Simultaneous profiling of native-state proteomes and transcriptomes of neural cell types using proximity labeling

Domain: biology
Published: 2026-03-31
Editorial outcome: major_revision

Abstract
Phenotyping cells at transcriptomic and proteomic levels is an essential step to understanding cellular contributions to development, aging, injury, and disease. Since proteome and transcriptome level abundances modestly correlate, complementary profiling of both is needed. We report a method called simultaneous protein and RNA -omics (SPARO) to capture the cell type-specific transcriptome and proteome simultaneously in vitro using BV2 microglial and HEK293 cell lines and in vivo using astrocytic and neuronal Cre driver mice crossed with Rosa26-TurboID knock-in mice. SPARO leverages TurboID to biotinylate RNA-interacting cytosolic proteins, enabling enrichment of proteins for proteomics and protein-associated RNA for transcriptomics. We validate SPARO first using well-controlled in vitro systems to verify that the proteomes and transcriptomes obtained reflect the global proteomes and transcriptomes. The effect of neuroinflammatory activation by lipopolysaccharide is also faithfully captured. We apply SPARO to obtain native-state proteomes and transcriptomes from astrocytes and neurons, thereby validating the approach in vivo. We interrogate mRNA-protein concordance and discordance, providing insights into molecular processes that exhibit uniform or cell type-specific patterns.

Introduction
The transcriptome and proteome of a cell provide complementary molecular information about its phenotype, state, and cellular functions. Quantifying RNA and protein levels in cell types using high-throughput -omics approaches uncover mechanisms and molecular pathways that contribute to development, aging, injury, and disease. RNA-sequencing can be leveraged to record different transcript species, expression patterns, gene structure, splicing patterns, and post-transcriptional modifications1. On the other hand, mass spectrometry (MS)-based quantitative proteomics provides information on protein abundances, localization, post-translational modifications, structure, cell-to-cell interactions, and effector functions2. Importantly, the number of RNA transcripts for a given gene does not always correlate with its protein abundance3,4. This is straightforward to explain from a biological standpoint by considering dynamics of RNA stability, translational regulation, protein half-life, chaperone proteins, cellular sequestration, as well as other post-transcriptional and post-translational events5–9. This contributes to observed discordances between mRNA and protein abundances and molecular signatures in cells and tissues.

Measuring RNA and protein levels simultaneously can reveal changes at the RNA-level that are not reflected at the protein-level, and vice-versa, and enables exploration of mechanisms regulating mRNA-protein correlation. This can have biological implications as well as highly practical consequences that impact choice of RNA or protein markers in experiments (e.g., choice of RNA probe for in situ hybridization or choice of protein marker for spatial studies). Also, the level of discordance between mRNA and protein, and the mechanisms that control mRNA and protein levels may vary across different cell types, tissues, and biological contexts, and cannot be ascertained from bulk tissue -omics approaches. Therefore, we need new approaches to jointly measure mRNA and protein levels in cell type-specific contexts.

Several attempts have been made to quantify this general concordance (or discordance) between transcriptomic and proteomic levels4,10–18. Simultaneous sampling of the transcriptome and proteome can be readily accomplished under in vitro monoculture conditions. However, under in vivo settings, these methods involve collecting RNA and protein from separate physical samples and rely on cell type purification using mechanical approaches, which, by themselves, impact transcriptomic and molecular characteristics of the cells19. The ability to purify intact cells from complex tissues like the brain also varies across cell type. For example, adult neurons are difficult to isolate without loss of their cellular and synaptic architecture20,21.

A general approach to obtain cell type-specific transcriptomes or proteomes from tissues that are independent of cell type isolation involves protein tagging. For cell type-specific in vivo transcriptomics, ribosomal subunits (e.g., Rpl22) can be tagged (e.g., HA) in a cell type-specific manner using Cre/lox genetics, allowing capture of ribosome-associated mRNA species (translatome). For cell type-specific proteomics, metabolic labeling with non-canonical amino acids22–27 and proximity-labeling by biotin ligases28–39 have been employed. There have also been several efforts to achieve joint mRNA and protein measurements, using RiboTag40–43 and Translating Ribosome Affinity Purification (TRAP)44–47 to capture mRNAs and nascent peptides from a single tagged ribosomal protein under a cell type-specific promoter. However, these approaches limit profiling to only ribosome-bound transcripts and nascent peptides rather than the broader proteome and transcriptome of the cell. Further, mammalian cells contain at least 79 different ribosomal proteins for cytosolic translation48, and ribosomes with different protein compositions translate different groups of mRNAs49. Therefore, ribosome affinity purification-based methods that target a single ribosomal protein may not capture the full complexity of the RNA species within a cell.

Another method to achieve joint mRNA and protein measurements is Single-Cell Protein And RNA Co-profiling (SPARC)50, which uses poly(A) oligo-dTs conjugated to magnetic beads that hybridize to mRNAs in conjunction with a homogeneous protein extension assay (PEA)51,52. SPARC, however, is limited by the 96-plex capability of the targeted proteins of the PEA assay. Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) also enables simultaneous measurement of transcriptomes and surface proteins in single cells, using antibodies conjugated to DNA oligonucleotides53. However, protein detection is restricted to the targeted antibody panel. Other proximity-labeling-based methods, such as APEX-seq54–56 and CAP-seq57, have also been developed for simultaneous molecular profiling at the RNA and protein levels. These approaches leverage the APEX2 and miniSOG enzymes, which can directly biotinylate nucleic acids as well as proteins. A limitation of these methods is that labeling must be performed ex vivo, requiring either the addition of hydrogen peroxide (APEX2) or light (miniSOG) to initiate the reaction. Due to the limitations of current methods, new tools for complementary molecular profiling of cell type-specific transcriptomes and proteomes are needed to gain a more global characterization of cell types in their native-states in a complex tissue environment.

To develop an approach for Simultaneous Protein and RNA Omics (SPARO), we leveraged the biotin ligase, TurboID. Using MS-based proteomics, we previously found that cytosolic TurboID biotinylates many RNA-binding proteins (RBPs) and ribosomal proteins both in vitro and in vivo, including Rpl22 that is used in the RiboTag approach36,37. Therefore, we hypothesized that transcripts associated with these proteins could also be purified simultaneously with biotinylated proteins. We developed and validated the SPARO approach to capture cell type-specific transcriptomes and proteomes using a microglial cell line in vitro, and neurons and astrocytes in vivo. This allowed us to investigate the concordance and discordance between mRNA and protein levels, and to examine how patterns of mRNA-protein discordance are conserved or vary across cell types. SPARO represents an approach to simultaneously quantify RNA and protein profiles with potentials for broad applications in vitro and in vivo.

Results
Validation of SPARO in BV2-TurboID cells in vitro

To test whether we could leverage TurboID-based cytosolic proteomic labeling to simultaneously capture cellular proteomes and transcriptomes, we first used our previously validated, stably transduced mouse microglia BV2 cell line that expresses V5-TurboID-NES (BV2-TurboID)37,58. In this construct, the TurboID biotin ligase is restricted to the extranuclear compartment via a nuclear export sequence (NES) to bias towards biotinylation of the cytosolic proteome (Fig. 1 a). By starting with a homogeneous cell line, we could quantitatively assess how the transcriptome and proteome of BV2 cells using the SPARO approach compare with whole-cell transcriptomes and proteomes from global cell lysates. Furthermore, to test whether SPARO could also capture cellular changes in microglia induced by an inflammatory stimulus, we treated BV2 control and BV2-TurboID cells with lipopolysaccharide (LPS) (Fig. 1b). Immunocytochemistry (ICC) confirmed the that the TurboID enzyme (via V5) and biotinylation are primarily restricted to the cytosol, under unstimulated and LPS-stimulated conditions (Supplementary Fig. 1a). Following this validation, we lysed cells in a buffer that maintains RNA-protein interactions and then processed for simultaneous transcriptomics (mRNA-seq) and proteomics (label-free quantitative mass spectrometry or LFQ-MS). Immunoblot analyses confirmed the presence of biotinylated proteins (via streptavidin) and the TurboID protein (via V5) in BV2-TurboID cells, which were absent in non-TurboID control cells both before (Supplementary Fig. 1b) and after streptavidin bead affinity purification (henceforth “pulldown”) (Fig. 1c). We detected only endogenously biotinylated proteins in the BV2 control cells (Fig. 1c). After streptavidin pulldown, we confirmed protein enrichment using a silver stain (Supplementary Fig. 1b).

Bioanalyzer RNA analysis showed comparably high levels of RNA in global BV2 lysates (Supplementary Fig. 1d). Bioanalyzer RNA analysis displayed RNA of high integrity in the global lysates (mean RIN = 9.9, Supplementary Fig. 1d) and pulldowns (mean RIN = 9.8), but low integrity in the non-TurboID controls (mean RIN = 6.9, Fig. 1d and Supplementary Fig. 2a and b). At the pulldown level, high quality RNA was enriched only when proteomic biotinylation was present in BV2 microglia (p = 0.004, unpaired t test).

We next performed LFQ-MS and RNA-seq on global BV2-TurboID samples and pulldowns. Raw intensity and processed intensity values are found in Supplementary Data 2, 3, and 4. Raw RNA-seq and processed counts values are found in Supplementary Data 5 and 6, respectively. We first assessed the overlap of the number of proteins between the TurboID pulldowns and the global proteome. We observed 44% (untreated) and 51% (LPS-treated) overlap of TurboID pulldown proteomes with the ground truth global BV2 proteomes, fairly consistent with prior observations in BV2-TurboID cells37. This is likely a result of the NES tag on TurboID that biases the proteome towards the cytosolic compartment and against the nuclear and mitochondrial compartments (Fig. 1e). Next, to benchmark the fidelity of our TurboID pulldown transcriptome, we compared our RNA-seq datasets between the global BV2 samples and our pulldown populations. We observed between 95% (untreated) and 98% (LPS-treated) overlap of TurboID pulldown transcriptomes with the ground truth global BV2 transcriptome (Fig. 1f). We next asked whether the pulldown proteomes and transcriptomes could adequately capture changes in BV2 cells upon exposure to LPS (Fig. 1g, left). Multidimensional scaling (MDS) analysis of the transcriptomes identified distinct untreated and LPS-treated transcriptomes regardless of sample type (global vs pulldown) (Fig. 1g, right) suggesting that LPS-effect was equally captured by the TurboID pulldown and global transcriptomes. We also defined the background noise in the system by comparing mRNAs enriched from non-TurboID BV2 cells with those from BV2-TurboID cells (Supplementary Fig. 2). The “noise” that we found consisted primarily of many non-coding RNAs (e.g., Gdap10, Xist and Malat1), pseudogenes (e.g., H2-K2 and Pisd-ps1) and splicing factors (e.g., Luc7l3 and Rsrp1) enriched in the BV2 non-TurboID LPS controls (p ≤ 0.05, ≥ 1-log -fold change, Supplementary Fig. 2c and d, Supplementary Data 7).

To test the fidelity of the pulldowns to their respective global samples, we next performed correlation analysis of our LFQ-MS and RNA-seq data, respectively. At the proteomic level, we observed modest correlation between LPS-treated pulldown and the global LPS-treated BV2-TurboID proteomes (Pearson’s r = 0.28, p < 0.0001, n = 1455 shared proteins, Fig. 1h). Notably, we observed a much higher correlation between the LPS-treated pulldown transcriptomes and global BV2-TurboID transcriptomes (Pearson’s r = 0.99, p < 0.0001, n = 12742 shared transcripts, Fig. 1i). We also compared the correlation between the untreated global and pulldown proteomes and transcriptomes, respectively (Supplementary Fig. 3a and d). To confirm the cytosolic bias of the V5-TurboID-NES recombinant protein, we differentially compared the pulldown and global BV2-TurboID transcriptomes and proteomes. Differential enrichment analysis of BV2 transcriptomic and proteomic data are found in Supplementary Data 7 and 8, respectively. At the transcriptomic level, we found that mitochondrial-encoded genes (e.g., COX1, CYTB and ND2) were highly enriched in the global samples (p ≤ 0.05, ≥ 1-log -fold change, Supplementary Fig. 4a) as compared to the pulldowns, consistent with inability of cytosolic TurboID to label intra-mitochondrial proteins and their associated transcripts. At the proteomic level, the global proteome was enriched in nuclear, endoplasmic reticulum (ER) and mitochondrial-related terms, consistent with undersampling of these subcellular compartments by TurboID labeling (p ≤ 0.05, ≥ 1-log -fold change, Supplementary Fig. 5a).

SPARO also detected proteomic and transcriptomic changes driven by LPS treatment (Fig. 1j and m, Supplementary Fig. 3, 4 and 5). As expected, GO analysis of differentially enriched proteins (DEPs) between TurboID pulldowns revealed overrepresented ontologies of LPS-treated cells including Regulation of Lipopolysaccharide-mediated Signaling Pathway, Cellular Response to Molecule of Bacterial Origin, and Regulation of Cytokine Production (Fig. 1k, Supplementary Data 9). The changes that we observed upon LPS treatment measured by TurboID pulldown were still modestly correlated with LPS changes in the global proteome (Pearson’s r = 0.48, n = 519 shared proteins, p ≤ 0.05, Fig. 1l). However, this correlation was substantially improved when specifically considering proteins that exhibit high differential abundance between LPS-treated and control BV2-TurboID cells (Pearson’s r = 0.80, n = 69 shared proteins with p ≤ 0.05 and ≤ -1 or ≥ 1-log -fold change, Fig. 1l). We also found canonical pro-inflammatory microglial activation markers (e.g., Stat1, Nfkb2, and Ehd1) enriched in the LPS-treated BV2-TurboID pulldowns and anti-inflammatory microglial activation markers (e.g., Mrc1/Cd206, Mgl2 and Arg1) enriched in the control pulldowns (p ≤ 0.05, ≥ 1-log -fold change, Fig.1l).

Similar to the proteome, we performed differential enrichment and GO analyses of differentially expressed genes (DEGs) in the LPS-treated and non-LPS treated pulldown transcriptomes (Supplementary Data 10). We observed the same canonical reactive microglial response enriched in LPS treated and untreated samples (Fig. 1m-o). Unlike at the protein level, the LPS-driven transcriptomic changes correlated highly between the TurboID RNA pulldown and the LPS-enriched global transcriptome (Pearson’s r = 0.82, n = 6798 shared transcripts with p ≤ 0.05, Fig. 1o). This correlation was further improved when specifically considering genes that exhibit high differential enrichment between LPS-treated and control BV2-TurboID cells (Pearson’s r = 0.89, n = 999 shared genes, p ≤ 0.05 and ≤ -1-log or ≥ 1-log -fold change, Fig. 1o). Additional transcriptomic and proteomic GO analysis data comparing either LPS-treated versus untreated global or pulldown samples and pulldown versus global samples can be found in Supplementary Data 11-16.

Overall, these results suggest that SPARO can simultaneously capture the cellular transcriptome and proteome and effectively captures inflammatory effects of LPS in an in vitro mammalian system.

Validation of SPARO in vivo using cortical astrocytes and neurons

We next wanted to test whether we could capture cell type-specific transcriptomes and proteomes simultaneously in vivo while retaining the native state of these cells. We crossed Rosa26TurboID/wt (TurboID) mice with appropriate inducible Cre-ERT2 mice, to drive Cre expression specifically in Camk2a-expressing excitatory neurons or in Aldh1l1-expressing astrocytes. We chose the Aldh1l1-Cre/ERT2 mice and Camk2a-Cre/ERT2 mouse lines for their extensive validation with regard to specificity of Cre recombination42,59. Our breeding scheme resulted in the generation of Aldh1l1CreERT2/wt/Rosa26TurboID/wt (astrocyte-TurboID) and Camk2aCreERT2/wt/Rosa26TurboID/wt (neuron-TurboID) mice. We used Aldh1l1CreERT2/wt or Camk2aCreERT2/wt as Cre-only controls. We induced Cre-mediated recombination by intraperitoneal tamoxifen at 7 weeks of age, followed by a 3-week gap, and then biotin water supplementation for 2 weeks (Fig. 2a). Based on our prior study, V5-TurboID-NES presence and biotin treatment does not have adverse effects on the mice and does not impact homeostatic cellular functions31,33,36. Following biotin treatment (age 2.5-3 months), we harvested and lysed cortical tissue in the same buffer as the in vitro studies to maintain RNA-protein interactions and then processed for simultaneous transcriptomics and proteomics using SPARO (Fig. 2a). Immunofluorescence imaging of the cortex showed robust biotinylation in TurboID brains compared to controls (Fig. 2b and Supplementary Fig. 6a). Moreover, the biotinylation signal co-localized with the astrocytic marker, S100 (in astrocyte-TurboID mice) and neuronal marker, -III-tubulin (in neuron-TurboID mice) confirming cell targeting (Fig. 2b). Importantly, our prior work validated that the biotinylation signal present in astrocyte- and neuron-TurboID animals is cell type-specific via the absence of signal in other brain cell types, and without reactive gliosis36. Immunoblot analysis of cortical lysates at the global level (Supplementary Fig. 6b and c) and after streptavidin affinity purification displayed biotinylated proteins from astrocyte-TurboID and neuron-TurboID pulldowns not seen in non-TurboID controls (Fig. 2c). The presence of the TurboID recombinant protein via V5 presence was also only seen in astrocyte-TurboID and neuron-TurboID pulldowns and not non-TurboID controls (Fig. 2c) providing biochemical verification of the presence of TurboID in these cells.

Following streptavidin pulldown, we determined protein enrichment using a silver stain (Supplementary Fig. 6d and e) and RNA integrity using a Bioanalyzer RNA quality control (QC) assay (Fig. 2d, Supplementary Fig. 6f). In the TurboID-expressing samples, we consistently found RNA of higher integrity (mean RIN = 8.0) when compared to the non-TurboID samples (mean RIN = 5.2, p = 0.04, unpaired t test). We next prepared samples for LFQ-MS. Raw intensity and processed intensity values are found in Supplementary Data 17-19, respectively. Notably, MDS analysis of the proteomes showed separation between neuronal and astrocytic proteomes (Fig. 2e). We also confirmed the cell type specificity of the proteomes including enrichment of canonical astrocytic proteins e.g., Hepacam, Glul, Aqp4, Plpp3, p ≤ 0.05, ≥ 1-log -fold change) and GO terms (e.g., astrocyte end-foot, glial cell projection and astrocyte differentiation) in astrocytic pulldowns and neuronal proteins (e.g., Map2, Ncam1, Mapt, p ≤ 0.05, ≥ 1-log -fold change) and GO terms (e.g., synaptic signaling dendrite and neuron projection,) in neuronal pulldowns (Fig. 2f and g, Supplementary Data 20 and 21). These data are consistent with our prior work using astrocyte-TurboID and neuron-TurboID systems36. It is important to highlight that the lysis buffer used in the current studies was a homogenization buffer intended to maintain RNA-protein interactions, as opposed to 8 M urea lysis buffer in prior work36.

Like the proteome, we conducted MDS, differential enrichment and GO analysis of the neuronal and astrocytic transcriptomes. Raw RNA-seq and processed counts values are found in Supplementary Data 22 and 23, respectively. Like the proteome, MDS analysis identified distinct neuronal and astrocytic transcriptomes (Fig. 2h), each of which were enriched for canonical cell type specific markers (Fig. 2i and j, Supplementary Data 24 and 25), suggesting that the pulldowns are cell type enriched. To further validate the cell type specificity of the pulldown transcriptomes, we compared the relative gene expression of top astrocytic and neuronal genes from Zhang et al., 201460 between the global cortex and the cell type enriched pulldowns (Supplementary Fig. 7). The cell type enriched gene lists can be found in Supplementary Data 26. The astrocyte-TurboID pulldown transcriptomes exhibited a high expression of astrocytic genes (e.g., Tnc, Sox9 and Aqp4) and a low expression of neuronal genes (e.g., Tmem130, Slc10a4 and Npy) (Supplementary Fig. 7a), and the converse was true for the neuron-TurboID pulldown transcriptomes (Supplementary Fig. 7b).

We included a 5-day repeated LPS paradigm in our original astrocyte SPARO study to examine effects of LPS-induced neuroinflammation on astrocyte profiles (Supplementary Fig. 8). We observed modest effects of LPS at both the proteome (p ≤ 0.05, ≥ 1-log -fold change, n = 11 up with LPS, n = 35 up in non-LPS controls, Supplementary Data 18) and transcriptome levels (p ≤ 0.05, ≥ 1-log -fold change, n = 141 up with LPS, n = 41 up in non-LPS controls, Supplementary Data 21), suggesting that the LPS effect may have been partially washed out during the one-week interval between the last LPS dose and euthanasia (Supplementary Fig. 8b and c). Despite this, we observed modest yet meaningful effects of LPS on the astrocyte pulldown transcriptome using the SPARO approach, including upregulation of canonical reactive genes (e.g., Lcn2 and Serpina3g, Supplementary Fig. 8b). We also performed GO analysis of DEGs and found synapse- and translation-related terms enriched with LPS treatment (Supplementary Fig. 8d, Supplementary Data 27). In contrast, LPS-induced changes were minimal at the level of the astrocyte proteome (Supplementary Fig. 8c), suggesting a discordance between LPS-induced effects on astrocytes at the transcriptomic and proteomic levels. We did not find any GO terms enriched from DEPs, due to the small number of DEPs within each sample group.

These results demonstrate the application of the SPARO approach to obtain biologically meaningful proteomes and transcriptomes of astrocytes and neurons while retaining their native states in adult mouse cortex.

The SPARO- and RiboTag-enriched astrocytic transcriptomes are similar

We next benchmarked SPARO against RiboTag, a gold-standard in vivo cell type-specific transcriptomic profiling method40,41,61. RiboTag mice express a hemagglutinin tag (HA) modified allele of the Rpl22 gene (Rpl22-HA), a major component of the polyribosome complex40,41. In the presence of Cre recombinase, HA-tagged polyribosomes can be isolated from target cell types using an anti-HA antibody and immunoprecipitation methods. The mRNAs interacting with the ribosomes during translation can then be extracted and quantified to obtain a snapshot of actively translated transcripts (translatome) in specific cell types. To assess the similarity between the TurboID-enriched and RiboTag-enriched transcriptomes, we compared both methods using cortical Aldh1l1-expressing astrocytes in vivo (Fig. 3a). We bred Aldh1l1CreERT2/wt mice with ARpl22tm1.1Psam or TurboID mice to generate astrocyte-RiboTag and astrocyte-TurboID mice, respectively. We administered tamoxifen to astrocyte-TurboID, astrocyte-RiboTag and Aldh1l1CreERT2/wt or Rpl22tm1.1Psam (control) mice at 7 weeks of age, followed by a 3-week gap, then performed cortical tissue lysis, pulldown of biotinylated or HA-tagged proteins, LFQ-MS, and RNA-seq (Fig. 3a).

Immunofluorescent imaging of the cortex showed the presence of HA recombinant protein in astrocyte-RiboTag brains, which co-localized with the astrocytic marker, S100 (Fig. 3b). TapeStation RNA QC analysis from bulk cortical lysates from control and astrocyte-RiboTag samples showed RNA of high integrity (mean RIN = 8.7) across all samples (Supplementary Fig. 9a). Astrocyte-RiboTag pulldowns also showed high-quality RNA (mean RIN = 8.7), whereas RiboTag-only controls did not produce reportable RIN values (Supplementary Fig. 9b). We next prepared samples for RNA-seq. Raw RNA-seq and processed counts values are found in Supplementary Data 28 and 29, respectively. Importantly, transcript overlap between the astrocyte-TurboID and astrocyte-RiboTag pulldowns was 95% (Fig. 3c). The cell type specificity of RiboTag and SPARO transcriptomes (Supplementary Fig. 9c), as well as the correlation across datasets (Pearson’s r = 0.93, n = 17050 shared transcripts, Fig. 3d) were both robust. Next, we examined whether highly cell type-specific astrocytic genes from Zhang et al., 2014 were correlated across the TurboID- and RiboTag-enriched transcriptomes. We found that the mRNA abundances were highly correlated (Pearson’s r = 0.69, n = 50 genes, Fig. 3e).

We next performed differential enrichment analysis and GO analysis between the astrocyte-TurboID and astrocyte-RiboTag pulldown transcriptomes (Supplementary Fig. 9d-f, Supplementary Data 30 and 31) and identified several differences in signatures. We hypothesized that these differences arise from the fact that RiboTag enriches all RNAs bound to ribosomal complexes that contain the ribosomal protein, Rpl22, whereas the SPARO approach captures mRNAs interacting with many biotinylated proteins that interact with RNA in astrocytes. To test this, we performed LFQ-MS on the astrocyte-RiboTag pulldowns and compared the results to the astrocyte-TurboID pulldown proteome. The astrocyte-RiboTag raw intensity and processed intensity values are found in Supplementary Data 32 and 33, respectively. For this comparison, we first identified proteins in the astrocyte-TurboID or astrocyte-RiboTag enriched over Cre-only or RiboTag-only controls, respectively (≥ 1-log -fold change) and then determined the number of RNA-binding proteins (RBPs and ribosomal proteins) enriched in each dataset (Supplementary Data 34). We found that the TurboID approach captured many RBPs (n = 918 total proteins with 754 unique to TurboID) when compared to the RiboTag approach (n = 336 total proteins with 172 unique to RiboTag) and 164 RBPs shared (Fig. 3f). Next, we filtered the RBP list for only ribosomal proteins. We found that both approaches captured many ribosomal proteins with slightly more ribosomal proteins detected in RiboTag (n = 11 unique to TurboID and n = 21 unique to RiboTag) with 41 ribosomal common proteins (Fig. 3g). Abundance analysis of ribosomal proteins in the astrocyte-TurboID pulldowns relative to the Cre-only controls (via row Z score) confirmed strong enrichment (Fig. 3h).

We next examined transcriptomic features, including mean transcript length, GC content, and exon number. DEGs (p ≤ 0.05, ≥ 1-log -fold change, Supplementary Fig. 9d, Supplementary Data 35) between astrocyte-RiboTag and astrocyte-TurboID pulldowns showed significant differences across all three features. Mean transcript length (p < 0.0001, Mann Whitney test), and exon number (p = 0.029, Mann-Whitney test) were higher in the TurboID pulldowns while percent GC content (p = 0.002, Mann Whitney test) was higher in the RiboTag pulldowns (Supplementary Fig. 9g). These differences may reflect the broader nature of TurboID labeling, which captures many RBPs not limited to those associated with active translation.

Because TurboID biotinylates many RNA-interacting proteins, we tested whether SPARO could measure small non-coding RNAs (ncRNAs). Small RNA extraction and sequencing of astrocyte-TurboID pulldowns and bulk cortex detected over 400 miRNAs in both sample types (Supplementary Fig. 10a and b, Supplementary Data 36 and 37). MDS analysis revealed clear separation of bulk cortex and astrocyte pulldown miRNAomes (Supplementary Fig. 10c). Differential enrichment analysis identified 126 miRNAs enriched in astrocyte pulldowns and 102 miRNAs enriched in bulk cortex (p ≤ 0.05, ≥ 1-log -fold-change, Supplementary Fig. 10d, Supplementary Data 38). The most enriched astrocyte pulldown miRNA was mmu-miR-760.

Together, these findings could explain why the astrocyte-RiboTag and astrocyte-TurboID pulldown transcriptomes show overall similarities in transcript detection and abundance yet diverge in specific signatures.

Correlation of the SPARO transcriptomes and proteomes of cortical astrocytes and neurons in vivo

We next evaluated the concordance between the in vivo proteomes and transcriptomes of astrocytes and neurons obtained using SPARO (Fig. 4a). The raw correlation between 1,934 mRNA and protein pairs was modest in both astrocytes (Pearson’s r = 0.29, Supplementary Fig. 11a, Supplementary Data 39) and neurons (Pearson’s r = 0.27, Supplementary Fig. 11b, Supplementary Data 40). To determine if these generally low correlations were simply a product of the complexity of an in vivo brain environment, we also performed similar transcriptome and proteome correlation analysis of the in vitro BV2-TurboID cells and found similar results (Pearson’s r = 0.24, Supplementary Fig. 8c, Supplementary Data 41). We did observe the highest concordance between global BV2-TurboID cells (Pearson’s r = 0.66, Supplementary Fig. 11c, Supplementary Data 42) and bulk cortical transcriptomes and proteomes (Pearson’s r = 0.47, Supplementary Fig. 11f, Supplementary Data 43), which lack any cell type specificity. To benchmark our in vivo SPARO correlations to published datasets, we used RNA data from the Zhang et al., 201460 transcriptomic study of mouse brain cell types and Sharma et al., 201562 proteome of acutely isolated mouse astrocytes and neurons. Like our findings in SPARO, the correlation between mRNA and protein abundances was modest for all pairs (Pearson’s r = 0.36 for astrocytes and r = 0.29 in neurons) as well as after limiting the analysis to mRNA-protein pairs found in SPARO (r = 0.41 in astrocytes and r = 0.21 in neurons, Supplementary Fig. 11e, Supplementary Data 44 and 45).

Quantifying concordance is further complicated by innate technical differences between LFQ-MS and RNA-sequencing. For example, the dynamic range and sensitivity of both approaches differ vastly. Therefore, to better normalize against these biases, we calculated percentile ranked abundances for proteins and transcripts in each dataset (1,934 gene and protein pairs that overlap) and classified these mRNA-protein pairs into distinct groups based on level of mRNA-protein rank discordance (Fig. 4b and Supplementary Fig. 11g). In both astrocytes and neurons, and in the bulk cortex, we observed that most mRNA-protein pairs fell into either concordantly low or high groups (Log % rank values between 75 and 100 for both the x and y-axis). Together, these findings verify modest levels of concordance between protein and mRNA abundances62–64, although mRNAs that tend to be highly expressed in neurons and astrocytes, and in the cortex in general, also tend to be highly abundant at the proteomic level.

We next classified mRNA and protein pairs from the astrocyte and neuronal pulldowns into either concordant (mRNA ~ protein, > 0.75 in black) or discordant (protein >> mRNA, > - 0.50 in blue, or mRNA << protein levels, > 0.50 in red) groups based on a rank differential of abundance values (Supplementary Fig. 11h, Supplementary Data 40 and 41). We wondered whether there were examples of canonical cell type-specific markers that fall into either highly concordant or discordant quadrants. To designate astrocytic and neuronal enriched canonical markers, we used a unionized marker list65–67 generated from the Zhang et al., 201460 transcriptomic data and the Sharma et al., 201562 proteomic data from astrocytes and neurons (Fig. 4c and Supplementary Fig. 11i and j, Supplementary Data 46). A subset of example markers belonging to each category are shown in Fig. 4d. We repeated the same analysis for the neuronal pulldown proteomes and transcriptomes (Fig. 4e). These findings nominate cell type-specific astrocytic and neuronal markers that may be preferentially suitable for either transcriptomic or protein-based studies, as well as those that are highly abundant at both mRNA and protein levels.

The observed mRNA-protein discordance can arise from many biological phenomena, which may also vary across cell types. To determine whether discordant proteins and mRNAs were consistent across astrocytes and neurons, we assessed the level of overlap in mRNA-protein pairs across astrocytes and neurons in 3 quadrants (high mRNA and high protein, high mRNA and low protein, low mRNA and high protein, Supplementary Data 47). Across all 3 groups, most concordant mRNA-protein pairs (high RNA, high protein) were present in both cell types (Supplementary Fig. 11k). Many discordant mRNA-protein pairs were also shared across cell types (Fig. 4f and g). Interestingly, we observed a pattern in which the types/categories of discordant genes were generally conserved across cell types, with specific subtypes of genes that are cell type specific. For example, within the discordant group where protein exceeded mRNA (protein >> mRNA) abundance, we found overrepresented ontologies of cytoskeletal pathways in both astrocytes and neurons. However, within astrocytes, only terms related to microtubules were enriched, as opposed to actin machinery in neurons (Fig. 4f). For the discordant group of genes in which mRNA exceeded protein (mRNA >> protein) abundance, we found a consistent signature in both neurons and astrocytes of ontologies related to mitochondria. However, within astrocytes this signature comprised of genes involved in aerobic respiration, whereas neurons were biased towards genes involved with mitochondrial fusion and GTPase activity (Fig. 4g). This pattern most likely reflects the metabolic differences that exist between these two cell types. After performing GO analyses on the mRNA and protein pairs in each concordant and discordant quadrant, we also looked at the overlap of the GO terms across the two cell types. The cell type-specific GO enrichment patterns of mRNA and protein pairs in the concordant and discordant groups generally matched our results from GO analyses at the level of mRNA-protein pairs (Fig 4f and g, Supplementary Fig. 12, Supplementary Data 48).

To validate our discordant mRNA-protein findings, we performed concordance/discordance analyses on the BV2-TurboID globals and SPARO pulldowns (Supplementary Fig. 13a, Supplementary Data 49 and 50), and the Zhang et al., 201460 Sharma et al., 201562 published astrocytic and neuronal datasets (Supplementary Fig. 13b, Supplementary Data 51 and 52). Across the independent experiments and analyses of published work--the BV2-TurboID samples (global and pulldown), Zhang vs Sharma astrocyte and neuron global datasets--we found cytoskeletal-related GO terms enriched in the discordant protein >> mRNA blue group (Supplementary Fig. 13c and d). We also found some mitochondrial-related GO terms enriched in the discordant mRNA >> protein groups from the BV2-TurboID SPARO pulldowns and the Zhang vs Sharma astrocyte and neuron global datasets (Supplementary Fig. 13c and d). These findings show that the SPARO discordant mRNA-protein phenotypes are likely not due to the labeling nature of TurboID and may be due to biological processes in these cells.

After evaluating the correlation between the paired proteomes and transcriptomes of each TurboID model, we aimed to uncover mechanisms mediating concordance and discordance between mRNA and protein abundances in the bulk cortex, astrocytes and neurons. We found no relationship between the level of mRNA-protein discordance with the average RNA features: % GC content, number of exons and transcript length or protein half-life68 (Supplementary Fig. 14, Supplementary Data 53 and 54), suggesting that these mRNA characteristics and protein synthesis dynamics are unlikely on their own to explain the observed discordance.

Together, these findings emphasize an important observation about discordant mRNAs and proteins across these two cell types. In general, the broad classes of mRNAs and proteins that are highly discordant in abundance are well conserved between neurons and astrocytes. It is only in the nuanced subtypes of these gene classes where each cell type exhibits a uniquely discordant repertoire of proteins and transcripts.

Removal of NES from TurboID does not alter the correlation between mRNA-protein abundances in vitro

After identifying concordant and discordant phenotypes of cortical astrocytes and neurons using SPARO in vivo, we wanted to further confirm that the discordant mRNA-protein phenotypes are not due to the labeling nature of cytosolic TurboID. To investigate this, we infected HEK293 cells with a TurboID lentivirus with or without the NES and performed SPARO (Supplementary Fig. 15a and b). IF and immunoblot analysis confirmed the presence of biotinylated proteins (via streptavidin) in HEK293 TurboID-NES and noNES cells and only endogenously biotinylated proteins in the non-TurboID control cells. (Supplementary Fig. 15c). TapeStation RNA QC analysis was otherwise comparable between constructs (Supplementary Fig. 15d).

We next performed DIA LFQ-MS and RNA-seq on global HEK293 samples and pulldowns (Supplementary Fig. 16-18). Raw intensity and processed intensity values are found in Supplementary Data 55 and 56, respectively. Raw RNA-seq and processed counts values are found in Supplementary Data 57 and 58, respectively. We observed 92% overlap between the TurboID-NES and TurboID-noNES pulldown transcriptomes (Supplementary Fig. 15e, top), as well as 85% overlap of detected peptides between the TurboID-NES and TurboID-noNES pulldowns (Supplementary Fig. 15e, bottom). However, MDS analysis of the transcriptomes and proteomes identified distinct TurboID-NES and TurboID-noNES pulldown groups (Supplementary Fig. 16d).

To assess differences between the TurboID-NES and noNES pulldowns, we also examined the correlation between the transcriptomes and proteomes, respectively. Surprisingly, we found a very high correlation between the mRNA and protein abundances between the respective TurboID pulldowns (Supplementary Fig. 15f, Pearson’s r = 0.99, n = 17,998 shared genes and Supplementary Fig. 15g, Pearson’s r = 0.93, n = 7333 shared proteins), suggesting that TurboID-enriched pulldowns with and without the NES are highly similar. To define the labeling bias of the TurboID-NES and TurboID-noNES enzymes, we differentially compared the pulldown transcriptomes and proteomes. Differential enrichment analysis of HEK293 transcriptomic and proteomic data are found in Supplementary Data 59 and 60, respectively. Proteomic GO analyses can be found in Supplementary Data 61-63 and transcriptomic GO analyses can be found in Supplementary Data 64-66. At the transcriptomic level, we found few DEGs in the TurboID-noNES pulldown group and nearly triple the amount in the TurboID-NES pulldown group (n = 81 Up in the TurboID-noNES and n = 261 Up in TurboID-NES, p ≤ 0.05, ≥ 1-log -fold change, Supplementary Fig. 15h). We found nuclear and endoplasmic reticulum (ER)-associated genes (e.g., HSPA5, SDF2L1 and H2BC21) to be the most highly enriched in the TurboID-noNES group (p ≤ 0.05, ≥ 1-log -fold change, Supplementary Fig. 15h). GO analyses also confirmed nuclear, ER and mitochondrial-associated cellular processes to be enriched in the TurboID-noNES pulldowns (Supplementary Fig. 16g). Notably, some nuclear-associated genes (e.g., ZNF75A, HPS6 and EID2) also appeared in the TurboID-NES pulldowns (p ≤ 0.05, ≥ 1-log -fold change, Supplementary Fig. 15h). At the proteomic level, we found many DEPs in the TurboID-noNES pulldowns and very few in TurboID-NES pulldowns (n = 791 Up in the TurboID-noNES and n = 45 Up in TurboID-NES, p ≤ 0.05, ≥ 1-log -fold change, Supplementary Fig. 15i). We found DEPs of many different cellular subcompartments (e.g., RABL3 (ER), SCP2 (peroxisomes and nucleoplasm), GANAB (ER), NFKBIA (cytosol), and ETFA (mitochondria) to be enriched in the TurboID-noNES group (p ≤ 0.05, ≥ 1-log -fold change, Supplementary Fig. 15i). In contrast, many nucleoplasm-associated proteins appeared in the TurboID-NES pulldowns (e.g., SREK1IP1, RP9, HOXD13 and NGFR). Many mitochondrial carboxylases (e.g., PCCA and MCCC1) were also enriched and these have been identified as endogenously biotinylated proteins important for cell metabolism69,70. These findings are in alignment with the BV2-TurboID-NES versus global differential enrichment analyses (Supplementary Fig. 2 and 3) and further confirms that TurboID restricted to the extranuclear compartment can still interact and biotinylate some proteins in other cellular compartments.

We also examined the correlation between mRNA and protein abundances in the TurboID-NES and TurboID-noNES pulldowns. In both cases, we observed modest correlations (TurboID-NES: Pearson’s r = 0.31, n = 6,748 pairs, TurboID-noNES: r = 0.35, Supplementary Fig. 15l–m). These values are comparable, though slightly higher, than those observed in the in vitro BV2-TurboID-NES and in vivo astrocyte- and neuron-TurboID pulldowns (r = 0.24-0.27, Supplementary Fig. 11a-c). We also differentially compared the pulldown and global transcriptome and proteomes with or without the NES, respectively (Supplementary Fig. 17 and 18, Supplementary Data 59).

Finally, we tested if the removal of the NES on the TurboID enzyme would yield the same mRNA-protein discordant phenotypes as seen in the BV2-TurboID cells in vitro, astrocytes and neurons in vivo, and published datasets. The raw correlation between 6,748 mRNA and protein pairs was modest in the global (Pearson’s r = 0.47, Supplementary Fig. 19a), TurboID-NES pulldowns (Pearson’s r = 0.31, Supplementary Fig. 19b) and TurboID-noNES pulldowns (Pearson’s r = 0.35, Supplementary Fig. 19c). Again, we performed the percentile rank transformation and differential calculation to classify mRNA-protein pairs as concordant or discordant (Supplementary Fig. 19d, Supplementary data 67, 68, and 69). Using GO analysis, we observed some cytoskeletal-associated terms in the discordant protein>mRNA blue group across all datasets (Supplementary Fig. 19e, Supplementary Data 70-72), like the SPARO and Zhang versus Sharma published datasets analyses (Supplementary Fig. 13). We also again observed the discordant mRNA>protein mitochondrial phenotype across all datasets (Supplementary Fig. 19f, Supplementary Data 70-72).

Methods
Cloning, plasmid and lentivirus generation

Through Emory’s Integrated Genomics Core Custom Cloning Division, the TurboID-NES construct (Addgene plasmid #107169) used in our prior study37, was edited to generate a construct without the NES (TurboID-noNES). Restriction enzyme sites were introduced with respective forward and reverse primers (Supplementary Data 1). PCR products were then subcloned into the TurboID-NES backbone. TurboID-NES and TurboID-noNES plasmids were transformed into DH5-⍺ competent E. coli cells (NEB, C2987I) according to manufacturer’s instructions. Plasmid DNA was purified following the Miracle-prep (Miraprep) instructions143 (QIAprep Spin Miniprep Kit) and resulting concentration measured using a Qubit. Lentivirus (LV) using the TurboID-NES and TurboID-noNES plasmids were generated through Emory’s Viral Vector Core.

In vitro studies

Mouse microglia BV2 cells were cultured in 0.2 µm vacuum filter sterilized Dulbecco’s Modified Eagle Medium (DMEM) supplemented with high glucose and L-glutamine containing 1% penicillin-streptomycin, and 10% fetal bovine serum. BV2 cells were incubated in 100 mm cell culture dish at 37°C and 5% CO until reaching 80-90% confluency. Cells were treated with 100 ng/mL of LPS for 48 h and 200 µM of biotin for 24 h during the second day of LPS treatment.

Human embryonic kidney 293 cells (HEK293) were cultured in DMEM containing 1% penicillin-streptomycin and 10% fetal bovine serum. HEK293 cells were incubated in 6-well culture dishes in the same condition as the BV2 cells described above. At 30% confluency, HEK293 cells were infected with 3 µL of the two LVs generated (TurboID-NES, viral titer: 3.9x108 IU/mL or TurboID-noNES, viral titer: 1.7x109 IU/mL). LV-containing media was removed after 3 days, and successful infection was determined by cellular GFP signal using the ECHO Revolve fluorescence microscope (Echo, USA) prior to lysis. HEK293 cells were grown to 80-90% confluency cells before biotin treatment (200 µM for 24 h).

The 48 h LPS and 24 h biotin treatment paradigms were used to model similar treatment and labeling kinetics for in vivo SPARO studies.

Immunocytochemistry

BV2 control and BV2-TurboID cells were seeded at a density of 1 x 105 cells onto autoclaved and ethanol-treated coverslips placed in a 12-well plate. Seeded cells were treated with 100 ng/mL LPS. After 24 hours, the cells were supplemented with external biotin (200 μM), along with LPS, for an additional 24 hours. After treatments, cells were washed three times with PBS and then fixed with 4% paraformaldehyde (Thermo Scientific, J19943-K2) in PBS for 30 min at RT. Fixed cells were permeabilized and blocked simultaneously using 5% goat serum diluted in 0.25% TBST for 1 h at room temperature. Each well was then incubated with 1 mL of antibody cocktail containing rabbit anti-V5 (1:500, Abcam, 206566) and Streptavidin-DyLight 488 (1:500, Invitrogen, 21832) prepared in 1% Goat serum diluted in TBS, overnight at 4 °C. The next day, cells were washed three times with 0.25% TBST, followed by incubation with anti-rabbit Alexa Fluor 594 secondary antibody (1:500, Invitrogen, A21207) prepared in 1% goat serum in TBS for 2 hours at room temperature. Nuclei were stained with 4′,6-diamidino-2-phenylindole (DAPI) for 5 min. After final washes, coverslips were mounted onto glass slides using Fluoromount-G (Invitrogen, 00-4958-02). Images were captured at 60X using a Keyence BZ-X810 microscope.

In vivo studies

Mice were housed in the Division of Animal Resources vivarium at Emory University under a standard environment (12 h light/ 12 h dark cycle, temperature 72°F, humidity range 40-50%) with access to food and water ad libitum. All animal-related studies (PROTO-201700821) were conducted with the approval of Emory’s Institutional Animal Care and Use Committee following the National Institute of Health’s “Guide for the Care and Use of Laboratory Animals” and reported in accordance with the ARRIVE guidelines.

Transgenic approach for Cre recombinase expression in neurons and astrocytes using Rosa26TurboID/wt mice:

Neuronal TurboID cohort: Rosa26TurboID/wt (Jackson Labs, Strain No. 037890) were crossed with Camk2aCreERT2 (Jackson Labs, Strain No. 012362) mice to obtain heterozygous Rosa26TurboID/wt/Camk2aCreERT2/wt (“neuron-TurboID”, n = 3, 1 M and 2 F littermate mice. Heterozygous littermate mice Camk2aCreERT2/wt (“Camk2a”, n = 2, 1 M and 1 F) were used as controls.

Astrocytic TurboID cohort: Rosa26TurboID/wt (Jackson Labs, Strain No. 037890) were crossed with Aldh1l1CreERT2 (Jackson Labs, Strain No. 031008) mice to obtain heterozygous Rosa26TurboID/wt/Aldh1l1CreERT2/wt (“astrocyte-TurboID”, n = 8, 6 M and 2 F). Heterozygous littermate mice Aldh1l1CreERT2/wt (“Aldh1l1”, n= 3, 2 M and 1 F) were used as controls. For astrocyte-TurboID LPS studies, systemic LPS at 0.75/kg/dose x 5 days was given via i.p. injection during the first week of biotin water supplementation. Based on previous work and our own, we validated neuron- and astrocyte-specificity and nonleaky Cre activity of the Camk2aCreERT2 and Aldh1l1CreERT2 models, respectively42,59.

Astrocytic RiboTag cohort: RiboTag mice, Rpl22tm1.1Psam (Jackson Labs, Strain No. 029977) were crossed with Aldh1l1CreERT2 (Jackson Labs, Strain No. 031008) mice to obtain heterozygous Rpl22tm1.1Psam/Aldh1l1CreERT2/wt (“astrocyte-RiboTag”, n = 3, 3 F). Heterozygous littermate mice Rpl22tm1.1Psam (“RiboTag”, n= 2, 1 M and 1 F) were used as controls.

All mice were given tamoxifen (75 mg/kg) intraperitoneally (i.p.) for 5 days at 6 weeks of age and allowed 3 weeks of Cre recombination. After Cre recombination, TurboID mice were given water supplemented with biotin (37.5 mg/L) for 2 weeks until euthanasia at 3-4 months of age. RiboTag mice were euthanized at 2.5 months of age. Mice from each cohort were anesthetized using ketamine/xylazine (ketamine 87.5 mg/kg, xylazine 12.5 mg/kg) followed by transcardial perfusion with ice-cold 1X PBS. The brain was immediately removed and hemisected along the mid-sagittal line. The left hemisphere was immediately dissected to obtain the cortex and then snap frozen using dry ice. The right hemisphere was used for immunohistological studies.

Immunohistochemistry

For immunohistochemistry studies, mice were euthanized and perfused with ice-cold 1X PBS, and the brain was dissected. One hemisphere was immediately transferred into 4% PFA (Cat No. J19943.K2) and fixed overnight for immunohistological analysis. After PFA fixation, the brain was washed with 1X PBS 3 times to remove residual PFA and transferred into 30% sucrose solution until sectioning. After removing extra sucrose, the fixed and sucrose-saturated hemisphere was embedded in the Tissue-Tek optimal cutting temperature compound (Cat No. 4583, Sakura) and frozen on dry ice. Serial 40 µm sagittal sections of the brain were generated using a cryostat (Leica Biosystems, CM1850) and stored free-floating in cryoprotective media (Glycerin, Ethylene Glycol and 0.1 M Phosphate Buffer in ratio of 2.5: 3.0: 5.0) at -20°C. For immunofluorescence (IF) staining, 3-4 brain sections from each mouse were washed 3 times with 1X TBS and then blocked and permeabilized by incubating in 1X TBS containing 0.25% Triton X-100 (TBS-T) and 5% goat serum for 1 h at room temperature. Following, sections were incubated with primary antibodies diluted in 1X TBS-T containing 1% goat serum overnight at 4°C. Next, the sections were washed 3X with 1X TBS and then incubated with fluorophore-conjugated secondary antibodies for 2 h at room temperature in the dark. All primary and secondary antibodies were used at dilution 1:500 including HA-tag (Cell Signaling Technology, 3724S), S100β (Proteintech, 15146-1-AP), βIII-Tubulin (Biolegend, 802001), Streptavidin Protein, DyLight™ 488, and Donkey anti-Rabbit- Alexa Fluor™ 594. Brain sections were then incubated for 5 minutes in nuclear staining reagent DAPI (1 µg/mL) dissolved in 1X TBS. The samples were further washed 3X with 1X TBS, mounted onto glass slides and coverslipped using ProLong Diamond Antifade Mountant (P36965, ThermoFisher). Images of the same brain regions for each mouse were captured using a Keyence fluorescence microscope (Keyence BZ-X810). Images were processed and analyzed using Image J software (FIJI Version 2.14).

Cell and tissue homogenization

BV2 control, BV2-TurboID, HEK293 control, HEK293-TurboID-NES and HEK293-TurboID-noNES cells were washed three times with cold 1X PBS containing 100 µg/mL of cycloheximide. Next, cells were incubated with 1 mL of cold homogenization buffer (10 mM HEPES pH 7.4, 150 mM KCl, 10 mM MgCl2 supplemented with 0.5 mM DTT, 0.1% v/v RNasin, 0.1% v/v SUPERasin, 1X HaltTM protease and phosphatase inhibitors, 100 µg/mL cycloheximide) for 10 minutes, scaped and collected into a 1.5 mL Eppendorf LoBind tube. For brain samples, frozen Camk2a and Aldh1l1 controls and neuron-TurboID and astrocyte-TurboID mouse cortices (up to 50 mg) were lysed in same cold homogenization buffer as the BV2 cells with RNase-free glass beads and a Bullet Blender Homogenizer (Next Advance). RiboTag control and astrocyte-RiboTag mouse cortices were lysed in cold RiboTag homogenization buffer (50 mH Tris-HCl pH 7.5, 100 mM KCl, 12 mM MgCl2, 1% NP-40, 1 mM DTT, 1% v/v sodium deoxycholate 0.1% v/v RNasin, 0.1% v/v SUPERasin, 1X Halt protease and phosphatase inhibitors, 100 µg/mL cycloheximide) similarly to the TurboID samples. All homogenates underwent 3 rounds of sonication (5 s of active sonication at 25% amplitude followed by a 10 s incubation on ice) followed by centrifugation for 10 min at 2000 x g at 4°C. The supernatants were transferred to a new tube and protein concentrations were calculated using a Pierce BCA assay for proteomic studies. To account for variability in infection efficiency, the HEK293 cell lysates per condition (control uninfected, TurboID-NES and TurboID-noNES) were pooled prior to streptavidin affinity purification.

Immunoblotting

To confirm the presence of biotinylated proteins, 17 µg of cell or tissue lysates were resolved on a 4-12% Bis-Tris gel, transferred onto a nitrocellulose membrane, blocked with StartingBlock for 30 min and then probed with a streptavidin-AlexaFluor 680 antibody (ThermoFisher, S32358, dilution: 1:10,000 in StartingBlock) for 1 h at room temperature. To confirm TurboID construct presence, the membrane was probed with a rabbit anti-V5 primary antibody (dilution: 1:500 in StartingBlock) overnight and then incubated with a goat anti-rabbit HRP-conjugated secondary antibody (Jackson ImmunoResearch, 111-035-003, dilution: 1:10,000 in StartingBlock). Immunoblots were imaged with an Odyssey Infrared Imaging System (LI-COR Biosciences), or BioRad chemiluminescence system.

Biotinylated protein and ribosomal protein enrichment for transcriptomics

Following confirmation of biotinylated proteins and V5 presence via immunoblotting, biotin-tagged proteins were enriched for using streptavidin-coated magnetic beads36. For each sample in the BV2, astrocyte-TurboID and neuron-TurboID studies, 25 µL of streptavidin beads (ThermoFisher, 88817) in a 1.5 mL Eppendorf LoBind tube were washed 3 times with 1 mL of homogenization buffer on rotation for 2 min. The streptavidin beads were incubated with 300 µg of protein lysate from each sample with additional wash buffer (10 mM HEPES pH 7.4, 150 mM KCl, 10 mM MgCl2 supplemented with 0.5 mM DTT, 1% v/v NP-40 substitute, 0.1 % v/v RNasin, 0.1% v/v SUPERasin, 1X HaltTM protease and phosphatase inhibitors, 100 µg/mL cycloheximide) to create a final volume of 250 µL on rotation for 1 h at 4°C. For the HEK293 studies, 17 µL of streptavidin beads and 200 µg of protein lysate from each pooled sample was used. Next, the beads were washed four times with cold high salt buffer (10 mM HEPES pH 7.4, 350 mM KCl, 10 mM MgCl2 supplemented with 0.5 mM DTT, 1% v/v NP-40 substitute, 0.1 % v/v RNasin, 0.1% v/v SUPERasin, 1X HaltTM protease and phosphatase inhibitors). After RiboTag control and Astrocyte-RiboTag cortical lysis, 800 µL of lysate was incubated with 4 µL of anti-hemagglutinin (HA) antibody (Biolegend, 901513) for 4 hours on end-over-end rotation at 4°C. For each sample, 25 µL of A/G beads (ThermoFisher, 88803) in a 1.5 mL Eppendorf LoBind tube were washed 3 times with 400 µL of RiboTag homogenization buffer. The A/G beads were incubated with 100 µL of HA-tagged lysate overnight on end-over-end rotation at 4°C. Next, the A/G beads were washed four times with cold RiboTag high salt buffer (50 mM Tris-HCl pH: 7.5, 300 mM KCl, 12 mM MgCl2, 1% v/v NP-40, 0.50 mM DTT, 100 µg/mL cycloheximide).10 mM HEPES pH 7.4, 350 mM KCl, 10 mM MgCl2 supplemented with 0.5 mM DTT, 1% v/v NP-40 substitute, 0.1 % v/v RNasin, 0.1% v/v SUPERasin, 1X HaltTM protease and phosphatase inhibitors). After final washes, the streptavidin and A/G beads were resuspended in 100 µL of wash buffer and then added to 700 µL of Trizol and stored at -80°C until RNA extraction. For global/input lysate/animal, 50 µL of lysate remained at 4°C during the protein enrichment protocols.

RNA extraction

After protein enrichment using streptavidin and A/B beads, RNA was extracted from the beads and or global lysates using a miRNeasy Mini Kit (Qiagen Inc., 217004) following the manufacturer’s instructions. RNA was eluted in RNase-free water and analyzed for purity and concentration using either an Agilent 2100 Bioanalyzer with the RNA 6000 Nano or Pico Kit, or Agilent 4200 TapeStation System prior to RNA-sequencing library preparation.

RNA-sequencing and processing

After assessing RNA quality, cDNA library preparation was performed. The SMART-Seq® v4 PLUS Kit (Takara Bio) was used following the manufacturer’s protocol. For small RNA-sequencing library preparation, a QIAseq miRNA NGS kit (Qiagen, 331565) was used. Global sequencing of 40 million paired-end reads per sample was completed using the Illumina-based platform at Admera Health. FASTQ files were evaluated for quality using FastQC144 and then mapped to the mouse genome, GRCm38 (mm10), or GRCh38, human genome (hg38), using the Spliced Transcripts Alignment to a Reference (STAR)145 (for mRNA-seq) or Bowtie 2146 (for smRNA-seq) aligners with the paired-end option. The featureCounts147 package was used to generate a raw counts data matrix from mapped reads. Lowly abundant transcripts across all samples (average count value ≤ 10) were filtered out. After generating the filtered, raw counts data matrices, we used DESeq2148 to normalize the matrices and perform differential gene expression-based statistics. For astrocyte-TurboID versus astrocyte-RiboTag studies, we used the same astrocyte samples used in neuron-TurboID comparison. RNA-sequencing was completed independently and the resulting FASTQ files were processed and analyzed together. For the Zhang et al., 201460 transcriptomes versus the Sharma et al., 201562 proteomes of acutely isolated astrocytes and neurons, lowly abundant transcripts were filtered out using an average FPKM value of ≤ 3.

Biotinylated or HA-tagged polyribosome protein enrichment for proteomics

For each sample in the BV2, astrocyte-TurboID and neuron-TurboID studies, 42 µL of streptavidin beads in a 1.5 mL Eppendorf LoBind tube were washed 2 times with 1 mL of RIPA lysis buffer (50 mM Tris, 150 mM NaCl, 0.1% SDS, 0.5% sodium deoxycholate, 1% Triton X-100) on rotation for 2 min. The beads were incubated with 500 µg of protein lysate from each sample with additional RIPA lysis buffer to create a final volume of 500 µL on rotation for 1 h at 4°C. For the HEK293 studies, 13 µL of streptavidin beads and 150 µg of protein lysate from each pooled sample was used. Next, the beads were quickly centrifuged, placed on a magnetic rack and the supernatant was transferred to a new 1.5 mL Eppendorf LoBind tube and stored at -80°C. The beads were washed with the following buffers on rotation at room temperature: twice with 1 mL of RIPA lysis buffer for 8 min, once with 1 M KCl for 8 min, once with 1 mL 0.1 M sodium carbonate for ~10 s, once with 1 mL 2 M urea in 10 mM Tris-HCl (pH 7.6) for ~10 s, and twice with 1 mL RIPA lysis buffer for 8 min. After the final RIPA wash, the beads were resuspended in 1 mL of 1X PBS, transferred to a new tube and washed two more times with 1X PBS on rotation for 2 min. To confirm biotinylated protein enrichment, 10% of the streptavidin bead volume was transferred to a new 1.5 mL Eppendorf LoBind tube and boiled in 30 μL of 2X Laemmli protein loading buffer (Bio-Rad, 1610737) supplemented with 2 mM biotin and 20 mM dithiothreitol (DTT) at 95°C for 10 min to elute the biotinylated proteins. Following, 1/3 or the eluate was resolved on a 4-12% Bis-Tris gel, transferred onto a nitrocellulose membrane, blocked with StartingBlock for 30 min and incubated overnight with a rabbit anti-V5 antibody (Abcam, ab206566) on a shaker at 4°C. Next, the membrane was incubated with a goat anti-rabbit HRP-conjugated secondary antibody (Jackson ImmunoResearch, 111-035-003, dilution: 1:10,000 in StartingBlock) and streptavidin-AlexaFluor 680 antibody (ThermoFisher, S32358, dilution: 1:10,000 in StartingBlock) for 1 h at room temperature. The remaining 2/3 of protein eluate was resolved on a 4-12% Bis-Tris gel for a silver stain (ThermoFisher, 24612). Immunoblots were imaged with an Odyssey Infrared Imaging System (LI-COR Biosciences), or BioRad chemiluminescence system. Silver-stained gels were imaged using a Canon scanner (CanoScan LIDE 300).

For astrocyte-RiboTag-based proteomics of HA-tagged polyribosomes and associated proteins, 50 µL of A/G beads were incubated with 200 µL of anti-HA lysate per sample. This ratio was used to approximately match the astrocyte-TurboID bead to lysate ratio. The rest of the protocol matched the RNA pulldowns with the addition of RNA depletion via 50 µg/mL of RNaseA (Thermo Scientific, EN0531) in PBS for 15 min at room temperature on end-to-end rotation. After, the beads were washed 3 times with PBS prior to preparation for MS.

Protein digestion

To prepare enriched biotinylated proteins from BV2, astrocyte-TurboID, and neuron-TurboID for MS-based proteomics, the remaining 90% volume of the streptavidin beads were resuspended in 50 mM ammonium bicarbonate (Na2CO3). Next, the bound proteins were reduced with 1 mM DTT for 30 min at room temperature and then alkylated with 5 mM iodoacetamide (IAA) in the dark for 30 min. Following, proteins were subsequently digested overnight with 0.5 µg of lysyl (Lys-C) endopeptidase (Wako, 127-06621) and then 1 µg of trypsin (ThermoFisher, 90058) on the shaker at room temperature. After digestion, the resulting peptide mixtures were acidified to a final concentration of 1% formic acid and 0.1% trifluoroacetic acid, desalted with an HLB column (Waters, 186003908), and dehydrated using a vacuum centrifuge (SpeedVac Vacuum Concentrator). The same approach was used for HA-tagged polyribosomes from astrocyte-RiboTag samples. To prepare global lysates for MS-proteomics, 50 µg of protein from each sample was reduced in 5 mM DTT for 30 min at room temperature and then alkylated with 10 mM IAA for 30 min in the dark. After, each sample was diluted (4-fold) with 50 mM ammonium bicarbonate (ABC) buffer and then digested with 1 µg of Lys-C endopeptidase on the shaker overnight at room temperature. Next, samples were diluted (4-fold) with 50 mM ABC buffer and digested with 2 µg of trypsin on the shaker overnight at room temperature. After digestion, the peptide mixtures were acidified, desalted, and dried down as described above.

To prepare enriched biotinylated proteins from HEK293 TurboID-NES and TurboID-noNES samples for MS-based proteomics, the remaining 90% of the streptavidin beads were resuspended in 5% SDS in 50 mM triethylammonium bicarbonate (TEAB). Proteins were reduced with 10 mM DTT for 1 hour at room temperature, followed by alkylation with 20 mM iodoacetamide (IAA) in the dark for 30 minutes. The proteins were then acidified with phosphoric acid. A mixture of 100 mM TEAB and 90% methanol (MeOH) was added, and the samples were transferred to S-Trap micro spin columns (Protifi). Binding and washing steps were performed according to the S-Trap protocol. Digestion was carried out by adding 0.5 µg Lys-C and 1 µg trypsin, followed by incubation at 37 °C for 18 hours. Peptides were eluted sequentially with 50 mM TEAB, 0.2% formic acid, and 50% acetonitrile, then dried using a SpeedVac. The same protocol was followed for the global lysates with 25 µg of protein from each sample used as input. Peptide concentration was determined by fluorescence-based BCA assay. Finally, samples were injected volumetrically into the mass spectrometer.

Mass spectrometry

Mass spectrometry preparation and acquisition parameters were selected based on conditions established in our prior work31,33,36 and detailed here. For BV2, astrocyte-TurboID, and neuron-TurboID studies, dried peptides were reconstituted in 15 µL of loading solution (0.1% formic acid and 0.03% trifluoroacetic acid in water), and 7-8 µL was injected onto a self-packed 25 cm analytical column (100 µm inner diameter) packed with 1.7 µm Waters CSH particles. Peptides were separated using either an Easy-nLC 1200 or a Dionex 3000 RSLCnano nanoLC system. The gradient began at 1% solvent B (80% acetonitrile containing 0.1% formic acid) and increased to 5% over 10 seconds, followed by a 55-minute linear gradient to 35% solvent B. The run concluded with a 4-minute, 50-second wash at 99% solvent B. Following, an Orbitrap Lumos Tribrid MS with a high-field asymmetric waveform ion mobility spectrometry (FAIMS Pro)149 interface was used to obtain all mass spectra at a compensation voltage of -45V. BV2-TurboID in vitro and astrocyte-TurboID and neuron-TurboID in vivo studies were completed using data-dependent acquisition (DDA) LFQ-MS using top-speed mode with a 3 s cycle time. Full MS scans were acquired in the Orbitrap at 120,000 resolution across an m/z range of 400-1,600, with an automatic gain control (AGC) target of 400,000, a maximum injection time of 50 ms, and the RF lens set to 30%. MS/MS spectra were generated by higher-energy collision dissociation (HCD) and analyzed in the ion trap using a normalized collision energy of 35% and an isolation window of 0.7 m/z. The AGC target for MS/MS scans was 10,000 with a maximum injection time of 35 ms. Dynamic exclusion was enabled for 30 s with a mass tolerance window of 10 ppm.

For astrocyte-RiboTag and HEK293 studies, dried peptides were separated on a 75 µm x 60 cm analytical column (Thermo Fisher Scientific) using a 135-min (astrocyte-RiboTag) or 115-min (HEK293) gradient from 1-99% solvent B (80% acetonitrile, 0.1% formic acid) at 300 nL/min. MS analysis was performed on an Orbitrap Exploris 480 (Thermo Fisher Scientific) in positive ion mode. Full MS scans were acquired at 120,000 resolution (m/z 400-1000), and data-independent-acquisition (DIA) MS/MS scans at 30,000 resolution (m/z 150-2000) using 10 m/z isolation windows with 1 m/z overlap and HCD fragmentation at 28% normalized collision energy.

Protein identification and quantification

DDA raw MS files for global and pulldown protein from BV2-TurboID in vitro and astrocyte- and neuron-TurboID in vivo studies along with respective controls were searched separately using the Andromeda150 search engine integrated into MaxQuant151 (version v2.4.2.0), against the 2020 downloaded mouse UniProt proteome (https://www.uniprot.org/proteomes/UP000000589) including sequences for V5 and TurboID. DIA Raw MS files from the astrocyte-RiboTag pulldown and the HEK293-TurboID (NES versus noNES) global and pulldown experiments were searched separately using the Pulsar search engine integrated into Spectronaut (version 19)152. The astrocyte-RiboTag search was performed against the same 2020 mouse UniProt proteome described above (with the HA sequence included). The HEK293-TurboID studies were searched against the 2025 downloaded human UniProt proteome (https://www.uniprot.org/proteomes/UP000005640). Search parameters were determined by our prior work31,33,36,37,153 and described here. Methionine oxidation (+15.9949 Da) and protein N-terminal acetylation (+42.0106 Da) were variable modifications (up to 5 allowed per peptide); cysteine was assigned as a fixed carbamidomethyl modification (+57.0215 Da). Only fully tryptic peptides were considered with up to 2 missed cleavages in the database search. A precursor mass tolerance of +/-20 ppm was applied prior to mass accuracy calibration and +/-4.5 ppm after internal calibration. Other search settings included a maximum peptide mass of 4600 Da, a minimum peptide length of 6 residues, 0.05 Da tolerance for Orbitrap and 0.6 Da tolerance for ion trap MS/MS scans. Quantification settings were as follows: re-quantify with a second peak finding attempt after protein identification has completed; match MS1 peaks between runs; a 0.7 min retention time match window was used after an alignment function was found with a 20 min RT search space. Peptide spectral match false discovery rate (FDR) was set to 1%. The MaxQuant or Spectronaut output data (raw intensity values) were uploaded to R (version 4.3.1) for normalization, log transformation, and differential abundance analyses.

To account for endogenously biotinylated and non-specific proteins captured during streptavidin affinity purification, mean intensity values from TurboID-negative control pulldown samples were subtracted from the TurboID-positive pulldown samples by group. To account for variability in TurboID expression, biotinylated protein abundance, and streptavidin affinity purification efficiency, intensity values were normalized depending on the dataset. For BV2 in vitro studies comparing the abundance values from global protein and enriched pulldown samples, samples were normalized by taking each sample’s log-transformed summed protein intensity median and setting it to zero. The BV2-TurboID pulldown groups (untreated and LPS-treated) intensity values were normalized based on TurboID abundance. We did not find a difference in TurboID abundance in the other SPARO datasets, so this normalization approach was only completed on the BV2-TurboID pulldown groups. For astrocyte-TurboID and neuron-TurboID in vivo studies, enriched pulldown samples were normalized by subtracting the difference from the mean of the log-transformed summed protein intensity for 71 ribosomal subunit proteins present in all TurboID-positive pulldown samples. Proteins were filtered out if they had missing values in 2/3 samples across groups. Missing values were imputed from normal distribution for each sample. Nomination of proteins as either ribosomal or RNA-binding were determined using the EMBL RBPbase (v0.23 alpha), a database that integrates high-throughput RNA-binding protein detection studies (https://apps.embl.de/rbpbase/).

Data analysis and visualization

We utilized differential enrichment analysis, multidimensional scaling (MDS), and gene set enrichment analysis (GSEA) to analyze transcriptomic and proteomic data from in vitro and in vivo studies. Data analysis and visualization was completed using R software (version 4.3.1) and Prism (GraphPad, version 10). Venn diagrams were generated using either a protein missingness (present in 2/3 samples) or low abundant transcript (average count value ≤ 10) filter per sample group. For transcriptomic studies, the DESeq2148 Wald test was used to identify DEGs between groups. For proteomic studies, unpaired two-sided (equal variance assumption) t test equivalent calculations using F value from ANOVA run strictly with 2 comparison groups were performed with the parANOVA suite of functions in R (https://github.com/edammer/parANOVA) to identify DEPs between groups. DEGs and DEPs are presented as volcano plots. GSEA was applied using the GOparallel function (https://github.com/edammer/GOparallel) that scrapes monthly updated .GMT formatted GO from the Bader Lab website (https://baderlab.org/) as gene symbol lists submitted to one-tailed Fisher’s exact test for GO enrichment of overlapping symbols in gene lists of interest, including DEPs and DEGs. GO terms meeting Fisher exact significance of p value ≤ 0.05 (i.e., a z ≥ 1.96) were considered. Protein and gene input lists were significantly differentially biochemically enriched over global or background, in addition to gene lists that were different by treatment condition (p ≤ 0.05, ≥ 1-log -fold change). To assess average GC content, number of exons, and average transcript length of transcriptomic data, we used the biomaRt R package154,155 and the BioMart/Ensembl databases156. To examine protein half-life information of proteomic data, we used values from Fornasiero et al., 201868.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "- What are the noteworthy results?\nIn this paper, the authors claim to have developed a new approach that allows simultaneous cell type specific RNA and\nproteomic profiling by adapting TurboID. Specific claims include:\n1) TURBO ID allows enrichment-via-biotinlyation of both cytosolic RNA and protein.\n2) This was validated in an in vitro system (BV2) cells\n3) The method is sensitive enough to detect changes in response to a stimulus known to change proteome and\ntranscriptome (LPS on BV2 cells).\n4) The method works in vivo as tested in comparing neurons to astrocytes (comparing to existing benchmark data)\n5) They examine mRNA/protein concordance & discordance and report how this pattern differs by cell at the level of GO\nterms.\nThey also generally argue this will be a useful method for biological discoveries in the future.\n- Will the work be of significance to the field and related fields?\nBeing able to simultaneously measure RNA and protein in the same cell type, in vivo, is a potentially transformative\nadvance. As such, this is a very clever adaptation of existing tools, and a neat proof of concept study comparing two cell\ntypes in the brain. I think it could open up new questions on gene and protein regulation in the brain and represents a nice\nbody of work potentially suitable for this journal. But, it is a bit fuzzy on what kind of new questions one could ask with it right\nnow. I have a couple of recommendations below of where they could address that. On the whole, I think it is a quite cool\nidea, but it could benefit from a more concrete description of a couple clear use cases.\nHow does it compare to the established literature? If the work is not original, please provide relevant references.\nIt reminds me a bit of Alice Ting’s Apex-Seq, but since that requires Biotin-Phenol I am not sure it would work in vivo, but it\nmight be worth comparing this to that a bit in discussion or intro.\n- Does the work support the conclusions and claims, or is additional evidence needed?\nFor the most part, the work does support the conclusions. But it is a bit unclear where the authors think this new approach\nmight make impact in the future. For example, this is a rather vague sentence to lead off the discussion: “Measuring\ntranscriptomic and proteomic levels is important to identify cellular and molecular mechanisms of biology.” The last para of\nthe discussion also has several similarly vague sentences about the contribution this new method could make. Can the\nauthors make these sections more concrete?\nIn general the discussion is a bit of a missed opportunity, as it spends a larger balance of the space restating the findings\nand less time on describing that would be done next. Or discussing technical considerations on implementing this. For\nexample, is this time course of labeling required (something that might be addressed in experiments, see below)?\nThere is also probably one more set of experiments needed to understand the noise of the method in general (see ‘Is the\nMethodology Sound’ section).\n- Are there any flaws in the data analysis, interpretation and conclusions? - Do these prohibit publication or require revision?\nOverall, I view my comments as requiring revision rather than prohibiting publication.\nFigure S7d: TurboID and RiboTag have substantial differences (thousands of DEGs in Figure S10) that are rather\nunderplayed here. I would recommend a couple of analyses digging into these differences more. Maybe something similar to\nwhat they do in Figure S10?\nThe authors spend some time on page 8 speculating why the transcriptome and proteome are sometimes discordant. One\nadditional source of such discordance could simply be cellular compartment: it seems like even in vivo the TURBO-ID is\nrestricted to the cytoplasm? Yet the transcripts that code for all proteins in the cell (nuclear, mitochondria, etc), are largely\ntranslated in the cytoplasm. So some of the discordance could simply reflect that the mature translated protein ends up\nsomewhere in the cell where it cannot be readily tagged. Likewise, any excreted proteins might be lost to TurboID labeling\n(including potentially Lcn2, which they highlight in their discussion). When the come back discussing discordance on page\n10, they do bring up this possibility that it is driven by cellular compartments in part. However, they may wish to revise these\nsections, as the different cellular compartments is probably the simplest explanation and should be raised earlier.\nThe difficulty in interpreting RNA/Protein comparisons is the part of the paper I find the most challenging and that would\nmake me hesitant to bring the approach into my own lab. Is there anything that could be done informatically to address this?\nFor example, if they limit the analysis comparing RNA to Protein to the proteins that are in the cytoplasm (potentially by GO),\ndoes the correlation improve between RNA and Protein? Does the ability to use things like protein half lives (or perhaps\ncodon usage) to predict Protein/RNA discordance improve? And can they start to look at cell differences in RNA/Protein\ndiscordance in a more insightful way if limiting their analysis to proteins that have a better chance of being tagged (i.e., the\ncytoplasmic ones)? On the whole, this comparisons seems to be one of the motivating aspects of the paper, so it would be\nnice to have some recommendations on how to approach these questions with the current technique. (I appreciate that the\nauthors also discuss future method changes like removing the NES that might address this experimentally).\n- Is the methodology sound? Does the work meet the expected standards in your field?\nThey need to better account for sex in their work (and in future studies, may want to balance for it, though there is clearly no\nway to travel back in time to change samples now). For example, in Figure 2i. Did the neuron line samples have more\nfemales than the astrocyte line samples? Otherwise can the authors explain why Xist (a female-only transcript) is a top\ngene? Actually, this comes up across several other figures (e.g., Fig S7d, which also shows a lot of Y chromosome genes).\nAh, as I get to discussion, it does seem like the sexes were not well balanced in the experiment. Hmm.. the authors may\nwish to flag sex-chromosome derived ‘DEG/DEPs’ as being potential false positives (driven by sex rather than cell type)\nwith a column in their supplemental tables and explicitly exclude them from volcano plots.\nThe method would benefit from a more quantitative characterization of the noise, as it is a new method. Did the author\nsequence mouse brain samples that did not have the Turbo allele? I agree from the tapestations there is less RNA coming\ndown without it, but some is still there. Knowing what is in that RNA (and potentially filtering/subtracting it from the true\nsamples) is important to characterize noise, eliminate sources of false positives in some analyses (e.g., Riboseq vs. their\nmethod) and potentially improve signal to noise in the future. Potentially similar experiments might be done with proteomics\ntoo.\nCan the authors address to what extent is it necessary to feed the animals biotin? I assume there is some biotin present in\nthe cells in vivo regardless. How does signal change with vs. without the added biotin in the diet?\nLikewise, is there a relatively cheap and fast way to explore how features of the biotin pulse to the animal change the yield\n(like just I.F. or quantifying tape station, streptavidin immunoblot or silver stain) that would give a sense of how much the\ntiming and magnitude of that pulse needs to be optimized by labs adopting this approach? Or if this is already done in prior\npapers, perhaps allude to that in methods.\n- Is there enough detail provided in the methods for the work to be reproduced?\nYes. The methods were clearly written.\nTypos/minor notes:\nShould the A be underlined and bolded when defining the acronym for SPARO?\nFigure 1l disagrees with the results text on the magnitude of a correlation (.68 vs .83).\nFigure 1 legend about 1d: mRNA is usually not visible on a tapestation, just the rRNA.\n“Hepacam, Glu, Aqp4, Plpp3, p ≤ 0.05, ≥ 1-fold change” should this be “> 1 log2 fold-change”?\nFigure S10: If you calculate a logFC of RNA vs Protein, can you then compare the protein half lives to that? For ‘a’ does\nseem to be a relationship between quadrant and protein half life coloration, though it seems to disappear by b and c. Being\nable to more directly compare Protein/RNA to half life might have more explanatory power.\nI am also confused by the next sentence “Less than 5% of gene products exhibit cell type-specific expression patterns (via >\n4-fold enrichment), and as a result, bulk tissue -omics studies cannot completely resolve changes occurring at the level of\nindividual cell types55,59” I am trying to understand how the first clause of the sentence results in the second clause.\n“which is in line with prior studies57–59” can the authors highlight more quantitatively the extent of correlation seen in prior\nstudies? Is it similar to their .24-27 (Figure S8) Are their bulk results (.47) more comparable in magnitude of correlation to\nthose prior studies?\nThis sentence does not make much sense “There are also limited resources to determine which allele of a gene yields a\nparticular protein isoform.” Aren’t all these mice inbred? And thus homozygous at most alleles?", "reviewer_2": "NCOMMS-25-08576\nSimultaneous profiling of native-state proteomes and transcriptomes of neural cell types using proximity labeling\nThe manuscript by Ramelow et al. outlines the development and application of a novel method for complementary profiling\nof cell type-specific proteomes and transcriptomes called “SPARO” (simultaneous protein and RNA -omics). They validate\ntheir approach using a microglial cell line in vitro and then in neurons and astrocytes in vivo using the same mouse lines for\ncell type-specific, cytosolic expression of biotin ligase TurboID that they described previously in Rayaprolu et al., 2022,\nNature Communications. The main difference between the work in this manuscript and their previous work is that they\nperform both proteomics and transcriptomics to investigate the correlation between mRNA and protein abundances, of which\nis a useful analysis to glean insight on post-transcriptional regulatory processes. Owing to the cell type-specificity of their\napproach, the authors compare the concordance between neuronal and astrocytic proteomes and transcriptomes. Although\nSPARO, in theory, builds upon their previously published work, there are limitations that need to be addressed in order for\nthem to complete a meaningful assessment of mRNA/protein concordance. As is, the conclusions made from their\nmRNA/protein concordance analysis are lackluster and lack experimental validation.\nMajor criticisms/suggestions:\n1. Regarding Fig.1: The drastic difference in TurboID expression between LPS-treated and non-treated BV2 cells is\nconcerning. Why is the expression in the non-treated cells (as indicated by the V5 immunolabeling in Fig.1c) about half of\nthat in the LPS-treated cells? The streptavidin labeling is also dramatically different between the LPS-treated and untreated\ncells (Fig.1c/Fig.S1a). To be able to compare the LPS-treated and nontreated transcriptomes/proteomes, the level of\ncytosolic TurboID expression needs to be similar. Otherwise, differences in mRNA and protein abundances may reflect\ndifferences in the extent of biotinylation/labeling, rather than bona fide differences in expression. In the methods, the authors\noutline how they perform median centering of the log2 transformed intensity values to account for variability in TurboID\nexpression, biotinylated protein abundance, and streptavidin affinity purification efficiency. Given the magnitude of these\ndifferences however, I am not confident that this normalization method is sufficient. For their transcriptomic data analysis, the\nauthors do not account for this variability. In Fig.1e, the LPS pulldown proteome has almost 20% more proteins than the non-\nLPS pulldown proteome. In Fig.1f, the LPS pulldown transcriptome has 58% more mRNAs. Is this reflecting true changes in\nprotein/mRNA abundances or is it because of differences in the extent of biotinylation/labeling?\n2. Regarding Fig.1: Does the 48 h LPS treatment lead to increased expression of TurboID? This needs to be investigated\nand taken into consideration when interpreting results. The authors should include immunofluorescent images and\nquantification of the V5 and streptavidin labeling of the BV2 control cells and BV2-TurboID cells before and after LPS (or\nmock) treatment.\n3. Throughout the manuscript, the authors mistakenly attribute what is likely Agilent TapeStation images as “RNA gel\nelectrophoresis”. The authors need to relabel this (perhaps as “RNA ScreenTape analysis” or something similar) because\nthe Agilent TapeStation does not use traditional gel electrophoresis, but rather microfluidics. The details are missing in the\nmethods. The authors state that “high levels of rRNA and mRNA were detected”, but there are no real bands except the 28S\nRNA (~4000 nt) and 18S rRNA (~2000 nt) bands in Fig.1d. The authors should perform standard RNA polyacrylamide gel\nelectrophoresis instead.\n4. Overall, validation of their findings is lacking (e.g., LPS treatment experiments in Fig.1 and Fig.S6). Validation (e.g.,\nimmunofluorescence imaging) is essential.\n5. Regarding Fig.1e: The authors find only ~50% overlap between the biotinylated cytosolic proteome and the bulk\nproteomes. They say that this is likely because the biotinylated cytosolic proteome doesn’t contain the proteomes of the\nnuclear and mitochondrial compartments. The authors should look into this further to back up their claim. Why not subset\ntheir bulk proteome data for cytosolic proteins and then do the comparison? Biotinylation by TurboID requires certain amino\nacids to be present in a given protein and accessible. The authors could also determine the extent of the cytosolic proteome\n(using annotated datasets) that is captured in the pulldown proteome and check to see which, if any, cytosolic proteins aren’t\nbeing captured.\n6. Regarding Fig.3: The authors compare the abundance/enrichment of ribosomal proteins in the cre-only control vs.\nastrocyte-TurboID pulldowns and are able to capture 66 RPs and 67 RNA-binding proteins. A more important comparison\nwould be between the astrocyte-TurboID pulldown and the astrocyte-RiboTag HA IP eluate.\n7. Regarding Fig.3: The authors state that “a notable difference between the SPARO and RiboTag transcriptomes is that in\nSPARO, TurboID biotinylates 133 proteins that interact with RNA (e.g., ribosomal and RBPs), while the RiboTag approach\nlabels one ribosomal protein”. To back up their claim, the authors should report how many of these 133 proteins are detected\nin the RiboTag IP HA eluates. I would assume that several RBPs will be detected, even without UV crosslinking.\n8. Regarding Fig.4: Without whole-cell TurboID expression, mRNA/protein concordance analysis is problematic. A major\npoint of simultaneous proteomic/transcriptomic profiling is to be able to do this analysis in the first place. With cytosolic\nTurboID, even with 2 weeks of labeling, caution and care needs to be taken in interpreting the mRNA/protein concordance\nfor luminal proteins localized within membrane-bound organelles as well as any protein localized within\ndouble-membrane-bound organelles. The highly discordant genes (mRNA >> protein) related to mitochondria in both astrocytes\nand neurons should be validated. Is the observed discordance because these mRNAs are co-translationally imported at the OMM,\nand therefore escaping cytosolic biotinylation?\n9. Validation of mRNA-protein discordant astrocytic/neuronal markers is lacking.\nMinor criticisms/suggestions:\n1. The gain should be increased in the silver stain image in Fig.S1c. The figure legend states that “several proteins were\nenriched in the BV2-TurboID samples”, but it’s not obvious.\n2. The authors mention how assessments of protein/mRNA concordance are confounded by differences in the sensitivity of\nLFQ-MS and RNA-sequencing. Did they authors do data-independent acquisition (DIA), which is more sensitive than data-\ndependent acquisition (DDA)?\n3. In the discussion, the authors mention potential brain region-specific differences. Why do the authors not compare their\ndata (cortex) with the Soto et al., 2023 dataset (striatum)?", "reviewer_3": "In this manuscript, Ramelow and co-authors present SPARO, a new method to simultaneously measure transcriptomes and\nproteomes from living systems. They first benchmark this method against global transcriptomics and global proteomics using\nan in vitro model, then go on to apply this method in an in vivo mouse model to simultaneously capture cell-type specific\nproteomes and transcriptomes in the brain. From this data, they go on to interrogate protein-mRNA concordance and\ndiscordance across cell types.\nI enjoyed reading this manuscript, and I think that this method would be a valuable addition to the scientific community. The\nin vivo cell-type specific profiling in particular was quite compelling, and certainly addresses a technological gap. I do,\nhowever, have some questions and concerns that I would like to see addressed prior to recommending for publication. I\nacknowledge that there are many points raised below, but I will preface that I am not asking for any additional experiments - I\nwould just like some clarification on the authors' methodologies and interpretation of the data, the inclusion of some\nadditional analyses, and more discussion in the main text on the points outlined below.\nMy major concerns are largely surrounding the in vitro method validation upon which the rest of the work is based, in\nparticular the technical robustness of both the transcriptomic and proteomic components of the TurboID enrichment method\nfor SPARO and the MS data acquisition and analysis. Due to these concerns, I also do not think this method would be\nsuitable for spatial transcriptomics as mentioned in the discussion. Further, due to the bias introduced by TurboID labeling,\nI have concerns for SPARO being used to characterize RNA/protein concordance and discordance, although I do recognize\nthat it could still be very useful for comparing changes in these patterns between conditions and for proposing proteomic or\ntranscriptomic biomarkers. Below I have outlined these concerns and others, as well as some minor points to be addressed:\n1. I appreciate the time the authors took in the introduction to discuss other technologies in this space to highlight current\ngaps, however there were two additional methods that I recommend the authors include. The first is CITE-Seq from Illumina,\nwhich allows simultaneous quantitation of transcriptomes and proteins from single cells - I think the differences (and\nadvantages) of SPARO are quite easy to highlight (limited panel of proteins that can be quantified in CITE-seq, custom\nantibody reliance, cost). The other more relevant methods are APEX-seq and CAP-seq (see publications below), which are\nother proximity labeling methods that can be used to capture cell-type specific proteomes and transcriptomes using very\nsimilar workflows to SPARO. The main differences are that first, the proximity labeling enzymes used in these methods,\nAPEX2 and miniSOG, can biotinylate nucleic acids directly like they can biotinylate proteins, which could allow for fewer\nfalse negatives compared to SPARO (although looking at the high correlation between your SPARO transcriptomes and\nglobal transcriptomes, I don’t think this is actually an issue), but also allows harsh washing through the entire enrichment\nprotocol which could allow for fewer false positives (which I am concerned could be an issue for SPARO). The second major\ndifference is that these methods require either the addition of hydrogen peroxide (APEX2) or light (miniSOG) to initiate\nlabeling, therefore they would most likely need to be done ex-vivo (although there are a few in vivo studies for both).\nSPARO has a clear advantage here, as the simple biotin labeling allows for non-invasive, live labeling in the intact animal.\na. Fazal, F. M. et al. (2019) Atlas of Subcellular RNA localization revealed by APEX-seq. Cell, 178, 473-490.\nb. Padron, A. et al. (2019) Proximity RNA labeling by APEX-seq reveals the organization of translational initiation\ncomplexes and repressive RNA granules. Molecular Cell, 75, 875-887.\nc. Li, R. et al. (2022) Metabolic incorporation of electron-rich ribonucleosides enhances APEX-seq for profiling spatially\nrestricted nascent transcriptome. Cell Chemical Biology, 29, 1218-1231.\nd. Wang, P. et al. (2019) Mapping spatial transcriptome with light-activated proximity-dependent RNA labeling.\nNature Chemical Biology, 15, 1110-1119.\n2. Why did you choose to carry out the TurboID biotinylation for 24 hours? This is quite excessive to the 10 min labeling time\nrecommended for in vitro overexpression systems, particularly for the TurboID-NES construct. Yet, I was surprised to see\nthat the extent of biotinylation observed in the streptavidin blot of whole cell lysates was much lower than what I would have\nexpected for this long of a labeling period (Supplementary Figure 1a, comparing the biotinylation signal in the +TurboID\nsamples to the signal from the endogenously biotinylated proteins). Are your TurboID expression levels very low? Also,\ndoes it appear that TurboID expression and thus biotinylation increases with LPS stimulation? If so, what are your thoughts\nabout this? It would be nice to have the V5 blot shown for the whole-cell lysate blots in addition to the pulldown blots.\n3. I am a bit concerned about the efficiency of your pulldown, as there is robust biotinylation shown in the whole cell lysate\n(Fig S1a) but very little in the eluate (Fig. 1c, S1c), in the silver it looks like it’s mostly endogenously biotinylated proteins\npulled down? Did you blot your flow through to show that you successfully depleted your whole cell lysate of biotinylated\nmaterial? Inefficient capture could explain the low now numbers you see in your MS data.\n4. Does Figure 1d really show that you only enrich RNA with proteomic biotinylation? While you do have stronger\nenrichment of the two darkest bands (rRNA?) with TurboID labeling, it appears to my eye that RNA of other sizes are\nactually more strongly enriched in the non-Turbo samples - definitely not “negligible”, looks like many RNAs could be non-\nspecifically binding. Had you happened to run RNA sequencing on the TurboID negative samples? If so, how many\ntranscripts did you detect/quantify compared to TurboID positive samples and how do these compare to the bulk RNA seq\ndata?\n5. I am quite concerned that you only quantify 1455 proteins in your global proteome. Honestly, this is very low and a red\nflag, so please explain. For global proteomics, I would expect at least 7000 but up to 9000+ proteins. Only 1038 proteins is\nalso very low for a TurboID enriched proteome. For TurboID-NES, I would expect detection of at least 3000 but up to 5000+\nproteins. I know that you state in your discussion that more sensitive MS approaches are likely to increase the depth of the\nproteome, but the numbers you have here are far below what would be expected from a Lumos Tribrid MS instrument.\n6. Is the fidelity of the TurboID vs global RNA-seq datasets suspicious given the large apparent bias in the proteomics\nresults that are claimed to be due to nuclear exclusion? Are there not several nuclear specific RNAs that you would expect\nto miss for the same reason (not a leading question, genuinely asking as a non-expert)? Could this very high overlap\nbetween the TurboID and global datasets further suggest that many of these RNAs being detected are ones that are being\nnon-specifically enriched and not due to the TurboID labeling?\n7. Figure 1g is honestly a bit confusing for me. What is this dimensional scaling based off of, intensity as stated in the\nlegend, or log2FC as stated in the figure? If Log2FC, what is the Log2FC based off of since all four conditions are plotted\nseparately? How are you dealing with missing values? The default imputation option in MaxQuant is probably not the best\nfor this type of data. I’m not very familiar with MDS, but assuming I can read this like a PCA, this figure is saying that the\ndifferences in proteins detected by global proteomics is lower than the differences detected by TurboID proteomics, but\nopposite for the RNA data? Is there a technical or biological reason that would explain this? Could this also be due to batch\neffects? DDA LFQ is very sensitive to technical variability, you discuss this caveat as well as tell readers of more reliable\nquantitation methods like TMT or DIA in the discussion. As for this MDS plot, please explain more or remove entirely - as is,\nthis is not very meaningful. Honestly, showing the correlation between methods and the volcanoes within the methods\ncomparing the +/- LPS may be sufficient to show that you are successfully detecting the effect of LPS using the different\nmethods.\n8. I saw in the methods that you are using a reference proteome from 5 years ago (and an old version of MaxQuant), which is\nquite dated considering how often these reference proteomes get updated. Is this simply when the experiment was\nconducted? If not, please justify why you are using this as the reference proteome. Also, does this reference proteome match\nwith the appropriate reference genome for the RNA-seq data? If there are discrepancies this could explain some of the\ndifferences observed instead of being due to the biology.\n9. For Fig. 1h, please explain where the 1455 shared proteins are coming from, because Fig. 1e indicates that only 1232\nproteins are shared between the +LPS global and pulldown proteomes. Also, there should be more discussion on why there\nis such low correlation in the protein abundances between the pulldown and global proteome datasets - likely, it is not\nsimply due to the nuclear exclusion of TurboID, but also how many solvent-exposed and sterically-accessible lysines a\nprotein has to be biotinylated by TurboID, how reactive these lysines are, how well a protein is able to be pulled down, etc. A\nprotein may be very abundant in the cytosol and highly detected by global proteomics, but if it isn’t capable of being labeled\nby TurboID then it will not appear in the pulldown data.\n10. For Fig. 1i, please explain where the 12742 shared transcripts are coming from, because Fig. 1f indicates that only\n11947 transcripts are shared between the +LPS global and pulldown transcriptomes. Again here, I worry that the high\ncorrelation between the global and pulldown datasets is suggestive that most of the RNA signal is not due to TurboID\nenrichment. As discussed in the point above, due to the chemical nature of TurboID labeling, there is a bias in the proteins\nenriched which I would expect to also bias the associated RNAs being pulled down.\n11. Was the same cell lysis/sample preparation used for the global transcriptomics and the RNA enrichment for SPARO?\nIf so then it is encouraging that mitochondrial RNAs were only enriched in the global (Supplementary Fig. 2a), this would be\nsomething that supports the TurboID enrichment is actually working for the RNA workflow of SPARO, but again it is\ndiscouraging that you are not depleting nuclear RNAs as well. It is also interesting that you are so strongly enriching\nACACA, which encodes an endogenously biotinylated carboxylase - this is something that we would expect to see enriched\nin the protein pulldown but why do you think you are enriching this RNA so strongly? Looking at your GO term enrichment,\nare you surprised that the mitochondrial doesn’t come through more strongly, particularly in the non-LPS dataset? Your most\nenriched gene sets appear to be cytoplasmic translation and ribosomal terms, not mitochondrial terms. I wonder if you would\nhave a clearer signal if you used fewer gene sets, and perhaps rolled up into more general subcellular components (e.g.\ncytosol, nucleus, mitochondria, etc.)\n12. For Supplementary Figure 3, looking at the GO term enrichment, it is not immediately obvious to me that you are more\nbiased to cytosolic proteins in the pulldown data and enriching more nuclear proteins in the global data. And given the\nmitochondrial enrichment you saw in the global RNA data, why do you not see this same signature in the global proteomic\ndata? Again, I suggest trying to simplify these categories and your signature may become clearer, for example “Haus\ncomplex” and “protein dimerization activity” are not useful terms for me as a reader to reach the same conclusion that you\nhave stated in the main text.\n13. Not necessary but if you happen to have IF data of your TurboID-NES construct in the BV cell line it would be useful to\nsee so you can show that it is indeed nuclear excluded, and that you have a matching biotinylation pattern.\n14. Looking at your methods, it appears that the TurboID transcriptome and proteome enrichments were done separately,\nam I interpreting this correctly? I understand that these were both likely done from the same biological sample, but if the\npulldowns were done separately then this adds another source of possible technical variability. In the future, is there any\nway that you could do the RNA pulldown first, elute the RNAs in a way that preserves the proteins on bead, apply the\nstringent washes for the protein pulldown to the same beads, and on bead digest to elute the peptides?\n15. I am by no means an expert in the BV cellular response to LPS, but I do not find the GO term enrichment shown in Fig.\n1k, Supplementary Fig. 1g very useful for showing that you clearly enrich an LPS-treatment signature, again because you\nused so many gene sets in your analysis. For the RNA data (Fig. 1n, Supplementary Fig. 1j), at least there are at least terms\nwith “LPS”, is there a reason you think that these are absent in the protein data? Is there perhaps a list of well\nvalidated proteins that are known to be up-regulated/down-regulated in response to LPS treatment that you could highlight on\nthe volcano plot? I see that you later highlight a few proteins, but is there a more comprehensive list that you could show?\n16. For Fig. 1l, the log2FC(+/-LPS) between global and pulldown shared proteins is basically the same as your protein\nabundance correlation, which I thought would be higher because you are “controlling” for factors of labeling and pulldown\nefficiency in the +/-LPS comparison. It is encouraging that your highest differentially enriched proteins correlate better\nbetween the methods, but how do you explain why so many more proteins were found to be differentially\nenriched with the TurboID vs global experiment? Is it possible that the TurboID experiment is detecting not just changes in\nprotein abundance, but also changes in protein localization? Also, again as a non-expert, would you really expect to enrich\nanti-inflammatory markers in the -LPS dataset?\n17. I know I am belaboring this point, but does the fact that your RNA Log2FC correlation data is so high compared to the\nprotein data again suggest that the pulldown is not working the same for the RNA and protein enrichment? For example, are\nthere not nuclear-specific RNAs that are differentially expressed in response to LPS that you would see biased towards the\nglobal dataset (this is not a leading question, I genuinely don’t know the answer to this)? There really needs to be more\ndiscussion on why the correlation between the methods is so different for RNA vs protein, I am concerned that this may be\ndue to the RNA data being largely based on non-specific binders and the proteomic data being due to poor enrichment\nefficiency. Regarding the transcriptomic robustness, however, I am encouraged by your in vivo data that shows SPARO can\nbe used to capture cell-type specific transcriptomes, suggesting that there is minimal non-specific binding of RNAs from\nother cells in the lysate.\n18. For Fig. 2b, there are cells that are positive for biotinylation but negative for the astrocyte marker (S100beta) or neuron\nmarker (beta III tubulin), do you think these are still astrocytes/neurons (non-expert here) or are you concerned that this other\ncell population will convolute the data? Also, again non-expert here, in Supplementary Fig. 4f, to my eye it appears that the\nTurboID expression is affecting the gross morphology/distribution pattern shown by the cellular markers in the red channel.\nFor example, in the Cre-only control, it looks like the astrocyte maker is evenly distributed through the FOV but with TurboID\nexpression the cells have higher density at the top but are less dense going down the FOV. For the neuron marker, the FOV\nfor the TurboID expressing samples appear significantly denser than the Cre-only control. Are these differences due to\nnormal variation or was this differential pattern something that was seen consistently with TurboID expression? I know in the\nmain text you state that there were no adverse effects on the mice or cellular functions, but it would still be worth noting this\ndifferential pattern post-TurboID labeling if it is consistent.\n19. For Supplementary Fig. 5, the astrocyte data is quite compelling and clear, while the neuron data is a bit less clear. I\nthink this is interesting because what it suggests is that some cell types like astrocytes may be more difficult to purify in large\namounts thus resulting in a weaker signal, and SPARO provides a superior method to obtaining enough signal to clearly\nprofile this type of cell. The neurons on the other hand appear to be easier to purify and thus obtain enough material for high\nneuronal signal (maybe because they’re more abundant/larger?), as SPARO and the cell enrichment give more similar\nresults of the top 50 genes overall, however SPARO has a clear advantage in having higher specificity as there is clear de-\nenrichment of other cell types (astrocytes) compared to the cell enrichment. This may be worth discussing this data a bit\nmore to emphasize the advantages that SPARO can provide for rarer/smaller cells and for purity.\n20. For Supplementary Fig. 7b, I’m wondering if at this point I’m just not used to looking at RNA gels, because the negative\ncontrols still appear to have a lot of RNA to my eye, not “low-levels”. Also is there a reason that the bands are shifted\nbetween samples? Is this a composite from different gel images? Also, where is the SPARO QC data for this experiment?\nAre you using the SPARO data from the previous experiment in Fig. 2 or did you generate new samples?\n21. For Fig. 3c and d, are you surprised that the RiboTag and TurboID data overlap so highly, given that RiboTag would\npresumably only deliver actively translated RNAs, while TurboID would presumably also deliver non-coding RNAs? Is this\nreflected in Supplementary Fig. 7d volcano? I know there is some speculation in your main text why there are some of the\ndifferences that you see, but I would find a more definitive discussion on why the difference isn’t greater to be more helpful\n(maybe due to abundance most of the RNAs captured even by TurboID are mostly ribosomally associated? Maybe Rpl22 is\nin most ribosomes in this cell type?).\n22. For Supplementary Fig. 8a-d, I’m just really not sure that SPARO or similar techniques should be used to measure\nRNA/protein concordance, due to the bias introduced by the labeling chemistry and enrichment. Even without considering\nthe cell type specificity, this trepidation is supported by the fact that the RNA/protein abundance correlation is much lower for\nSPARO BV-2 when compared to the global datasets for the cortex. Can you please show the correlation for the RNA/protein\nabundances from the global BV2 datasets so we have a more direct comparison to panel c?\n23. For Supplementary Fig. 8g-h, the markers seem evenly distributed across the abundances of both protein and RNA, are\nyou surprised they are not more biased towards the top right corner, or higher on the respective protein vs. RNA axes if they\ndeemed a marker using proteomic vs. transcriptomic data?\n24. For Supplementary Fig. 8i, Fig. 4f and g, and Supplementary Fig. 9, I do enjoy the discussion in the main text about the\nbiological explanations for the differences in RNA/protein concordance/discordance between cell types (although you go on\nto temper the biological significance of these findings in the discussion), but is it possible that the concordant/discordant\nmRNA-protein pairs overlap so highly between cell types in part because of the labeling chemistry of TurboID and the\nSPARO protocol rather than solely due to biologically meaningful reasons? For example, if a protein is not efficiently labeled\nby TurboID and pulled down, then it could consistently appear to be lower abundance than the RNA. Again, it would be\nreally great to see how all of this data looks like for the BV-2 SPARO vs global datasets.\nMinor points:\n25. I understand that you probably chose to use TurboID-NES because you had already generated the models for previous\nstudies, but just wanted to point out that biotin ligases like TurboID generally localize diffusely to the cytosol and nucleus\nwhen untagged, so the untagged enzyme would probably suit your purposes better in the future so you capture nuclear\nproteins/RNAs as well as cytosolic ones.\n26. Please consider changing “bulk” to “global” because from my perspective, both of the “bulk” and “pulldown” samples are\nbulk samples (pooled from many cells), but in the non-pulldown (“bulk”) samples, you are measuring the whole-cell, i.e.\nglobal, proteome or transcriptome.\n27. Typos: protein enrichment using a silver stain (Supplementary Fig. 1c); levels of RNA in bulk BV2 lysates\n(Supplementary Fig. 1b)\n28. Supplementary Figure 1d is not discussed in the main text, and it also has an incomplete figure legend.\n29. For Fig. 1h-i and Supplementary Fig. 1e, h, please label on the plots if the data shown is +/- LPS. Also in the main text,\nstate that you see similar results with +/- LPS so the readers don’t have to dig through the supplementary just to learn this.\n30. Typo: SPARO also detected proteomic and transcriptomic changes driven by LPS treatment 170 (Fig. 1j and m,\nSupplementary Fig. 1f and i)\n31. For Fig. 1l, in the main text you claim that the Pearson coefficient is 0.84 but the plot shows 0.68 - please correct to reflect\nthe true value.\n32. For Fig. 2c, the V5 expression only provides biochemical verification that you have specific TurboID expression in\ngeneral, not verification of cell-type specificity (this is your IF data). Please remove or edit this sentence.\n33. For Fig. 2d, I again don’t agree that the amount of RNA enriched in the non-TurboID controls is “negligible”, as I can\nclearly see it on the gel, particularly on the neuron samples, please consider using a different word.\n34. Supplementary Fig. 4e is not mentioned in the main text, is this just an extended figure of what is shown in Fig. 2d?\n35. For Fig. 2f, how many proteins were quantified in this proteomic experiment? Please state somewhere in the main text or\non the figure.\n36. In Supplementary Fig. 6, is the LPS-treatment dose in the legend correct? 75 mg/kg/day seems very high. In the\nmethods, the unit is missing.\n37. In regards to the main text on Supplementary Fig. 10, consider changing “mRNA characteristics and protein synthesis\ndynamics” to “mRNA and proteostasis dynamics” as half life is really a balance of synthesis and degradation.\nThank you for taking the time to respond to this lengthy review. I hope that you find my comments useful and I wish you the\nbest of luck in your submission!"}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have thoroughly addressed my remarks. I appreciate the extra benchmarking of the approach, which will be\nhighly helpful to future users. I think it will now make a useful contribution to the field, adding an interersting new set of tools\nfor profiling of cell types.", "reviewer_3": "Thank you to the authors for taking the time to respond so thoroughly to the points raised by me and the other reviewers. The\nauthors' clarifications have assuaged my concerns, and I think the manuscript has been strengthened by the additional\nfigures and text. I recommend that this study be published with the author's revisions."}, "author_response": "Simultaneous profiling of native-state proteomes and transcriptomes of neural cell types using\nproximity labeling\nChristina C. Ramelow1-3, Eric B. Dammer2,6, Hailian Xiao1,2, Lihong Cheng2,4, Prateek Kumar5,\nClaudia Espinosa-Garcia5, Maureen M. Sampson3, Dilpreet Kour5, Ruth S. Nelson5, Sneha\nMalepati5, Rashmi Kumari5, Wooyoung Jang5, Qi Guo2,6, Pritha Bagchi7, Duc M. Duong6,7,\nNicholas T. Seyfried1,2,6,7, Steven A. Sloan3,#,*, Srikant Rangaraju1,5,#,*\n1Department of Neurology, Emory University, Atlanta, GA\n2Center for Neurodegenerative Disease, Emory University\n3Department of Human Genetics, Emory University\n4Department of Pharmacology and Chemical Biology, Emory University\n5Department of Neurology, Yale University, New Haven, CT\n6Department of Biochemistry, Emory University\n7Emory Integrated Proteomics Core, Emory University\n#Co-senior authors\nProximity labeling | transcriptomics | proteomics | microglia | astrocytes | neurons | mRNA-protein\nconcordance\n*Correspondence: S.R. (srikant.rangaraju@yale.edu)\nS.A.S (steven.a.sloan@emory.edu)\nWe wish to thank the reviewers for their thorough and very constructive reviews and suggestions.\nAll reviewers found consensus strengths in the novelty and application of the SPARO approach.\nSeveral highlighted shared weaknesses related to the “noise”/background of SPARO and\nexpressed concerns with the TurboID-NES enzyme being cytosolic and how this biases the\nfindings of the correlation between mRNA-protein abundances.\nWe have now addressed individual concerns to the best of our abilities, updated our manuscript\nwith new experiments, additional analyses and discussion, all marked in the revised manuscript\nas blue text. We strongly believe that the updated manuscript has been improved after integrating\nreviewer recommendations and hope that we have satisfactorily addressed Reviewer comments.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\n- What are the noteworthy results?\n1. For the most part, the work does support the conclusions. But it is a bit unclear where the\nauthors think this new approach might make impact in the future. For example, this is a rather\nvague sentence to lead off the discussion: “Measuring transcriptomic and proteomic levels is\nimportant to identify cellular and molecular mechanisms of biology.” The last para of the\ndiscussion also has several similarly vague sentences about the contribution this new method\ncould make. Can the authors make these sections more concrete? In general the discussion is a\nbit of a missed opportunity, as it spends a larger balance of the space restating the findings and\nless time on describing that would be done next.\nResponse: We appreciate the reviewer’s feedback. We have made significant changes to the\nDiscussion section as suggested. We have first emphasized the gaps in the field and how the\nSPARO approach, based on our key findings, overcomes these technical challenges. We have\nhighlighted the major findings in the initial section of the discussion as well. Towards the end of\nthe discussion, we have extended the discussion on potential applications of SPARO to different\ncell types, tissue types, disease models, sub-regions of organs/tissues as well as the capture of\nnon-coding RNA species with SPARO. To support this new and exciting application, we have also\nincluded new data where we captured astrocyte-specific microRNA signatures that were distinct\nfrom that of bulk cortical tissue. Furthermore, we have discussed how our SPARO transcriptome\ncompares to the RiboTag transcriptome, now highlighting what is similar and dissimilar, as\nrequested by the reviewers. Lastly, we have highlighted limitations of our study, and how future\nmodifications to the SPARO approach could potentially address these.\n2. ...or discussing technical considerations on implementing this [approach]. For example, is\nthis time course of labeling required (something that might be addressed in experiments, see\nbelow)?\nResponse: This is an important point and led us to perform a new treatment duration experiment\nso we could include a robust discussion on the topic (see below).\nThe initial reason we chose to use longer labeling paradigms in BV2 cells was to match the\nprolonged in vivo time course we had previously established. We acknowledge that the efficiency\nof TurboID allows rapid labeling within 10 minutes in cells in culture1. However, because we do\nnot have such temporal control of labeling in vivo (in the brain), the 24-hour of biotinylation\nseemed to be a reasonable starting point, so that both in vitro and in vivo systems would have\nsimilar biotinylation durations. We have observed no cell toxicity with 24-hour paradigms in BV2\ncells. As previously published2, we also found minimal impact of 24-hour biotinylation on global\ncell proteomes, cell viability or cytokine production in response to lipopolysaccharide or cellular\nrespiration (Seahorse assay). Furthermore, the 24-hour time point allows for detection of immune-\nrelated, signaling. and scaffold proteins that may not be captured in a shorter treatment window3.\nSimilarly, when labeling Camk2a-expressing neurons or parvalbumin (PV)-expressing\ninterneurons with TurboID in vivo4–6, we also did not observe electrophysiological alterations in\nboth neuronal sub-types despite 2 weeks of biotinylation.\nTo experimentally address the reviewer’s question about the duration of biotin labeling, we\nperformed a biotin treatment duration experiment in the Camk2a-CreERT2/TurboID (neuron-\nTurboID) mice and found that 1 week of biotin water is sufficient to biotinylate neuronal proteins.\nPlease see streptavidin immunoblot below. Based on this finding, we predict that 1 week of\nbiotin water supplementation can be adapted for future studies. The data for this finding is now\nincluded in Supplementary Fig. 6g and below.\nWe did include +biotin and -biotin immunoblot data in our original in vivo TurboID paper4 showing\nthat whole cell biotinylation levels in HEK293 cells without TurboID does not change with or\nwithout addition of biotin in the cell culture media. We expect the same to be the case in vivo as\nwell. To confirm this, we performed an additional in vivo experiment in wild-type, Cx3cr1-\nCreERT2, Camk2a-CreERT2 and neuron-TurboID mice and assessed the extend of biotinylation\nsignal via bulk brain immunoblot with either no biotin or 2 weeks of biotin water. We found that\nthe level of endogenous biotinylation in brain lysates from wild-type mice (without exogenous\nbiotin supplementation) and non-TurboID animals (with exogenous biotin supplementation) is the\nsame (please see figure below for the reviewer only), although we have not performed SPARO\nstudies on these ‘negative’ conditions. While it may also seem intuitive to have a neuron-TurboID\ngroup without biotin, this is not an ideal control because (1) even without biotin supplementation,\nthe presence of TurboID will lead to biotinylation using biotin in regular chow, and (2) excessive\nbiotinylation by TurboID without supplementation, will lead to a relative biotin deficiency which will\nbe toxic to mice and create a significant confound.\nWe have also added our rationale for the 24-hour (in vitro) and two-week (in vivo) biotin\ntreatment duration to the methods section of the manuscript.\n3. There is also probably one more set of experiments needed to understand the noise of\nthe method in general (see ‘Is the Methodology Sound’ section).\nResponse: In response to this concern, and to better quantify the level of noise in this system,\nwe performed a new set of experiments. Our goal was to understand what RNAs we would\n“enrich” in non-TurboID samples, which could be defined as the background noise in the system.\nTherefore, we performed differential enrichment analysis of the pulldown transcriptome between\nthe BV2 non-TurboID controls (n = 2 biological replicates, lanes 4 and 5 indicated by black arrows\nin panel a below) and the BV2-TurboID (n = 3 biological replicates, lanes 10-12 indicated by green\narrow in panel a below) samples treated with LPS.\nThe “noise” that we found consisted primarily of many non-coding RNAs (e.g., Gdap10, Xist and\nMalat1), pseudogenes (e.g., H2-K2 and Pisd-ps1) and splicing factors (e.g., Luc7l3 and Rsrp1)\nenriched in the BV2 non-TurboID LPS controls. We predict that these transcripts are enriched in\nthe BV2 non-TurboID controls simply because they are generally highly abundant in mammalian\ncells7–9.\nWe also found that Acaca is a highly abundant transcript in the BV2 non-TurboID controls. Acaca\nis an endogenously biotinylated protein10,11 that is a rate-limiting enzyme in fatty acid synthesis\n(converts acetyl-CoA to malonyl-CoA)12. We hypothesize that the Acaca protein can regulate its\nown regulation via interaction with Acaca transcript. Indeed, one paper in 201913 provided\nevidence for interaction between a bacterial carboxypeptidase (equivalent to mammalian Acaca)\nand its mRNA, provide a ‘dimmer’ type regulatory mechanism for a rate-limiting metabolic enzyme\nto regulate mRNA to protein translation via direct protein-mRNA interaction.\nIt is also possible that these RNA species interact with endogenously biotinylated proteins10,11\nallowing them to be captured from non-TurboID RNA pulldowns. It is also important to note here,\nthat only LPS-treated BV2 non-TurboID-derived RNA pulldowns yielded cDNA libraries for RNA-\nseq while those from non-LPS BV2 non-TurboID cells did not. There may be several possible\nexplanations for why the noise RNA pulldowns may change with LPS treatment, some due to\ntechnical reasons and some potentially biological reasons. While we cannot resolve these\npossibilities in this study, users of SPARO must plan to use several different non-TurboID\nnegative control pulldowns to account for these variabilities in experiments. These data have been\nadded to Supplementary Fig. 2 and provided below.\n4. Figure S7d: TurboID and RiboTag have substantial differences (thousands of DEGs in\nFigure S10) that are rather underplayed here. I would recommend a couple of analyses digging\ninto these differences more. Maybe something similar to what they do in Figure S10?\nResponse: Thank you to the reviewer for their insightful recommendation. We agree that\nadditional analyses like Supplementary Fig. 10 is helpful to investigate differences between the\nastrocyte-TurboID and astrocyte-RiboTag pulldown transcriptomes.\nFor this new analysis, we investigated the average transcript length, percent GC content, and\nnumber of exons using bioMart/ENSEMBL of the highly enriched DEGs in the astrocyte-TurboID\nversus astrocyte-RiboTag differential gene expression analysis from Supplementary Fig. 9d (via\np ≤ 0.05, ≥ 1-log -fold change). We found that all three RNA characteristics are significantly\n2\ndifferent between the SPARO TurboID and RiboTag-enriched astrocytic transcriptomes. Mean\ntranscript length (****p < 0.0001, Mann Whitney test), and exon number (*p = 0.029, Mann-\nWhitney test) were higher in the TurboID pulldowns while percent GC content (**p = 0.002, Mann\nWhitney test) was higher in the RiboTag pulldowns. These differences may reflect the broader\nnature of TurboID labeling, which captures many RNA-binding proteins not limited to those\nassociated with active translation.\nWe also found a significant difference in the average percent GC content between the astrocyte-\nTurboID and astrocyte-RiboTag pulldowns. We predict that this may be indirectly related to\ntranscript length. The average percent GC content may also be related to the classes, or functions\nof transcripts captured by each method. There is also the possibility that because the RiboTag\napproach is primarily biased towards ribosome-associated, actively translated mRNAs, that these\ntranscripts are more enriched in housekeeping transcripts with a higher GC content. In contrast,\nTurboID labeling may recover a broader pool of RNA-interacting proteins, including those\nassociated with longer, less GC-rich transcripts at earlier stages of RNA processing. We have\nnow included this new analysis in Supplementary Fig. 9g and below for the reviewer.\nIt is possible that TurboID biotinylates RNA-interacting proteins at multiple stages of the mRNA\nlife cycle, including during nuclear processing events such as splicing, consistent with the\nexpectation that pre-spliced transcripts are generally longer and contain more exons than\nribosome-associated, spliced mRNAs.\nWe predict that these differences may be associated with the nature of TurboID biotinylating many\nproteins that interact with RNA that are not only being actively translated. To further investigate\nthis, we also performed MS on proteins enriched by the RiboTag pulldown and contrasted these\nproteomes with SPARO pulldown proteomes, expecting to find differences in how each approach\ncan differentially enrich ribosomal and RNA-binding proteins, thereby explaining differences in the\nRiboTag translatome and SPARO transcriptomes (see new Fig. 3f and g provided below and\nReviewer 2, Comment 27 response). As predicted, RiboTag pulldown captures a smaller cadre\nof RNA-binding and larger proportion of ribosomal (translational) proteins, while SPARO captures\nmany more RNA-binding proteins while also capturing many ribosomal proteins, albeit with some\ndifferences across methods.\nIt may be possible that TurboID is capable of biotinylating RNA-interacting proteins at different\nstages of the mRNA life cycle (e.g., TurboID biotinylates an RNA-binding protein that translocate\nto the nucleus to assist with mRNA processing such as splicing). It is likely that pre-spliced\ntranscripts (assessable by biotinylated RNA-binding proteins) are generally longer than post-\nspliced transcripts (i.e., transcripts associated with ribosomes for translation).\nTaken together, our new analyses provide several probable reasons for observed differences\nbetween SPARO and RiboTag transcriptomes, despite their strong correlations. We certainly see\nexamining these differences as a future direction of the SPARO approach.\nWe have now included this analysis in Fig 3f and g and Supplementary Fig. 9g as well as\nin the results and discussion sections.\n5. The authors spend some time on page 8 speculating why the transcriptome and proteome\nare sometimes discordant. One additional source of such discordance could simply be cellular\ncompartment: it seems like even in vivo the TURBO-ID is restricted to the cytoplasm? Yet the\ntranscripts that code for all proteins in the cell (nuclear, mitochondria, etc), are largely\ntranslated in the cytoplasm. So some of the discordance could simply reflect that the mature translated\nprotein ends up somewhere in the cell where it cannot be readily tagged. Likewise, any excreted\nproteins might be lost to TurboID labeling (including potentially Lcn2, which they highlight in their\ndiscussion). When the come back discussing discordance on page 10, they do bring up this\npossibility that it is driven by cellular compartments in part. However, they may wish to revise\nthese sections, as the different cellular compartments is probably the simplest explanation and\nshould be raised earlier.\nResponse: We thank the reviewer for highlighting this issue. For both the in vitro and in vivo\nSPARO experiments, the TurboID construct contains a nuclear export sequence. Therefore,\nTurboID is localized to the cytosol of the cell. We have added immunocytochemistry images of\nthe BV2 non-TurboID control and BV2-TurboID microglial cells in culture to emphasize that the\npresence of TurboID via V5 and biotinylation signal is primarily in the cytosol. Please see ICC\nimages added to Supplementary Fig. 1a and below. Moreover, at the transcriptomic-level\ncomparing the BV2-TurboID pulldowns versus global samples (+/- LPS treatment), we find that\nthe majority of DEGs upregulated in the global group are mitochondrial-encoded transcripts (e.g.,\nCOX1, CYTB and ND1). This provides additional evidence that the TurboID enzyme is biased\naway from the mitochondrial compartment. Please see Supplementary Fig. 4a and b.\nWe have now revised this section and brought it up earlier as suggested to better highlight\nthe cytosolic localization of TurboID being a contributing factor to discordance between\nthe transcriptome and proteome.\nIn addition, to address the next point in Comment 6, we performed a new SPARO experiment in\nHEK293 cells comparing the transcriptomes and proteomes of TurboID-NES and TurboID-noNES\npulldowns. This new experiment is now included in the manuscript as Supplementary Figs. 15-\n19).\n6. The difficulty in interpreting RNA/Protein comparisons is the part of the paper I find the\nmost challenging and that would make me hesitant to bring the approach into my own lab. Is there\nanything that could be done informatically to address this? For example, if they limit the analysis\ncomparing RNA to Protein to the proteins that are in the cytoplasm (potentially by GO),\ndoes the correlation improve between RNA and Protein? Does the ability to use things like protein half lives\n(or perhaps codon usage) to predict Protein/RNA discordance improve? And can they start to look\nat cell differences in RNA/Protein discordance in a more insightful way if limiting their analysis to\nproteins that have a better chance of being tagged (i.e., the cytoplasmic ones)? On the whole,\nthis comparisons seems to be one of the motivating aspects of the paper, so it would be\nnice to have some recommendations on how to approach these questions with the current technique. (I\nappreciate that the authors also discuss future method changes like removing the NES that might\naddress this experimentally).\nResponse: We certainly take this concern to heart, and it was shared by other reviewers as well.\nTherefore, to better experimentally address the limitations of the cytosolic TurboID (TurboID-NES),\nwe conducted a new in vitro SPARO experiment in HEK293 cells with a TurboID construct\nthat lacks the NES.\nWe found that the noNES versus NES pulldown transcriptomes are highly similar (92% overlap\nin transcripts detected and mRNA abundances highly correlated r = 0.99, Supplementary Fig\n15e and fbelow). We found a similar trend with the pulldown proteomes (85% overlap in peptides\ndetected and protein abundances highly correlated r = 0.93, Supplementary Fig 15e and g\nbelow). Moreover, when we assessed the correlation between the paired pulldown\ntranscriptomes and proteomes, the correlation is similar between the TurboID-noNES and NES\nSPARO approaches (r = 0.31 (NES) and 0.35 (noNES), Supplementary Fig 15l and m below).\nTogether, these findings suggest that the noNES and NES TurboID enzymes in vitro yield similar\nproteomic and even more similar transcriptomic profiles and that removing the NES to restrict\nTurboID to the cytoplasm does not greatly increase the correlation between mRNA-protein\nabundances. Moreover, these findings suggest that the mRNA-protein correlation being modest\nis likely not simply explained away by the compartment restriction of TurboID.\nWe have also made a new Rosa26-TurboID mouse line without the NES and the line is currently\nbeing validated. We fully intend on applying the SPARO approach using this new mouse, but we\nare at least 2 years away from a definitive experiment to confirm that the in vitro noNES versus\nNES findings translate in vivo.\nWe have adjusted the manuscript by adding the new HEK293 TurboID-NES versus noNES\nSPARO study to Supplementary Fig. 15 and below for the reviewer.\nComment 6 response continued:\nTo address the reviewer’s recommendation to limit the correlation analysis to cytosolic proteins,\nwe also performed this analysis by filtering the proteins based on the bioMart/ENSEMBL GO term\nID for cytosol \"GO:0005829\" and provided the data for the reviewer below. After filtering for\ncytosolic proteins (n = 626 - 742), we found that the correlation between the abundance of mRNAs\nand proteins remains modest (r = 0.28 – 0.36), similar to the unfiltered correlation plots in\nSupplementary Fig. 11 (r = 0.24 - 0.27).\nWe have provided the correlation plots below for the reviewer and briefly mentioned this\nfinding in the discussion section of the manuscript.\n7. Is the methodology sound? Does the work meet the expected standards in your field?\nThey need to better account for sex in their work (and in future studies, may want to balance for\nit, though there is clearly no way to travel back in time to change samples now). For example, in\nFigure 2i. Did the neuron line samples have more females than the astrocyte line samples?\nOtherwise can the authors explain why Xist (a female-only transcript) is a top gene? Actually, this\ncomes up across several other figures (e.g., Fig S7d, which also shows a lot of Y chromosome\ngenes). Ah, as I get to discussion, it does seem like the sexes were not well balanced in the\nexperiment. Hmm.. the authors may\nwish to flag sex-chromosome derived ‘DEG/DEPs’ as being potential false positives (driven by sex rather than cell type)\nwith a column in their supplemental tables and explicitly exclude them from volcano plots.\nResponse: Thank you to the reviewer for their Comment. Although we are not powered to explore\nsex differences with our current dataset, this is an important future direction of the SPARO\napproach. Because Xist is an DEGs in the in vivo neuronal pulldowns in which 2/3 of the mice are\nfemale, we had hypothesized that the enrichment of Xist may be attributed to a sex-related\ndifference. However, after performing the differential expression analysis of the BV2-TurboID LPS\n(n=3) versus BV2 non-TurboID LPS pulldowns (n=2), Xist showed up as a DEGs in the BV2 non-\nTurboID control samples, suggesting that this may be a non-specific/background or noise-\nassociated transcript, or a non-coding RNA that interacts with an endogenously biotinylated\nprotein. Supplementary Fig. 2 and below in Comment 8. Interestingly, the BV2-microglial cell\nline is derived from female mice, and we had hypothesized that it would not be differentially\nexpressed when comparing the BV2 groups.\nWe have added the sex of the mice to the legends of Fig. 2, Fig. 3, and Supplementary Fig. 6\nand Supplementary Fig. 8 and nominated sex-chromosome derived DEGs/DEPs in the\nSupplemental Data Tables.\n8. The method would benefit from a more quantitative characterization of the noise, as it is\na new method. Did the author sequence mouse brain samples that did not have the Turbo allele?\nI agree from the tapestations there is less RNA coming down without it, but some is still there.\nKnowing what is in that RNA (and potentially filtering/subtracting it from the true samples) is\nimportant to characterize noise, eliminate sources of false positives in some analyses (e.g.,\nRiboseq vs. their method) and potentially improve signal to noise in the future. Potentially similar\nexperiments might be done with proteomics too.\nResponse: Regarding the important point on quantifying noise, we absolutely agree that this is a\nsignificant addition to describe the new SPARO approach. As described above in Comment 3\nand 7, we performed differential expression analysis of the BV2-TurboID LPS (n=3) versus BV2\nnon-TurboID LPS pulldowns (n=2) and found that many of the DEGs up in the BV2 non-TurboID\nLPS pulldowns are non-coding genes, pseudogenes, or splicing factors. We predict that these\ntranscripts are enriched in the BV2 non-TurboID controls because they are generally highly\nabundant in mammalian cells7–9. There is also the possibility that these non-specific/background\nor noise-associated transcripts may interact with ubiquitous RNA-binding proteins, or are\nassociated with protein complexes (e.g., ribonucleoproteins) making them more likely to be pulled\ndown. It is important to note that for the differential analysis comparison, only 2 biological\nreplicates are reported for the BV2 non-TurboID LPS pulldowns. The three non-LPS and third\nLPS BV2 non-TurboID controls failed library preparation quality control parameters and were\nomitted from the sequencing project.\nThe data for this finding can now be found in Supplementary Fig. 2 and below as well as\nprovided brief mentions in results and discussion sections of the manuscript.\n9. Can the authors address to what extent is it necessary to feed the animals biotin? I assume\nthere is some biotin present in the cells in vivo regardless. How does signal change with vs.\nwithout the added biotin in the diet?\nResponse: The reviewer is correct in stating that there is endogenous biotin in cells. In the\nabsence of exogenous biotin, TurboID can utilize endogenous biotin14 and ATP to create a\nreactive intermediate, biotin-AMP that can be quickly added to lysine residues of proteins that are\nclose-by15,16.\nHowever, within a cell, there is not enough endogenous biotin to support sufficient labeling of\ncellular proteins leading to poor signal in streptavidin pulldowns1. Additionally, because the\nTurboID enzyme is efficient in using free biotin to tag proteins, this creates a competition for a\ncell’s normal machinery that critically needs biotin (e.g., carboxylase enzymes for different\nmetabolic pathways)10. Therefore, exogenous biotin supplementation is standard in TurboID\nproximity labeling protocols.\nAs described in Comment 2 above as well, we did include +biotin and -biotin immunoblot data in\nour original in vivo TurboID paper4 showing that whole cell biotinylation levels in HEK293 cells\nwithout TurboID does not change with or without addition of biotin in the cell culture media. We\nexpect the same to be the case in vivo as well. To confirm this, we performed an additional in vivo\nexperiment in wild-type, Cx3cr1-CreERT2 (no TurboID), Camk2a-CreERT2 (no TurboID) and\nneuron (Camk2a)-TurboID mice and assessed the extend of biotinylation signal via bulk brain\nimmunoblot with either no biotin or 2 weeks of biotin water. We found that the level of endogenous\nbiotinylation in brain lysates from wild-type mice (without exogenous biotin supplementation) and\nnon-TurboID animals (with exogenous biotin supplementation) is the same (please see figure\nbelow for the reviewer only), although we have not performed SPARO studies on these\n‘negative’ conditions. While it may also seem intuitive to have a neuron-TurboID group without\nbiotin, this is not an ideal control because (1) even without biotin supplementation, the presence\nof TurboID will lead to biotinylation using biotin in regular chow, and (2) excessive biotinylation by\nTurboID without supplementation, will lead to a relative biotin deficiency which will be toxic to mice\nand create a significant confound.\n10. Likewise, is there a relatively cheap and fast way to explore how features of the biotin\npulse to the animal change the yield (like just I.F. or quantifying tape station, streptavidin\nimmunoblot or silver stain) that would give a sense of how much the timing and magnitude of that\npulse needs to be optimized by labs adopting this approach? Or if this is already done in prior\npapers, perhaps allude to that in methods.\nResponse: We agree that optimizing the biotin treatment duration is important. For the SPARO\nin vivo studies in this manuscript, we used two weeks of biotin water supplementation based on\nprior studies by our team as described in Comment 2 and 9 above. In these studies, we found\nthat two weeks of biotin water treatment showed no toxicity on neuronal function using\nelectrophysical recordings of Camk2a-expressing and PV-expressing neurons4–6.\nTo address the concern around the timing of biotin treatment, we performed a new experiment\nwith the Camk2a-CreERT2 control and Camk2a-CreERT2/TurboID (neuron-TurboID) mouse\nmodels with biotin duration for 2, 5, 7 (1 week) and 14 days (two weeks). We found that at least\n1 week of biotin water supplementation is sufficient to display robust biotinylation of the neuronal\nproteome. The data for this finding can be found in Supplementary Fig. 6g and below.\nTypos/minor notes:\n11. Should the A be underlined and bolded when defining the acronym for SPARO?\nResponse: Thank you for noticing this typo. We have fixed the formatting in the main text.\n12. Figure 1l disagrees with the results text on the magnitude of a correlation (.68 vs .83).\nResponse: We have fixed this discrepancy in the results section.\n13. Figure 1 legend about 1d: mRNA is usually not visible on a tapestation, just the rRNA.\nResponse: Our apologies for over extrapolating. The reviewer is correct that the Bioanalyzer gel\nimage primarily shows rRNA (18S and 28S subunits), the most abundant RNA in a cell. We have\nremoved “mRNA” from the description. We have also better described the rationale to use the\nBioanalyzer as a means to assess RNA quality and integrity prior to RNA-sequencing.\n14. “Hepacam, Glu, Aqp4, Plpp3, p ≤ 0.05, ≥ 1-fold change” should this be “> 1 log2 fold-\nchange”?\nResponse: Thank you for noticing this error. We have changed the language to be ≥ 1-log -fold\n2\nchange.\n15. Figure S10: If you calculate a logFC of RNA vs Protein, can you then compare the protein\nhalf lives to that? For ‘a’ does seem to be a relationship between quadrant and protein half life\ncoloration, though it seems to disappear by b and c. Being able to more directly compare\nProtein/RNA to half life might have more explanatory power.\nResponse: Thank you for the reviewer’s suggestion. We have included this additional analysis\nand have directly compared the protein half-lives of each concordant/discordant group, within\nbulk, astrocyte SPARO and neuron SPARO -omes and added this comparison next to the scatter\nplots in Supplementary Fig. 14 and provided below in the right panels, as requested. For the\nbulk cortical samples, we observed statistically significant differences in protein-half lives when\ncomparing the concordant (mRNA ~ protein) and discordant (mRNA > protein) groups and the\ndiscordant protein > mRNA and mRNA > protein groups (****p < 0.001, determined by Tukey’s post-\nhoc test following one-way ANOVA). For the astrocyte- and neuron-TurboID pulldown groups, we found\nobserved no statistically significant differences in protein-half lives for these comparisons.\nsuggesting that proteostasis alone may not explain protein-mRNA discordance in our SPARO\ndatasets. This comes with the caveat, however, that reference protein half-lives (Fornasiero et\nal., 201817) are valid for our datasets, since the tissue/sample types are comparable. However,\nsince true reference datasets for protein turnover in astrocytes or neurons in vivo in the brain do\nnot exist, the best we could do is assume that whole brain-level half-lives may be valid at the level\nof two predominant cell types in the brain (neurons and astrocytes).\n16. I am also confused by the next sentence “Less than 5% of gene products exhibit cell type-\nspecific expression patterns (via > 4-fold enrichment), and as a result, bulk tissue -omics studies\ncannot completely resolve changes occurring at the level of individual cell types55,59” I am trying\nto understand how the first clause of the sentence results in the second clause.\nResponse: Our apologies for the confusion. We have removed this statement.\n17. “which is in line with prior studies57–59” can the authors highlight more quantitatively the\nextent of correlation seen in prior studies? Is it similar to their .24-27 (Figure S8) Are their bulk\nresults (.47) more comparable in magnitude of correlation to those prior studies?\nResponse: Prior studies have also reported modest correlations (0.4-0.59) between the\ntranscriptome and proteome of neural cells at the global/bulk level (see table below).\nModel system Pearson’s Dataset size Reference\nr (mRNA-protein\npairs)\nAcutely isolated 0.47 n.d. Sharma et al., 2015\nmicroglia 10.1038/nn.4160\nAcutely isolated 0.40 n.d. Sharma et al., 2015\nastrocytes 10.1038/nn.4160\nCultured primary 0.43 - 0.45 n.d. Sharma et al., 2015\nneurons 10.1038/nn.4160\nHematopoietic mouse 0.59 425 Tian et al., 2004\ncell lines 10.1074/mcp.M400055-MCP200\nInterferon-responsive 0.4 2154 Prakash et al., 2023\nreactive astrocytes https://doi.org/10.1002/glia.24494\nIn addition to the reference values denoted in these studies, we were also curious to see whether\nthe mRNA-protein pairs evaluated by SPARO reflected similar patterns in other published\ndatasets. Of course, the main caveat of this approach is that other datasets are either only\ntranscriptomic or proteomic, but we believed this analysis could also help validate our findings to\nsome degree. We used RNA data from the Zhang et al, 201418 transcriptomic study of mouse\n mouse\nCNS cell types and Sharma et al., 201519 proteome of acutely isolated mouse astrocytes and\nneurons. Similar to our findings in SPARO, the correlation between mRNA and protein\nabundances was modest for all pairs (r = 0.36 for astrocytes and r = 0.29 in neurons) and for\nfiltering of the mRNA-protein pairs found in SPARO Supplementary Fig. 8a and b (r = 0.41 in\nastrocytes and r = 0.21 in neurons). We have included the Zhang versus Sharma analysis below and in\nSupplementary Fig. 11e). We believe these findings suggest that the correlation\ncoefficients found in Supplementary Fig. 11a-c are likely comparable to other studies.\n18. This sentence does not make much sense “There are also limited resources to determine\nwhich allele of a gene yields a particular protein isoform.” Aren’t all these mice inbred? And thus\nhomozygous at most alleles?\nResponse: The reviewer is correct that commonly used lab mice such as C57BL/6, which were\nused in our studies, are inbred and essentially homozygous at most loci. We have revised the\nstatement to clarify that short-read bulk RNA-seq alone cannot resolve protein isoform differences\nthat result from alternative splicing or post-translational modifications.\nReviewer #2 (Remarks to the Author):\nMajor criticisms/suggestions:\n19. Regarding Fig.1: The drastic difference in TurboID expression between LPS-treated and\nnon-treated BV2 cells is concerning. Why is the expression in the non-treated cells (as indicated\nby the V5 immunolabeling in Fig.1c) about half of that in the LPS-treated cells? The streptavidin\nlabeling is also dramatically different between the LPS-treated and untreated cells\n(Fig.1c/Fig.S1a). To be able to compare the LPS-treated and nontreated\ntranscriptomes/proteomes, the level of cytosolic TurboID expression needs to be similar.\nOtherwise, differences in mRNA and protein abundances may reflect differences in the extent of\nbiotinylation/labeling, rather than bona fide differences in expression.\nResponse: We appreciate the reviewer’s concern regarding the differences in TurboID\nabundance values between the untreated and LPS-treated BV2 cells. We had used the V5\nimmunoblot to confirm the presence of the TurboID recombinant protein (See Fig. 1c or panel a\nbelow). Our initial goal of the in vitro BV2 LPS experiments was to confirm that SPARO is capable\nof capturing an immune response from the BV2 cells, not to suggest that LPS exposure changes\nthe correlation between the BV2 transcriptome and proteome.\nTo more comprehensively address this concern, we evaluated the V5 and streptavidin\nimmunoblot signal (via ImageJ densitometric analysis from the immunoblot image in panel a\nbelow) and the abundance of TurboID (via LFQ-MS) in each of the BV2 sample groups (see\nfigure below for the reviewer). As indicated by the reviewer, we found that the streptavidin (panel\nb) and V5 (panel c) immunoblot signals are significantly higher in the LPS-treated pulldowns\n(unpaired, two-sided T test, ** p = 0.009). Furthermore, we also found that that the abundance of\nTurboID is not significantly different across the global samples (unpaired T test, p = 0.65), but\nsignificantly different in the pulldowns (unpaired, two-sided T test, **** p < 0.001, panel d).\nMoreover, based on our differential enrichment analysis comparing the global LPS-treated versus\nthe non-LPS untreated BV2-TurboID proteome, TurboID is not differentially enriched (p = 0.79\nand log2-fold change = 0.24, unpaired, two-sided T test). However, at the pulldown level, TurboID\nis slightly higher in LPS-treated samples (p = 0.04 and log2-fold change = 0.99, unpaired, two-\nsided T test). We predict that during LPS stimulation, TurboID is more present in the cytosol of\nthe BV2 cells and therefore detected at higher levels in the LPS pulldown samples.\nBased on these findings, we decided to perform a TurboID protein abundance normalization\napproach for the +/- LPS pulldown samples prior to differential enrichment analysis. We have\nadjusted the manuscript to reflect these changes in Fig. 1j-l and the main text.\n20. In the methods, the authors outline how they perform median centering of the log2\ntransformed intensity values to account for variability in TurboID expression, biotinylated protein\nabundance, and streptavidin affinity purification efficiency. Given the magnitude of these\ndifferences however, I am not confident that this normalization method is sufficient.\nResponse: Many thanks to the reviewer for this important observation. We chose the median\nzero-centering normalization approach to be able to differentially compare the pulldown and\nglobal samples. We initially planned to normalize our BV2 proteomic data based solely on TurboID\nprotein abundance. However, as shown in figure provided below for the reviewer, panel a,\nTurboID levels differed in the LPS-treated pulldown proteomes consistently by 0.84 log (summed\n2\nintensity) unit, i.e., the mean abundance of TurboID was ~79% lower without LPS treatment.\nHowever, importantly, the abundance of total protein pulled down (panel b) demonstrated a much\nmore muted difference [44% lower] in non-LPS treated samples relative to the LPS treated\npulldowns. Therefore, a normalization equalizing TurboID levels would over-correct the proteome\nin global. Please note that sample ID “_01” in the red group and “_02” in the yellow group in panel\na are BV2 global samples without TurboID, so we would not expect TurboID peptide abundance\nto be present in these samples. Separately, the proteomic abundances of global proteomes (panel\nb, red and yellow boxes) were relatively overloaded compared to other samples. Given this\nobservation, and our goal to assess differential enrichment between the BV2-TurboID-enabled\npulldowns and global samples, we chose the median zero-centering as our normalization\napproach, which successfully aligned the quantitative proteomes (panel d).\nAfter evaluating the abundance of TurboID in the pulldown samples (See Comment 19 response\nabove), we decided to move forward with a TurboID abundance normalization approach for\nthe +/- LPS pulldown proteome comparison. We have replaced the BV2 proteomic plots in Fig.\n1j-l and adjusted the text in the main manuscript to reflect these changes.\n21. For their transcriptomic data analysis, the authors do not account for this variability. In\nFig.1e, the LPS pulldown proteome has almost 20% more proteins than the non-LPS pulldown\nproteome. In Fig.1f, the LPS pulldown transcriptome has 58% more mRNAs. Is this reflecting true\nchanges in protein/mRNA abundances or is it because of differences in the extent of\nbiotinylation/labeling?\nResponse: Thank you allowing us the opportunity to better visualize and communicate the\nnumber of peptides and transcripts detected in the BV2 in vitro SPARO studies. We have adjusted\nthe Venn diagrams in Fig 1e and f (and provided the below for the reviewer) to include the total\nnumber of peptides or transcripts detected in the BV2 global and pulldown samples.\n22. Regarding Fig.1: Does the 48 h LPS treatment lead to increased expression of TurboID?\nThis needs to be investigated and taken into consideration when interpreting results. The authors\nshould include immunofluorescent images and quantification of the V5 and streptavidin labeling\nof the BV2 control cells and BV2-TurboID cells before and after LPS (or mock) treatment.\nResponse: As described on Comment 19 above and the barplot below, we found that that the\nabundance of TurboID is not significantly different across the global samples (unpaired T test, p\n= 0.65), but significantly different in the pulldowns (unpaired, two-sided T test, **** p < 0.001, panel d).\nMoreover, based on our differential enrichment analysis comparing the global LPS-treated versus\nthe non-LPS untreated BV2-TurboID proteome, TurboID is not differentially enriched (p = 0.79\nand log -fold change = 0.24, unpaired, two-sided T test). However, at the pulldown level, TurboID\n2\nis slightly higher in LPS-treated samples (p = 0.04 and log -fold change = 0.99, unpaired, two-\n2\nsided T test). We predict that during LPS stimulation, TurboID is more present in the cytosol of\nthe BV2 cells and therefore detected at higher levels in the LPS pulldown samples.\nBased on this observation, we have decided to move forward with a TurboID abundance\nnormalization approach for the +/- LPS pulldown proteome comparison. We have replaced the\nBV2 proteomic plots in Fig. 1j-l and adjusted the text in the main manuscript to reflect these\nchanges.\nWe have also added immunocytochemistry images of the BV2 non-TurboID control and BV2-\nTurboID microglial cells (+/- LPS) in culture to Supplementary Fig. 1a and Reviewer 1\nComment 5.\n23. Throughout the manuscript, the authors mistakenly attribute what is likely Agilent\nTapeStation images as “RNA gel electrophoresis”. The authors need to relabel this (perhaps as\n“RNA ScreenTape analysis” or something similar) because the Agilent TapeStation does not use\ntraditional gel electrophoresis, but rather microfluidics. The details are missing in the methods.\nThe authors state that “high levels of rRNA and mRNA were detected”, but there are no real bands\nexcept the 28S RNA (~4000 nt) and 18S rRNA (~2000 nt) bands in Fig.1d. The authors should\nperform standard RNA polyacrylamide gel electrophoresis instead.\nResponse: We thank the reviewer for pointing out the mistaken terminology related to RNA gel\nelectrophoresis and the lack of methodological detail regarding our RNA QC analysis. We\nacknowledge that the term “RNA gel electrophoresis” and “high levels of rRNA and mRNA were\ndetected”, was inaccurately used in the manuscript to describe the Agilent Bioanalyzer or\nTapeStation assay results. As the reviewer mentioned, the Agilent Bioanalyzer and TapeStation\nassays do not use traditional gel-based electrophoresis, bur rather microfluidics.\nWe appreciate the reviewer’s suggestion to perform standard RNA polyacrylamide gel\nelectrophoresis. However, for the purposes of this study, the Bioanalyzer and TapeStation\nanalyses were sufficient to assess RNA integrity prior to RNA-sequencing. We acknowledge that\nstandard RNA polyacrylamide gel electrophoresis can offer complementary information (e.g.,\nRNA size distribution) and will consider including this in future experiments.\nWe have revised the manuscript from “RNA gel electrophoresis” to “Bioanalyzer or Tapestation\nRNA QC analysis”. We have also updated the methods section to state that we used either the\nAgilent 2100 Bioanalyzer or TapeStation systems and the RNA 6000 Nano or Pico Kit as a proxy\nfor RNA integrity and not to measure mRNA abundance. Moreover, we have added the RNA\nintegrity number (RIN) to the figure legends and the results sections of the manuscript.\n24. Overall, validation of their findings is lacking (e.g., LPS treatment experiments in Fig.1 and\nFig.S6). Validation (e.g., immunofluorescence imaging) is essential.\nResponse: We tried to validate our findings related to LPS stimulation in BV2 cells by comparing\nDEPs from our LPS versus no-LPS BV2-TurboID SPARO pulldown proteome to our prior work\nby Sunna et al., 2023 that contains an LPS activation of BV2-TurboID cells component2. We found\nsome immune-related microglial-associated DEPs to be shared across the two LPS datasets\nincluding Stat1 (involved in inflammatory signaling cascades20) , Sqstm1 (related to autophagy\nfunction21), and Gbp2 (associated with control immune responses in microglia22).\nWe have also added immunocytochemistry images of the BV2 non-TurboID control and BV2-\nTurboID microglial cells (+/- LPS) in culture to Supplementary Fig. 1a and Reviewer 1\nComment 5. We have also updated the BV2 pulldown proteome GO plots in Fig. 1k and\nprovided the plot for the reviewer below to demonstrate that the BV2 cells were activated by\nLPS. We had added a statement on validating our LPS findings in the discussion section of the\nmanuscript.\n25. Regarding Fig.1e: The authors find only ~50% overlap between the biotinylated cytosolic\nproteome and the bulk proteomes. They say that this is likely because the biotinylated cytosolic\nproteome doesn’t contain the proteomes of the nuclear and mitochondrial compartments. The\nauthors should look into this further to back up their claim. Why not subset their bulk proteome\ndata for cytosolic proteins and then do the comparison?\nResponse: Thank you to the reviewer for the suggestion to subset the SPARO proteomic data\nfor cytosolic proteins (Reviewer 1 in Comment 6 shared the same recommendation). We\nperformed this analysis by filtering the proteins based on the bioMart/ENSEMBL GO term ID for\ncytosol \"GO:0005829\" and provided the data for the reviewer below. After filtering for cytosolic\nproteins (n = 626 - 742), we found that the correlation between the abundance of mRNAs\nand proteins remains modest (r = 0.28 – 0.36), similar to the unfiltered correlation plots in\nSupplementary Fig. 11 (r = 0.24 - 0.27).\nAdditionally, we re-evaluated the GO analysis in Supplementary Fig. 5c and d (protein BV2T\npulldown versus globals, +/- LPS) and filtered the GO terms for mitochondrial, nuclear and ER-\nassociated terms. We found some, but very few mitochondrial, nuclear and ER-associated terms\nin the BV2T pulldown proteomes (See updated GO barplots below).\nThese BV2 findings are also similar to the new HEK293 SPARO experiment we conducted in vitro\nwith a TurboID construct that lacks the NES (please see Supplementary Fig. 15 and below,\nand Comment 6 above). The goal of this experiment was to experimentally address the\nlimitations of the cytosolic TurboID (TurboID-NES).\nWe found that the noNES versus NES pulldown transcriptomes are highly similar (92% overlap\nin transcripts detected and mRNA abundances highly correlated r = 0.99, Supplementary Fig\n15e and fbelow). We found a similar trend with the pulldown proteomes (85% overlap in peptides\ndetected and protein abundances highly correlated r = 0.93, Supplementary Fig 15e and f\nbelow). Moreover, when we assessed the correlation between the paired pulldown\ntranscriptomes and proteomes, the correlation is similar between the TurboID-noNES and NES\nSPARO approaches (r = 0.31 (NES) and 0.35 (noNES), Supplementary Fig 15l and m below).\nTogether, these findings suggest that the noNES and NES TurboID enzymes in vitro yield similar\nproteomic and even more similar transcriptomic profiles and that removing the NES to restrict\nTurboID to the cytoplasm does not greatly increase the correlation between mRNA-protein\nabundances. Moreover, these findings suggest that the mRNA-protein correlation being modest\nis likely not simply explained away by the compartment restriction of TurboID.\nThese findings suggests that even when TurboID has a NES, that the enzyme can briefly enter\nsub-cellular compartments other than the cytosol to biotinylate proteins, or that TurboID\nbiotinylates these proteins before they are destined for a specific cellular sub-compartment and\ntherefore, show up in our pulldown datasets.\n26. Biotinylation by TurboID requires certain amino acids to be present in a given protein and\naccessible. The authors could also determine the extent of the cytosolic proteome (using\nannotated datasets) that is captured in the pulldown proteome and check to see which, if any,\ncytosolic proteins aren’t being captured.\nResponse: Based on the analysis above in Comment 7, we filtered the SPARO proteome based\non the mouse bioMart/ENSEMBL GO term ID for cytosol \"GO:0005829\" (n = 3554 total proteins).\nOf the 3554-input protein list, we found 742 proteins in the in vivo astrocyte and neuron datasets\nand 626 proteins in the in vitro BV2 dataset classified as “cytosolic”. This suggests that ~20%\n((742 or 626)/3554 *100) of cytosolic proteins are being captured by SPARO using this approach.\nHowever, it is important to note that not seeing a protein detected by MS does not mean that is it\nabsent from the cell.\n27. Regarding Fig.3: The authors compare the abundance/enrichment of ribosomal proteins\nin the cre-only control vs. astrocyte-TurboID pulldowns and are able to capture 66 RPs and 67\nRNA-binding proteins. A more important comparison would be between the astrocyte-TurboID\npulldown and the astrocyte-RiboTag HA IP eluate. 7. Regarding Fig.3: The authors state that “a\nnotable difference between the SPARO and RiboTag transcriptomes is that in SPARO,\nTurboID biotinylates 133 proteins that interact with RNA (e.g., ribosomal and RBPs), while the\nRiboTag approach labels one ribosomal protein”. To back up their claim, the authors should report\nhow many of these 133 proteins are detected in the RiboTag IP HA eluates. I would assume that\nseveral RBPs will be detected, even without UV crosslinking.\nResponse: Thank you to the reviewer for the suggestion. To address this, we have performed a\ndata independent acquisition label-free quantitative mass spectrometry experiment on the\nastrocyte-RiboTag immunoprecipitation samples and compared the presence of the RNA-\ninteracting proteins between the astrocyte-TurboID and RiboTag pulldowns. We compared the\nenriched proteins (via log2-fold-change of 1 compared to the Aldh1l1-CreERT2 or RiboTag only\ncontrols) present in either the astrocyte-TurboID or astrocyte-RiboTag samples. The “133 proteins\nthat interact with RNA” reported in the manuscript was determined based on ribosomal and\nproteins that contain an RNA-binding “RRM-containing” motif (n = 453 total proteins) from the\nHGNC/HUGO database. We realized that this list does not contain a comprehensive list of RNA-\nbinding proteins (RBP) (i.e. including other RNA-binding motifs). To more comprehensively\nassess RBPs in each dataset, we evaluated the enriched proteins and compared them to a list of\nmouse RBPs reported in the EMBL RBPbase (https://apps.embl.de/rbpbase/), a database that\nintegrates high-throughput RBP detection studies. We found that the astrocyte-TurboID\npulldowns contained many more RBPs (n = 918 total and n = 754 unique) when compared to the\nastrocyte-RiboTag pulldowns (n = 336 total and n = 172 unique). Furthermore, when we subset\nthe RBP list to only ribosome proteins, we found that the astrocyte-RiboTag pulldowns contained\nslightly more ribosomal proteins (n = 62 total and n = 21 unique) when compared to the astrocyte-\nTurboID pulldowns (n = 52 total and n = 11 unique). This analysis suggests that SPARO can\ncapture many proteins that interact with RNA, especially, RBPs, which may contribute to the\ndifferences between the astrocyte-TurboID and astrocyte-RiboTag transcriptomes. Furthermore,\nit is important to note that the astrocyte-TurboID pulldown proteome was captured using a data-\ndependent acquisition (DDA) LFQ-MS approach and the astrocyte-RiboTag IP proteome was\ncaptured using a data-independent acquisition (DIA) LFQ-MS. It is possible that the astrocyte-\nRiboTag unique RBPs may still be present in the astrocyte-TurboID pulldowns. As mentioned in\nthe discussion, more alternative MS approaches with increased sensitivity and depth are a future\ndirection of SPARO. Overall, these findings reinforce that SPARO/TurboID biotinylation provides\nbroader access to RBPs than RiboTag, while RiboTag preferentially enriches ribosomal proteins.\nWe have reported these new findings in Fig. 3f and g and below for the reviewer as well as\nupdated the manuscript.\n28. Regarding Fig.4: Without whole-cell TurboID expression, mRNA/protein concordance\nanalysis is problematic. A major point of simultaneous proteomic/transcriptomic profiling is to be\nable to do this analysis in the first place. With cytosolic TurboID, even with 2 weeks of labeling,\ncaution and care needs to be taken in interpreting the mRNA/protein concordance for luminal\nproteins localized within membrane-bound organelles as well as any protein localized within\ndouble-membrane-bound organelles.\nResponse: We appreciate the reviewer’s concerns about the limitations of TurboID-NES and\nevaluation the correlation between mRNA and protein abundances. This concern was also shared\nby Reviewer 1 (Comment 6). As described in Comment 6 and 25, we performed a new SPARO\nexperiment in HEK293 cells lentivirally infected with TurboID-noNES versus NES. We found that\nthe noNES versus NES pulldown transcriptomes are highly similar (92% overlap in transcripts\ndetected and mRNA abundances highly correlated r = 0.99, Supplementary Fig 15e and f\nbelow). We found a similar trend with the pulldown proteomes (85% overlap in peptides\ndetected and protein abundances highly correlated r = 0.93, Supplementary Fig 15e and f\nbelow). Moreover, when we assessed the correlation between the paired pulldown\ntranscriptomes and proteomes, the correlation is similar between the TurboID-noNES and NES\nSPARO approaches (r = 0.31 (NES) and 0.35 (noNES), Supplementary Fig 15l and m below).\nTogether, these findings suggest that the noNES and NES TurboID enzymes in vitro yield similar\nproteomic and even more similar transcriptomic profiles and that removing the NES to restrict\nTurboID to the cytoplasm does not greatly increase the correlation between mRNA-protein\nabundances.\nWe have also made a new Rosa26-TurboID mouse line without the NES and the line is currently\nbeing validated. We fully intend on applying the SPARO approach using this new mouse, but we\nare at least 2 years away from a definitive experiment to confirm that the in vitro noNES versus\nNES findings translate in vivo.\nWe have adjusted the manuscript by adding the new HEK293 TurboID-NES versus noNES\nSPARO study to Supplementary Fig. 15 and 16 and hope these findings may help lessen\nconcerns of the validity of using the TurboID-NES to study mRNA-protein concordance.\n29. The highly discordant genes (mRNA >> protein) related to mitochondria in both astrocytes\nand neurons should be validated. Is the observed discordance because these mRNAs are co-\ntranslationally imported at the OMM, and therefore escaping cytosolic biotinylation?\nResponse: To better understand the technical labeling biases of TurboID-NES and validate our\ndiscordant mRNA-protein findings, we performed new correlation and concordance/discordance\nanalyses on the BV2 global, BV2 SPARO pulldowns, HEK293 TurboID-NES and HEK293\nTurboID-noNES SPARO, and published datasets.\nFor the published datasets, we used RNA data from the Zhang et al, 201418 transcriptomic study\nof mouse CNS cell types and Sharma et al., 201519 proteome of acutely isolated mouse astrocytes\nand neurons. Of course, the main caveat of this approach is that other datasets are either only\ntranscriptomic or proteomic, but we believed this analysis could also help validate our findings to\nsome degree. The Zhang versus Sharma correlation and concordance/discordance analyses\nwere filtered for mRNA-protein pairs found in the in vivo SPARO datasets (purple and blue data\npoints below). Like our findings in SPARO, the correlation between mRNA and protein\nabundances was modest for all pairs (r = 0.36 for astrocytes and r = 0.29 in neurons) and for\nfiltering of the mRNA-protein pairs found in SPARO Supplementary Fig. 10a and b (r = 0.41 in\nastrocytes and r = 0.21 in neurons). We have included the Zhang versus Sharma analysis in\nComment 17 and in Supplementary Fig. 11e). We believe these findings suggest that the\ncorrelation coefficients found in Supplementary Fig. 10 are likely comparable to other studies.\nAcross many independent experiments and analyses of published work--the BV2s (global and\npulldown), HEK293 TurboID-NES SPARO pulldowns, Zhang versus Sharma astrocyte and\nneuron global datasets—we found cytoskeletal-related GO terms enriched in the discordant\nprotein>mRNA darkblue group (see new Supplementary Fig. 13 and 19 and below),\nwhich\nmatches our observations in the in vivo SPARO astrocyte and neuron datasets in Fig. 4f). We\nalso found some mitochondrial-related GO terms enriched in the discordant mRNA>protein\ngroups from the BV2 SPARO pulldowns and the Zhang versus Sharma astrocyte and neuron\nglobal datasets. These findings were also in alignment with the in vivo SPARO astrocyte and\nneuron datasets in Fig. 4g.\nNext, as discussed previously, we tested if the removal of the NES on the TurboID enzyme would\nyield the same discordant phenotypes. Using the HEK293 TurboID-noNES datasets, we observed\nsome cytoskeletal-associated terms in the discordant protein>mRNA darkblue group (again like\nthe other SPARO datasets). We also found that the discordant mRNA>protein mitochondrial\nphenotype is also present in the TurboID-noNES SPARO dataset. Other cellular compartments-\nrelated GO terms also showed up as being discordant in the mRNA>protein group (e.g., Nuclear\nmembrane, Lysosomal lumen, ER-Golgi membrane). However, we did not see the mitochondrial\ndiscordant phenotype in the BV2 global samples. This may be because of the few number of\nmRNA-protein pairs included in the analysis (n = 1328 via DDA LFQ-MS), as compared to the\nHEK SPARO pulldowns (n = 6748 via DIA LFQ-MS).\nTogether, we believe these findings suggest that the discordant phenotypes seen in the\nSPARO datasets may be biologically meaningful, especially, the protein>mRNA\ncytoskeletal phenotype.\nRegarding validation of discordant markers, we have thought deeply about this validation query\nbut there are many challenges to addressing this is a rigorous manner. To validate a\nmRNA>protein hit, we need a highly precise and sensitive manner to measure both the mRNA\nand protein using an approach that is not MS. Antibody-based measures of protein (in\nimmunofluorescence microscopy) are not linear with absolute protein abundances and as the\nreviewer knows, suffer from many failures due to poorly validated proteins. For mRNA, the\napproach of choice likely includes RNA-scope or other in situ hybridization approaches. While this\nmay be measuring the mRNA level, the protein measurement will be lacking, making it very\nchallenging to truly validate a mRNA>protein or for that matter, a protein>mRNA target as well.\nWe did look at brain atlases of in-situ hybridization and IHC studies of brain, and not surprisingly,\nmany of the used antibodies show signal, which is not very convincing, and are limited to few\nantibody vendors. Therefore, we do not feel confident is using these sources for validation studies.\nWhile we used MS for all of our studies, we also point out that there is independent validation of\nthese across datasets which include different cell types. Using bulk tissues, neuron-specific and\nastrocyte-specific -omes, we arrived at similar types of mRNA-protein pairs in concordant and\ndiscordant groups, suggesting that are indeed capturing biological phenomena rather than\nfindings due to random chance.\nBy performing the new study in human cells (HEK293) using TurboID-NES and TurboID-noNES,\nwe also arrived at similar groups of mRNA-protein pairs, in concordant and discordant groups,\nagain providing an additional layer of validation. We therefore humbly suggest that our findings\nare more about types of mRNA-protein pairs that tend to be concordant or discordant, rather than\nabout identifying specific pairs for validation. We have therefore also decreased the emphasis on\nspecific genes/proteins in this analysis, but rather focused on shared ontologies.\n30. Validation of mRNA-protein discordant astrocytic/neuronal markers is lacking.\nResponse: As described in Comment 29 above, we have thought critically about how to validate\ndiscordant markers. To validate a mRNA>protein hit, we need a highly precise and sensitive\nmanner to measure both the mRNA and protein using an approach that is not MS. Antibody-\nbased measures of protein (in immunofluorescence microscopy) are not linear with absolute\nprotein abundances and as the reviewer knows, suffer from many failures due to poorly validated\nproteins. For mRNA, the approach of choice likely includes RNA-scope or other in situ\nhybridization approaches. While this may be measuring the mRNA level, the protein\nmeasurement will be lacking, making it very challenging to truly validate a mRNA>protein or for\nthat matter, a protein>mRNA target as well.\nWe did look at brain atlases of in-situ hybridization and IHC studies of brain, and not surprisingly,\nmany of the used antibodies show signal, which is not very convincing, and are limited to few\nantibody vendors. Therefore, we do not feel confident is using these sources for validation studies.\nWhile we used MS for all of our studies, we also point out that there is independent validation of\nthese across datasets which include different cell types. Using bulk tissues, neuron-specific and\nastrocyte-specific -omes, we arrived at similar types of mRNA-protein pairs in concordant and\ndiscordant groups, suggesting that are indeed capturing biological phenomena rather than\nfindings due to random chance.\nFurthermore, we performed new correlation and concordance/discordance analyses on the BV2\nglobal, BV2 SPARO pulldowns, HEK293 TurboID-NES and HEK293 TurboID-noNES SPARO,\nand the Zhang versus Sharma published datasets. Across many independent experiments and\nanalyses of published work--the BV2s (global and pulldown), HEK293 TurboID-NES SPARO\npulldowns, Zhang versus Sharma astrocyte and neuron global datasets—we found cytoskeletal-\nrelated GO terms enriched in the discordant protein>mRNA darkblue group (see new updated\nSupplementary Fig. 13, 15 and 19), which matches our observations in the in vivo SPARO\nastrocyte and neuron datasets in Fig. 4f). We also found some mitochondrial-related GO terms\nenriched in the discordant mRNA>protein groups from the BV2 SPARO pulldowns and the Zhang\nversus Sharma astrocyte and neuron global datasets. These findings were also in alignment with\nthe in vivo SPARO astrocyte and neuron datasets in Fig. 4g.\nWe therefore humbly suggest that our findings are more about types of mRNA-protein pairs that\ntend to be concordant or discordant, rather than about identifying specific pairs for validation. We\nhave therefore also decreased the emphasis on specific genes/proteins in this analysis, but rather\nfocused on shared ontologies.\nMinor criticisms/suggestions:\n31. Minor criticisms/suggestions:\nThe gain should be increased in the silver stain image in Fig.S1c. The figure legend states that\n“several proteins were enriched in the BV2-TurboID samples”, but it’s not obvious.\nResponse: Thank you to the reviewer for the suggestion. We have adjusted the image using\nImageJ to visualize the differences more easily, replaced the previous figure in Supplementary\nFig. 1c, and provided the image below for the reviewer. We use the silver stain as a proxy to\nassess signal-over-noise prior to mass spectrometry-based proteomics. We have revised the\nfigure legend and main text to state that the silver stain showed stronger protein signal in the\nTurboID-expressing cells compared to the non-TurboID controls, and biotinylation patterns (via\nstreptavidin immunoblot) showed more biotinylated signal with several bands in the TurboID-\nexpressing pulldown samples when compared to the controls.\n32. The authors mention how assessments of protein/mRNA concordance are confounded by\ndifferences in the sensitivity of LFQ-MS and RNA-sequencing. Did they authors do data-\nindependent acquisition (DIA), which is more sensitive than data-dependent acquisition (DDA)?\nResponse: We agree that DIA offers improved sensitivity and proteome coverage compared to\nDDA, and we view DIA as an important future direction for this work.\nTo address the reviewer’s question, we performed an additional experiment comparing the BV2-\nTurboID-enriched SPARO transcriptome to a BV2-TurboID proteome generated using DIA-MS (n\n= 2 biological replicates). Panel a below shows the original correlation analysis from now\nSupplementary Fig. 11c (DDA-MS), while panel b presents the new DIA-MS analysis.\nIn this new DIA-MS experiment, we observed a slightly higher moderate correlation between\nmRNA and protein abundance (Pearson’s r = 0.35, n = 2548 mRNA-protein pairs), compared to\nthe DDA-MS analysis (Pearson’s r = 0.24, n = 1328 mRNA-protein pairs). Notably, the datasets\nwere generated using different lysis protocols: SPARO buffer was used in the DDA-MS\nexperiment (panel a) to preserve RNA–protein interactions, while 8 M urea was used in the DIA-\nMS experiment (panel b), which may influence the sets of proteins captured. Additionally, the\nproteome filtering criteria differed slightly: in the DDA-MS dataset, proteins were retained if\npresent in 2/3 replicates, whereas in the DIA-MS dataset, proteins were kept if present in both\nreplicates. These differences in lysis conditions and filtering stringency likely contribute to the\nvariation in mRNA-protein pair counts across analyses.\nThus, while this comparison should be interpreted with caution, the findings suggest that DIA-MS\nmay better capture the cellular proteome using the TurboID-NES SPARO approach and provide\nslightly improved correlation with the transcriptome. As noted, we consider DIA-MS a promising\ndirection for future studies.\n33. In the discussion, the authors mention potential brain region-specific differences. Why do\nthe authors not compare their data (cortex) with the Soto et al., 2023 dataset (striatum)?\nResponse: We thank the reviewer for their recommendation. We have not undertaken region-\nspecific differences of the SPARO approach yet. We agree with the reviewer that this is a logical\nextension of SPARO, and we see this as a future direction of this work once the validity of the\napproach is established.\nReviewer #3 (Remarks to the Author):\nWe sincerely appreciate Reviewer 3’s highly supportive and constructive Comments. We have\nattempted to address these concerns in a systematic manner and believe that these\nmodifications have significantly improved the quality and rigor of our manuscript.\n34. I appreciate the time the authors took in the introduction to discuss other technologies in\nthis space to highlight current gaps, however there were two additional methods that I recommend\nthe authors include. The first is CITE-Seq from Illumina, which allows simultaneous quantitation\nof transcriptomes and proteins from single cells - I think the differences (and advantages) of\nSPARO are quite easy to highlight (limited panel of proteins that can be quantified in CITE-seq,\ncustom antibody reliance, cost). The other more relevant methods are APEX-seq and CAP-seq\n(see publications below), which are other proximity labeling methods that can be used to capture\ncell-type specific proteomes and transcriptomes using very similar workflows to SPARO. The main\ndifferences are that first, the proximity labeling enzymes used in these methods, APEX2 and\nminiSOG, can biotinylate nucleic acids directly like they can biotinylate proteins, which could allow\nfor fewer false negatives compared to SPARO (although looking at the high correlation between\nyour SPARO transcriptomes and global transcriptomes, I don’t think this is actually an issue), but\nalso allows harsh washing through the entire enrichment protocol which could allow for fewer\nfalse positives (which I am concerned could be an issue for SPARO). The second major\ndifference is that these methods require either the addition of hydrogen peroxide (APEX2) or light\n(miniSOG) to initiate labeling, therefore they would most likely need to be done ex-vivo (although there are a\nfew in vivo studies for both). SPARO has a clear advantage here, as the simple biotin labeling\nallows for non-invasive, live labeling in the intact animal.\na. Fazal, F. M. et al. (2019) Atlas of Subcellular RNA localization revealed by APEX-seq. Cell, 178,\n473-490.\nb. Padron, A. et al. (2019) Proximity RNA labeling by APEX-seq reveals the organization of\ntranslational initiation complexes and repressive RNA granules. Molecular Cell, 75, 875-887.\nc. Li, R. et al. (2022) Metabolic incorporation of electron-rich ribonucleosides enhances APEX-\nseq for profiling spatially restricted nascent transcriptome. Cell Chemical Biology, 29, 1218-1231.\nd. Wang, P. et al. (2019) Mapping spatial transcriptome with light-activated proximity-dependent\nRNA labeling. Nature Chemical Biology, 15, 1110-1119.\nResponse: Thank you for bringing these methods and studies to our attention. We agree with all\nof the reviewer’s points. APEX and miniSOG approaches, via direct biotinylation of nucleic acids\nin addition to proteins, is likely to lead to highly stable biotinylation of both types of molecules\n(proteins and nucleic acids) allowing harsher/more stringent washing conditions than what is\npossible in SPARO. This direct labeling of nucleic acids is not likely to occur with SPARO, as the\nreviewer stated. Certainly, future in vivo applications of APEX2 and miniSOG for a SPARO-like\npipeline, seem to be very logical extensions.\nAs the reviewer alludes to, it is truly exciting to see how all these proximity labeling methods can\nbe theoretically adapted to in vivo systems in animal models, potentially in organoid systems, for\nsimultaneous proteomics and transcriptomics profiling. Furthermore, the relative advantages of\neach could be harnessed to address specific biological questions pertaining to protein-RNA\ninteractions, protein-DNA interactions, subcellular localization and mis-localization of proteins.\nWe have now incorporated these references in the Introduction and have also included a\nfew points related to this in the Discussion section.\n35. Why did you choose to carry out the TurboID biotinylation for 24 hours? This is quite\nexcessive to the 10 min labeling time recommended for in vitro overexpression systems,\nparticularly for the TurboID-NES construct. Yet, I was surprised to see that the extent of\nbiotinylation observed in the streptavidin blot of whole cell lysates was much lower than what I\nwould have\nexpected for this long of a labeling period (Supplementary Figure 1a, comparing the biotinylation\nsignal in the +TurboID samples to the signal from the endogenously biotinylated proteins).\nResponse: We wish to clarify that our in vitro studies using BV2 cells were intended purely to test\nthe initial feasibility of SPARO, and whether the resulting transcriptomes and proteomes are valid\n(using the whole cell -omes as gold standards). The confirmation of validity is best possible when\nthe pulldown proteomes and transcriptomes can be directly compared to the whole cell proteome\nand transcriptome in a monoculture system. This validation-check would not be possible in vivo\nin a multicellular system while retaining native states of the cell. Eventually, our goal was to apply\nSPARO in vivo, using neurons and astrocytes as examples of major brain cell types. Since the\nbiotinylation in vivo is prolonged (days to weeks), we chose to use longer labeling paradigms in\nBV2 cells as well. We acknowledge that the efficiency of TurboID allows rapid labeling within 10\nminutes in cells. But since we do not have such temporal control of labeling in vivo, the 24-hour\nof biotinylation seemed reasonable to us, so that both in vitro and in vivo systems would have\nlonger biotinylation durations. We have observed no cell toxicity with 24-hour paradigms in BV2\ncells. As previously published2, we also found minimal impact of 24-hour biotinylation on whole\ncell proteomes, cell viability or cytokine production in response to lipopolysaccharide or cellular\nrespiration (Seahorse assay). Similarly, when labeling Camk2a-expressing neurons or\nparvalbumin (PV)-expressing interneurons with TurboID in vivo4–6, we also did not observe\nelectrophysiological alterations in both neuronal sub-types despite 2 weeks of biotinylation.\nWith regard the reviewer’s question about the apparent lower level of labeling in immunoblots,\nplease note that we limited exposures of streptavidin blots, to avoid over-saturated blots. Related\nto this, the cell lines we used have been positively selected based on puromycin resistance with\nverification of >90% cells expressing GFP (TurboID-NES-2A-GFP). In the absence of cellular\ntoxicity and confirmation of stable transduction, we are reassured that this is not due to any\ntechnical issues.\n36. Are your TurboID expression levels very low? Also, does it appear that TurboID expression\nand thus biotinylation increases with LPS stimulation? If so, what are your thoughts about this?\nIt would be nice to have the V5 blot shown for the whole-cell lysate blots in addition to the\npulldown blots.\nResponse: We appreciate the reviewer’s question regarding the abundance of TurboID.\nReviewer 2 shared similar concerns in Comment 19 above.\nTo address this concern, we evaluated the V5 pulldown signal (via ImageJ densitometric analysis\nfrom the V5 immunoblot image in panel a figure below provided for the reviewer) and the\nabundance of TurboID (via LFQ-MS) in each of the BV2 sample groups. We found that the V5\nimmunoblot signal is significantly higher in the LPS-treated pulldowns (**p = 0.009, unpaired t-\ntest). Furthermore, we also found that that the abundance of TurboID is not significantly different\nacross the global samples (p = 0.65, unpaired t-test), but significantly different in the pulldowns\n(****p < 0.001, unpaired t-test). Moreover, based on our differential enrichment analysis\ncomparing the global LPS-treated versus the non-LPS untreated BV2-TurboID proteome,\nTurboID is not differentially enriched (p = 0.79 and log -fold change = 0.24, unpaired t-test).\nlog -fold change = 0.99, unpaired t-test).\napproach for the +/- LPS pulldown differential enrichment analysis.\nIn summary, we agree that LPS increased TurboID levels to some extent, but we do not believe\nthis impacts the main results or conclusions of the BV2 in vitro study. Regarding the possible\nunderlying reason for LPS-induced increase in TurboID, we note that we observed this trend in\nour prior study2 as well. We suspect that LPS stimulation may partly impact cytosolic localization\nof TurboID.\nRegarding the reviewer’s comment on detecting V5 in the global lysates via immunoblotting, we\ntried to, but were unsuccessful, likely due to it being too lowly abundant in the global samples.\n37. I am a bit concerned about the efficiency of your pulldown, as there is robust biotinylation\nshown in the whole cell lysate (Fig S1a) but very little in the eluate (Fig. 1c, S1c), in the silver it\nlooks like it’s mostly endogenously biotinylated proteins pulled down?\nResponse: Thank you for the opportunity to expand on the efficiency of the streptavidin pulldowns\nof the BV2 cells. For the global cell lysates (Supplementary Fig. 1b now) and the pulldown\nimmunoblots (Fig. 1c), the reviewer is correct that there are differences in biotinylation signal\nintensity. It is important to note that the pulldown immunoblot and silver stain (Supplementary Fig.\n1d and below) are based on a very small 10% aliquot of the streptavidin beads (~4-5 uL since the\npulldown was performed with 42 uL of beads). 1/3 of the 10% bead aliquot were used to assess\nbiotinylation signal via immunoblot and the remaining 2/3 of bead volume was used for the silver\nstain. Because the silver stain was overdeveloped, we agree that is it hard to see that there is\nmore protein enrichment in the TurboID-positive samples when compared to the non-TurboID\ncontrols. We have adjusted the image using ImageJ to visualize the differences more easily and\nreplaced it in Supplementary Fig. 1c and provided the new image below. We also note that\nsilver stain is not quantitative, but qualitative and does not show linear increases in signal with\nincreasing protein. Therefore, we rely on the silver stain simply to confirm that protein signal in\nthe pulldowns from TurboID cells is greater than signal from non-TurboID cells (signal greater\nthan noise). This serves as ‘go-no go’ checkpoint before proceeding to MS studies. We also wish\nto emphasize that the ratio of streptavidin beads to input protein was determined in our prior\noptimization studies where we varied input protein versus beads to find the right balance of\npulldown efficiency (sensitivity) and specificity. These studies (using dot plots) from supernatants,\nmade us arrive at the currently used ratio of 42 uL beads for 500 ug of protein. We therefore used\nthe same ratio for all our subsequent studies. It is also true that efficiency of biotinylation may\nvary by cell type, in vitro versus in vivo systems, and based on relative proportion of labeled cells\nin a mixed cellular lysate. Even if the pulldown efficiency in our SPARO studies was slightly\nsuboptimal, our intention is to prioritize specificity over sensitivity, so that non-specific binding to\nbeads is minimized.\n38. Did you blot your flow through to show that you successfully depleted your whole cell\nlysate of biotinylated material? Inefficient capture could explain the low now numbers you see in\nyour MS data.\nResponse: We unfortunately did not check the flow through for remaining biotinylated proteins in\nthe experiments.\n39. Does Figure 1d really show that you only enrich RNA with proteomic biotinylation? While\nyou do have stronger enrichment of the two darkest bands (rRNA?) with TurboID labeling, it\nappears to my eye that RNA of other sizes are actually more strongly enriched in the non-Turbo\nsamples - definitely not “negligible”, looks like many RNAs could be non-specifically binding.\nResponse: The reviewer is correct that Fig. 1d does not show only enriched RNA with proteomic\nbiotinylation and that the strong peaks correspond to 28S and 18S rRNA, which is only a proxy\nfor RNA quality, but not necessarily quantitative measures of mRNA abundance. We agree that\nthe use of “negligible” to describe the RNA enriched from the non-TurboID control pulldowns is\nunclear.\nReviewer 1 (Comment 3) shared a similar concern on assessing the background/non-specific\nbinding of the SPARO transcriptome. We agree that this is a valuable addition to the manuscript.\nIn response to this concern, and to better quantify the level of noise in this system, we performed\na new set of experiments. Our goal was to understand what RNAs we would “enrich” in non-\nTurboID samples, which could be defined as the background noise in the system. Therefore, we\nperformed differential enrichment analysis of the pulldown transcriptome between the BV2 non-\nTurboID controls (n = 2 biological replicates, lanes 4 and 5 indicated by black arrows in panel a\nbelow) and the BV2-TurboID (n = 3 biological replicates, lanes 10-12 indicated by green arrows\nin panel a below) samples treated with LPS. Despite some RNA band signal in the non-TurboID\ncontrols, there was very little RNA to sequence and many of the controls failed RNA-sequencing\nquality control due to low concentration and RNA integrity score (RIN) (concentration < 1.5 ng/uL\nand RIN < 6, panel b below).\nThe “noise” that we found consisted primarily of many non-coding RNAs (e.g., Gdap10, Xist and\nMalat1), pseudogenes (e.g., H2-K2 and Pisd-ps1) and splicing factors (e.g., Luc7l3 and Rsrp1)\nenriched in the BV2 non-TurboID LPS controls (panel c and d below). We predict that these\ntranscripts are enriched in the BV2 non-TurboID controls simply because they are generally highly\nabundant in mammalian cells7–9.\nWe also found that Acaca is a highly abundant transcript in the BV2 non-TurboID controls. Acaca\nis an endogenously biotinylated protein10,11 that is a rate-limiting enzyme in fatty acid synthesis\n(converts acetyl-CoA to malonyl-CoA)12. We hypothesize that the Acaca protein can regulate its\nown regulation via interaction with Acaca transcript. Indeed, one paper in 201913 provided\nevidence for interaction between a bacterial carboxypeptidase (equivalent to mammalian Acaca)\nand its mRNA, provide a ‘dimmer’ type regulatory mechanism for a rate-limiting metabolic enzyme\nto regulate mRNA to protein translation via direct protein-mRNA interaction.\nIt is also possible that these RNA species interact with endogenously biotinylated proteins10,11\nallowing them to be captured from non-TurboID RNA pulldowns. It is also important to note here,\nthat only LPS-treated BV2 non-TurboID-derived RNA pulldowns yielded cDNA libraries for RNA-\nseq while those from non-LPS BV2 non-TurboID cells did not. There may be several possible\nexplanations for why the noise RNA pulldowns may change with LPS treatment, some due to\ntechnical reasons and some potentially biological reasons. While we cannot resolve these\npossibilities in this study, users of SPARO must plan to use several different non-TurboID\nnegative control pulldowns to account for these variabilities in experiments. These data have been\nadded to Supplementary Fig. 2 and provided below.\n40. Had you happened to run RNA sequencing on the TurboID negative samples? If so, how\nmany transcripts did you detect/quantify compared to TurboID positive samples and how do these\ncompare to the bulk RNA seq data?\nResponse: Reviewer 1 (Comment 3) shared a similar concern on assessing the\nbackground/non-specific binding of the SPARO transcriptome. As described in Comment 39\nabove and new Supplementary Fig. 2, we found that that the background/non-specific binding\nRNAs in the non-TurboID control pulldowns are essentially “noise”, mostly dominated by non-\ncoding RNAs (e.g., Gdap10, Xist and Malat1), pseudogenes (e.g., H2-K2 and Pisd-ps1) and\nsplicing factors (e.g., Luc7l3 and Rsrp1) enriched in the BV2 non-TurboID LPS controls.\nWhen we compared the BV2 TurboID negative pulldown LPS samples to the BV2 LPS global\nsamples, we found a similar phenotype with non-coding RNA Xist, pseudogene LOC101056014\nand splicing factors (e.g., Sf3b1 and Rsrp1) enriched in the negative pulldowns. Please see\ndifferentiation gene expression heatmap provided below for the reviewer.\n41. I am quite concerned that you only quantify 1455 proteins in your global proteome.\nHonestly, this is very low and a red flag, so please explain. For global proteomics, I would expect\nat least 7000 but up to 9000+ proteins. Only 1038 proteins is also very low for a TurboID enriched\nproteome. For TurboID-NES, I would expect detection of at least 3000 but up to 5000+\nproteins. I know that you state in your discussion that more sensitive MS approaches are likely to increase\nthe depth of the proteome, but the numbers you have here are far below what would be expected\nfrom a Lumos Tribrid MS instrument.\nResponse: Thank you for allowing us to better explain the number of proteins detected using\nsingle shot LFQ-MS, using DDA, for the BV2-TurboID samples and how the Venn diagrams were\ngenerated in Fig. 1e. We apologize for any confusion caused by how we represented the results.\nThe total number of proteins detected by MS before any data cleanup is 2873, which is similar to\nother TurboID-NES studies from our lab using a DDA-MS approach2. We do wish to explain how\nwe applied stringent thresholds which also contributed to lower than expected “hits”. A 50%\nmissingness filter is common in MS studies, but because we had three biological replicates per\ngroup and did not want to have false-positive results due to a liberal missingness filter, we chose\na more stringent missingness filtering of a protein was kept if it was present in 2/3 biological\nsamples The Venn diagrams in Fig. 1e were generated based on this missingness filtering\napproach. The number of proteins detected in the global samples is 1455 (unique) plus the 1038\n(overlap with the pulldown), which is 2493 proteins. In contrast, the number of proteins detected\nin the pulldown samples is 102 (unique) plus 1038 (overlap with global), which is 1140 proteins.\nFor the differential enrichment volcanoes in Fig. 1j, the missingness filter was the same as above,\nbut for only the pulldown sample groups and applied across the whole intensity matrix.\nWe have adjusted the representation of the Venn diagrams in Fig. 1e and f and below for\nthe reviewer. We have also included brief statements in the manuscript to clarify the\nnumber of proteins detected and how missingness was handled.\nAnother difference with the SPARO method compared to prior work with TurboID in BV2 cells, is\nlysis buffer. The SPARO buffer contains various salts, cycloheximide, RNase inhibitors, etc. to\nmaintain protein-RNA interactions, as used in TRAP and RiboTag protocols. It is possible that\nprotein extraction using this buffer is less comprehensive as compared to other lysis buffers such\nas 8 M buffer. When we compared the BCA assay protein concentration values between the\nSPARO buffer and 8M urea buffer of BV2 cells, we found SPARO buffer lysis to be ~4-5 ug/uL\nand for 8 M urea lysis, we found ~6-7 ug/mL.\nThe last point we wish to make regarding the number of identified proteins will be higher if one\nvaries several parameters, including fractionation, longer gradients, FAIMS, newer and more\nsensitive and faster MS instruments, and importantly, DIA versus DDA. At the time of the work,\ngiven resources at hand, we chose the DDA and MS parameters used in our studies. We also\nemphasize that while the depth of the proteome could be optimized, the main results and\nconclusions of this manuscript, will not be impacted (i.e., the SPARO approach works in vitro and\nin vivo).\n42. Is the fidelity of the TurboID vs global RNA-seq datasets suspicious given the large\napparent bias in the proteomics results that are claimed to be due to nuclear exclusion? Are there\nnot several nuclear specific RNAs that you would expect to miss for the same reason (not a\nleading question, genuinely asking as a non-expert)? Could this very high overlap between the\nTurboID and global datasets further suggest that many of these RNAs being detected are ones\nthat are being non-specifically enriched and not due to the TurboID labeling?\nResponse: We thank the reviewer for this thoughtful and important question. The dichotomy is\nthat the agreement between the SPARO transcriptome and global transcriptome is very high,\nwhile this is not the case at the level of the proteome. One therefore wonders why this is the case.\nOne reason is the cytosolic labeling bias of TurboID. While TurboID labels mostly extra-nuclear\nproteins, nuclear proteins are still labeled (e.g., nuclear RNA-binding proteins such as Eif3g, Fus,\nSrsf1 and others in TurboID-NES pulldowns). It is therefore possible that sufficient nuclear RNAs\nare labeled. Since the sensitivity of RNA-seq is far greater than DDA MS, we suspect that this\nlikely explains this dichotomy. It is also important to look at the RNA yields from pulldowns as\ncompared to global RNA extracts. The pulldown RNA yield (from TurboID BV2 cells) is orders of\nmagnitude lower than global RNA yields, even after accounting for dilutional differences. Yet, the\npulldown transcriptomes are highly correlated with the global transcriptome. Biologically, this\nmeans that even if a very small fraction of the global transcriptome is associated with biotinylated\nproteins in TurboID cells, that small proportion is still representative of the global transcriptome.\nThe next question is whether we are detecting non-specific RNA in a single cell system (BV2),\nleading to spuriously high agreement between whole cell and pulldown transcriptomes. Our\nargument against this possibility is cDNA library generation failed on majority of samples obtained\nfrom non-TurboID pulldowns (in vitro and in vivo), confirming that very little mRNA was present in\npulldowns from non-TurboID cells. If non-specific RNA was being enriched in vivo at a high level,\nwe would not observe cell type differences comparing astrocyte to neuron transcriptomes, using\nthe SPARO approach.\nThe reviewer is also correct that certain nuclear-specific RNAs such as unspliced pre-mRNAs,\nnuclear-retained non-coding RNAs (e.g., Malat1 and Xist), and incompletely exported mRNAs,\nare generally expected to be underrepresented in TurboID-NES-based pulldowns, as these\nspecies are not engaged in translation or localized to the cytoplasm where TurboID-NES labeling\noccurs.\nHowever, our results suggest that the overlap between the TurboID-enriched and global\ntranscriptome datasets is not due to non-specific enrichment. First, we assessed background\nRNA capture by comparing BV2-TurboID LPS pulldowns to BV2 non-TurboID LPS control\npulldowns. We observed that nuclear-enriched RNAs such as Xist, Malat1, and pseudogenes\n(H2-K2, Pisd-ps1), as well as splicing factors (Luc7l3, Rsrp1), were significantly enriched in the\nnon-TurboID controls (see Supplementary Fig. 2 andComment 21 above), indicating that these\nare likely non-specifically retained RNAs absent from true TurboID proximity labeling-based\nSPARO transcriptome.\nNext, when comparing TurboID pulldowns to global transcriptomes (+/- LPS treatment), we\nobserved relatively few differentially expressed genes, and the most enriched transcripts in the\nbulk samples were mitochondrial-encoded (e.g., COX1, CYTB, ND2), RNAs expected to be\nexcluded from the TurboID pulldown due to their localization in mitochondria, which are not\nefficiently labeled by TurboID-NES (see Supplementary Fig. 4a and b).\nIn summary, these results support the specificity of the TurboID-based approach for capturing\ncytoplasmic, translation-associated RNAs, and argue against non-specific enrichment as the\ndriver of overlap with the bulk transcriptome. However, future SPARO studies should assess the\nnon-translation-associated transcriptome such as RNAs interacting with RNA-binding proteins\nsince TurboID-NES also biotinylates many of these proteins.\nWe have included the above discussion points in the updated manuscript, and present additional\nsupplemental figure (Supplementary Fig. 2 to emphasize some of these points.\n43. Figure 1g is honestly a bit confusing for me. What is this dimensional scaling based off of,\nintensity as stated in the legend, or log2FC as stated in the figure? If Log2FC, what is the Log2FC\nbased off of since all four conditions are plotted separately?\nResponse: The multidimensional scaling (MDS) analysis in Fig. 1g and Fig. 2e and h is based\non log2-transformed gene expression or protein abundance values, not log2 fold changes from a\nspecific groupwise comparison. The distances between samples are calculated using the root-\nmean-square log2 fold change across the top 500 most variable genes or proteins between each\npair of samples. This means the \"log2FC\" indicated in the figure refers to the average\nexpression/abundance difference between individual samples, not predefined experimental\ngroups. Similar to a PCA plot, the axes correspond to the first two principal coordinates (PCs) that\nbest represent these pairwise distances. A distance of 1 unit roughly indicates a 1-unit root-mean-\nlog2 fold change across the top variable features between samples. Also, like a PCA, we can’t\ncompare PCs across datasets, but only within groups within a dataset.\n44. How are you dealing with missing values? The default imputation option in MaxQuant is\nprobably not the best for this type of data.\nResponse: Handling of missingness is a critical starting point of analysis of data, particularly DDA\nlabel-free proteomics data and we sincerely appreciate the Reviewer’s request for clarification.\nWe do not use (nor recommend) the default imputation option in MaxQuant because we agree\nthat it is not appropriate for this type of data. We have now explicitly stated our handling of\nmissingness approach in the Methods section as follows:\n“Proteins were filtered out if they had missing values in 2/3 samples across groups. Missing\nvalues were imputed from normal distribution biased towards the left tail (low abundances) for\neach sample using the R software. This is directly adapted from Perseus-style imputation, which\nassumes that missing values in LFQ-MS data are missing because of low abundance, rather than\nmissingness at random.”\n45. I’m not very familiar with MDS, but assuming I can read this like a PCA, this figure is saying\nthat the differences in proteins detected by global proteomics is lower than the differences\ndetected by TurboID proteomics, but opposite for the RNA data? Is there a technical or biological\nreason that would explain this? Could this also be due to batch effects? DDA LFQ is very sensitive\nto technical variability, you discuss this caveat as well as tell readers of more reliable\nquantitation methods like TMT or DIA in the discussion.\nResponse: The reviewer is correct that conceptually a MDS plot is like a PCA plot in terms of\nunderstanding broad variance expression/abundance patterns between experimental samples\nand groups. For the proteomic groups, dim 1 describes 97% of the variance, which separates\nglobal and pulldown groups. Dim 2 describes only 9% of the variance, which separates the\npulldown groups (+/- LPS). For the transcriptomic groups, dim 1 describes 37% of the variance,\nwhich separates untreated and LPS treated groups (regardless of global versus pulldown sample\ntype). Dim 2 describes 12% of the variance, which separates global and pulldown groups (+/- LPS\ntreatment). Fig. 1h, Supplementary Fig. 1e, and Supplementary Fig. 3 (BV2 proteomic analyses)\nand Fig. 1i, Supplementary Fig. 1h and Supplementary Fig. 2 (BV2 transcriptomic analyses)\nsupport these patterns. We predict that the BV2-TurboID pulldown and global transcriptomes are\nsimilar based on the nature of translation primarily occurring in the cytoplasm and the TurboID-\nNES enzyme biotinylating proteins that interact with RNA. We also expect the TurboID pulldown\nproteomics to capture a more dynamic, context-dependent subset of the proteome, including\nsignaling factors, RNA-binding proteins, and stress-responsive components. This can naturally\nresult in greater variability between conditions (e.g., +/- LPS) than what is seen in the global\nproteome, which is dominated by abundant housekeeping proteins. The larger effect of LPS on\nthe pulldown proteome as compared to the global proteome, was also observed previously2,\nsupporting that this finding is not due to technical, but biological reasons.\nIn contrast, the bulk and TurboID-associated transcriptomes are more similar, likely because both\nare sampling RNAs from the cytoplasmic, translation-competent pool, where TurboID labeling\nand translation intersect. This reduces differential signal between pulldown and global samples\nat the RNA level.\nTechnically, we acknowledge that label-free DDA proteomics can be sensitive to batch effects\nand missing values, which could contribute to apparent variance in the MDS. However, we\nperformed batches of experiments so that all experimental groups were present in each batch.\nTherefore, differences across batches do not explain these differences. The levels of missingness\n(before imputation) also did not vary systematically across experimental groups, further\nsupporting our argument. Related to this, we also confirm that samples from TurboID pulldown\nand global samples were also randomized during LFQ-MS runs to minimize any effects of drift\nover time with MS instrumentation. As mentioned, we address this limitation in the discussion and\nhighlight that alternative quantification strategies, such as TMT or DIA, offer improved\nreproducibility and quantification depth. These are valuable next steps to strengthen the\nconclusions drawn from TurboID-NES-SPARO proteomics.\n46. As for this MDS plot, please explain more or remove entirely - as is, this is not very\nmeaningful. Honestly, showing the correlation between methods and the volcanoes within the\nmethods comparing the +/- LPS may be sufficient to show that you are successfully detecting the\neffect of LPS using the different methods.\nResponse: We acknowledge Reviewer 3’s concern with the MDS plot reported in Fig. 1 in\nComments 43, 45, and here. In response, we have simplified the MDS plots as well as provided\nmore discussion on the interpretation of these plots.\n47. I saw in the methods that you are using a reference proteome from 5 years ago (and an\nold version of MaxQuant), which is quite dated considering how often these reference proteomes\nget updated. Is this simply when the experiment was conducted? If not, please justify why you are\nusing this as the reference proteome. Also, does this reference proteome match with the\nappropriate reference genome for the RNA-seq data? If there are discrepancies this could explain\nsome of the differences observed instead of being due to the biology.\nResponse: We appreciate the reviewer’s careful attention to the reference proteome and\nsoftware version used. The reviewer is correct that we used the 2020 UniProt Mus musculus\nreference proteome and MaxQuant version 2.4.2.0 (released in 2023). For the BV2 in vitro\nexperiments, the 2020 proteome was the most current available at the time of experiment and\nanalysis. The in vivo experiments were completed subsequently, and we retained the same\nproteome and software version across all datasets to ensure analytical consistency and\ncomparability.\nWe acknowledge that reference proteomes and genome assemblies are periodically updated,\npotentially offering improved annotations. However, the 2020 UniProt proteome was\ncomprehensive and widely used at the time of analysis, and we feel that it remains appropriate\nfor the scope of our study.\nRegarding the genome reference, we had used mm10/GRCm38, released in 2011. This assembly\nis based on the 2009 C57BL/6J reference genome, which is also the source for the 2020 UniProt\nproteome (as noted on https://www.uniprot.org/proteomes/UP000000589). Therefore, to address\nthe reviewer’s concern: Yes, the genome version and UniProt version used for our analyses are\nmatched to each other. However, to assess whether newer genome versions would substantially\naffect our findings, we re-analyzed a subset of the in vivo TurboID pulldown data (astrocyte and\nneuron) using the mm39/GRCm39 assembly. The results from multidimensional scaling (MDS)\nand differential enrichment analyses were highly consistent between the mm10- and mm39-\naligned datasets (see panels a–e below), supporting the use of mm10 in our original analysis.\nWe therefore concluded that discrepancies between the proteomes and transcriptomes are not due\nto version differences of genome, reference UniProt proteome or software versions.\nWe agree that newer annotations may offer advantages, and we plan to adopt the most recent\nreference versions in future work.\n48. For Fig. 1h, please explain where the 1455 shared proteins are coming from, because\nFig. 1e indicates that only 1232 proteins are shared between the +LPS global and pulldown\nproteomes. Also, there should be more discussion on why there is such low correlation in the\nprotein abundances between the pulldown and global proteome datasets - likely, it is not simply\ndue to the nuclear exclusion of TurboID, but also how many solvent-exposed and sterically-\naccessible lysines a protein has to be biotinylated by TurboID, how reactive these lysines are,\nhow well a protein is able to be pulled down, etc. A protein may be very abundant in the cytosol\nand highly detected by global proteomics, but if it isn’t capable of being labeled by TurboID then\nit will not appear in the pulldown data.\nResponse: We apologize for any confusion caused by how we represented the results. The\nshared proteins between the LPS-treated BV2-TurboID pulldown and global samples for the Venn\ndiagrams in Fig. 1e and the correlation plot in Fig. 1h were generated based on different\nmissingness filtering approaches. The total number of proteins detected by MS before any data\ncleanup is 2873, which is similar to other TurboID-NES studies from our lab using a DDA-MS\napproach2. We do wish to explain how we applied stringent thresholds which also contributed to\nlower than expected “hits”. A 50% missingness filter is common in MS studies, but because we\nhad three biological replicates per group and did not want to have false-positive results due to a\nliberal missingness filter, we chose a more stringent missingness filtering of a protein was kept if\nit was present in 2/3 biological samples The Venn diagrams in Fig. 1e were generated based on\nthis missingness filtering approach. The number of proteins detected in the global samples is\n1455 (unique) plus the 1038 (overlap with the pulldown), which is 2493 proteins. In contrast, the\nnumber of proteins detected in the pulldown samples is 102 (unique) plus 1038 (overlap with\nglobal), which is 1140 proteins. For the differential enrichment volcanoes in Fig. 1j, the\nmissingness filter was the same as above, but for only the pulldown sample groups and applied\nacross the whole intensity matrix. We have adjusted the representation of the Venn diagrams in\nFig. 1e (also provided below for the reviewer) and included brief statements in the manuscript\nto clarify the number of proteins detected and how missingness was handled.\nFurthermore, for the correlation plot, protein isoforms were collapsed into 1 gene symbol based\non a max mean calculation using the WGCNA R package.\nRegarding the point as to “why there is such low correlation in the protein abundances between\nthe pulldown and global proteome datasets - likely, it is not simply due to the nuclear exclusion of\nTurboID”, we completed an additional SPARO experiment in HEK293 cells comparing the\nTurboID-noNES and NES enzymes (See Comment 6, 25, 28, and new Supplementary Fig. 14-\n18). In this experiment, we performed a DIA LFQ-MS approach and detected many more peptides\n7-8k (Supplementary Fig. 15e). When we performed correlation analyses between the global\nand pulldown protein abundances, we found the correlation to be higher (r = 0.58 for the global\nversus TurboID-NES, panel g and r = 0.68 for the global versus TurboID-noNES, panel h), which\nis higher than our DDA LFQ-MS SPARO studies and DIA LFQ-MS experiment reported on\nComment 32 (r = 0.35, n = 2548 mRNA-protein pairs). As the reviewer noted, the differences\nmay be attribute to accessibility of lysine residues for biotinylation of TurboID.\n49. For Fig. 1i, please explain where the 12742 shared transcripts are coming from, because\nFig. 1f indicates that only 11947 transcripts are shared between the +LPS global and pulldown\ntranscriptomes. Again here, I worry that the high correlation between the global and pulldown\ndatasets is suggestive that most of the RNA signal is not due to TurboID enrichment. As discussed\nin the point above, due to the chemical nature of TurboID labeling, there is a bias in the proteins\nenriched which I would expect to also bias the associated RNAs being pulled down.\nResponse: Our apologies for not clearly describing and visualizing the number of transcripts\ndetected by RNA-seq and for the value discrepancies across data analyses. Similarly to the LFQ-\nMS data, lowly expressed transcripts were filtered out of the RNA-seq data for each sample group\n(a transcript was kept if the row mean was >/= 10). The Venn diagrams in Fig. 1f were generated\nusing this filtering approach per sample group to capture unique and common transcripts across\nsample types. For differential enrichment and correlation analysis, the row mean >/= 10 was\napplied across the whole gene expression data frame, which reasonably limits the analyses to\ntranscripts that are not very-low abundance (~35k genes to 12-16k depending on the dataset).\nWe have adjusted the representation of the Venn diagram in Fig. 1f and provided it below for\nthe reviewer as well as adjusted the manuscript text to better communicate filtering thresholds.\nRNA-seq is more sensitive compared to DDA MS. Low-abundance transcripts are typically\nidentified (with low count values) while in MS data, low abundance proteins are not identified\n(missing).\nAs also stated in responses above (Reviewer 1 Comment 3 and Reviewer 3 Comment 21), the\ninability to perform RNA-seq on many non-TurboID samples provides evidence that non-specific\nmRNA enrichment in the BV2 samples is very low and therefore, less likely to explain our findings.\nThe ability to distinguish the transcriptomes of two cell types (astrocytes versus neurons) in vivo\nusing SPARO also confirms that contamination via non-specific RNAs is likely to be minimal. We\nalso agree with the reviewer that since TurboID biotinylates proteins and not RNAs, using the\nAPEX2 system that biotinylates nucleic acids, could theoretically yield lower “noise” if adapted to\nan in vivo SPARO-type pipeline, which is certainly an excellent experiment to consider in the\nfuture.\n50. Was the same cell lysis/sample preparation used for the global transcriptomics and the\nRNA enrichment for SPARO?\nIf so then it is encouraging that mitochondrial RNAs were only enriched in the global (Supplementary Fig. 2a), this would be\nsomething that supports the TurboID enrichment is actually working for the RNA workflow of SPARO, but again it is\ndiscouraging that you are not depleting nuclear RNAs as well.\nResponse: Yes, the same cell lysis/sample preparation was used for the global and pulldown\ntranscriptomics. The same approach was used for the proteomics pipeline. We also suspect that\nTurboID does not enter the mitochondria although proteins destined for mitochondrial localization,\ncan be biotinylated before entering the mitochondria. Accordingly, nuclear encoded mitochondrial\nproteins can be labeled with TurboID-NES while mitochondrial DNA-encoded transcripts are not\nsampled. This clear compartmentalization is not the case for cytosolic and nuclear compartments.\nThe NES preferentially excludes TurboID to the cytosol, but this does not mean that a low level\nof TurboID cannot be transiently present in the nucleus. Furthermore, biotinylated RNA-binding\nproteins will enter the nucleus even if biotinylated by TurboID in the cytosol. Therefore, it is\nconceivable that an entirely nuclear-localized RNA species may still interact with a biotinylated\nRNA-binding protein that enters the nucleus, allowing capture of that RNA species by SPARO\nwhen using TurboID-NES.\n51. It is also interesting that you are so strongly enriching ACACA, which encodes an\nendogenously biotinylated carboxylase - this is something that we would expect to see enriched\nin the protein pulldown but why do you think you are enriching this RNA so strongly?\nResponse: We appreciate the Reviewer’s astute observation, and have also become very\ninterested in Acaca at various stages of the SPARO study. Remarkably, Acaca is a top hit in the\nSPARO transcriptome both in vitro and in vivo. As the reviewer noted, Acaca is an endogenously\nbiotinylated protein10,11 that is a rate-limiting enzyme in fatty acid synthesis (converts acetyl-CoA\nto malonyl-CoA)12.\nAfter performing differential enrichment analysis between the LPS-treated BV2-TurboID and BV2\nnon-TurboID controls (see Comment 3 above), we found that Acaca is a highly abundant\ntranscript in the BV2 non-TurboID controls. We hypothesize that the endogenously biotinylated\nAcaca protein can regulate its own regulation via interaction with Acaca transcript. Indeed, one\npaper in 201913 provided evidence for interaction between a bacterial carboxypeptidase\n(equivalent to mammalian Acaca) and its mRNA, provide a ‘dimmer’ type regulatory mechanism\nfor a rate-limiting metabolic enzyme to regulate mRNA to protein translation via direct protein-\nmRNA interaction.\nBecause Acaca is an important rate-limiting enzyme)12, we also predicted that is it a highly\nabundant gene. When examining the Zhang et al., 2014 RNA datasets from acutely isolated\nmouse neural cells (via brainrnaseq.org below), we found that the Acaca transcript is abundant\nacross many neural cell types.\nOverall, we agree that this is a very exciting and fundamentally important finding which warrants\ncareful investigation, on its own merit. The reason for Acaca being highly enriched in the BV2\nnon-TurboID controls is an avenue for future exploration.\n52. Looking at your GO term enrichment, are you surprised that the mitochondrial doesn’t\ncome through more strongly, particularly in the non-LPS dataset? Your most enriched gene sets\nappear to be cytoplasmic translation and ribosomal terms, not mitochondrial terms. I wonder if\nyou would have a clearer signal if you used fewer gene sets, and perhaps rolled up into more\ngeneral subcellular components (e.g. cytosol, nucleus, mitochondria, etc.)\nResponse: Thank you to the reviewer for recommending that we simplify the GO output.. As\nindicated by the reviewer, more mitochondrial-associated terms show up in the RNA global\nsamples when compared to the BV2-TurboID pulldowns. We have revised the GO barplots in\nnew Supplementary Fig. 4 to highlight more general subcellular compartments, as\nrequested and provided the updated barplots below.\n53. For Supplementary Figure 3, looking at the GO term enrichment, it is not immediately\nobvious to me that you are more biased to cytosolic proteins in the pulldown data and enriching\nmore nuclear proteins in the global data. And given the mitochondrial enrichment you saw in the\nglobal RNA data, why do you not see this same signature in the global proteomic data? Again, I\nsuggest trying to simplify these categories and your signature may become clearer, for example\n“Haus complex” and “protein dimerization activity” are not useful terms for me as a reader to reach\nthe same conclusion that you have stated in the main text.\nResponse: This is an excellent suggestion. We filtered the GO ontology terms for mitochondrial,\nnuclear and ER-associated terms to better show that the BV2-TurboID proteomic pulldowns\ncontain very few of these terms as compared to the global samples.\nPlease see the new GO barplots that have been updated in Supplementary Fig. 5 and for\nthe reviewer below.\n54. Not necessary but if you happen to have IF data of your TurboID-NES construct in the BV\ncell line it would be useful to see so you can show that it is indeed nuclear excluded, and that you\nhave a matching biotinylation pattern.\nResponse: We have added immunocytochemistry images of the BV2 non-TurboID control and\nBV2-TurboID microglial cells in culture to highlight that the presence of TurboID via V5 and\nbiotinylation signal is indeed primarily localized to the cytosol. Please see ICC images added to\nSupplementary Fig. 1a and below.\n55. Looking at your methods, it appears that the TurboID transcriptome and proteome\nenrichments were done separately, am I interpreting this correctly? I understand that these were\nboth likely done from the same biological sample, but if the pulldowns were done separately then\nthis adds another source of possible technical variability. In the future, is there any way that you\ncould do the RNA pulldown first, elute the RNAs in a way that preserves the proteins on bead,\napply the stringent washes for the protein pulldown to the same beads, and on bead digest to\nelute the peptides?\nResponse: The reviewer is correct that the TurboID transcriptome and proteome enrichments\nwere performed separately, but importantly, from the same biological samples. We agree that\nprocessing RNA and protein from the same streptavidin pulldown would reduce technical\nvariability and represents a promising future direction for this work. In the current study, we used\nTRIzol to extract RNA directly from biotinylated proteins bound to the beads, which partly\nprecludes subsequent protein analysis due to the denaturing nature of phenol and guanidinium-\nbased reagents. To enable sequential RNA and protein recovery from the same pulldown in the\nfuture, we plan to explore milder RNA elution conditions such as low-ionic-strength buffers or\nheat-based elutions that preserve protein integrity for downstream proteomic analysis, including\non-bead digestion.\n56. I am by no means an expert in the BV cellular response to LPS, but I do not find the GO\nterm enrichment shown in Fig. 1k, Supplementary Fig. 1g very useful for showing that you clearly\nenrich an LPS-treatment signature, again because you used so many gene sets in your analysis.\nFor the RNA data (Fig. 1n, Supplementary Fig. 1j), at least there are at least terms with “LPS”,\nis there a reason you think that these are absent in the protein data? Is there perhaps a list of well\nvalidated proteins that are known to be up-regulated/down-regulated in response to LPS\ntreatment that you could highlight on the volcano plot? I see that you later highlight a few proteins,\nbut is there a more comprehensive list that you could show?\nResponse: Thank you again to the reviewer for giving us the opportunity to better visualize and\ncommunicate the GO barplots in the manuscript. To address the reviewers concern about LPS-\nrelated terms not appearing in the BV2T pulldown proteomic barplots, we filtered the GO output\nfor immune-related terms associated with microglia activation via LPS and found many immune-\nspecific terms (see new barplot below for the reviewer that has replaced the barplot in Fig.\n1k). As you can see, there are LPS/immune-activation-related GO terms enriched in the BVT2 +\nLPS pulldowns, confirming that they are not absent from our protein data.\nWe predict that the LPS/immune-related GO terms in Fig. 1n below have a much larger Z score\nenrichment due to the fact that the transcriptomic response is more robust than the proteomic\nresponse. This may be because there is a strong mRNA response downstream of inflammatory\nregulators, however, the actual translation of these proteins is more carefully regulated or requires\nadditional signals. Additionally, it is likely that some LPS-stimulation-associated proteins may also\nbe secreted. We have adjusted the main manuscript text to reflect these changes to show that\nSPARO can be used to capture an immune response in BV2 microglia in vitro.\nRegarding the reviewer’s comment on utilizing a well validated proteomic marker list of LPS-\nstimulated BV2 microglia, we tried to validate our findings using published work. We compared\nthe DEPs from our LPS versus no-LPS BV2-TurboID SPARO pulldown proteome to our prior\nstudy by Sunna et al., 2023 that contains an LPS activation of BV2-TurboID cells component2.\nWe found some immune-related microglial-associated DEPs to be shared across the two LPS\ndatasets including Stat1 (involved in inflammatory signaling cascades20) , Sqstm1 (related to\nautophagy function21), and Gbp2 (associated with control immune responses in microglia22).\nWe view our LPS global lysates as our “references” of LPS activation, which is why we generated\nthe log -FC correlation plot in Fig. 1l and below, that demonstrates that the effect of LPS\n2\nactivation on BV2 cells at the global level is recapitulated at the pulldown level.\n57. For Fig. 1l, the log2FC(+/-LPS) between global and pulldown shared proteins is basically\nthe same as your protein abundance correlation, which I thought would be higher because you\nare “controlling” for factors of labeling and pulldown efficiency in the +/-LPS comparison. It is\nencouraging that your highest differentially enriched proteins correlate better between the\nmethods, but how do you explain why so many more proteins were found to be differentially\nenriched with the TurboID vs global experiment?\nResponse: Based on our findings that there is more TurboID in the BV2T + LPS pulldowns\n(See Reviewer 1 Comment 1 and 2), we decided to normalize the +/- LPS protein pulldowns by\nTurboID abundance and regenerated the differential abundance analysis and the scatter plot in\nFig. 1l (provided below). As you can see, the correlation of all shared proteins does improve\nfrom 0.3 in Fig. 1h to 0.48 in the updated Fig. 1l. When examining the highly enriched proteins via\nlog2-fold-change of 1, the correlation coefficient increases to 0.80.\nRegarding the reviewer’s comment on the number of proteins differentially enriched between the\npulldown and global BV2-TurboID samples, we predict that TurboID-NES can capture proteins\nbeing trafficked to different cellular compartments. Our BV2 GO analysis data found in\nSupplementary Fig. 5 (See Comment 58 below) that shows “Protein Localization to ER” as an\noverrepresented ontology in the pulldown groups. Additionally, the HEK293 TurboID-NES and\nTurboID-noNES protein GO analyses found in Supplementary Fig. 18 show cytoskeletal-related\nontologies, which are crucial for protein trafficking. Some of these proteins may appear\nmoderately abundant in the global samples, but due to the enrichment nature of TurboID, they\nappear highly abundant in the pulldown samples. The global lysates are also likely dominated by\nhighly abundant proteins. Furthermore, many proteins are not equally represented across cellular\ncompartments.\n58. Is it possible that the TurboID experiment is detecting not just changes in protein\nabundance, but also changes in protein localization?\nResponse: Based on the updated GO plots in Supplementary Fig. 5 and below, it is possible\nthat TurboID is detecting some changes in protein localization as some (although few)\nmitochondrial, nuclear, and ER-associated terms appear in the BV2 SPARO protein pulldowns\n(+/- LPS). We did not find any ubiquitin or proteasome-associated GO terms enriched in the\nprotein pulldowns when compared to the globals (+/- LPS), but it is possible proteostasis-\nassociated changes could be detected with more sensitive MS approaches.\n59. Also, again as a non-expert, would you really expect to enrich anti-inflammatory markers\nin the -LPS dataset?\nResponse: Anti-inflammatory proteins (that go down with LPS) will decrease with LPS, but not\nentirely be absent in the microglial proteome. Therefore, we expect microglial proteins to be\nlabeled and enriched using the TurboID approach, even if LPS increases or decreases their\nabundance. The only anti-inflammatory proteins that should be absent from the SPARO\nproteome, should be those that are completely lost from the cell, but it is unlikely that many such\nproteins exist. On the contrary, many acute stress response and immediate early genes are\nactivated, and their proteins increase from undetectable to high levels (e.g., IRG1). These will be\nexpected to increase, and accordingly, we see such proteins at both input and pulldown levels,\nchanging with LPS.\n60. I know I am belaboring this point, but does the fact that your RNA Log2FC correlation data\nis so high compared to the protein data again suggest that the pulldown is not working the same\nfor the RNA and protein enrichment? For example, are there not nuclear-specific RNAs that are\ndifferentially expressed in response to LPS that you would see biased towards the global dataset\n(this is not a leading question, I genuinely don’t know the answer to this)?\nResponse: We do not think that technical differences in pulldown efficiency are responsible for\nthis. We suspect that this is primarily because TurboID labels cytosolic proteins including RBPs\nwhich can of course then enter the nucleus, even at low levels, sufficient to label nucleus-\nexclusive RNAs. We have performed small RNA-seq on the astrocyte-TurboID pulldowns and\nh and\nhave indeed found some miRNAs, snRNAs, piRNAs, snoRNAs and circRNAs. Since this analysis\nis preliminary, we have only included the miRNA data in this manuscript as new Supplementary\nFig. 10 and below for the reviewer.\nTo address the labeling biases of TurboID-NES, we performed a new SPARO study in HEK293\ncells with and without the NES (See new Supplementary Figs. 15-19 and Comments 6, 25, 28\nand 48).\nWe have accordingly made a new Rosa26-TurboID mouse line without the NES and the line is\ncurrently being validated. We fully intend on applying the SPARO approach using this new mouse,\nbut we are at least 2 years away from a definitive experiment.\n61. There really needs to be more discussion on why the correlation between the methods is\nso different for RNA vs protein, I am concerned that this may be due to the RNA data being largely\nbased on non-specific binders and the proteomic data being due to poor enrichment efficiency.\nRegarding the transcriptomic robustness, however, I am encouraged by your in vivo data that\nshows SPARO can be used to capture cell-type specific transcriptomes, suggesting that there is\nminimal non-specific binding of RNAs from other cells in the lysate.\nResponse: We agree that this point needs a more comprehensive discussion in our manuscript.\nWe have therefore included a paragraph in the discussion highlighting this dichotomy and\nprovided possible explanations.\n62. For Fig. 2b, there are cells that are positive for biotinylation but negative for the astrocyte\nmarker (S100beta) or neuron marker (beta III tubulin), do you think these are still\nastrocytes/neurons (non-expert here) or are you concerned that this other cell population will\nconvolute the data?\nResponse: This is a very fair point that needs a justification. Morphologically, the biotinylated\ncells that are negative for S100β or β-III tubulin look like the cell types that should be labeled\n(astrocytes and neurons, respectively). IHC of brain tissues can suffer from incomplete\npenetrance by antibodies which could explain this difference. We have not seen any biotinylation\nin glial cells in Camk2a-expressing TurboID brains. Similarly, we have not seen neuronal or\nmicroglial labeling in Aldh1l1-expressing TurboID brains. The proteomes of the two, show very\nclear differences between the populations, including canonical markers expected to be present,\nand lack of markers not expected to be present. Therefore, even if there was a low level of non-\nspecific labeling, this has not impacted our results or conclusions. The markers used (S100β,\nβ-III tubulin) are also not perfect markers. Not all astrocytes make S100β and not all neurons have\nlevels of β-III tubulin detectable by IHC. Finally, it is well known that the Camk2a-cre-ERT2 and\nAldh1l1-cre-ERT2 lines are highly specific to each cell type, particularly in the mouse cortex,\nwhich we used for our studies. Lastly, while S100βis localized to the cell body (more perinuclear)\nin astrocytes, astrocytes have a very large cell surface area including end-feet that wrap around\nblood vessels. In our prior work4, we showed that biotinylation in astrocytes colocalized with Aqp4,\na marker of astrocyte end-feet. Therefore, a combination of all the above factors can easily explain\nwhy some biotinylated cells do not show up as positive with the markers we chose. We hope this\ndiscussion allays the reviewer’s concerns about potential non-specificity.\n63. Also, again non-expert here, in Supplementary Fig. 4f, to my eye it appears that the\nTurboID expression is affecting the gross morphology/distribution pattern shown by the cellular\nmarkers in the red channel. For example, in the Cre-only control, it looks like the astrocyte maker\nis evenly distributed through the FOV but with TurboID expression the cells have higher density\nat the top but are less dense going down the FOV. For the neuron marker, the FOV for the TurboID\nexpressing samples appear significantly denser than the Cre-only control. Are these differences\ndue to normal variation or was this differential pattern something that was seen consistently with\nTurboID expression? I know in the main text you state that there were no adverse effects on the\nmice or cellular functions, but it would still be worth noting this differential pattern post-TurboID\nlabeling if it is consistent.\nResponse: These differences are due to normal variation.\n64. For Supplementary Fig. 5, the astrocyte data is quite compelling and clear, while the\nneuron data is a bit less clear. I think this is interesting because what it suggests is that some cell\ntypes like astrocytes may be more difficult to purify in large amounts thus resulting in a weaker\nsignal, and SPARO provides a superior method to obtaining enough signal to clearly profile this\ntype of cell. The neurons on the other hand appear to be easier to purify and thus obtain enough\nmaterial for high neuronal signal (maybe because they’re more abundant/larger?), as SPARO and\nthe cell enrichment give more similar results of the top 50 genes overall, however SPARO has a\nclear advantage in having higher specificity as there is clear de-enrichment of other cell types\n(astrocytes) compared to the cell enrichment. This may be worth discussing this data a bit\nmore to emphasize the advantages that SPARO can provide for rarer/smaller cells and for purity.\nResponse: Thank you to the reviewer for their thoughtful discussion in regards to Supplementary\nFig. 5. We have pondered similar thoughts related to the bulk cortex versus neuron-TurboID\npulldown proteomes. As mentioned by the reviewer, the bulk cortical proteome is likely dominated\nby neuron-specific proteins, so the comparison to the astrocyte-TurboID pulldowns shows a\n“clearer” astrocyte enrichment phenotype when compared to the same analysis for the neuron-\nTurboID pulldowns.\nThe advantage of SPARO to potentially capture RNA and protein-level information from less\nabundant cell types is an outstanding addition to the manuscript and we view exploring this as a\nfuture direction of the SPARO approach. We have added a brief section to the discussion\nsection on this.\nWe did perform an in vivo SPARO pilot study on PV-expressing interneurons, which are less\nabundant than Camk2a-expressing neurons. We were able to obtain neuron-like SPARO\ntranscriptomes and proteomes from the PV-interneurons, but are still validating our findings and\ntherefore the sensitivity of SPARO on lowly abundant neural cell types.\n65. For Supplementary Fig. 7b, I’m wondering if at this point I’m just not used to looking at\nRNA gels, because the negative controls still appear to have a lot of RNA to my eye, not “low-\nlevels”.\nResponse: Despite some RNA band signal in the RiboTag-only controls, there wasn’t much RNA\nto sequence and the RNA integrity was very low (concentration < 300 pg/uL and no RIN score\nreported. Please see the table below from the TapeStation assay.\nWe have added the RIN scores to the Supplementary Fig. 9 legend.\nLane RIN RNA conc. [pg/uL) Sample info\n1 - 3750 Ladder\n2 - 289 RiboTag only control\n3 - 192 RiboTag only control\n4 8.8 2200 Astrocyte-RiboTag\n5 8.8 1270 Astrocyte-RiboTag\n6 8.8 973 Astrocyte-RiboTag\n66. Also is there a reason that the bands are shifted between samples? Is this a composite\nfrom different gel images? Also, where is the SPARO QC data for this experiment?\nAre you using the SPARO data from the previous experiment in Fig. 2 or did you generate new samples?\nResponse: The RNA QC was completed at Admera Health using an RNA ScreenTape assay.\nThe image is a direct output from the Agilent TapeStation system, which generates a digital gel-\nlike representation of each sample run. Minor shifts in the apparent migration between lanes can\noccur due to how the software renders the electropherogram traces into a gel view, rather than\nreflecting actual differences in migration during electrophoresis. All samples were run on the same\nTapeStation assay and are shown together, not as a composite from separate gels.\nWe appreciate the reviewer for informing us that we did not explicitly state that the astrocyte-\nTurboID SPARO transcriptomic data compared to the astrocyte-RiboTag data in Fig. 3 and\nSupplementary Fig 9 is from Fig. 2. We did perform STAR genome alignment and eaturecounts\nwith all the FASTQ files together prior to data cleanup and differential expression analysis. We\nhave added a statement clarifying this in the methods section.\n67. For Fig. 3c and d, are you surprised that the RiboTag and TurboID data overlap so highly,\ngiven that RiboTag would presumably only deliver actively translated RNAs, while TurboID would\npresumably also deliver non-coding RNAs? Is this reflected in Supplementary Fig. 7d volcano?\nI know there is some speculation in your main text why there are some of the\ndifferences that you see, but I would find a more definitive discussion on why the difference isn’t greater to be more\nhelpful (maybe due to abundance most of the RNAs captured even by TurboID are mostly\nribosomally associated? Maybe Rpl22 is in most ribosomes in this cell type?).\nResponse: We were also surprised that the RiboTag and TurboID data overlap so highly. We\nagree with the reviewer that TurboID can capture more than actively translated RNAs such as\nnon-coding RNAs. While not included in our data, we have performed small RNA-seq on the\nastrocyte-TurboID pulldowns and have indeed found some miRNAs, snRNAs, piRNAs, snoRNAs,\nand circRNAs. Since this analysis remains preliminary, we have not included this aspect in this\nmanuscript.\nAs requested by Reviewer 1 in Comment 4, we performed an additional analysis that looked at\naverage transcript length, percent GC content, and number of exons of DEGs between the\nTurboID and RiboTag astrocytic transcriptomes. We found that all three RNA characteristics are\nsignificantly different between the SPARO TurboID and RiboTag-enriched astrocytic\ntranscriptomes (See updated Supplementary Fig. 9g and below).\nRiboTag/TurboID differences may also reflect the broader nature of TurboID labeling, which\ncaptures many RNA-binding proteins not limited to those associated with active translation (see\nnew Fig. 4f and Reviewer 2, Comment 27 response). It is possible that TurboID biotinylates\nRNA-interacting proteins at multiple stages of the mRNA life cycle, including during nuclear\nprocessing events such as splicing, consistent with the expectation that pre-spliced transcripts\nare generally longer and contain more exons than ribosome-associated, spliced mRNAs. It may\nbe possible that TurboID can biotinylate RNA-interacting proteins at different stages of the mRNA\nlife cycle (e.g., TurboID biotinylates an RNA-binding protein that translocate to the nucleus to\nassist with mRNA processing such as splicing). It is likely that pre-spliced transcripts (assessable\nby biotinylated RNA-binding proteins) are generally longer than post-spliced transcripts (i.e.\ntranscripts associated with ribosomes for translation).\nWe also found a significant difference in the average percent GC content between the astrocyte-\nTurboID and astrocyte-RiboTag pulldowns. We predict that this may be indirectly related to\ntranscript length. The average percent GC content may also be related to the classes, or functions\nof transcripts captured by each method. There is also the possibility that because the RiboTag\napproach is primarily biased towards ribosome-associated, actively translated mRNAs, that these\ntranscripts are more enriched in housekeeping transcripts with a higher GC content. In contrast,\nTurboID labeling may recover a broader pool of RNA-interacting proteins, including those\nassociated with longer, less GC-rich transcripts at earlier stages of RNA processing. We see\nexamining these differences as a future direction of the SPARO approach after it has been\nvalidated.\nRegarding the last point about Rpl22, when the RiboTag approach were engineered, Rpl22 was\nselected because it was identified as a core ribosome protein subunit from large-scale yeast and\nmammalian protein tagging studies and when tagged with HA, was incorporated into\npolyribosomes23,24. It is possible that Rpl22 is highly abundant in astrocytes. This may not be the\ncase for other cell types in CNS or peripheral tissues highlighting another strength of the SPARO\nmethod. Although, Rpl22 does have a paralog, Rpl22-like1 (Rpl22L1), that can increase in the\nabsence of Rpl22 that could be biotinylated by TurboID in other cell models.\n68. For Supplementary Fig. 8a-d, I’m just really not sure that SPARO or similar techniques\nshould be used to measure RNA/protein concordance, due to the bias introduced by the labeling\nchemistry and enrichment. Even without considering the cell type specificity, this trepidation is\nsupported by the fact that the RNA/protein abundance correlation is much lower for SPARO BV-\n2 when compared to the global datasets for the cortex. Can you please show the correlation for\nthe RNA/protein abundances from the global BV2 datasets so we have a more direct comparison\nto panel c?\nResponse: We appreciate the reviewer’s concern on the labeling biases of TurboID and how this\nmay confound the mRNA-protein abundance correlations. However, based on our new analyses\nwith the HEK293 TurboID-noNES versus NES findings (Supplementary Fig. 15 and 16 and\nComments 1, 6, 25, 28, and 48 above) where we found few differences between the SPARO\ntranscriptomes and higher correlation between the pulldown and global datasets, we believe this\nis evidence that SPARO can be used to at least make reasonable inferences about mRNA-protein\nabundances.\n69. For Supplementary Fig. 8g-h, the markers seem evenly distributed across the abundances\nof both protein and RNA, are you surprised they are not more biased towards the top right corner,\nor higher on the respective protein vs. RNA axes if they deemed a marker using proteomic vs.\ntranscriptomic data?\nResponse: For the analyses in Supplementary Fig. 8g-h (now Supplementary Fig. 11i and j),\nwe did not know what to expect for the distribution of cell type markers. The Zhang et al., 201418\nand Sharma et al., 201519 studies from which we identified cell- type-specific markers from, is\nderived from acutely isolated postnatal neural cell types. Because a comparative analysis of\nmRNA-protein abundances within cell type markers has not been completed using the native, in\nvivo environment, we were not entirely surprised.\n70. For Supplementary Fig. 8i, Fig. 4f and g, and Supplementary Fig. 9, I do enjoy the\ndiscussion in the main text about the biological explanations for the differences in RNA/protein\nconcordance/discordance between cell types (although you go on to temper the biological\nsignificance of these findings in the discussion), but is it possible that the concordant/discordant\nmRNA-protein pairs overlap so highly between cell types in part because of the labeling chemistry of TurboID and the\nSPARO protocol rather than solely due to biologically meaningful reasons? For example, if a protein is not efficiently labeled\nby TurboID and pulled down, then it could consistently appear to be lower abundance than the RNA. Again, it would be\nreally great to see how all of this data looks like for the BV-2 SPARO vs global datasets.\nResponse: To better understand the technical labeling biases of TurboID-NES and validate our\ndiscordant mRNA-protein findings, we performed correlation and concordance/discordance\nanalyses on the BV2 global, BV2 SPARO pulldowns, HEK293 TurboID-NES and HEK293\nTurboID-noNES SPARO, and the Zhang et al., 201418 versus Sharma et al., 201519 datasets. The\nZhang versus Sharma correlation and concordance/discordance analyses were filtered for\nmRNA-protein pairs found in the in vivo SPARO datasets.\nFor the BV2 (global and pulldown), HEK293 TurboID-NES SPARO pulldown, Zhang versus\nSharma astrocyte and neuron global datasets, we found cytoskeletal-related GO terms enriched\nin the discordant protein>mRNA darkblue group (see new Supplementary Figs. 13, 15 and 19),\nas seen in the in vivo SPARO astrocyte and neuron datasets in Fig. 4f). We also found some\nmitochondrial-related GO terms enriched in the discordant mRNA>protein groups from the BV2\nSPARO pulldowns and the Zhang versus Sharma astrocyte and neuron global datasets. These\nfindings were also in alignment with the in vivo SPARO astrocyte and neuron datasets in Fig. 4g.\nAlthough, we did not see the mitochondrial discordant phenotype in the BV2 global samples.\nNext, we tested if the removal of the NES on the TurboID enzyme would yield the same discordant\nphenotypes. Using the HEK293 TurboID-noNES datasets, we some cytoskeletal-associated\nterms in the discordant protein>mRNA darkblue group (again like the other SPARO datasets. We\nalso found that the discordant mRNA>protein mitochondrial phenotype is also present in the\nTurboID-noNES SPARO dataset. Other cellular compartments-related GO terms also showed up\nas being discordant in the mRNA>protein group (e.g., Nuclear membrane, Lysosomal Lumen,\nER-Golgi membrane). Although, we did not see the mitochondrial discordant phenotype in the\nBV2 global samples. This may be because of the few number of mRNA-protein pairs included in\nthe analysis (n = 1328 via DDA LFQ-MS), as compared to the HEK SPARO pulldowns (n = 6748\nvia DIA LFQ-MS).\nThese findings suggest that the discordant phenotypes seen in the SPARO datasets may\nbe biologically meaningful, especially, the protein>mRNA cytoskeletal phenotype.\nMinor points:\n71. I understand that you probably chose to use TurboID-NES because you had already\ngenerated the models for previous studies, but just wanted to point out that biotin ligases like\nTurboID generally localize diffusely to the cytosol and nucleus when untagged, so the untagged\nenzyme would probably suit your purposes better in the future so you capture nuclear\nproteins/RNAs as well as cytosolic ones.\nResponse: Thank you for the outstanding suggestion. Based on shared concerns on the possible\nlimitations of the TurboID-NES enzyme, we performed a new SPARO experiment with HEK293\ncells with a TurboID-noNES and the transcriptomes are highly similar and (see Comments 1,\n6, 25, 28, 48, 68 and 70 above and Supplementary Figs. 12-18).\n72. Please consider changing “bulk” to “global” because from my perspective, both of the\n“bulk” and “pulldown” samples are bulk samples (pooled from many cells), but in the non-pulldown\n(“bulk”) samples, you are measuring the whole-cell, i.e. global, proteome or transcriptome.\nResponse: Thank you for the suggestion to change “bulk” to “global”. We agree that this is more\nappropriate and have changed it in the manuscript.\n73. Typos: protein enrichment using a silver stain (Supplementary Fig. 1c); levels of RNA in\nbulk BV2 lysates (Supplementary Fig. 1b)\nResponse: We have corrected this typo.\n74. Supplementary Figure 1d is not discussed in the main text, and it also has an incomplete\nfigure legend.\nResponse: Thank you for acknowledging this error. We realized that the Venn diagram\nSupplementary Fig. 1d is not needed since we have the differential enrichment and correlation\nanalyses, so we have removed it from the manuscript.\n75. For Fig. 1h-i and Supplementary Fig. 1e, h, please label on the plots if the data shown is\n+/- LPS. Also in the main text, state that you see similar results with +/- LPS so the readers don’t\nhave to dig through the supplementary just to learn this.\nResponse: We have adjusted the plots to show +/- LPS.\n76. Typo: SPARO also detected proteomic and transcriptomic changes driven by LPS\ntreatment 170 (Fig. 1j and m, Supplementary Fig. 1f and i).\nResponse: We have corrected this typo.\n77. For Fig. 1l, in the main text you claim that the Pearson coefficient is 0.84 but the plot shows\n0.68 - please correct to reflect the true value.\nResponse: Thank you for identifying these typos. We have corrected them.\n78. For Fig. 2c, the V5 expression only provides biochemical verification that you have specific\nTurboID expression in general, not verification of cell-type specificity (this is your IF data). Please\nremove or edit this sentence.\nResponse: Thank you for acknowledging this overextrapolation. The reviewer is correct, and we\nhave edited the text to state that the immunoblot V5 detection indicates the presence of the\nTurboID recombinant protein and not cell type specificity.\n79. For Fig. 2d, I again don’t agree that the amount of RNA enriched in the non-TurboID\ncontrols is “negligible”, as I can clearly see it on the gel, particularly on the neuron samples,\nplease consider using a different word.\nResponse: Thank you for the suggestion. We agree that “negligible” is not the best choice of\nword. We have edited the text from “negligible” to “lower levels of RNA as compared to …” .\n80. Supplementary Fig. 4e is not mentioned in the main text, is this just an extended figure of\nwhat is shown in Fig. 2d?\nResponse: Yes, Supplementary Fig. 4e is an extended figure for Fig. 2d. We have clarified the\nfigure legends and the results section.\n81. For Fig. 2f, how many proteins were quantified in this proteomic experiment? Please state\nsomewhere in the main text or on the figure.\nResponse: Our apologies for not stating this. We have added the number of proteins quantified\nto Fig. 2 panel e and f as well as the number of transcripts quantified to panel h and i.\n82. In Supplementary Fig. 6, is the LPS-treatment dose in the legend correct? 75 mg/kg/day\nseems very high. In the methods, the unit is missing.\nResponse: 75 mg/kg/day is a typo. Our apologies. The correct dosage is 0.75mg/kg/dose x 5\ndays. We have updated the figure legend and methods section. This figure is now Supplementary\nFig. 8.\n83. In regards to the main text on Supplementary Fig. 10, consider changing “mRNA\ncharacteristics and protein synthesis dynamics” to “mRNA and proteostasis dynamics” as half life\nis really a balance of synthesis and degradation.\nResponse: Thank you for the suggestion. We have edited the text.\nReferences\n1. Cho, K. F. et al. Proximity labeling in mammalian cells with TurboID and split-TurboID.\nNat Protoc 15, 3971–3999 (2020).\n2. Sunna, S. et al. Cellular Proteomic Profiling Using Proximity Labeling by TurboID-NES in\nMicroglial and Neuronal Cell Lines. Molecular & Cellular Proteomics 22, 100546 (2023).\n3. Mulvey, C. M. et al. Spatiotemporal proteomic profiling of the pro-inflammatory response to\nlipopolysaccharide in the THP-1 human leukaemia cell line. Nat Commun12, 5773 (2021).\n4. Rayaprolu, S. et al. Cell type-specific biotin labeling in vivo resolves regional neuronal and\nastrocyte proteomic differences in mouse brain. Nat Commun13, 2927 (2022).\n5. Kumar, P. et al. Native-state proteomics of Parvalbumin interneurons identifies unique\nmolecular signatures and vulnerabilities to early Alzheimer’s pathology. Nat Commun 15,\n2823 (2024).\n6. Goettemoeller, A. M. et al. Entorhinal cortex vulnerability to human APP expression promotes\nhyperexcitability and tau pathology. Nat Commun15, 7918 (2024).\n7. Hon, C.-C. et al. An atlas of human long non-coding RNAs with accurate 5′ ends. Nature543,\n199–204 (2017).\n8. Salmena, L. Pseudogenes: Four Decades of Discovery. in Pseudogenes (ed. Poliseno, L.)\nvol. 2324 3–18 (Springer US, New York, NY, 2021).\n9. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human\ngenome. Nature 489, 57–74 (2012).\n10. Chapman-Smith, A. & Cronan Jr, J. E. Molecular Biology of Biotin Attachment to Proteins.\nThe Journal of Nutrition129, 477S-484S (1999).\n11. Tong, L. Structure and function of biotin-dependent carboxylases. Cell. Mol. Life Sci.70, 863–\n891 (2013).\n12. Saggerson, D. Malonyl-CoA, a Key Signaling Molecule in Mammalian Cells. Annu. Rev. Nutr.\n28, 253–272 (2008).\n13. Meades, G., Benson, B. K., Grove, A. & Waldrop, G. L. A tale of two functions: enzymatic\nactivity and translational repression by carboxyltransferase. Nucleic Acids Research 38,\n1217–1227 (2010).\n14. Branon, T. C. et al. Efficient proximity labeling in living cells and organisms with TurboID. Nat\nBiotechnol 36, 880–887 (2018).\n15. Choi Rhee, E., Schulman, H. & Cronan, J. E. Promiscuous protein biotinylation by Escherichia\ncoli biotin protein ligase. Protein Science 13, 3043–3050 (2004).\n16. Roux‐, K. J., Kim, D. I., Raida, M. & Burke, B. A promiscuous biotin ligase fusion protein\nidentifies proximal and interacting proteins in mammalian cells. Journal of Cell Biology 196,\n801–810 (2012).\n17. Fornasiero, E. F. et al. Precisely measured protein lifetimes in the mouse brain reveal\ndifferences across tissues and subcellular fractions. Nat Commun9, 4230 (2018).\n18. Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons,\nand vascular cells of the cerebral cortex. J Neurosci 34, 11929–11947 (2014).\n19. Sharma, K. et al. Cell type– and brain region–resolved mouse brain proteome. Nat Neurosci\n18, 1819–1831 (2015).\n20. He, Y., Taylor, N., Yao, X. & Bhattacharya, A. Mouse primary microglia respond differently to\nLPS and poly(I:C) in vitro. Sci Rep11, 10447 (2021).\n21. Pesti, I. et al. Bafilomycin 1A Affects p62/SQSTM1 Autophagy Marker Protein Level and\nAutophagosome Puncta Formation Oppositely under Various Inflammatory Conditions in\nCultured Rat Microglial Cells. IJMS 25, 8265 (2024).\n22. You, J.-E. et al. Exploring the Role of Guanylate-Binding Protein-2 in Activated Microglia-\nMediated Neuroinflammation and Neuronal Damage. Biomedicines 12, 1130 (2024).\n23. Sanz, E. et al. Cell-type-specific isolation of ribosome-associated mRNA from complex\ntissues. Proc. Natl. Acad. Sci. U.S.A. 106, 13939–13944 (2009).\n24. Sanz, E., Bean, J. C., Carey, D. P., Quintana, A. & McKnight, G. S. RiboTag: Ribosomal\nTagging Strategy to Analyze Cell Type Specific mRNA Expression In Vivo. Current Protocols\nin Neuroscience 88, (2019)."}]

Decision letter