Multi-modal dissection of cell-type specific TDP-43 pathology in the motor cortex

Domain: chemistry
Published: 2026-03-09
Editorial outcome: major_revision

Abstract
Cytoplasmic TDP-43 pathology is a pathological sign of ALS/ALS-FTD and a converging disease event across different genotypes, phenotypes and CNS areas. To understand this process and target it therapeutically, we need to define which cell types are affected and which cell-type specific effects make them particularly vulnerable. We coupled flow-cytometry nuclear sorting and sequencing with single-nucleus multi-omic ATAC-seq and RNA-seq and spatial transcriptomics to define the transcriptional cell type of affected neurons in the post-mortem ALS/ALS-FTD motor cortex (30 ALS, 20 ALS-FTD & 32 control samples). Here, we show that mainly excitatory cortical neurons are affected by TDP-43 pathology and define the cell types that are affected the most: intratelencephalic L2-3-LINC00507-FREM3, L3-5-RORB-LNX2, L3-5-RORB-ADGRL4 & L6-THEMIS-LINC00343 neurons and extratelencephalic L5-FEZF2-NTNG1 neurons. Transcriptional aberrations by TDP-43 pathology, like cryptic exon inclusion, are cell-type specific and affect distinct gene sets in each cell type, highlighting the need to address TDP-43 pathology in a cell-type specific manner.

Introduction
Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease that affects the brain and spinal cord and rapidly progresses to severe disability and fatal end1,2. While ALS is primarily characterized by motor symptoms, up to 50% of ALS patients display an overlap with the cognitive disability profile of frontotemporal dementia (FTD), another devastating neurodegenerative disease affecting the frontal and temporal lobes of the brain1–3. Together, ALS and FTD represent two ends of a spectrum of neurodegenerative disorders (“ALS-FTD”) that share clinical, genetic and pathological features4,5. ALS-FTD is associated with the degeneration of specific populations of cortical neurons in different regions of the frontotemporal cortex, including the giant pyramidal Betz cells and the von Economo neurons6,7. The pathologically defining feature of degenerating cells in ALS-FTD is the presence of proteinaceous cytoplasmic inclusions rich in ubiquitinated aggregates of the protein TDP-43 (TARDBP gene) that can be observed in >95% of the ALS patients and >50% of the FTD patients1,7–9.

TDP-43 aggregation is believed to not only represent a pathological correlate of degeneration, but also to play an active role in the degenerative process. Indeed, translocation of TDP-43 to the cytoplasm and aggregation results in different cellular aberrations, including de-repression of transposable elements10, expression of endogenous retroviral proteins11, erroneous splicing and inclusion of cryptic exons (CE)12–14, altered poly-adenylation site usage15, changes in gene expression10 and cellular stress. These molecular aberrations are well-described, and some of them are ‘upstream’ of other ALS-FTD genes, like STMN216. In addition, it is also likely that pathology spreads from a neuron to its adjacent and to its interconnected cells, affecting progressively more cells and areas. However, only a subset of the brain cells is affected by TDP-43 aggregation and degeneration in ALS-FTD, a phenomenon termed ‘selective vulnerability’. The molecular basis and the genetic/epigenetic predisposition factors for the selective vulnerability are not well understood.

Histopathological studies have demonstrated that not only the ‘canonical’ vulnerable neurons (i.e., Betz cells, the L5b extra-telencephalic neurons), but also neurons from different cortical layers are affected by TDP-43 pathology7. However, the molecular and transcriptional identity of these neurons have not been defined, despite three recent attempts: Liu et al. could demonstrate enrichment of upper layer (L1-L3) neuronal markers in bulk RNA-seq of TDP-43 depleted nuclei from the middle frontal cortex10, while Gittings et al. and Wang et al. could successfully impute TDP-43 pathology of single-nucleus RNA-sequencing, but remained limited to the largest affected group of L2-3 ITC neurons, both in the dorso-lateral prefrontal cortex (DLPF)17,18. Recent advances in single-nucleus RNA-sequencing have enabled the highly resolved description of different cortical cell types19. Still, the transcriptional cell types affected by TDP-43 pathology and its effects in them have not been defined yet. The targeting of specific molecular processes upstream or downstream of TDP-43 pathology require the precise resolution of the cell-type identity and of the transcriptional events.

Here, we characterize the cell-type specificity of TDP-43 pathology in the primary ALS/ALS-FTD motor cortex by employing a multi-modal approach that enables highly-resolved identification of cell types and direct identification of single nuclei with TDP-43 pathology. We found that TDP-43 pathology affects predominantly specific cell types and results in distinct transcriptional alterations. These effects were cell-type specific and distinct from the general transcriptional changes in the ALS/ALS-FTD motor cortex, which were partly associated with differential chromatin accessibility.

Results
Multi-modal ATAC-seq and RNA-seq in the same nucleus reveals an increased resolution of cell-type identity in the motor cortex
We generated a multi-modal, multi-cohort single-nucleus dataset of the diseased and unaffected human primary motor cortex (‘M1’) by sampling fresh-frozen postmortem tissue from 30 patients with sporadic ALS (‘ALS’), 10 patients with sporadic ALS-FTD (‘ALS-FTD’), 7 familial ALS-FTD patients harboring the pathogenic C9ORF72 Hexanucleotide repeat (HRE) (‘ALS-FTDC9ORF72’), and 32 neurologically unaffected controls (‘Ctrl’, study design in Fig. 1a) from a total of six different cohorts. ALS and ALS-FTD were diagnosed based on the standardized criteria at the respective centers and pathologically confirmed by TDP-43 inclusions post mortem (47/47 cases, Supplementary Fig. 1). Classification into sporadic/familial disease was based on medical records and a genotyping panel testing for pathogenic variants in 43 ALS-associated genes (clinical and demographic characteristics of the cohort in Supplementary Data 1 and Supplementary Data 2, list of tested ALS-associated genes in Methods). By utilizing the 10X Genomics Multiome Assay for Transposase-Accessible Chromatin by sequencing (ATAC) + RNA-seq Kit, we acquired a total of >245,000 nuclei with simultaneous chromatin accessibility and transcriptome profiles, resulting in >180,000 high-quality nuclei after thorough quality control (“Methods”, Supplementary Fig. 2). To alleviate confounding by batch effects, we applied an experimental design where control and disease case samples were processed together in different 10X Genomics 3’GEX reaction wells and some samples repeated on different 10X reaction wells (Supplementary Data 3). We then integrated all samples into a fully-merged, integrated dataset by carefully modeling the batch effects with deep learning (scVI for GEX and peakVI for ATAC), aiming for a robust clustering of nuclei to cell communities independent from batch and the donor disease status. To reveal cell identity and group cells with similar chromatin accessibility profiles and transcriptome programs, we leveraged both RNA and ATAC modality by employing the weighted-nearest-neighbor (WNN) framework. This combines latent spaces from chromatin accessibility and the gene expression modalities into a single latent space, accommodating the need for a single representation of the dataset in a reduced dimensionality space. This approach enabled the fine resolution of the nuclei into discrete cell types that could not be identified using either of the modalities alone (Fig. 1b–d and Supplementary Fig. 3), with 58 cell clusters identified from the transcriptomic profiles alone, 64 with the chromatin accessibility profiles alone, and 75 with WNN. WNN nuclei clusters were assigned to a cell type by marker expression and then grouped hierarchically into major cell types and classes (see next section). All major human cortex cell types (excitatory neurons, inhibitory neurons, astrocytes, microglia, oligodendrocytes and oligodendrocyte precursor cells) were detected, with a strong overrepresentation of excitatory neurons, as expected for proper sampling from the M1 area. Vascular, epithelial, and peripheral immune cells were also detected, but due to their very low numbers that prevent statistically robust conclusions (1,045 nuclei, 5 clusters), we excluded them from further analysis. All major cell types were similarly represented in each disease group (Figs. 1e, 2e), with significant variation between the samples. The major cell-type WNN clusters were well-characterized by marker features from both modalities (Fig. 1f, g), confirming the proper segmentation of the nuclei into major cell classes.

Hierarchical annotation of cell-type identity and compositional analysis
To leverage the full power of the single-nuclei dataset, we modeled cell identity with a hierarchical cell-type annotation, which allows for the balancing between the advantages and limitations of single-nucleus resolution. We grouped the 70 cell clusters (75 total without 5 endothelial and lymphocyte clusters) to 14 major cell-types by inspecting the expression of marker genes and the correlation of the expression profiles between the clusters (Fig. 2a–c; complete hierarchical annotation tree in Supplementary. Fig. 4; hierarchical clustering dendrograms by gene expression, by chromatin accessibility, and based on both modalities in Supplementary. Fig. 5–7; list of major-cell-type markers in Supplementary. Data 4, 5). Neuronal cell clusters were organized in 10 major cell types (6 inhibitory & 4 excitatory) and glial cells clusters in 4 major cell types corresponding to the four major glial cell types: astrocytes (7 cell clusters), microglia (one cell cluster), oligodendrocytes (one cell cluster) and oligodendrocyte precursor cells (‘OPC’, 5 cell clusters). The glutamatergic/excitatory neuron types were characterized by the expression of the markers LINC00507 (4 cell clusters), RORB (11 cell clusters), THEMIS (3 cell clusters) and FEZF2 (5 cell clusters). As expected, the large, transcriptionally active nuclei of the excitatory neurons were associated with a higher number of detected features (both genes and chromatin accessibility peaks) and number of reads per feature (UMIs for gene expression and fragments for chromatin accessibility). The GABAergic/inhibitory interneurons were characterized by the expression of the markers PVALB, SST, VIP, TAFA1, LAMP5 and PAX6 (6, 7, 9, 5, 5 and 1 cell clusters, respectively). Correlation analysis of the gene expression profiles in the 14 major cell types demonstrates high correlation of the expression across all genes and samples inside three major groups: inhibitory neurons, excitatory neurons and glial cells (Fig. 2a, b), with some significant correlation of the expression profile of FEZF2-excitatory neurons also with inhibitory neurons. Hierarchal clustering of the major cell types in the reduced latent space spanned by the 50 top latents from chromatin accessibility and 50 top latents from gene expression demonstrated that the association of the major cell types is also preserved when considering the chromatin accessibility profiles (Fig. 2a). Similarly, the major cell type associations were similar when considering the whole gene expression data (Fig. 2c, 10,000 randomly sampled nuclei). All 14 major cell types were well-represented in the three disease groups, both ALS-FTD types (familial with C9ORF72 HRE and sporadic), both sexes and all batch and cohort groups (Fig. 2d), with a significant variation of numbers between donors (Supplementary. Fig. 8 and Supplementary. Data 22–24). RORB- and LINC00507-excitatory neurons and oligodendrocytes were the most abundant cell types, representing ~two thirds of the entire dataset (64% together; RORB: 16%, LINC00507: 21%, oligodendrocytes: 26%) (Supplementary. Data 6). Of note, neuronal cell clusters, especially inhibitory, were more diverse in their expression profiles, resulting in a higher number of smaller cell clusters than in glial cells at the same clustering resolution. For example, all oligodendrocytes clustered in a single large, homogenous cell cluster when clustering was performed at the same community resolution, together with all other nuclei in the dataset. Due to their comparably low numbers and highly correlated expression profiles, we combined VIP- and TAFA1-inhibitory neurons (‘Inh VIP/TAFA1’) and LAMP5- and PAX6-inhibitory neurons (‘Inh LAMP5/PAX6’) to facilitate further analysis. Our dataset reached an increased cell-type resolution compared to previous studies. It was comparable to previous reports regarding sequencing depth (UMIs per nucleus, genes detected per nucleus), number of nuclei per cluster, and cell-type proportions, but presented with a lower number of nuclei per sample (Supplementary Fig. 9). Furthermore, the set of detected (expressed) genes and the magnitude of expression were highly correlated to previous studies, as well as the KEGG terms enriched in this ‘background’ gene list (Supplementary Fig. 10). Remarkably, the segmentation of nuclei to different well-defined cell identities was highly concordant between our study and the studies of Pineda et al.20, Li et al.21 and Gittings et al.18 (Supplementary Fig. 9k–n), demonstrating the robustness and reproducibility of cell-type identification.

Similarly to previous reports with single-nucleus sequencing in ALS/ALS-FTD17,18,20,21, we did not observe a general change of a specific cell type in ALS, ALS-FTD or C9ORF72-ALS-FTD (Supplementary Fig. 11 and Supplementary Fig. 21). To ensure no technical bias, we analyzed cell-type proportions with three statistical frameworks: deep-learning based Bayesian compositional analysis with scCODA, hierarchical with tascCODA, and statistical modeling by mixed linear models with MASC. We also excluded bias by samples with lower total cell numbers by repeating the MASC analysis only with samples with a high number of nuclei. Still, we observed a comparable proportion of cell clusters, cell types, major cell types and cell classes, except for increased astrocytes and Exc RORB neurons (in ALS and ALS-FTD resp.) and reduced LAMP5 Inh neurons and microglia (in ALS and ALS-FTD resp) with MASC, which could not be confirmed with the other methods. However, we observed some stratification, with decreased neuron numbers (e.g., Exc LINC00507 neurons) in single samples, pointing to heterogeneity due to underlying phenotypical variation.

Spatial transcriptomics allow for the mapping of the transcriptional cell types to their histological counterparts
Next, we sought to map the defined major transcriptional cell types to their histological correlates in the human cortex. ALS-FTD affects specifically the motor and the DLPF cortex in a progressive manner, which is thought to reflect the spread of pathology7. These areas are characterized by histologically defined morphological layers ‘L1’-‘L6’ rich in neuronal somata of interconnected glutamatergic and GABAergic neurons, with pathology progressing from deep to upper cortical layers. The large pyramidal neurons of the deep layer L5b in the motor and the DLPF cortex are the only extratelencephalic neurons and traditionally considered to be the selectively vulnerable cells in ALS/ALS-FTD (‘Betz cells’ in the motor cortex). Nonetheless, TDP-43 pathology is found also in cells of other layers, mostly superficial and deep layers including L2, L3 and L67. For the histological mapping of the major cell types, we utilized the publicly available LIBD spatial transcriptomic dataset of the adult human DLPF cortex, consisting of 12 slides from 3 neurologically unaffected adult cases and well-annotated cortical cell layers22. We built a cell-type characterizing gene expression signature for all major cell types from our multi-omic dataset and quantified it in the spatial transcriptomic dataset (Fig. 3) by gene score expression and by spot deconvolution of cell types (numbers and list of cell-type markers in Supplementary Data 4 and Supplementary Data 5, negative control with gene signatures composed of randomly sampled genes in Supplementary. Fig. 12). We found that the excitatory neuronal types were found across all cortical layers, with Exc LINC00507 cells predominantly in L1 and L2/3 layers (hereafter ‘L2-3 intratelencephalic neurons’), Exc RORB cells in L3-5 (hereafter ‘L3-5 intratelencephalic neurons'), and Exc THEMIS and Exc FEZF2 cells in L5/6 (hereafter ‘L6 intratelencephalic’ and “L5-6 Near-projecting, extra-telencephalic and corticothalamic’ neurons, respectively) (Fig. 3a–d). Inhibitory neurons were more diffusely distributed across all layers, with Inh LAMP5/PAX6, Inh TAFA1/VIP and Inh SST cells predominantly in the upper cortical layers L1-3 and Inh PVALB neurons in all cortical layers (Fig. 3e) (hereafter referred to as ‘interneurons’ with their markers). As expected, glial cells were concentrated in the underlying white matter (all types), as well as L1 (OPCs, astrocytes and microglia) (Fig. 3e). Surprisingly, we observed an apparent enrichment of microglia in L1 in this highly validated dataset, but we could not identify whether this is an absolute enrichment of microglia or a relative enrichment due to lower numbers of other cell types without further studies. To identify the transcriptional equivalent of the large pyramidal cells of layer Vb of the motor cortex, we calculated a score based on 410 gene expression markers of extratelencephalic neurons19 (Supplementary. Data 5). Interestingly, the signature was enriched very strongly and specifically only in one cell type, Exc FEZF2-NTNG1 cells, which we identified as the neuronal community hosting the Betz cells of the motor cortex. Correspondingly, the Exc FEZF2-NTNG1 signature was found in very rare, single spots with very high signature expression in the spatial transcriptomic data, as expected for large but rare cells (Fig. 3f and Supplementary Fig. 13). Furthermore, the Exc FEZF2-NTNG1 cells overlapped with very high accuracy with the VAT1L neurons from Pineda et al.20, the L5 extratelencephalic neurons from Gittings et al.18 and the Exc L5 ET FEZF2-ADRA1A neurons from Li et al.21, unequivocally confirming their identity (Supplementary Fig. 9n).

The ALS/ALS-FTD motor cortex is transcriptionally altered across hierarchical cell types
We next investigated in detail the transcriptional changes in ALS and ALS-FTD motor cortex. The cell-type unaware analysis (i.e., all cells from a donor are combined to a single sample) confirmed robust transcriptional changes in ALS and ALS-FTD (Supplementary Fig. 14). We employed surrogate variable analysis (SVA) to account for hidden batch variables like post-mortem interval (PMI) and onset site, as due to incompleted data, we were unable to directly include these variables as covariates in the differential gene expression (DGE) analysis. Combining DGE analysis with SVA strongly increased the sensitivity of the analysis while maintaining the specificity of the results, as demonstrated by systematically increasing the number of covariates in the analysis (Supplementary Fig. 14). We identified 667 differentially expressed genes (‘DEGs’) in ALS (absolute log2 fold-change > 0, fdr < 0.05; sample permutation control: 3 DEGs) and 3,827 DEGs in ALS-FTD (sample permutation control: 19 DEGs) (Supplementary. Fig. 14, lists of DEGs in Supplementary. Data 7–9), even despite the lower number of samples (and thus decreased statistical power) in the ALS-FTD analysis (ALS/Ctrl: n=30/32; ALS-FTD/Ctrl: n=17/32). Thus, transcriptomic changes were stronger in ALS-FTD. Comparison of DEG numbers between C9ORF72-HRE positive and sporadic ALS-FTD cases revealed that the strong transcriptomic changes are found in both disease subgroups (Supplementary Fig. 15). Comparing the magnitude and direction of regulation (up/down) revealed a strong correlation of gene changes in ALS and ALS-FTD, with >96% of the common DEGs changed concordantly (n=173/180, Pearson’s r=0.90, ****p<0.0001). ALS/ALS-FTD DEGs showed a large overlap with known KEGG pathways of ALS (Supplementary Fig. 14). DEGs were enriched in pathways associated with aerobic metabolism (‘oxidative phosphorylation’, ‘metabolic pathways’) and neurodegenerative diseases (‘ALS’, ‘Parkinson’s disease’, ‘Huntington’s disease’, ’prion disease’), probably driven by the high overlap of the genes annotated to these pathways with the oxidative phosphorylation pathways. DEGs included known targets of TDP-43 pathology, including STMN2, EPHA4, NPTX2 and its receptor NPTXR, which were all down-regulated in ALS/ALS-FTD. Taken together, this low-resolution analysis demonstrates robust transcriptomic remodeling in ALS/ALS-FTD, with strong overlap between both disease groups, thus confirming its functional relevance.

To assess the contribution of different cell types, we next investigated differential expression events across the 12 major cell types. Increasing the resolution increased the power to detect DEGs and demonstrated transcriptomic changes in specific major cell types (Fig. 4). Transcriptomic changes in ALS were almost exclusively in excitatory neurons, with some DEGs also in VIP and Parvalbumin interneurons and in astrocytes, while in ALS-FTD, more DEGs were detected in all major cell types (Fig. 4a–d). In general, transcriptional dysregulation was balanced between up- and down-regulated genes, with slightly more down-regulated genes in most cell types. In both disease groups, the highest number of DEGs was detected in L2-3 intratelencephalic (‘L2-3 ITC’) and L3-5 intratelencephalic (‘L3-5 ITC’) excitatory neurons. The number of detected DEGs strongly correlated with the total number of reads per major cell type, demonstrating that direct comparison of the strength of transcriptomic dysregulation between the major cell types is not feasible without equilibrating the statistical power (mostly for read numbers) (Fig. 4a–d). At the same time, it suggests some specificity of the differences between major cell types; for example, a relatively high number of DEGs were detected in oligodendrocytes in ALS-FTD, despite the comparable library size to Parvalbumin interneurons. Similarly, in both ALS and ALS-FTD, more DEGs were detected in L3-5 ITC excitatory neurons than in L2-3 ITC excitatory neurons, despite their lower numbers and smaller total library sizes (Fig. 4c, d).

Across the 11 L3-5 ITC excitatory neuron cell types, we found the most DEGs in RORB ERBB4 and RORB LNX2 cells in ALS, and in RORB CUX2 and RORB LNX2 cells in ALS-FTD. From the 4 L2-3 ITC excitatory cell types, we found the most DEGs in the most abundant cell type, LINC00507 FREM3 both in ALS and ALS-FTD (Fig. 4e–l). DEGs found in ALS and ALS-FTD were strongly expressed in layers L2-6 and less in L1 and the white matter (Fig. 4m–o; 1,197/1,315 genes from ALS signature expressed in the spatial data, 3,851/4,666 from the ALS-FTD signature) in the neurologically unaffected. Together, we identified 5,471 genes which were differentially expressed in at least one major cell type in at least one condition (Fig. 4m and Supplementary Data 10). 510 of these genes were detected both in ALS and ALS-FTD (Supplementary Data 11). Many of the ALS/ALS-FTD genes were similarly regulated in major cell types also when statistical significance was not reached (i.e., fdr > 0.05, Supplementary Fig. 16). Among the signatures were genes associated with familial ALS/ALS-FTD: SOD1, TARDBP, TBK1, ALS2, BSCL2, DCTN1, GLE1, GRN, HNRNPA1, NEFH, PFN1, SETX, UBQLN2, VAPB, VEGFA and with known association to ALS/ALS-FTD: NEFL, STMN2, UNC13A, NPTX1, NPTX2 and NPTXR, amongst others.

Interestingly, gene expression was regulated more concordantly between different cell types in a disease group than between disease groups for the same cell type (Supplementary Fig. 17). These findings are highly concordant with Pineda et al.20 and confirm that the robust transcriptomic changes in major cell types are more ‘disease-specific’ than ‘cell-type specific’. Altogether, transcriptomic changes were highly convergent both across cell types and across disease groups.

In summary, transcriptomic analysis across hierarchical cell types in ALS/ALS-FTD reveals predominantly concordant changes across cell types and disease genotypes/phenotypes, indicating common underlying factors. Thus, we next explored the roles of epigenetic remodeling and TDP-43 pathology.

Chromatin remodeling is associated with a subset of the ALS/ALS-FTD transcriptional changes
Next, we investigated the hypothesis that at least a part of the transcriptomic signature of ALS/ALS-FTD is associated with underlying changes in chromatin accessibility as a cumulative proxy measure for epigenetic remodeling. We exploited the simultaneous profiling of chromatin accessibility and gene expression in the same nucleus across all cells in the dataset to detect significant correlations (‘peak links’) between chromatin accessibility regions (‘peaks’) and expression of nearby genes, which are more difficult to detect in designs with less observations (i.e., on a sample level or not measured in the same cell) (Fig. 5a). In total, we detected >53,000 significant associations between 36,721 unique peaks (~18% of all peaks) and >6000 genes (~18% of all expressed genes), with most genes associated to <20, but up to 68 peaks, and most peaks associated to one gene or a maximum of 12 genes across all peaks (Fig. 5b). Peaks associated with gene expression were enriched in promoter elements (Fig. 5c). Of the 5,471 genes in the ALS/ALS-FTD transcriptomics signature, 2,271 (42%) were annotated a peak link, showing a >4-fold enrichment of chromatin-associated peaks in the signature (compared to a 1,000-fold random sampling from genes with comparable expression levels). This suggests that ALS/ALS-FTD genes are specifically associated with chromatin accessibility.

Differential accessibility analysis identified differentially accessible peaks (“differentially accessible regions, ‘DARs’) in several major cell types: astrocytes (n=531/52 DARs in ALS/ALS-FTD resp.), oligodendrocytes (n=46/24 DARs in ALS/ALS-FTD resp.), excitatory L3-L5 ITC neurons (n=51/2,277 DARs in ALS/ALS-FTD resp.), excitatory L6 ITC neurons (n=1/5 DARs in ALS/ALS-FTD resp.) and excitatory L2-3 ITC neurons (n=33/2,973 DARs in ALS/ALS-FTD resp.) (Fig. 5d, f). Similarly to the DEGs, DARs were often shared between cell types and their regulation correlated strongly between cell types where they were differentially accessible (Fig. 5e, g, h). The accessibility of significant DARs was strongly positively correlated to the expression of the associated genes across cell types and disease groups (Fig. 5i, yellow dots). To investigate whether chromatin accessibility peaks are associated with SNVs previously identified in GWAS studies, we compiled a list of 222 unique ALS GWAS SNVs from the GWASdb collected from different studies. In total, 24 SNVs overlapped with chromatin accessibility regions. Analysis of the combined chromatin accessibility of these positions revealed no increased/decreased accessibility in them, except for oligodendrocytes in ALS-FTD (Supplementary Fig. 18). Taken together, this data demonstrated that a portion of the transcriptomic changes in ALS/ALS-FTD motor cortex is associated with chromatin changes.

Nuclear TDP-43 pathology selectively affects excitatory L2-3 intratelencephalic, L5 extratelencephalic and L6 intratelencephalic neurons
To determine which ALS/ALS-FTD DEGs were associated with TDP-43 pathology (e.g., resulting from or leading to it) in a cell-type specific manner, we aimed to define the transcriptional signature of nuclear TDP-43 depletion. Of note, the highly cited previous report has described a TDP-43 pathology transcriptomic signature, but it was derived with bulk sequencing10. This type of analysis returns cell-type markers next to the specific DEGs, because only some cell types will be affected by TDP-43 pathology. Indeed, as the authors of this study themselves, we found a strong enrichment of cell-type markers in the bulk-sequencing derived TDP-43 pathology signature (Supplementary Fig. 19). However, it is crucial to distinguish specifically up-/down-regulated genes from enriched cell-type markers. Therefore, we performed fluorescence-activated nuclei sorting coupled with single-nuclei RNA-seq (FANS-snRNA-seq) based on the TDP-43 signal in neuronal nuclei (Fig. 6a and Supplementary Fig. 20). TDP-43 pathology in vulnerable neurons is characterized primarily by depletion from the nucleus, mostly accompanied by its phosphorylation and aggregation in the cytoplasm. Thus, neuronal nuclei with TDP-43 pathology can be identified by the decrease in TDP-43 signal. We selected isolated nuclei singlets from 10 ALS-FTD samples with confirmed presence of TDP-43 pathology by selecting nuclei with high relative signal of NeuN (RBFOX3, a marker of neuronal nuclei; found also in some oligodendrocytes, Fig. 6b) and sorted them into nuclei with TDP-43 pathology (TDP-43 Low) and without TDP-43 pathology (TDP-43 High). Around 30–40% of all nuclei were positively selected (Fig. 6e; no debris, singlets, DAPI+, NeuN+), of which <6% were TDP-43 Low (Fig. 6f). As expected, ~1% of all neuronal nuclei were TDP-43 Low. We then employed the nuclei fractions for single-nucleus RNA-seq with the 10× 3’GEX kit and acquired a total of 12,225 nuclei transcriptome profiles (7189 TDP-43 High, 5036 TDP-43 Low) after QC filtering. To identify the cell type of the nuclei affected by TDP-43 pathology, we transferred the well-defined cell-type labels from the high-resolution ALS-FTD multiome nuclei dataset to the FANS-seq nuclei based on the expression of anchor features with Seurat (“Methods”). Clustering and visualization with Bonsai23 and UMAP confirmed that despite the strong expected transcriptomic changes by TDP-43 pathology, nuclei still primarily clustered by cell type and thus the cell type can be identified (Fig. 6h–l). More than 95% of the selected nuclei were neuronal, as intended (Fig. 6m). Even though the RBFOX3 expression was lower in inhibitory than in excitatory neurons (Fig. 6b), we found that the proportions of excitatory (~75%) and inhibitory neurons (~25%) in the TDP-43 High data was comparable to the unsorted multi-omic dataset (Fig. 6n) and previous reports17,24, confirming that the sorting strategy was appropriate and did not passively enrich excitatory neurons due to stronger RBFOX3 expression. While all 8 neuronal cell types (4 inhibitory, 4 excitatory) and oligodendrocytes were detected in the TDP-43 High nuclei fraction, we detected almost exclusively excitatory neurons in the TDP-43 Low nuclei fraction (>95%) (Fig. 6n). All major excitatory cell types were affected, but not equally represented, with an enrichment of L2-3 ITC and L5-6 ITC- and ETC neurons (Fig. 6o and Supplementary Data 25). Again, TARDBP expression was not lower in L2-3 intratelencephalic neurons (Fig. 6c, d), suggesting that the expansion of this cell type in the TDP-43 Low fraction is specific. Similarly, Somatostatin interneurons were the most represented interneurons in TDP-43 Low nuclei, despite their relatively high TDP-43 expression (Fig. 6d), reiterating the specificity of the TDP-43-based sorting. Thus, nuclear TDP-43 depletion in the motor cortex is highly selective for specific neuronal cell types.

To definitely confirm that nuclei in the TDP-43 Low fraction are nuclei from cells affected by TDP-43 pathology, we sought to reproduce known features of TDP-43 pathology: previously described gene signatures, cryptic exon (CE) inclusion and alternative poly-adenylation. Cell-type unaware (“pseudo-bulk”) DGE analysis revealed a robust dysregulation of gene expression in TDP-43 Low nuclei compared to TDP-43 High nuclei, with >1600 differentially expressed genes (Fig. 7a and Supplementary Data 12). More than 40% of the DEGs were amongst the DEGs previously described in bulk-RNA-seq TDP-43 FANS in the frontal cortex (Liu et al.)10 and in single-nucleus RNA-seq of nuclei predicted to be affected by TDP-43 pathology (Gittings et al.)18 (Fig. 7b). The dysregulation of the genes found in common with these studies was highly concordant both in direction (up- vs. down-regulated) and magnitude (Fig. 7c, d). Thus, transcriptomic signatures confirmed TDP-43 pathology in the TDP-43 Low fraction. The known and recently well-described targets of TDP-43 pathology, STMN2 and NPTX2 were also down-regulated in TDP-43 Low nuclei, in accordance with previous reports. Interestingly, NPTXR, the receptor for NPTX2 was also down-regulated, in line with previous findings with iCLIP data that NPTXR splicing is regulated by TDP-4325. In addition, we analyzed CE inclusion in TDP-43 Low nuclei. Of the 66 previously described alternative splicing events in TDP-43 pathology (Ma et al.)14 with bulk RNA-seq, two could be previously detected in 10X Genomics 3’GEX single-nucleus RNA-seq data: STMN2 and KALRN CEs18. We could detect reads spanning the CE junctions in both genes with significantly higher counts than the previous report, but only in the TDP-43 Low nuclei (Fig. 7e–g). Differential poly-adenylation usage analysis also demonstrated >1000 differentially used distal poly-adenylation sites. Differential poly-adenylation was visible e.g., at the previously described genes MARK3 and ELP115,26,27 (Fig. 7h–j). Thus, we could confirm with high confidence TDP-43 pathology in the TDP-43 Low nuclei.

Transcriptional effects of TDP-43 pathology are cell-type specific
Next, we asked whether the transcriptional effects of TDP-43 pathology are cell-type specific, as suggested by previous studies28. Differential gene expression analysis with DESeq2 and pseudo-bulk in each cell type revealed variable DEG numbers in the four major excitatory cell types (Fig. 8a and Supplementary Data 13–16). The highest number of DEGs was found in L5/L6 NP, ETL and CTH neurons (FEZF2+), despite their lower numbers and library sizes (and thus decreased statistical power) (Fig. 8d). Similarly, CE inclusion was cell-type specific, with most counts of CE junctions in L2-3 ITC neurons. However, compared to the total number of cells and counts, L5-6 NP, ETL, and CTH, and L6 ITC neurons had the highest CE counts (Fig. 8b). Interestingly, STMN2 and KALRN CEs were cell-type specific, with more STMN2 CE counts in L5-6 NP, ETL & CTH neurons and more KALRN CE counts in L6 ITC neurons (Fig. 8b). Compositional analysis of cell subtypes in each major cell type revealed the specific cell subtypes most affected: L2-3 LINC00507+FREM3+ ITC, L3-L5 RORB+ADGRL4+ and RORB+LNX2+ ITC, L5 ETC (FEZF2+NTNG1+) and L6 THEMIS+LINC00343+ ITC neurons (Fig. 8c). Despite the relatively large number of DEGs detected in each major cell type, most DEGs were specific to one of the cell types (Fig. 8e, f), concordant with an enrichment of cell-subtype markers due to the different proportions of cell subtypes. Nevertheless, some DEGs that were found in more than one major cell type were dysregulated concordantly (Fig. 8f), with notable exceptions in L5-6 NP, ETL & CTH neurons. In line with this, hierarchical clustering based on the DGE changes demonstrated that transcriptomic changes are more distinct in L5-6 NP, ETL & CTH neurons (Fig. 8f). DGE analysis with MAST showed a high number of DEGs in all five most affected cell types (Supplementary Data 17–21). In addition, we observed that the counts of CE reads were mostly found in these 5 cell types, further defining them as the transcriptional cell types affected by TDP-43 pathology.

Cell-type specific transcriptomic changes in the ALS/ALS-FTD motor cortex are associated with chromatin changes and distinct from the TDP-43 transcriptional changes
Next, we investigated how TDP-43-pathology-associated transcriptional changes relate to the general transcriptomic changes we observed in the ALS-FTD motor cortex. Hierarchical clustering of the log2-fold changes for each cell type and all genes significant in at least one comparison from the ALS-FTD vs. Ctrl and the TDP-43 Low vs. TDP-43 High nuclei demonstrated that these are two distinct sets of transcriptomic changes (Fig. 9a). Still, a significant proportion (n=373, ****p<0.0001 OR 2.92, 95% CI 2.54-3.34) of the TDP-43 Low DEGs were found also in the ALS/ALS-FTD DEGs, with a significant enrichment of a sole KEGG term “Axon guidance” (Fig. 9b). On a major excitatory neuron type level, overlap of TDP-43 Low DEGs with the ALS-FTD DEGs was variable but significantly enriched for all four major cell types (Fig. 9c), with little overlap to changes associated with chromatin accessibility. Among the chromatin accessibility changes, several signaling pathways were enriched uniquely for dysregulated genes associated with chromatin accessibility changes, but not with TDP-43 pathology. Interestingly, the ‘Axon guidance’ KEGG term enrichment in TDP-43 DEGs was driven by 7 genes, including EPHA3, EPHA4 and EPHA6 that were also dysregulated on the level of chromatin accessibility.

Methods
Humansamplesandethics
AllexperimentswithhumanmaterialwereperformedinaccordancewiththeDeclarationofHelsinki,andtheresearchapproachhasbeenapprovedbytheEthicsCommitteeoftheUniversityofUlm(Nr.135/20-FSt/TR). Human flash-frozen postmortem tissues were obtained from: The Netherlands Brain Bank, Netherlands Institute for Neuroscience, Amsterdam (open access: www.brainbank.nl) (33 Samples), Edinburgh Brain and Tissue Bank (22 Samples), Massachusetts General Hospital (6 Samples), Laboratory for Neuropathology of Ulm University (8 samples) (Ethical approval No. 54/08), Hannover Medical School for Neurology (11 Samples), and Clinical Neuroanatomy of Ulm University (2 Samples). All material has been collected from donors for or from whom a written informed consent for a brain autopsy and the use of the material and clinical information for research purposes had been obtained. Clinical diagnosis was based on the standardized criteria set by each center (further information available at each center upon enquiry) and confirmed pathologically by the presence of pTDP-43 pathology, using immunohistochemistry as described in detail in Bolborea et al.30 (Supplementary Fig. 1). Demographic, clinical and neuropathological information are provided in Supplementary Data 1 & 2. Complete demographic data (ethnicity, PMI, onset/phenotype) was not available for all samples. Biological sex was listed as reported from the cohort records, validated in silico from the sequencing data and referred to as ‘sex’.

Studydesign
Thestudyincludes82samplesintwodatasets:themulti-omicdatasetand the FANS-Seq dataset. 72 samples are unique for the multi-omic dataset: ALS (n=30), ALS-FTD (n=10), and age- and sex-matched controls without neuropathology (n=32). 7 samples were included in both datasets (ALS-FTD), and 3 samples are unique to the FANS-Seq dataset (ALS-FTD). The cohort/center information for the samples in the FANS-Seq dataset is not specified to preserve privacy (privacy concerns due to small sample size). The multi-omic dataset is comprised of 79 samples in the age range 33–96 years (mean: 66.53, median: 65.5, IQR: 56.25-77.75). One sample had missing age information. Age distribution was significantly different between the ALS and Control groups, and not significant between ALS-FTD and Control groups and between ALS and ALS-FTD groups (Wilcoxon rank-sum test with alpha 0.05). Male/female distribution in the whole cohort was 61%/39% and not significantly enriched in any case group (Fisher’s Exact Test, p-value > 0.05). The FANS-Seq dataset is comprised of 10 samples. After sorting and sequencing, TDP-43-High nuclei were available from 10 of these samples and TDP-43-Low nuclei from 8 of these samples. The age range was 60–75 years (mean: 67.38, median: 66.50, IQR: 64.0-70.75). Age distribution was not significantly different between the TDP-43-High and TDP-43-Low groups (Wilcoxon rank-sum test with alpha = 0.05; groups ages are not identical because from two sample donors only TDP-43-High nuclei were successfully sorted and sequenced). Male/female distribution in the whole cohort was 70%/30% and not significantly enriched in one of the TDP-43 High/Low groups (70%/30% & 62.5%/37.5%, Fisher’s Exact Test with alpha 0.05). Due to the heterogeneity of data collected at the different centers comprising the study cohort, more comprehensive population data could not be used for covariate modeling. Therefore, covariates were extensively modeled with surrogate variable analysis as indicated in the manuscript. Blinding was not possible during sample collection, as specific, matched samples had to be selected and pooled together. The investigators were blinded to group allocation during sample preparation, sequencing, and pre-processing of the data during data analysis. Blinding of the data analysts was not relevant for the downstream data analysis, as it is performed computationally, based on analysis statistics, and not by subjective judgment of the investigators. RIN scores were used to evaluate bulk RNA quality for each sample before library preparation. All patients’ samples were analyzed regarding pathogenic variants in 43 known ALS-associated genes (ALS2, ANG, ARHGEF28, ATXN2, BSCL2, CCNF, CHCHD10, CHMP2B, DCTN1, ERBB4, FIG4, FUS, GBE1, GLE1, GRN, HNRNPA1, HNRNPA2B1, HSPB1, HSPB8, MAPT, MATR3, MME, NEFH, NEK1, OPTN, PFN1, PRPH, SETX, SIGMAR1, SOD1, SPG11, SPG20, SQSTM1, TAF15, TARDBP, TBK1, TUBA4A, UBQLN2, VAPB, VCP, VEGFA, VPS54) using targeted gene sequencing. C9ORF72 hexanucleotide repeat expansion (HRE) was investigated by repeat-primed PCR and Southern blotting. Samples were dissected from the primary motor cortex (Brodmann area 4; BA4).

Nucleiisolationofhumanbraintissue
Nucleiwereisolatedfromflash-frozenbraintissue,usinganOptiPrep density gradient (Merck #D1556). For each donor, ~20 mg of tissue was dissected from the gray matter of the motor cortex using a dry-ice pre-chilled scalpel blade. Two to four tissue donors were pooled for the preparation of the single-nuclei suspension and loaded on a single well on the Chromium Next GEM Chip J (10X Genomics). Later, the individuals were demultiplexed based on genetic polymorphism (see below). The tissue (approximately 80mg) was mechanically homogenized in a 7 ml glass tissue grinder (Merck, #D9063) on ice containing 1.4 ml of ice-cold homogenization buffer (320 mM Sucrose, 5 mM CaCl2, 3 mM Mg(Ac)2, 10 mM Tris-HCl (pH 8.0), 0.1 mM EDTA (pH 8.0), 0.1% NP-40, 1 mM β-mercaptoethanol, 0.4 µ/µl) using ~15 strokes with a loose pestle, followed by 15 strokes with a tight pestle, until all tissue parts were dissolved. The homogenized tissues were filtered through a 70 µm Flowmi cell strainer (Bel-Art, #136800070) and afterwards through a 40 µm strainer (Bel-Art, #136800040). Then, 700 µl of homogenate was mixed with 450 µl of working solution (50% OptiPrep, 5 mM CaCl2, 3 mM Mg(Ac)2, 10 mM Tris-HCl (pH 8.0), 0.1 mM EDTA (pH 8.0), 1 mM β-mercaptoethanol) and layered on top of an OptiPrep density gradient consisting of 300 µl of 40% OptiPrep (40% OptiPrep, 96 mM Sucrose, 5 mM CaCl2, 3 mM Mg(Ac)2, 10 mM Tris-HCl (pH 8.0), 0.1 mM EDTA (pH 8.0), 0,03% NP-40, 0.12 U/µl RiboLock) at the bottom of a tube, followed by 750 µl of 30% OptiPrep (30% OptiPrep, 134 mM Sucrose, 5 mM CaCl2, 3 mM Mg(Ac)2, 10 mM Tris-HCl (pH 8.0), 0.1 mM EDTA (pH 8.0), 1 mM β-mercaptoethanol, 0,04% NP-40, 0.17 U/µl RiboLock). Nuclei were pelleted at the interface of the OptiPrep density gradient by centrifugation at 10,000 xg for 5 min at 4°C. The nuclear pellet was collected by aspirating ~200 µl from the interface and transferred to a 1.5 ml DNA-LoBind tube (Eppendorf, #0030108051). The nuclei were washed three times in 250 µl washing buffer (2% BSA containing 0.12 µ/µl RiboLock) by centrifugation at 2000 xg for 3 min at 4°C. During the final wash step, the nuclei suspension was again filtered through a 40 µm Flowmi cell strainer to remove debris and aggregates. After the last centrifugation step, the pellet was resuspended in 50 µl of 1X nuclei buffer (1X nuclei buffer (20X stock provided from 10X Genomics, #2000207), 1 mM DTT, 1 µ/µl RiboLock). As a quality control, the number and quality of the nuclei was assessed upon DAPI staining in a hemocytometer.

Generationofmulti-omicsingle-nucleilibrariesandsequencing
Multi-omic single-nuclei ATAC and gene expression libraries from the same nuclei were prepared using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression reagents (10X Genomics, #PN-1000283). Sample processing and library preparation was performed according to manufacturer instructions (user guide CG000338 Rev F, from 10X Genomics). Two to four samples were multiplexed per single 10X Chip well (Supplementary Data 3). Single-nuclei ATAC libraries were sequenced using the NovaSeq 6000 SP PE150 (Illumina), and single-nuclei gene expression libraries were sequenced on a NovaSeq 6000 S4 PE150 (Illumina).

IsolationofgenomicDNAforgenotypinganddemultiplexing
A piece of tissue of approximately 15 mg was dissected with a scalpel blade and supplemented with 500 µl nuclear lysis buffer (2.5 M NaCl, 0.5 M EDTA), 10 µl proteinase K (10 mg/ml) and 20 µl of 20% SDS. The sample was incubated overnight in a gently shaken water bath. The next day, 180 µl of 6 M NaCl solution was added, and the tube was shaken vigorously. The sample was centrifuged twice at 1500 xg for 15 min at room temperature. After each centrifugation, the supernatant was transferred to a new tube. Then, 2 volumes of absolute ethanol were added to the supernatant. Careful inversion of the tube allowed the DNA to condense and to become visible. The DNA strand was washed briefly in 70% ethanol and transferred to a tube containing 30 µl of water. The DNA was incubated overnight at 4°C until it was soluble enough for quantification on the NanoDrop One.

Genotype-basedsampledemultiplexing
To reduce batch effects and facilitated doublet removal by genotype identification, we pooled two to four sample donors for each 10X Multiome reaction well. Later, we identified the donor of origin of each nucleus in the dataset with a combination of in silico and in vitro sex- and SNP-based demultiplexing. For sample demultiplexing, we used Souporcell, a genotype-free bioinformatic tool to perform SNP-based demultiplexing31. First, reads are remapped using minimap2 and variants are called using freebayes. Vartrix is then used to count alleles per cell. Finally, cells are clustered based on the number of reads supporting each allele of the called variants. Cluster (i.e., donor) SNPs are then analyzed and SNPs and SNP combinations that uniquely identify a donor were genotyped on DNA isolated from each sample separately by Sanger sequencing after PCR with primers flanking the SNPs from both sides. DNA was purified from the same motor cortex samples that were used for the generation of the single-cell data.

Fluorescence-activatednucleisorting(FANS)
Nuclei isolation and fluorescence-activated sorting were performed based on a protocol by Policicchio et al. (dx.doi.org/10.17504/protocols.io.bmh2k38e) and Liu et al.10 with adaptations. Approximately 300 mg of primary motor cortex tissue was homogenized in 3.2 ml of homogenization buffer, and the total homogenate, together with the working solution, was distributed onto four OptiPrep gradients. Nuclei from the different OptiPrep gradients were pooled during the transfer from the interface to a new tube. Following the washing steps, the nuclei were finally resuspended in 1 ml staining buffer (PBS supplemented with 0.5% BSA and 0.12 µ/µl RNase inhibitor) and incubated for 15 min on ice. Nuclei were then stained with fluorescently-labeled antibodies targeting TDP-43 (1:500, CoraLite488-conjugated TDP-43, #CL488-10782, Proteintech) and NeuN (1:500, Milli-Mark™ Anti-NeuN-PE antibody, #FCMAB317PE, Sigma-Aldrich). Further, nuclei were stained with 1 µg/ml DAPI (#D9542, Sigma-Aldrich) to identify intact single nuclei. In addition, 5% of the total nuclei were stained with one of the respective antibodies or DAPI to be used as single-stained controls in subsequent flow cytometry experiments. Staining was performed in staining buffer for 90 min at 4°C in a rotating wheel. After incubation, nuclei were pelleted by centrifugation at 2000 xg for 3 min at 4°C, re-suspended in 500 µl staining buffer and subjected to FANS performed by the Cytometry Core Facility at the University of Ulm using a FACSAria II SORP (BD). Single neuronal nuclei (NeuN+) were sorted for either TDP-43 high or low content. Immediately after sorting, single-nuclei gene expression libraries were prepared according to the Chromium Next GEM Single Cell 3’ Reagent kit v3.1 user manual (CG000315 Rev F, from 10X Genomics). The libraries were sequenced on a NovaSeq X 25B with PE150.

FANSRNA-seqdatapre-processing
FANS-seq data was demultiplexed and converted from BCL files to FASTQ files with bcl2fast2 (Illumina). FASTQ files were processed with Cellranger count (v9.0.1) (10X Genomics) with the GRCh38-2024-A genomic reference (10X Genomics), including introns. For each sample, ambient RNA signal was checked with SoupX32, corrected with CellBender (v0.3.0)33 and converted to Seurat-compatible h5 files with PyTables (v3.10.2). Nuclei doublets were detected with scDblFinder (v1.20.2) with a number of artificial doublets, which provided stable doublet detection for all samples34.

Multi-omicsequencingdatapre-processing
BCL files were demultiplexed and converted to FASTQ files with bcl2fast2 (Illumina). FASTQ files from both single-nucleus ATAC-seq (snATAC-seq) and single-nucleus RNA-seq (snRNA-seq) were processed using the ‘count’ function in Cellranger-arc (2.0.2) using the reference GRCh38-2020-A-2.0.0 (10x Genomics). RNA count matrices were merged using Seurat (v.4.4.0)35. To identify accessible chromatin regions, peak calling was performed using MACS2 (v.2.2.6)36 with the options ‘--extsize 200, --shift-extsize/2’. Consensus genomic regions (‘peaks’) were generated with Signac (v1.10.0)37 with the ‘reduce’ function and filtered to retain only genomic regions found in at least 5 samples. Peaks < 30 base-pairs (bp) or > 2500 bp were excluded.

Nucleifiltering
Multi-omic nuclei were filtered in the merged dataset according to the following parameters per nucleus: Total number of RNA counts >1000, total detected fragments >1000, ratio of mono-nucleosomal to nucleosome-free fragments <3, transcription start site (TSS) enrichment score >1, fraction of ATAC-seq reads in peaks >0.2, percent of GEX-reads in mitochondrial genes <10%, percent hemoglobin counts <1% and percent of reads in ribosomal genes <10% (Supplementary Fig. 2). Filtering of the FANS-seq dataset was performed in the merged dataset, with median absolute deviation (MAD) filtering: >5 MAD of log1p RNA counts, percent of reads in the mitochondrial genes in the uncorrected data <5% and percent of reads in ribosomal genes <5%.

Dataintegration
For data integration and batch correction of the multi-omic dataset, we utilized the single-cell variational inference (scVI) toolkit38,39. scVI uses probabilistic modeling and a latent variable framework to correct for batch variation in single-cell RNA sequencing data, minimizing technical artifacts across the experimental batches. We included the percentage of mitochondrial reads as a continuous covariate and sex as a categorical covariate, as gonosomal genes were retained. To tune the hyperparameters, we utilized a Gaussian process-based optimization with the ‘gp_minimize’ function from scikit-optimize40. We made two adjustments to the default settings, resulting in an optimized Evidence Lower Bound (ELBO) score. Specifically, we increased the number of latents to 50 and the number of nodes per hidden layer to 200. For the ATAC model, we employed PeakVI, a deep generative model for single-cell chromatin accessibility analysis41. The model was set up with ‘atac_peak_region_fragments’ as a continuous covariate. The best ELBO score was obtained increasing the number of latents to 50 and the number of nodes per hidden layer to 332. The covariates were included in all layers of the decoder. All other parameters were kept at their default settings. The FANS-seq dataset was integrated with Harmony42 and the SCT-transformed data with Seurat implementation’s default parameters35.

Cellclustering,dimensionalityreductionandcell-typeannotation
Dimensionality reductions were computed with PCA, UMAP43 (Supplementary Fig. 3) and Bonsai-Scout23. For the multi-omic single-nucleus dataset, UMAP was calculated on the basis of the 50 latent variables from the scVI model (RNA-seq), the peakVI model (ATAC-seq), or both (WNN analysis); for the FANS-seq dataset, on the first 30 PCA components of the normalized data. For Bonsai-Scout, the clustering was performed on the whole transcriptomic count matrix and randomly sampled 10,000 nuclei for the multi-omic dataset, and on the whole transcriptomic count matrix for the FANS-seq dataset. Cell types were annotated manually by generating lists of differential cell type markers and curating cell types based on the current literature, foremost comparing to Bakken et al.19, Pineda et al.20 and Gittings et al.18. Cell-type gene expression and chromatin accessibility markers were calculated for each hierarchical cell type against all other cells using only nuclei from the neurologically unaffected group (‘Ctrl’) with Seurat (‘FindMarkers’ function) and presto (https://github.com/immunogenomics/presto) with Seurat’s Wilcox test implementation35. Hierarchical cell-type annotation to major cell types and cell type classes was performed manually (Supplementary Fig. 4). Data from unsupervised clustering is provided in Supplementary Figs. 5–7. For visualization of marker feature expression in the major cell types (Fig. 1f), metacells were constructed with Cicero using the WNN UMAP reduced coordinates and 100 k-nearest neighbors44. 100 metacells were then randomly sampled per group and plotted. For visualization of the chromatin accessibility profiles across the major cell types (Fig. 1g), the cumulative, scaled accessibility was plotted for each cluster with Signac (‘CoveragePlot’). For the hierarchical clustering of the major cell types based on the ‘average’ cell per group (Fig. 1f, g), all cells and all features (genes or genomic regions) were used, and the dendrogram plotted with Seurat (‘BuildClusterTree/PlotClusterTree’) with the default metric and method (Euclidean distance, complete linkage). FANS-seq dataset was annotated by label transfer from the multi-omic dataset with Seurat with ‘FindTransferAnchors’ and ‘TransferData’ functions with log1p-normalized data, PCA, 30 principle components and ‘k.anchor=5’. Discordant hierarchical annotations of the same nucleus were preserved for downstream analysis for the respective hierarchical annotation levels.

Compositionaldataanalysis
The compositional analysis to identify changes in the relative cell-type proportions across the disease groups was performed with scCODA, a Bayesian-model-based classifier which outputs a measure of the likelihood that a specific cell type or a node in the hierarchical cell-type tree is specifically changed as opposed to passively due to the compositional nature of the data45. We simulated compositional changes with automatic cell type reference choice and the default parameters. Furthermore, we analyzed compositional cell-type data with mixed linear modeling based on MASC46.

Differentialexpression/accessibilitytesting
To capture the transcriptomic perturbations associated with ALS/ALS-FTD across different cell types, we utilized two complementary approaches for modeling of differential gene expression in combination with the hierarchical cell type annotation: ‘pseudo-bulk’ and model-based single-cell analysis. The pseudo-bulk analysis is based on the aggregation of all counts from a given cell type per sample (person) and captures robustly transcriptomic changes that are characteristic for the disease across different patients; the model-based approach benefits from the increased power of single nuclei as observations and captures transcriptomic changes with increased sensitivity, but reduced power to generalize across patients. The combination of both methods allows for sufficient sensitivity at high resolution (i.e., comparison of small clusters/rare cells) and improved specificity at low resolution (larger cell clusters, like major cell types), while decreasing the disadvantages associated with both approaches. We used surrogate variable analysis (SVA)47 to compensate for known and hidden batch effects (e.g., PMI, onset site, cohort) in pseudo-bulk, and used fixed plus random effects to accommodate sample heterogeneity and batch effects in the mixed models. We further used sample label permutations and random gene samples as negative controls to control the specificity of the results.

Differential expression/accessibility of pseudo-bulk samples was performed with DESeq248 after sample-wise (i.e., donor) aggregation of the raw counts for the respective cell type analyzed. Then, differential expression/accessibility was performed with Wald’s test with covariates as specified in the results section. We estimated the gene-wise dispersion in a multigroup fashion (Ctrl+ALS+ALS-FTD instead of Ctrl+ALS and Ctrl+ALS-FTD separately) to increase the stringency and specificity of the analysis: DESeq2 analysis was run on all samples together and then the results extracted for each disease group vs. controls comparison. For SVA, surrogate variables (SVs) were extracted with the sva package and the SVs included as covariates in the DESeq model. For ranking, visualization, and correlation analyses, log2-fold changes were shrunken with apeglm49. Hierarchical analysis was performed with the BulkR package (https://github.com/vgrozd/BulkR). Subsetting of FASTQ and BAM files for control analyses was performed with seqtk (https://github.com/lh3/seqtk) and 10X Genomics’ ‘subset-bam_linux’ (https://github.com/10XGenomics/subset-bam), respectively.

Functionalanalysis
The annotation of transcripts to gene types was acquired from the ENSEMBL database accessed over the Bioconductor ecosystem (‘Ens.db.Hs.86’ and ‘org.Hs.eg.db’ packages). Importantly, the genes which are expressed in the human motor cortex are heavily annotated to pathways and KEGG/GO terms related to neurodegenerative diseases (Supplementary Fig. 10). All enrichment analyses were therefore conducted against a background (“universe”) of the respective cell type/dataset. Comparison of sets of differential expression results were performed with the ‘BulkR’ R package (https://github.com/vgrozd/BulkR). Enrichment analyses were performed with ShinyGO50, StringDB51, fgsea52 and clusterProfiler53 R packages. Gene type annotations and alternative names between the 10X CellRanger reference genome versions 2020-A and 2024-A were converted with the specification files provided with the 10X CellRanger reference genomes’ release notes. Gene/genomic region module expression resp. accessibility scores were calculated with Seurat (“AddModuleScore”) against a background control with random features (n=500 unless specified otherwise) with the default random seed (‘1’). For cell-type specific module scores, only markers with q-value (fdr) < 0.05 and fold change > 5 (up-regulated) were used, and only markers for which both HGNC gene symbol and an ENSEMBL gene id were available were kept to allow for the simultaneous use of the markers for the single-nucleus multiome dataset and the human cortex spatial transcriptomics dataset. Data was handled with tidyverse and Pandas, GenomicRanges, magrittr, qs/qs2; single-cell datasets with Seurat, Scanpy and SingleCellExperiment. Data was visualized with the R packages ggplot2, complexHeatmap, pheatmap, ggrastr, complexUpset, ggnewscale, patchwork, scattermore, ggraph, data.tree, igraph, networkD3, ggrepel, ggpubr, dendextend, and with the IGV browser.

Crypticexonandalternativepoly-adenylationanalysis
Cryptic splicing analysis was performed as described in Gittings et al.18. Exon junctions were extracted from BAM files from CellRanger ‘count’ output with regtools’ ‘junction extract’ with a minimum anchor length of 6 bp, minimum intron size of 30 bp and a maximum intron size of 500,000 bp54. Introns were then clustered with Leafcutter’s ‘leafcutter_cluster_regtools.py -m 10 -p 0.0001’55 and counts for the CE-specific junctions counted. For cell-type or group analyses, barcoded BAM files were split with sinto (https://github.com/timoast/sinto) ‘filter barcodes’ and merged with samtools’ ‘merge’56. Bedgraph files for visualization were generated with bedtools’ ‘genomecov’57. Alternative poly-adenylation analysis was performed with DaPars’ ‘python DaPars_main.py’58 with the default parameters.

Analysisofpubliclyavailabletranscriptomicsdata
For spatial transcriptomics, we accessed the publicly available LIBD dataset22 over the accompanying spatialLIBD package59. The dataset consists of 12 slides processed with 10X Visium Spatial from 3 neurologically unaffected controls, 2 pairs 300 μm apart from each other, and has been extensively characterized before22,59. Histological layers L1-L6 & white matter (WM) have been annotated by Maynard et al.22. Spatial gene expression and score heatmaps were plotted with the spatialLIBD59 and ggplot2 packages. For comparison of the differential gene expression (DGE) analysis results between the study of Liu et al. and ours, we performed the DGE analysis since the list of original results could not be accessed. In brief, we queried the dataset with the ‘GEOquery’ R package60, aligned the reads with ENCODE’s standard parameters for RNA-seq with STAR61, and generated a count matrix with RRsubread’s ‘featureCounts’62. DGE was then performed with DESeq2 with sex as a covariate.

Annotationandlinkageofchromatinaccessibility
Chromatin accessibility was correlated to gene expression across all nuclei in the dataset with Signac (‘LinkPeaks’ function) with the default settings and a 500 Kbp window from both ends of each peak. Chromatin peaks were annotated as a genomic element with the ChIPseeker package63 (‘peakAnno’ function) and summarized to one of 6 categories: ‘Intergenic’, ‘5’ UTR’, ‘Exon’, ‘Intron’, ‘3’ UTR’ and ‘Promoter’, the last comprising peaks up to 3 Kbp around the TSS.

Statisticsandreproducibility
Sample sizes were estimated from previous studies and recommendations for genomics studies. No statistical method was used to predetermine sample size. No data were excluded from the analyses. Vascular and immune cells were excluded due to low numbers that prevent proper analysis. The experiments were randomized in two ways: a random label permutation control was used were applicable as a ‘negative’ control; multiple samples were pooled per reaction well and random anonymized IDs assigned to samples after demultiplexing. The investigators were not blinded to allocation during experiments and outcome assessment. To increase reproducibility, multiple samples from different centers were included in the study cohort and raw data, processed data and data analysis code are provided with the study. Random seeds used for sampling and centroid/clustering initiation are specified in the source code and methods. For methods for which the random seed is not specified, the default random seed implemented in these methods/algorithms was used. We used sample permutation as a ‘negative’ control for unspecific results by supervised mixing of sample labels, i.e., random permutations were generated with a constraint such that the number of disease cases and controls are balanced in each random group and that the proportions of sex labels is comparable to the case-control comparison. Statistical overrepresentation was tested with Fisher’s exact test. For all analyses with multiple target variables, the false discovery rate was controlled with the Benjamini-Hochberg adjustment of p-values64. Findings were considered ‘statistically significant’ with an fdr q-value < 0.05 until stated otherwise. ANOVA was performed with the R ‘stats’ package, effect sizes with the R packages ‘effsize’ and ‘effectsize’ and confusion matrices with accuracy with the R ‘caret’ package.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors have done a very thorough job in generating and describing single-nucleus multi-omic data from the motor cortex of ALS and ALS-FTD patients, alongside FANS sorted single-nucleus RNA-seq to identify nuclei with low TDP-43. Both these datasets will be tremendously useful to the wider field. All analyses have been done carefully and using the current state of the art tooling. I have only a few suggestions.\n\nGiven that the authors generated genotyping data to aid with demultiplexing of the single-nucleus data, it was a shame that this data wasn’t incorporated into the paper, for example by looking for ALS-associated common variants that overlapped their chromatin accessibility regions, or even mapping QTLs if the sample size allows. Is this something the authors would consider?\n\nThe enrichment of neurodegenerative disease KEGG pathways in the DEGs used throughout the manuscript is likely due to all of those “disease” pathways having strong overlaps with the genes in the Oxidative Phosphorylation KEGG pathway. I would suggest the authors either remove these assertions, or refine it by testing only the genes that are unique to each disease pathway. Alternatively, the authors could perform enrichment with lists of GWAS and rare variant-linked genes for ALS.\n\nWere the authors able to assess cryptic splicing of STMN2 and KALRN in the TDP-43 low fraction of single cells, given that those two genes have been previously demonstrated to be detectable in single cell data? PMID: 37466726.\nSimilarly, did the authors investigate whether NPTX2 was upregulated in the TDP-43 low fraction, given its recent demonstration as a TDP-43 target? PMID: 38355792\n\nMinor comments:\nPlease use commas as thousand separators throughout.\nLine 42 - STMN2 typo\nLine 56 - the vast majority of polymorphisms detected by GWAS are in non-coding regions, I would change “some” to “most”.\nFig. 4 - typo, ALS-FTD has 17 donors, not 30\n(Remarks on code availability)\nCode appears well organised. No README provided which would be helpful.", "reviewer_2": "This manuscript describes concurrent RNA and chromatin accessibility sequencing of single nuclei from ALS, ALS-FTD and control motor cortex. This dataset emerges as a few other single cell datasets have been published, although this is the first using the technology that allows for both transcriptome and epigenome profiling in the same cell. The analysis pipeline is sophisticated, and a large number of cases are studies. However, the results are sometimes inconsistent with what is known which may be driven by relatively low coverage and/or cells per case. Moreover, while this is an impressive amount of data and analysis, I am not sure what additional insights these analyses have put forward towards our understanding of ALS and ALS/FTD. These issues include the following:\n\n1. The cases/demographics file is not well formatted and is incomplete. In addition to demographic information, the reader needs information on the number of cells, number of reads per cell, number of cell types, etc. on a per case basis. While it is impressive that there is a large number of cases that were included, it does not seem that each case was sequenced to equal depth when examining figure 2d. Based on the number of cases and the number of total nuclei, the average number of cells per has is on the order of 2000-2500, with many cases likely being much lower, as evidenced by figure 4a where some cases have no nuclei from some cellular subtypes. This is problematic because some of the analyses are likely underpowered if there are not sufficient numbers of cells per case (and the number of cells per case is used as a variable in some analyses).\n2. The lack of differences in proportion of various cell types in disease versus control is worrisome that this dataset is not capturing the molecular events in disease. This may be related to the low number of cells per case (point #1).\n3. Figure 2A contains the data shown in figure 1D but broken up into different parts of the figure. As this data is repeated and is not really contributing additional data to figure 2, and since the breaking up of the UMAP further distorts the UMAP which is already non-linear, I suggest not including these dot plots.\n4. Page18 lines 438-439 states that the data was compared to “previous studies” which I do not see in the supplementary figure 11 – unless they mean just mapping their data using GSEA to KEGG pathways. This should be clarified.\n5. As two large single cell datasets have been published in the last couple of years on this topic, a comparison of findings across datasets would be important to show either consistent (or discordant) results.\n6. Figure 3 is problematic and appears to show that this analysis pipeline, matching single cell transcriptomes to existing spatial transcriptomic data, is not working properly. RORB is known to be a layer 4 gene, but they report it highest in layer 5. Similar problems exist for Themis (layer 5, not 6), FEZF2 (layer 5, not 6), SST (layers 2,3,5, not layer 1), microglia (equally dispersed throughout the CNS). This is important because the same methods are used later to map the authors’ new data – and so if the method cannot do this accurately for some known genes (such as Figure 4N), it casts doubt on the new data localization.\n7. Figure 3f shows a single dot in a very small inset as evidence of localization of Betz cells. This is not convincing.\n8. The Venn Diagram in figure 4N, while significantly “enriched” in terms of overlap (likely due to the high statistical power when compared against the entire transcriptome), is striking for how the vast majority of the results do not overlap.\n9. Page 21, line 508, states that DARs “correlated strongly”. While some p-values are very low due to the high number of datapoints, the r values (which is used to judge the strength of a correlation) are very low suggestive of a very weak correlation.\n10. Figure 5i, can the authors clarify on which dataset the Spearman correlations were performed? It appears to be just for the data that is both differentially expressed and differentially accessible which is fine but just needs to be explicitly stated.\n11. For the FANS study, the authors need to provide the fluorescence dot plots to judge the quality of the staining/sorting.\n12. One issue with this method is that burst/broken nuclei can appear to be TDP-43 dim. This may be occurring here as there seems to be a global downregulation of transcripts.\n13. The validation of the FANS material is primarily based on assessment of differential polyadenylation that has recently been linked to loss of nuclear TDP-43. However, the finding of differential polyadenylation has been highly variable between research groups, and appears to be highly cell specific. Thus, more robust validation of proper sorting requires more strongly established methods such as evaluation of cryptic splicing (which admittedly is very difficult to do using single cell transcriptomics due to its 3’ bias). If too sparse, expression of cryptically spliced genes would be helpful. Comparison of their FANS data with the existing FANS dataset which has been highly validated by many groups would also be helpful even if these were from different brain regions as there is likely to be high overlap between different brain regions.\n14. Figure 6J and 6K suggests that loss of nuclear TDP-43 shows no selectivity for any cell type which runs counter to what is known about TDP-43 pathology.\n15. Von Economo neurons are not present in the dorsolateral prefrontal cortex (page 16). It is also stated that TDP-43 pathology is found in cells of all layers (page 16) which needs a citation. I am not sure if this has formally been demonstrated, with pathologists characterizing TDP-43 pathology as present in superficial and deep layers with a predilection towards superficial layers. I do not believe that molecular demonstration that TDP-43 pathology affects all neuron subtypes has been done.\n16. There is a focus in various parts on Von Economo neurons. However, it is known that TDP-43 pathology in most cases actually preferentially involves superficial cortical layers. The Seeley group has highlighted that VENs are also selectively vulnerable in FTLD-TDP (although not in other forms of FTD). A more balanced presentation of selectively vulnerable cell types would be helpful.\n17. Figure 7, I was not sure what “case” refers to in the circular track. I understand that it refers to ALS versus ALS/FTD, but what does it mean that a certain cell type is associated with one group or the other? While I can understand, for example, that the oligodendrocyte is an “ALS-FTD” cell type with a certain amount of DEGs in ALS-FTD, what if a cell type shows significant DEGs in both ALS and ALS-FTD? How is one group selected over the other and why not show the data for both groups?\n18. The circular track is also somewhat misleading in that the color scales seem arbitrary and may be exaggerating findings that were chosen to be highlighted, with some colors being binary versus graded. The choice of cutoff values for the various scales is not well described or justified.\n19. The pathology in supplementary figure1 is of poor quality and not convincing.\n\nMinor comments:\nPage 14, line 330, I believe this should be Figure 2d (not 2b).\nPage 18, line 429, it probably should read “We identified 67 DEGs in ALS ….”\n(Remarks on code availability)\nWhile I did not review the actual code, I did review their statistical methods which are well described.", "reviewer_3": "Overall, these datasets have the potential to be useful to the ALS/FTD research community.\nWhile this is not the first study to combine snRNA-seq and snATAC-seq in ALS-FTD patient tissue (ref 47), the addition of FANS-snRNA-seq of a subset of samples allows for a novel comparison between the transcriptional signature of TDP-43-depleted neuronal nuclei with other cells in the motor cortex of ALS or ALS-FTD patients. Indeed, the FANS-snRNA-seq dataset alone is likely to be a useful resource, complementing the highly cited dataset generated by Liu et al (ref 10).\nWhile these datasets will likely be useful resources, the authors should provide more context to describe the biological significance of their results. This is especially important given two counterintuitive results from this study: 1) a lack of cell type reductions in ALS or ALS-FTD motor cortex, and 2) convergent gene expression changes across varied cell types. These topics are addressed by the authors in the Discussion, but still warrant further investigation to ensure that these are true biological outcomes and not driven by technical artifacts.\n\nOther key points that the authors must address to resolve concerning issues in the manuscript:\n1) The abstract should be more detailed to highlight the key findings. The authors refer to a “well-defined transcriptomic signature of disease,” but no details/highlights are provided.\n2) The authors control for various possible batch effects related to nuclear isolation and library prep. Were batch effects also considered between the 6 cohorts of postmortem tissue? This is very briefly assessed in Figure 2d in terms of cell-type proportions between groups, but other possible batch effects between cohorts are not mentioned.\n3) As a related point, post-mortem interval (PMI) varied widely, from ~4 hours to 96 hours. Can any batch effects (or outliers) be explained by a transcriptional signature based on where the samples were collected/stored and/or by differences in PMI?\n4) As the authors note, it is surprising that no cell types were robustly reduced in ALS or ALS/FTD samples, as these diseases are characterized by the death of neurons. The same brain region was assessed for all samples - because neuronal loss in this region could correlate with the location of symptom onset (bulbar vs spinal), have the authors considered this variable in their analyses?\n5) Similarly, it is hard to interpret the fact that gene expression changes are convergent across cell types. This may be an important, though surprising, biological insight; however, the expectation would be that some cells (e.g., excitatory neurons) are primarily affected by disease while other cells (e.g., astrocytes, oligodendrocytes) undergo distinct transcriptional changes based on their unique cellular functions. This point warrants a deeper explanation – and careful consideration for any technical artifacts (or batch effects) that explain this result.\n6) Given the importance of TDP-43 function in ALS/FTD, I do not think it’s sufficient to say that splicing analysis and analysis of cryptic exons are outside the scope of this study. There should be an effort to characterize alternative splicing events and particularly CEs known to be regulated by TDP-43. A recent paper (Gittings et al, PMID: 37466726) found that cryptic exons could be detected in snRNA-seq data from C9-ALS/FTD frontal cortex. Admittedly, this was most apparent in one sample that was deeply sequenced. However, assessing TDP-43 regulated splicing events in these data would provide another layer of information to compare with differential gene expression or chromatin accessibility between groups.\n7) In Figures 3 and 4, the authors mention that they identified transcriptional signatures common to previous analyses and known to be associated with ALS, but more detail would be helpful since the Figures do not highlight specific DEGs. Highly relevant findings (STMN2, etc.) would be useful to mention specifically.\n8) I do not see a supplementary file that includes DEGs from the FANS-snRNA-seq analysis. Please include this in any future submissions.\n9) For the FANS-snRNA-seq experiment, approximately 25% of nuclei sorted based on NeuN/RBFOX3 expression were characterized as ‘TDP-43-low.’ This far exceeds the expected % of cells exhibiting TDP-43 pathology in ALS-FTD motor cortex. This warrants explanation, as it is a major discrepancy and undermines the veracity of the entire FANS-snRNA-seq data set.\n10) The TDP-43 low population also has a different proportion of cell types, with over-representation of LINC00507 excitatory neurons. Using snRNA-seq data from healthy controls, do these neurons (or a subset of these neurons) normally have lower TDP-43 expression? An additional control is necessary to ensure that the DEGs associated with TDP-43 depletion are not driven by differences in cell type proportion between the TDP-43 high and low groups. Could this be modeled by doing DEG analysis on two sets of randomly sampled nuclei comprised of the same cell type proportions as the TDP-43 high/low groups?\n\nOther important but less substantive issues that must be addressed are as follows:\n11) Overall, I noticed many small typos while reading the paper. I would recommend carefully re-reading the text for small typos. Examples:\no Fig S4 and S5,6: ‘it parent cell population,’ ‘better suitable’\no Page 17: ‘randomly samples genes’\no Figure 3: ‘annotation of the sport’\n12) The supplementary tables are not clearly labeled. If the file names are set by journal guidelines, the authors should include a brief description of the data within each file. For example, Suppl Tables 6-8 contain DEGs from pseudobulk analyses, but it is unclear which groups are being compared in each Table. All tables should include a description of the data within the file if the file names cannot be fully descriptive.\n13) Methods: FANS was performed “as previously described” and a link is provided, but I do not see a citation.\n14) Results: patients were “diagnosed based on standardized criteria (methods)” but I do not see this information in the Methods section. This is a minor point because these samples do have confirmed TDP-43 pathology in the motor cortex.\n15) Demographics information is limited to age and sex. Is information on patient race/ethnicity available?\n16) The organization of Figure 2 should be improved. 2b and 2c blend into one another. Additionally, the Figure 2d legend refers to the data as %, but the figure represents the data as proportions (i.e., 0.25 versus 25%).\n17) Fig S5, S6, and S7 all have the same title. The authors should add details to define each figure (RNA-seq, ATAC-seq, etc.).\n18) Page 24: “oligodendrocytes are affected by TDP-43 pathology in ALS-FTD, but not ALS” – this needs a citation.\n19) Figure S10 G, H – the scale bars are set to highlight a lack of differences between C9 and sALS… but it seems that there are a few DEGs/DARs. It would be easier to interpret these panels if the axes were set lower (e.g., +/- 500).\n20) Figure S11 uses “HC” as healthy control instead of Ctrl like other figures.\n21) Figure S11 “Increasing dot size represents higher (red) or lower (blue) normalized enrichment score (‘NES’) for the respective term” - It is unclear to me if this is the same as saying blue dots represent downregulated genes, while red dots represent upregulated genes. If not, it would be more intuitive to see what pathways are enriched for up- vs down-regulated genes.\n(Remarks on code availability)\nThis is not an area of expertise for me, so should be evaluated by someone more knowledgeable in computation and bioinformatics.", "reviewer_4": "The authors used multi-modal single-nucleus profiling in the primary motor cortex of ALS and ALS-FTD to understand gene signatures and epigenetic regulatory mechanisms. The novelty of the study lies in their discovery of a distinct subset of transcriptomic changes not linked to TDP-43 pathology. Extensive bioinformatic data analyses were performed, providing a valuable single-nucleus dataset for the ALS field. However, the lack of a detailed molecular mechanism evaluation is a weak point of this study. The study could be significantly improved by performing experimental validation assays to support their hypothesis. Currently, no detail evaluation has been conducted for their findings.\n(Remarks on code availability)"}, "author_response": "We are grateful to the Reviewers for their effort and constructive criticism, which helped us to improve the manuscript. We have thoroughly revised the study, including a significant amount of new data and new experiments. Below is the point-to-point response to each issue raised. We thank the Reviewers again.\nReviewer #1:\nReviewer #1 (Remarks to the Author):\nThe authors have done a very thorough job in generating and describing singlenucleus\nmulti-omic data from the motor cortex of ALS and ALS-FTD patients,\nalongside FANS sorted single-nucleus RNA-seq to identify nuclei with low TDP-\n43. Both these datasets will be tremendously useful to the wider field. All analyses\nhave been done carefully and using the current state of the art tooling. I have only\na few suggestions.\nGiven that the authors generated genotyping data to aid with demultiplexing of the\nsingle-nucleus data, it was a shame that this data wasn’t incorporated into the\npaper, for example by looking for ALS-associated common variants that\noverlapped their chromatin accessibility regions, or even mapping QTLs if the\nsample size allows. Is this something the authors would consider?\nResponse to Reviewer #1:\nPoint 1: Given that the authors generated genotyping data to aid with demultiplexing of the\nsingle-nucleus data, it was a shame that this data wasn’t incorporated into the paper, for\nexample by looking for ALS-associated common variants that overlapped chromatin\naccessibility regions, or even mapping QTLs if the sample size allows. Is this something\nthe authors would consider?\nReply: Following the reviewer’s suggestion we analyzed ALS-associated common variants\nthat overlapped in chromatin accessible regions. We found 24 unique SNPs from two list of\nGWAS SNPs associated to ALS (222 total SNPs) overlapping chromatin accessibility\npeaks. We investigated the chromatin accessibility of these peaks and did not observe a\nsignificant differential accessibility in ALS or ALS-FTD. We added these data as new Suppl.\nFig. 18 (Results section ‘Chromatin remodeling...’, L 651-658 ).\nPoint 2: The enrichment of neurodegenerative disease KEGG pathways in the DEGs used\nthroughout the manuscript is likely due to all of those “disease” pathways having strong\noverlaps with the genes in the Oxidative Phosphorylation KEGG pathway. I would suggest\nthe authors either remove these assertions, or refine it by testing only the genes that are\nunique to each disease pathway. Alternatively, the authors could perform enrichment with\nlists of GWAS and rare variant-linked genes for ALS.\nReply: The Reviewer is right that disease pathways have strong overlaps between each\nother and with genes in the Oxidative Phosphorylation KEGG pathway. We thank for this\nuseful remark! We analyzed in detail the enrichment and found that most of the\naforementioned pathways are enriched just by the fact that gene expression comes from the\n(motor) cortex, whose genes are heavily annotated to AD, PD, ALS, etc. Using only genes\nexpressed in the motor cortex as a background alleviates this problem. We have added to\nthe manuscript this important observation (Methods section ‘Functional analysis’, L 336-339;\nnew Suppl. Fig. 10; Results section ‘The ALS/ALS-FTD motor cortex is transcriptionally\naltered...’, L 575-578).\nPoint 3: Were the authors able to assess cryptic splicing of STMN2 and KALRN in the TDP-\n43 low fraction of single cells, given that those two genes have been previously\ndemonstrated to be detectable in single cell data? PMID: 37466726.\nReply: We thank the Reviewer for this comment since cryptic exons are increasingly\nrecognized as highly relevant in ALS. In the revised manuscript, we have incorporated cryptic\nexon detection as an integral part of the analysis. We were able to detect the cryptic exons in\nSTMN2 and KALRN in the TDP-43 Low fraction. We have added this data to the revised\nmanuscript (new Fig. 7 in all TDP-43 Low cells & Fig. 8 in a cell-type specific manner\nof TDP-43 Low cells ; Results sections ‘Nuclear TDP-43 pathology selectively affects...’ and\n‘Transcriptional effects of TDP-43 pathology...’, L 719-724 & L 738-740).\nPoint 4: Similarly, did the authors investigate whether NPTX2 was upregulated in the TDP-\n43 low fraction, given its recent demonstration as a TDP-43 target? PMID: 38355792\nReply: We followed this suggestion and investigated NPTX2 expression in our data. Indeed,\nwe found a significant down-regulation of NPTX2 in the TDP-43 Low fraction and,\ninterestingly, of its receptor NPTXR. Furthermore, we found a downregulation of NPTX2 also\nin the muti-omic data set regarding Exc-RORB (L3-5 ITC) and Exc-LINC00507 (L2/3 ITC)\nneurons in ALS and Exc-THEMIS (L6 ITC) and Exc-LINC00507 (L2/3 ITC) neurons in ALS-\nFTD. We have included this analysis in the revised manuscript (Results section ‘The\nALS/ALS-FTD motor cortex is transcriptionally altered...’, L 577 & L 613, Results section\n‘Nuclear TDP-43 pathology selectively affects’, L 717-719).\nPoint 5: Please use commas as thousand separators throughout.\nReply: As suggested we added commas as thousand separators in the whole manuscript\nincluding figures and supplemental figures.\nPoint 6: Line 42 - STMN2 typo\nReply: We thank the reviewer for his/her/their attention and corrected this typo (L 82).\nPoint 7: Line 56 - the vast majority of polymorphisms detected by GWAS are in non-coding\nregions, I would change “some” to “most”.\nReply: We thank the Reviewer. This part is not anymore a part of the ‘Introduction’ section.\nPoint 8: Fig. 4 - typo, ALS-FTD has 17 donors, not 30\nReply: We apologize for this typo and corrected it (Fig. 4).\nReviewer #2 (Remarks to the Author):\nThis manuscript describes concurrent RNA and chromatin accessibility\nsequencing of single nuclei from ALS, ALS-FTD and control motor cortex. This dataset\nemerges as a few other single cell datasets have been published, although this is the first\nusing the technology that allows for both transcriptome and epigenome profiling in the same\ncell. The analysis pipeline is sophisticated, and a large number of cases are studies.\nHowever, the results are sometimes inconsistent with what is known which may be driven\nby relatively low coverage and/or cells per case. Moreover, while this is an impressive\namount of data and analysis, I am not sure what additional insights these analyses have put\nforward towards our understanding of ALS and ALS/FTD. These issues include the\nfollowing:\n1. The cases/demographics file is not well formatted and is incomplete. In addition to\ndemographic information, the reader needs information on the number of cells, number of\nreads per cell, number of cell types, etc. on a per case basis. While it is impressive that there\nis a large number of cases that were included, it does not seem that each case was\nsequenced to equal depth when examining figure 2d. Based on the number of cases and the\nnumber of total nuclei, the average number of cells per has is on the order of 2000-2500, with\nmany cases likely being much lower, as evidenced by figure 4a where some cases have no\nnuclei from some cellular subtypes. This is problematic because some of the analyses are\nlikely underpowered if there are not sufficient numbers of cells per case (and the number of\ncells per case is used as a variable in some analyses).\nPoint in the summary: The analysis pipeline is sophisticated, and a large number of cases\nare studies. However, the results are sometimes inconsistent with what is known which may\nbe driven by relatively low coverage and/or cells per case.\nReply: We have now dedicated a dedicated part of the revised manuscript to the\ncomparison to the previous studies (L486-494). We show that both our multi-omic dataset is\ncomparable by major quality criteria to the three largest previous studies, including by\ncoverage. We further demonstrate that the cell-type identification and the main findings are\nstrongly concordant with the previous studies: most changes in upper layer excitatory\nneurons, concordant changes across excitatory neurons, no significant effects by the\nC9ORF72 HRE genotype, more pronounced differences in ALS-FTD, oligodendrocyte and\nastrocyte component. To address the concern with low number of cell per case, we have\nperformed additional analyses (see point # by Reviewer #2).\nMoreover, while this is an impressive amount of data and analysis, I am not sure what\nadditional insights these analyses have put forward towards our understanding of ALS and\nALS/FTD.\nReply: We have explicitly highlighted the additional insights that our study delivers in the\nrevised manuscript. The most important are:\n- With the new experimental data, we are the first to analyze the cell-type specific\neffects of TDP-43 pathology in the ALS/FTD patients’ motor cortex. Most\nimportant, we show that the previous extensive report (Liu et al.) delivers mostly\nmarkers for affected cell types, and not genes which are affected by TDP-43\npathology in these cell types.\n- We define for a first time with high resolution which subtypes of neurons are\naffected by TDP-43 pathology in the ALS/ALS-FTD motor cortex\n- We deliver novel sets of TDP-43 pathology-affected genes in the 5 mostly\naffected cell subtypes\n- All these findings were enabled by our multi-omic approach, which allowed our\nstudy to be the first to reach such high cell-type resolution (Fig. 1)\n- We demonstrate that TDP-43 pathology-related transcriptomic changes make up\nonly a small part of the total transcriptomic changes observed in the ALS/ALS-\nFTD motor cortex\nPoint 1. The cases/demographics file is not well formatted and is incomplete. In addition to\ndemographic information, the reader needs information on the number of cells, number of\nreads per cell, number of cell types, etc. on a per case basis. While it is impressive that\nthere is a large number of cases that were included, it does not seem that each case was\nsequenced to equal depth when examining figure 2d. Based on the number of cases and\nthe number of total nuclei, the average number of cells per has is on the order of 2000-\n2500, with many cases likely being much lower, as evidenced by figure 4a where some\ncases have no nuclei from some cellular subtypes. This is problematic because some of the\nanalyses are likely underpowered if there are not sufficient numbers of cells per case (and\nthe number of cells per case is used as a variable in some analyses).\nReply: We thank the Reviewer and have addressed this point. We have provided\nthe requested data in the new Suppl. Tables 22-25.\nWe had considered the number of cells per donor already in the original report. For cell-type\nabundance analyses, we have incorporated this variable as covariate in the mixed-effects\nlinear model MASC. For differential gene expression analysis, we have used pseudo-bulk\napproach only down to the level of major cell types. Du to the very low and highly variable\nnumber of cells per donor at the highest resolution level, ‘cell types/subtypes’, we have\nemployed MAST for DGE analysis on this level.\nPoint 2. The lack of differences in proportion of various cell types in disease versus control\nis worrisome that this dataset is not capturing the molecular events in disease. This may be\nrelated to the low number of cells per case (point #1).\nReply: We thank the Reviewer for this point, which helped to improve the quality of the\nstudy. We dedicated an extensive investigation to this observation. In sum, we demonstrate\nthat the lack of differences in proportions does agree with previous – comparable – studies\nand is attributable to experimental noise due to the nature of the measurement. This and the\nfact that we recapitulate previously established transcriptomic changes (downregulation of\nSTMN2, of NPTX2, inclusion of cryptic exons, changes in the same cell types as previously\ndescribed, etc.) demonstrate that our study does faithfully capture the molecular events of\nthe disease.\nThree lines of evidence suggest that the lack of cell-type differences in our study are not\nindicative of poor quality of the dataset or ‘not capturing the molecular events in disease’:\n1) Very careful modeling and control for bias (see below)\n2) Same findings by the previous studies (please see table below)\n3) Previously described phenomenon that single-cell/nucleus RNA-seq often doesn’t\nreflect IHC censuses (e.g. Pineda et al.)\nDetailed description of each argument 1-3:\n1) We have shown in our analysis that there’s no statistically robust depletion or\nexpansion of any specific cell type except in some ALS or ALS-FTD samples using plain\nstatistics (repeated Wilcox test), and using sophisticated modeling with three different\nframeworks:\n- Deep-learning aided Bayesian statistical modelling with CODA,\n- Hierarchical analysis with tascCODA\n- Linear mixed models with random and fixed effects.\nMoreover, we repeated the analysis over three hierarchical levels of cell-type identification. To\naddress the Reviewer’s concern, we also repeated the analysis only considering samples with\nhigh number of cells (>2000). None of the analyses showed a significant change in any cell\ntype in ALS, ALS-FTD or C9-ALS-FTD, with the exception of Astrocytes and L3-5 ITC Exc\nneurons in less stringent analyses. We added these results as the new Suppl. Fig. 11.\n2) Our observation is in accordance with 5 previous studies of C9-ALS/ALS-FTD\nmotor cortex. None of the 5 studies (see table below) found loss of specific neuronal cell\ntypes in ALS/ALS-FTD in the motor cortex with snRNA-seq. Please note that changes were\nfound often in the DLPF or frontal cortex, and more often in FTD; but not in ALS/ALS-FTD in\nthe motor cortex, what we have in our study. Moreover, Li et al., a very-well controlled study,\nalso found no reduction of any cell type in the motor cortex of ALS and FTD not only by\nsingle-cell compositional analysis, but also with flow cytometry. Our findings thus do not\nsurprise, but re-affirm previous analyses.\nFor-review-table 1. Previous observations of cell-type proportions in comparable\nstudies. Where authors found differences in cell type abundance, we have highlighted in\nred the difference to our study.\nStudy Disease group Brain area Method Finding\nLi et al. 2023 C9-ALS, C9- BA4 motor snRNA-Seq No significant\nALS-FTD cortex changes\nLi et al. 2023 C9-ALS, C9- BA4 motor Flow cytometry\nNo significant\nALS-FTD cortex of same\nchanges\nsamples\nGittings et al. C9-ALS, C9 Frontal cortex snRNA-Seq L5 ETC\nFTD reduced only in\nC9-FTD, but not\nC9-ALS or C9-\nALS-FTD\nLimone et al. sALS BA4 motor snRNA-Seq No significant\n2022 cortex changes\nWang et al. C9 ALS-FTD BA9 DLPF snRNA-Seq Decrease in\n2025 cortex Exc neurons\nPineda et al. C9 ALS, C9- BA4 motor snRNA-Seq Reduction of\nsALS, sALS- DLPF cortex only in DLPF\nFTD cortex, but not\nmotor\nPineda et al. C9 ALS, C9- BA4 motor IHC/IFC Reduction of\nsALS, sALS- DLPF cortex motor cortex\nFTD (ALS) and\nDLPF cortex\n(ALS-FTD)\n3) Previous studies have described a pronounced discrepancy between single-\ncell/nucleus compositional data and IHC/flow cytometry censuses (Li et al., Pineda et al.).\nPossible factors include the nature of the single-cell/nucleus data (compositional) and the way\na cell-type identity is determined in these experiments. Indeed, Pineda et al. demonstrated a\nmarked loss of L5b ETC neurons ins ALS motor cortex with IHC, despite no differences in the\nsingle-nucleus RNA-seq dataset from the same samples. The authors of this study\nthemselves conclude in their ‘Limitations of the study’ section: “Another limitation of the assay\nand use of human tissues is the expected variability in dissections and nuclei capture resulting\nin highly variable cell type yields even from high quality and rigorously dissected tissues,\nmaking it a poor method for evaluating changes in cell type\ncomposition. This mandates the use of histology, or similar approaches, to validate\nsuch changes”\nIn sum, we acknowledge that this question is of central importance and have improved the\ntext accordingly: (Result section “Hierarchical annotation of cell-type identity...”, L 495-507 &\nDiscussion L 828-835). We have followed the Reviewer’s suggestion and checked with\nhigh-cell number samples only. All new data listed above is included in the new Suppl. Fig.\n11 and Suppl. Fig. 15. We will also be happy to include the comparison to the literature\n(table above) in the revised manuscript, but such data is usually out of the scope of\nresearch articles and better fits a systematic review article.\nPoint 3. Figure 2A contains the data shown in figure 1D but broken up into different parts of\nthe figure. As this data is repeated and is not really contributing additional data to figure 2,\nand since the breaking up of the UMAP further distorts the UMAP which is already non-\nlinear, I suggest not including these dot plots.\nReply: We thank the Reviewer for this recommendation and acknowledge that this UMAP\nrepresentation is redundant and had just illustrative value, as was indicated in the original\ntext. We have removed these parts of the figure and have added a Bonsai representation of\nthe data to aid the reader in comprehending the structure of the data (please see modified\nFig. 2).\nPoint 4. Page18 lines 438-439 states that the data was compared to “previous studies”\nwhich I do not see in the supplementary figure 11 – unless they mean just mapping their\ndata using GSEA to KEGG pathways. This should be clarified.\nReply. We have clarified this issue and the text (Results section ‘Transcriptomic remodeling\nof the ALS.ALS-FTD motor cortex, L 571-572). For the comparison to previous studies, see\nnext point by Reviewer #2.\nPoint 5. As two large single cell datasets have been published in the last couple of years\non this topic, a comparison of findings across datasets would be important to show either\nconsistent (or discordant) results.\nReply: We have acknowledged the Reviewer’s suggestion and are very thankful for it, as it\nhelped us to significantly improve the manuscript.\nOur study robustly agrees with previous findings: similar changes between major cell types,\nmore pronounced differences between phenotypes, most changes in L2/3 and L5/6 neurons,\ndysregulation in oligodendrocytes in ALS-FTD, and similar differentially expressed genes when\nsame analysis applied (e.g. the TDP-43 low pseudobulk analysis). In particular, our results are\nmostly in agreement on these points with Li et al. and Pineda et al., which were very\ncomprehensive and robust studies. We have added these comparisons to the revised\nmanuscript (L495-497, L615-619, L828-835). We also compared the FANS-seq dataset to the\ntwo previous report most similar to it: Liu et al. and Gittings et al. (L716-725).\nPoint 6. Figure 3 is problematic and appears to show that this analysis pipeline, matching\nsingle cell transcriptomes to existing spatial transcriptomic data, is not working properly.\nRORB is known to be a layer 4 gene, but they report it highest in layer 5. Similar problems\nexist for Themis (layer 5, not 6), FEZF2 (layer 5, not 6), SST (layers 2,3,5, not layer 1),\nmicroglia (equally dispersed throughout the CNS). This is important because the same\nmethods are used later to map the authors’ new data – and so if the method cannot do this\naccurately for some known genes (such as Figure 4N), it casts doubt on the new data\nlocalization.\nReply: We are very grateful to the Reviewer for this constructive critique, which made us\nrealize that the naming of the major transcriptomic cell types may be confusing to some\nreaders. We have therefore thoroughly revised the naming scheme for the cell types, which\nare now described with their localization/morphology, similar to other reports in the field\n(Gittings et al.). Indeed, Fig. 3 does not demonstrate the expression of the RORB, THEMIS\nand FEZF2 genes in different cortical layers, but of the cell classes which are named after\nthese markers (although these markers are not exclusively expressed only in one class).\nInterestingly, as the Reviewer remarks, the spatial data demonstrates an unexpected\nenrichment of microglia in L1 of the motor cortex. While the enrichment of microglia in the\nwhite matter relative to the grey matter is well-known, we were not aware that the\nenrichment of microglia in L1 has not been described so far. Given that the LIBD dataset is\ncarefully controlled and highly validated, we have added this important information to the\nrevised manuscript (Results section ‘Spatial transcriptomics allow for the mapping’, L 538-\n539).\nPoint 7. Figure 3f shows a single dot in a very small inset as evidence of localization of Betz\ncells. This is not convincing.\nReply: We understand the Reviewer’s skepticism and have made an effort to demonstrate\nthe identification of the Betz cells more convincingly. In the new Supp. Fig. 9 n, we show\nthat Exc FEZF2 NTNG1 cells overlap almost perfectly with the annotations of Betz cells\nfrom three different previous studies, thus unequivocally demonstrating their identity as L5\nETC neurons. We have also added additional spatial transcriptomics images to\ndemonstrate the correct localization of the transcriptomic correlate of Betz cells in the new\nSuppl. Fig. 13. Importantly, as ETN markers are very specific for Betz cells, it is expected\nthat they are expressed very concentrated and intensive in single spots that lie beneath\nBetz cells. Furthermore, as they are very rare (< 0.4 percent in the multiome dataset), they\nare expected to be very sparse in the cross-sectional images of the spatial data. We have\nspecified this in the revised text (Results section ‘Spatial transcriptomics allow for the\nmapping...’, L 544-550).\nPoint 8. The Venn Diagram in figure 4N, while significantly “enriched” in terms of overlap\n(likely due to the high statistical power when compared against the entire transcriptome),\nis striking for how the vast majority of the results do not overlap.\nReply: This is indeed an interesting observation. We have characterized it further in the new\nSuppl. Fig. 16, 17 to show that many of the non-overlapping genes are regulated in the same\nmanner (direction, magnitude), but do not reach significance in one of the groups (ALS or\nALS-FTD), suggesting that the changes are more similar than in seems, but the strength of\ndysregulation varies between the both groups. Still, the changes are less concordant\nbetween the disease groups (e.g. L2/3 ITC nuclei ALS vs ALS-FTD) than between the cell\ntypes in the same disease group (e.g. L2/3 ITC nuclei vs L3-5 ITC nuclei, both in ALS),\nsuggesting a ‘primarily disease-specific’ and secondary ‘cell-type specific’ changes. This is in\nstrong agreement with the observations of Pineda et al. (Fig. 1 A “4” and “5” in Pineda et al.)\nand Li et al. (concluding remarks in ‘Results’ in Li et al.). We have added this valuable insight\nto the revised manuscript (Results section ‘The ALS/ALS-FTD motor cortex is\ntranscriptionally altered’, L 614-623).\nPoint 9. Page 21, line 508, states that DARs “correlated strongly”. While some p-values are\nvery low due to the high number of datapoints, the r values (which is used to judge the\nstrength of a correlation) are very low suggestive of a very weak correlation.\nReply: The Reviewer refers to Fig 5 h, but the text mentioned comments Fig. 5 I, where r-\nvalues are in the range 0.7-0.87, which is strong correlation. We have refined the text to\nexplicitly reference subpanel “5 i” to make clear which data does this statement describe\n(Results section ‘Chromatin remodeling is associated with a subset’, L 647-651).\nPoint 10. Figure 5i, can the authors clarify on which dataset the Spearman correlations were\nperformed? It appears to be just for the data that is both differentially expressed and\ndifferentially accessible which is fine but just needs to be explicitly stated.\nReply: As correctly observed by the Reviewer, the correlations are performed on both\ndifferentially expressed and differentially accessible datapoints only. We have specified this\nexplicitly (Results section ‘Chromatin remodeling is associated with a subset’, L 647-651).\nWe thank the Reviewer for pointing this out.\nPoint 11. For the FANS study, the authors need to provide the fluorescence dot plots to\njudge the quality of the staining/sorting.\nReply: The Reviewer is absolutely right about this. We have provided the fluorescence dot\nplots along with percentages in the new Fig.6 e-g and in the new Suppl. Fig. 20.\nPoint 12. One issue with this method is that burst/broken nuclei can appear to be TDP-43 dim.\nThis may be occurring here as there seems to be a global downregulation of transcripts.\nReply: We thank the Reviewer for this critical remark. It is indeed very important to control\nthat TDP-43 Low nuclei are not just burst/broken nuclei. We have added a substantial new\namount of data to the study to increase the quality of the FANS-seq dataset. We have\nseveral lines of strong evidence that the TDP-43 Low nuclei are not just burst/broken:\n1) The strongest evidence comes from our analysis of gene expression and RNA processing\nin TPD-43 Low vs. TDP-43 High nuclei. Not only could we reproduce previously validated\ntranscription programs of TDP-43 pathology, but also specific alternative poly-adenylation\ntargets (MARK3, ELP1) and the inclusion of cryptic exons specific for TDP-43 pathology\n(STMN2, KALRN). Thus, it is highly unlikely that the TDP43 Low fraction of sorted nuclei is\nmainly debris. All these data is demonstrated in the new Fig. 7.\n2) The gating strategy in the fluorescence-activated nuclei sorting was designed such that\nnot only typical debris, but also all putative debris was not selected (Suppl. Fig. 20, first\ngate). Furthermore, nuclei were positively selected for uniform signal (the singlet selection)\nand for DAPI, which would have had a reduced signal due to DNA release if nuclei were\nruptured (Suppl. Fig. 20 2 , 3rd and 4th gate). This strategy is now demonstrated in the new\nnd\nSuppl. Fig. 20.\n3) The gating/selection of TDP-43 High against TDP-43 Low nuclei was designed to be relative to\nthe NeuN fluorescence signal (please see new Fig. 6, panel ‘e’: the selection gates for TDP-43\nHigh and TDP-43 Low are ‘diagonal’ instead of ‘horizontal’ meaning that nuclei were only sorted as\n“TDP-43 Low” if their TPD-43 signal was proportionally lower ( i.e. lower than expected due to the\nstrength of their NeuN signal). Furthermore, Fig. 6 e demonstrates that a lot of nuclei with poorer\nsignal were sorted to “TDP-43 Low” as well, so no bias is expected.\n4) Next, we acknowledge that the global down-regulation of transcripts (including non-\nsignificant) was problematic. We have inspected its roots and found out it’s a combination of\nthe lower sequencing depth of TDP-43 Low nuclei and of the implementation of L2FC in Li et\nal. 1, which we used. We performed control DGE analyses with equal numbers of nuclei or\ncounts and found out this implementation is sensitive to different library sizes (This was not a\nproblem in Li et al., since they had comparable library size between the groups they\ncompared). To overcome this problem, we increased the overall sequencing depth of the\ndataset; we performed further FACS experiments and increased the sample size of the\ndataset, and, finally, proceeded to using the MAST model fold change estimates, which in\ncontrast to the implementation of Li et al. is not sensitive to lower library sizes in one of the\ngroups. We specifically thank the Reviewer for this remark, as this was a very important\naspect to control for bias and would have invalidated the conclusions about pathological\nTDP-43 cells and its consequence on transcriptomics.\nPoint 13. The validation of the FANS material is primarily based on assessment of\ndifferential polyadenylation that has recently been linked to loss of nuclear TDP-43.\nHowever, the finding of differential polyadenylation has been highly variable between\nresearch groups, and appears to be highly cell specific. Thus, more robust validation of\nproper sorting requires more strongly established methods such as evaluation of cryptic\nsplicing (which admittedly is very difficult to do using single cell transcriptomics due to its 3’\nbias). If too sparse, expression of cryptically spliced genes would be helpful. Comparison of\ntheir FANS data with the existing FANS dataset which has been highly validated by many\ngroups would also be helpful even if these were from different brain regions as there is likely\nto be high overlap between different brain regions.\nReply: We thank the Reviewer for these constructive suggestions and have followed both of\nthem. The Reviewer is right that cryptic exons in STMN2 and KALRN are only included in\nTDP-43 pathology, while APA may just reflect the differences in cell types between TDP-43\nand TPD-43 High nuclei. We have performed further rounds of FANS-seq and more rounds\nLow\nof sequencing to increase the size, quality and sequencing depth of the FANS-seq dataset.\nThe increased quality of the dataset allowed us to reliably quantify the inclusion of known\nTDP-43-specific cryptic exons. We also compared our data to Liu at al. and observed very\nconcordant results. All these new data is provided in the new Fig 7.\nPoint 14. Figure 6J and 6K suggests that loss of nuclear TDP-43 shows no selectivity for\nany cell type which runs counter to what is known about TDP-43 pathology.\nReply: The Reviewer is correct with the expectation that TDP-43 pathology should be\nselective for cell types. The new, improved FANS-seq dataset allowed us to indeed\ndemonstrate this: TDP-43 Low nuclei are selectively enriched for L2-L3 intratelencephalic\nneurons, as well as for L5-6 near-projecting, extratelencephalic and corticothalamic, and\nintratelencephalic neurons. TDP-43 pathology is specific for specific subtypes like Exc\nLINC00507 FREM3, Exc THEMIS LINC00343 and Exc FEZF2 NTNG1 and can be\ndemonstrated not only by their enrichment in the TDP-43 Low fraction, but also by strong\ntranscriptional dysregulation and inclusion of cryptic exons in these cell types. These new\ndata are central to the conclusions of the revised manuscript and are included in the new\nFig. 6-9 (new Results sections ‘Nuclear TDP-43 pathology selectively affects excitatory’ and\n‘Transcriptional effects of TDP-43 pathology are cell-type specific’, L659-754).\nPoint 15. Von Economo neurons are not present in the dorsolateral prefrontal cortex (page\n16). It is also stated that TDP-43 pathology is found in cells of all layers (page 16) which\nneeds a citation. I am not sure if this has formally been demonstrated, with pathologists\ncharacterizing TDP-43 pathology as present in superficial and deep layers with a\npredilection towards superficial layers. I do not believe that molecular demonstration that\nTDP-43 pathology affects all neuron subtypes has been done.\nReply: Indeed, Von Economo neurons are not generally expected in the DLPF cortex, even\nthough some studies have demonstrated this 2. Several pathological studies have\ndemonstrated that TDP-43 pathology may affect different layers of the motor cortex 3 . We\nhave added these to the text (Results section ‘Spatial transcriptomics allow for the mapping,\nL 519-520). The Reviewer is correct that, to the best of our knowledge, there have not been\na molecular demonstration that TDP-43 pathology affects all neuron subtypes so far. We\nhave addressed this issue with our new data, providing for a first time highly-resolved\ncharacterization of the transcriptional cell types affected by TDP-43 pathology. We have\nimproved the text to specify more precisely that TPD-43 pathology affects all excitatory\nneuron types in the motor cortex (Results section ‘Nuclear TDP-43 pathology selectively\naffects excitatory’, L 694-699).\nPoint 16. There is a focus in various parts on Von Economo neurons. However, it is known\nthat TDP-43 pathology in most cases actually preferentially involves superficial cortical\nlayers. The Seeley group has highlighted that VENs are also selectively vulnerable in FTLD-\nTDP (although not in other forms of FTD). A more balanced presentation of selectively\nvulnerable cell types would be helpful.\nReply: We agree with the Reviewer on this point and have revised the manuscript for a\nmore balanced presentation of the selectively vulnerable cell types. Our new data clearly\ndemonstrates that not only extratelencephalic neurons, but also intratelencephalic neurons\nfrom different cortical layers, mostly the superficial layers L2/3 are subject to TDP-43\npathology (Results section ‘Nuclear TDP-43 pathology selectively affects excitatory’, L 697-\n700).\nPoint 17. Figure 7, I was not sure what “case” refers to in the circular track. I understand that\nit refers to ALS versus ALS/FTD, but what does it mean that a certain cell type is associated\nwith one group or the other? While I can understand, for example, that the oligodendrocyte is\nan “ALS-FTD” cell type with a certain amount of DEGs in ALS-FTD, what if a cell type shows\nsignificant DEGs in both ALS and ALS-FTD? How is one group selected over the other and\nwhy not show the data for both groups?\nReply: We understand that this data visualization can be challenging and have reduced the\ncomplexity of this figure. We also added new labels to make clear that each leave in the\ndendrogram represents the changes for a specific cell type in a specific disease group\ncomparison (new Suppl. Fig. 17). We thank the Reviewer.\nPoint 18. The circular track is also somewhat misleading in that the color scales seem\narbitrary and may be exaggerating findings that were chosen to be highlighted, with some\ncolors being binary versus graded. The choice of cutoff values for the various scales is not\nwell described or justified.\nReply: We removed the figure in question from the revised manuscript (see previous point),\nsince we feel the critique is reasonable and the figure was not balanced enough. For the\nrecord, the Reviewer is absolutely correct about this - the color schemes were chosen for\nbetter contrast to make the figure more readable, but this seems to be misleading to readers.\nPoint 19. The pathology in supplementary figure1 is of poor quality and not convincing.\nReply: We agree with the Reviewer and provide new IHC pathology images from two\ndifferent pathologists in the new Suppl. Fig. 1.\nMinor points:\nPage 14, line 330, I believe this should be Figure 2d (not 2b).\nThanks, we have corrected this inconsistency.\nPage 18, line 429, it probably should read “We identified 67 DEGs in ALS ....”\nWe apologize for these inconsistency and have corrected it.\nReviewer #2 (Remarks on code availability): While I did not review the actual code, I did\nreview their statistical methods which are well described.\nReply: We thank the Reviewer for acknowledging the quality of our analysis. We have\nadded even more details to the description of the statistical methods in hope to provide a\nrobust and reproducible analysis (please see changes marked in red in the revised\n‘Methods’ section, e.g. L 357-380).\nReviewer #3 (Remarks to the Author):\nOverall, these datasets have the potential to be useful to the ALS/FTD research\ncommunity.\nWhile this is not the first study to combine snRNA-seq and snATAC-seq in ALSFTD patient\ntissue (ref 47), the addition of FANS-snRNA-seq of a subset of\nsamples allows for a novel comparison between the transcriptional signature of TDP-43-\ndepleted neuronal nuclei with other cells in the motor cortex of ALS or ALS-FTD patients.\nIndeed, the FANS-snRNA-seq dataset alone is likely to be a useful resource, complementing\nthe highly cited dataset generated by Liu et al (ref 10).\nWhile these datasets will likely be useful resources, the authors should provide more\ncontext to describe the biological significance of their results. This is especially important\ngiven two counterintuitive results from this study: 1) a lack of cell type reductions in ALS or\nALS-FTD motor cortex, and 2) convergent gene expression changes across varied cell\ntypes. These topics are addressed by the authors in the Discussion, but still warrant further\ninvestigation to ensure that these are true biological outcomes and not driven by technical\nartifacts.\nResponse to Reviewer #3:\nPoint in the summary. While these datasets will likely be useful resources, the authors\nshould provide more context to describe the biological significance of their results. This is\nespecially important given two counterintuitive results from this study: 1) a lack of cell type\nreductions in ALS or ALS-FTD motor cortex\nReply: We have extensively addressed this issue, which was raised also by Reviewer\n#2. See detailed reply to Point #2 by Reviewer #2. In short, we show that cell census is\nunprecise with this method and the lack of cell-type reduction agrees with the previous\nstudies.\nPoint in the summary. 2) convergent gene expression changes across varied cell types.\nThese topics are addressed by the authors in the Discussion, but still warrant further\ninvestigation to ensure that these are true biological outcomes and not driven by technical\nartifacts.\nReply: We have addressed this issue and added a new analysis in the revised manuscript\n(new Suppl. Fig. 16 & 17), as well as commented on its concordance with previous studies\nResults section ‘The ALS/ALS-FTD motor cortex is transcriptionally altered’, L 614-619).\nOther key points that the authors must address to resolve concerning issues in the\nmanuscript:\nPoint 1. The abstract should be more detailed to highlight the key findings. The authors refer\nto a “well-defined transcriptomic signature of disease,” but no details/highlights are\nprovided.\nReply: We agree with the Reviewer. Since we thoroughly re-worked the study and got\nnew findings, the abstract is now completely revised, taking care to highlight precisely the\nkey findings (‘Abstract’, L 36-56).\nPoint 2. The authors control for various possible batch effects related to nuclear isolation\nand library prep. Were batch effects also considered between the 6 cohorts of postmortem\ntissue? This is very briefly assessed in Figure 2d in terms of cell-type proportions between\ngroups, but other possible batch effects between cohorts are not mentioned.\nReply: This is a very important point and we have commented on it in the revised Methods\nsection. Since we were unable to consider PMI, onset site, BMI and other possible batch\neffects as the data was incomplete, we used SVA analysis to account for hidden/unknown\nbatch variables in the differential gene expression analysis. We have revised the manuscript\nto explicitly state this (Methods section ‘Differential expression/accessibility testing’, L 314-\n317 and Results section ‘The ALS/ALS-FTD motor cortex is transcriptionally altered’, L 555-\n559).\nPoint 3. As a related point, post-mortem interval (PMI) varied widely, from ~4 hours to 96\nhours. Can any batch effects (or outliers) be explained by a transcriptional signature based\non where the samples were collected/stored and/or by differences in PMI?\nReply: This is correct and likely. Since we couldn’t properly include PMI as a covariate, we\nmodelled batch covariates with SVA (please see response to the previous point).\nPoint 4. As the authors note, it is surprising that no cell types were robustly reduced in ALS or\nALS/FTD samples, as these diseases are characterized by the death of neurons. The same\nbrain region was assessed for all samples - because neuronal loss in this region could\ncorrelate with the location of symptom onset (bulbar vs spinal), have the authors considered\nthis variable in their analyses?\nReply: We thank the Reviewer for this useful comment. Unfortunately, we did not have\nenough onset data to stratify samples by it. We have explicitly highlighted this in the section\nabout limitations of this study (Discussion section ‘Limitations of the study’, L 870-873).\nPoint 5. Similarly, it is hard to interpret the fact that gene expression changes are\nconvergent across cell types. This may be an important, though surprising, biological\ninsight; however, the expectation would be that some cells (e.g., excitatory neurons) are\nprimarily affected by disease while other cells (e.g., astrocytes, oligodendrocytes) undergo\ndistinct transcriptional changes based on their unique cellular functions. This point warrants\na deeper explanation – and careful consideration for any technical artifacts (or batch effects)\nthat explain this result.\nReply: We agree with the Reviewer and have dedicated a part of the revised Results to this\nquestion. Indeed, convergent gene expression changes across cell types are somewhat\nsurprising, although we also find divergent changes in glial cells. In the new Suppl. Fig. 17,\nwe show that changes in glial cell are clustered in a distinct group away from neuronal\nchanges.\nPoint 6. Given the importance of TDP-43 function in ALS/FTD, I do not think it’s sufficient to\nsay that splicing analysis and analysis of cryptic exons are outside the scope of this study.\nThere should be an effort to characterize alternative splicing events and particularly CEs\nknown to be regulated by TDP-43. A recent paper (Gittings et al, PMID: 37466726) found that\ncryptic exons could be detected in snRNA-seq data from C9-ALS/FTD frontal cortex.\nAdmittedly, this was most apparent in one sample that was deeply sequenced. However,\nassessing TDP-43 regulated splicing events in these data would provide another layer of\ninformation to compare with differential gene expression or chromatin accessibility between\ngroups.\nReply: We fully agree with the Reviewer and have added a significant amount of new\nexperimental data to the study, which helped to increase the quality of the FANS-Seq\ndataset. Please see point 13 von Reviewer #2. We were now able to perform further\nanalyses and demonstrate the inclusion of cryptic exons in TDP-43 Low cells. These data are\nprovided in the new Fig 7-8 and Results sections ‘Nuclear TDP-43 pathology selectively\naffects excitatory’ and ‘Transcriptional effects of TDP-43 pathology’.\nPoint 7. In Figures 3 and 4, the authors mention that they identified transcriptional\nsignatures common to previous analyses and known to be associated with ALS, but\nmore detail would be helpful since the Figures do not highlight specific DEGs. Highly\nrelevant findings (STMN2, etc.) would be useful to mention specifically.\nReply: The Reviewer is right that specific well-known targets should be mentioned\nspecifically to help the reader assess the agreement with known findings. We have explicitly\nshown STMN2, NPTX2 and EPHA4 in the revised manuscript (Results section ‘The\nALS/ALS-FTD motor cortex is transcriptionally altered’, L 610-613).\nPoint 8. I do not see a supplementary file that includes DEGs from the FANS-snRNA-\nseq analysis. Please include this in any future submissions.\nReply: We apologize for this inconsistency. We have included a list of the DEGs from\nthe FANS-Seq analysis as the new Suppl. Tab. 12-21.\nPoint 9. For the FANS-snRNA-seq experiment, approximately 25% of nuclei sorted based\non NeuN/RBFOX3 expression were characterized as ‘TDP-43-low.’ This far exceeds the\nexpected % of cells exhibiting TDP-43 pathology in ALS-FTD motor cortex. This warrants\nexplanation, as it is a major discrepancy and undermines the veracity of the entire FANS-\nsnRNA-seq data set.\nReply: We apologize that we have not presented these data more clearly. Indeed, after\nsorting, we proceeded to single-nucleus RNA-sequencing on purpose with similar numbers\nof TDP-43 High and TDP-43 Low nuclei to enable meaningful statistical analyses (which\nhave higher power if the two groups to compare are of similar size). In reality, many more\nTDP-43 High nuclei were found than TDP-43 Low, as expected. We added the quantification\nof 8 rounds of FANS-seq (6x from the final data and 2x from establishing runs) as the new\nFig. 6 f and g. The numbers of TDP-43 Low nuclei after sorting were as expected – between 1\nand 3 % of all neuronal nuclei. We thank the Reviewer for this remark which clearly improved\nthe way the data is presented.\nPoint 10. The TDP-43 low population also has a different proportion of cell types, with over-\nrepresentation of LINC00507 excitatory neurons. Using snRNA-seq data from healthy\ncontrols, do these neurons (or a subset of these neurons) normally have lower TDP-43\nexpression? An additional control is necessary to ensure that the DEGs associated with TDP-\n43 depletion are not driven by differences in cell type proportion between the TDP-43 high\nand low groups. Could this be modeled by doing DEG analysis on two sets of randomly\nsampled nuclei comprised of the same cell type proportions as the TDP-43 high/low groups?\nReply: We acknowledge the Reviewer’s remarks, which are very important to ensure the\nquality of the data and support the conclusions. We have addressed all of them:\n1) LINC00507 (now “L2-L3 Intratelencephalic”) neurons do not have much lower TDP-43\nexpression than the other neuronal types, it is even higher than that of other excitatory\nneurons. We now show this in the new Fig. 6 b-d.\n2) The fact that only 1-3 % of sorted nuclei are in the “TDP-43 Low” gate demonstrates that\nthis is not a passive selection of excitatory neurons, which indeed have lower TDP-43\nexpression, but constitute 75% of all neuronal nuclei, not < 3%. In addition, RBFOX3 (NeuN)\nexpression is markedly lower in most types of inhibitory neurons than in excitatory neurons\n(new Fig. 6 b), but the NeuN signal of the TDP-43 High and TDP-43 Low gates runs “in\nparallel”, demonstrating that the TDP-43 Low gate collects specifically nuclei with\ndisproportionately low TDP-43 signal, not just with decreased expression.\n3) The Reviewer is absolutely right and this is a central part of the revised study: the new\ndata has enabled us to perform cell-type specific analyses to overcome the bias from\ndifferent cell type proportions (new Fig. FANS 7-8, new Suppl. Tab. 17-21). Indeed, we show\nthat the previous studies and the cell-type unaware analysis are enriched in subtype\nmarkers, exactly as this Review expected (new Suppl. Fig. 19).\nMinor points:\nPoint 11. Overall, I noticed many small typos while reading the paper. I would recommend\ncarefully re-reading the text for small typos. Examples:\no Fig S4 and S5,6: ‘it parent cell population,’ ‘better suitable’\no Page 17: ‘randomly samples genes’\no Figure 3: ‘annotation of the sport’\nReply: We apologize for these and have carefully revised the text to eliminate remaining\ntypos.\nPoint 12. The supplementary tables are not clearly labeled. If the file names are set by\njournal guidelines, the authors should include a brief description of the data within each file.\nFor example, Suppl Tables 6-8 contain DEGs from pseudobulk analyses, but it is unclear\nwhich groups are being compared in each Table. All tables should include a description of\nthe data within the file if the file names cannot be fully descriptive.\nReply: The Reviewer is right, it is hard to tell when downloading and opening a\nsupplementary data file which data is shown. We have included the description of the data to\neach single file. We thank the Reviewer for this suggestion!\nPoint 13. Methods: FANS was performed “as previously described” and a link is provided,\nbut I do not see a citation.\nReply: We have added the appropriate citation to the text (Liu et al., Policichio et al.).\nPoint 14. Results: patients were “diagnosed based on standardized criteria (methods)” but I\ndo not see this information in the Methods section. This is a minor point because these\nsamples do have confirmed TDP-43 pathology in the motor cortex.\nReply: We have added this information also in the Methods section now (Methods\nsection ‘Human samples and ethics’, L 115-117).\nPoint 15. Demographics information is limited to age and sex. Is information on\npatient race/ethnicity available?\nReply: Unfortunately, since the cohort is assembled from multiple centers, this data is not\navailable in sufficient quality for the dataset. We have specified this in Methods section\n(Methods section ‘Human samples and ethics’, L 120-121).\nPoint 16. The organization of Figure 2 should be improved. 2b and 2c blend into one\nanother. Additionally, the Figure 2d legend refers to the data as %, but the figure represents\nthe data as proportions (i.e., 0.25 versus 25%).\nReply: We thank the Reviewer for this constructive suggestion. We have revised Figure 2 to\nenhance its consistency and readability.\nPoint 17. Fig S5, S6, and S7 all have the same title. The authors should add details to\ndefine each figure (RNA-seq, ATAC-seq, etc.).\nReply: This is correct, the titles are misleading. We have modified them to clarify what the\nseparate figures show.\nPoint 18. Page 24: “oligodendrocytes are affected by TDP-43 pathology in ALS-FTD, but\nnot ALS” – this needs a citation.\nReply: After further research, we realized that this statement was erroneous and have\nremoved it from the text. We are very grateful to the Reviewer for pointing out this error.\nPoint 19. Figure S10 G, H – the scale bars are set to highlight a lack of differences between\nC9 and sALS... but it seems that there are a few DEGs/DARs. It would be easier to interpret\nthese panels if the axes were set lower (e.g., +/- 500).\nReply: This is true, the aim of this visualization is to demonstrate the lack of differences b/w\nC9 and sALS-FTD. However, since it seems that the few DEGs/DARs may be interesting to\nthe reader, we have added the numbers of DEGs above the bars in this chart.\nPoint 20. Figure S11 uses “HC” as healthy control instead of Ctrl like other figures.\nReply: We have corrected this inconsistency.\nPoint 21. Figure S11 “Increasing dot size represents higher (red) or lower (blue) normalized\nenrichment score (‘NES’) for the respective term” - It is unclear to me if this is the same as\nsaying blue dots represent downregulated genes, while red dots represent upregulated\ngenes. If not, it would be more intuitive to see what pathways are enriched for up- vs down-\nregulated genes.\nReply: We thank the Reviewer for this comment, which helped us to make the data\npresentation more clear. Indeed, red dots specify pathways which are enriched in the DEG\nlist, and blue dots pathways which are decreased. We have specified this clearly in the\nrevised Suppl. Fig. 14 legend.\nReviewer #4 (Remarks to the Author):\nThe authors used multi-modal single-nucleus profiling in the primary motor cortex\nof ALS and ALS-FTD to understand gene signatures and epigenetic regulatory\nmechanisms. The novelty of the study lies in their discovery of a distinct subset of\ntranscriptomic changes not linked to TDP-43 pathology. Extensive bioinformatic\ndata analyses were performed, providing a valuable single-nucleus dataset for\nthe ALS field. However, the lack of a detailed molecular mechanism evaluation is\na weak point of this study. The study could be significantly improved by\nperforming experimental validation assays to support their hypothesis. Currently,\nno detail evaluation has been conducted for their findings.\nResponse to Reviewer #4:\nPoint 1. The authors used multi-modal single-nucleus profiling in the primary motor cortex\nof ALS and ALS-FTD to understand gene signatures and epigenetic regulatory\nmechanisms. The novelty of the study lies in their discovery of a distinct subset of\ntranscriptomic changes not linked to TDP-43 pathology. Extensive bioinformatic\ndata analyses were performed, providing a valuable single-nucleus dataset for\nthe ALS field. However, the lack of a detailed molecular mechanism evaluation is\na weak point of this study. The study could be significantly improved by\nperforming experimental validation assays to support their hypothesis. Currently,\nno detail evaluation has been conducted for their findings.\nReply: We thank the Reviewer for the effort. The Reviewer directly refutes the study on the\nsole grounds of lack of molecular validation studies. While we respect this view, we consider\nmulti-omic approaches as well-established in the genomics field and they do provide novel\nmechanistic insights, which do not require the immediate translation to molecular biology\nassays in the same manuscript (e.g.\nNat Commun. 2024 Jul 10;15(1):5815 ; Nat Commun. 2024 Dec 5;15(1):10609 ;\n4 5\nNat Commun. 2025 Jan 2;16(1):319) 6. If more specifics are provided, we will be\nhappy to provide wet lab validation wherever possible.\nReferences\n1. Li, J., et al. Divergent single cell transcriptome and epigenome alterations in ALS and\nFTD patients with C9orf72 mutation. Nature communications 14, 5714 (2023).\n2. Fajardo, C., et al. Von Economo neurons are present in the dorsolateral\n(dysgranular) prefrontal cortex of humans. Neuroscience Letters 435, 215-218 (2008).\n3. Brettschneider, J., et al. Stages of pTDP-43 pathology in amyotrophic lateral sclerosis.\nAnnals of neurology 74, 20-38 (2013).\n4. Wang, Q., et al. Single cell transcriptomes and multiscale networks from persons with\nand without Alzheimer's disease. Nature communications 15, 5815 (2024).\n5. Rosario, S.R., et a!. Integrative multi-omics analysis uncovers tumor-immune-gut axis\ninfluencing immunotherapy outcomes in ovarian cancer. Nature communications 15, 10609 (2024).\n6. Watson, B.R., et a!. Spatial transcriptomics of healthy and fibrotic human liver at single-cell\nresolution. Nature communications 16, 319 (2025).\nWe are grateful to the Reviewers for their effort and positive feedback after the manuscript\nrevisions. Below is the point-to-point response to each new issue raised. We thank the\nReviewers again."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I am pleased to see that the authors have impressively strengthened their manuscript with the addition of the cryptic exon analysis and the deeper dive into the FANS-seq data. I have no further criticisms, though I implore the authors to make sure that all sequencing data, metadata, and count matrices are made publicly available at the point of publication.\n(Remarks on code availability)\nThe code is exceptionally well organized. I congratulate the authors for being so transparent.", "reviewer_2": "I greatly appreciate the author's willingness to address the many comments I made in the first round of reviews. This revised manuscript is greatly improved and I have a lot more confidence in the findings. Significant improvements include comparison of their results with the prior studies, and improved presentation and validation of their FANS dataset. I only have a few minor comments.\n1. The \"enrichment\" of microglia in superficial layer 1 is interesting, but I would consider that this might not be an increase in superficial microglia but rather a decrease in other cellular elements. Given the paucicellular nature of layer 1, it is not surprising that other cell types are reduced which overall would appear like an increase of microglia. However, histologically, there is no accumulation of microglia in layer 1.\n2. I appreciate the wider maps shown in supplementary figure 13 for the rare Ecx FEZF NTNG1 cells. However, in some panels, there are dots that seem to indicate these are also found in superficial layers. This is likely due to the fact that different color scales are used for each pane (ranging from 0 to 6 versus 0 to 3). I suggest making all the panels use the same color scale, even if that means some panels do not identify these rare cells (as expected).\n3. The legend for supplemental figure #1 needs to be updated to include a description of panels e to k.\n4. I appreciate that the FANS dot plots are now shown. For the gating, it appears that DAPI singlets and doublets were included. If this is true, I would adjust the description in the methods where it says that only \"single nuclei\" were obtained.\n5. There is a typo on page 29 where \"find\" should be \"found\"\n(Remarks on code availability)", "reviewer_3": "The authors have provided substantive updates and clarifications to the original manuscript, which will make it more useful to the ALS research community.\n(Remarks on code availability)"}, "author_response": "We are grateful to the Reviewers for their effort and positive feedback after the manuscript revisions. Below is the point-to-point response to each new issue raised. We thank the Reviewers again.\n\nReviewer #1:\nWe thank the Reviewer for the positive feedback. The raw sequencing data, the processed sequencing data and the metadata have been made public on EGA and Zenodo.\n\nReviewer #2:\n1. The \"enrichment\" of microglia in superficial layer 1 is interesting, but I would consider that this might not be an increase in superficial microglia but rather a decrease in other cellular elements. Given the paucicellular nature of layer 1, it is not surprising that other cell types are reduced which overall would appear like an increase of microglia. However, histologically, there is no accumulation of microglia in layer 1.\nReply: We thank the Reviewer for this useful insight. We have included it in the manuscript with a revision in the ‘Results’ section:\n“Surprisingly, we observed an apparent enrichment of microglia in L1 in this highly validated dataset, but we could not identify whether this is an absolute enrichment of microglia or a relative enrichment due to lower numbers of other cell types without further studies.”\n2. I appreciate the wider maps shown in supplementary figure 13 for the rare Ecx FEZF NTNG1 cells. However, in some panels, there are dots that seem to indicate these are also found in superficial layers. This is likely due to the fact that different color scales are used for each pane (ranging from 0 to 6 versus 0 to 3). I suggest making all the panels use the same color scale, even if that means some panels do not identify these rare cells (as expected).\nReply: We thank the Reviewer for this constructive suggestion. Unfortunately, the function in the spatial imaging package used to plot this images does not include an option to specify the scale, so we were unable to plot all panels on the same scale.\n3. The legend for supplemental figure #1 needs to be updated to include a description of panels e to k.\nReply: The Reviewer is right and we apologize for this error. We have updated the legend of Suppl. Fig. 1.\n4. I appreciate that the FANS dot plots are now shown. For the gating, it appears that DAPI singlets and doublets were included. If this is true, I would adjust the description in the methods where it says that only \"single nuclei\" were obtained.\nReply: The Reviewer highlights an important point. The singlet selection was based on the forward and sideward scatter. Although discrete “multiplets’ can be seen in the DAPI plots, we restrained from filtering out the putative “doublets” in the DAPI gate, since brain cortical nuclei are very heterogenous in the DAPI signal and in our experience may appear as a bimodal population in DAPI: the small, compact nuclei of the glial cells and the much larger nuclei of the neurons. We have indicated this in the legend of Suppl. Fig. 20:\n“Then, stained and intact nuclei from smaller and larger cells were selected based on DAPI signal…”\n5. There is a typo on page 29 where \"find\" should be \"found\"\nReply: We thank the Reviewer for pointing this out. We have corrected the typing error:\n“In addition, we observed that the counts of cryptic exon reads were mostly found in these 5 cell types, further…”\n\nReviewer #3:\nWe thank the Reviewer for the positive feedback and the constructive suggestions."}]

Decision letter