Af-CUT&Tag: a sensitive and antibody-free chromatin profiling method using genetically encoded tags and high-affinity binders fused to Tn5

Domain: earth_science
Published: 2026-01-17
Editorial outcome: major_revision

Abstract
Conventional chromatin profiling techniques are often limited by antibody availability and performance. Here, we introduce Af-CUT&Tag, a target antibody-free method that overcomes these limitations by using CRISPR-integrated peptide tags (HiBiT/ALFA-tag) recognized by engineered binders (LgBiT/NbALFA) fused to a Tn5 transposase. Af-CUT&Tag eliminates dependence on traditional target antibodies, achieving robust specificity and sensitivity with as few as 500 cells. It provides high-quality chromatin profiles, with improved signal-to-noise ratios and library quality compared with conventional antibody-based counterparts, while also enabling single-cell resolution (scAf-CUT&Tag). Applying Af-CUT&Tag to Hippo effectors (YAP1/TAZ) during liver regeneration reveals dynamic chromatin remodeling, including YAP1/TAZ-mediated control of lipid metabolism (e.g., Lpin1, Fasn) and heme clearance (Hpx, Trf). We further identify miR-122 as a critical regulator of these processes, impacting liver regeneration. The versatility of Af-CUT&Tag in cell lines, bulk tissues, and single nuclei establishes it as a powerful tool for studying gene regulation in development, disease, and regeneration.

Introduction
Chromatin landscape analysis has revolutionized our understanding of gene regulation by enabling precise mapping of transcription factor binding sites, DNA-binding proteins, and nucleosome features1. This advanced resolution allows systematic profiling of histone modifications, transcription factor positioning, and chromatin structure, facilitating deeper insights into epigenetic regulation2. Among these, transcription factors play a central role in regulating gene expression by binding to specific DNA sequences, recruiting co-regulators, and forming transcriptional complexes3,4. Understanding these regulatory mechanisms is essential for elucidating the underlying processes that govern cellular function, differentiation, and responses to environmental stimuli5.

Current methods for transcription factor (TF) profiling such as CUT&Tag6 have emerged as powerful tools in transcriptional regulation and epigenetics, offering superior efficiency, reduced starting material, and high sensitivity compared to techniques like ChIP-seq1, ChEC-seq7, and CUT&RUN8,9. By utilizing a transposase enzyme to insert sequencing adapters into chromatin fragments, CUT&Tag yields high-resolution fragment libraries with low background noise, making it ideal for chromatin profiling10.

Recent advancements have focused on enhancing precision and accuracy through the fusion of nanobodies and Tn5 transposase, such as Nano-CUT&Tag11 and NTT-seq12. These developments have significantly improved the versatility of CUT&Tag, enabling the simultaneous analysis of multiple epigenetic modifications within single cells13–15. Nanobodies, or single-domain antibodies (VHH), are derived from the variable regions of camelid heavy-chain antibodies and are smaller and more stable than conventional antibodies16. Their integration enhances the targeting specificity for particular chromatin regions, which is critical for gaining deeper insights into gene regulation and cellular differentiation.

However, despite the advantages of CUT&Tag or Nano-CUT&Tag, their reliance on conventional target antibodies for chromatin targeting poses significant challenges. Target antibody-based methods are limited by the quality of antibodies, their high molecular weight, and low cell penetration, which can lead to potential targeting errors17,18. Additionally, protein post-translational modifications—such as phosphorylation19,20, methylation, and acetylation—can disrupt antibody binding21, further complicating the accurate profiling of epigenetic features. These limitations highlight the need for alternative approaches that bypass dependence on target antibodies, providing more robust and flexible solutions for epigenetic studies.

In this study, we develop a target antibody-free chromatin profiling method: Af-CUT&Tag. This approach leverages high-affinity small peptide tags, such as HiBiT22–24 and ALFA-tag25,26, and their corresponding binders or nanobodies, coupled with the Tn5 transposase. These peptide-based systems offer enhanced stability27, sub-nanomolar-level affinity28, and resistance to interference from protein modifications. By tagging proteins of interest (POIs) with small peptides and using CRISPR for knock-in of these tags29, Af-CUT&Tag allows precise and efficient chromatin profiling across different cell types, tissues, and proteins, as exemplified by uncovering YAP/TAZ-mediated Hippo pathway dynamics during liver regeneration. Importantly, this method is unaffected by protein modifications and provides a universal platform for chromatin landscape analysis, paving the way for more comprehensive and accurate studies of gene regulation and chromatin dynamics.

Results
Development and validation of the HiBiT/LgBiT-Tn5-based Af-CUT&Tag system
To create a more direct and efficient method for studying protein-DNA interactions, we developed a target antibody-free CUT&Tag system using genetically encoded tags. Our approach relies on fusing the Tn5 transposase to high-affinity protein binders. We selected two short peptide tags, HiBiT23 and ALFA-tag25, which exhibit nanomolar-level affinities and high resistance to disruption due to their tight binding configuration (Fig. 1a, b).

In vitro binding assays confirmed that both systems exhibited nanomolar-level affinities, with the dissociation constants (Kd) for the HiBiT/LgBiT-Tn5 and ALFA-tag/NbALFA-Tn5 interactions measured at 0.58 nM and 0.202 nM, respectively (Fig. 1c, d). The affinity of the HiBiT/LgBiT-Tn5 system was comparable to that of the native interaction, whereas the affinity of NbALFA-Tn5 was reduced by approximately sevenfold compared with NbALFA alone, although it remained very high (Supplementary Fig. 1a). Despite the higher affinity of the NbALFA-Tn5 system, its poor solubility resulted in reduced protein purity (Supplementary Fig. 1c). In contrast, fusion with LgBiT significantly improved the solubility, yield, and overall quality of the LgBiT-Tn5 protein, making it more robust and efficient to purify (Supplementary Fig. 1b). Given these advantages, we selected the HiBiT/LgBiT-Tn5 system as the focus of our study.

To validate this target antibody-free protein-DNA interaction system, we co-transfected SW480 or DLD1 colorectal cancer cells with RNP complex-targeting transcription factor plasmids and donor template plasmids. Gene-edited cells with HiBiT and ALFA-tag knock-ins were efficiently generated in 96-well plates using dual fluorescence labeling and flow cytometry sorting. HiBiT and ALFA-tags were inserted into the C-terminus of RNA polymerase II’s largest subunit, RPB1, which regulates transcription of protein-coding and non-coding genes. A similar approach was applied to the transcription factor CTCF (Supplementary Fig. 1d).

Subsequently, LgBiT-Tn5 was assembled with the transposon adapter, and its activity was validated in HiBiT knock-in cell lines by detecting chemiluminescence from furimazine, the NanoLuc substrate (Fig. 1e, f and Supplementary Fig. 1e). The catalytic stability of LgBiT-Tn5 at varying concentrations in different buffers showed minimal deviation from the Nano-Glo® Lytic Buffer standard (Supplementary Fig. 1f). Additionally, LgBiT-Tn5 and NbALFA-Tn5 tagged with 3xFlag proteins were purified and used with Flag nanobody magnetic beads to enrich HiBiT and ALFA-tagged endogenous target protein from cell lysates, confirming stable interactions between the fusion proteins and their tagged targets (Supplementary Fig. 2a).

The DNA cleavage activity of LgBiT-Tn5 and NbALFA-Tn5 was confirmed, with Tn5-tagmented DNA fragments predominantly ranging from 200 to 300 base pairs (Fig. 1g and Supplementary Fig. 2b). Both fusion proteins exhibited equivalent transposase activity with control Tn5. To assess system versatility, Tn5 was fused with high-affinity binders, including G4PID30 and SG4 nanobody31 targeting DNA G-quadruplexes, avidin for biotin, and affibodies for EGFR and HER232. All constructs demonstrated robust DNA cleavage activity in vitro (Supplementary Fig. 2c). These results indicate that binder-fused Tn5 proteins can specifically bind targets and insert sequencing adapters near TF-binding sites, offering a specific, and streamlined protein-DNA interaction system suitable for high-throughput sequencing (Fig. 1h).

Af-CUT&Tag Provides High-Resolution Mapping of RNA Polymerase II
After incubation with Concanavalin A-coated paramagnetic beads to immobilize the cells, the cells were permeabilized, and the fusion protein LgBiT-Tn5 or NbALFA-Tn5, pre-assembled with adapters, was introduced. Owing to its lower molecular weight (70 kDa) compared to antibodies (150 kDa), the fusion protein exhibited increased permeability through both cellular and nuclear membranes. Thus, nuclear extraction for subsequent membrane penetration was unnecessary. LgBiT-Tn5 or NbALFA-Tn5 specifically binds to the HiBiT or ALFA-tag at the protein of interest’s C-terminus, forming a stable protein complex. After multiple washes to remove unbound fusion protein, Mg2+ was added to activate the Tn5 transposase, generating high-quality libraries for investigating TF-DNA interactions (Fig. 1h).

To evaluate system specificity, library sequencing was performed using LgBiT-Tn5 in SW480 RPB1-HiBiT cells and SW480 WT cells lacking HiBiT integration. Two parallel replicates were included in the experimental group. To minimize nonspecific genomic interactions of LgBiT, purified full-length Nanoluciferase was used as a blocking agent, as it does not bind the HiBiT subunit. This effectively reduced LgBiT’s nonspecific binding. The results showed no prominent peaks in SW480 WT cells following LgBiT-Tn5 cleavage, confirming the specificity of the system (Fig. 2a).

To evaluate the signal-to-noise ratio of Af-CUT&Tag relative to other methods, we conducted experiments using CUT&Tag and nano-CUT&Tag with the same RPB1 antibody in 100,000 SW480 RPB1-HiBiT cells. Each method included two replicates and was benchmarked against ATAC-seq data generated within-house LgBiT-Tn5. CUT&Tag and nano-CUT&Tag systems required antibody incubation, whereas Af-CUT&Tag did not (Fig. 1h). Downsampling of ~8 million reads aligned to the human genome revealed similar chromatin landscape patterns among the methods, with Af-CUT&Tag achieving high-quality data in promoter regions within the 100,000-cell dataset (Fig. 2a). Due to the fact that the RNAPII-S2P antibody labels elongating RNA polymerase II, CUT&Tag and nano-CUT&Tag datasets exhibited stronger signals across gene bodies compared to Af-CUT&Tag (Supplementary Fig. 3a). We also included WT cells, which had not undergone gene editing, as a positive control for RNA Pol II profiling and performed a quantitative comparison of genome-wide enrichment, further confirming that HiBiT knock-in does not appear to markedly affect the binding of the target protein to DNA (Fig. 2a and Supplementary Fig. 3b). Remarkably, this approach enabled profiling from as few as ~500 cells, yielding signal levels comparable to ~50,000-cell datasets, with minimal background noise (Supplementary Fig. 3c, d). Crucially, our Af-CUT&Tag approach profiles total RPB1, whereas the antibody-based CUT&Tag and nano-CUT&Tag targeted the elongating form (RNAPII-S2P). This distinction accounts for the observed enrichment of Af-CUT&Tag signals at promoter regions compared to the gene body enrichment seen with the antibody-based methods.

For a quantitative comparison, we downsampled all samples to 8 million mapped reads and used deepTools to generate heatmaps of transcription start site (TSS) enrichment across all genes. The results revealed that Af-CUT&Tag produced moderately higher signals than CUT&Tag and nano-CUT&Tag across most TSS sites, despite exhibiting a narrower dynamic range (~4/5 of CUT&Tag’s). This may be attributed to the closer spatial proximity of the transposase to the target protein, and to the fact that Af-CUT&Tag captures both phosphorylated and unphosphorylated forms of RPB1 (Fig. 2b).

To further assess method performance, the TSS profile plots were calculated and plotted for RPB1 Af-CUT&Tag, CUT&Tag and nano-CUT&Tag datasets (Fig. 2c). Af-CUT&Tag demonstrated significantly higher signal accumulation at these sites, highlighting its efficiency in distinguishing POI regulatory states using fewer reads. Library quality analysis indicated enhanced performance for Af-CUT&Tag in terms of promoter region coverage and complexity (Fig. 2d, e). Replicates for Af-CUT&Tag RPB1 profiling showed high reproducibility (Fig. 2f). Comparison of RNA PII read counts against accessible sites (identified through ATAC-seq) further revealed that Af-CUT&Tag exhibited fewer nonspecific accessible sites, distinguishing RNAPII TSS-binding sites more effectively (Supplementary Fig. 3e).

To further validate the system, homemade NbALFA-Tn5 was tested in DLD1 cells with endogenous ALFA-tagged RPB1 knock-in alleles. Comparisons were made between two parallel replicates and DLD1 WT cells lacking ALFA-tag in heatmaps, and profile plots to Nano-CUT&Tag (Supplementary Fig. 3f–h). These findings highlight the versatility and reliability of Af-CUT&Tag.

Af-CUT&Tag enables profiling of CTCF binding across genome in bulk and single-cell levels
To assess the applicability of Af-CUT&Tag for profiling abundant transcription factor binding sites, we profiled the CCCTC-binding factor (CTCF) DNA-binding protein. Equal concentrations of LgBiT-Tn5 and NbALFA-Tn5 were introduced into SW480 cells with HiBiT or ALFA-tag knocked-in alleles, respectively. Following identical experimental protocols, CTCF antibody was used concurrently as a control. Each sample contained 50,000 cells, and the read depth was uniformly maintained across all datasets. We selected a subset of approximately 8 million mapped reads for downstream analysis, revealing distinct patterns of chromatin domains regulated by CTCF. The chromatin landscapes generated by these three methods exhibited high similarity (Fig. 3a). A genome-wide enrichment comparison using CTCF antibody targeting WT control and HiBiT knock-in cells further indicated that the introduction of tagged proteins did not appear to significantly affect CTCF binding to DNA (Fig. 3a and Supplementary Fig. 4a). The heatmaps and profile plots for the CTCF dataset were generated for Af-CUT&Tag (HiBiT and ALFA-tag) and Nano-CUT&Tag datasets (Fig. 3b and Supplementary Fig. 4b), all of which exhibited significant enrichment near the transcriptional start site.

To confirm that Af-CUT&Tag captures specific protein-DNA interactions rather than general chromatin accessibility, we first performed motif analysis on CTCF peaks. As expected, the canonical CTCF binding motif was the most significantly enriched motif (Supplementary Fig. 4c). We then compared our data to ATAC-seq, which further supported the system specificity of Af-CUT&Tag in effectively distinguishing CTCF binding sites (Fig. 3c). Replicates for CTCF profiling using Af-CUT&Tag (HiBiT and ALFA-tag) exhibited high similarity, reaffirming the method’s reproducibility (Supplementary Fig. 4d).

To evaluate the effectiveness of epigenetic regulation validation targeting HiBiT protein, multiple indicators were compared, including peak occurrence in promoters, peak occurrence in introns, PCR Bottlenecking Coefficient 1 (PBC1), Non-Redundant Fraction (NRF), and duplication ratio, using CTCF and HiBiT antibodies for CTCF CUT&Tag and Af-CUT&Tag (HiBiT) libraries (Supplementary Fig. 4e). As expected, sequencing library quality derived from HiBiT was enhanced, irrespective of antibody-free or antibody-dependent methods. Additionally, TSS profile plots were calculated and generated by HiBiT based analysis (Supplementary Fig. 4f). Consistent with expectations, the use of either LgBiT-Tn5 or HiBiT-targeting antibody based classical CUT&Tag produced similar genomic landscapes. The overarching patterns closely resembled those obtained via CTCF antibody-based approaches, all of which displayed notably low background noise (Supplementary Fig. 4g).

Af-CUT&Tag enables single-cell applications in liver regenerative tissues
Since Af-CUT&Tag does not require antibody binding and the entire reaction, from the addition of fusion transposase to adapter integration, occurs within digitonin-treated, semi-permeabilized cells, fragmented DNA remains within each cell nucleus. Consequently, we developed a straightforward strategy to generate chromatin profiles from individual cells, which we have termed single-cell Af-CUT&Tag (scAf-CUT&Tag) (Fig. 3d). We employed a barcoding multiplexing strategy analogous to previously published methods33,34, incorporating barcode sequences from ACT-seq35. SW480 CTCF-HiBiT cells were divided into 96 wells by volume, with a density of 10,000 cells per well. Each well was treated with a separate LgBiT-Tn5 complex, carrying unique 5’ and 3’ sequence barcode combinations. Following the removal of any unbound complexes, the cells were pooled and redistributed into a second 96-well plate using FACS, with 200 cells per well (Fig. 3d). The transposition reaction was initiated by adding tagmentation buffer containing Mg2+ and terminated by the addition of EDTA and protease. Library construction and amplification were performed in each well using a second set of unique index barcodes. After sequencing, the samples were demultiplexed to obtain single-cell data.

The aggregate of single-cell CTCF enrichment peaks aligns closely with the profiles generated in bulk samples (Fig. 3e), exhibiting a high correlation (Pearson’s r=0.88) (Supplementary Fig. 4h). Cells were ranked by their genome-wide read counts, and unique fragments for each cell are displayed in individual tracks. The majority of reads from single cells fall within the CTCF binding peaks defined by bulk analysis, indicating a high recovery rate for single-cell chromatin profiling and effective enrichment of CTCF binding motifs (Supplementary Fig. 4i). Additionally, the average enrichment pattern of annotated gene TSS regions demonstrates high reproducibility in single cells (Fig. 3f). Fraction of reads in single SW480 CTCF-HiBiT cells falling within the designated CTCF binding peaks reaches an optimal value (Median=0.28) (Fig. 3g). Thus, Af-CUT&Tag can be used to profile POIs in single cells and can be further extended to multiplex chromatin profiling through the introduction of diverse tags.

Af-CUT&Tag efficiently profiles chromatin landscapes bound by the POI in vitro and in vivo through combination with CRISPR-based tag knockin
Injury, disease, and aging often result in tissue damage, which triggers intrinsic cellular repair mechanisms to facilitate regenerative processes and restore organ function36. The Hippo pathway is foundational to the orchestration of organ growth and regeneration, as extensively established in prior research37. Within this pathway, the downstream effectors YAP1 and TAZ play critical roles in coordinating reparative functions across diverse tissue types38. However, many aspects of how these effectors modulate downstream gene expression to drive tissue repair remain poorly understood, particularly in the context of regeneration and repair.

The HiBiT-tagged protein exhibits diverse functionalities39,40, serving not only as a nanoluciferase reporter gene system but also as a tool in the Af-CUT&Tag system to investigate transcriptional regulatory mechanisms of YAP1 and TAZ. We validated this approach using YAP1- or TAZ-HiBiT knock-in cell models. This system allowed real-time monitoring of changes in YAP1 or TAZ expression levels via chemiluminescence detection, with signal stability persisting for over 40 min without notable attenuation (Supplementary Fig. 5a). Additionally, it effectively captured changes in YAP1 and TAZ expression levels following treatment with MG-132 (a proteasome inhibitor) and CHX (cycloheximide, an inhibitor of eukaryotic protein synthesis), aligning with results from Western blot experiments (Supplementary Fig. 5b, c).

The system also proved effective for characterizing the regulatory states of YAP1 and TAZ. We generated heatmaps to examine genomic loci enriched by these proteins and compared the TSS profiles between the Af-CUT&Tag and CUT&Tag datasets (Supplementary Fig. 5d, e). Genome browser visualizations further revealed enriched signals of YAP1 and TAZ at the CCN1 gene promoter region in a subset of samples (Supplementary Fig. 5f). These result was confirmed in DLD1 TAZ-ALFA-tag knock-in cells (Supplementary Fig. 5g).

To investigate liver regeneration mechanisms, we constructed recombinant rAAV2/8 vectors encoding TBG-Cre, U6 promoter-sgRNA, and Yap1/Taz-HiBiT knock-in templates. By administering AAV injection, Cas9 expression was induced in Cas9 knock-in mouse liver cells, enabling precise HiBiT tag insertion at the C-terminus of Yap1 or Taz (Fig. 4a). Subsequently, partial hepatectomy (PHx) was performed, revealing significantly higher HiBiT protein expression in knock-in mice compared to controls, while YAP1/TAZ antibody stainings showed no overall protein level changes (Fig. 4b). Liver tissues were collected 24 h post-surgery for further analysis, and HiBiT protein expression was confirmed by immunohistochemical staining (Supplementary Fig. 6a).

Resected and residual liver tissues were categorized as pre- and post-regeneration samples and subjected to nuclear extraction. Following DAPI staining, 100,000 nuclei per sample were isolated. Af-CUT&Tag (LgBiT-Tn5) and Nano-CUT&Tag (antibody-based) were employed to elucidate Yap1/Taz-mediated regulatory mechanisms in early liver repair and regeneration. Six parallel experiments were performed to ensure reproducibility and reliability.

Graphical analyses of average read counts in the Af-CUT&Tag and Nano-CUT&Tag datasets revealed significantly enhanced signal accumulation at TSS regions in Af-CUT&Tag profiles. Heatmap analyses corroborated these trends (Supplementary Fig. 6b). Replicates demonstrated substantial reproducibility in Yap1 and Taz profiling using Af-CUT&Tag (Supplementary Fig. 6c). Comparative analyses of FRiP, promoter peaks, reads in promoters, and library quality metrics (e.g., PBC1, NRF, duplication ratio) further underscored the superior performance of Af-CUT&Tag in complex hepatocellular tissue (Fig. 4c, d). Motif enrichment analysis of pre-regeneration liver samples from Yap1-HiBiT and Taz-HiBiT knock-in mice revealed significant enrichment of canonical TEAD4 and AP-1 binding motifs across all profiling strategies, including LgBiT-Tn5, anti-HiBiT, and anti-Yap1/Taz-based Af-CUT&Tag and Nano-CUT&Tag approaches (Supplementary Fig. 6d).

scAf-CUT&Tag enables single-cell applications in liver regenerative tissues
We further applied Af-CUT&Tag to the single-cell analysis of regenerating liver tissues. To enhance HiBiT-tagged protein expression, we constructed a recombinant AAV vector encoding TBG-Yap1-HiBiT, packaged the virus, and administered it to mice for liver-specific overexpression of Yap1-HiBiT. Liver tissues were collected pre- and 24h post-partial hepatectomy, and nuclei were extracted and fixed. Using barcoding multiplexing strategy, sequencing data were demultiplexed to obtain single-cell datasets. A total of 17,416 barcodes were identified in the PHx-pre samples, while 17,855 barcodes were identified in the PHx-post samples. Similar to other NGS-based approaches41–43, scAf-CUT&Tag generated unique reads for each nucleus. To ensure data quality, libraries with excessively high or low unique read distributions were excluded from the analysis. After filtering, the mean number of unique reads per nucleus was 1153 for PHx-pre and 5844 for PHx-post (Fig. 4e). Subsequently, clustering analysis was performed on 12,775 nuclei from the PHx-pre sample and 12,812 nuclei from the PHx-post sample, yielding eight distinct single-cell datasets for each condition (Fig. 4f).

To ensure that single-cell isolation and library preparation retained the high specificity observed in bulk Af-CUT&Tag, single-cell datasets from pre- and post-PHx were correlated with bulk samples (Supplementary Fig. 7a, b). Single-cell datasets with the highest correlation to bulk samples were selected, and nuclei from pre- and post-PHx were ranked by genome-wide read counts, with unique fragments from each nucleus displayed on individual tracks. The aggregated single-cell profiles closely resembled Yap1 bulk CUT&Tag profiles across various genomic loci, exhibiting similarly high signal-to-noise ratios (Fig. 4g). As a proof of concept, although AAV-mediated expression of Yap1-HiBiT in liver tissues was heterogeneous, the exceptional sensitivity of Af-CUT&Tag rendered it suitable for single-cell transcriptional regulatory analysis in tissues.

Investigating YAP1/TAZ-mediated early liver regeneration mechanisms via Af-CUT&Tag
To further investigate the genes potentially regulated by YAP1/TAZ-associated chromatin binding events after liver resection, we identified the differential binding peaks (|log2FC|>1, p-value<0.05) from bulk data using the edgeR44 package. These peaks represent genomic regions with altered YAP1/TAZ occupancy, and were annotated to the corresponding genes using the ChIPseeker45 package. The volcano plot illustrates the distribution of differential binding peaks across the different groups, with peaks associated with genes involved in fatty acid metabolism highlighted (Fig. 5a, b). Gene Ontology (GO) enrichment analysis on the genes corresponding to the differential binding peaks at 24h post-regeneration revealed that upregulated genes were linked to regeneration, immune response, inflammation, hemostasis, and iron ion transport, while downregulated genes were associated with fatty acid metabolism, acyl-CoA processes, and organic acid biosynthesis (Fig. 5c). These pathways were not significantly enriched in the Nano-CUT&Tag antibody-treated group (Supplementary Fig. 8a). Heatmaps of peak enrichment values further highlighted the superior sensitivity of Af-CUT&Tag (Fig. 5d).

After partial hepatectomy, Yap1/Taz promoted the expression of Hemopexin (Hpx) and Serotransferrin (Trf) to clear free heme released during erythrocyte rupture, providing antioxidant and immunomodulatory effects crucial for initiating liver regeneration (Supplementary Fig. 9a). Spatial transcriptomic analysis revealed that Hpx and Trf were significantly upregulated at 24h and returned to baseline by 72h (Supplementary Fig. 9b).

Lipid metabolic homeostasis is vital for liver regeneration46, with acute lipid accumulation playing a critical early role. At 24h post-hepatectomy, Yap1/Taz enrichment in the promoters of fatty acid metabolic enzymes and synthases (e.g., Acox1, Lpin1, Fasn, Scd1) was markedly reduced, especially in the Af-CUT&Tag system (Fig. 5e). Notably, Lpin1, essential for lipid metabolism, causes metabolic dysfunction and impairs regeneration when deficient47,48. Reduced chromatin accessibility and decreased RNA Pol II enrichment in these genes evidenced chromatin remodeling (Supplementary Fig. 8b). Except for transient Acox1 upregulation, these genes were significantly downregulated 24h post-regeneration, exhibiting spatiotemporal expression differences (Fig. 5f).

Additionally, genes associated with fatty acid metabolism, including Cyp4a32, Acsl1, and Thrsp, showed decreased peak enrichment values (Fig. 5d). Analysis of the GSE180012 dataset confirmed that genes regulated by Yap1/Taz, such as Lpin1, Scd1, Thrsp, and Hpx, exhibited similar expression trends 24h post-hepatectomy (Supplementary Fig. 9c). Differential gene expression analysis during liver regeneration demonstrated that the expression levels of several other fatty acid synthases were significantly downregulated within the first 24h, recovering only thereafter (Supplementary Fig. 9e).

Subsequently, we assessed lipid accumulation 24h after partial hepatectomy, revealing a marked increase (Fig. 5g). This raised the question of the mechanisms underlying the origin of these accumulated lipids. Transcriptomic analysis at 24h post-hepatectomy showed that most genes involved in fatty acid metabolism or synthesis were significantly downregulated during this period (Supplementary Fig. 9d). In contrast, the additional accumulation of hepatic lipids can also arise from two sources: lipid droplet enlargement via fusion and enhanced fatty acid uptake. Examination of genes associated with lipid droplet fusion and storage, such as Plin2 and Cidec, revealed early upregulation prior to 24h, followed by a return to baseline expression levels (Fig. 5h, i). Similarly, fatty acid transport genes like Cd36 and Fabp449 were upregulated at 24h, contributing to rapid lipid droplet accumulation (Supplementary Fig. 9f, g).

Notably, although Plin2 and Cidec are regulated by Yap1/Taz, the enrichment of Yap1/Taz at the promoters of these two genes had largely returned to pre-hepatectomy levels by the 24-h mark, with a slight decrease even observed (Supplementary Fig. 9h). Fatty acid transport genes, primarily regulated by the PPAR signaling pathway50, also reached peak upregulation within the first 24h, followed by a gradual decline in expression levels thereafter (Supplementary Fig. 9f, g).

These findings highlight the dynamic nature of lipid accumulation during liver regeneration. In the early stages, Yap1/Taz-driven lipid droplet fusion and PPAR-enhanced fatty acid transport promote acute lipid accumulation. Subsequently, Yap1/Taz mediates the inhibition of lipid metabolism and synthesis, reducing inflammation and oxidative stress from metabolic byproducts and providing energy and a favorable environment for regeneration.

miR-122 identified by Af-CUT&Tag plays a crucial role in early liver regeneration
An intriguing aspect pertains to the notable increase in the enrichment of Yap1/Taz on Mir122 (Fig. 6a), which is notably abundant in the liver, comprising an impressive 70% of all microRNAs, and exhibits multifaceted robust functionalities by regulating the expression of a series of downstream genes51. We also observed a significant increase in both the chromatin accessibility and H3K27 acetylation of Mir122 (Fig. 6b). The actual levels of miR-122 in the liver were validated by microRNA-specific RT-qPCR, concomitant with the acute inflammatory response induced by liver injury, inflammatory factors such as Tnf-α, Il-6 and Cxcl-1 show an upregulation (Fig. 6c and Supplementary Fig. 10a).

To ascertain whether the upregulation of miR-122 is one of the significant determinants in liver regeneration, the Cas9 knockin animal was adopted for specific in vivo miR-122 depletion in hepatocytes via AAV-TBG-Cre vectors carrying Mir122-sgRNA, with vectors expressing Cre recombinase were used as control (Fig. 6d). Subsequently, partial hepatectomy was performed and miR-122 expression levels were assessed, and the regenerated liver tissue was examined 24h post-surgery. The results showed that, compared to the control group, the liver-specific miR-122 knockout group exhibited increased lipid accumulation (Fig. 6e), and H&E staining revealed a certain degree of inflammation (Fig. 6f). Transcriptomic analysis following miR-122 depletion supported these findings, showing upregulation of inflammation-related pathways and a marked suppression of lipid metabolism pathways (Fig. 6g, h). We then assessed liver regeneration following miR-122 inhibition, observing a reduction in the proportion of Ki-67+ hepatocytes (Fig. 6i) and a significant decrease in the percentage of PCNA-positive hepatocytes (Fig. 6j). These results confirm that excessive lipid accumulation and inflammatory responses are critical factors affecting the early stages of liver regeneration.

Correspondingly, the downstream target genes of miR-122 involved in iron ion metabolism and fatty acid metabolism, such as Hfe, Tfr2, Ndrg3 (downregulation of these three genes maintains iron ion homeostasis), and E2f1 (regulates fatty acid metabolism), demonstrate a favorable declining trend during liver regeneration as miR-122 expression increases. However, the loss of miR-122 does not reverse this phenomenon; it merely decelerates the trend (Supplementary Fig. 10b). Interestingly, miR-122 expression initially increases and then decreases during liver regeneration. Conversely, E2F1, a transcription factor regulating lipid metabolism, displays an expression pattern inversely correlated with miR-12252 (Supplementary Fig. 10c), aligning with the lipid metabolism dynamics observed during liver regeneration.

Thus, Yap1/Taz likely facilitates liver tissue regeneration and repair by regulating the expression of genes such as Mir122. This phenomenon was maximally captured utilizing Af-CUT&Tag, with DNA peaks highly enriched in the regulatory regions of target genes, demonstrating the system’s exceptional sensitivity in complex tissues. In contrast, antibody-dependent Nano-CUT&Tag signals considerably attenuate in comparison.

Methods
Ethical Statement
All animal experiments in this study were conducted in compliance with relevant ethical regulations and were approved by the Institutional Animal Care and Use Committee of Xiamen University Laboratory Animal Center (ethics approval number: XMULAC20230071). The study protocol was reviewed and approved by the appropriate ethics review board at Xiamen University. All procedures involving animals were performed in accordance with institutional guidelines to ensure humane treatment.

Biological materials
Human RKO (Cat#TCHu116), DLD1 (Cat#CC0507), and SW480 (Cat#CCL-228) cell lines were obtained from the Shanghai Institute of Biochemistry and Cell Biology, Guangzhou Cellcook Biotech Company, and the American Type Culture Collection (ATCC), respectively, and cultured according to the suppliers’ instructions. We used the following antibodies: Rabbit Control IgG (Abclonal, AC005), Phospho-Rpb1 CTD (Ser2) (Cell Signaling Technology, 13499), CTCF (Cell Signaling Technology, 3418), YAP1 (Cell Signaling Technology, 14074), TAZ (Cell Signaling Technology, 72804), HiBiT (Promega, N7200), Histone H3 (acetyl K27) (Abcam, ab4729), Ki-67 (Abcam, ab279653), PCNA (Santa Cruz, sc-56), Goat anti-rabbit secondary antibody (Thermo Fisher Scientific, 31210), and Goat anti-mouse secondary antibody (Thermo Fisher Scientific, 31160). C57BL/6JGpt-Rosa26tm1(CAG-LSL-Cas9-tdTomato)/Gpt mice were purchased from GemPharmatech (T002249). Mice were maintained under standard laboratory conditions with a normal light/dark cycle, ambient room temperature, and standard humidity.

Generation of isogenic HiBiT/ALFA-tagged cell lines
To generate endogenous HiBiT/ALFA-tagged YAP1/TAZ cell lines, sgRNA targeting the stop codon regions were co-transfected with donor vector contained the following elements: left and right homology arms, as well as linker and HiBiT or ALFA-tag using pSpCas9(BB)-2A-GFP (PX458) and pAAV-mCherry-donor. The transfected cells were sorted 48 h later, and GFP/mCherry-double-positive cells were selected and plated into individual well of 96 well plate, Homozygous or heterozygous clones were selected for further analysis. All homozygous/heterozygous knock-ins were confirmed by Sanger sequencing and western blotting. A list of the oligos and the sequences of the sgRNAs is provided in Supplementary Data 1.

Flow cytometry
Prior to flow cytometry analysis, approximately 1×106 cells expressing pSpCas9(BB)-2A-GFP and pAAV-mCherry-donor were treated with trypsin and then washed with PBS. The cells were subsequently sorted and examined using a BD FACSAria FUSION cell sorter, with data analyzed using BD FACSDiva™ Software. The instrument employed 488- and 561-nm excitation lasers, and emission signals were detected using filters at 525/20 nm (wavelength/bandwidth) for GFP and either 610/20 nm or 615/20 nm for mCherry. The sorted cells, both bulk populations and single cells, were placed into either 12 well plates or 96 well plates containing DMEM, RPMI-1640, and L-15 with 10% FBS and 1% penicillin-streptomycin.

LgBiT/NbALFA-Tn5 construction, purification and activity test
Using the pETM11-His-SUMO3-LgBiT-Tn5 expression vector, sequences of LgBiT were PCR amplified from the LgBiT vector. The C terminus of LgBiT was separated from the transposase by a 7-residue flexible linker peptide composed of GSSGGGS. The pTXB1-Tn5 plasmid was a gift from Rickard Sandberg (Addgene plasmid #60240). Previously published sequences coding for NbALFA were synthesized as a gene fragment (Tsingke), with purification steps for NbALFA-Tn5 followed as LgBiT-Tn5. Sequences of other binder-Tn5 fusion proteins were designed according to Bartosovic and Castelo-Branco11. A His-3×FLAG tag was added to the N terminus of each binder, separated by a GGGGS linker to eliminate the need for tag-cleavage steps. Protein purification was performed following the same column-based procedure as described for LgBiT-Tn5. The amino acid sequences of the proteins are provided in Supplementary Data 3.

The pETM11-His-SUMO3-LgBiT-Tn5 plasmid was transformed into E. coli ER2566 Star following the manufacturer’s protocol. Each colony tested was inoculated into 10 mL LB medium and growth was continued at 37°C for 12 h. The overnight culture was used to inoculate 1.5 L of LB medium supplemented with 50 µg/mL kanamycin and incubated at 37°C with shaking. When the culture reached an optical density (OD600) of ~1.0, the temperature was reduced to 20°C, and protein expression was induced with 0.25 mM isopropyl β-D-1-thiogalactopyranoside (IPTG). The culture was then incubated overnight at 20°C before harvesting. Cells were harvested by centrifugation (3900×g, 10 min). The bacterial pellet was frozen and stored at -80°C. A frozen pellet was resuspended in 50 mL Binding Buffer (20 mM HEPES-KOH at pH 7.2, 0.8 M NaCl, 1 mM EDTA, 10% glycerol, 0.2% Triton X-100) including 1× Roche Complete EDTA-free protease inhibitor tablets. The lysate was sonicated for a total of 15 min using a 2s on/3s off pulse cycle at an output level of 6 (60% amplitude), while keeping the sample chilled on ice throughout the process. The lysate was centrifuged at 12,000×g for 30 min at 4°C, and the supernatant was loaded onto a HisTrap HP column (Cytiva). The column was washed with 10× column volume of Wash buffer 1 (20 mM HEPES pH 7.2, 800 mM NaCl, 20 mM imidazole, 10% Glycerol and 1 mM EDTA) and 20× column volume of Wash buffer 2 (20 mM HEPES pH 7.2, 1.5 M NaCl, 20 mM imidazole, 10% Glycerol and 1 mM EDTA) sequentially. The recombinant His-SUMO3-LgBiT Tn5 was eluted by 5× column volume of Elution buffer (25 mM HEPES pH 7.2, 800 mM NaCl, 300 mM imidazole, 10% Glycerol and 1 mM EDTA). Each fraction was analyzed by SDS-PAGE, and the one containing a high concentration of recombinant Tn5 was combined and concentrated to 2 mL using Millipore Amicon ultra-15 centrifugal filter unit (30 kDa) to remove the imidazole. The eluent was stored in dialysis buffer (25 mM HEPES pH 7.2, 800 mM NaCl, 0.1 mM EDTA, 10% Glycerol and 1 mM DTT). SENP2 protease was added at a 1:100 ratio to remove the SUMO3 tag from SUMO3-LgBiT-Tn5, followed by incubation with His magnetic beads to eliminate the His-tagged SUMO3, yielding purified LgBiT-Tn5. The concentrated LgBiT-Tn5 was then loaded onto Superdex™ 75 Increase 10/300 GL column with Buffer A. The first peak (peak at 8 mL) was collected and combined, which was the dimer form of recombinant LgBiT-Tn5. The LgBiT-Tn5 dimer fractions were concentrated using Millipore Amicon ultra-15 centrifugal filter unit (30 kDa), and mixed with 100% glycerol to make the glycerol up 50%. The final recombinant LgBiT-Tn5 was stored at -20°C.

To generate the LgBiT-Tn5 adapter transposome, The lyophilized DNA oligos were dissolved in annealing buffer (40 mM Tris pH 8, 50 mM NaCl) to get 100 µM. The oligo sequences are as follows: Tn5MErev, 5′-CTGTCTCTTATACACATCT-3’; Tn5ME-A, 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-3’; Tn5ME-B, 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-3’. One volume of Tn5MErev was mixed with one volume of Tn5ME-A or Tn5ME-B to get 50 µM Adaptor A/B. The adaptor A/B was annealed in a thermocycler with the program: 75°C, 15 min; 60°C, 10 min; 50°C, 10 min; 40°C, 10 min; 25°C, 30 min; 4°C, hold. The equal volume of adaptor A and adaptor B were mixed to get 50 µM adaptor mix. Assembly of LgBiT-Tn5 transposome was performed by mixing 2 µL LgBiT-Tn5 (26 µM), 2.4 µL adapter mix (50 µM) and 10.6 µL dialysis buffer. The mixture was incubated at room temperature for 60 min and then stored at 4°C for several days, then mixed with 100% glycerol to make the glycerol up 50%, The final LgBiT-Tn5 adapter transposome was stored at -20°C.

Liver tissue nuclei extraction
Mice were sacrificed. Subsequently, 0.1-0.2 g of liver tissue was excised and washed with 2 mL of swelling buffer (10 mM Tris-HCl pH 7.5, 2 mM MgCl2, 3 mM CaCl2, 1× Protease inhibitor cocktail). The liver was then dounced in 1 mL of swelling buffer (dounced 10 times, piston A) and incubated at 4°C for 20 min. Afterward, the liver was dounced again 20 times (piston B), and 1 mL of swelling buffer was added. The cells were filtered using a 100 µm cell strainer and spun at 400×g at 4°C for 10 min. The supernatant was removed, and the cells were resuspended in 0.5 mL of swelling buffer + 10% glycerol. Slowly vortex while adding drop by drop 0.5 mL of lysis buffer (Swelling buffer + 10% glycerol + 1% NP-40). Incubate for 5 min at 4°C and add 1.5 mL of lysis buffer. Spin at 600×g at 4°C for 5 min, remove the supernatant, and wash the nuclei again with 1.5 mL of lysis buffer. Spin at 600×g at 4°C for 5 min, remove the supernatant, and resuspend the nuclei in 1 mL of freezing buffer (50 mM Tris-HCl pH 7.5, 5 mM MgCl2, 0.1 mM EDTA, 40% glycerol, and 1× Protease inhibitor cocktail). Finally, the nuclei were stained with DAPI and counted.

Af-CUT&Tag bulk
Nuclei or permeabilized cells were counted and centrifuged for 3 min at 600×g at room temperature. Aliquots of nuclei or cells (100–50,000 nuclei or cells), were washed twice in 0.5 mL Wash Buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 0.5 mM Spermidine, 1× Protease inhibitor cocktail). Concanavalin A coated magnetic beads (Vazyme #N515) were prepared and 10 µL of activated beads were added per sample and incubated at RT for 10-15 min. And then nuclei or permeabilized cells were directly suspended with 100 µL antibody buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 2 mM EDTA, 0.5 mM spermidine, 0.05% Digitonin, 1% BSA, 1× protease inhibitors) with a cocktail of blocking protein (Nanoluciferase composed of LgBiT and SmBiT, final concentration: 2 µM) and LgBiT-Tn5 or NbALFA-Tn5 (final concentration: 0.04 µM), and then incubated on a rotator at room temperature for 30 min. Subsequently, cells were washed three times with 200 µL high salt wash buffer (20 mM HEPES pH 7.5, 300 mM NaCl, 0.5 mM spermidine, 0.01% Digitonin, 1× protease inhibitors). The nuclei or cells were then resuspended in 50 µL tagmentation buffer (20 mM HEPES pH 7.5, 300 mM NaCl, 0.5 mM spermidine, 10 mM MgCl2, 0.01% Digitonin, 1× protease inhibitors). The samples were incubated for 1 h at 37°C. Tagmentation steps were performed in 0.2 mL tubes to minimize cell loss. To stop tagmentation, 1 µL of 0.5 M EDTA, 1 µL of 10% SDS and 0.5 µL of 20 mg/mL proteinase K was added to 50 µL of sample, which was incubated at 55°C for 10 min. To extract the DNA, 20 µL VAHTS Particles G beads (Vazyme #N903) were added to each tube with vortexing, incubated for 5 min at room temperature. Tubes were placed on a magnet stand to clear, then the liquid was carefully withdrawn. Without disturbing the beads, beads were washed twice in 200 µL 80% ethanol. After allowing to dry ~5 min, 35 µL of 10 mM Tris pH 8 was added, the tubes were vortexed, quickly spun and allowed to sit for 5 min. Tubes were placed on a magnet stand and the liquid was withdrawn to a fresh tube.

To amplify libraries, 35 µL of DNA was mixed with 2 µL of a universal i5 and a uniquely barcoded i7 primer, using a different barcode for each sample. A volume of 1 µL of TAE and 5× TAB (Vazyme #TD601) were added and mixed. The sample was placed in a thermocycler with a heated lid using the following cycling conditions: 72°C for 3 min (gap filling); 98°C for 30 s; 15 cycles of 98°C for 15 s, 60°C for 30 s and 72°C for 30 s; final extension at 72°C for 5 min and hold at 4°C. Post-PCR clean-up was performed by adding 1.5× volume of VAHTS DNA Clean Beads (Vazyme #N411), and libraries were incubated with beads for 5 min at RT, washed twice gently in 80% ethanol, and eluted in 22 µL 10 mM Tris pH 8.0.

Single-cell Af-CUT&Tag
The preparation of the LgBiT-Tn5 complex with various barcodes was conducted in a total of 192-well PCR strip tubes, each containing a unique combination of 5′ and 3′ complex barcodes, with sequences referenced from ACT-seq35. The following components were mixed in each well: 1.5 µL of 26 µM recombinant LgBiT-Tn5 protein, 0.75 µL of 50 µM 5′ composite barcode, 0.75 µL of 50 µM 3′ composite barcode, and 4 µL of dialysis buffer. The strip tubes were incubated at 25°C for 60 min to form the complex.

Wash and permeabilize 2 million HiBiT-tagged cells or nuclei as described above. Resuspend all permeabilized cells in antibody buffer and add NanoLuciferase. Distribute the sample into 96 wells at a density of 10,000 cells or nuclei per 50 µL. Add 1 µL of the LgBiT-Tn5 complex to each well and incubate at room temperature for 30 min. Centrifuge the PCR strip tubes at 300×g for 3 min, retaining ~10 µL of solution to remove unbound complexes. Add 100 µL of Dig-300 wash buffer to each well and repeat the centrifugation and wash steps three times. Resuspend the cell or nuclear solution and pool it into a 15 mL centrifuge tube. Centrifuge the tube containing the pooled cells at 600×g for 3 min and carefully remove all but ~20 µL of the solution. Resuspend the cells in 400 µL of Dig-300 buffer and filter using a cell strainer test tube. Using a FACS instrument, distribute the cells or nuclei into 96 clean wells, with each well containing 200 cells or nuclei. Initiate the tagmentation reaction by adding 25 µL of tagmentation buffer to each well and incubate at 37°C for 60 min. Subsequently, terminate the reactions and extract DNA as described above.

For library preparation, 1.25 µL of 10 µM 5′ library index barcode was added to each of the 96 wells, with each well receiving a 3′ barcode with a unique sequence. Each well then received 18 µL of DNA, 1.25 µL of 10 µM universal 5’ library index barcode, 0.5 µL of TAE, and 5 µL of 5× TAB. Twenty cycles of PCR amplification were performed using the program described above. DNA purification was consistent with the aforementioned bulk protocol. All barcode sequences and unique library primer sequences are provided in Supplementary Data 2.

ATAC-seq
Harvest cells by centrifuging at 600×g for 5 min, discard the supernatant, and resuspend in PBS. Centrifuge a single-cell suspension containing the desired number of target cells at 600×g for 5 min, discard the supernatant, and resuspend in 50 µL of pre-cooled lysis buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.02% digitonin, 0.1% Tween-20). Allow the cells to lyse by placing them on ice for 5 min. Centrifuge at 500×g for 10 min at 4°C to collect the cell nuclei and immediately continue to transposition reaction. To make the transposition reaction mix, combine the following: 10 µL 5× TD Buffer (50 mM Tris pH 7.5, 25 mM MgCl2, and 50% dimethyl formamide), 16.5 µL PBS, 0.5 µL 10% Tween-20, 0.5 µL 1% digitonin, 18.5 ddH2O and 4 µL LgBiT/NbALFA-Tn5 Transposase (~1 µM). Resuspend nuclei in the transposition mix and incubate at 37°C for 30 min. PCR mix (Vazyme #TD601) and adapters (Vazyme TD #202) were used for library amplification, the total volume is 50 µL. The fragmented DNA library was initially incubated with a 0.55× (27.5 µL) volume of DNA purification magnetic beads (Vazyme #N411). After discarding the magnetic beads, the supernatant was retained, and a 1.2× (60 µL) volume of DNA magnetic beads was added to further purify the DNA fragment library.

CUT&Tag and Nano-CUT&Tag bulk
CUT&Tag was performed as described in Henikoff6, and all reagents and transposase used in this assay were obtained from the Hyperactive Universal CUT&Tag Assay Kit for Illumina (Vazyme Biotech Co., Ltd). Nano-CUT&Tag was performed as described in Bartosovic and Castelo-Branco11, with NanoRabbit/NanoMouse-Tn5 expressed and purified according to the sequences provided by the authors, and all salt concentrations strictly matched those specified in the original protocol. The sequencing library was prepared consistent with Af-CUT&Tag.

DNA sequencing
The size distribution of libraries were determined by Agilent 2100 TapeStation analysis, and concentration of the libraries were determined by Qubit 3.0. Paired-end Illumina sequencing was performed on the barcoded libraries following the manufacturer’s instructions.

Bulk Af-CUT&Tag data processing
The raw reads were first filtered to remove the adapter sequences and low-quality sequences using fastp (version 0.20) with options: -g -q 30 -u 50 -n 15 -l 36 -w 4, clean reads were mapped to the mouse mm10 or human hg19 reference genome using Bowtie2 (version 2.4) with options: --end-to-end --very-sensitive --no-mixed --no-discordant --phred33 -I 10 -X 700. Duplicate reads were removed with Picard MarkDuplicates (version 2.27) with default parameters, mitochondrial reads, reads from unmapped and reads with a mapping quality <30 were removed using SAMtools (version 1.15). BigWig files were generated with deepTools bamCoverage (version 3.3.1) tools from reads with --binSize 10 --normalizeUsing RPGC. The results were visualized in the Integrative genomics viewer IGV (version 2.16.2). TSS heatmap and profile plot are plotted using the plotHeatmap and plotProfile functions respectively. Peak calling was performed on each individual sample by MACS2 (version 2.2) with options: -c control_sample --bdg --SPMR --gsize hs --keep-dup all --qvalue 0.05 --shift -75 --extsize 150 --nomodel --call-summits --nolambda. Use the ChIPseeker R package to annotate the genes corresponding to the peaks. Homemade Python scripts were used to calculate the quality control indicators of the sample. For more details see https://github.com/xmuzhanglab/Workflow.

Single-cell Af-CUT&Tag data processing
The single-cell raw data were split into individual samples at the single-cell level using custom Python scripts. Sequencing adapters were removed, and quality control was performed using methods commonly employed in bulk sequencing analysis. The sequences were then aligned to the reference genome. To ensure data quality, reads that could not be aligned were filtered out, along with cells that had an insufficient number of reads. Cells with an excessively high number of reads, which could indicate doublets, were also excluded. Subsequently, the genome was divided into bins of 10,000 bases using bamCoverage (version 3.3.1), and the read coverage for each bin was calculated. The results were converted into bedGraph files using the map tool in BEDtools (version 2.30). Dimensionality reduction was performed using scikit-learn for PCA and UMAP, followed by k-means clustering analysis to identify distinct cell populations.

Differential binding peak analysis
To compare the accessibility differences of YAP1/TAZ pre and post PHx, we first utilized UCSC tools BigWigAverageOverBed download from GitHub (https://github.com/kevlim83/ucsctools) to calculate the average score of BigWig over each peak and filtered out peaks with scores below 10 in both pre- and post-resection samples. Subsequently, we employed edgeR (version 3.36.0) R package to identify differential peaks between the samples with cutoff p value <0.01, FDR <0.05 and |log2FC| >1.

GO enrichment
To investigate the functional impact of differentially accessible peaks, we utilized ChIPseeker (version 1.30.3) to annotate the upregulated and downregulated peaks to their nearest genes. We then performed functional enrichment analysis separately for these genes using the clusterProfiler (version 4.2.2) R package.

Bio-layer interferometry (BLI)
The determination of HiBiT/LgBiT-Tn5 and ALFA-tag/NbALFA-Tn5 binding affinities was conducted using the Octet K2 Protein Analysis System and Streptavidin (SA) biosensors. Specifically, biotinylated ExoIII-HiBiT and GFP-ALFA-tag proteins were immobilized onto the SA biosensors. Serial dilutions of LgBiT/LgBiT-Tn5 and NbALFA/NbALFA-Tn5 proteins, prepared in assay buffer (1× PBS, pH 7.4, 0.05% Tween-20), were employed to investigate their binding interactions with the immobilized peptides. To mitigate any potential interference from the assay buffer, an equivalent volume of PBST buffer was introduced to the ExoIII-HiBiT and GFP-ALFA-tag samples as a control. The resultant response data were normalized using Octet Data Analysis Studio 12.2.

AAV2/8 Preparation and Injections
C57BL/6JGpt-Rosa26tm1(CAG-LSL-Cas9-tdTomato)/Gpt mice were used for liver-specific gene knock-in and knock-out. AAV vectors carrying the U6-sgRNA (targeting the stop codon region of mYap1/Taz) and the donor contained the following elements: left and right homology arms, as well as linker and HiBiT, with TBG-Cre as the last element for liver-specific knock-in. AAV vector carrying TBG-Yap1-HiBiT was used for liver-specific expression of Yap1-HiBiT. AAV vector carrying the U6-sgRNA (targeting Mir122) and TBG-Cre for liver-specific knock-out. AAV vectors with Cre recombinase and no sgRNA were used as control viruses. The nucleotide sequences utilized are provided in Supplementary Data 1.

AAV vectors were packaged into serotype 2/8 capsids. For liver-specific knock-in, ~2×1012 vg per mouse of AAV 2/8 virus were injected via the tail vein into 6-week-old male C57BL/6JGpt-Rosa26tm1(CAG-LSL-Cas9-tdTomato)/Gpt mice. For tissue-specific expression of Yap1-HiBiT or tissue-specific gene knockout, 2–5×1011 vg per mouse was administered. Control mice were injected with AAV 2/8 virus containing only TBG-Cre.

Immunoprecipitation
Cells (DLD1 TAZ-HiBiT, DLD1 TAZ-ALFA-tag, and DLD1 WT) were seeded in 10-cm dishes, trypsinized, and lysed in 1 mL of IP lysis buffer (25 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1 mM EDTA, 1% NP-40, 5% glycerol) containing protease inhibitor cocktail, pre-chilled on ice. After incubating on ice for 30 min with occasional mixing, the lysates were centrifuged at 12,000×g for 10 min at 4°C, and the supernatants were collected. Protein concentrations were measured, and 100 µL of each sample was reserved as input. For immunoprecipitation, 800 µg of total protein was incubated with 1.5 µg of Flag-LgBiT-Tn5 or Flag-NbALFA-Tn5 protein at 4°C for 3 h. Anti-FLAG Nanobody Magarose Beads (15 µL, Alpalife bio) were then added, and the samples were incubated overnight at 4°C with rotation. The next day, the beads were washed, and the proteins were eluted in 45 µL of 1× loading buffer. Input samples were mixed with 5× loading buffer. Both input and IP samples were analyzed by western blotting.

Nano-Glo® HiBiT Lytic Detection System
To assess the activity of the NanoLuc large subunit (LgBiT) infusion proteins, cells with stable knock-in of the HiBiT tag were enzymatically dissociated and resuspended. The cells were evenly divided into three groups, each further split into three replicates containing approximately 1×105 cells per replicate. Experimental groups were treated with an equimolar amount of LgBiT or LgBiT-Tn5 Protein, while the control group received no additional protein. The Nano-Glo® HiBiT detection reagent (Promega, catalog number: N3040) was prepared by diluting LgBiT (1:100) or LgBiT-Tn5 Protein (1:100) and Nano-Glo® HiBiT Lytic Substrate (1:50) in an appropriate volume of room-temperature Nano-Glo® HiBiT Lytic Buffer, followed by gentle inversion to mix. Equal volumes of the cell suspension and detection reagent were then combined in each well. After incubation at room temperature for 10 min, luminescence was measured using a microplate reader with the following settings: LUM-EndPoint mode, single label detection, 700 nm blocker, top-read mode, measurement time of 1000 ms, and a default Z focus of 8.5 mm.

Western blotting
Trypsinized cell pellets were resuspended in lysis buffer (50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 0.5% SDS, 0.125% sodium deoxycholate, and 1% Triton X-100 in Millipore water) containing protease inhibitor cocktail (Sigma). The liver tissue samples were homogenized and then lysed in lysis buffer. Protein concentration was determined using a standard BCA assay (TIANGEN) and 30 µg per sample was separated by SDS-PAGE and transferred to a PVDF membrane (Millipore). Membranes were incubated with antibodies in TBS-Tween (0.2%) containing 5% milk at 4°C overnight. Antibodies against YAP1 (Cell Signaling Technology, 14074), TAZ (Cell Signaling Technology, 72804), HiBiT (Promega, N7200) and actin (Abcam, ab20272) were used, and then with the corresponding horseradish peroxidase-labeled secondary antibody. Protein levels were measured using a standard ECL kit and images were acquired using a C-Series Imaging System (Azure Biosystems C500).

Stem-Loop RT-qPCR for miR-122
For detection of miR-122, total RNA was reverse-transcribed using stem-loop primer (RT miR-122: 5’-GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACCAAAC-3’; RT U6: 5’-AACGCTTCACGAATTTGCGT-3’). Specific oligonucleotide primers were used for miR-122 and U6 genes: miR-122 F: 5’-TGGAGTGTGACAATGGTGTT-3’ and R: 5’-CCAGTGCAGGGTCCGAGGT-3’; U6: F: 5’-CTCGCTTCGGCAGCACA-3’, and R: 5’-AACGCTTCACGAATTTGCGT-3’. RT reaction was carried out in Biometra T Gradient Thermocycler (16°C for 30 min, 42°C for 30 min, 85°C for 5 min and taken out the sample at 4°C). The amplification reactions were carried out and the expression levels of miR-122 were quantified as described above. miR-122 was normalized with U6 snRNA.

Histology and immunohistochemistry
Liver tissue samples were fixed in 4% paraformaldehyde and embedded in paraffin. For histopathological evaluation, sections were stained with hematoxylin and eosin (H&E) according to standard protocols. For detection of Ki-67 (1:200 dilution) and PCNA (1:200 dilution), immunostaining was performed via standard protocols. Antigen retrieval was performed for 20 min in citrate buffer by heating in a boiling water bath at 100°C for 10 min. Endogenous peroxidase activity was blocked by incubating with 3% H2O2 for 10 min at room temperature. Then, tissue sections were blocked with 5% goat serum for 30 min and then incubated with primary antibodies overnight at 4°C. After rinsing, the tissue sections were incubated with biotinylated goat anti-rabbit/mouse IgG for 10 min and streptavidin-peroxidase for 10 min. DAB solution was then added for colorization. After a brief rinse, the sections were stained with hematoxylin for nuclear staining. Finally, the sections were dehydrated with ethanol and xylene and sealed with a neutral resin. Images were acquired at 100×, 200×, and 400× magnifications using a Leica DM4B microscope. Samples in the negative control group were treated according to the same protocol, except that the primary antibody was replaced with the control IgG.

Oil red O staining
Liver tissue samples were fixed in 4% paraformaldehyde, dehydrated using a sucrose gradient, and embedded in O.C.T. compound. The embedded tissues were then sectioned into frozen sections. These sections were rinsed with phosphate-buffered saline (PBS) to remove any residual OCT. Subsequently, the sections were incubated in 60% isopropanol for 20 s and stained with freshly prepared Oil Red O working solution (Beyotime #C0157S) for 5 min at room temperature. After staining, the sections were briefly rinsed with 60% isopropanol to remove unbound stain, followed by thorough washing with distilled water. The nuclei were counterstained with hematoxylin for 10 s, and the sections were then rinsed with running tap water for 5 min to remove excess hematoxylin. Finally, the stained sections were mounted with a suitable aqueous mounting medium and examined under a Leica DM4B microscope.

Statistics & reproducibility
All statistical analyses were performed using GraphPad Prism 9.3 (San Diego, CA, USA). Bioinformatics analysis was performed with R (version 3.6.1) (http://www.R-project.org/). The number of biological replicates for each experiment is indicated in the corresponding figure legend. Unless otherwise specified, all values were calculated from at least three independent biological replicates. Unpaired t-test was used for all analyses. For all the statistical analyses between the control group and the experimental group, the results are shown in the figures as follows: *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, and N.S. (not statistically significant).

No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.

Visualization
The genome browser tracks were created using pyGenomeTracks or IGV and further formatted in Adobe Illustrator. Volcano plot, heatmaps, boxplots, and GSEA plot were created using ggplot2 (version 3.5.1).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In the manuscript “Af-CUT&Tag: A Sensitive and Antibody-Free Chromatin Profiling Method Using Genetically Encoded Tag and High-Affinity Binder-Tn5”. Xindong et al. developed Af-CUT&Tag to profile chromatin-associated proteins by modifying CUT&Tag. Instead of using antibodies to tether Tn5 transposase to a protein of interest, Af-CUT&Tag employs CRISPR-mediated knock-in of peptide tags (e.g., HiBiT, ALFA-tag) at the protein terminus. Tn5, fused to binders or nanobodies, binds these tags, enabling targeted chromatin profiling. Single-cell Af-CUT&Tag was also developed to extend this approach. The authors applied Af-CUT&Tag to profile RNA Polymerase, CTCF, YAP1, and TAZ1 in both cell lines and tissues. In pre- and post-partial hepatectomy, they observed dynamic YAP/TAZ1 binding changes at genes associated with regeneration and fatty acid metabolism. Notably, they identified a strong increase in YAP/TAZ1 binding at Mir122, correlating with its upregulation after hepatectomy, and demonstrated that Mir122 plays a crucial role in liver regeneration. This antibody-free Af-CUT&Tag method has potential applications where antibodies are unavailable for CUT&Tag. However, technical concerns must be addressed to confirm that it profiles protein binding rather than chromatin accessibility. That is, in the abstract, the authors claim that their method provides “chromatin accessibility analysis”. However, examination of their results suggests that what they are profiling is not directed specifically towards the intended target, but rather to open chromatin regions genome-wide, an artifact. Additionally, more appropriate experiments and data analysis are needed to ensure that the conclusions are well-supported and not misleading.\nMajor Concerns:\n(1) The key strength of Af-CUT&Tag should be profiling proteins of interest on chromatin, not mapping chromatin accessibility. Therefore, the authors should conduct a thorough analysis to confirm that this method effectively profiles proteins on chromatin", "reviewer_168": "Line 177) and ‘Library quality analysis indicated superior performance for Af-CUT&Tag in terms of promoter region coverage’ (Line 187) is not appropriate. It is important that the same target epitopes are being compared to evaluate relative performance.\nAdditionally, to confirm that RPB1 Af-CUT&Tag signals reflect specific chromatin binding, the authors should provide a genome-wide quantitative comparison between WT and RPB1-HiBiT cells, beyond browser tracks. Since RNAPIIS2P marks elongating RNAPII, CUT&Tag and nano-CUT&Tag datasets should show stronger signals in gene bodies than at TSS, yet the current analysis focuses only on TSSs. To better support the claim in Line 171 that 'HiBiT knock-in does not affect the binding of the target protein to DNA,' it would be valuable to assess whether HiBiT knock-in alters RNAPIIS2P signals in gene bodies and total RPB1 signals at TSS by comparing WT and RPB1-HiBiT knock-in cell lines.\nMinor Concerns:\n(1) Please provide references to support the claim in Lines 73-75: ‘Protein post-translational modifications—such as phosphorylation, methylation, and acetylation—can disrupt antibody binding.’ Additionally, no experimental evidence was provided to support the statement that ‘Af-CUT&Tag enhances targeting stability, resistance to protein modifications’ in Lines 31-32 and 461-462. Please either include supporting data or revise these claims accordingly.\nRather, since Af-CUT&Tag chromatin profiling relies on knock-in tags to proteins, one of its main limitations is that it cannot distinguish whether the detected signals correspond to the protein in its modified or unmodified state. For example, in the case of RPB1, Af-CUT&Tag signals alone cannot determine whether the protein carries S2-phosphorylation or not. This limitation should be acknowledged in the manuscript.\n(2) Line 102-103: The KD values in the figure (0.58 nM, 0.024 nM) differ from those in the text (0.57 nM, 0.023 nM). Please ensure consistency.\n(3) Extended Data Fig. 1C shows that LgBiT-Tn5 binding to ExoIII-HiBiT is concentration-dependent. This binding is critical for targeted Tn5 binding and tagmentation in Af-CUT&Tag. Please include the final LgBiT-Tn5 concentration in the Methods section. Additionally, the authors should confirm that the same amount of LgBiT-Tn5 was used across all Af-CUT&Tag profile comparisons, such as for RPB1 Af-CUT&Tag in RPB1-HiBiT and WT controls.\n(4) Extended Data Fig. 1C shows a more pronounced decline in LgBiT-Tn5 binding to ExoIII-HiBiT in Af-CUT&Tag Dig-300 buffer. Fig. 1F confirms LgBiT-Tn5 binding to RPB1-HiBiT and CTCF-HiBiT after cell lysis in Lytic Buffer; however, it does not show whether this binding remains stable in Dig-300 buffer after Tn5 binding. If unstable, this could compromise the specificity of protein-of-interest chromatin binding signals in Af-CUT&Tag.\n(5) Please clarify Extended Data Fig. 2A labels for Input and IP lanes, as they currently suggest one Input and three IP samples per gel.\n(6) Lines 140-141: The section title, 'Establishment of Af-CUT&Tag for antibody-free transcription factor binding profiling analysis,' is misleading, as the results presented focus solely on RNAPII profiling. Since no transcription factor binding data is shown, the title should be revised for accuracy.\n(7) Please explain why in vitro Tn5 activity was tested at 55°C instead of 37°C in Fig. 1G and Extended Data Fig. 2B, as this is the in situ tagmentation temperature in Af-CUT&Tag.\n(8) In Lines 190-191, comparing RPB1 Af-CUT&Tag, RNAPIIS2P CUT&Tag, and nano-CUT&Tag signals to accessible sites is not a valid way to claim that RPB1 Af-CUT&Tag exhibits fewer nonspecific accessible sites. Since RNAPII transcription increases chromatin accessibility, RPB1 is expected to overlap with ATAC-seq peaks. Instead, CTCF, YAP1, and TAZ should be analyzed at accessible sites to assess nonspecific accessibility in Af-CUT&Tag.\n(9) Since TSS regions are highly accessible, plotting CTCF at TSSs is not ideal. Instead, CTCF should be plotted at motifs in Fig. 3 and Extended Data Fig. 4. Additionally, testing whether true CTCF binding sites can be distinguished from accessible regions in CTCF Af-CUT&Tag is essential to confirm that LgBiT-Tn5 and NbALFA-Tn5 were indeed tethered to CTCF-HiBiT/ALFA to produce true CTCF chromatin binding signals.\n(10) Please specify in the figure legends whether the correlation maps in Fig. 2F, Fig. 3C, Extended Data Fig. 4E and Extended Data Fig. 6C, D were generated using signals from TSS or another region.\n(11) PMID: 26258633 showed that most YAP/TAZ binding occurs at cis-regulatory regions distal to TSSs, which contain both TEAD and AP-1 motifs. However, Fig. 4D shows that ~50% of YAP1 Af-CUT&Tag peaks are in promoters, which are highly accessible. The authors should confirm that these signals are not merely reflecting chromatin accessibility. Additionally, it would be valuable to examine whether YAP/TAZ Af-CUT&Tag peaks contain TEAD and AP-1 motifs.\n(12) In Extended Data Fig. 6B, YAP1 and TAZ1 Nano-CUT&Tag signals using HiBiT and YAP1/TAZ antibodies appear reduced in pHx-post compared to pHx-pre, whereas this reduction is not observed in Af-CUT&Tag profiles. This inconsistency raises concerns about whether introducing the HiBiT tag at the YAP1/TAZ1 C-terminus affects their chromatin binding.\n(13) Please specify what region was used to generate the UMAP in Fig. 4F, such as peaks, TSS, or another region. Additionally, it would be interesting to know whether YAP1 distribution in UMAP space changes in pHx-post compared to pHx-pre. Were there any specific cell populations and genomic regions in these populations that showed differential YAP1 signals in pHx-post compared to pHx-pre?\n(14) Lines 357-358: The authors claim that 'after partial hepatectomy, Yap1/Taz promoted the expression of Hemopexin (Hpx) and Serotransferrin (Trf)' based on increased Yap1/Taz signals in Fig. 5D. However, an increase in Yap1/Taz signals at these genes does not necessarily mean that Yap1/Taz promotes their expression. To support this claim, the authors should check whether RPB1 signals also increase after partial hepatectomy.\n(15) Line 374: The authors state, ‘Analysis of the GSE180012 dataset confirmed that genes regulated by Yap1/Taz, such as Lpin1, Scd1, Thrsp, and Hpx, exhibited similar expression trends 24 h post-hepatectomy.’ However, it is unclear what the GSE180012 dataset represents. Additionally, the rationale for using this dataset to generate Fig. 6A and Extended Data Fig. 8A, B is not explained. What were the authors aiming to demonstrate?\n(16) How Yap1/Taz binding changes correlate with gene expression changes after partial hepatectomy remains unclear. It would be more effective to describe Yap1/Taz changes alongside gene expression changes in a unified way rather than presenting them separately across different paragraphs in Lines 362-393.\n(17) Which figure corresponds to the statement in Lines 392-393: ‘Fatty acid transport genes, primarily regulated by the PPAR signaling pathway, showed declining expression after 24 h’?\n(18) The authors show that genes associated with lipid droplet fusion and storage, such as Plin2 and Cidec, exhibit early upregulation prior to 24 hours after partial hepatectomy. However, they only demonstrate that Yap1/Taz chromatin signals remain largely unchanged after 24 hours. It is unclear how this supports the conclusion that ‘In the early stages, Yap1/Taz-driven lipid droplet fusion…… for regeneration’ (Lines 395-399).\n(19) The manuscript requires grammatical and scientific refinements for clarity and accuracy. For example, in Lines 260-261, 'binded by' should be changed to 'bound by,' and 'in-vitro' and 'in-vivo' should be corrected to 'in vitro' and 'in vivo.' In Lines 260-261, 'on the Mir122 genome' should be revised to 'on Mir122.' In Line 130, 'enzyme-digested' should be changed to 'Tn5-tagmented' to reflect the correct terminology, as tagmentation and digestion are distinct processes. In Lines 342-343, 'the differential accessibility regions of Yap1/Taz' is misleading, as chromatin accessibility and protein binding are distinct; accessibility regions refer to open chromatin, whereas protein binding sites represent specific interactions. Additionally, Af-CUT&Tag is designed to profile proteins on chromatin, so it should not be described as a chromatin accessibility method. YAP1/TAZ are not transcription factors, as they lack a DNA-binding domain and do not bind DNA directly.", "reviewer_2": "In this manuscript, Wang et al. introduce Af-CUT&Tag, an antibody-free method for profiling chromatin-associated proteins using genetically encoded peptide tags. The authors tag several chromatin-associated factors (RNA Pol II, CTCF, YAP, and TAZ) with small peptide tags (ALFA-tag and HiBiT) and use nanobody-Tn5 fusion proteins to selectively target the tagged proteins. This approach is validated in both cultured cells and mouse liver tissue, demonstrating its applicability across different biological systems. The study is technically rigorous, and the results are clearly presented.\nHowever, I have several critical points:\n1. While Af-CUT&Tag is convincingly shown to work, it represents an incremental improvement over existing technologies. It does not enable fundamentally new types of experiments beyond what is already achievable with CUT&Tag, nano-CUT&Tag, or ChIP-seq. The main benefit is circumvention of antibody limitations, which—although useful—is not transformative for most applications.\n2. A major drawback of the method is its reliance on genome editing to insert peptide tags. This restricts its use to systems amenable to genetic modification, which excludes many primary tissues, patient-derived samples, or developmental stages. Furthermore, the method is not compatible with detecting post-translational modifications (e.g., histone marks), which is a major use case for antibody-based methods.\n3. The second part of the manuscript applies Af-CUT&Tag to identify YAP/TAZ binding sites in mouse liver. While the application is interesting, the transition from method development to biological discovery feels abrupt. The manuscript would benefit from a clearer narrative thread connecting the two parts, possibly by framing the biological question earlier and showing how the method uniquely enables this analysis.\nOverall, this is a technically sound and interesting study describing a novel approach that is likely to be of value to the chromatin profiling field. However, given the incremental nature of the advance and the limitations discussed above, I would recommend that the manuscript be considered for publication in a more specialized journal.", "reviewer_3": "The authors present Af-CUT&Tag, a novel variation of existing CUT&Tag technologies. CUT&Tag is a transposase-based method designed to precisely map protein-DNA interactions or chromatin modifications in cells. Unlike traditional approaches such as CUT&Tag and Nano-CUT&Tag, which rely on primary antibodies to recognize the DNA-binding target protein or chromatin modification, Af-CUT&Tag eliminates the need for primary antibodies by utilizing modified cell lines or transgenic mice expressing the DNA-binding target proteins fused to small peptide tags. These tags are then specifically recognized by high-affinity binders fused to Tn5, which in turn introduces DNA sequences that can be used for NGS-based identification of the respective target sites.\nWhile Af-CUT&Tag may in fact represent a potentially valuable addition to the CUT&Tag toolbox — particularly in cases requiring high spatial resolution or where suitable antibodies are unavailable — the reviewer finds the data presentation and – at least in part – the data interpretation highly problematic. At least in the current state, the reviewer believes that the manuscript is not suited for the general scientific readership of Nature Communications.\nDue to the abundance of individual points of criticism, these can only be listed here by way of example.\nGeneral Concerns:\n1. Although the authors compare Af-CUT&Tag with existing technologies such as CUT&Tag and Nano-CUT&Tag, they fail to describe these methods in sufficient detail for readers outside the field to understand the concepts without consulting the cited literature.\n2. Abbreviations are not consistently explained throughout the manuscript.\n3. The manuscript includes an overwhelming 85 (!) display items, distributed across six main figures and eight extended data figures. While the main figures include captions, the content of individual panels often does not align with the stated themes. For example, Figures 6A–6D are clearly related to the subject matter of Figure 5. In the extended data figures, the authors do not even make any attempt to find common themes for each figure, and the figures are presented without captions.\n4. The figure legends generally lack sufficient details required for readers to understand the experiments and interpret the results.\n5. The authors aim to demonstrate two independent proof-of-concept adaptor systems: HiBiT/LgBiT and ALFA-tag/NbALFA. While they successfully expressed and purified LgBiT-Tn5 in soluble form using a standard E. coli expression system, they were unable to establish suitable conditions for NbALFA-Tn5 expression. As a result, as stated in the text, the final NbALFA-Tn5 preparation exhibited low solubility and suffered from poor yield and purity. It would be important to show data on the purification of these key proteins, or at least on the final preparations. In the reviewer's eyes this is exactly what supplementary figures should be good for. Despite the specific issues reported for NbALFA-Tn5, the authors proceeded with the planned experiments and included data generated using the poorly characterized NbALFA-Tn5 fusion, which presumely has sub-optimal features.\n6. Figure labeling is often too small to read clearly (e.g., Fig. 1A, B).\n7. The explanatory illustrations lacks consistency: Tn5 is consistently shown as a dimer, whereas the fusion partner (NbALFA or LgBiT) is depicted only once. If Tn5 is a dimer, also the direct fusion partner should be present twice.\n8. In Figure 1H, the laborious generation of stable cell lines or transgenic animals required for Af-CUT&Tag is omitted. Instead, the method is portrayed as a quick and simple alternative, which is at least misleading.\n9. Data consistency: In Figure 2B/C, Af-CUT&Tag applied to RNA Pol II yields narrower gene distance distributions than antibody-based methods, consistent with reduced spatial displacement between the POI and Tn5. However, this effect is not observed for CTCF (Figure 3B) or RNA Pol II in a different experiment (Suppl. Fig. 3E/F). This inconsistency should be specifically addressed – especially since the authors highlight the narrower distribution in Fig. 2B/C as a major advantage. Have alternative explanations, such as poor antibody quality, been considered?\n10. Figure clarity: Several figures fail to convey what they are intended to show. For example:\n• Fig. 1G: Tagmented DNA is barely visible (possibly due to the loading dye used)\n• Suppl. Fig. 2C: Tagmented DNA is not visible at all.\n• Fig. 6G (left): The figure is unclear, and the legend does not clarify what the reader is expected to see.\n11. Some primary data is inconsistent with the reported numerical values. For instance, in Figure 1D, the dissociation curves for both affinity pairs trend upwards, which should making it impossible to extract valid dissociation rate constants (kd). Nevertheless, the authors report defined kd values (see Suppl. Fig. 1) and even derive dissociation constants (KD) that surprisingly closely match published values. Furthermore, the concentrations used (up to 240–250 nM) are excessively high — more than 1,000-fold above the derived KD even at the lowest concentrations tested (30–50 nM). This, at best, is an indicator of poor experimental design. Consequently, the calculated association constants are ~100-fold lower than expected for nanobody/target interactions in general and particularly in contrast to the well-characterized NbALFA/ALFA-tag pair.\n12. The Methods section should clearly describe what was done, yet it often reads like a step-by-step lab protocol, rather than a concise account of the procedures actually performed.\n13. The description of experimental results is often erratic, and in some cases, conclusions are stated before the relevant data is presented.\n14. Overall, the manuscript is exceedingly difficult to read and understand. In addition to the fact that the phrasing and language are often not scientifically clear and concise, this is a result of a combination of surprising discrepancies:\na. Too many unreflected details but too little concise information\nb. Too many display items with minimal impact (e.g. large panels with sequencin results) while important data is missing (e.g. purity of Tn5 fusions)\nSpecific Points:\n• Figure 1C: BLI readouts are generally given in nanometers (nm), not nanomolar (nM). Values given in nM are incorrect and misleading.\n• Supplementary Figure 1C: The legend claims to address the binding stability of the NanoLuc system in various buffers, yet the experiment actually measures luminescence over time, which may reflect combined effects influenced by multiple factors (protein stability, substrate availability, product inhibition), but rather not binding stability. Additionally, the experimental setup is unclear.\n• Supplementary Figure 2A: Critical experimental details for the Western blot—such as the detection system, loading controls, and a specific IP protocol—are missing. Full blots should be provided.\n• Supplementary Figure 2C: The source of the data shown on the right side is unclear. The agarose gel on the left shows no tagmented gDNA and cannot resolve fragments smaller than 100 bp. Therefore, it cannot be the source of the data.\nTitle:\nThe title is misleading. The method uses two alternative tag systems, not one. Furthermore, Tn5 is not itself a high-affinity binder — rather, high-affinity binders are fused to Tn5.\nIntroduction:\nA short, concise introduction to existing systems and the conceptual innovations of Af-CUT&Tag is missing. The authors claim that their method does not use antibodies — but nanobodies are antibody fragments and are still considered antibodies. Therefore, NbALFA-based Af-CUT&Tag is not truly \"antibody-free.\" What they actually mean is that conventional IgG-type antibodies are not used as the reagents that confer specificity towards the respective target structures.\nLine-by-Line Issues:\n• Line 109: “NbALFA-Tn5 suffers from poor solubility, leading to reduced protein purity.” This statement should be supported by data. If it holds true, the best possible solution would be to remove all data obtained using this suboptimal protein preparation from the manuscript.\n• Line 110/111: “Thus, the HiBiT/LgBiT-Tn5 system was selected as the focus of our study.” Yet extensive data are still shown using the poorly soluble NbALFA-Tn5.\n• Lines 132ff: The relevance of data on G4PID, the SG4 nanobody, etc., to this study is unclear. The targeting function implied in the text is in fact possible and even likely, but not demonstrated experimentally.\n• Line 145f: Proteins in general cannot pass intact membranes. Even monomeric LgBiT-Tn5 (~70 kDa) is too large to pass nuclear pores by passive diffusion. The references 30/31 given by the authors do not seem to be appropriate here. These papers do not detail on any aspects of nanobodies penetrating the plasma membrane or the nuclear membrane or nuclear pore complexes at all.\n• Line 233: The text implies that Af-CUT&Tag is performed in “intact cells,” but in fact uses digitonin treated, semi-permeabilizes cells. This fact is actually hard to find in the manuscript. Semi-permeabilized cells are far from being intact. They are dead and leaky. This distinction should be clarified.\n• Line 314ff: While standard procedures (e.g., AAV vector construction) are described in detail, critical steps—such as infection of mice for integration of Yap1-HiBiT—are omitted.\n• Line 324ff: Exact cell numbers are not critical here and could be omitted.\nDiscussion:\nWhile the discussion focuses on the advantages of Af-CUT&Tag — such as independence from antibody specificity — its limitations should also be acknowledged. For instance, Af-CUT&Tag cannot distinguish between different posttranslational protein modification states of a given target. It also cannot be used to analyze different patterns of DNA modifications. Depending on the scientific question, this might be a rather significant drawback. A more balanced discussion would strengthen the manuscript.\nThe section from line 467 onward, focusing on AI-generated binders, seems completely out of place:\nFirst, AI-designed high-affinity binders are not yet widely available nor are they generally validated. The text implies otherwise, which is misleading. Second, the authors aim to promote Af-CUT&Tag as a method that bypasses target-specific binders. Why then pivot to advocating AI-designed target-specific binders?", "reviewer_4": "The manuscript by Xindong Wang et al. [NCOMMS-24-13551-T] developed a modified CUT & Tag method called antibody-free(Af)- CUT & Tag method. In this method, high-affinity small peptide tags (HiBit and ALFA-tag) and their Tn5 transposase -binders were used. The result is the useful method that can be used extensively with good sensitivity. It is expected to contribute to many researchers with its excellent analytical methods.\nAlthough the authors’ conclusion is interesting, there are several minor concerns about the conclusion. Taking all into consideration, this manuscript is mostly acceptable for publication in Nature Communications.\nMinor comments\n1. In Fig. 1C and D and lines 103, Kd=0.58 (nM) and Kd=0.024 (nM), but different values (0.57 and 0.023) in the text.\n2. In Fig. 4-6, although the roles of YAP1 and TAZ in the liver have some similarities, there are also many differences. For example, hepatocarcinogenesis due to disruption of the Hippo pathway is YAP1-dependent but not TAZ; shouldn't the differences between YAP1 and TAZ be discussed more?\n3. In Fig.5, the role of miR-122 in liver regeneration is not so significant. Is it possible that in selecting miR-122, the emphasis on commonality between YAP1 and TAZ resulted in the exclusion of important factors? In other words, should you have selected a specific target for YAP1?"}, "author_response": "Response to Reviewers’ Comments\nWe thank the reviewers for their careful reading and constructive critiques. We have\nrevised the manuscript extensively (text, figures, legends, Methods and\nSupplementary information) and performed additional experiments and analyses\nspecifically requested to demonstrate Af-CUT&Tag specificity, provide essential QC\ndata, and clarify conceptual points. Below we summarize the major changes and then\nrespond point-by-point.\nSummary of changes\n1. New specificity analyses: motif enrichment for CTCF (canonical CTCF motif\nstrongly enriched), quantitative comparisons between wild-type (WT) and HiBiT/ALFA\nknock-in cells (Spearman ρ), Af-CUT&Tag versus ATAC-seq overlap analyses (new\nFig. 3C, Supplementary Fig. 4C), and comparative motif enrichment analyses for\nYAP1/TAZ in mouse liver (Supplementary Fig. 6D). These directly address concerns\nabout chromatin accessibility versus true protein-DNA occupancy.\n2. Protein QC: added SDS-PAGE analysis for LgBiT-Tn5 and NbALFA-Tn5\n(Supplementary Fig.1B and C).\n3. BLI & luminescence corrections: re-ran BLI binding measurements under\noptimized storage buffer conditions to avoid precipitation, and corrected labeling and\nunits in figures and legends. Clarified that the luminescence assays were not direct off-\nrate measurements, and included corrected K reporting (Fig. 1D and Supplementary\nD\nFig. 1A).\n4. Figures and legends: reorganized figures to reduce the number of main figures,\nmoved many panels to Extended Data, and substantially expanded figure legends.\n5. Methods: supplemented missing protocols for immunoprecipitation and\nchemiluminescence detection; added precise fusion protein concentrations and wash\nprotocols (0.04 μM LgBiT-Tn5/NbALFA-Tn5 for all Af-CUT&Tag experiments) to ensure\nconsistency of experimental conditions.\n6. Clarifications and limitations: explicitly acknowledge that the Af-CUT&Tag system\ncannot directly detect post-translational modification (PTM) states and requires\ngenome editing for tag insertion. Additionally, we have moderated the language\nregarding “accessibility” throughout the Abstract, Introduction, and Discussion to more\naccurately reflect the method’s capabilities and limitations.\nPoint-by-point Response to all Reviewer\nReviewer #1 (Remarks to the Author):\nIn the manuscript “Af-CUT&Tag: A Sensitive and Antibody-Free Chromatin Profiling\nMethod Using Genetically Encoded Tag and High-Affinity Binder-Tn5”. Xindong et al.\ndeveloped Af-CUT&Tag to profile chromatin-associated proteins by modifying\nCUT&Tag. Instead of using antibodies to tether Tn5 transposase to a protein of interest,\nAf-CUT&Tag employs CRISPR-mediated knock-in of peptide tags (e.g., HiBiT, ALFA-\ntag) at the protein terminus. Tn5, fused to binders or nanobodies, binds these tags,\nenabling targeted chromatin profiling. Single-cell Af-CUT&Tag was also developed to\nextend this approach. The authors applied Af-CUT&Tag to profile RNA Polymerase,\nCTCF, YAP1, and TAZ1 in both cell lines and tissues. In pre- and post-partial\nhepatectomy, they observed dynamic YAP/TAZ1 binding changes at genes associated\nwith regeneration and fatty acid metabolism. Notably, they identified a strong increase\nin YAP/TAZ1 binding at Mir122, correlating with its upregulation after hepatectomy, and\ndemonstrated that Mir122 plays a crucial role in liver regeneration. This antibody-free\nAf-CUT&Tag method has potential applications where antibodies are unavailable for\nCUT&Tag. However, technical concerns must be addressed to confirm that it profiles\nprotein binding rather than chromatin accessibility. That is, in the abstract, the authors\nclaim that their method provides “chromatin accessibility analysis”. However,\nexamination of their results suggests that what they are profiling is not directed\nspecifically towards the intended target, but rather to open chromatin regions genome-\nwide, an artifact. Additionally, more appropriate experiments and data analysis are\nneeded to ensure that the conclusions are well-supported and not misleading.\nReviewer #1 (Remarks to the Author):\nIn the manuscript “Af-CUT&Tag: A Sensitive and Antibody-Free Chromatin Profiling\nMethod Using Genetically Encoded Tag and High-Affinity Binder-Tn5”. Xindong et al.\ndeveloped Af-CUT&Tag to profile chromatin-associated proteins by modifying\nCUT&Tag. Instead of using antibodies to tether Tn5 transposase to a protein of interest,\nAf-CUT&Tag employs CRISPR-mediated knock-in of peptide tags (e.g., HiBiT, ALFA-\ntag) at the protein terminus. Tn5, fused to binders or nanobodies, binds these tags,\nenabling targeted chromatin profiling. Single-cell Af-CUT&Tag was also developed to\nextend this approach. The authors applied Af-CUT&Tag to profile RNA Polymerase,\nCTCF, YAP1, and TAZ1 in both cell lines and tissues. In pre- and post-partial\nhepatectomy, they observed dynamic YAP/TAZ1 binding changes at genes associated\nwith regeneration and fatty acid metabolism. Notably, they identified a strong increase\nin YAP/TAZ1 binding at Mir122, correlating with its upregulation after hepatectomy, and\ndemonstrated that Mir122 plays a crucial role in liver regeneration. This antibody-free\nAf-CUT&Tag method has potential applications where antibodies are unavailable for\nCUT&Tag. However, technical concerns must be addressed to confirm that it profiles\nprotein binding rather than chromatin accessibility. That is, in the abstract, the authors\nclaim that their method provides “chromatin accessibility analysis”. However,\nexamination of their results suggests that what they are profiling is not directed\nspecifically towards the intended target, but rather to open chromatin regions genome-\nwide, an artifact. Additionally, more appropriate experiments and data analysis are\nneeded to ensure that the conclusions are well-supported and not misleading.\nMajor Concerns:\n(1) The key strength of Af-CUT&Tag should be profiling proteins of interest on\nchromatin, not mapping chromatin accessibility. Therefore, the authors should conduct\na thorough analysis to confirm that this method effectively profiles proteins on\nchromatin"}]

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