Mice, homozygous for an Oct4-eGFP transgene (The Jackson Laboratory: B6; 129S4- Pou5f1 ^tm2Jae /J), were mated for CUT&RUN collections. Mice, homozygous for an Oct4-eGFP transgene (The Jackson Laboratory: B6; 129S4-Pou5f1 ^tm2Jae /J) and heterozygous for the Taf4b-deficiency mutation (in exon 12 of the 15 total exons of the Taf4b gene that disrupts the endogenous Taf4b gene), were mated for mRNA collections. Timed matings were estimated to begin at day 0.5 by evidence of a copulatory plug. The sex of the embryos was identified by confirming the presence or absence of testicular cords. Genomic DNA, isolated from tail biopsies using QIAGEN DNeasy Blood & Tissue Kits (Cat #: 69506), was used for PCR genotyping assays.

All animal protocols were reviewed and approved by Brown University Institutional Animal Care and Use Committee and were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. Gonads were dissected out of embryos into cold PBS.

Testes were gathered from embryonic mice (E13.5-E18.5), briefly fixed with 10% formalin and paraffin-embedded. The Molecular Pathology Core at Brown University completed tissue preparation and performed sectioning. After the serial sections were prepared, antigen retrieval was performed using a 0.1% antigen unmasking solution (Vector Laboratories, H-3300) in a steamer for 20 min. Samples were then permeabilized in 0.1% sodium citrate and Triton X-100 for 10 min, washed with 0.1% PBST (Triton X-100), and incubated in blocking buffer (PBS with 3% goat serum, 1% BSA, and 0.1% Triton X-100) for 1 hour. This was then followed by primary antibody incubation for 24 h at 4°C prepared in the blocking buffer. The samples were then washed in PBST, incubated with secondary antibody for 1 h at 37°C, washed in PBST, and mounted in DAPI-containing Vectashield Mounting Medium (Vector Laboratories H-1200, Burlingame, CA). Primary antibodies used were rat anti-TRA98 (1:100, abcam, ab82527), anti-KI67 rabbit mAb (1:100, Cell Signaling Technology D3B5, 9129S) and anti-PCNA (1:100, Cell Signaling Technology, 13110T). For immunofluorescence, the secondary antibodies used were Alexa Fluor 555-conjugated anti-rat IgG (1:500; abcam ab150158) and Alexa Fluor 488-conjugated anti-rabbit IgG (1:500; abcam ab150081). Images were all acquired at the same exposures and received the same immunofluorescence preparation. Visualization and counting were completed via blinded assessment of TRA98-expressing cells colocalized with MKI67 on a Zeiss Axio Imager M1 and Zeis software. A two-way ANOVA was used to determine significance using GraphPad Prism 9 version 9.3.1. for MacOS (GraphPad Software, Boston, Massachusetts United States, www.graphpad.com) with an n of 3-5 mice.

To dissociate gonadal tissue into a single-cell suspension, embryonic gonads were harvested and placed in 0.25% Trypsin/EDTA and incubated at 37°C for 15 and 25 min for E14.5 and E16.5 gonads, respectively, as previously described (Gura et al., 2020). Eppendorf tubes were flicked to dissociate tissue halfway through and again at the end of the incubation. Trypsin was neutralized with FBS. Cells were pelleted at 1,500 RPM for 5 min, the supernatant was removed, and cells were resuspended in 100 μL PBS. The cell suspension was strained through a 35 μm mesh cap into a FACS tube (Gibco # 352235). Propidium iodide (1:500) was added to the cell suspension as a live/dead distinguishing stain. Fluorescence-activated cell sorting (FACS) was performed using a Becton Dickinson FACSAria III in the Flow Cytometry and Cell Sorting Core Facility at Brown University. A negative control of a non-transgenic mouse gonad was used for each experiment to establish an appropriate GFP signal baseline. Dead cells were discarded and the remaining cells were sorted into GFP⁺ and GFP⁻ samples in PBS at 4°C for each embryo.

For RNA-seq analysis, GFP⁺ cells from each individual embryo were kept in separate tubes and were then spun down at 1,500 RPM for 5 min, had PBS removed, and were then resuspended in Trizol (ThermoFisher # 1556026). If samples had roughly less than 50 µL of PBS in the tube, Trizol was added immediately. The number of cells for each sample can be found in Supplementary Table S1. Samples were stored at −80°C.

For CUT&RUN, germ cells from all the gonads were pooled prior to FACS. Sorted germ cells were then spun down at 1,500 RPM for 5 min and were resuspended in 300 µL of PBS, then split into three Eppendorf tubes. These three tubes of germ cells were then used for CUT&RUN. The number of cells for each sample were as follows: Replicate 1 germ cell samples had approximately 56,000 cells per tube (obtained from 22 embryos) and Replicate 2 germ cell samples had approximately 131,000 cells per tube (obtained from 28 embryos).

All computational scripts regarding single cell RNA-seq (scRNA-seq) used in this publication are available to the public: https://github.com/mg859337/Gura_et_al._TAF4b_male_transcription. SRP194420, SRP158811, and SRP178196 were downloaded from NCBI SRA onto Brown University’s high-performance computing cluster at the Center for Computation and Visualization. We used Law et al. and Nguyen et al., which enriched for germ cells via FACS sorting (Law et al., 2019; Nguyen et al., 2020). We then combined the data with Tan et al., who collected the whole testis, thus establishing our comprehensive observation window from E12.5 to P7 (Tan et al., 2020). The fastq files were aligned using Cell Ranger (v 6.0.0) count and then aggregated using Cell Ranger aggr. The cloupe file created from Cell Ranger aggr was used as input for Loupe Cell Browser (v 5.0). We selected the germ cells by first assessing the log-normalized expression of germ cell marker Dazl within the complete 71,584 cell data set. Then, using unbiased clustering, we eliminated somatic cell clusters based on their mean Dazl expression being less than 0.85. The data set underwent further quality control and filtering based on fitting the metrics of 12.5–15.8 log2-normalized unique molecular identifiers, 10.6–12.8 log2-normalized features, and lastly, less than 24% mitochondrial transcript ratio (Quatredeniers et al., 2023).

Embryonic germ cells resuspended in Trizol were shipped to GENEWIZ (GENEWIZ Inc., NJ) on dry ice. Sample RNA extraction, sample QC, library preparation, sequencing, and initial bioinformatics were done at GENEWIZ. RNA was extracted following the Trizol Reagent User Guide (ThermoFisher Scientific). Glycogen was added (1 μL, 10 mg/mL) to the supernatant to increase RNA recovery. RNA was quantified using Qubit 2.0 Fluorometer (Life Technologies, Carlsbad, CA, United States) and RNA integrity was checked with TapeStation (Agilent Technologies, Palo Alto, CA, United States) to see if the concentration met the requirements.

SMART-Seq v4 Ultra Low Input Kit for Sequencing was used for full-length cDNA synthesis and amplification (Clontech, Mountain View, CA), and Illumina Nextera XT Library Preparation Kit was used for library preparation. The sequencing libraries were multiplexed and clustered on a lane of a flowcell. After clustering, the flowcell was loaded onto an Illumina HiSeq 4000 according to manufacturer’s instructions. The samples were sequenced using a 2 × 150 Paired End (PE) configuration. Image analysis and base calling were conducted by the HiSeq Control Software (HCS) on the HiSeq instrument. Raw sequence data (.bcl files) generated from Illumina HiSeq were converted into fastq files and de-multiplexed using bcl2fastq (v 2.17). One mismatch was allowed for index sequence identification.

All computational scripts regarding RNA-seq used in this publication are available to the public: https://github.com/mg859337/Gura_et_al._TAF4b_male_transcription. All raw fastq files were initially processed on Brown University’s high-performance computing cluster. Reads were quality-trimmed and had adapters removed using Trim Galore! (v 0.5.0) with the parameters–nextera -q 10. Samples before and after trimming were analyzed using FastQC (v 0.11.5) for quality and then aligned to the Ensembl GRCm38 using HiSat2 (v 2.1.0) (Andrews, 2010; Pertea et al., 2016). Resulting sam files were converted to bam files using Samtools (v 1.9) (Li et al., 2009).

To obtain TPMs for each sample, StringTie (v 1.3.3b) was used with the optional parameters -A and -e. A gtf file for each sample was downloaded and, using RStudio (R v 4.0.2), TPMs of all samples were aggregated into one comma separated (csv) file using a custom R script. To create interactive Microsoft Excel files for exploring the TPMs of each dataset: the csv of aggregated TPMs was saved as an Excel spreadsheet (Supplementary Table S2). Colored tabs were added to set up different comparisons, and a flexible Excel function was created to adjust to gene name inputs. To explore the Excel files, please find the appropriate tab (named “Quick_Calc”) and type in the gene name of interest into the highlighted yellow boxes.

To obtain count tables, featurecounts (Subread v 1.6.2) was used (Liao et al., 2014). Metadata files for each dataset were created manually in Excel and saved as a csv. These count tables were used to create PCA plots by variance-stabilizing transformation (vst) of the data in DESeq2 (v 1.22.2) and plotting by ggplot2 (v 3.1.0) (Love et al., 2014; Wickham, 2016). DESeq2 was also used for differential gene expression analysis, where count tables and metadata files were used as input. We accounted for the litter batch effect in our mouse germ cells by setting it as a batch parameter in DESeq2. For the volcano plot, the output of DESeq2 was used and plotted using ggplot2. DEG lists were used for ClusterProfiler (v 3.16.1) input to create dotplots of significantly enriched gene ontology (GO) categories for all DEGs. The highest-confidence protein-protein interactions were identified using STRING, with unconnected proteins not shown in the images (Szklarczyk et al., 2019). Net plots were constructed using Bioconductor packages in R after normalization and QC. The top three GO processes category nets for both Up and Downregulated DEGs were cross-referenced with the TAF4b-bound gene TSS list.

The CUT&RUN performed in E16.5 germ cells followed the protocol in Hainer and Fazzio, 2019 (Hainer and Fazzio, 2019). CUT&RUN antibodies were as follows: polyclonal rabbit TAF4b (as previously described (Grive et al., 2016)), monoclonal rabbit H3K4me3 (EMD Millipore # 05-745R), rabbit IgG (ThermoFisher # 02–6102), pA-MNase was a generous gift from Dr. Thomas Fazzio, UMASS Med. For library preparation, the KAPA HyperPrep Kit (Roche Cat. No 07962363001) was used with New England Biolabs NEBNext Multiplex Oligos for Illumina (NEB #E7335). After library amplification through PCR, libraries were size-selected through gel extraction (∼150–650 bp) and cleaned up using the QIAGEN QIAquick Gel Extraction Kit (Cat. # 28704). CUT&RUN libraries in EB buffer were shipped to GENEWIZ (GENEWIZ Inc., NJ) on dry ice. Sample QC, sequencing, and initial bioinformatics were done at GENEWIZ.

The sequencing libraries were validated on the Agilent TapeStation (Agilent Technologies, Palo Alto, CA, United States), and quantified by using Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA) as well as by quantitative PCR (KAPA Biosystems, Wilmington, MA, United States). The sequencing libraries were clustered on flowcells. After clustering, the flowcells were loaded onto the Illumina HiSeq instrument (4,000 or equivalent) according to manufacturer’s instructions. The samples were sequenced using a 2 × 150bp Paired End (PE) configuration. Raw sequence data (.bcl files) generated from Illumina HiSeq were converted into fastq files and de-multiplexed using bcl2fastq (v. 2.20). One mismatch was allowed for index sequence identification.

All computational scripts regarding CUT&RUN data analysis used in this publication are available at: https://github.com/mg859337/Gura_et_al._TAF4b_male_transcription and based on other CUT&RUN publications (Hainer and Fazzio, 2019). All raw fastq files were initially processed on Brown University’s high-performance computing cluster. Reads were quality-trimmed and had adapters removed using Trim Galore! (v 0.5.0) with the parameter -q 10 (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). Samples before and after trimming were analyzed using FastQC (v 0.11.5) for quality and then aligned to the Ensembl GRCm39 using Bowtie2 (v 2.3.0). Fastq screen (v 0.13.0) was used to determine the percentage of reads uniquely mapped to the mouse genome in comparison to other species. Resulting sam files were converted to bam files, then unmapped, duplicated reads, and low quality mapped were removed using Samtools (v 1.9). Resulting bam files were split into size classes using a Unix script. For calling peaks, annotating peaks, and identifying coverage around TSSs, Homer (v 4.10) was used (Heinz et al., 2010). For gene track visualization, the final bam file before splitting into size classes was used as input to Integrative Genomics Viewer (IGV) (Robinson et al., 2011). A custom genome was created using a genome fasta and gtf file for Ensembl GRCm39.

Pie charts were created using data from Homer output and Venn diagrams were created using BioVenn. Dotplots of Promoter-TSS peaks were made using ClusterProfiler. TSS plots were created using the “tss” function of Homer and plotted using Microsoft Excel. All plots produced in RStudio were saved as an EPS file type and then opened in Adobe Illustrator in order to export a high-quality JPEG image. Excel file of the composite CUT&RUN replicates is presented as several unique tabs in Supplementary Table S3.

Analyzing single-cell RNA-seq data allowed us to examine the gene expression within ProSpg at finer resolution, which we reprocessed from published publicly available data sets (Tan et al., 2020; Gura et al., 2022; Quatredeniers et al., 2023). In our analysis, we examined the expression profile of Taf4b over time to evaluate what genes and biological processes are possibly modulated concurrently or downstream of TAF4b expression. After applying our computational workflow (see Methods), the complete data set consisted of 17,310 germ cells in our uniform manifold approximation and projection (UMAP) (Figure 1A). We then assessed the expression of Taf4b compared to its paralog Taf4a, which is ubiquitously expressed in somatic cells. Across all time points, Taf4a mRNA expression was notably lower than Taf4b mRNA expression (Figures 1B,C). When observing the expression of Taf4b mRNA over the E12.5-P7 time course, we detected that Taf4b expression peaks at P0 (Figure 1C). The dynamic range of Taf4b-expressing cells across time suggests Taf4b may act more akin to a dimmer switch than on/off regulation. Because previous experiments demonstrated that the Taf4b-deficient gonad exhibited changes in germ cell proliferation, we compared this newly characterized Taf4b expression profile to known proliferation markers, such as Mki67 (Sun and Kaufman, 2018). We saw an inverse expression pattern of Mki67 and Taf4b, where the significantly reduced expression of Mki67 between E16.5-P0 points to the characteristic quiescent period of T1 ProSpg (Figure 1D). The absence of Mki67 transcripts helps illustrate quiescence because its mRNA is expressed during S phase up until mitotic exit. Plk1 mRNA, another marker of cell proliferation, is decreased similarly from E16.5 to P2 (Figure 1E) (Liu et al., 2017). The combined decreased markers of proliferation and increased Taf4b expression led us to hypothesize that Taf4b may play a role in the cell cycle transitions during ProSpg development.

We were intrigued to observe a clear increase in Taf4b mRNA during the ProSpg transition to quiescence. To better understand the potential function of TAF4b during this transition, we compared global mRNA differences between Taf4b-wildtype, -heterozygous, and -deficient ProSpg at E16.5. We first generated mouse embryos that were transgenic for Oct4-eGFP and either wildtype, heterozygous, or deficient for Taf4b, then sorted for GFP⁺ germ cells from the male embryonic testis. This provided germ cell-isolated samples from all three genotypes to perform bulk RNA-seq. Our ProSpg RNA-seq samples consisted of 7 Taf4b-wildtype, 9 Taf4b-heterozygous, and 8 Taf4b-deficient E16.5 embryos from six different litters (Supplementary Table S1). Although Taf4b-heterozygous male mice do not display overt signs of impaired spermatogenesis (Falender et al., 2005), including them as a unique hypomorphic and genotypic group was important. A PCA plot of these samples indicates that samples cluster primarily by genotype. While Taf4b-wildtype (WT) replicates display the tightest clustering, they do overlap with the Taf4b-heterozygous (Het) replicates, indicating a shared transcriptional profile (Figure 2A). This was expected as both genotypes are fertile. In contrast, the Taf4b-deficient (Def) replicates were much more dispersed and non-overlapping with the other two genotypes, suggesting large-scale transcriptional dysregulation. There were three Def samples that clustered more closely with the WT and Het samples. However, we completed the downstream analysis with all replicates because there were subtle but statistically significant transcriptional differences in these three samples compared to WT/HET. Overall, we detected 4551 DEGs between the Taf4b-wildtype and -deficient ProSpg; 3820 were defined as protein-coding with an adjusted p-value <0.05 (Figure 2B). Some genes noted to be dysregulated were Mki67 and Plk1 (Liu et al., 2017; Sun and Kaufman, 2018). These markers of proliferation should decrease during this quiescent time point but are significantly upregulated in the Taf4b-deficient ProSpg (Figure 2B). Using gene ontology (GO) of the DEGs, we determined which biological processes are disrupted in the absence of Taf4b. This GO analysis indicated cell cycle regulation is the primary disruption driven by Taf4b ablation, followed by chromatin modification and gland development. (Figures 2C,D).

After observing global transcriptional dysregulation with 1,474 downregulated genes and 3077 upregulated genes, we sought to parse out what direction particular biological processes are due to Taf4b ablation. Of the 1,259 downregulated genes that were protein-coding, the top three biological process gene ontology categories were cilium movement, negative regulation of the immune system process, and cell killing (Figures 3A,C). From the upregulated genes, 2,561 were protein-coding, and we saw that the biological process categories are primarily related to cell cycle, DNA repair, and covalent chromatin modification (Figures 3B,D). Surprisingly, there were almost twice as many upregulated genes than downregulated genes in the absence of TAF4b, many of which encoded critical components of the cell cycle and chromatin regulatory machinery.

Mitotic ProSpg enter a characteristic period of quiescence between E13.5 and E16.5. Based upon our scRNA-seq and bulk RNA-seq data, we hypothesized that TAF4b modulated some aspect of ProSpg quiescence. We performed immunofluorescence on Taf4b-wildtype and Taf4b-deficient testis sections to detect TRA98 (a germ cell marker) and MKi67 (a marker of cell proliferation) from E13.5 to E18.5 (Grive et al., 2014; Sun and Kaufman, 2018). Representative images of each genotype and time point are shown (Figure 4A), as well as the quantification of the MKi67^- quiescent wildtype and Taf4b-deficient ProSpg (indicated by the TRA98-only signal) at each timepoint (Figure 4B). At E13.5, the majority of Taf4b-deficient M ProSpg were still proliferating as indicated by the nuclear localization of MKi67 (Figure 4A). At E14.5, there is a significant reduction of ProSpg entering quiescence in the Taf4b-deficient compared to wildtype controls (Figure 4B). Interestingly, the percentage of quiescent cells at both E16.5 to E18.5 did not differ significantly between controls and Taf4b-deficient Pro-Spg. These data indicate that although Taf4b-defcient ProSpg can largely achieve quiescence, they struggle with its timely entry and this transition. We also compared staining for the proliferating cell nuclear antigen (PCNA), which is a DNA replication and repair protein, across matched Taf4b-wildtype and Taf4b-deficient testis sections (Figure 4C). At E16.5, there is a significant increase in the percent of Taf4b-deficient ProSpg expressing PCNA compared to matched wildtype controls and this difference is non-significant at E18.5 (Figure 4D). Taken together, these data indicate that Taf4b-deficient ProSpg are altered in their timing of ProSpg quiescence entry.

To distinguish which DEGs identified in our E16.5 bulk RNA-seq experiment were direct targets of TAF4b, we performed Cleavage Under Targets and Release Using Nuclease (CUT&RUN), a genome mapping technique to identify enrichment of specific proteins and histone modifications in the genome (Skene and Henikoff, 2017; Hainer and Fazzio, 2019). We isolated two replicates of wildtype Oct4-eGFP E16.5 male germ cells using FACS and examined the genomic localization of TAF4b. H3K4me3 served as a positive control and marked active promoters, and IgG served as a negative control. CUT&RUN data analysis using the program Homer identified 64,891 H3K4me3 peaks and 7,861 TAF4b peaks in Replicate 1 and 1,730 H3K4me3 peaks and 848 TAF4b peaks in Replicate 2. The differences in the number of peaks called can be attributed to stochastic differences in sample preparation and starting material. However, it is necessary to note that these differences did not affect the assay’s specificity because we still see the same top motifs and the greater than 80% overlap between each replicate. We also found that 73% and 88% of TAF4b peaks were classified as localizing to promoters/transcription start site (“promoter-TSS”) for Replicate 1 and 2, respectively (Figure 5A). Importantly, 617 TAF4b peaks overlapped between the two replicates allowing us to perform GO analysis of the shared TAF4b-bound gene promoter-TSSs between the two replicates. These top five categories include mRNA processing, proteasomal degredation, RNA splicing, covalent chromatin modification, and negative regulation of the cell cycle (Figure 5B). When plotting the enrichment profile of TAF4b relative to TSSs, we found the highest TAF4b enrichment was located just upstream of the TSS (Figure 5C). This initial chromatin mapping of TAF4b indicates that the majority of the binding sites for TAF4b are just upstream of the TSS which is consistent with where TFIID is known to bind to core promoters.

We then explored the motifs associated with TAF4b-enriched peaks within our CUT&RUN data and found consistent enrichment of CCAAT and GC motifs, which are associated with the NFY complex and the Sp/Klf transcription factor family, respectively (Figures 6A,B) (Suske, 2017). Importantly, these motifs were shared between the two CUT&RUN replicates. We then compared DEGs from our RNA-seq experiment to direct targets of TAF4b from the CUT&RUN to identify a subset of genes directly modulated by TAF4b at E16.5. When comparing our DEGs to the “promoter-TSS” peaks of TAF4b, we found 876 DEGs that had at least one peak near their TSS. GO analysis of these 876 TAF4b bound and differentially expressed genes reveals that the most notable biological process affected is “regulation of the mitotic cell cycle” (Figure 6C). A volcano plot of these TAF4b-bound E16.5 DEGs revealed far more upregulated DEGs (674 genes) than downregulated DEGs (202 genes) in Taf4b-deficient ProSpg (Figure 6D). As examples of TAF4b-bound DEGs, we present TSS gene tracks for Nuclear Transcription Factor Y Subunit Alpha (Nfya), Protein Regulator of cytokinesis 1 (Prc1), H2A histone family member X (H2afx), Kruppel-like factor 6 (Klf6), and Cyclin Dependent Kinase 20 (Cdk20), with TAF4b and H3K4me3 enrichment compared with control IgG (Figure 6E). Nfya is a component of the NFY transcription factor complex and interestingly TAF4b peaks were located between the TSS of Nfya and another gene Oard1 which was not a DEG. Prc1 encodes a microtubule-binding protein that plays a role in mitosis and binds to Plk1 another critical mitosis gene (Hernández-Ortega et al., 2019). H2afx encodes an essential DNA damage response protein and has been previously found to be increased in Taf4b-deficient oocytes (Grive et al., 2016). Klf6 encodes a transcription factor that plays a role in several developmental processes such as cell proliferation and differentiation (Hu et al., 2021). Cdk20, decreased in Taf4b-deficient ProSpg, encodes a positive regulator of the cell cycle by activating CDK2 and Cyclin D (Lai et al., 2020). A protein-protein interaction network of these 876 TAF4b-bound DEGs revealed Cdk1 and other cell cycle-related genes as a major group of associated genes and interestingly the entire NFY complex (encoded by Nfya, Nfyb, and Nfyc) was present (Supplementary Figure S1).

When integrating our CUT&RUN “promoter-TSS” peaks with TAF4b DEGs, we again found an enrichment for SP/KLF transcription factor binding motifs (Figure 7A). However when we looked at the integrated data we no longer observed NFY in the top five overall binding motifs. The surprising absence of NFY sites in this analysis prompted us to further parse these data into TAF4b peaks whose levels of mRNA decreased (Downregulated DEGs) or increased (Upregulated DEGs) in TAF4b-deficient ProSpg. We first looked at the occupancy of TAF4b and noted that it centered around the −50 bp region upstream of the TSS, further supporting a role for TAF4b in core promoter recognition and transcription initiation. It was also observed that there was a slight difference in the median being approximately 5 bases futher upsteam in the Up DEGs as compared to the Down DEGs (Figure 7B). Interestingly, we discovered that NFY is actually the top Down DEG motif while Sp1 is the top Up DEG motif (Figures 7C,D). The differences in top motifs between decreased and increased mRNA levels may allude to the multiple modes of transcriptional modulation by TAF4b. As follow-up, we mapped TAF4b targets to the GO analysis from bulk RNA-seq to determine which GO categories were directly modulated. Most notable in this analysis were the categories of cell cycle regulation, covalent chromatin modification, and DNA repair which had increased mRNA expression levels in the absence of TAF4b and also contained 7 genes that had TAF4b-bound promoters (Figure 7E). Finally, we quantified the overall fraction of CUT&RUN peaks that were also DEGs and found significantly more mRNAs that were increased in the absence of TAF4b to contain a TAF4b-bound promoter (Figure 7F). Overall, these data suggest that TAF4b specifically controls the levels of cell cycle and chromatin regulating gene expression programs required for ProSpg to properly navigate the transition from cycling to quiescence during embryonic mammalian testes development.