Apell, Z. | bioRxiv Science

bioRxiv2026

3D chromatin compartment of round spermatids encodes the spatiotemporal program of histone-to-protamine exchange in spermiogenesis

Sperm formation requires a radical chromatin reorganization, where nucleosomes are replaced by transition proteins (TNPs) and subsequently by protamines (PRM1 and PRM2). Although essential for fertility, the regulatory logic governing this exchange is unknown, but its presumed to be stochastic and unregulated. Using endogenously tagged PRM mouse models and stage-resolved, genome-wide profiling, we revise the order of histone-to-protamine exchange and show that the chromatin remodeling process is highly programmed. Imaging and biochemical experiments reveal a direct histone-to-PRM1 exchange, while TNPs appear after PRM1 but precede PRM2 incorporation. This temporal uncoupling of PRM1 and PRM2 incorporation coincides with dynamic, region-specific chromatin remodeling that is not governed by histone acetylation but is instructed by the three-dimensional nuclear architecture of round spermatids. Therefore, by integrating ATAC-seq, CUT&Tag, and Hi-C, we define a compartment-encoded "blueprint" that prescribes the assembly of the mature sperm epigenome and establishes a complex molecular hierarchy for the histone-to-protamine exchange.

genomics↗

bioRxiv2025

Chrom-Sig: de-noising 1-dimensional genomic profiles by signal processing methods

MotivationModern genomic research is driven by next-generation sequencing experiments such as ChIP-seq, CUT&Tag, and CUT&RUN that generate coverage files for transcription factor binding, as well as ATAC-seq that yield coverage files for chromatin accessibility. Due to the inherent technical noise present in the experimental protocols, researchers need statistically rigorous and computational efficient methods to extract true biological signal from a mixture of signal and noise. However, existing approaches are often computationally demanding or require input or spike-in controls. ResultsWe developed Chrom-Sig, a Python package to quickly de-noise 1-dimensional genomic coverage tracks by computing the empirical null distribution without prior assumptions or experimental controls. When tested on 19 ChIP-seq, CUT&RUN, ATAC-seq, snATAC-seq datasets, Chrom-Sig can effectively decompose the data into signal and noise. Notably, Chrom-Sig performs de-noising and peak calling in 1-2 hours using around 20 GB of memory. The de-noised signal corroborates with biologically meaningful results: CTCF CUT&RUN data retained a higher percentage of peaks overlapping CTCF binding motifs and ATAC-seq and RNA Polymerase II data resulted in enhancers and promoters. We envision Chrom-Sig to be a highly versatile and general tool for current and future genomic technologies. AvailabilityChrom-Sig is publicly available at https://github.com/minjikimlab/chromsig under the MIT license. Contactminjilab@umich.edu

bioinformatics↗