{"paper_id":"0bcfa9bb-33c8-4f19-8168-159c818089bf","body_text":"1 \nDirect measurement of sub-kilobase chromatin structure reveals that linker histone H1 1 \nbroadly compacts chromatin, with differential impact amongst epigenetic states 2 \n 3 \nHera Canaj1*, Irene Duba1*, Andres Mansisidor1†, Andrew Scortea1†, Ryan Johnson1, Hugo 4 \nPinto2, Arnold Ou1, Nicole Pagane1, Juhee Pae3, Dmitry Fyodorov2, Arthur I. Skoultchi2‡, Viviana 5 \nI. Risca1‡ 6 \n 7 \n 8 \n1. Laboratory of Genome Architecture and Dynamics, The Rockefeller University, New 9 \nYork, NY 10 \n2. Department of Cell Biology, Albert Einstein College of Medicine, New York, NY 11 \n3. Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY 12 \n 13 \n* Denotes equal contribution. 14 \n† Denotes equal contribution. 15 \n 16 \n‡ To whom correspondence should be addressed: vrisca@rockefeller.edu; 17 \narthur.skoultchi@einsteinmed.edu.  18 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 2 \nAbstract 19 \n 20 \nChromatin compaction by linker histone H1 family proteins is a long-standing model for 21 \ntranscriptional repression. However, the biophysical and conformational details of such 22 \ncompaction in situ, at the kilobase- and sub-kilobase length scale relevant to the activity of 23 \ntranscriptional regulatory elements, remain under debate. Rather than inferring such compaction 24 \nfrom indirect measurements of features like DNA accessibility, we sought to directly probe sub-25 \nkilobase contacts between nearby nucleosomes. We developed an improved version of 26 \nradiation-induced correlated cleavage with sequencing (RICC-seq), which we term RICC-seq 27 \n2.0, and used it in parallel with Micro-C to cross-validate our measurements of chromatin 28 \nstructure in both diverse cell types with different levels of linker histone and different levels of 29 \nchromatin compaction, as well as a CRISPRi system for pan-H1 depletion. Using this system, 30 \nwe find that chromatin fiber de-compaction upon H1 depletion is global across the genome, 31 \nreducing the contrast in inter-nucleosome contacts between acetylated chromatin and the rest 32 \nof the genome. Surprisingly, this does not dramatically change higher-order chromatin 33 \norganization such as nuclear compartments. Nevertheless, we observe a broad increase in 34 \naccessibility at tens of thousands of sites and an increase in expression of over a thousand 35 \ngenes, which are enriched in polycomb repressive complex targets. Investigating the local 36 \nchromatin compaction at upregulated genes as opposed to genes that do not change 37 \ntranscription, we observe that upregulated genes are not specifically de-compacted. Rather, our 38 \ndata support a model in which linker histone globally induces local compaction of nucleosome 39 \ncontacts and an increase in linker lengths, and repression by PRC1/2 is particularly dependent 40 \non these local features of chromatin architecture. 41 \n 42 \n 43 \n  44 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 3 \nMain Text  45 \n 46 \nCompaction of the chromatin fiber has been invoked as a mechanism for transcriptional 47 \nrepression since the earliest investigations into the organization of chromatin fibers in vivo and 48 \nin vitro1–4. In situ, microscopy shows that the density of silent heterochromatin is significantly 49 \nhigher than the density of transcriptionally active euchromatin5,6. In vitro, chromatin fibers with 50 \nregularly spaced nucleosomes collapse into compact 30 nm-diameter structures, supporting the 51 \n“30-nm fiber” model of transcriptional repression7. H1 linker histones are a family of proteins 52 \nessential for development in metazoans, which bind the dyad positions of core nucleosomes via 53 \ntheir globular domain and interact with the linker DNA entering and exiting the nucleosome via 54 \ntheir unstructured, positively charged N-terminal and C-terminal domains 8. In a variety of 55 \nchromatin reconstitution experiments, linker histones have been shown to promote the 56 \ncompaction of chromatin fibers or chromatin domains into higher density structures 7,9–12.  57 \n 58 \nAlthough the simple 30-nm fiber model dominated the field for decades, efforts to assess in vivo 59 \nor in situ chromatin fiber structure enabled by advances in electron microscopy and X-ray 60 \nscattering did not identify the expected long-range regular higher-order helices posed by the 30-61 \nnm fiber model 13–16. Chromatin was therefore proposed to be an unstructured, liquid-like 62 \n“polymer melt” of nucleosomes3. This updated model of chromatin as a liquid is consistent with 63 \nthe recently observed propensity of unmodified chromatin to form phase-separated liquid 64 \ncondensates12,17.  However, even in condensates, local structural motifs of chromatin fibers 65 \ndictated by nucleosome modifications and the geometry of inter-nucleosome stacking 66 \ninteractions dictated by linker DNA lengths and architectural proteins such as linker histones can 67 \nmodulate phase separation behavior17. For example, linker histones were shown to increase the 68 \ndensity of chromatin condensates12, and sequencing-based methods for mapping local 69 \nchromatin interactions, such as Micro-C and RICC-seq, found short-range zig-zag 70 \ntetranucleosome folding signatures18–20. Super-resolution imaging found that chromatin fibers 71 \nconsist of small clusters of nucleosomes, termed “clutches”, the size of which is modulated by 72 \nfactors including linker histones21. In vitro FRET measurements and simulations both point to 73 \nsuch clusters or tetranucleosome motifs being highly dynamic22,23.  74 \n 75 \nA full understanding of chromatin fiber structure and behavior, and the regulation of its 76 \ninteractions with the proteins that carry out DNA-based processes, including DNA replication, 77 \ntranscription, and DNA repair, therefore requires us to reconcile the long-range disorder and the 78 \npotential local order of chromatin. This is particularly important as the local interactions of 79 \nnucleosomes determine the accessibility and binding affinity of individual loci to these proteins. 80 \nFor example, the spacing of nucleosomes, which, as a result of DNA’s helical nature and its 81 \nrelative stiffness on the length scale of typical inter-nucleosome linker lengths (~30-70 bp), 82 \nstrongly determines the geometry of nearby nucleosome stacking, is tightly controlled by 83 \nnucleosome remodelers, forming regular arrays in some areas of the genome17,24–28. Linker 84 \nhistones are one of the strongest determinants of nucleosome spacing, with high linker histone 85 \nexpression correlating with longer average linker lengths and a large nucleosome repeat length 86 \n(NRL)29. Long linker segments can create boundaries between nucleosome interaction 87 \ndomains30. Structural studies show that chromatin modifiers (“writers”) can bridge nucleosomes 88 \nas they propagate the histone modification from one nucleosome to another, either alone, like 89 \nthe polycomb repressive complex 2 (PRC2) or through dimerization31,32, as is the case for the 90 \nH3K9me3-binding protein HP1. The efficiency of deposition of H3K27me3 by PRC2 in vitro is 91 \nenhanced by compacted chromatin fibers6,33. On the other hand, the efficiency of the 92 \nmodification H3K36me2 by the methyltransferase NSD2 is inhibited by chromatin fiber 93 \ncompaction6.  94 \n 95 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 4 \nWe therefore sought to understand how local chromatin fiber compaction on the scale of a few 96 \nnucleosomes—the length scale relevant to protein binding at regulatory regions on DNA—is 97 \nregulated by linker histone in different chromatin contexts and what its consequences are for 98 \ntranscriptional repression. Locus-specific chromatin compaction in situ is very difficult to 99 \nmeasure, and proxies for compaction such as DNA accessibility measured by ATAC-seq have 100 \nhistorically been used instead. To address this challenge, we sought to use two complementary 101 \napproaches that rely on different fundamental operating principles: Micro-C18 and radiation-102 \ninduced correlated cleavage with sequencing (RICC-seq)20.   103 \n 104 \nMicro-C, which relies on cell crosslinking, micrococcal nuclease (MNase) digestion of chromatin, 105 \nand proximity ligation of DNA ends, can probe nucleosome-nucleosome contacts and chromatin 106 \norganization on the kilobase scale, and differences in local contacts, such as a zig-zag-like 107 \nsignature in nucleosome contact probabilities, between yeast and mouse embryonic stem cells 108 \n(mESCs) 18. This is a priori attractive as a potential measure of compaction, but uncertainties 109 \nabout artifacts caused by sequence and particularly by the accessibility bias of MNase cleavage 110 \nmake it difficult to determine whether the differential signal observed is due to compaction or 111 \naccessibility.  112 \n 113 \nThis uncertainty motivated our use of an orthogonal method to Micro-C in order to validate 114 \nresults and gain more sensitivity to local changes in nucleosome contacts. RICC-seq20 relies on 115 \nspatial clusters of DNA damage events, within a few nanometers of each other, that produce 116 \ncharacteristic single-stranded DNA fragment lengths in irradiated cells. The peaks in the 117 \nfragment length distribution (FLD) reflect the lengths of frequently occurring DNA loops 118 \nspanning self-contact points that are simultaneously cleaved within a diameter of ~8 nm. The 119 \nprimary peaks observed in RICC-seq FLDs from human fibroblasts correspond to a single DNA 120 \nwrap around a nucleosome (~78 nt), a full nucleosome unit (~180 nt), and contacts between the 121 \nDNA gyres of stacked alternating nucleosomes (~270 nt and ~360 nt). Using chromatin fiber 122 \nsimulations, we explored how the locations and strengths of these peaks vary with chromatin 123 \nfiber geometry, indicating that RICC-seq FLDs have the potential to be sensitive to nucleosome 124 \nspacing, nucleosomal DNA wrapping (which alters DNA entry/exit angles), and the strength of 125 \nattractive interactions between nucleosomes. This indicated to us that RICC-seq should be able 126 \nto detect the effects of linker histone H1 on local chromatin compaction, beyond what is already 127 \nknown about its effects on nucleosome spacing and linker lengths.  128 \n 129 \nBefore applying RICC-seq to this problem, we had to overcome its limitations: the protocol was 130 \nlong, requiring more than a week to complete, did not compensate for sample-to-sample 131 \nvariations in DNA fragment length capture bias to enable quantitative comparisons between 132 \ndifferent samples, and exhibited significant sequence bias in the final libraries. Here, we develop 133 \nan optimized RICC-seq 2.0 protocol that solves these challenges, and apply it, together with 134 \nMicro-C, to measure DNA-DNA contacts on the sub-kilobase length scale in cells with varying 135 \nlevels of H1 linker histones. We find that linker histone has a dramatic genome-wide effect on 136 \nkilobase-scale chromatin compaction, and that changes in accessibility and transcription are 137 \nconcentrated in regions silenced by the polycomb repressive complexes (PRC1/2).  138 \n 139 \n 140 \n 141 \n 142 \n 143 \n 144 \n 145 \n 146 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 5 \nResults 147 \n 148 \nRICC-seq 2.0 improves robustness, reduces sequence bias, and allows cross-sample 149 \ncomparison  150 \n 151 \nIn order to use RICC-seq to assess the chromatin fiber compaction effects of linker histone 152 \nacross cell types and perturbation conditions, we improved on the original RICC-seq method in 153 \nseveral ways.  154 \n 155 \nFirst, we addressed a challenge we encountered when scaling up the RICC-seq protocol to 156 \nlarger numbers of samples and conditions: library preparations would sometimes fail, yielding 157 \nno peaks in the fragment length distribution. The original protocol used a high heat denaturation 158 \nstep to dissociate radiation-cleaved single-stranded DNA (ssDNA) fragments from the higher 159 \nmolecular weight genomic DNA prior to elution. We found that this heat-elution method caused 160 \nthe appearance of a large number of additional ssDNA breaks at heat-labile sites throughout the 161 \ngenome, overwhelming the DNA cleavage signal from the original radiation-induced breaks. 162 \nThese heat-labile sites have been previously documented as a product of DNA irradiation34. 163 \nSmall variations in the precise timing of heat denaturation would cause more or fewer of these 164 \nbreaks, leading to a lack of robustness in the RICC-seq protocol. Replacement of heat 165 \ndenaturation with high-pH (NaOH incubation) denaturation and avoidance of high heat (above 166 \n65˚C) in subsequent library processing was sufficient to generate a more robust ssDNA elution 167 \nand library preparation (Figure 1a-b).  168 \n 169 \nSecond, we addressed another challenge of using RICC-seq—the length and complexity of the 170 \nprotocol—which was partly due to the necessity for end-repair in agarose plugs and multiple 171 \ngel-based size selections and amplifications to strike a balance between maintaining as much of 172 \nthe insert size distribution as possible while removing dimers of ligated sequencing adapters. To 173 \nstreamline sequencing adapter ligation to the eluted ssDNA, we used the Single Reaction 174 \nSingle-stranded LibrarY (SRSLY) protocol developed for ancient DNA and cell-free DNA 175 \nsequencing, which uses single-strand binding protein (SSB) to stabilize and blocked adapters 176 \nwith random-heptamer splint overhangs to capture the end-repaired ssDNA fragments35. This 177 \nallowed us to proceed from ligation to PCR without the need for size selection (Figure 1a). 178 \n 179 \nTogether, these changes produced a more robust RICC-seq 2.0 protocol that can capture 180 \nssDNA fragments from irradiated cells across a broader range of GC contents and fragment 181 \nlengths (Figure 1c). In particular, RICC-seq 2.0 demonstrates an improved efficiency of capture 182 \nfor fragments that are both long and GC-rich (Figure 1d).  183 \n 184 \nThird, because RICC-seq can be sensitive to the length bias introduced by sample handling and 185 \nPCR, we developed a spike-in and normalization strategy to account for such sample-specific 186 \nbiases and allow us to quantitatively compare samples across experiments, cell types and 187 \nperturbations. To create a “standard candle” library, we digested Schizosaccharomyces pombe 188 \nchromatin with MNase into a nucleosome ladder, quantified its FLD using capillary 189 \nelectrophoresis prior to library preparation. Known quantities of this S. pombe spike-in were 190 \nthen added to RICC-seq libraries prior to SRSLY adapter ligation and library preparation (Figure 191 \n1a). Spike-in reads were computationally isolated to calculate their own FLD and a length bias 192 \ncorrection factor was calculated by fitting an exponential to the ratio of the post-sequencing FLD 193 \nand pre-sequencing FLD (Figure 1e). The absolute amount of spike-in was used to scale FLDs 194 \nfor comparisons between no-irradiation controls, irradiated genomic DNA, and irradiated cell 195 \nsamples (Figure 1f).  The length-dependent correction factor (Figure 1e) was then used to 196 \ncorrect length bias in RICC-seq FLDs (Figure 1f). Lastly, to enhance the contrast of peaks in the 197 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 6 \nFLD over the background of DNA fragments caused by random, uncorrelated breaks, we 198 \ncalculated the ratio of the scaled and corrected FLD from irradiated cells to the scaled and 199 \ncorrected FLD from irradiated genomic DNA from the same cell sample (maintained in 0.5M Tris 200 \npH 8.0 as a quencher for radiation-induced radicals, approximating intracellular quenching) 201 \n(Figure 1f). These procedures allowed us to directly compare replicates and different 202 \nexperimental samples that may have been subject to different length biases (Figure 1g).    203 \n 204 \nRICC-seq 2.0 is sensitive to chromatin compaction differences across species 205 \n 206 \nThe combination of a more robust protocol and spike-in normalization allowed us to apply RICC-207 \nseq 2.0 (Figure 1) to a broad range of cell samples, including budding yeast (Figure 2a). 208 \nBudding yeast does not express a canonical member of the H1 linker histone family, but 209 \nexpresses Hho1p, which is homologous to the H5 linker histone found in chicken erythrocytes 210 \nand binds nucleosomes 36. However, its expression level is much lower than mammalian linker 211 \nhistones: a ratio of 0.3 molecules per nucleosome 29,36. Budding yeast therefore has short inter-212 \nnucleosome linkers and a short NRL, and a largely open chromatin conformation with little clear 213 \ndistinction between euchromatin and heterochromatin as is found in metazoans. At the other 214 \nextreme, the linker histone to nucleosome ratio rises even higher to ~1.3 in the transcriptionally 215 \ninactive nuclei of chicken erythrocytes, in which the primary histone variant is H529. Chicken 216 \nerythrocytes have been used as a model system for highly compacted chromatin fibers, as they 217 \nrepresent one of the few cell types in which electron microscopy reveals structures resembling 218 \n30-nm diameter fibers, albeit more disordered ones than reconstituted in vitro 5,9,37.  219 \n 220 \nSeeking to understand the dynamic range of RICC-seq 2.0 FLDs as a function of varying 221 \ncompaction and linker histone levels, we applied it to four sample types: S. cerevisiae, human 222 \nBJ-5ta fibroblasts, naïve mouse B cells, and chicken erythrocytes (Figure 2a-h). After correcting 223 \nfor length bias and calculating the ratio of the irradiated cell sample FLDs to their corresponding 224 \nirradiated genomic DNA FLDs, we compared them directly (Figure 2i). We found two main 225 \neffects. First, moving from budding yeast, with an average NRL of 163 bp, to BJ-5ta human 226 \nfibroblasts, with an average NRL of 186 38, mouse B cells with a NRL estimated to be ~192 227 \n(based on human lymphoblastoid cells28), to chicken erythrocytes, with an average NRL of 212 228 \nbp 29, we observed a shift in the location of the higher-order contact (third and fourth) peaks of 229 \nthe RICC-seq FLD toward longer fragment lengths, consistent with the increase in FLD. 230 \nImportantly, we also observed that the inter-nucleosome contact peaks were more prominent 231 \ncompared to the sub-nucleosomal (first) and mono-nucleosome (second) peak in cell types with 232 \nmore compact chromatin, such as human fibroblasts and to a greater extent, mouse B-cells. 233 \nChicken erythrocytes had the most extreme example, with a high fourth peak at ~400 nt.  234 \n 235 \nThe correlation between the linker histone level and the inter-nucleosome stacking signal, which 236 \nwe interpret as a measure of local chromatin fiber compaction differences between cell types, 237 \nmotivated us to perturb the linker histone level in a well-characterized system to more precisely 238 \nanalyze its effects, context dependence and functional consequences.  239 \n 240 \nLinker histone depletion by CRISPRi leads to genome-wide reduction of nucleosome 241 \nrepeat length and loss of zig-zag alternating nucleosome contacts 242 \n 243 \nUsing a doxycycline-inducible dCas9 K562 cell line, we designed CRISPRi guides against the 244 \nfour H1 subtypes that are the most abundantly expressed in K562 cells: H1.2, H1.3, H1.4 an 245 \nH1.5, as well as scrambled controls (Figure 3a). The guide RNAs were stably transfected. 246 \ndCas9 induction for five days led to a reduction in the H1:nucleosome ratio from ~0.75 to ~0.2, 247 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 7 \nas quantified by HPLC (Figure 3b-c). Over the five-day timeline of the experiment, cell doubling 248 \ntime was not qualitatively different, indicating maintenance of viability (Figure 3d).  249 \n 250 \nWe applied Micro-C (Figure 3e-f) to cells with CRISPRi-depleted H1 (H1-low) and control cells 251 \nexpressing scrambled CRISPR guides (scr-CTRL) at the 5-day time point. Due to potential 252 \ndifferences in global accessibility upon reduction of linker histone and the sensitivity of Micro-C, 253 \nas with other MNase-based assays, to the precise MNase concentration, we titrated and 254 \noptimized the MNase concentration for each condition independently until similar chromatin 255 \ndigestion profiles were obtained, as assayed by capillary electrophoresis.  256 \n 257 \nAnalysis of the short-range (< 1.5 kb) Micro-C contact probability curves (Figure 3f) revealed 258 \nthat both scr-CTRL cells and H1-low cells exhibit a series of peaks corresponding to contacts 259 \nbetween integer nucleosome steps proceeding down the fiber (N+1, N+2, …), with two main 260 \ndifferences between the curves (Figure 3g-h). Most prominent is a shift in peak location 261 \ncorresponding to a drop in the NRL upon H1 depletion (Figure 3g,i). However, a second, more 262 \nsubtle but significant effect is a difference in the relative heights of the contact frequency peaks. 263 \nWhile scr-CTRL cells exhibit a staircase-like pattern in which pairs of contact peaks are of 264 \nsimilar height (N+2 and N+3, N+4 and N+5, …), this pattern was subdued and the peak heights 265 \napproached a smoothly decreasing function in the H1-low cells (Figure 3h). We quantified this 266 \nthrough the ratio of odd and even nucleosome contact probability peaks and found the effect to 267 \nbe significant across our biological replicates (Figure 3j).  268 \n 269 \nAlthough matching the global MNase digestion profiles between samples should mitigate some 270 \nof the accessibility biases of Micro-C on a genome-wide scale, concerns that the Micro-C results 271 \nmay not fully reflect local folding of the chromatin fiber nevertheless remain. To validate that our 272 \nobserved change in not only NRL but also nucleosome contact (and hence chromatin fiber 273 \nfolding) patterns are not an artifact of differential MNase digestion, we applied the RICC-seq 2.0 274 \nprotocol to the same cells, using a S. pombe spike-in to normalize fragment length histograms 275 \nbetween samples. RICC-seq does not rely on enzymatic digestion and should therefore not be 276 \ninfluenced in the same way by changes in the accessibility to proteins. Its cleavage events are 277 \nmediated by ionizing radiation that penetrates the whole nucleus and by highly diffusible 278 \nspecies—primarily, hydroxyl radicals20. In genome-wide analysis, we found that the RICC-seq 279 \nresults corroborated our findings from Micro-C (Figure 3k-p). The inter-nucleosome peaks shift 280 \nto lower fragment lengths in the H1-low RICC-seq FLD, indicating a lower NRL (Figure 3k), and 281 \nthe strength of the fourth peak, which was most strongly correlated with chromatin compaction 282 \nand linker histone levels in our cross-species comparison (Figure 2), dropped significantly 283 \n(Figure 3k-l). Smaller significant changes were also present in the second (mono-nucleosome) 284 \nRICC-seq FLD peak (Figure 3k-l), but we do not draw a strong conclusion from this segment of 285 \nthe FLD because it exhibited more variability between biological replicates.  286 \n 287 \nWe then sought to determine to what extent these effects on the short-range chromatin fiber 288 \ncompaction evident in the RICC-seq data were a direct result of linker histone depletion, as 289 \nopposed to indirect effects, such as from cell stress responses. We performed a washout 290 \nexperiment in which the H1-low and scr-CTRL cells were depleted of H1 for five days, as before, 291 \nand then cultured in doxycycline-free media for five more days to allow for H1 levels to return. 292 \nWe found that the strength and location of the fourth peak returned upon dCas9 washout 293 \n(Figure 3m-p). Overall, this led us to conclude that linker histone H1 has a direct effect on short-294 \nrange stacking between alternating nucleosomes—nucleosome N to N+2 zig-zag contacts—in 295 \nthe context of intact chromatin.  296 \n 297 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 8 \nShort-range zig-zag stacking contrast between euchromatin and heterochromatin 298 \ndepends on linker histone levels 299 \n 300 \nNext, we asked how the dependence of nucleosomal zig-zag contacts depend on the local 301 \nepigenetic context. Segmenting the Micro-C contacts by overlap with histone mark ChIP-seq 302 \npeaks—H3K27 acetylation to mark active promoters and enhancers, and H3K27 trimethylation 303 \nand H3K9 trimethylation to mark the two primary types of heterochromatin—we found that there 304 \nwas a subtle change in the zig-zag signal of the first eight Micro-C contact peaks (Figure 4a-b). 305 \nQuantitating the zig-zag signature using the odd-even peak height ratio, we found that there 306 \nwere differences in compaction between the three chromatin states, with H3K9me3 chromatin 307 \nhaving the strongest zig-zag and H3K27me3 the weakest (Figure 4c). H1 depletion reduced the 308 \nzig-zag signature such that the H3K27me3 heterochromatin in H1-low cells had a similar level to 309 \nH3K27 acetylated chromatin in scr-CTRL cells (Figure 4c).  310 \n 311 \nWe cautiously interpret the zig-zag signature in short-range Micro-C contact data on a global 312 \nlevel as a measure of short-range chromatin compaction because differences in the propensity 313 \nfor cleavage by MNase can be controlled at a global level by tuning the MNase concentration. 314 \nHowever, artifacts caused by differential digestion by MNase cannot be mitigated if they occur 315 \nbetween different sets of genomic loci within the same sample, as would be expected for 316 \nheterochromatic versus euchromatic loci. Indeed, we observed that in libraries with different 317 \nextents of digestion, as measured by the effective fragment size of the mononucleosome peak 318 \n(Figure 4d), the zig-zag signature depended on the amount of digestion, with an inverse 319 \ncorrelation between the strength of the zig-zag signature and the size of the mononucleosome 320 \nfragment (Figure 4d-e). We therefore concluded that Micro-C is not a reliable measure of 321 \ndifferences true chromatin compaction within the same sample, and validation of results by an 322 \northogonal method is needed. 323 \n 324 \nWe analyzed our RICC-seq 2.0 data segmented by epigenetic state in the same mode, in order 325 \nto determine whether the patterns of zig-zag contacts suggested by the Micro-C data could be 326 \northogonally validated by a non-enzymatic method (Figure 4f-k). We monitored the irradiated 327 \ngenomic DNA (gDNA) control from both scr-CTRL and H1-low cells to ensure that the peak 328 \nchanges we observed were not driven by pre-existing DNA damage that could be differential 329 \nbetween genomic loci (Figure 4h,i). Although we observed some weak peaks in the H1-low 330 \ngDNA control consistent with small amounts of contaminating DNA fragments with damage 331 \nbetween nucleosomes, they were not correlated with the irradiated cell peaks in a way that 332 \nwould explain the observed differences. To normalize against differences in the gDNA, we 333 \ncalculated the ratio between the length bias-corrected, epigenetic state-specific RICC-seq cell 334 \nFLDs to the similarly corrected gDNA FLDs (Figure 4j-k). The results we observed partially 335 \nagree with Micro-C data. Interpreting the strength of the fourth peak (~330-420 nt) as a measure 336 \nof the population-averaged stacking of alternating nucleosomes by zig-zag chromatin fiber 337 \ncompaction, we found that acetylated chromatin indeed does have very low compaction. 338 \nHowever, the level of compaction between H3K27me3 heterochromatin and H3K9me3 339 \nheterochromatin appears similar by RICC-seq, as opposed to the higher H3K9me3 compaction 340 \nsuggested by Micro-C. The location of the fourth peak is shifted (Figure 4j, arrow) between 341 \nH3K9me3 and H3K27me3 chromatin, consistent with the difference in NRL observed by Micro-342 \nC.  343 \n 344 \nThe difference in local chromatin fiber compaction between heterochromatic regions and 345 \nacetylated euchromatic regions is consistent with the removal of linker histone H1 from 346 \nacetylated chromatin causing unfolding of the fiber 10,39. We therefore compared the epigenetic 347 \nstate compaction landscape between scr-CTRL cells and the H1-low cells. We found that most 348 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 9 \nof the difference in chromatin fiber compaction between epigenetic states in the scr-CTRL 349 \nlandscape is gone in H1-low cells (Figure 4g,k). 350 \n 351 \nChanges to long-range chromatin compartments, domains, and loops are minimal after 352 \nfive-day H1 depletion 353 \n 354 \nThe dramatic loss of local chromatin fiber compaction upon H1 depletion observed with short-355 \nrange Micro-C curves and RICC-seq 2.0 motivated us to ask how this short-range 356 \ndecompaction relates to long-range chromosome folding features. We performed Hi-C to obtain 357 \na sensitive measure of long-range compartment changes. The large-scale (1 Mb resolution) 358 \nbalanced contact matrices appeared similar between scr-CTRL and H1-low cells (Figure 5a) 359 \nand indeed, HiC-Rep analysis at 500 kb resolution showed that the difference between 360 \nconditions was comparable to the difference between replicates (Figure 5b). 361 \n 362 \nWe then analyzed compartment changes between scr-CTRL and H1-low using the compartment 363 \nscore (c-score). We found that, in contrast to what was observed using the same analysis for H1 364 \ndepletion by conditional triple knockout in mouse T-cells6, the changes in c-score in K562 cells 365 \ndepleted of H1 by five days of CRISPRi were subtle, and only weakly weighted toward B-to-A 366 \ntransitions (Figure 5c).  367 \n 368 \nVisual analysis of chromatin domains shows little change with H1 depletion (Figure 5d), and 369 \ncalls of domain boundaries location (Figure 5e) and strength (Figure 5f) showed that there are 370 \nno substantial domain changes on the global scale. Calling chromatin loops showed a general 371 \nloss of loops with H1 depletion (Figure 5i), though the low specificity of loop calling suggests this 372 \nmay in fact reflect an overall weaking of loop strength (Figure 5h). This small loss of loop 373 \nstrength affects both CTCF and non-CTCF loops(Figure 5j).  374 \n 375 \nTranscriptional de-repression upon H1 depletion preferentially occurs in polycomb 376 \nrepressive complex target genes 377 \n 378 \nConsidering the relatively subtle changes in long-range genome organization, we next 379 \nwondered about the effects of global de-compaction of chromatin and the loss of compaction 380 \ncontrast between epigenetic states on functional outcomes like transcriptional regulation, and its 381 \nassociated features such as DNA accessibility and histone modifications.  382 \n 383 \nWe performed poly(A)-capture RNA-seq on scr-CTRL and H1-low cells at five days of H1 384 \ndepletion to compare against our chromatin compaction results. We found that that the vast 385 \nmajority of changing genes were up-regulated in their transcription (1525 significantly 386 \nupregulated and 32 downregulated with p < 0.05 and |log2(fold-change)| > 1) (Figure 6a). To 387 \ndetermine which regulators may be responsible for the changes in gene expression, we 388 \nperformed ChEP-MS to identify changes in protein abundance on chromatin (Figure 6b). We 389 \nfound that the transcription factor GATA1, which is highly expressed in K562 cells, dramatically 390 \nincreased its association with chromatin in H1-low cells, while the BAF complex component 391 \nSMARCC2, the chromatin-binding nucleoporin NUP153, the H3K4-targeting histone 392 \ndemethylase KDM1B and its methyltransferase KMT2A, the neuron-specific transcription factor 393 \nTBR1, the polycomb repressive complex2 (PRC2) member SUZ12, and the repressive CBX1 394 \n(HP1-beta) protein were decreased in their association. We next used ENRICHR to determine 395 \nthe upstream regulators most likely to explain the change in gene expression (Figure 6c) and 396 \nfollowed up with top hits GSEA analyses (Figure 6d). We found that the most significantly 397 \nupregulated gene sets upon H1 depletion are those regulated by the PRC2 complex member 398 \nSUZ12 and PRC1 complex members CBX8 and CBX2, which are respectively involved in 399 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 10 \ndepositing and sensing the H3K27me3 histone mark. Surprisingly, although the association of 400 \nGATA1 with chromatin was highly significant, GATA1 targets were not enriched in the 401 \nupregulated gene set (Figure 6c,e).  402 \n 403 \nNext, we investigated how chromatin accessibility responds to loss of H1 using ATAC-seq at 5 404 \ndays of CRISPRi. We found that as with transcription, differential accessibility is biased toward 405 \ngains across thousands of ATAC-seq peaks, which are more enriched in distal intergenic 406 \nregions relative to peaks with stable accessibility (Figure 6f,g).   407 \n 408 \nThe increase in accessibility and the de-repression of PRC1/2 target genes led us to 409 \nhypothesize that loss of H3K27me3 upon H1 depletion may explain the observed increase in 410 \ntranscription. CUT&Tag for H3K27me3 showed widespread changes, with regions changing in 411 \nboth directions but dominated by a loss of H3K27me3 (Figure 6h,i). To tie accessibility changes 412 \nto epigenetic state, we then asked where the newly accessible sites fell, relative to the existing 413 \nepigenetic context. We then quantified changes in accessibility in regions that lost H3K27me3 414 \ncompared to those where the signal remained unchanged, as well as in other regions marked 415 \nby several additional epigenetic marks (Roadmap Epigenomics 40) (Figure 6k). Consistent with 416 \nthe gene regulation results, we saw that the regions with net increases in accessibility are 417 \nheterochromatic—those marked by H3K27me3 in scr-CTRL or parental K562 cells and those 418 \nmarked by H3K9me3 in parental K562 cells 40 (Figure 6k).  However, the fold-change of 419 \naccessibility in regions losing H3K27me3 was not higher when compared to all H3K27me3 420 \nregions. Similarly, when we investigated the change in H2K27me3 between control and H1-low 421 \ncells specifically focusing on genes that increased in transcription, we found that the local 422 \nH3K27me3 landscape stayed at a similar level (Figure 6l). What was notable, was that the 423 \ngenes that were upregulated upon H1 loss had a much higher level of H3K27me3 signal near 424 \ntheir promoters than genes that were not de-repressed, regardless of H1 depletion (Figure 6l).  425 \n 426 \nTogether, these results suggest that gene de-repression and the gain of accessibility does not 427 \nrequire complete local loss of promoter proximal H3K27me3 and that the mechanism of de-428 \nrepression is not simply a direct consequence of local H3K27me3 loss.  429 \n 430 \n 431 \nChromatin de-compaction by H1 depletion is genome-wide, except for regions that were 432 \nalready de-compacted and accessible 433 \n 434 \nWe next looked at the regions that gain accessibility in H1-low versus scr-CTRL–spanning 435 \npromoter proximal and distal sites. We found that H3K27me3 signal flanking these peaks of 436 \naccessibility is largely maintained in H1-low (Figure 7a), indicating that accessibility gains do not 437 \ngenerally require local depletion of H3K27me3 at these regulatory elements. The local 438 \ndifference in the CUT&Tag signal observed is likely to be driven by the change in accessibility, 439 \nas a sharp CUT&Tag peak at the center of the newly opened ATAC-seq peaks (Figure 7a).  440 \n  441 \nWe then turned to RICC-seq as a measure of chromatin compaction to determine whether there 442 \nare focal changes in chromatin compaction at regions where DNA accessibility or H3K27 443 \ntrimethylation are changing. We did not observe any change in chromatin compaction by RICC-444 \nseq between genomic regions that lose K27me3 and those that do not (Figure 7b). The primary 445 \ndifference remains between the scr-CTRL and the H1-low sample at all H3K27me3-marked 446 \nsites, regardless of their change in the histone mark between the two conditions.  447 \n 448 \nFor genomic regions that change accessibility, on the other hand, we did observe changes in 449 \ncompaction (Figure 7c-e). We found a distinction in the average RICC-seq FLD between 450 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 11 \nunchanging ATAC-seq peaks (Figure 7c) and those that become accessible upon H1 depletion 451 \n(Figure 7d). Unchanging ATAC-seq peaks are already quite decompacted, with FLDs similar to 452 \ngenome-wide acetylated chromatin (Figure 7e) and resembling the decompacted chromatin of 453 \nbudding yeast (Figure 2). The regions that gain accessibility, however, began with a FLD very 454 \nsimilar to the genome-wide average and decompacted to a FLD comparable to the genome-455 \nwide H1-low FLD upon H1 depletion (Figure 7e).  456 \n 457 \nDiscussion 458 \n 459 \nWe set out to understand the relationship between chromatin structure, transcriptional 460 \nregulation, DNA accessibility and histone marks. By improving the RICC-seq protocol to RICC-461 \nseq 2.0, we obtained a protocol that could be reliably applied to a variety of sample types with 462 \nvarying levels of chromatin compaction. We verified that cell types with very different NRLs and 463 \nglobal levels of chromatin compaction, spanning de-compacted budding yeast cells through 464 \nmammalian cell types and hyper-compacted, transcriptionally inactive chicken erythrocytes, 465 \nproduced different RICC-seq FLDs, demonstrating that the method is sensitive to changes in 466 \nchromatin compaction. 467 \n 468 \nWe were particularly motivated to make direct in situ measurements of chromatin compaction in 469 \na model of linker histone depletion because linker histone H1 has so often been invoked as an 470 \narchitectural protein that uses chromatin compaction as its mechanism for broad-based 471 \ntranscriptional repression. Our results show that a dramatic reduction in total H1 levels leads to 472 \nnot only an increase in accessibility at thousands of sites and the upregulation of thousands of 473 \ngenes, but it also causes chromatin decompaction at the tri-nucleosome length scale, which we 474 \ncould measure using two orthogonal methods—Micro-C and RICC-seq 2.0. We did not see 475 \nstrong changes in long-range chromatin organization over the same time scale, suggesting that 476 \nthat chromatin structure at this scale is not directly coupled to H1 density and transcriptional 477 \nregulation. This underscores the importance of maintenance of chromatin compaction by linker 478 \nhistone in regulating both the accessibility of many sites across the genome and the 479 \ntranscriptional repression of a large set of genes. We also observed a modest but broad-based 480 \nloss in H3K27me3 signal as measured by CUT&Tag, indicating that in this system, linker histone 481 \nplays a role in the maintenance of the H3K27me3 mark, as has been observed in other 482 \nsystems, including T-cells6 and B-cells41, as well as in K562 cells in which H1 is depleted via 483 \nCRAMP1 knockout42.  484 \n 485 \nSurprisingly, the changes in chromatin compaction upon linker histone depletion are remarkably 486 \nuniform across the genome. Although DNA accessibility is particularly enriched in H3K27me3-487 \ndecorated regions of the genome upon H1 depletion, this is not accompanied by specific 488 \ndecompaction at H3K27me3 regions, any more than is happening in the rest of the genome. 489 \nRICC-seq is, however, sensitive to changes in compaction elsewhere. We observed a difference 490 \nin the FLD shift between regions that maintained accessibility and those that gained it—those 491 \nwith pre-existing accessibility had a more de-compacted FLD in control cells, and experienced 492 \nonly a modest change in the FLD and in the compaction contact peak upon H1 depletion.  493 \n 494 \nOur results support a model in which linker histone H1 is not locally inducing compaction at 495 \nparticular loci, but is rather working genome-wide to compact most chromatin, with the 496 \nexception of acetylated regions where it is removed and chromatin can de-compact10,39. This is 497 \nconsistent with FRAP data showing that in vivo, linker histones are remarkably dynamic43, which 498 \nwould permit their broad distribution across the genome, and with electron microscopy and 499 \nsuper-resolution microscopy, which show a broad change in both chromatin density and 500 \nnucleosome clutch size 6,21. There may be variability in H1 density between chromatin types, 501 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 12 \nand chromatin compaction at the tri-nucleosome scale may have different sensitivity to the linker 502 \nhistone level as compared to other architectural or functional features. Indeed, some threshold 503 \neffects in chromatin fiber architecture have been observed with linker histone density changes in 504 \nsilico44.  505 \n 506 \nOverall, we find that H1 acts to modulate the global nucleosome repeat length and local 507 \ncompaction of chromatin, but that some chromatin states may be more dependent on this 508 \ncompaction than others. PRC1/2 repression of both accessibility and gene expression is 509 \nparticularly sensitive. The exact nature of this sensitivity may be a combination of H1’s effects 510 \non both chromatin compaction and NRL. In vitro experiments show that PRC2 deposition of 511 \nH3K27me3 preferentially occurs on long-NRL chromatin45, but compaction also promotes 512 \ndeposition of H3K27me3 and prevents deposition of its antagonistic mark H3K36me2 6. 513 \n 514 \nOur results highlight that there is a close regulatory relationship between H1-dependent sub-515 \nkilobase chromatin compaction, DNA accessibility, histone marks and transcriptional 516 \nregulation—and that it is more immediate than the long-range compartmentalization of the 517 \nnucleus. In a system with as much complexity and redundancy as chromatin, directly measuring 518 \nchromatin compaction as a distinct physical variable, rather than inferring it from other methods, 519 \ncan help define more precise mechanistic models of transcriptional repression. 520 \n 521 \nDisclosures 522 \n 523 \nV.I.R. is a co-inventor on a patent application covering a chromatin conformation capture 524 \nmethod. 525 \n 526 \nAcknowledgments 527 \n 528 \nWe would like to thank the Risca Lab, and Skoultchi Lab, and the members of the Rockefeller 529 \nChromatin Supergroup, as well as Ari Melnick, Ethel Cesarman, and Yael David for helpful 530 \ndiscussions. We also thank the support of the Rockefeller University Genomics Resource 531 \nCenter and High Performance Computing Resource Center. This work was supported by a NIH 532 \nNew Innovator Award to V.I.R. (DP2GM150021), a Rita Allen Foundation Scholar Award to 533 \nV.I.R., a Hirschl/Weill-Caulier Career Scientist Award to V.I.R., a NSERC post graduate 534 \nscholarship award to H.C., and a HFSP Postdoctoral Fellowship to A.O.  H.D. P. and A.I.S. were 535 \nsupported by NIH grant R01GM147165 and D.V.F. by NIH grant R01HD114814. 536 \n 537 \n 538 \n  539 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 13 \nFigure Legends 540 \n 541 \nFigure 1. Improved RICC-seq 2.0 protocol reduces sequence bias, improves recovery of 542 \nlong high-GC fragments and allows quantitative comparison between samples. 543 \n 544 \na) Schematic of the new RICC-seq protocol, incorporating the SRSLY ssDNA library 545 \npreparation, including spike-in.  546 \nb) Schematic depicting fragments produced by RICC-seq and the nucleosome-nucleosome 547 \ncontacts that generate the corresponding peak distributions. 548 \nc) Fragment-length distribution plot over increasing genome %GC with fragments mapping to 549 \nRoadmap Epigenomics 40 H3K27me3, H3K9me3, and H3K27ac peaks shown for RICC-seq 1.0 550 \nand 2.0 methods.  551 \nd) Contour plot of fragment lengths captured and %GC of representative BJ sample from RICC-552 \nseq 1.0 and RICC-seq 2.0. 553 \ne) MNase-digested fission yeast chromatin ladder is shown before sequencing as a Capillary 554 \nelectrophoresis (TapeStation)  trace and after sequencing as a Fragment Length Distribution 555 \n(FLD), both smoothed (5 nt rolling average) and aligned by the falloff of the mononucleosome 556 \npeak. Ratio of post- to pre-sequencing distributions shown with exponential curve fit after 300bp 557 \nwhich is extrapolated back and used to correct samples within an experiment.  558 \nf) Representative BJ-5ta fibroblast sample with the respective corrections applied 559 \ng) Multiple BJ fibroblast samples shown with the respective corrections applied n=2 biological 560 \nreplicates shown each with n=2 technical replicates.  561 \n 562 \n 563 \nFigure 2. RICC-seq 2.0 applied across organisms with increasing H1 levels and 564 \nchromatin compaction reveals an increase in nucleosome stacking contacts.  565 \n 566 \na,c,g,e) Representative spike-in–scaled FLDs for each species before length-bias correction  567 \nb,d,h,f) Replicate FLDs after length-bias correction with 95% CIs, max-normalized to the 568 \nmononucleosome signal; condition means shown with 95% CI  569 \ni) Per-organism corrected replicate FLDs averages shown. Chicken n=2 technical replicates, 570 \nMouse B cell n=2 technical replicates, BJ n=3 technical replicates of 2 biological replicates, 571 \nyeast n=3 technical replicates of two biological replicates. 572 \n 573 \nFigure 3. H1 depletion shortens nucleosome repeat length and reduces contacts 574 \nassociated with nucleosome-nucleosome stacking interactions, which reemerge upon 575 \nwash-out. 576 \n 577 \na) Experimental design for generating H1-low and scrambled control (scr-CTRL) cells, with five 578 \ndays doxycycline (dox) induction and matched 5 days dox-washout (rescue) conditions for 579 \ninduction of dCas9 in cells with constitutively expressed, stably transfected CRISPRi guides 580 \nRNAs targeting H1.2, H1.3, H1.4, and H1.5 (H1-low) or scrambled guides (scr-CTRL). 581 \nb) HPLC of H1 subtypes in H1-low cells relative to scr-CTRL. 582 \nc) Quantification of HPLC of H1:H2B ratio depletion on day 5 of dox induction.  583 \nd) Doubling time shown for samples before and after induction on dox for scr-CTRL (blue) and 584 \nH1-low (pink). n=1 biological replicate. 585 \ne-f) Schematic of Micro-C workflow and resulting short-range contact probability histogram.  586 \ng) Micro-C contact frequency curve and h) maximum contacts for scr-CTRL and H1-low 587 \nconditions i) nucleosome repeat length (NRL) quantification n=2, j) odd/even contact frequency 588 \npeak maximum quantification of n=2 biological replicates shown. 589 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 14 \nk) RICC-seq fragment length distribution in scr-CTRL and H1-low cells, spike-in–scaled, depth-590 \nmatched within biological replicate, normalized to the 180 nt peak maximum, interpolated to a 591 \ncommon 55–450 bp grid, and smoothed (10 nt rolling average). Shaded bands denote 95% CI 592 \n(t-interval) across the relevant replicates n= 3 biological replicates shown. 593 \nl) Ratio taken over scr-CTRL and H1-low; pink boxes mark contiguous Welch-significant runs 594 \n(p<0.05; calculated within 5 nt windows) for the indicated pairwise comparison.  595 \nm) Condition means across dox washout rescue experiment (scr-CTRL vs H1-low). n=2 H1 low 596 \nwith dox, n=2 H1low dox washout, n= 2 Scr with dox n=1 scr-CTRL dox washout. n: technical 597 \nreplicates. 598 \nn) Ratio of technical replicate means from (m) ±95% CI for scr-CTRL, scr-CTRL dox-off.  599 \no) Ratio of technical replicate means from (m) ±95% CI for H1-low and H1-low dox-off.  600 \np) Ratio of technical replicate means from (m) ±95% CI for H1-low dox-off/scr-CTRL dox-off; 601 \nshaded segments denote Welch test-significant runs (p<0.05; 5 nt windows).  602 \n 603 \n 604 \nFigure 4: H1 depletion phenocopies compaction structure of active chromatin.  605 \n  606 \nNucleosome-nucleosome contacts as measured by Micro-C in epigenetic regions defined by 607 \npublished WT K562 ChIP datasets in a) scr-CTRL b) and H1-low conditions 40. Two biological 608 \nreplicates shown each. 609 \nc) Ratio of N/N+odd contacts and N/N+even nucleosome contacts in epigenetic state regions. 610 \nError bar: standard deviation between biological replicates, n=2.   611 \nd) Capillary electrophoresis (TapeStation) traces of the fragment size distribution produced by 612 \nMNase titration with different amounts of enzyme. The estimated fragment length of the 613 \nmononucleosome peak is indicated.  614 \ne) Micro-C contact probability curves for the libraries obtained from the MNase titration in (d).  615 \nf-g) RICC-seq FLDs from irradiated cells and matched gDNA controls in h-i) 616 \n j-k) scr-CTRL cells and H1-low cells, subset by histone mark 40 gapped peaks. 1 biological 617 \nreplicate shown.   618 \n 619 \nFigure 5: Long-range chromatin structure is only weakly affected by 5-day H1 depletion 620 \n 621 \na) HiC contact maps of scr-CTRL and H1-low cells, ICE corrected by cooltools. Chr 4, 1 Mb 622 \nresolution, 80-100 M paired end reads per HiC replicate.   623 \nb) Genome-wide reproducibility analysis within (teal and pink) and between (gray) conditions, 624 \nusing HiC-Rep at 500 kb resolution. 625 \nc) Compartment scores (c-scores) in matched genomic bins between scr-CTRL and H1-low 626 \nHiC contact data at 100 kb resolution. C-score is calculated with cscoretool and compartment 627 \nshifts are defined as |∆c-score| > 0.2, compartment changes where a bin’s c-score changes sign 628 \nand A-shift or B-shift where bins shift within the same compartment.   629 \nd) Example contact maps showing domains in scr-CTRL and H1-low Micro-C. Chr 4 zoom, 100 630 \nkb resolution, 80-100 M paired end reads per HiC replicate 631 \ne) Example domain boundaries called by cooltools in scr-CTRL and H1-low Micro-C. Chr 1: 32 632 \nMb – 34.7 Mb.  633 \nf) Genome-wide boundary strength distributions for different window sizes for scr-CTRL and 634 \nH1-low Micro-C.    635 \ng) Example contact maps showing loops in scr-CTRL and H1-low Micro-C. Chr 4 zoom, 10 kb 636 \nresolution, 80-100 M paired end reads per HiC replicate.  637 \nh) Aggregate peak analysis of loops called by HICCUPS centered around loops called only in 638 \nscr-CTRL, only in H1-low, or in both datasets.  639 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 15 \ni) Overlap of loops called by juicertools HICCUPS in scramble control and H1-low Micro-C. 640 \nOverlap is defined as both anchors having 90% overlap.   641 \nj) Aggregate peak analysis of loops called by HICCUPS centered around CTCF loops, defined 642 \nas those overlapping RAD21 and SMC ChIP peaks (called by juicertools motif), and non-CTCF 643 \nloops. APA is site +/- 10 kb with 1 kb bins.  644 \n 645 \nFigure 6: Transcriptional upregulation and increase in accessible chromatin noted upon 646 \nH1 depletion while opportunistic TF binding does not drive expression changes.  647 \na) ERCC spike-in normalized RNA-seq. 1525 genes are significantly upregulated upon H1 648 \ndepletion with |log2(fold-change)| >1 and padj < 0.05 (Benjamini-Hochberg). H1-5, H1-2, H1-3 649 \namong the significantly downregulated genes at –1.64, -1.69 and –1.39 log2 fold change 650 \nrespectively. 651 \nb) CHEP-seq volcano plot of H1-low vs. scr-CTRL proteins associated with chromatin, n=3 652 \nbiological replicates for each condition, significance threshold defined as |log2(fold-change)| >1.  653 \nc) ENRICHR analysis of transcription factors (TFs) with significantly enriched targets in 654 \nupregulated genes in H1-low vs. scr-CTRL. TF target gene sets based on ENCODE TF 2015 655 \nChIP-seq peak overlap with target TSS in K562 cells.  656 \nd) Gene set Enrichment analysis (GSEA) of top hits identified by ENRICHR (c) based on 657 \nHarmonizome ENCODE Transcription Factor Targets database. Normalized enrichment score 658 \n(NES): CBX8 1.615, CBX2 1.56 and SUZ12 1.17, respectively.  659 \ne) GSEA of GATA1 (identified by CHEP-seq, (b)) target genes based on H1-low vs. scr-CTRL 660 \nfold-change based on Harmonizome ENCODE Transcription Factor Targets database. 661 \nNES:0.859 p=1.00.  662 \nf) ATAC-seq peaks base mean over fold change with 2678 peaks significantly upregulated in 663 \nH1-low condition. |log2(fold-change)| >1 and padj. <0.05 (Benjamini-Hochberg).  664 \ng) ATAC-seq peaks annotated by genomic features. 665 \nh) M-A plot of H3K27me3 CUT&Tag fold-change at H3K27me3 CUT&Tag peaks. 2453 peaks 666 \nsignificantly downregulated in H1 low condition (|log2(fold-change)|>1 and padj. <0.05, Benjamini-667 \nHochberg criterion). 668 \ni) H3K27me3 H1-low upregulated, H1-low downregulated and unchanging peaks annotated by 669 \ngenomic feature. 670 \nj) H3K27me3 signal over H1-low downregulated and all H3K27me3 peaks. Peak centers 671 \nshown +/- 8kb with a bin size 100 bp.  672 \nk) Enrichment of H1-low/scr-CTRL Log2FC ATAC-seq signal over H3K27me3 CUT&Tag peaks 673 \ncompared to published Roadmap Epigenomics ChIP-seq peaks for K562 40 674 \nl) H3K27me3 signal over upregulated vs unchanging genes measured by RNA-seq. Signal 675 \nplot over +/-5 kb around TSS of genes with a bin size of 100 bp.  676 \n  677 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 16 \nFigure 7: Chromatin fiber compaction is unaffected by H3K27me3 depletion but varies 678 \nwith changes in accessibility  679 \na) ATAC-seq accessibility signal over Cut&Tag H3K27me3 peaks +/- 5 kb around peak centers 680 \nshown with a bin size of 100 bp.  681 \nb) RICC-seq FLD plot over downregulated vs unchanging H3K27me3 peaks in H1-low vs scr-682 \nCTRL. n= 1 biological replicate shown as ratio over subset genomic DNA control. Signal 683 \nnormalized to mononucleosome peak.   684 \nc) RICC-seq FLD plot over unchanging ATAC-seq peaks and genome-wide smoothed (30 nt 685 \nrolling average). n=1 biological replicate shown corrected by biological replicate-specific 686 \ncorrection factor and ratio over similarly subset and corrected sample-matched genomic DNA 687 \ncontrol. Signal normalized to mononucleosome peak.   688 \nd) RICC-seq FLD plot over upregulated ATAC-seq peaks and genome-wide, as in (c). 689 \ne) RICC-seq FLD plot over upregulated ATAC-seq peaks, H3K27me3, and H3K27ac, as in (c). 690 \n 691 \nMethods 692 \n 693 \nCell culture and preparation 694 \n 695 \nBJ-5ta Fibroblasts 696 \nCells were grown in DMEM supplemented with 10% FBS and 1% Penicillin-Streptomycin. Cells 697 \npassaged every three days at 1:3 splits when cells are about 75% confluent. To harvest, cells 698 \nwere contact inhibited and trypsinized with 4 ml Trypsin to lift then quench with 8 mL media. 699 \nCells were washed and spun down with PBS and 2 million cells per technical replicate 700 \nharvested per plug.  701 \n 702 \nBudding yeast 703 \nFor RICC-seq experiments on budding yeast, we used Saccharomyces cerevisiae W303 704 \nRAD5+ wild-type strains (Gift from Xiaolan Zhao). Overnight starter cultures were diluted into 705 \nfresh YPD to an initial OD600 of ~0.1 and grown at 30°C with shaking for at least 4 hours to allow 706 \ncells to enter mid-log growth phase with an OD600 of 0.5–1.0. Cells were harvested in 50 mL 707 \nconical tubes by centrifugation at ~900 × g for 3 min, washed once in 25 mL PBS, transferred to 708 \n1.5 mL microcentrifuge tubes, and washed again in 1 mL PBS (30 s at ~900 × g between 709 \nwashes). Pellets were resuspended in PBS and mixed 1:1 with molten low-melting-point (LMP) 710 \nagarose. Yeast cells were embedded directly in agarose plugs without prior zymolyase 711 \ntreatment, as pilot experiments indicated it was not required for efficient lysis and downstream 712 \nprocessing. The cell–agarose suspensions were immediately cast into plug molds and solidified 713 \non ice for 10 min. Plugs were released into 2 mL tubes (3 plugs per tube) and irradiated on ice 714 \nin a 50 mL conical tube with 1000 Gy X-ray ionizing radiation at ~120 Gy/min over the course of 715 \n8 minutes and 20 seconds, while non-irradiated tubes were kept on ice for the same duration. 716 \nFollowing irradiation, plugs were immediately incubated in 950 µL RICC lysis buffer 717 \nsupplemented with 50 µL Proteinase K at 25°C with gentle shaking for 48 h. After cell lysis, the 718 \nrest of the RICC-seq protocol proceeded as described further below.  719 \n 720 \n 721 \nChicken Red blood cell Whole chicken blood was ordered from Pel-Freez Biologicals, Whole 722 \nChicken Blood, Non-Sterile with Alsever’s Media, Cat. No. 33133-1. Cell concentration was 723 \ndetermined using a hemocytometer corresponding to ~9.8 × 10^8 cells/mL. A 5 mL aliquot was 724 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 17 \ntransferred to a 15 mL conical tube and pelleted at 200 × g for 5 min, the supernatant was 725 \nremoved, and the cells were washed twice in PBS at 200 × g, 5 min for each wash. The pellet 726 \nwas spun again at 200 × g for 5 min and kept on ice, then resuspended in 2.5 mL PBS to 727 \ngenerate a suspension at 1.5 × 10^2 cells/mL. Cells were aliquoted at 750 µL into 2 mL tubes, 728 \nequilibrated at 37°C for 1 min, and mixed 1:1 (v/v) with pre-warmed 2% low–melting point 729 \nagarose. The solution was pipetted carefully into plug molds, avoiding bubbles and allowed to 730 \nsolidify on ice. Plugs were then transferred into 400 µL cold PBS and either irradiated with 300 731 \nGy ionizing radiation on ice or kept on ice as non-irradiated 0 Gy and PLC controls. Following 732 \nirradiation, plugs were incubated in 1,170 µL lysis buffer supplemented with 30 µL Proteinase K; 733 \nsamples were kept on ice for 2–3 h post-lysis and then transferred to room temperature for 734 \novernight incubation. 735 \n 736 \nK562 H1 depletion and scrambled control cell culture and induction K562 cells expressing a 737 \ndox-inducible dCas9-KRAB-P2A-mCherry were generated by lentiviral transduction with the 738 \nTET-ON vector pAAVS1-NDi-CRISPRi (addgene #73497). Transduced cells were selected with 739 \n200ug/ml G418 and inducible dCas9-KRAB-P2A-mCherry cells were further selected with 740 \nfluorescence-activated cell sorting (FACS) after 3 days of Doxycycline treatment (1ug/ml). 741 \nSelected cells were allowed to grow in the absence of doxycycline and then transduced with 742 \npU6-sgRNA EF1Alpha-puro-T2A-BFP (addgene #60955) which was engineered with 4 in 743 \ntandem U6-sgRNA expression cassetes, each expressing a subtype specific H1 sgRNA namely 744 \nH1.5 (GGCAGGAGCGGTTTCCGACA), H1.2 (GGCTGCCGCCGGCTATGATG), H1.3 745 \n(GGCTGCCGCCGGCTATGATG) and H1.4 (GGCCAAGCCTAAGGCTAAAA). As control, cells 746 \nwere also transduced with a non targeting pU6-sgRNA EF1Alpha-puro-T2A-BFP 747 \n(GCACTACCAGAGCTAACTCA). Cells expressing constitutive high levels of sgRNAs were 748 \nselected by combining puromycin selection (10ug/ml) and FACS to collect the top BFP positive 749 \ncells. H1 depletion was induced by supplementing culture media with doxycycline 1ug/ml for 5 750 \ndays and H1 content assayed by RP-HPLC of acid extracted histones. 751 \n 752 \nCells were grown in IMDM media (Thermo Scientific #12440046) supplemented with 10% heat-753 \ninactivated FBS (Sigma-Aldrich F4135) and 1% Penicillin-Streptomycin. Cells were passaged 754 \nevery two days, seeding 2.4 M cells in a T150 flask. Starting five days before experimental 755 \nharvest, doxycycline was added to the growth media of both H1-low and scr-CTRL cells to a 756 \nconcentration of 3 mg/mL. Cells were grown in the doxycycline media for five days, following the 757 \nregular splitting schedule, then harvested for experiments on day five. 2 million cells per 758 \ntechnical replicate harvested.  759 \n 760 \nNaïve B cell isolation 761 \nSpleens from wild-type C57BL/6J mice (Jackson Laboratories, strain 000664) were 762 \nmechanically dissociated and passed through a 40-µm strainer. Red blood cells were lysed 763 \nusing ACK buffer (Lonza). Resting B cells were then enriched by negative selection using anti-764 \nCD43 (Ly48) magnetic microbeads (MACS, Miltenyi Biotech), according to the manufacturer’s 765 \ninstructions. Briefly, the cell suspension was incubated with 30 µl of CD43 magnetic beads 766 \ndiluted in 270 µl of PBS per spleen for 20 mins at 4 °C. The mixture was then applied to an LS 767 \nMACS Column on MACS Separator. The flow-through containing naïve B cells was collected 768 \nand resuspended in PBS supplemented with 0.5% bovine serum albumin (BSA) and 2 mM 769 \nEDTA. 770 \n 771 \n 772 \n 773 \n 774 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 18 \nRICC-seq 2.0 library preparation 775 \n 776 \nRICC-seq 2.0 was performed on multiple different cell types each with their own growing 777 \ntechniques and harvest conditions as outlined above. Adherent cells were grown to ~100% 778 \nconfluence and rested ~1 day to enrich G0. Cells were were washed in warm PBS (Gibco 779 \n14190-144), detached with 0.05% Trypsin-EDTA or Accutase at 37 °C (Invitrogen 25300-054), 780 \ncounted with trypan blue (Gibco 15-250-061), pooled, and pelleted (200–300 RCF, 4 min). 781 \nPellets were resuspended in PBS to 50 M cells/mL and embedded to final concentration 1% 782 \nlow-melt agarose (Sigma Type VII-A, A0701-25G) kept at 37 °C, using Bio-Rad plug molds 783 \n(1703713). 2 million cells per plug were used for the K562 cells harvested 5 days post 784 \ndoxycycline induction.  785 \n 786 \nPlugs were irradiated to a total dose of 300 Gy for most conditions except 1000 Gy for yeast 787 \nand lysed overnight (24-48 h, RT/20 °C with shaking) in RICC lysis buffer containing 20% N-788 \nlauroylsarcosine (Sigma L7414-50ML), Proteinase K (NEB P8107), and EDTA (Thermo 789 \n15575020), then washed for ~5 h as follows: TE + 1 mM PMSF, 30 min at 4 °C; TE + 1 mM 790 \nPMSF, 45 min at 4 °C; TE, 60 min RT; TE, 60 min RT; TE + RNase A 0.1 mg/mL (Sigma R4642-791 \n250MG), 45 min at 37 °C; TE + RNase A 0.1 mg/mL, 45 min at 37 °C; TE, 60 min RT.  792 \n 793 \nGenomic DNA control plugs were equilibrated in 0.5 M Tris-HCl pH 8 on ice (three 15-min 794 \nexchanges, then +400 µL Tris) and irradiated identically, followed by the same washes. ssDNA 795 \nwas eluted by incubating plugs in 0.1 N NaOH (200 µL, 15 min), neutralized with 1 M Tris-HCl 796 \npH 7.5 (100 µL) + 2 mM EDTA (RT, ~4 h), and purified with the Zymo RNA Clean & 797 \nConcentrator-5 kit (R1016) using modified speeds (binds at 3,800 RCF; washes at 10,000 798 \nRCF); columns were loaded with 600 µL RNA Binding Buffer then 900 µL absolute ethanol and 799 \neluted twice in 10 µL 10 mM Tris pH 8 (total ~18 µL). K562 eluate samples were purified with 800 \ncustom-made carboxyl-coated beads to facilitate batch processing46.   801 \n 802 \nSpike-ins were included post elution into each sample before library preparation. A genomic 803 \nyeast DNA ladder digested with MNase was included for cross-experiment comparability. 804 \n 805 \nEnds were dephosphorylated with rSAP (NEB M0371) in CutSmart buffer (21 µL total; 37 °C, 1 806 \nh), then 5′-phosphorylated with T4 PNK (Enzymatics Y9040L) in the presence of DTT (125 mM 807 \nstock to 5 mM final; Sigma 43815-1G) and ATP (500 µM stock to 5 µM final) in a 25 µL reaction 808 \n(37 °C, 1 h); MgCl₂ (Invitrogen AM9530G) and spermidine (Sigma 85558-1G).  809 \n 810 \nSRSLY ligation was done with SRSLY P5/P7 adapters, T4 DNA ligase (Enzymatics L6030-LC-L) 811 \nwith 1× ligase buffer and 18.5% PEG-8000 (50% stock) at 37 °C for 1 h; adapters were added at 812 \n~250 nM each. Post-ligation clean-up used Zymo R1016 as above, eluting in 12 µL 10 mM Tris 813 \npH 8. K562 were cleaned up using custom-made size selection beads46.  814 \n 815 \n Libraries were barcoded by PCR in 50 µL with KAPA HiFi PCR Mix (2×; KAPA KK2602), 2 µM 816 \neach i5/i7 barcode primer, and 21 µL template; cycling was 98 °C 3 min; 5 initial cycles of 98 °C 817 \n30 s, 65 °C 30 s, 72 °C 1 min; 72 °C 1 min. A side-reaction qPCR on a 5 µL aliquot determined 818 \nadditional cycles to retain exponential amplification (BJ, K562 typically 12 total cycles). gDNA 819 \ncontrol, 0 Gy, and irradiated conditions were processed in parallel through all steps. Rad source 820 \n1800 Q4 X-ray Irradiator used delivering a dose rate of 123.1 Gy/min on the top shelf. BJ, K562, 821 \nChicken red blood cells were irradiated with a total dose of 300 Gy and Budding Yeast at 1000 822 \nGy. 823 \n 824 \n 825 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 19 \n 826 \n 827 \nHi-C library preparation 828 \n 829 \nHiC was performed using the HiC 3.0 protocol followed by NEBNext Ultra II library preparation. 830 \nBriefly, pellets of 5 M cells were crosslinked first with 1% formaldehyde in HBSS for 10 minutes, 831 \nthen with 3 mM DSG for 40 minutes, and subsequently snap frozen in liquid nitrogen. Cells were 832 \nlysed with a Dounce homogenizer in a buffer containing 0.2% NP-40, followed by chromatin 833 \nsolubilization by SDS, quenched with Triton X-100. Chromatin was digested with a cocktail of 834 \nDdeI (400 U) and DpnII (400 U), incubated overnight at 37°C. DNA ends were then biotinylated 835 \nusing Klenow polymerase and a dNTP mix containing biotin-14-dATP, then proximity ligation 836 \nwas performed using T4 DNA ligase. After biotin fill-in and proximity ligation, crosslinks were 837 \nreversed and proteins digested with an overnight 65°C incubation with Proteinase K. DNA was 838 \nthen purified and concentrated via phenol-chloroform extraction followed by ethanol precipitation 839 \nand passage through Amicon filter tubes. End repair was performed with T4 polymerase and 840 \ndATP/dGTP, then DNA was sonicated to obtain a distribution of sequenceable DNA lengths, 841 \nwhich was further size selected using AMPure XP beads. At this point, we switched to NEBNext 842 \nUltra II library preparation, following the manufacturer’s protocol, then sequenced samples on 843 \nan Illumina NovaSeq X Plus platform to a depth of 50 - 90 million read pairs per replicate for two 844 \ntechnical replicates. 845 \n 846 \nIn-situ Micro-C library preparation 847 \n 848 \nTwo biological replicates of Micro-C were performed using an in-situ proximity ligation-based 849 \nprotocol, adapted for our purposes from previously published Micro-C protocols, as described 850 \nbelow. 851 \n  852 \nCrosslinking 853 \nPellets of 10 million cells were crosslinked with 1% formaldehyde for 10 minutes, quenched with 854 \n1 M Tris-HCl pH 7.5, washed with DPBS, then crosslinked with 3 mM DSG (synthesized in 855 \nhouse) for 45 minutes. DSG crosslinking was quenched with 1 M Tris-HCl pH 7.5, and cells 856 \nwere washed with DPBS before snap freezing in liquid nitrogen. 857 \n  858 \nMNase titration 859 \nCell pellets were thawed on ice and resuspended in buffer MB128. The cell suspension was split 860 \ninto five aliquots of 2 million nominal cells, 2-3 of which aliquots were used for MNase titration, 861 \nand 2-3 of which were used as experimental samples. Experimental samples were kept on ice 862 \nwhile the MNase titration was took place (typically about three hours). Titration was carried out 863 \nby extracting nuclei by incubating cells in MB1 for 20 minutes, spinning down and resuspending 864 \nnuclei in MB1, then adding varying volumes of MNase (NEB M0247S) to aliquots. Typically, 865 \nvolumes between 2 and 6 uL of 1:10 diluted MNase produced the desired level of digestion. 866 \nMNase digestion was performed for 10 minutes at 37°C on a thermomixer, then stopped with 867 \nEGTA and incubation at 65°C. Digested chromatin was treated with Proteinase K (NEB P8107) 868 \nand RNase A (Sigma-Aldrich R4642) and incubated for 2 hours at 65°C to reverse crosslinks 869 \nand digest proteins. DNA was then purified with phenol-chloroform extraction followed by 870 \npassage through a Zymo DNA Clean & Concentrator kit. The digestion profiles of the titration 871 \nsamples were assessed by quantifying the DNA by Qubit and running 1 ng on a TapeStation. 872 \nMNase concentrations leading to a major mononucleosome peak and small dinucleosome peak 873 \nwere identified and used for the full protocol with the remaining aliquots left on ice. 874 \n  875 \nMicro-C 876 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 20 \nNuclei were spun down, resuspended in MB1 with the predetermined concentration of MNase, 877 \nand digested as in the titration. The digested sample was washed with MB228 then end repaired 878 \nand end labeled with PNK (NEB M0201L) and Klenow polymerase (NEB M0210M) with 879 \nbiotinylated dATP and dCTP. After enzyme inactivation with EDTA and incubation at 65°C, the 880 \nsample was washed with MB328. Proximity ligation was carried out using T4 ligase (NEB 881 \nM0202L) and unligated fragments were digested with Exonuclease III (NEB M0206L). The 882 \nsample was then incubated overnight with Proteinase K and RNase A at 65°C to reverse 883 \ncrosslinks and digest proteins. DNA was purified with phenol-chloroform extraction followed by 884 \npassage through a Zymo DNA Clean & Concentrator kit, then ligated mononucleosome pairs 885 \nwere selected for by running the sample on a 2% agarose gel, excising and extracting the 886 \ndinucleosome-sized band, and pulling down with streptavidin beads. Library preparation was 887 \nthen carried out using the NEBNext Ultra II kit, following manufacturer’s instructions. Libraries 888 \nwere sequenced twice on an Illumina Novaseq X Plus to approximately 100-400 million 2 x 150 889 \nbp paired end reads per replicate per run. Reads from the two runs were combined for a final 890 \ndepth of 500 - 600 million reads per technical replicate, or 1.6 - 2 billion reads per biological 891 \nreplicate. 892 \n 893 \nATAC-seq library preparation 894 \n 895 \nATAC-seq was performed following the Omni-ATAC-seq protocol, using Tn5 made in-house with 896 \nthe Open-Tn5 method. Tn5 was loaded with Illumina adaptors by incubating equal volumes of 1 897 \nmg/mL Tn5 and 1 𝜇M adaptor together for 10 minutes at room temperature immediately before 898 \nbeing used for tagmentation. Briefly, the Omni-ATAC-seq protocol involved harvesting cells in 899 \naliquots of 50,000 cells – in this case, we did two separate harvests (biological replicates) with 900 \n3-5 aliquots (technical replicates) per condition – followed by light permeabilization in a buffer 901 \ncontaining 0.1% NP-40, 0.1% Tween, and 0.01% digitonin. The samples were then tagmented 902 \nwith Tn5 at 37°C for 30 minutes, cleaned up with a Zymo DNA Clean & Concentrate kit, and 903 \nPCR amplified with barcoded primers for an optimized number of cycles determined with a side 904 \nqPCR reaction. Following amplification, libraries were cleaned up, quantified, and sequenced on 905 \nan Illumina NextSeq 2000 platform to a depth of 30 - 50 million read pairs per technical 906 \nreplicate. 907 \n 908 \nCUT&Tag library preparation 909 \n 910 \nCut&Tag libraries were prepared using the Epicypher protocol. K562 cells were lightly fixed 911 \nusing 0.1% formaldehyde for 1 minute, spun down resuspended in cell freezing media and 912 \nfrozen down post day 5 doxycycline induction of H1 depletion. Pellets were then hawed, spun 913 \ndown, washed in PBS and 100,000 cells per technical replicate counted. The cut and tag 914 \nprotocol proceeded as outlined in the Epicypher v1.7 protocol. TN5 for these CUT&Tag libraries 915 \nwas made in house using our previously published protocol47. The antibody used for mapping 916 \nH3K27me3 was CST 9733 at a 1:100 final concentration.  917 \n 918 \nRNA extraction and RNA-seq library preparation 919 \n 920 \nTriplicate cultures of cells expressing H1 sgRNAs and Scramble sgRNA were induced with 921 \nDoxycycline 1ug/ml for 5 days. Cells were backdiluted during treatment in order to maintain cell 922 \ncultures in exponential growth phase. Five mmillion cells were collected on day 5. One million 923 \ncells were ressuspended in Trizol for RNA extraction and four million cells used for acid extraction 924 \nand HPLC for validation of H1 depletion. Total RNA was extracted using Direct-zol RNA Miniprep 925 \nKit (Zymo Research). Standard mRNA -Seq (poly(A) selection) was performed at Azenta Life 926 \nSciences. Libraries were performed incorporating unique molecular identifers during adapter 927 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 21 \nligation and External RNA Controls Consortium (ERCC) spike -ins were added to ech sample 928 \nbefore reverse transcription. 929 \n 930 \nChromatin Enrichment for Proteomics (ChEP) 931 \n 932 \nChromatin-bound proteins were isolated using the ChEP protocol as described by Kustatscher et 933 \nal.48 with minor modifications. Briefly, cells were formaldehyde -crosslinked (1%, 10  min), nuclei 934 \nwere isolated under hypotonic conditions, and chromatin was enriched by sequential high -935 \nstringency washes under denaturing conditions (2% SDS, 8 M urea). Crosslinked chromatin was 936 \nsonicated and used for quantitative Mass Spectrometry analysis. 937 \n 938 \nMass-spectrometry  939 \n 940 \nSamples were alkylated with 30mM IAA for 45min at RT in the dark. Reactions were then desalted 941 \ninto 50mM NH4HCO3 using ZebaSpin 7k columns (ThermoFisher) and eluates were 942 \nsupplemented with trypsin (0.1mg/ml) and digested for 2h at 37C. At the end of the 2h, samples 943 \nwere supplemented with additional trypsin and digestions allowed to proceed overnight. 944 \nDigestions were quenched with 1% formic acid, dried in SpeedVac and then resuspended in 130 945 \nµl MS Sample Buffer (0.1% formic acid, 1% acetonitrile in water). 946 \n 947 \nLCMS analyses were performed on a TripleTOF 5600+ mass spectrometer (AB SCIEX) coupled 948 \nwith M5 MicroLC system (AB SCIEX/Eksigent) and PAL3 autosampler. LC separation was 949 \nperformed in a trap-elute configuration, which consists of a trap column (LUNA C18(2), 100 Å, 5 950 \nμm, 20 X 0.3 mm cartridge, Phenomenex) and an analytical column (Kinetex 2.6 μm XB-C18, 100 951 \nÅ, 50 X 0.3 mm microflow coumn, Phenomenex). The mobile phase consisted of water with 0.1% 952 \nFA (phase A) and 100% ACN containing 0.1% FA (phase B). 953 \n 954 \nPeptides in MS Sample Buffer were injected into a 50-μl sample loop, trapped and cleaned on 955 \nthe trap column with 3% mobile phase B at a flow rate of 25 μl/min for 4 min before being 956 \nseparated on the analytical column with a gradient elution at a flow rate of 5 μl/min. The gradient 957 \nwas set as follows: 0–24 min: 3% to 35% phase B, 24–27 min: 35% to 80% phase B, 27–32 958 \nmin: 80% phase B, 32–33 min: 80% to 3% phase B, and 33–38 min at 3% phase B. An equal 959 \nvolume of each sample (30 μl) was injected four times, once for information-dependent 960 \nacquisition (IDA), immediately followed by DIA/SWATH in triplicate. Acquisitions of distinct 961 \nsamples were separated by a blank injection (80 µl MS Sample Buffer) to prevent sample 962 \ncarryover. The mass spectrometer was operated in positive ion mode with EIS voltage at 5200 963 \nV, Source Gas 1 at 30 psi, Source Gas 2 at 20 psi, Curtain Gas at 25 psi, and source 964 \ntemperature at 200°C. 965 \n 966 \nRICC-seq data processing 967 \n 968 \nRICC-seq library alignment and fragment length distribution (FLD) generation 969 \nAlignment. Illumina FASTQ paired end reads were aligned with Bowtie2 to prebuilt 970 \nBowtie2Index references (e.g., hg38/hg19/mm10), applying a mapping-quality cutoff (MAPQ ≥ 971 \n30) and optional removal of reference blacklist regions (defaults provided per genome when 972 \navailable). Per-sample reads to the yeast genome E2F were also aligned and used for 973 \ndownstream spike in correction and length bias normalization.  974 \nSubsetting. For each input BAM, we performed a name sort (samtools sort -n) to ensure proper 975 \npairing semantics for downstream intersection. Paired-end alignments were then intersected 976 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 22 \nwith each provided BED/peak set using bedtools pairtobed (criterion: overlap of the paired 977 \nfragment span with the feature set; -type ospan). The script iterates over all BAM×BED 978 \ncombinations. The resulting per-combination subset BAMs (reads whose paired fragment spans 979 \noverlapped the specified regions) were carried forward to downstream analyses. 980 \n 981 \nFragment length histogram generation. For each input BAM, we computed paired-end insert-982 \nsize distributions with Picard Toolkit (Broad Institute) CollectInsertSizeMetrics. The task 983 \nexcludes PCR/optical duplicates. Picard was invoked with DEVIATIONS=10.0 to capture long-984 \ntail fragments up to mean ± 10 SD, MINIMUM_PCT=0.05 to require ≥5% of pairs for a stable 985 \nestimate, and HISTOGRAM_WIDTH=700. For each BAM, the script writes (i) a PDF histogram 986 \n<basename>_hist.pdf and (ii) a tabulated metrics log 987 \n<basename>_full_hist_graphwithoutdups.log (median/mean/stdev, read counts, and percentile 988 \ncutoffs) for downstream QC. The log files for both spike in controls and samples were then 989 \ncompiled to use for plotting Fragment Length Distributions (FLD) and correcting.  990 \nFLD normalization and correction  991 \nFragment length distributions were corrected in two stages. First, per-sample spike-in 992 \nnormalization was applied using a biological replicate-specific scaling factor defined as the ratio 993 \nof the mean spike-in read depth of all technical replicates within a biological replicate (REF𝑏) to 994 \nthe individual sample’s spike-in depth (𝐷𝑖). Each replicate distribution was multiplied by 995 \nREF𝑏/𝐷𝑖to equalize spike-in coverage across replicates. 996 \nSecond, to correct for fragment-length bias, we used the spike-in–scaled curve and its length-997 \nbias–corrected counterpart calculated by comparing the spike-in ladder FLD fragment loss after 998 \nsequencing to the ladder input on TapeStation. For each technical replicate, a length-bias 999 \ncorrection factor was computed per base pair. Within each biological replicate, these per-1000 \ntechnical replicate correction curves were averaged to obtain a mean biological replicate-1001 \nspecific falloff profile which was used to correct each averaged biological replicate curve.  1002 \nEach curve was then normalized to the signal of the mononucleosome at 180 bp and lightly 1003 \nsmoothed (10 bp rolling average). These biological replicate-level normalized profiles were used 1004 \nto compute condition means ± 95 % confidence intervals and to perform per-base Welch’s t-1005 \ntests, with significant contiguous regions (≥ 5 bp) highlighted in downstream figures. 1006 \nTo summarize a condition (e.g., SCRM or dH1), we stacked all available replicates from that 1007 \ncondition and compute the condition mean curve as the pointwise average across biological 1008 \nreplicates. 95% confidence intervals were obtained using a Student’s t interval across biological 1009 \nreplicates, i.e., mean ± 𝑡0.975, 𝑛−1 ⋅ SD/√𝑛, where 𝑛 is the number of biological replicates at that 1010 \nbp (using exact 𝑡for small 𝑛, ~1.96 as 𝑛grows). For between-condition comparisons we plotted 1011 \nthe pointwise dH1/SCRM mean ratio and assessed significance at each bp with a two-sided 1012 \nWelch’s t-test computed over the underlying biological replicate curves, shading only significant 1013 \nsegments of length ≥ 5 bp as significant differences. 1014 \nAll shown subset RICC-seq curves were length bias corrected by the previously calculated 1015 \nrespective biological replicate correction factor and divided by the corrected genomic DNA 1016 \ncontrol (PLC) curve to produce the signal over the genomic background per condition.  1017 \nHiC and Micro-C analysis 1018 \n 1019 \nAlignment and QC filtering 1020 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 23 \nBasic alignment and quality control (QC) filtering for both HiC and Micro-C was done based on 1021 \nthe Dovetail Genomics analysis pipeline (micro-c.readthedocs.io), in which ligation pairs were 1022 \naligned with bwa mem (v 0.7.17) using two-sided alignment, then valid ligation events were 1023 \nidentified with pairtools parse (v 0.3.0). PCR duplicates were removed and final bam and pairs 1024 \nfiles were created with pairtools split. From pairs files, ICE balanced mcool files with a base 1025 \nresolution of 500 bp were made using cooler cload and zoomify (v 0.8.6) with default 1026 \nparameters, and hic files with a base resolution of 500 bp were made using juicer_tools pre (v 1027 \n1.22.01) with default parameters. Pairs, mcool, and hic files were then used for subsequent 1028 \nanalyses, described below. 1029 \n  1030 \nHiC analysis 1031 \nP(s) curves were made from balanced mcool files at 10 kb resolution using cooltools (v 0.5.4) 1032 \nexpected_cis with smoothing. For correlation analysis with HiCRep49 , we converted our HiC 1033 \npairs files to full contact matrices at 500 kb resolution using juicer and straw (v 1.6)50, then 1034 \ncomputed pairwise SCC scores between all replicates, within and between conditions. 1035 \nChromosome compartment analysis was carried out using the cscoretool package (v1.1)51 . 1036 \nFirst, cscoretool was run on each chromosome for each condition, using pairs files at 100 kb 1037 \nresolution. We next calculated Spearman’s correlation coefficient for each chromosome’s c-1038 \nscores with H3K36me3 ChIP signal (from ENCODE52 ENCSR000AKR) and flipped the sign of 1039 \nthe c-score for negatively correlated chromosomes so that positive c-scores consistently 1040 \ncorrespond to the gene-dense A compartment (supp fig ref). Chromosomes for which correlation 1041 \ndid not pass a significance threshold of p < 0.01 were excluded from the analysis. Differential 1042 \ncompartment scores were found by subtracting scrambled control c-scores from H1-low c-1043 \nscores in matched genomic bins, and shifts were defined as |∆ c-score | > 0.25, and negative to 1044 \npositive = B to A, positive to negative = A to B, increasing within positive or negative = A-shifted, 1045 \ndecreasing within positive or negative = B-shifted. 1046 \n  1047 \nMicro-C domain and loop calling 1048 \nDomain analysis was performed using the cooler/cooltools suites. From balanced mcool files, 1049 \nthe cooltools insulation tool was used to find insulation strength and call domain boundaries at a 1050 \nbase resolution of 10 kb. Loop calling was performed on hic files filtered to remove inward 1051 \nligations (see below). Loops were called at 5 kb resolution using the juicer hiccups tool with KR 1052 \nnormalization. CTCF loops were identified with juicer motifs using publicly available SMC3 1053 \n(ENCSR000EGW), RAD1 (ENCSR000FAD), and CTCF (ENCSR000EGM) ChIP-seq in K56252. 1054 \nDifferential loops were found by calling loops on biological replicates, finding reproducible loops 1055 \nwithin conditions, and then comparing the consensus loop lists between conditions. Aggregate 1056 \npeak analysis was performed using the juicer apa tool. 1057 \n  1058 \nShort-range contact probability 1059 \nContact probability is calculated from pairs files by first separating reads based on ligation 1060 \norientation, subtracting positions of cis pairs to find contact distance, then plotting a histogram of 1061 \nthose contact distances. It is necessary to separate pairs by ligation orientation because contact 1062 \nprobability curves for each orientation are shifted relative to each other – this is due to the fact 1063 \nthat read positions will correspond to the entry or exit of each nucleosome in a pair depending 1064 \non their ligation orientation, and accordingly, the contact distance will include or not the lengths 1065 \nof the nucleosomes. Pairs are separated by ligation orientation based on the strand information 1066 \nas follows: +/- pairs were designated “inward” ligations, -/+ pairs were designated “outward” 1067 \nligations, and +/+ or -/- pairs were designated “tandem” ligations. To look at short-range contact 1068 \nprobability in specific genomic regions, bam files were first intersected with bed regions using 1069 \nbedtools intersect (v 2.30.0) (cite), then pairs with read IDs matching reads in the intersected 1070 \nbam were used for contact distance calculation. 1071 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 24 \n  1072 \nNucleosome contact peaks are called from the contact probability curves by finding where the 1073 \nsecond derivative of the curve is negative. NRL is calculated from these peaks by finding the 1074 \naverage basepair distance between maxima and N/N+odd:N/N+even ratios of nucleosome 1075 \ncontacts are calculated by summing N/N+3 and N/N+5 contact probabilities, summing N/N+2 1076 \nand N/N+4 contact probabilities, and diving the two values. A higher ratio indicates an 1077 \nenrichment of N/N+odd contacts. 1078 \n 1079 \nRNA-seq analysis 1080 \n 1081 \nRNA-seq data was analyzed by first extracting UMIs and filtering for unique reads using fastp (v 1082 \n0.24.0) (cite) with parameters --umi_loc per_read --umi_skip 2 --umi_len 5 to match the UMI 1083 \nscheme used by Genewiz. The UMI-filtered data was then aligned to an index composed of 1084 \ncombined GRCh38 and ERCC transcriptomes using STAR alignment (v 2.7.11b) (cite) 1085 \n(parameters: --outFilterType BySJout --outFilterMultimapNmax 15 --alignSJoverhangMin 8 --1086 \nalignSJDBoverhangMin 1 --outFilterMismatchNmax 500 --outFilterMismatchNoverReadLmax 1087 \n0.05 --alignIntronMin 20 --alignIntronMax 1000000). A counts matrix of paired-end fragments 1088 \nover genes was made from the aligned reads using featureCounts (subread v 2.0.6) (Liao Y, 1089 \nSmyth GK and Shi W (2014)) and the combined gencode.v38-ERCC transcriptome, then 1090 \nDESeq2 (v 1.42.0) was run on genes with at least 10 total counts, using ERCC genes as the 1091 \ncontrol gene set with estimateSizeFactors. 1092 \n 1093 \n 1094 \nATAC-seq data processing  1095 \n 1096 \nATAC-seq data was aligned using Bowtie253 filtered, and shift-corrected using deeptools 1097 \nalignmentSieve parameter, which shifts the plus strand by +4 and the minus strand by –5 to 1098 \naccount for the Tn5 homodimer which leaves 9bp of DNA between the two Tn5 molecules. 1099 \nPeaks were called using macs254,55 reproducible peaks were found between replicates using 1100 \nIrreproducible discover rate (IDR)56 , and a master peak set was made by listing reproducible 1101 \npeaks from both conditions and concatenating adjacent peaks. GenomicRanges,, an R 1102 \npackage, is used to load the master peak for further downstream analysis in R. Differential peak 1103 \nanalysis was carried out with DESeq2 separately on peaks within 5 kb of a TSS and those 1104 \nfurther than 5 kb from any TSS. Fragment length distributions were found from filtered bam files 1105 \nusing samtools57.  1106 \n 1107 \nFragment length distributions (FLD) were plotted over ATAC-seq FLD bed files that intersected 1108 \nK562 ChromHmm regions. To get bed files that consisted of accurate fragment lengths, we 1109 \nmade ATAC-seq bams that had replicates merged by condition using samtools, then bedpe files 1110 \nwere created using bedtools bamtobed with the parameter bedpe. Finally, using awk to get the 1111 \nchromosome name, forward read start coordinates and reverse read end coordinates, which 1112 \nrepresents the true length of a fragment read and saved that to a bed file. Next, to get the 1113 \nfragments that intersected chromHmm regions, we used bedtools intersect to find and record 1114 \nonly the ATAC-seq true fragments that overlapped any of the chromHmm regions. This 1115 \nrepresents our ATAC-seq fragments found in chromHmm regions bed file.  1116 \n   1117 \n 1118 \nCUT&Tag data processing  1119 \n 1120 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 25 \n CUT&Tag data was aligned with BWA mem58, filtered, with duplicates removed. Peaks were 1121 \ncalled using Sicer259, then concatenated the peak set from each replicate from both conditions 1122 \nto create a master peak set.   1123 \n  1124 \nBigwig files were created using deeptools60 bamCoverage with reads extended and CPM 1125 \nnormalized.  1126 \n  1127 \nDifferential peak analysis was done with DESeq261 using the master peak set as regions for 1128 \naligned reads to be tallied over. Up and down peaks are those that pass two thresholds; 1) Padj 1129 \nvalue < 0.05, and 2) Log2fold change > |2|. Principle component analysis (PCA) plots were 1130 \ngenerated using DESeq2 results to show the variance between conditions and replicates to 1131 \nensure unwanted batch effects were not playing a pivotal role underlying the data.   1132 \n  1133 \nChIPseeker62,63 is used to annotate the differential peaks. This gives insights into the distribution 1134 \nof peaks in various regions of the genome, as seen in the legend of the plot labeled features.   1135 \n  1136 \nHeat maps and profile plots were generated using deeptools. To show the reliability of peaks 1137 \ncalled, we took the differential peaks and plotted the bigwig signal over the center of said 1138 \ndifferential peaks with 5kb up and down stream. This region illustrates the difference in signal 1139 \nbetween the two conditions H1low and Scrambled.    1140 \nWe also plotted the Cut&Tag H3K27me3 signal over Pro-seq nascent transcription regions to 1141 \ndiscover if H1 linker histone affecting compaction plays a pivotal role in nascent transcription.   1142 \nAnother profile/heatmap plot generated by our pipeline shows our Cut&Tag H3K27me3 signal 1143 \nover differential ATAC-seq peaks. The interaction between chromatin compaction state and loss 1144 \nof H1 illustrates that as we lose linker histone, there is more accessibility.  1145 \n  1146 \nNextflow pipeline data processing   1147 \n 1148 \nNextflow64 is used to create a reproducible and scalable pipeline that incorporates many of the 1149 \ntools mentioned in the methods section. We have two pipelines engineered to handle 1150 \nepigenomic sequencing techniques. The first Risca Lab pipeline (NEXDEP) can process fastq 1151 \nreads from ATAC-seq, Cut&Tag, Cut&Run, ChIP-seq assays to align, filter, give quality control 1152 \nmetrics and preprocess data to produce bam files with sequence alignment information. The 1153 \nsecond Risca Lab pipeline was engineered specifically to call peaks from previously mentioned 1154 \nassays and provide downstream analysis and plots such as heatmaps, MA-plots, PCA plots, 1155 \nand peak annotation information, along with many other custom analytical Nextflow workflow 1156 \ntechniques that partially aided in completion of this study.  1157 \n 1158 \nIDA and data analyses 1159 \n 1160 \nIDA was performed to generate reference spectral libraries for SWATH data quantification. The 1161 \nIDA method was set up with a 200 ms TOF -MS scan from 300 to 1,250 Da, followed by MS/MS 1162 \nscans in a high-sensitivity mode from 100 to 1,500 Da of the top 25 precursor ions above 100 cps 1163 \nthreshold (80 ms accumulation time, 100 ppm mass tolerance, rolling collision energy, and 1164 \ndynamic accumula -tion) for charge states (z) from +2 to +5. IDA files were searched using 1165 \nProteinPilot (version 5.0.2, ABSciex) with a default setting for tryptic digest and IAA alkylation 1166 \nagainst a protein sequence data-base. 1167 \n 1168 \nThe Homo sapiens proteome FASTA file (82,493 protein entries, UniProt UP000005640) 1169 \naugmented with sequences for common contaminants was used as a reference for the search. 1170 \nUp to two missed cleavage sites were allowed. Mass tolerance for precursor and fragment ions 1171 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 26 \nwas set to 100 ppm. A false discovery rate (FDR) of 5% was used as the cutoff for peptide 1172 \nidentification. 1173 \n 1174 \n 1175 \n 1176 \n 1177 \nSWATH acquisitions and data analyses 1178 \n 1179 \nFor SWATH (SWATH-MS, Sequential Window Acquisition of All Theoretical Mass Spectra) acqui-1180 \nsitions (Zhu et al., 2014), one 50-ms TOF-MS scan from 300 to 1,250 Da was performed, followed 1181 \nby MS/MS scans in a high-sensitivity mode from 100 to 1,500 Da (15 ms accumulation time, 100 1182 \nppm mass tolerance, +2 to +5 z, rolling collision energy) with a variable -width SWATH window 1183 \n(Zhang et al., 2015). DIA data were quantified using PeakView (version 2.2.0.11391, ABSciex) 1184 \nwith SWATH Ac -quisition MicroApp (version 2.0.1.2133, ABSciex) against selected spectral 1185 \nlibraries generated in Pro -tein-Pilot. Retention times for individual SWATH acquisitions were 1186 \ncalibrated using 25 or more pep-tides for plectin (PLEC, UniProt Q15149) and myosin-9 (MYH9, 1187 \nUniProt P35579), two abundant pro-teins that were highly representative in the IDA ion library and 1188 \nall SWATH acquisitions. The following software settings were utilized: up to 25 peptides per 1189 \nprotein, 6 transitions per peptide, 95% peptide confidence threshold, 5% FDR for peptides, XIC 1190 \nextraction window 10 minutes, and XIC width 100 ppm. 1191 \n 1192 \n  1193 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 27 \nReferences 1194 \n 1195 \n1. Felsenfeld, G. & McGhee, J. D. Structure of the 30 nm chromatin fiber. Cell 44, 375–377 1196 \n(1986). 1197 \n2. Li, G. & Reinberg, D. Chromatin higher-order structures and gene regulation. Curr. Opin. 1198 \nGenet. Dev. 21, 175–186 (2011). 1199 \n3. Maeshima, K., Iida, S. & Tamura, S. Physical Nature of Chromatin in the Nucleus. Cold 1200 \nSpring Harb. Perspect. Biol. 13, (2021). 1201 \n4. Mansisidor, A. R. & Risca, V. I. Chromatin accessibility: methods, mechanisms, and 1202 \nbiological insights. Nucleus 13, 236–276 (2022). 1203 \n5. Ou, H. D. et al. ChromEMT: Visualizing 3D chromatin structure and compaction in 1204 \ninterphase and mitotic cells. Science 357, (2017). 1205 \n6. Willcockson, M. A. et al. H1 histones control the epigenetic landscape by local chromatin 1206 \ncompaction. Nature 589, 293–298 (2021). 1207 \n7. Routh, A., Sandin, S. & Rhodes, D. Nucleosome repeat length and linker histone 1208 \nstoichiometry determine chromatin fiber structure. Proc. Natl. Acad. Sci. U. S. A. 105, 1209 \n8872–8877 (2008). 1210 \n8. Fyodorov, D. V., Zhou, B.-R., Skoultchi, A. I. & Bai, Y. Emerging roles of linker histones in 1211 \nregulating chromatin structure and function. Nat. Rev. Mol. Cell Biol. 19, 192–206 (2018). 1212 \n9. Robinson, P. J. J. & Rhodes, D. Structure of the “30 nm” chromatin fibre: a key role for the 1213 \nlinker histone. Curr. Opin. Struct. Biol. 16, 336–343 (2006). 1214 \n10. Robinson, P. J. J. et al. 30 nm chromatin fibre decompaction requires both H4-K16 1215 \nacetylation and linker histone eviction. J. Mol. Biol. 381, 816–825 (2008). 1216 \n11. Song, F. et al. Cryo-EM study of the chromatin fiber reveals a double helix twisted by 1217 \ntetranucleosomal units. Science 344, 376–380 (2014). 1218 \n12. Gibson, B. A. et al. Organization of Chromatin by Intrinsic and Regulated Phase 1219 \nSeparation. Cell 179, 470-484.e21 (2019). 1220 \n13. Nishino, Y. et al. Human mitotic chromosomes consist predominantly of irregularly folded 1221 \nnucleosome fibres without a 30-nm chromatin structure. EMBO J. 31, 1644–1653 (2012). 1222 \n14. Eltsov, M., MacLellan, K. M., Maeshima, K., Frangakis, A. S. & Dubochet, J. Analysis of 1223 \ncryo-electron microscopy images does not support the existence of 30-nm chromatin fibers 1224 \nin mitotic chromosomes in situ. Proceedings of the National Academy of Sciences 105, 1225 \n19732–19737 (2008). 1226 \n15. Maeshima, K., Hihara, S. & Eltsov, M. Chromatin structure: does the 30-nm fibre exist in 1227 \nvivo? Curr. Opin. Cell Biol. 22, 291–297 (2010). 1228 \n16. Luger, K., Dechassa, M. L. & Tremethick, D. J. New insights into nucleosome and 1229 \nchromatin structure: an ordered state or a disordered affair? Nat. Rev. Mol. Cell Biol. 13, 1230 \n436–447 (2012). 1231 \n17. Chen, L. et al. Nucleosome spacing can fine-tune higher-order chromatin assembly. Nat. 1232 \nCommun. 16, 6315 (2025). 1233 \n18. Hsieh, T.-H. S. et al. Resolving the 3D Landscape of Transcription-Linked Mammalian 1234 \nChromatin Folding. Mol. Cell 78, 539-553.e8 (2020). 1235 \n19. Hsieh, T.-H. S. et al. Mapping Nucleosome Resolution Chromosome Folding in Yeast by 1236 \nMicro-C. Cell 162, 108–119 (2015). 1237 \n20. Risca, V. I., Denny, S. K., Straight, A. F. & Greenleaf, W. J. Variable chromatin structure 1238 \nrevealed by in situ spatially correlated DNA cleavage mapping. Nature 541, 237–241 1239 \n(2017). 1240 \n21. Ricci, M. A., Manzo, C., García-Parajo, M. F., Lakadamyali, M. & Cosma, M. P. Chromatin 1241 \nfibers are formed by heterogeneous groups of nucleosomes in vivo. Cell 160, 1145–1158 1242 \n(2015). 1243 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 28 \n22. Ding, X., Lin, X. & Zhang, B. Stability and folding pathways of tetra-nucleosome from six-1244 \ndimensional free energy surface. Nat. Commun. 12, 1091 (2021). 1245 \n23. Kilic, S. et al. Single-molecule FRET reveals multiscale chromatin dynamics modulated by 1246 \nHP1α. Nat. Commun. 9, 235 (2018). 1247 \n24. Portillo-Ledesma, S. et al. Nucleosome Clutches are Regulated by Chromatin Internal 1248 \nParameters. J. Mol. Biol. 433, 166701 (2021). 1249 \n25. Portillo, S., Tsao, L. H. & Schlick, T. Nucleosome Clutches in Chromatin are Tightly 1250 \nRegulated by Nucleosome Positions and Linker Histone Density. Biophysical Journal vol. 1251 \n118 8a–9a Preprint at https://doi.org/10.1016/j.bpj.2019.11.3370 (2020). 1252 \n26. Collepardo-Guevara, R. & Schlick, T. Chromatin fiber polymorphism triggered by variations 1253 \nof DNA linker lengths. Proc. Natl. Acad. Sci. U. S. A. 111, 8061–8066 (2014). 1254 \n27. Abdulhay, N. J. et al. Massively multiplex single-molecule oligonucleosome footprinting. 1255 \nElife 9, (2020). 1256 \n28. Gaffney, D. J. et al. Controls of nucleosome positioning in the human genome. PLoS Genet. 1257 \n8, e1003036 (2012). 1258 \n29. Woodcock, C. L., Skoultchi, A. I. & Fan, Y. Role of linker histone in chromatin structure and 1259 \nfunction: H1 stoichiometry and nucleosome repeat length. Chromosome Research vol. 14 1260 \n17–25 Preprint at https://doi.org/10.1007/s10577-005-1024-3 (2006). 1261 \n30. Oberbeckmann, E., Quililan, K., Cramer, P. & Oudelaar, A. M. In vitro reconstitution of 1262 \nchromatin domains shows a role for nucleosome positioning in 3D genome organization. 1263 \nNat. Genet. 56, 483–492 (2024). 1264 \n31. Grau, D. et al. Structures of monomeric and dimeric PRC2:EZH1 reveal flexible modules 1265 \ninvolved in chromatin compaction. Nat. Commun. 12, 714 (2021). 1266 \n32. Leicher, R. et al. Single-molecule and in silico dissection of the interaction between 1267 \nPolycomb repressive complex 2 and chromatin. Proc. Natl. Acad. Sci. U. S. A. 117, 30465–1268 \n30475 (2020). 1269 \n33. Yuan, W. et al. Dense chromatin activates Polycomb repressive complex 2 to regulate H3 1270 \nlysine 27 methylation. Science 337, 971–975 (2012). 1271 \n34. Stenerlöw, B., Karlsson, K. H., Cooper, B. & Rydberg, B. Measurement of prompt DNA 1272 \ndouble-strand breaks in mammalian cells without including heat-labile sites: results for cells 1273 \ndeficient in nonhomologous end joining. Radiat. Res. 159, 502–510 (2003). 1274 \n35. Troll, C. J. et al. A ligation-based single-stranded library preparation method to analyze cell-1275 \nfree DNA and synthetic oligos. BMC Genomics 20, 1023 (2019). 1276 \n36. Freidkin, I. & Katcoff, D. J. Specific distribution of the Saccharomyces cerevisiae linker 1277 \nhistone homolog HHO1p in the chromatin. Nucleic Acids Res. 29, 4043–4051 (2001). 1278 \n37. Scheffer, M. P., Eltsov, M. & Frangakis, A. S. Evidence for short-range helical order in the 1279 \n30-nm chromatin fibers of erythrocyte nuclei. Proc. Natl. Acad. Sci. U. S. A. 108, 16992–1280 \n16997 (2011). 1281 \n38. Clerkin, A. B., Pagane, N., West, D. W., Spakowitz, A. J. & Risca, V. I. Determining 1282 \nmesoscale chromatin structure parameters from spatially correlated cleavage data using a 1283 \ncoarse-grained oligonucleosome model. bioRxiv (2024) doi:10.1101/2024.07.28.605011. 1284 \n39. Shimada, M. et al. Gene-Specific H1 Eviction through a Transcriptional 1285 \nActivator→p300→NAP1→H1 Pathway. Mol. Cell 74, 268-283.e5 (2019). 1286 \n40. Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human 1287 \nepigenomes. Nature 518, 317–330 (2015). 1288 \n41. Yusufova, N. et al. Histone H1 loss drives lymphoma by disrupting 3D chromatin 1289 \narchitecture. Nature 589, 299–305 (2021). 1290 \n42. Matthews, R. E. et al. CRAMP1 drives linker histone expression to enable Polycomb 1291 \nrepression. Mol. Cell 85, 2503-2516.e8 (2025). 1292 \n43. Misteli, T., Gunjan, A., Hock, R., Bustin, M. & Brown, D. T. Dynamic binding of histone H1 to 1293 \nchromatin in living cells. Nature 408, 877–881 (2000). 1294 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n 29 \n44. Portillo-Ledesma, S., Wagley, M. & Schlick, T. Chromatin transitions triggered by LH density 1295 \nas epigenetic regulators of the genome. Nucleic Acids Res. 50, 10328–10342 (2022). 1296 \n45. Lee, C.-H. et al. Distinct stimulatory mechanisms regulate the catalytic activity of polycomb 1297 \nrepressive complex 2. Mol. Cell 70, 435-448.e5 (2018). 1298 \n46. Canaj, H. et al. Deep profiling reveals substantial heterogeneity of integration outcomes in 1299 \nCRISPR knock-in experiments. bioRxiv 841098 (2019) doi:10.1101/841098. 1300 \n47. Soroczynski, J. et al. OpenTn5: Open-Source Resource for Robust and Scalable Tn5 1301 \nTransposase Purification and Characterization. bioRxivorg (2024) 1302 \ndoi:10.1101/2024.07.11.602973. 1303 \n48. Kustatscher, G., Wills, K. L. H., Furlan, C. & Rappsilber, J. Chromatin enrichment for 1304 \nproteomics. Nat. Protoc. 9, 2090–2099 (2014). 1305 \n49. Yang, T. et al. HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted 1306 \ncorrelation coefficient. Genome Res. 27, 1939–1949 (2017). 1307 \n50. Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C 1308 \nexperiments. Cell Syst. 3, 95–98 (2016). 1309 \n51. Zheng, X. & Zheng, Y. CscoreTool: fast Hi-C compartment analysis at high resolution. 1310 \nBioinformatics vol. 34 1568–1570 Preprint at https://doi.org/10.1093/bioinformatics/btx802 1311 \n(2018). 1312 \n52. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human 1313 \ngenome. Nature 489, 57–74 (2012). 1314 \n53. Langmead, B., Wilks, C., Antonescu, V. & Charles, R. Scaling read aligners to hundreds of 1315 \nthreads on general-purpose processors. Bioinformatics 35, 421–432 (2019). 1316 \n54. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008). 1317 \n55. Gaspar, J. M. Improved peak-calling with MACS2. bioRxiv (2018) doi:10.1101/496521. 1318 \n56. Li, Q., Brown, J. B., Huang, H. & Bickel, P. J. Measuring reproducibility of high-throughput 1319 \nexperiments. aoas 5, 1752–1779 (2011). 1320 \n57. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–1321 \n2079 (2009). 1322 \n58. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. 1323 \nBioinformatics 26, 589–595 (2010). 1324 \n59. Xu, S., Grullon, S., Ge, K. & Peng, W. Spatial clustering for identification of ChIP-enriched 1325 \nregions (SICER) to map regions of histone methylation patterns in embryonic stem cells. 1326 \nMethods Mol. Biol. 1150, 97–111 (2014). 1327 \n60. Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data 1328 \nanalysis. Nucleic Acids Res. 44, W160-5 (2016). 1329 \n61. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for 1330 \nRNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). 1331 \n62. Wang, Q. et al. Exploring epigenomic datasets by ChIPseeker. Curr. Protoc. 2, e585 1332 \n(2022). 1333 \n63. Yu, G., Wang, L.-G. & He, Q.-Y. ChIPseeker: an R/Bioconductor package for ChIP peak 1334 \nannotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015). 1335 \n64. Di Tommaso, P. et al. Nextflow enables reproducible computational workflows. Nat. 1336 \nBiotechnol. 35, 316–319 (2017). 1337 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\na\nd\nc\ne\nf\ng\nRICC-seq 1.0 RICC-seq 2.0\nPaired-End \nSequencing\nRemove 3’ and 5’ \nphosphates with rSAP\nP\nOH\nDenature with \n0.1M NaOH\nand elute ssDNA\nSRSLY adapter ligation \n& column clean-up\n-OH P-P--OH\nNNNNNNN NNNNNNNOH-\nNNNNNN NNNNNNOH-\n(Predominant \nunwanted product:\n3’ adapter dimer)\n(Illumina Read 1) (Illumina Read 2’) \nNNNNNN\nNNNNNN\nBarcoding\nPCR\nBarcode\nWash\nAdd back 5’\n phosphates\nwith PNK\nP\nGamma- \nirradiate at 0\n°C\nAgarose-\nembedded cells\nL\nyse, wash\nSoak in 5M Tris pH 8.0\nAdd spike-in DNA\nand SSB to stabilize Incubate 65˚C\nto inactivate rSAP\nand re-denature\nP\nP\nOH\nOHOH\nP OH\nOHP\nP OH\nPOH\nAdd SRSLY sequencing adapters\n(blocked)\nBead\nsize selection\n100 200 300 400 500 600 700\nF\nragment Length (nt)\n0\n200\n400\n600\n800\n1000Spike-in molarity \n0 100 200 300 400 500 600 700\nF\nragment Length (nt)\n0.0\n0.2\n0.4\n0.6\n0.8\n1.0\n100 200 300 400 500 600 700\nF\nragment Length (nt)\n0.0\n0.2\n0.4\n0.6\n0.8\n1.0 Pre-seq\nPost-seq \n100 200 300 400 500 600 700\nF\nragment Length (nt)\n0.0\n0.5\n1.0\n1.5\n2.0\n2.5\n3.0\nFit ≥300 bp\nSpike-in\nPre-sequencing\nSpike-in\nPost-sequencing\nSpike-in sequencing counts \nAligned spike-in fragment densities\nRatio of post- to pre-sequencing\nCounts uniformly scaled by spike-in\nCoutns corrected for length bias by spike-in\nRatio of 300 Gy to genomic DNA (PLC)\nCounts uniformly scaled by spike-in\nCoutns corrected for length bias by spike-in\nRatio of 300 Gy to genomic DNA \nFr\nagment Length (bp)\n0\n250\n500\n750\n1000\n20 40 60 80 20 40 60 80\nRICC 1.0 RICC 2.0\n% GC % GC\nRICC 2.0 Workflow\nSequencing countsFragment length (nt)\nb\n e-\nelectrons\nOH radicals cause\nbase damage and strand breaks \nwithin a ~3.5 nm radius\nX-ray\nIonization\nevent\ne-\n100 200 300 400 500\nF\nragment Length (nt)\n100 200 300 400 500\nFragment Length (nt)\n100 200 300 400 500\nFragment Length (nt)\n2000\n4000\n6000\n8000\n10000\n12000\n0\n2000\n4000\n6000\n8000\n10000\n0\n100 200 300 400 500\nFragment Length (nt)\nBJ fibroblast exp.1\nBJ fibroblast exp.2\nPLC\n100 200 300 400 500\nFragment Length (nt)\n100 200 300 400 500\nFragment Length (nt)\n2000\n4000\n6000\n8000\n10000\n0\n0.5\n0.75\n1.00\n1.25\n1.75\n0.25\n0\n1.5\n2.0\n2.5\n3.0\n3.5\n1.0\n0.5\n1000\n2000\n3000\n4000\n5000\n0\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n100 150 200 250 300 350 400 450\n0\n2000\n4000\n6000\nIrr (raw)\nPLC (raw)\n100 150 200 250 300 350 400 450\n0.0\n0.5\n1.0\n1.5\n2.0\n2.5\nYeast ±95% \nYeast mean\n100 150 200 250 300 350 400 450\n0\n1000\n2000\n3000\n4000\n5000\n100 150 200 250 300 350 400 450\n0.0\n0.5\n1.0\n1.5\n2.0\n100 150 200 250 300 350 400 450\n0\n1000\n2000\n3000\n4000\n5000\nChicken erythrocyte\n100 150 200 250 300 350 400 450\n0.0\n0.5\n1.0\n1.5\n2.0\n2.5\n100 150 200 250 300 350 400 450\nF\nragment Length (nt)\n0\n2000\n4000\n6000\n100 150 200 250 300 350 400 450\nF\nragment Length (nt)\n0\n1\n2\n3\nFragment Length (nt)\nF\nragment Length (nt)\nFragment Length (nt)\n100 150 200 250 300 350 400 450\nF\nragment Length (nt)\n2\nRatio of length-corrected counts: Cells / genomic DNA\nBudding Yeast + 95% CI\nBJ Fibroblast + 95% CI\nMouse B-Cell + 95% CI\nChicken Erythrocyte + 95% CI\n3\n1\n0.6\na\nc\nb\nd\ne\nf\ng h\ni\nFragment countsFragment countsFragment counts Fragment counts\nCounts ratio (cells / gDNA)\n(length bias-corrected)\nCounts ratio (cells / gDNA)\n(length bias-corrected)\nCounts ratio (cells / gDNA)\n(length bias-corrected) Counts ratio (cells / gDNA)\n(length bias-corrected)\nMouse naive B-cell\nHuman skin fibroblast (BJ)\nBudding yeast\nBJ fibroblast ±95% \nBJ fibroblast mean\nChicken erythrocyte ±95% \nChicken erthrocyte mean\nMouse B-cell ±95% \nMouse B-cell mean\nF\nragment Length (nt)\nFragment Length (nt)\nFragment Length (nt)\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\na\nf\nk\nWT K562\nrtTA G418\n+ G418\n+ dox\nsort for GFP\nBFP puro\nscrambled sgRNA\nBFP puro\nH1.2 - H1.5 sgRNA\nor\n+ puro\nsort for BFP\nscr CTRL\n K562\nWT levels of H1 with\nor without doxycycline\nH1-low K562\ndepleted of H1 upon\ndoxycycline induction\ndCas9-KRAB-GFP\nTRE\n0\n0\n.2\n0.4\n0.6\n0.8H1:H2B ratio\nscr\n+dox\nH1-low\n+dox\n0\n5\n1\n0\n15\n20\n25\n30\nscr\n-dox\nH1-low\n-dox\nscr\n+dox\nH1-low\n+dox\nDoubling time (hours)\nH2B\nH1cde\nH1b\nscr CTRL\nH1-low\nb c\nd\n0.001\n0.002\n0 500 1000 1500\nMicro-C contact frequency\nH1-low\nscr CTRL\n0.001\n0.002\n2 4 6\nMicro-C peak frequency\nH1-low\nscr CTRL 160\n170\n180\n190\n200NRL (bp)\nH1-low scr-CTRL\n0.9\n1.0Odd/Even contactsH1-low scr-CTRL\n0.001\n0.002\n0 500 1000 1500\nGenomic distance (bp)\nMicro-C contact frequency\nscr CTRL\nH1-low\ng h\ni\nj\nGenomic distance (bp)\nNucleosome peak number\n8\n1\n2\n3\n4\nRatio of corrected RICC-seq \nFLDs (Cells/gDNA)\nscr-CTRL mean (n=3)\nscr-CTRL 95% CI\nH1-low mean (n=3)\nH1-low 95% CI\n0.7\n0.8\n0.9\n1.0\n1.1\nH1-low / scr-CTRL\nH1-low / scr-CTRL (means)\nWelch p<0.05\nH1-low / scr-CTRL\nl\n100 150 200 250 300 350 400 450\n0.50\n0.75\n1.00\n1.25\n1.50\n1.75\n2.00\n2.25\nRatio of corrected RICC-seq\nFLDs (Cells / gDNA)\nscr-CTRL (n=2; with 95% CI)\nscr-CTRL dox-off (n=1; with 95% CI)\nH1-low (n=2; with 95% CI)\nH1-low dox-off (n=2; with 95% CI)\n320 340 360 380 400 420\nF\nragment length (nt)\n0.50\n0.75\n1.00\n1.25\n1.50\n1.75\n2.00\n2.25\n100 150 200 250 300 350 400 450\n1.00\n1.25\n1.50\n1.75 scr-CTRL dox-off / scr-CTRL\n100 150 200 250 300 350 400 450\n1.0\n1.2\nH1-low dox-off / H1-low\nWelch p<0.05\n100 150 200 250 300 350 400 450\n0.6\n0.8\n1.0\n1.2\nH1-low dox-off / scr-CTRL dox-off\nWelch p<0.05\nF\nragment length (nt)\n100 150 200 250 300 350 400 450\nFragment length (nt)\n100 150 200 250 300 350 400 450\nFragment length (nt)\nm\nn\no\np\nFA and DSG\ncrosslink\nMNase-digest DNA biotinylate,\nligate DNA\ne Micro-C workflow\nFragment length (nt)\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\nFragment length (Capillary electrophoresis A.U.)\n5e−04\n1e−03\n0 500 1000 1500\nContact distance (bp)\nMicro-C \ncontact probability\nDNA stain intensity \n(A.U.)\n131 bp (1.0 uL MNase)\n104 bp (1.2 uL MNase)\n124 bp (0.8 uL MNase)\nk\na\ni\njg\ne\nh\nf\nd\nc\n0.001\n0.002\n2 4 6 8\nH3K27ac\nH3K27me3\nH3K9me3\n0.001\n0.002\n2 4 6 8\nNucleosome contact number\nOdd/Even contacts\nH1-low scr-CTRL\n0.9\n1.0\n1.1\nH3K27acH3K27me3H3K9me3H3K27acH3K27me3H3K9me3\nscr-CTRL\nH1-low\n4.2\n5.8\n7.4\n9.0\n0 100 200 300 400 500\n5\n6\n7\n8\n0 100 200 300 400 500\nFragment Size (nt)\nGenome\nH3K9me3\nH3K27me3\nH3K27ac\n5\n6\n7\n0 100 200 300 400 500\n5.8\n7.2\n8.6\n10.0\n0 100 200 300 400 500\nMatched \ngDNA Control\nRICC-seq log2(Fragments/Mb)\nMatched \ngDNA Control\nscr-CTRL\nH1-low\nscr-CTRL H1-low\nMicro-C peak probability\n0.5\n1.0\n1.5\n2.0\n2.5\n3.0\n3.5\n4.0\nfull genome\nH3k9me3\nH3k27me3\nH3k27ac\n100 150 200 250 300 350 400 450\nFragment Size (nt)\n0.5\n1.0\n1.5\n2.0\n2.5\n3.0\nRatio of corrected RICC-seq \nFLDs (Cells/gDNA)\nRatio of corrected RICC-seq \nFLDs (Cells/gDNA)\n100 150 200 250 300 350 400 450\nFragment Size (nt)\nb\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\na\n0.965\n0.970\n0.975\n0.980\nHiC-Rep Spearman Correlation\nH1-low\nscr\nwithin\ncondition\nbetween\nconditions\nc\nd\nb\nj\nscr\nH1-low\n 1 Mb \n 1 Mb \n−1.0\n−0.5\n0.0\n0.5\n1.0\n−1.0 −0.5 0.0 0.5 1.0\nscr-CTRL c score\nH1−low c score\nA to B\nB to A\nB-shift\nA-shift\nNone\nscr\nH1-low\n# boundaries\nboundary strength\nH1-low 50 kb\n100 kb\n250 kb\n# boundaries\n50 kb\n100 kb\n250 kb\nscr\nscr\nH1-low\n 10 kb \n 10 kb \ne f\nScr-only loops H1-low-only loops\nScr\nScr H1-lowH1-low\nscr\nH1-low\n 5 kb \n 5 kb \nCommon loops\nScr\nH1-low\n2520 loops 2520 loops 836 loops 836 loops 1872 loops 1872 loops\n2998 loops 2998 loops 11960 loops 11960 loops\nCTCF: 23.8% decrease in loop number\nnon-CTCF: 28.0% decrease in loop number\nScr CTCF loops Scr non-CTCF loops\nScr\nScr H1-lowH1-low\nScr\n2520\nH1-\nlo w\n8361872\ng h\ni\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\n0.0\n0.1\n0.2\n0.3Enrichment ScoreFDR: 1.000\nPval: 1.000\nNES: 0.859\n0 2500 5000 7500 10000125001500017500\nR\nank in Ordered Dataset\n-5.0\n-2.5\n0.0\n2.5Ranked list metric\nPos\nNeg\nZero score at 13365\nGATA1 Targets\nun-\nchanging\ndown in \nH1-low\nup in \nH1-low\n0 25 50 75 100\nK27me3 CUT&Tag peaks (%)\nPromoter (<=1kb)\nPromoter (1−2kb)\nPromoter (2−3kb)\nPromoter (3−4kb)\nPromoter (4−5kb)\n5' UTR\n3' UTR\n1st Exon\nOther Exon\n1st Intron\nOther Intron\nDo\nwnstream (<=300)\nDistal Intergenic\na c\ng\nj\nf h\ne\n2.5\n0.0\n2.5\n5.0\n1e+01 1e+02 1e+03 1e+04 1e+05\nlog10 Base Mean\nlog2 Fold-change H1-low/scr-CTRL\nnot significant\npadj < 0.05\npadj < 0.05 &\nLFC > 1\n1525 \n32\n CHEP -Log10 P-value (H1-low vs. scr-CTRL)\n3\n3\n2\n1\n0\n0-6 -3\nCHEP log2 Fold-change (H1-low vs. scr-CTRL)\np-value\nlog2fc & \np-value\nb\n0.0\n0.2\n0.4\n0.6Enrichment ScoreFDR: 0.000\nPval: 0.000\nNES: 1.568\n0 2500 5000 7500 10000 125001500017500\nR\nank in Ordered Dataset\n-5.0\n-2.5\n0.0\n2.5Ranked list metric\nPos\nNeg\nZero score at 13365\nCBX2 Targets\n0.0\n0.2\n0.4Enrichment ScoreFDR: 0.000\nPval: 0.000\nNES: 1.178\n0 2500 5000 7500 1000012500 15000 17500\nR\nank in Ordered Dataset\n-5.0\n-2.5\n0.0\n2.5Ranked list metric\nPos\nNeg\nZero score at 13365\nSUZ12 Targets\nun-\nchanging\nup in \nH1-low\n \n0 25 50 75 100\nATAC-seq peaks (%)\nPromoter (<=1kb)\nPromoter (1−2kb)\nPromoter (2−3kb)\nPromoter (3−4kb)\nPromoter (4−5kb)\n5' UTR\n3' UTR\n1st Exon\nOther Exon\n1st Intron\nOther Intron\nDo\nwnstream (<=300)\nDistal Intergenic0.0\n2.5\n5.0\n10\n0.5\n10\n1\n10\n1.5\n10\n2\n10\n2.5\n10\n3\n10\n3.5\nATAC-seq peak Base Mean \nUpregulated peaks: 2678 (5.4%) \nDo\nwnregulated peaks: 11 (0%) \npadj < 0.05 only: 5142 (10.4%) \nNot significant: 41419 (84.1%)\n10\n1\n10\n2\n10\n3\n10\n4\nH3K27me3 CUT&Tag Broad Peak Base Mean\nH3K27me3 Log2 Fold-change \nH1-low vs scr-CTRL \nUpregulated peaks: 482 (3%) \nDownregulated peaks: 2453 (15.1%) \npad\nj < 0.05 only: 4060 (25%) \nNot significant: 9222 (56.9%)\n−5.0\n−2.5\n0.0\n2.5\nd\ni\n-8.0 center 8.0Kb\n1.0\n1.5\n2.0\n2.5\n3.0\n3.5\n4.0\n-8.0 center 8.0Kb\nscr-CTRL H3K27me3\n H1-low H3K27me3\n-8.0 center 8.0Kb\n -8.0 center 8.0Kb\n0\n2\n4\n6\n8\n10\n12\nH1 low H3K27me3 Scrambled H3K27me3\nDown H3K27me3 peaks All H3K27me3 peaks\nRPGC-normalized\nH3K27me3 CUT&Tag coverage\n-5.0 TSS 5.0Kb\n1.0\n1.5\n2.0\n2.5\n3.0\n-5.0 TSS 5.0Kb\nscr-CTRL H3K27me3\nH1-low H3K27me3\nscr-CTRL H3K27me3\n-5.0 TSS 5.0Kb\nH1-low-upregulated genes\nH1-low H3K27me3\n-5.0 TSS 5.0Kb\nUnchanging genes\n0\n2\n4\n6\n8\n10\nk\n−0.2\n−0.1\n0.0\n0.1\n0.2\nH3K27me3 all\nH3K27me3 down\nDNAse narrow peak\nH2A.Z narrow peak\nH3K27ac narrow peak\nH3K27me3 broad peak\nH3K36me3 broad peak\nH3K4me1 narrow peak\nH3K4me2 narrow peak\nH3K4me3 narrow peak\nH3K9me3 broad peak\n ATAC-seq H1-low vs. scr-CTRL\nlog2 Fold-change\nAccessibility enriched\nAccessibility depleted\nRoadmap K562 ChIP-seq dataCut and Tag\nl\nTF Adjusted P-value Odds Ratio\nCBX8\n2.99e-35 2.89\nCBX2 2.25e-30 2.73\nSUZ12 1.29e-24 2.42\nGATA1 1.00e+00 0.38\n...\nENRICHR ANAL\nYSIS OF\nUPSTREAM REGULATORS FOR\nGENES UP IN H1-low \n(K562 ENCODE TF 2015) \n0.0\n0.2\n0.4\n0.6Enrichment ScoreFDR: 0.000\nPval: 0.000\nNES: 1.615\n0 2500 5000 7500 10000125001500017500\nR\nank in Ordered Dataset\n-5.0\n-2.5\n0.0\n2.5Ranked list metric\nPos\nNeg\nZero score at 13365\nCBX8 Targets\nATAC-seq Log2 Fold-change \nH1-low vs scr-CTRL \nRPGC-normalized\nH3K27me3 CUT&Tag coverage\nUpregulated genes: 1525/22978 = 6.6% \nDownregulated genes: 32/22978 = 0.139%\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint \n\na b\n100 150 200 250 300 350 400 450\nFragment Size (nt)\n0.5\n1.0\n1.5\n2.0\n2.5\nCorrected RICC-seq 2.0 \nFLD ratio signal norm. to mono. nuc.\n(cells / gDNA)\n100 150 200 250 300 350 400 450\nFragment Size (nt)\n0.5\n1.0\n1.5\n2.0\n2.5\n100 150 200 250 300 350 400\nFragment Size (nt)\n0.5\n1.0\n1.5\n2.0\n2.5\n450\nCorrected RICC-seq 2.0 \nFLD ratio signal norm. to mono. nuc.\n(cells / gDNA)\n100 150 200 250 300 350 400 450\nFragment Size (nt)\nscr-CTRL- down peaks \nscr-CTRL- unchan\nging\nH1-low- unchanging\nH1-low- down peaks \nK27me3 Differential Peaks\n0.5\n1.0\n1.5\n2.0\n2.5\n3.0\n3.5\nc\nscr-CTRL- full gen\nome \nH1-low- full genome\nH1-low-ATAC unch.\nscr-CTRL- ATAC unch.\nscr-CTRL- full genome \nH1-low- full genome\nH1-low-ATAC up\nscr-CTRL- ATAC up\nH1-low-H3K27me3\nscr-CTRL- H3K27ac\nscr-CTRL- H3K27me3\nH1-low- H3K27ac\nH1-low- ATAC up\nscr-CTRL- ATAC up\nd e\n-10.0 center 10.0Kb\n1.5\n2.0\n2.5\n3.0\n3.5\n4.0\n-10.0 center 10.0Kb\nscr-CTRL H3K27me3\nup A TAC peaks\nH1low H3K27me3\nunchanging A TAC peaks\n0\n2\n4\n6\n8\n10\n12\n14\n-10.0 center 10.0Kb-10.0 center 10.0Kb\nscr-CTRL H3K27me3\nH1low H3K27me3\nCut&Tag\ninsertions\nCut&Tag normalized\ninsertion density\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 23, 2025. ; https://doi.org/10.64898/2025.12.19.695525doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}