Epigenomic analysis of formalin-fixed paraffin-embedded samples by cut&tag

ABSTRACT For more than a century, formalin-fixed paraffin-embedded (FFPE) sample preparation has been the preferred method for long-term preservation of biological material. However, the use

of FFPE samples for epigenomic studies has been difficult because of chromatin damage from long exposure to high concentrations of formaldehyde. Previously, we introduced Cleavage Under

Targeted Accessible Chromatin (CUTAC), an antibody-targeted chromatin accessibility mapping protocol based on CUT&Tag. Here we show that simple modifications of our CUTAC protocol either

in single tubes or directly on slides produce high-resolution maps of paused RNA Polymerase II at enhancers and promoters using FFPE samples. We find that transcriptional regulatory element

differences produced by FFPE-CUTAC distinguish between mouse brain tumors and identify and map regulatory element markers with high confidence and precision, including microRNAs not

detectable by RNA-seq. Our simple workflows make possible affordable epigenomic profiling of archived biological samples for biomarker identification, clinical applications and retrospective

studies. SIMILAR CONTENT BEING VIEWED BY OTHERS FITAC-SEQ: FIXED-TISSUE CHIP-SEQ FOR H3K27AC PROFILING AND SUPER-ENHANCER ANALYSIS OF FFPE TISSUES Article 26 June 2020 SOLID-PHASE CAPTURE

AND PROFILING OF OPEN CHROMATIN BY SPATIAL ATAC Article Open access 05 January 2023 CHROMATIN ACCESSIBILITY PROFILING BY ATAC-SEQ Article 27 April 2022 INTRODUCTION The standard workflow of

surgical specimens is from the operating room into formalin (~4% formaldehyde) for a few days and then embedding into paraffin, cut into sections for histological analysis and stored as

paraffin blocks. Even after long-term storage, formalin-fixed paraffin-embedded (FFPE) sections can be resurrected for application of modern sequencing-based genomic methodologies in ongoing

and retrospective studies1. FFPE sample preservation has been in use for over a century, with billions of cell blocks accumulated thus far, and no end in sight2. Most genomic studies using

FFPE samples have applied whole genome sequencing to identify mutations and aneuploidies, or whole exome sequencing to identify tissue-specific differences. However, chromatin profiling has

the potential of identifying causal regulatory element changes that drive disease. The prospect of applying chromatin profiling to distinguish regulatory element changes is especially

attractive for translational cancer research, insofar as mis-regulation of promoters and enhancers in cancer can provide diagnostic information and may be targeted for therapy3. However,

there has been limited progress in applying chromatin profiling techniques to FFPEs4. Although several methods have been developed for chromatin immunoprecipitation with sequencing

(ChIP-seq) using FFPEs5,6,7,8,9,10, ChIP-seq is not well-suited for small amounts of material that are typically available from patient samples. Furthermore, solubilization of such heavily

cross-linked material is extremely challenging, requiring strong ionic detergents and/or proteases in addition to controlled sonication or Micrococcal Nuclease (MNase) digestion treatments.

Alternatives to ChIP-seq for chromatin profiling include ATAC-seq11, DNase-seq12, NicE-seq13, FAIRE14,15 and enzyme-tethering methods such as CUT&RUN16 and CUT&Tag17. Modifications

to the standard ATAC-seq protocol were required to make it suitable for FFPEs, including nuclei isolation following enzymatic tissue disruption and in vitro transcription with T7 RNA

polymerase18,19. The same group also similarly modified CUT&Tag and included an epitope retrieval step using ionic detergents and elevated temperatures, which they termed FFPE tissue

with Antibody-guided Chromatin Tagmentation with sequencing (FACT-seq)20,21. However, FACT-seq is a 5-day protocol even before sequencing, and the many extra steps required relative to

CUT&Tag have raised concerns about experimental variability4. In this work, we wondered whether a fundamentally different approach to what has been described for FFPE-ATAC and FACT-seq

might overcome the obstacles that have thus far been encountered in chromatin profiling of FFPEs. Rather than enzymatically breaking down the tissue for nuclei isolation, we use only heat

and minimal shearing of the FFPE specimen, then follow our standard CUT&Tag-direct protocol with modifications. These include applying our Cleavage Under Targeted Accessible Chromatin

(CUTAC) strategy, which preferentially yields <120-bp fragments released by antibody-targeted paused RNA Polymerase II (RNAPII)22,23. Because of the small size of the fragments released

with CUTAC, it is relatively robust to the serious DNA degradation that occurs during cross-link reversal24, and by attaching to magnetic beads and following the single-tube

CUT&Tag-direct protocol, or by performing incubations directly on the slide, we minimize experimental variation. The resulting FFPE-CUTAC profiles could be used to confidently

distinguish different mouse brain tumors from one another and from normal brain tissue, identifying potentially key regulatory elements involved in cancer progression. RESULTS CUT&TAG

STREAMLINED PROTOCOL FOR WHOLE CELLS We originally introduced CUT&Tag with DNA purification by addition of SDS/Proteinase K followed by either phenol-chloroform-isoamyl alcohol

extraction and ethanol precipitation or SPRI bead binding and elution for PCR17. Later we streamlined the protocol so that it could be performed in single PCR tubes using a 58 oC incubation

in 0.1% SDS followed by excess Triton-X100, which sequesters the SDS in micelles, allowing efficient PCR22. However, this CUT&Tag-direct method was only suitable for up to ~50,000

nuclei, as more material was found to inhibit the PCR. To make CUT&Tag-direct applicable to whole cells, we have included 0.05% Triton-X100 in all buffers from antibody addition through

tagmentation, which maintains cells permeable without disrupting nuclei and improves bead behavior. We have also increased the concentration of SDS and included thermolabile Proteinase K in

the fragment release buffer. After digestion at 37 °C and inactivation at 58 oC, the SDS is quenched with excess Triton-X100 and the material is subjected to PCR, resulting in high yields

with 30,000-60,000 whole cells (Supplementary Fig. 1a). When applied to the H3K4me3 promoter mark, this modified CUT&Tag-direct protocol for native whole cells resulted in representative

profiles that match those of native or fixed nuclei using either the original organic extraction method or CUT&Tag-direct (Fig. 1a). Based on MACS2 peak-calling and Fraction of Reads in

Peaks (FRiP), we obtained slightly more peaks called and similar FRiP values for up to at least 100,000 native whole cells using the modified protocol (Fig. 1b, c), obviating the need to

purify nuclei for CUT&Tag-direct25 and AutoCUT&Tag26. TEMPERATURE-DEPENDENT PERMEABILIZATION OF FFPE SECTIONS FOR CUTAC To evaluate the ability of our approach to discriminate

between archived samples, we chose paraffin blocks of three related mouse CNS tumor types, driven by distinct mechanisms. We compared 10-micron sections from FFPE blocks of tyrosine kinase

active PDGFB-driven gliomas27, ZFTA-RELA gene fusion-driven ependymomas28, and YAP1-FAM118b gene fusion-driven ependymomas29 to one another and to FFPE blocks of normal mouse brain. The

difficulty of performing CUT&Tag-direct on FFPEs is exacerbated not only by the severe chromatin damage caused by heavy formalin fixation but also by the large amount of cross-linked

intra- and inter-cellular material that cells are embedded in. Both the FFPE-ATAC and FACT-seq methods require lengthy digestion with collagenases and hyaluronidase followed by 27-gauge

needle extraction and straining liberated nuclei for processing. We reasoned that harsh treatments might not be necessary if the cells can be permeabilized sufficiently, and we were

encouraged to attempt this approach by the fact that deparaffinized 5- to 10-micron FFPE samples on slides are routinely permeabilized for cytological staining with antibodies1. Also, there

has been recent progress in preventing the most severe DNA damage to FFPEs by careful attention to buffer and heating conditions24. Accordingly, we performed manual shearing of

deparaffinized 10-micron FFPE sections from tumor and normal mouse brains by dicing and scraping the tissue off slides with a razor blade followed by forcing the solution twenty times

through a 22-gauge needle. We found that the Concanavalin A (ConA) beads used for standard CUT&Tag, bound sufficiently well to sheared FFPE fragments. This meant that all steps from

antibody addition through to PCR could be performed on FFPEs following the same CUT&Tag-direct protocol used for nuclei and whole cells. In addition, the toughness of FFPE shards allowed

for hard vortexing and centrifugation steps that would have resulted in lysis of ConA bead-bound cells or nuclei. Formaldehyde cross-links are reversed by incubation at elevated

temperatures. Typical ChIP-seq, CUT&RUN and CUT&Tag protocols recommend cross-link reversal at 65 oC overnight in the presence of Proteinase K and SDS to simultaneously reverse

cross-links and deproteinize. However, the much more extreme formaldehyde treatments that are used in preparing FFPEs have required incubation temperatures as high as 90 oC for isolation of

PCR-amplifiable DNA for whole-genome sequencing24,30,31. High temperatures also contribute to epitope retrieval for ChIP-seq5,6,7,8,9,10 and FACT-seq20, and for cytological staining one

protocol calls for epitope retrieval at 125 oC at 25 psi in a pressure cooker32. To optimize the temperature of incubation for DNA recovery and epitope retrieval for CUTAC on FFPE samples

from mouse brain tumors, we incubated sheared FFPEs at temperatures ranging from 65 oC to 95 oC before ConA bead and antibody additions. We performed modified CUT&Tag-direct using

low-salt tagmentation (CUTAC) with RNAPII-Ser5p and/or RNAPII-Ser2,5p and H3K27ac antibodies. Upon DNA sequencing, the fraction of fragments that mapped to the mouse genome showed a strong

temperature dependence, where the highest temperatures (90-95 oC) showed the highest fraction mapping to the mouse genome (75%), and the lowest temperatures (65-70 oC) showed the lowest

fraction (13%) (Fig. 2b). A relationship between cross-link reversal and incubation temperature has been determined to follow the Arrhenius equation33. As temperature dependence of mouse

tagmented fragment recovery also followed the Arrhenius equation, cross-link reversal may be limiting for DNA fragment recovery. HIGH TEMPERATURES PREFERENTIALLY REDUCE TAGMENTATION OF

CONTAMINATING BACTERIAL DNA We were curious as to the identity of fragments generated by FFPE-CUTAC that did not map to the mouse genome. Using BLASTN against nucleotide sequences in Genbank

it became apparent that there was a single species that consistently rose to the top of the list for all samples, the gram-positive bacterium _Rhodococcus erythropolis_. Mapping fragments

to the _R. erythropolis_ genome, we found that the entire genome was represented as expected if this species is a major contaminant of the mouse brain FFPEs in our study. Consistent with

this interpretation, we found a high-temperature dependence of fragment release opposite that for mouse (Fig. 2c), consistent with _Rhodococcu_s fragments competing with mouse fragments in

the PCR. We also found a near-perfect anti-correlation between the fraction of fragments mapped to mouse and the fraction mapped to the _R. erythropolis_ genome (_R_2 = 0.996, _n_ = 59)

across all antibodies (Fig. 2d), with _Rhodococcus_ accounting for 1-15% of the total fragments. As bacterial DNA is not chromatinized, it is unlikely to be protected from melting as well as

mouse DNA, and so would not serve as a substrate for Tn5 tagmentation, which could account for the reduction in _Rhodococcus_ contamination with increasing temperature. To obtain a broader

representation of species contaminating our FFPEs, we performed BLASTN searches of the RefGene Genome Database using a sample of 300 multiply represented 50-bp reads not aligning to the Mm10

build of the mouse genome. A search of the bacterial genome subset returned hits to 208 species for ~2/3rd of the fragments, which implies that most of the unmapped reads were bacterial in

origin. Although no other bacterial species were nearly as abundant as _R. erythropolis_, summing the fragment counts mapped to the six most frequently represented other species accounted

for ~0.5–7% of the fragments and showed similar near-perfect anti-correlations to mouse (_R_2 = 0.989, Fig. 2d). Efficiency was highest for RNAPII Ser2,5p (85% mouse, 2.5% _Rhodococcus_) and

lowest for H3K27ac (38% mouse, 11% _Rhodococcus_). The lower efficiency of the histone modification than the RNAPII modifications might be attributed to susceptibility of lysine-rich

histone tails to formaldehyde adduct and cross-linking damage, in contrast to the 52-copy lysine-free YSPTSPS heptamer comprising the C-terminal domain of Rpb1. Efficiency for FFPEs was also

low for other histone modifications, resulting in poor signal-to-noise for the repressive H3K27me3 mark (Supplementary Fig. 2) and complete failures for H3K4me3 and H3K4me2, which were used

with the original CUTAC protocol22. In contrast, histone H3K27ac, a mark of active enhancers and promoters, resulted in high mappability (Fig. 2d), perhaps because unlike H3K4 and H3K27

methylations, H3K27ac is not known to be bound by “reader” proteins, which may cause epitope masking when cross-linked to their histone tail substrates. What is the source of _Rhodococcus_

and other bacterial contaminants in our FFPEs, which derive from multiple FFPE sample preparations over a 2-year span? _R. erythropolis_ isolates have been found to use paraffin wax as their

sole carbon source, forming thick biofilms34. The species has also been proposed as an industrial biodegrader for removing the paraffin wax that remains on the inner surfaces of oil tanker

holds after they are emptied35. We infer that most of the DNA fragments that do not map to mouse are derived from the paraffin used in embedding, with an advantage during PCR over the tissue

derived DNA in not having been subjected to formalin treatment. We interpret the near-perfect anti-correlations seen for these genomes in different samples as reflecting a very uniform

distribution of contamination for slides prepared at different times. FFPE-CUTAC PERFORMED DIRECTLY ON THE SLIDE Although high temperatures and stringent washes were unable to completely

eliminate bacterial contamination, we suspected that concanavalin A on the beads might have captured residual dead bacterial cells. To test this possibility we substituted amine-coated

paramagnetic beads for ConA beads and found that when followed by a centrifugation pulse at 3000 g before magnetizing, amine-coated bound sufficiently well to sheared FFPE fragments that we

obtained similar recoveries as with ConA beads. We also tested hot-aqueous deparaffinization by placing FFPE slides in a slide holder filled with cross-link reversal buffer and incubating

overnight at 85 oC (Fig. 2a), based on previous reports showing that hot water suffices to melt and float off paraffin without the need for organic chemical pretreatment36,37,38,39. Finally,

we tested a bead-free approach, in which overnight incubation at 85 oC was followed by incubations directly on the slide covered by plastic film and washes by immersion. We observed that

either using magenetic beads without ConA or performing FFPE-CUTAC directly on the slide resulted in 99% mappability, completely eliminating residual bacterial contamination (Supplementary

Data 1). SUBNUCLEOSOMAL FRAGMENT SIZES FROM FFPE-CUTAC SAMPLES Capillary gel profiles of FFPE-CUTAC libraries revealed insert sizes averaging ~60 bp (Supplementary Fig. 1b), despite

inclusion of a 1-minute 72 oC PCR extension step in each PCR cycle intended to capture larger fragments from degraded template DNA. After DNA sequencing, we observed subnucleosomal length

distributions showing 10-bp periodicities typical of CUT&Tag peaking at ~60 bp for all antibody series (Supplementary Fig. 3a). By separately plotting the fragment length distributions

for tumors and normal brains, we observed a conspicuous difference, where the length distribution was shifted with more longer fragments in tumor (median = 76 bp) relative to normal brain

tissue (median = 65 bp) (Fig. 2e and Supplementary Fig. 3b). Fragment length distributions of RNAPII-Ser5p FFPE-CUTAC data using the on-slide protocol confirmed that the tumors yielded

longer fragments than normal brain, with YAP1-FAM118b gene fusion-driven ependymomas showing the largest length increase relative to normal brain (Fig. 2f). In contrast, the two overall

length distributions of _Rhodococcus_ DNA fragments from the same tumor and normal samples closely superimposed (Supplementary Fig. 3b). This average shift to a longer fragment distribution

for tumors is also seen for mitochondrial DNA from the same samples when compared to either normal brain or CUT&Tag mitochondrial DNA profiles from native 3T3 fibroblasts (Supplementary

Fig. 3c). However, a small difference in the opposite direction was observed between liver tumor (median  = 63 bp) and normal (median = 68 bp) FFPEs (Supplementary Fig. 3d), which suggests

that the length differences seen between tumor and normal mouse brain are tumor-specific. Interestingly, both _Rhodococcus_ and mouse mitochondrial fragments from FFPEs displayed a much

weaker 10-bp periodicity relative to mouse brain FFPE nuclear and unfixed mouse mitochondrial fragments, respectively (Supplementary Fig. 3c), suggesting that the reduction in periodicity

seen for DNA unimpeded by nucleosomes (bacterial and mitochondrial) is the result of DNA damage caused by fixation and cross-link reversal. The strong periodicity seen for mouse CUTAC

profiles relative to non-chromatinized DNA of bacteria and mitochondria in the same samples might reflect partial protection from unreversed formadehyde fixation damage by RNAPII and other

chromatin regulatory complexes characteristic of open chromatin40. FFPE-CUTAC PRODUCES HIGH-QUALITY MAPS OF ACTIVE CHROMATIN To evaluate the accuracy and data quality of FFPE-CUTAC applied

to mouse brain tumors, we compared tracks between FFPE-CUTAC and FACT-seq or standard CUT&Tag from the same study20 using the same H3K27ac antibody (Abcam cat. no. 4729). Because of

differences in cell types, brain tumors in our study and kidney or liver in the FACT-seq study, we limited comparisons of tracks to housekeeping genes that are expected to be similarly

expressed in all cell types. Based on visual inspection of tracks from representative regions of the mouse genome, it is evident that H3K27ac CUTAC profiles show much cleaner profiles than

those obtained using FACT-seq, with higher sensitivity than the data obtained for CUT&Tag controls of frozen mouse kidney (Fig. 3a–d). Likewise, clean profiles were also seen for

RNAPII-Ser2,5p FFPE-CUTAC, where RNAPII-Ser2 phosphate marks elongating and RNAPII-Ser5 phosphate marks paused RNAPII. For a systematic analysis of data quality, we called peaks using

MACS241 and compared the number of peaks called and FRiP values. Both H3K27ac and RNAPII-Ser2,5p FFPE-CUTAC on RELA- and PDGFB-driven brain tumors showed much better sensitivity based on

number of peaks called and much higher FRiP values than either H3K27ac CUT&Tag on frozen kidney or FACT-seq on FFPEs (Fig. 3e, f). To determine the degree to which FFPE-CUTAC profiles

capture regulatory elements, we took advantage of the Candidate _cis_-Regulatory Elements (cCRE) database generated by the ENCODE project, which called putative regulatory elements from all

tissue types profiled. We used the 343,731 elements in the cCRE mouse database based mostly on DNAseI-seq, but also H3K4me3 and CTCF ChIP-seq. This resource provides a comprehensive standard

for FFPE-CUTAC performance, insofar as CUTAC profiles correspond closely to both ATAC-seq and DNAseI-seq profiles22. For each dataset we rank-ordered cCREs based on normalized counts

spanned by each element, which we plotted as a log-log cumulative curve, where a higher curve indicates better performance in distinguishing annotated sites from background. By this

benchmark, both H3K27ac and RNAPII-Ser2,5p FFPE-CUTAC brain datasets outperformed both FACT-seq on FFPEs and CUT&Tag on unfixed frozen kidney (Fig. 3g). We conclude that our FFPE-CUTAC

protocol provides high quality data, even when compared to standard CUT&Tag. FFPE-CUTAC PROFILES DISTINGUISH BRAIN TUMORS AND REVEAL GLOBAL UPREGULATION Nearly all strong peaks seen for

H3K27ac and RNAPII-Ser2,5p FFPE-CUTAC corresponded to putative regulatory elements from the cCRE database, with concordance between FFPE-CUTAC, FACT-seq and ChIP-seq (Fig. 3a–d). To identify

tumor-specific candidate regulatory elements we performed pairwise comparisons between three different mouse brain tumors (YAP1-, PDGFB- and RELA-driven tumors) and normal mouse brains. For

each of the 343,731 cCREs we averaged the normalized counts spanned by the cCRE and performed pairwise comparisons over all cCREs with Voom/Limma42, an Empirical Bayes algorithm, which uses

the other datasets as pseudo-replicates to increase statistical confidence. We applied this approach to datasets from multiple FFPE-CUTAC experiments using antibodies against RNAPII-Ser5p,

RNAPII-Ser2,5p and H3K27ac. We observed far more significant differences for comparisons between tumors and normal brains than between tumors, with more increases than decreases in tumors

relative to normal brains (Fig. 4a–c and Supplementary Data 2a-d). For example, using RNAPII-Ser5p, there were 10,321 cCREs that differed between YAP1 and normal brain, 518 between PDGFB and

normal brain, and 190 between RELA and normal brain at a False Discovery Rate (FDR) = 0.05, but only 10-63 cCREs that differed in pairwise comparisons between the three tumors (Fig. 4a and

Supplementary Data 2a). Compared to normal brain, 92-99% of the differences were increases in the tumors. Approximately similar results were obtained using RNAPII-Ser5p (Fig. 4b and

Supplementary Data 2b). For H3K27ac, the number of cCREs that increased was more extreme, with nearly half of the 343,371 cCREs significantly increased at the FDR = 0.05 level (Fig. 4c and

Supplementary Data 2d). These results demonstrate that FFPE-CUTAC using antibodies against RNAPII or H3K27 marks distinguishes between the tumors and the normal brain samples with nearly all

significant differences representing increases for the three tumors over normal brain. As FFPE-CUTAC data quality is very similar between RNAPII-Ser2,5p and H3K27ac (Fig. 3), we attribute

the conspicuous sensitivity differences in pairwise comparisons (Fig. 4a–c and Supplementary Data 2a–c) in part to the larger number of H3K27ac samples that Voom/Limma used for

pseudo-replicates in calculating FDR. To balance the contribution of samples from each genotype, we merged datasets from multiple FFPE-CUTAC experiments for each antibody (RNAPII-Ser5p,

RNAPII-Ser2,5p or H3K27ac) or antibody combination (RNAPII-Ser5p + RNAPII-Ser2,5p), then down-sampled to the same number of mapped fragments for each genotype. The three tumor and one normal

genotype, each represented by four different antibodies or antibody combination, were compared pairwise with Voom/Limma. We observed the most differences between RELA and Normal (1,657) and

between RELA and PDGFB (607) and the fewest differences between PDGFB and YAP1 (17) (Fig. 4d and Supplementary Data 2e). We conclude that FFPE-CUTAC can distinguish tumors from one another

and from normal brains based on differences in cCRE occupancy of active RNAPII and H3K27ac marks. INCREASES IN PAUSED RNAPII PINPOINT REGULATORY ELEMENT DIFFERENCES To identify gene

regulatory elements genome-wide that best distinguish tumor from normal and between tumors, we performed Voom/Limma analysis using the maximum normalized count within each cCRE, rather than

the average of normalized counts over the entire cCRE. The most significant difference among all RNAPII-Ser5p cCRE comparisons is a sharp peak in a coding exon of the PDGFB gene, which is

present in the PDGFB-driven tumors but absent in the normal brain (FDR = 5 × 10-5, Fig. 5a). This example serves as an internal control, as it corresponds to the virally expressed PDGF-beta

growth factor coding region that drives the tumor, even though this sample contained both normal brain and tumorous tissue. The other most significant and highly expressed differences

between tumors and normal brain identify loci that have been reported as implicated in tumor progression. Among these are the SET domain-containing 5 (_Setd5_) promoter (Fig. 5b)43, the

Phosphoglucokinase (_Pgk1_) promoter (Fig. 5c)44, which are also from the PDGFB-driven tumor and normal comparison, displaying clear differences between the tumors. Additionally, the cCREs

in these genes show high signal in the RELA-driven tumor and low signal in the YAP1-driven tumor. Even more striking differences are seen for the next two most significant differences at the

bidirectional promoter of the Insulin growth factor 2 (_Igf2_) (Fig. 5e) and the Collagen type 1 alpha 1 (_Col1a1_) gene promoter (Fig. 5d)45,46, where the RELA-driven tumor shows a strong

signal but there is no perceptible signal in the region for normal, PDGFB-driven and YAP1-driven samples. Conspicuous tumor-specific differences are also seen for four of the five cCREs with

the highest signals with FDR < 0.05, including an intronic enhancer in the Suppressor of cytokine signaling 3 (_Socs3_) gene (Fig. 5f)47, the promoter of the Nuclear paraspeckle assembly

transcript 1 (_Neat1_) long non-coding RNA gene (Fig. 5g)48, a proximal enhancer of the Cyclin D1 (_Ccnd1_) gene (Fig. 5h)49 and the C/EBPβ promoter (Fig. 5j)50. Additional genes implicated

in tumor progression are highlighted by these comparisons, including the Connective tissue growth factor (_Ccn2_) promoter (Fig. 5k)51 and an intronic enhancer of the Metallothionien 2 A

(_Mt2a_) gene (Fig. 5l)52. Finally, whereas the Testis Expressed 14 (_Tex14_) gene has not been reported to be implicated in cancer, this is the only one of the top 12 genes in which the

tumor/normal differences were inconspicuous (Fig. 5i), consistent with the supposition that increases in paused RNAPII at enhancers or promoters of the other genes are associated with tumor

progression. FFPE-CUTAC DISTINGUISHES TUMOR FROM NORMAL TISSUE WITHIN THE SAME FFPE On-slide FFPE-CUTAC (Fig. 2a) provided us with the opportunity to compare tumor with normal tissue on the

same slide. For this analysis we used ZFTA-RELA gene fusion-driven ependymomas (Fig. 6a) which are relatively large and cytologically distinct, whereas PDGFB-driven gliomas (Fig. 6b) are

more diffuse. We performed on-slide FFPE-CUTAC through tagmentation and manually harvested 6 sections from a single RELA slide and 7 sections from a single PDGFB slide separately into PCR

tubes. After sequencing, we performed Voom/Limma analysis comparing the sections identified cytologically as mostly tumor to sections identified as mostly normal. Results for RELA were very

similar to those obtained comparing tumor to normal brains, whereas results for PDGFB showed fewer significant cCREs at FDR = 0.05 (Fig. 6c). Similar results were obtained with two other

RELA slides, where the top upregulated cCRE was within the _Col1a_ gene (Fig. 6d and Supplementary Data 3), which was also the top RELA-versus-Normal hit in multiple-slide comparisons (Fig. 

4d). Interestingly, the top down-regulated gene in both replicate slides, _Mir124a-1hg_, is a microRNA methylation marker locus for _Helicobacter pylori_ infection that correlates with

gastric cancer driver gene methylation53. The entire locus is embedded in a cluster of 27 cCREs, and all replicates show a broad RNAPII signal in normal tissue but not RELA-driven tumor

encompassing the entire cluster (Fig. 6d). Indeed, the top 10 down-regulated cCREs are either _Mir124a-1hg_ or _Mir124a-2hg_ and these together with the next down-regulated cCRE, which is

over the _Mir670_ microRNA locus, account for 15 of the top 25 down-regulated cCREs (Supplementary Data 3). In contrast, these genes are far down the RNA-seq list ranked by false discovery

rate, as _Mir124a-1hg_ ranks 9,913, _Mir124a-2hg_ ranks 6,045 and _Mir670_ ranks 21,262 of 23,551 annotated mouse genes (Supplementary Data 4). FFPE-CUTAC DISTINGUISHES TUMORS FROM NORMAL

LIVER To test whether our results with mouse brain FFPEs generalize to a very different tissue type, we performed FFPE-CUTAC using FFPE sections prepared from intrahepatic cholangiocarcinoma

tumors and normal liver. We used FFPE sections that had been fixed in formalin for 7 days and after deparaffinization were incubated at 90 oC in cross-link reversal buffer for 8 h and

incubated with a 50:50 mixture of RNAPII-Ser5p and RNAPII-Ser2,5p antibodies, each at 1:50 concentration. Highly consistent results were obtained for samples ranging from 10% to 50% of a

section (~30,000–150,000 cells), with clean peaks over housekeeping genes for both liver tumor and normal liver (Fig. 7a–d). As was the case with brain tumor and normal tissues fixed in

formalin for 2 days, the number of peaks and fraction of reads in peaks (FRiP) were much higher than those from FACT-seq FFPE livers (Fig. 7e, f) and overlap with cCREs was also much higher

when down-sampled to the same number of fragments (Fig. 7g). Finally, volcano plots revealed net increases in cCRE RNAPII occupancy both in fold-change and FDR for liver tumors relative to

normal livers, similar to what we observed in comparing brain tumors to normal brains (Fig. 7h, i). We conclude that FFPE-CUTAC provides high-quality data for FFPEs from diverse tissue

types. COMPARISON BETWEEN FFPE-CUTAC AND STANDARD RNA-SEQ ON TRANSGENE-DRIVEN BRAIN TUMORS The murine brain tumors that we used in our study have served as models for the study of de novo

tumorigenesis28,29,54, with high-quality RNA-seq data available. To do an unbiased comparison between FFPE-CUTAC regulatory elements and processed transcripts mapped by RNA-seq, we first

determined whether there is sufficient overlap between cCREs and annotated 5’-to-3’ genes to fairly compare these very different modalities. Specifically, the 343,731 cCREs average 272 bp in

length, accounting for 3.4% of the Mm10 build of the mouse genome, whereas the 23,551 genes in RefGene average 49,602 bp in length, with an overlap of 54,062,401 bp or 2.0% of Mm10. In

other words, the 5’-to-3’ span of mouse genes on the RefGene list should capture all of the RNA-seq true positives and almost 60% (2.0/3.4 x 100%) of the cCREs. With most cCREs overlapping

annotated mouse genes, we can directly compare FFPE-CUTAC fragment counts to RNA-seq fragment counts by asking how well they correlate with one another over genes. Whereas FFPE-CUTAC

replicates and RNA-seq replicates are very strongly correlated to a similar extent, with “arrowhead” scatterplots (_R_2 = 0.955–0.997), comparisons between FFPE-CUTAC and RNA-seq samples are

“fuzzy” but nevertheless show strong correlations (_R_2 = 0.764–0.881) (Fig. 8a). We also determined the extent to which the same genes differ significantly between tumor and normal in the

two datasets. Using an FDR = 0.05 cut-off for both FFPE-CUTAC and RNA-seq, we found that 80–82% of genes were found in both lists: 52 of 63 for YAP1-driven tumors versus normal brains, 268

of 336 for PDGFB-driven versus normal and 1519 of 1896 for RELA-driven versus normal. However, there is a striking difference in the specificity with which these genes are identified as

illustrated by comparison of volcano plot displays: FFPE-CUTAC provides high specificity for regulatory elements, where significant differences between cCREs are almost exclusively at the

upregulated corner of the volcano plots (high positive log2 fold-change, high —log10 FDR) (Fig. 4). In contrast, about 1/3 to 1/2 of 23,551 genes show significant differences between these

tumors and normal brains using RNA-seq with massive, mostly symmetrical “volcanic eruptions” (Supplementary Fig. 4). To validate these comparisons, we aligned profiles of FFPE-CUTAC and

RNA-seq at YAP1 and at nine direct targets of YAP1, which were previously determined based in part on the RNA-seq data54. As expected, the FFPE-CUTAC profiles are enriched primarily at 5’

ends and RNA-seq at 3’ ends (Fig. 8b). Importantly, all ten examples showed full or partial concordance between FFPE-CUTAC and RNA-seq. We conclude that there is overall excellent agreement

between our FFPE-CUTAC data and previously published high-quality RNA-seq datasets. The very high specificity of FFPE-CUTAC data and its ability to identify and map differentially regulated

microRNAs, together with its simple implementations and potential for automation, make it an exceptional modality for discovery of functional biomarkers. DISCUSSION Fixation-related DNA and

chromatin damage has thus far impeded the practical application of chromatin profiling to FFPEs4. Here we have shown that improvements to the single-tube CUT&Tag-direct protocol to make

it suitable for whole cells, and together with heat treatment of FFPEs, provides high-quality CUTAC data. Using RNAPII antibodies provides a ground-truth interpretation of active chromatin

based on the transcriptional machinery itself, applicable to both promoters and enhancers55. RNAPII-based CUTAC mapping contrasts with the mapping of “open chromatin” inferred from enzymatic

[_e.g_. DNAseI hypersensitivity mapping56 and ATAC-seq11] or physical [_e.g_. FAIRE15 and Sono-seq57] methods, where results typically differ depending on the method used. We also showed

that all FFPE-CUTAC steps through tagmentation can be performed on the slides without using organic chemicals such as xylene or mineral oil. On-slide FFPE-CUTAC allows for direct comparisons

between dissected tumor and normal tissues from the same FFPE 5- or 10-micron section. As all steps through tagmentation are performed on the slide without noticeable tissue disruption

(Fig. 6a, b), FFPE-CUTAC is suitable for spatial applications using available platforms58,59. RNA-seq has been the preferred method for profiling the transcriptome, however, it is strongly

biased towards abundant transcripts, while transcription factors that drive development and are deregulated in cancer may be expressed at relatively low levels and can be difficult to

detect. Changes in TF mRNA abundance with cell type changes are swamped out by changes in more abundant mRNAs. mRNA abundance is also affected by complex regulatory mechanisms occurring

during and after transcriptional elongation, including multiple modes of processing and export to the cytoplasm, resulting in undifferentiated volcano plots that resemble erupting volcanos

(Supplementary Fig. 4). Our comparisons were against RNA-seq data from fresh or frozen tissue, whereas RNA-seq results from FFPEs are much poorer owing to serious degradation and off-target

reads60. In contrast, paused RNAPII is a critical checkpoint for transcriptional activation, and so its abundance at a regulatory element is a direct measure of transcriptional competence,

and cancer driver and tumor suppressor loci stand out (Figs. 5 and 6c, d). The 343,731 genomic sites annotated as candidate _cis_-regulatory elements (cCREs) in the mouse genome can

potentially provide direct information on transcriptional regulatory networks. We found that FFPE-CUTAC sensitively detects cCRE clusters spanning microRNA loci that gain RNAPII in the RELA

fusion-driven tumor. Strikingly, the 10 most down-regulated cCREs corresponded to two unlinked loci for the mouse Mir124a microRNA, which was previously described as a neuronal

differentiation factor61, a tumor suppressor in brain62 and a unique human biomarker for _H. pylori_ infection and gastric cancer risk53. Although Mir124a is one of the most abundantly

expressed microRNAs in the central nervous system61, we found that it is present at only average levels in RNA-seq data. As microRNAs are excised from polyadenylated RNAPII transcripts, and

RNA-seq relies on priming from the poly(A) tail, important microRNA biomarkers such as Mir124a are entirely missed. In contrast to RNA-seq, FFPE-CUTAC detects all classes of active RNAPII

genes. Furthermore, the much better discrimination of RNAPII that we observed for FFPE-CUTAC over cCREs than for high-quality RNA-seq data over genes encourages more general application of

FFPE-CUTAC technology for diagnosis, biomarker discovery and retrospective studies. Remarkably, the large majority of significant differences between tumors and normal brain corresponded to

increases in RNAPII and H3K27ac, a histone mark of active promoters and enhancers. Global hypertranscription is a general feature of aggressive human cancers63, and our consistent finding of

greater upregulation of RNAPII at cCREs in tumors relative to normal, even from the same mouse brains, provides support for this interpretation. In brain tumors, the RNAPII FFPE-CUTAC

fragment size distribution was increased relative to that of normal tissue, perhaps indicative of greater accessibility to the transcriptional apparatus. Cross-links and adducts resulting

from the long incubations in formaldehyde necessary for long-term preservation cause DNA breaks and lesions that are serious impediments for most genomic methods applied to FFPEs. Indeed,

standard CUT&Tag failed for the group that developed FACT-seq20, and we also failed to obtain usable profiles for repressive H3K27me3 and H3K9me3 and gene-body H3K36me3 histone epitopes.

We attribute these failures to the tight wrapping of DNA around lysine-rich histones, which are the most susceptible to cross-linking and formation of DNA adducts that result in DNA breaks

during high-temperature cross-linking reversal24. In contrast, nucleosome-depleted regions (NDRs) that are mapped using accessibility methods such as ATAC-seq11, NicE-seq13, FAIRE14,15 and

CUTAC22,23 are much better suited for FFPEs, as the protein machineries that occupy these sites are not especially lysine-rich. In particular, the YSPTSPS heptamer present in 52 tandem

copies on the C-terminal domain of the largest subunit of RNAPII presents abundant lysine-free epitopes for CUT&Tag, and the use of low-salt tagmentation after stringent washes allows

for tight binding of the Tn5 transposome within the confines of the NDR. We have previously shown that for epitopes such as H3K4 methylations22 and RNAPII epitopes23 that flank gaps in the

nucleosome landscape at promoters and enhancers, tagmentation preferentially releases subnucleosomal fragments. FACT-seq improves yield with in vitro transcription from a T7 promoter

inserted at single sites, however this strategy foregoes the advantage of the small size of NDRs at promoters and enhancers where nevertheless two Tn5s can fit with enough DNA in between for

sequence-based mapping. We might attribute the better data quality that we obtained using CUTAC relative to FACT-seq to the very low probability of two Tn5s inserting close enough to one

another and correctly oriented to produce a small amplifiable fragment by random chance. Curiously, H3K27ac FFPE-CUTAC detected cCREs even more sensitively than standard H3K27ac CUT&Tag

on frozen tissue, which might indicate that better reversal of cross-links at NDRs than at nucleosomes facilitates tagmentation within NDRs while nucleosomes remain relatively intractable.

Indeed, by avoiding the use of degradative enzymes and using only heat to expose epitopes in a suitable buffer, we found that bead-bound tissue shards from sheared FFPEs are much easier to

handle without damage than cells or nuclei, where lysis and sticking is a constant concern. We also discovered that DNA from _Rhodococcus erythropolis_, a species of bacteria that can live

on paraffin wax as its only carbon source, is abundant in the FFPE samples that we processed, and this unfixed DNA competes against formalin-damaged DNA from FFPEs during PCR. We found that

bacterial contamination was essentially eliminated in on-slide FFPE-CUTAC and when using some magnetic beads that lacked ConA but nevertheless bound sufficiently well to FFPE tissue

fragments. These observations suggest that sugars on the surface of contaminating bacterial remnants were captured by the Concanavalin A sugar-binding site and their DNA was released prior

to or during PCR. As CUT&Tag-direct has been fully automated26, we expect that our FFPE-CUTAC protocol will be suitable for institutional core facilities and commercial services,

maximizing reproducibility and minimizing costs. In conclusion, we have shown that RNAPII and H3K27ac chromatin profiling can be conveniently and inexpensively performed on FFPEs in single

PCR tubes or directly on slides. We use only heat in a suitable buffer to reverse the cross-links while making the tissue sufficiently permeable, followed by modified versions of our

CUT&Tag-direct protocol, which is routinely performed in many laboratories23,64. We found that data quality using low-salt tagmentation for antibody-tethered chromatin accessibility

mapping is sufficient to distinguish cancer from normal tissues and resolve closely similar brain tumors. Using FFPE-CUTAC, our study identified direct targets of cancer drivers in tumors

and microRNA loci not detectable by RNA-seq, validating our approach. METHODS ETHICAL STATEMENT This research was approved by the Fred Hutch Institutional Animal Care and Use Committee

(Protocol # 50842) and complies with all required ethical regulations. CELL LINES Human female K562 chronic myelogenous leukemia cells (American Type Culture Collection (ATCC) cat. no.

CCL-243) and mouse NIH 3T3 cells (ATCC cat. no. CRL-1658) were authenticated for STR, sterility, human pathogenic virus testing, mycoplasma contamination and viability at thaw. H1 (WA01)

male hESCs (WiCell cat. no. WA01-WB35186) were authenticated for karyotype, STR, sterility, mycoplasma contamination and viability at thaw. K562 cells were cultured in liquid suspension in

IMDM (ATCC) with 10% FBS added (Seradigm). H1 cells were cultured in Matrigel (Corning)-coated plates at 37 °C and 5% CO2 using mTeSR-1 Basal Medium (STEMCELL Technologies) exchanged every

24 h. K562 and 3T3 cells were harvested by centrifugation for 3 minutes at 1,000 g and then resuspended in 1× PBS. H1 cells were harvested with ReleasR (STEMCELL Technologies) using the

manufacturer’s protocols. MICE All animal experiments were done in accordance with protocols approved by the Institutional Animal Care and Use Committees of Fred Hutchinson Cancer Center

(protocol no. 50842) and followed National Institutes of Health guidelines for animal welfare. The RCAS/tv-a system used in this work has been described previously54. In brief, Jackson lab

mouse strain 3529 (FVB/N;C57BL/6;129/Sv Nestin) (N)/tv-a Cdkn2a null pups (P0-P1; male and female) or adults (5–7 week old, male and female) had been injected intracranially with DF-1 cells

expressing RCAS-PDGFB27, RCAS-REL A-ZFTA28, or RCAS-YAP1-FAM118b65. We used both male and female mice. Upon weaning (~P21), mice were housed with same-sex littermates, with no more than 5

per cage and given access to food/water ad libitum and monitored daily for the occurrence of tumor-related symptoms for the duration of the experiment. Mice were euthanized upon the

occurrence of predefined tumor-related symptoms: macrocephaly, lethargy, dehydration, poor grooming, hemiparesis, weight loss, seizures, jumpiness, or immobilization/paralysis. MOUSE TUMOR

AND NORMAL TISSUES AND FFPES Ntva;cdkn2a-/- mice were injected intracranially with DF1 cells infected with and producing RCAS vectors encoding either PDGFB27, REL A-ZFTA28, or

YAP1-FAM118b65. When the mice became lethargic and showed poor grooming, they were euthanized and their brains removed and fixed at least 48 h in Neutral Buffered Formalin. Tumorous and

normal brains were sliced into five pieces and processed overnight in a tissue processor, mounted in a paraffin block and 5- or 10-micron sections were placed on slides. Slides were stored

for varying times between 1 month to ~2 years before being deparaffinized and processed for FFPE-CUTAC. Healthy mouse liver or intrahepatic cholangiocarcinomas tumors harvested from

orthotopic models of intrahepatic cholangiocarcinoma mice with activating mutations of KrasG12D and deletion of p5366 were fixed in formalin for 7d before being sent to the Fred Hutch

Experimental Histopathology Shared Resource for FFPE processing. Ten brains and five livers were used in the experiments described. ANTIBODIES Primary antibodies: H3K4me3: Active Motif cat.

no. 39159, lot no. 18122006; H3K27ac: Abcam cat. no. ab4729, lot no. 1033973; RNAPII-Ser5p: Cell Signaling Technologies cat. no. 13523, lot 3; RNAPII-Ser2,5p: Cell Signaling Technologies

cat. no. 13546, lot 1; H3K27me3: Cell Signaling Technologies cat. no. 9733, lot 19; H3K4me2: Epicypher cat. no. 13-0027, lot 21090003-01; H3K36me3: Thermo cat. no. MAS-24687, lot VE2997961.

Secondary antibody: Guinea pig α-rabbit antibody (Antibodies online cat. no. ABIN101961, lot 46671). CUT&TAG-DIRECT FOR WHOLE CELLS Concanavalin A (ConA) coated magnetic beads (Bangs

Laboratories, cat. no. BP531) were activated just before use with Ca++ and Mn++ as described21. Frozen whole-cell aliquots were thawed at room temperature, split into PCR tubes and 5 µL ConA

beads were added with gentle vortexing. Briefly, nuclei were mixed with activated Concanavalin A beads and resuspended in Triton-wash buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 0.5 mM

spermidine, 0.05% Triton-X100 and Roche EDTA-free protease inhibitor). After successive incubations with primary antibody (≥1 h) and secondary antibody (1 h) in Wash buffer, the beads were

washed and resuspended in pAG-Tn5 preloaded with mosaic end adapters (Epicypher cat. no. 15-1117 1:20) in Triton-wash buffer for 1 h. Incubations were done at room temperature in 25 µL

volumes in PCR tubes. Tagmentation was performed for 1 h in 10 mM TAPS pH 8.5, 20% N,N-dimethylformamide, 5 mM MgCl2 at 55 °C. Fragment release was performed in 5 µl 1% SDS supplemented with

1:10 Thermolabile Proteinase K (New England Biolabs cat. no. P8111S) at 37 °C 1 h followed by 58 oC 1 h. SDS was quenched by addition of 15 µl 6% Triton-X100 and PCR was performed by

addition of 2 µl each barcoded 10 mM i5 and i7 primer solutions and 25 µl NEBNext 2X PCR Master mix (New England Biolabs cat. no. ME541L) (Supplementary Data 8) The following cycling

conditions were used: Cycle 1: 58 °C for 5 min; Cycle 2: 72 °C for 5 min; Cycle 3: 98 °C for 5 min; Cycle 4: 98 °C for 10 s; Cycle 5: 60 °C for 10 s; Repeat Cycles 4-5 11 times; 72 °C for 1 

min; Hold at 8 °C. Clean-up was performed using HighPrep PCR Cleanup Magbio Genomics cat. no. AC-60500 following manufacturer’s instructions. A detailed step-by-step protocol is available at

Protocols.io: https://doi.org/10.17504/protocols.io.x54v9mkmzg3e/v4. FFPES Mouse tissue (including normal brains and tumor bearing brains) were removed, fixed in 10% neutral-buffered

formalin for a minimum of 24 h and embedded into paraffin blocks. 5- or 10-µm serial sections were cut from formalin-fixed paraffin-embedded specimens and mounted on slides. FFPE-CUTAC In

most experiments, deparaffinization was performed in Coplin jars using 2-3 changes of histology grade xylene over a 20-minute period, followed by 3-5 minute rinses in a 50:50 mixture of

xylene:100% ethanol, 100% ethanol (twice), 95% ethanol, 70% ethanol and 50% ethanol, then rinsed in deionized water. Slides were stored in distilled deionized water containing 0.02% sodium

azide for up to 2 weeks before use. In experiments presented in Fig. 6 and Supplementary Data 1 and 6, FFPE slide were placed in 800 mM Tris-HCl pH8.0 in a slide holder and incubated at 85 

°C for 8–16 h, whereupon the paraffin melted and floated off the slide. Liquid was added beneath the surface so that any residual paraffin would drain out over the top of the slide holder.

Tissue sections on deparaffinized slides were diced using a razor and scraped into a 1.7 mL low-bind tube containing 400 µl 800 mM Tris-HCl pH8.0, 0.05% Triton-X100. For

xylene-deparaffinized samples, incubations were performed at 80–90 °C for 8–16 h or as otherwise indicated either in a heating block or divided into 0.5 mL PCR tubes after needle extraction.

Needle extraction was performed either before or after Concanavalin A (ConA) bead addition using a 1 ml syringe fitted with a 1” 22 gauge needle with 20 up-and-down cycles, and in some

cases was followed by 10 cycles with a 3/8” 26 gauge needle. In some experiments amine-coated (Polysciences cat. no. 86001-10) or glutathione-coated (Fisher cat. no. 88822) paramagnetic

beads were used in place of ConA beads. Other steps through to library preparation and purification followed the CUT&Tag-direct protocol as described above. A detailed step-by-step

protocol, is available on Protocols.io: https://doi.org/10.17504/protocols.io.14egn292zg5d/v1, with a comment box for help. ON-SLIDE FFPE-CUTAC FFPE slide were placed in 800 mM Tris-HCl

pH8.0 in a slide holder and incubated at 85 oC for 8–16 h, whereupon the paraffin melted and floated off the slide. Slides were cooled to room temperature, dipped in Triton-Wash buffer (10 

mM HEPES pH 7.5, 150 mM NaCl, 2 mM spermidine and Roche complete EDTA-free protease inhibitor), drained and excess liquid wicked off using a Kimwipe tissue. The sections were immediately

covered with 50-100 µL primary antibody added dropwise. Plastic film was laid on top starting at the bottom end and omitting bubbles as the meniscus progressed toward the frosted end of the

slide. The excess plastic film was folded under for a near-watertight seal. After ≥2 h incubation at room temperature (or overnight at ~8 oC) in a moist chamber, the plastic film was peeled

back, and the slide was submerged in Triton-Wash buffer for 10-20 min. This incubation/wash cycle was repeated for the guinea pig anti-rabbit secondary antibody (Antibodies Online cat. no.

ABIN101961) and for pAG-Tn5 preloaded with mosaic end adapters (Epicypher cat. no. 15-1117 1:20), followed by transfer of the slide to 10 mM TAPS pH 8.5. Tagmentation was performed in 5 mM

MgCl2, 10 mM TAPS pH 8.5, 20% (v/v) N,N-dimethylformamide in slide holders incubated at 55 oC for 1 h. Following tagmentation, slides were dipped in 10 mM TAPS pH 8.5, drained and excess

liquid wicked off. Individual sections were covered with 2 µL 10% Thermolabile Proteinase K (TL ProtK) in 1% SDS using a pipette tip to loosen the tissue. Tissue was transferred to a

thin-wall PCR tube containing 2 µL TL ProtK using a watchmaker’s forceps, followed by 1 µL TL ProtK and transfer to the PCR tube. Tubes were incubated at 37 oC for 1 h and 58 oC for 1 h

before PCR as described above. A detailed step-by-step protocol, is available on Protocols.io https://doi.org/10.17504/protocols.io.14egn292zg5d/v1. DNA SEQUENCING AND DATA PROCESSING The

size distributions and molar concentration of libraries were determined using an Agilent 4200 TapeStation. Up to 48 barcoded 96 barcoded libraries were pooled at approximately equimolar

concentration for sequencing. Paired-end 50x50 bp sequencing on the Illumina NextSeq 2000 platform was performed by the Fred Hutchinson Cancer Center Genomics Shared Resources. This yielded

1–20 million reads per antibody. Adapters were clipped by cutadapt version 4.1 with parameters --nextseq-trim 20 -m 20 -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCA -A

AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT -Z Clipped reads were aligned by Bowtie2 version 2.4.4 to the _Mus musculus_ mm10 and _Homo sapiens_ hg19 reference sequences from UCSC and to the

_Rhodococcus erythropolis_ complete genome (NZ_CP007255.1) from NCBI with parameters --very-sensitive-local --soft-clipped-unmapped-tlen --dovetail --no-mixed --no-discordant -q --phred33 -I

10 -X 1000 DATA ANALYSIS BLASTN searches of unmapped reads against the Nucleotide database were done on the NCBI web site

(https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome). We noticed the majority hit several bacteria so we narrowed the search to the

RefSeq Genome Database restricted to Bacteria (taxid:2). After further analysis of these BLAST hits we made a Bowtie2 reference sequence from five bacteria: NZ_CP007255.1 _Rhodococcus

erythropolis_ R138 NZ_JACNZU010000010.1 _Bacillus pumilus_ strain 167T-6 NZ_JAGEKP010000001.1 _Leifsonia_ sp. TF02-11 NZ_QCYC01000100.1 _Vibrio vulnificus_ strain Vv003 NZ_MLHV01000015.1

_Mycobacterium syngnathidarum_ strain 24999 To estimate library sizes in Supplementary Data 1, we used Picard Tools: MarkDuplicates http://broadinstitute.github.io/picard/ Properly paired

reads were extracted from the alignments by samtools version 1.14 bamtobed command into mapped fragment bed files and normalized count tracks were made by bedtools version 2.3067,68

genomecov command with scale (size_of_reference_sequence/total_counts). Normalized count tracks are the fraction of counts at each base pair scaled by the size of the reference sequence so

that if the counts were uniformly distributed across the genome there would be one at each position. Distributions of the lengths of the mapped fragments were made using the UNIX sort and

uniq -c command. Peaks were made by MACS2 version 2.2.641 from the mapped fragment bed files with parameters: macs2 callpeak -t <fragments > -f BEDPE -g hs --keep-dup all -p 1e-5 -n

<name > --SPMR For comparisons, the following datasets were downloaded from GEO: GSM5530653, GSM5530654 and GSM5530655 (mouse kidney H3K27ac FACT-seq replicates 1–2 and H3K27ac Frozen

CUT&Tag, respectively), GSM5530669 and GSM5530670 (mouse liver H3K27ac FACT-seq replicates 1–2) and GSE172688 (ENCODE ChIP-seq mouse post-natal forebrain). Random sub-samples of fixed

sizes were taken from the mapped fragment bed files using the UNIX shuff command and peaks were found by MACS2 for each sub-sample. Then the fraction of reads in peaks (FRiP) was computed

using the bedtools intersect command. Single-end data used for comparisons was 50 bp for kidney and 75 bp for liver. These read lengths were sufficiently similar to our paired-end median

adapter-trimmed fragment lengths (65-76 bp for brain and 63-68 bp for liver) that no adjustments were made in comparisons. cCRE overlaps were calculated for 10 million mapped fragments per

sample as the number of fragments with at least one base pair overlap with a cCRE. Differential analyses of FFPE-CUTAC and RNA-seq data were performed using the Voom/Limma option42 on the

Degust server (https://degust.erc.monash.edu/). Files for degust (https://degust.erc.monash.edu/) were made for a list of 343,731 Candidate _cis-_regulatory elements (cCREs) for _Mus

musculus_ from ENCODE (ENCFF427VRW) and for 23,551 genes from the _Mus musculus_ Mm10 refGene list from the University of California Santa Cruz Genome Resource. The refGene file contains

multiple transcripts for each gene so we winnowed it by using the region from the minimum start position to the maximum end position for each set of transcripts for a gene. For sums, we

added the normalized counts within each cCRE or gene region for analysis by the degust web site. For summits we took the maximum within each region. _Statistics and Reproducibility_ Overall

data quality was evaluated by peak-calling and FRiP at multiple levels of downsampling and by Voom/Limma (-log10FDR versus log2FoldChange) analysis, which is very sensitive to

reproducibility of replicates. No statistical method was used to predetermine sample size nor were data excluded from the analyses. The experiments were not randomized and Investigators were

not blinded to allocation during experiments and outcome assessment. REPORTING SUMMARY Further information on research design is available in the Nature Portfolio Reporting Summary linked

to this article. DATA AVAILABILITY The sequencing data generated in this study have been deposited in the NCBI GEO database under accession code GSE235876. Source data are provided with this

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Download references ACKNOWLEDGEMENTS We thank Christine Codomo, Doris Xu and Terri Bryson for technical assistance, Iris Luk for generating the cholangiocarcinoma model, Matthew Fitzgibbon

for bioinformatics support, the Fred Hutch Genomics Shared Resource for sequencing and data processing and the Fred Hutch Experimental Histopathology Shared Resource for FFPE processing of

liver samples. This work was supported by the Howard Hughes Medical Institute (S.H.) and grant # T32CA009515 from the National Cancer Institute (R.M.P.). AUTHOR INFORMATION AUTHORS AND

AFFILIATIONS * Basic Science Division, Fred Hutchinson Cancer Center, Seattle, WA, USA Steven Henikoff, Jorja G. Henikoff, Kami Ahmad & Derek H. Janssens * Howard Hughes Medical

Institute, Chevy Chase, MD, USA Steven Henikoff * Human Biology Division, Fred Hutchinson Cancer Center, Seattle, WA, USA Ronald M. Paranal, Zachary R. Russell, Frank Szulzewsky, Sita Kugel 

& Eric C. Holland Authors * Steven Henikoff View author publications You can also search for this author inPubMed Google Scholar * Jorja G. Henikoff View author publications You can also

search for this author inPubMed Google Scholar * Kami Ahmad View author publications You can also search for this author inPubMed Google Scholar * Ronald M. Paranal View author publications

You can also search for this author inPubMed Google Scholar * Derek H. Janssens View author publications You can also search for this author inPubMed Google Scholar * Zachary R. Russell

View author publications You can also search for this author inPubMed Google Scholar * Frank Szulzewsky View author publications You can also search for this author inPubMed Google Scholar *

Sita Kugel View author publications You can also search for this author inPubMed Google Scholar * Eric C. Holland View author publications You can also search for this author inPubMed 

Google Scholar CONTRIBUTIONS S.H. and E.C.H. conceived the study; S.H. and R.M.P. performed the experiments; F.S., Z.R.R., S.K. and E.C.H. provided critical materials; D.H.J. advised on the

methods; S.H. and J.G.H. analyzed the data; S.H. wrote the manuscript; S.H., J.G.H., K.A., S.K. and E.C.H. reviewed and edited the manuscript, and all authors approved the manuscript.

CORRESPONDING AUTHOR Correspondence to Steven Henikoff. ETHICS DECLARATIONS COMPETING INTERESTS S.H. is an inventor in a USPTO patent application filed by the Fred Hutchinson Cancer Center

pertaining to CUTAC and FFPE-CUTAC (application number 63/505,964). The other authors declare no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature Communications_ thanks

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formalin-fixed paraffin-embedded samples by CUT&Tag. _Nat Commun_ 14, 5930 (2023). https://doi.org/10.1038/s41467-023-41666-z Download citation * Received: 30 June 2023 * Accepted: 14

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