Aldh3, Slurp1, Tkt, and Krt12) were screened out for corneal epithelium, which showed an increased expression along the differentiation trajectory during corneal epithelial maturation. In conclusion, our study identified two different types of putative LSCs and several types of putative LPCs under normal homeostasis and upon wounding, which will facilitate the understanding of corneal epithelial regeneration and wound healing.Limbal stem cells (LSCs) reside discretely at limbus surrounded by niche cells and progenitor cells. The aim of this study is to identify the heterogeneous cell populations at limbus under normal homeostasis and upon wounding using single-cell RNA sequencing in a mouse model. Two putative LSC types were identified which showed a differentiation trajectory into limbal progenitor cell (LPC) types under normal homeostasis and during wound healing. They were designated as “putative active LSCs” and “putative quiescent LSCs”, respectively, because the former type actively divided upon wounding while the later type stayed at a quiescent status upon wounding. The “putative quiescent LSCs” might contribute to a barrier function due to their characteristic markers regulating vascular and epithelial barrier and growth. Different types of LPCs at different proliferative statuses were identified in unwounded and wounded corneas with distinctive markers. Four maturation markers ( Gpha2, Cd63, and Ifitm3) had less proliferative cells under normal homeostasis and expressed higher levels of Krt15 and Krt14 and a lower level of Krt12 than the “inner” LSCs (cluster 4 with markers Atf3 and Mt1-2), suggesting that the “outer” LSCs represent a more undifferentiated and quiescent stem cell population.The corneal epithelium is under constant renewal throughout life, which is supported by limbal stem cells (LSCs). Limbal stem cells are located at the basal limbal epithelium ,2 and arIn this study, we explored the heterogeneity of limbal epithelial stem and progenitor cells by analyzing the label-retaining cells from H2B-GFP mice using single-cell RNA sequencing under normal homeostasis and wound healing.The R26-M2rtTA; TetOP-H2B-GFP transgenic mice  were used in this study. The experiments were conducted under the protocol approved by the Institutional Animal Care and Use Committee (IACUC) at the University of North Carolina at Chapel Hill and were in compliance with the Association for Research in Vision and Ophthalmology Statement for the Use of Animals in Ophthalmic and Vision Research.w/v) water for 4 weeks, followed by a 4-week chase phase during which the corneas underwent two rounds of wound healing procedures  were fed with doxycycline (2 mg/mL) in sucrose   and cross-sectioned at 10 µm thickness using a cryostat . Eye sections were fixed with neutral 10% formalin  for 10 min at room temperature followed by a standard Hematoxylin and eosin stain  following the manufacturer’s protocol. The sections were mounted in mounting medium  for microscopic study.Cryosections of mouse eyes were fixed by 1% paraformaldehyde  for 15 min at room temperature, washed in phosphate buffered saline  4 times, and incubated in blocking buffer , 1% bovine serum albumin , and 0.4% Triton X-100  for 1 h at room temperature. The cells were incubated with primary antibody  in blocking buffer for 2 h at room temperature. Excess antibody with non-specific binding was washed away by PBST (PBS containing 0.025% Triton X-100). Secondary antibodies  were used in blocking buffer for 1h at room temperature, followed by PBST wash 3 times. Nuclei were counterstained by Hoechst 33342 . Images were taken by Olympus IX81 .Limbal epithelial cells were collected following a previously optimized protocol   , followed by PCR amplification and sequencing on NextSeq 500 high output . The sequencing data obtained from the NextSeq500 were demultiplexed using TGL analysis servers and the resulting FASTQ files were analyzed using the R version 3.6.0 of Seurat 2.3.4, which uses canonical correlation analysis to identify shared correlation structures and can visualize using dimension reduction procedures including PCA and t-SNE using 10 dimensions. Initial data screening for quality control was performed to exclude cells with low reads, low detected genes, and high apoptotic genes. Seurat’s analysis packages were used to perform unsupervised clustering of the GFP+ cells to sort them into different cell clusters based on the similarity of gene expression patterns. The clusters from both samples  were pooled together for the differentiation trajectory analysis.Single-cell RNA sequencing was performed using the 10x Genomics following the manufacturer’s protocol. In brief, the GFPHuman corneas were a generous gift from the eye bank “Miracles In Sight” (a non-profit eye bank). The corneas were embedded in OCT  and cryosectioned at 10 µm thickness. The sections were fixed in 1% paraformaldehyde (Electron Microscopy Sciences) in PBS (Sigma Aldrich) for 10 min at room temperature, incubated in 0.3% Triton–X100  in PBS (Sigma Aldrich) for 10 min, blocked with 5% BSA (Thermo Fisher Scientific) in PBS  for 30 min, and incubated with primary antibody  in PBS (Sigma Aldrich) containing 1% BSA (Thermo Fisher Scientific) overnight at 4 °C. After washing the excess antibody with PBS (Sigma Aldrich), the sections were incubated in secondary antibody  for 1 h and washed with PBS (Sigma Aldrich), followed by nuclear counterstain with Hoechst 33342 . Images were taken by Olympus IX81 .Mgarp, Aldh3, Slurp1, Tkt, Lypd2, Aqp5, Dbx2, Spink7, Piezo2, and Krt12) using the applied biosystems StepOnePlus PCR system and PowerUp™ SYBR® Green Master mix  following the standard fast cycling mode  and the default dissociation step . The primers used to detect the ten genes were shown in Corneal and limbal epithelial cells were isolated from human corneas following our previous protocol . In briet-test were used for multiple comparison and comparison between two specific groups. p < 0.05 was considered statistically significant.All data points were collected from 4–6 independent experiments. One-way ANOVA and Students’ +) cells. Under doxycycline induction, cells were labeled with GFP in a widespread pattern  and p63 under normal homeostasis  and the inclusion of limbal stem/progenitor cells in the label-retaining GFP+ cells.The H2B–GFP mice were fed with doxycycline (2 mg/mL) in sucrose water for 4 weeks during the pulse phase, followed by 2 rounds of a corneal epithelial wound-healing procedure during the subsequent 4-week chase phase . First, ithelium A,C, suggl cornea . This inTo avoid any damage to LSCs, the wounding was restricted to the central corneal epithelium, leaving the peripheral corneal epithelium untouched A. Due to+ cells for single-cell RNA sequencing. We obtained 11 clusters from 3136 cells in unwounded eyes  and the OS eye served as the unwounded control eye to minimize the variations according to individual, age, and sex. Limbal epithelial cells (LECs) and anterior limbal stromal cells were isolated from unwounded eyes and wounded eyes (pooled cells from 8 eyes for each sample), respectively, using an optimized protocol , followeded eyes A and 12 ded eyes B. The meKrt family including Krt12, Krt13, Krt14, Krt15, Krt17, Krt19, etc.), we segregated the clusters into epithelial clusters (8 clusters for unwounded eyes and 8 clusters for wounded eyes) and non-epithelial clusters (3 clusters for unwounded eyes and 4 for wounded eyes). The non-epithelial clusters fell into three categories: stromal cells , T cell34 [Trdc , Cd3g [3dc [Cd3g , and Trbg [Trbc1 ), and ma1 [Cxcl2 , Ccl4 [3l4 [Cd14 ). The epIn the dendrogram, the expression profiles of different clusters were compared and ranked so that fewer branches and shorter branches between two clusters indicate a lower difference in gene expression C. For thKrt12/Slurp1 [Krt13/Krt8/Krt19 [Krt12 and Krt13, which are the most widely used markers for corneal and conjunctival epithelium, respectively, as a criterion. They are shown in Krt12 expression fell into 3 main ranges in epithelial cell clusters from the unwounded eyes: “<0.9” , “5.5–20”, and “>180”, based on which we labelled them with a corneal differentiation score according to which “1” was “least differentiated”, “2” was “medium differentiated”, and “3” was “most differentiated”. The Krt13 expression fell into 2 ranges in epithelial clusters: “<0.8” and “>9”, based on which we labelled them with a conjunctival differentiation score according to which “1” was “undifferentiated”; “2” was “more differentiated”  and the low expression of maturation markers , indicating that they were at a comparable undifferentiated status. We observed that “putative LSC2” showed a decreased percentage in cell number in the isolated label-retaining epithelial cells  after wounding (20% in unwounded corneas V.S. 11% in wounded corneas) , while the expression of Fmo2 was slightly reduced after wounding (“8.9” in EC8 compared to “6.8” in EC16). The partially differentiated cell types with a higher expression of maturation markers in Branch 3 were identified as putative limbal progenitor cells (LPCs)   family genes (cell proliferation markers), suggesting a proliferative nature of this cell type.Among the three epithelial branches, Branch 3 is the only one that showed the differentiation trajectory from undifferentiated cells to partially differentiated cells under normal homeostasis and upon wounding. Therefore, we designated the undifferentiated cell types in Branch 3 as two putative limbal stem cell types (“putative LSC1” for EC7/15 and “putative LSC2” for EC8/16). Both putative LSC types had a comparable level on the high expression of epithelial progenitor markers (corneas) C. The recorneas) C, indicaor EC14) A. EC5 haKrt12 expression than the LPC1/2/3 and were thus in a further “differentiating” status (Krt13 (Mki67 and MCM family genes (cell proliferation markers) compared to the other clusters from the wounded corneas, indicating that EC13 is a proliferating epithelial progenitor cell after wounding.The cell types in Branch 2 were partially differentiated cells towards corneal epithelium and/or conjunctival epithelium. The cell types of EC3 and EC10 were designated as “differentiating LPC4”  due to their higher ” status A. The ces  was lower than the two putative LSC types and the three putative LPC types in “Branch 3” but higher than the “putative differentiating LPC4” and “putative CjPC” in “Branch 2”  showed a significantly higher expression in human corneal epithelium than in human limbal epithelium through real-time quantitative PCR , medium expression in the putative LPC types , and high expression in the putative differentiating LPC type (EC3/10) A. Their tive PCR B.+ cells. The label-retaining cells were collected from unwounded mouse corneas and from repetitively wounded mouse corneas, respectively, and were loaded for single-cell RNA sequencing. The cell clusters from the two samples (unwounded and wounded corneas) were analyzed together in a dendrogram for the differentiation trajectory. The epithelial clusters were identified by the expression of epithelial-specific cytokeratin genes and were segregated into “putative stem cells”, “putative progenitor cells”, and “putative differentiating progenitor cells” due to their distinctive increasing expression levels on maturation markers for the corneal and conjunctival epithelium. We observed a branch in the dendrogram (“Branch 3”) showing a classic differentiation trajectory from undifferentiated clusters to partially differentiated clusters under normal homeostasis and under wounded condition. Therefore, the two cell types of the undifferentiated clusters were designated as “putative LSC1” and “putative LSC2”, respectively. The differences between these two putative LSC types were studied. “Putative LSC1” and “putative LSC2” were at a comparable level of undifferentiated status because they showed a comparably high expression on the epithelial progenitor markers  and low expression on the maturation markers . Putative LSC2 showed a decreased percentage in cell number after repetitive wounding, which indicates that this type of cell was induced to divide upon wounding and was thus designated as “putative active LSCs”. This is consistent with our result on a delayed epithelial wound healing after the second wounding, which might be due to activated cell division and a partial exhaustion of the “putative active LSCs”. Putative LSC1 did not show this decreased percentage in cell number after wounding, indicating that they might stay in a relatively quiescent status upon wounding. They were thus designated as “putative quiescent LSCs”.Significant effort has been devoted to the study of LSCs and their difference from many research groups using different models and different research tools, including single-cell RNA sequencing. In this study, we employed three characteristics of LSCs to identify them and to study their behavior and expression: (1) LSCs are quiescent under normal homeostasis, (2) LSCs can differentiate into LPCs under normal homeostasis and under wounded condition, and (3) LSCs are undifferentiated cells with minimal expression of maturation markers. In this study, we used H2B–GFP transgenic mice to enrich LSCs in the label-retaining GFPFmo2. FMO2, a dimethylaniline monooxygenase, showed a 74-fold higher expression in enriched human LSCs in culture than in differentiated corneal epithelial cells [Fmo2 plays a functional role or whether its role is monooxygenase-independent in corneal limbus is still unknown.The molecular marker of “putative active LSCs” was al cells , suggestal cells ,44. No rEdn2 and Adm. Edn2 is reported to be expressed in a striped pattern on mouse corneas under normal homeostasis, which mimics the striped pattern of limbal stem/progenitor cells [Edn2 in the retina using transgenic knockin mice showed arrested vascular growth and accumulation of macrophages in the subretinal space [Adm, a vasodilator, plays an important role in endothelial vascular barrier functions  and in epithelial barrier functions [Adm signaling in maintaining the lymphatic barrier. Administration of ADM has been shown to improve intestinal epithelial barrier function by decreasing inflammation, increasing tight junctions, and decreasing intestinal epithelial cell permeability in a rat model of ulcerative colitis [Edn2 and Adm in “putative quiescent LSCs” may indicate their role in regulating barrier functions, including the angiogenic barrier, blood vessel endothelial barrier, lymphatic endothelial barrier, and limbal epithelial barrier functions in cornea. In addition, the data of single-cell RNA sequencing demonstrated that both Edn2 and Adm, as the characteristic markers for “putative quiescent LSCs”, showed a decreased expression in wounded corneas compared to unwounded corneas in this cell type , leading to a hypothesis that “putative quiescent LSCs” may regulate barrier functions by a high expression of the markers under normal homeostasis to form the barrier and a decreased expression of the markers under wound healing to allow a temporary leakage of the barrier to facilitate wound healing.The molecular markers of “putative quiescent LSCs” were or cells ,46. It ror cells ,49,50,51al space . One-monunctions ,55,56,57unctions , showingunctions . DisruptAtf3 for “aLSCs” and “putative quiescent LSCs”, Ifitm3 for “qLSCs” and “putative active LSCs”), although the rest of the cell markers were different and cell behavior was different upon wounding. Our data are also consistent with previous reports on the co-existence of quiescent stem cells and active stem cells in different epithelial and non-epithelial tissues, including cornea, hair follicle, gut, and bone marrow [Current data suggest that LSCs may contain at least two types of cells: one is quiescent upon corneal epithelial wounding, and the other is proliferative upon corneal epithelial wounding. Interestingly, a previous publication also reported the existence of two LSC populations in mice: one quiescent population (qLSC) at the outer limbus and one active population (aLSC) at the inner limbus . Our “pue marrow ,58.Many more questions are yet to be answered regarding the two types of putative LSCs. Are “putative quiescent LSCs” and “putative active LSCs” interconvertible? If yes, what factors promote the conversion? Do they have differences in regenerative capacity in vitro? Can we promote the in vitro propagation of LSCs by enriching or purifying a specific type of LSCs before culture? What mechanisms do the “putative quiescent LSCs” regulate the barrier functions? Do the “putative quiescent LSCs” have additional functions other than the potential barrier function? Is it possible that they regulate long-term epithelial regeneration and are not responsible for short-term wound healing? Will the markers be useful for diagnosing LSC deficiency? Will the markers of different LSC types be able to distinguish different types of LSC deficiency ?Ler5, which is a transcription factor regulating heat shock response, thermal stress, and ionizing radiation survival [Krt12, Slup1, etc.). Its characteristic marker is Lypd2, which has been shown to be highly expressed in superficial limbal epithelial cells while lowly expressed in corneal epithelium and basal and suprabasal limbal epithelium [Rdh10. Rdh10 plays an important role in normal eye development, and Rdh10-dificient mice lack cornea [In addition to the putative LSCs, different types of putative LPCs were screened out from our analysis. Under normal homeostasis, the “putative LPC1” was proliferative, which may contribute to the replenishment of the lost corneal epithelial cells due to normal wear and tear; the “putative LPC2” had a high expression of its marker survival ,60,61,62ithelium , indicatk cornea , indicatAldh3, Slurp1, Tkt, and Krt12) have been reported previously for their abundant expression in corneal epithelium and served as maturation markers for corneal epithelium [Ten markers were selected as maturation markers from our single-cell RNA sequencing data, of which four showed a higher expression in human corneal epithelium than human limbal epithelium. All four markers (ithelium ,37,64,65In conclusion, this study provides the atlas of different epithelial and non-epithelial cell populations at limbus under normal homeostasis and during wound healing. The discovery of different types of LSCs and LPCs under normal homeostasis and upon wounding may provide valuable information for understanding the dynamics of corneal epithelial regeneration.


The eukaryotic cell cycle is driven by the activity of cyclin-dependent kinases (CDKs). CDK activity rises over 50-fold during the cell cycle, from a low level in G1 to a high level in mitosis. However, it is not known whether the entire range of CDK activity is necessary for cell cycle progression, or whether cells can tolerate a reduction in CDK activity level. Here, in fission yeast, we show that sublethal CDK inhibition lengthens the time cells spend in mitosis but does not cause misordering of mitotic events. Maximum attainable CDK activity exceeds the amount necessary for mitosis, and thus forms a CDK activity buffer between sufficient and maximal possible CDK activities. This CDK activity buffer is needed for mitotic completion when CDK activity is compromised, and CDK inhibition only becomes lethal to cells when this buffer is exhausted. Finally, we explore what factors influence this CDK activity buffer, and find that it is influenced by CDK-counteracting phosphatases. Therefore, maximum attainable CDK activity is not necessary for mitosis but provides robustness to CDK activity reduction to ensure mitotic completion. Summary: CDK activity drives the eukaryotic cell cycle. We show that CDK activity is in surplus, and cells use this surplus to ensure mitotic completion despite CDK inhibition. Therefore, we asked whether cells were able to tolerate a lower level of CDK activity than the maximum attainable CDK activity and still be capable of undergoing mitosis.The major cell cycle events of DNA replication and mitosis are driven and ordered by the activity of cyclin-dependent kinases (CDKs) . Low CDKin vivo CDK activity using a fission yeast strain carrying an analogue-sensitive (as) variant of the gene encoding the only cell cycle CDK in fission yeast, cdc2 (cdc2-asM17 [referred to hereafter as cdc2(as)]-carrying strain is sensitive to inhibition by the ATP analogue 1-NmPP1  cells lost viability at 1-NmPP1 concentrations above 250 nM (cdc2(as) cells remained viable at 1-NmPP1 concentrations up to 125 nM, suggesting that cells may not require maximum levels of CDK activity for mitotic progression. We then examined the cell cycle profiles of 1-NmPP1-treated cells by using Cut3–GFP as a marker of CDK activity. Cut3 translocates into the nucleus at mitosis, and its nuclear levels serve as an in vivo readout of CDK activity . We conclude that sublethal 1-NmPP1 levels cause a slower progression into and through mitosis, and at lethal levels completely blocks the onset of mitosis.We confirmed this observation through time-lapse microscopy using Cut3–GFP as a marker of CDK activity. After release from a G2 block we observed that without any CDK inhibition, CDK activity rose and fell rapidly D. Cells It has previously been suggested that mitotic events occur at defined CDK activity thresholds , but thein vivo CDK activity in cells that naturally proceeded through mitosis and compared it to that in cells blocked in mitosis, after the addition of increasing amounts of CDK inhibitor. To carry out this experiment, we blocked cells in G2 and released them into either a normal mitosis or a metaphase block by depleting the mitotic APC/C targeting subunit Cdc20 (encoded by slp1 in fission yeast) using a thiamine-repressible promoter  cells became arrested, with high levels of nuclear Cut3, indicating high CDK activity A,B. In cTherefore, the maximum attainable CDK activity is above that needed for mitotic completion in a normal cell cycle. Thus, there is a CDK activity buffer between the CDK activity level that is sufficient and the maximum level that is achievable. CDK inhibition first consumes this buffer of CDK activity before becoming lethal to cells. This explains why in an unperturbed mitosis (Cdc20-ON state) addition of 1-NmPP1 up to 125 nM did not affect the CDK activity reached, as cells only needed to reach the mitotic threshold of CDK activity, which was still below the maximum attainable activity. However, when this activity buffer is depleted, CDK inhibition is sufficient to block mitosis, leading to lethality. Thus, during a normal mitosis, cells do not reach the maximum attainable level of CDK activity. Instead, cells trigger anaphase and cyclin destruction before the maximum CDK activity is reached.Fig. S2A).We hypothesised that increasing overall CDK activity would increase this CDK activity buffer and make cells more resistant to CDK inhibition. In eukaryotes, CDK activity is restrained by the activity of Wee1 kinase, which phosphorylates CDK residues Y15 and T14 . The T14ppa2) and the gene encoding Clp1 (clp1) in two different strains in a cdc2(as) background  and the Cdc14-type phosphatase Clp1 . Therefockground A.Fig. 4ppa2 had less effect on the level of tolerable CDK inhibition compared with deletion of clp1 , but we were unable to test whether these effects were additive because the ppa1Δppa2Δ strain is inviable (pab1) or the two B56 (par1 and par2) regulatory subunits did not result in increased cell viability at higher concentrations of 1-NmPP1 .PP2A is thought to be the major CDK-opposing phosphatase  but, in  of clp1 A. Remova of clp1 A. That dinviable . We alsoclp1Δ cells achieved similar maximum levels of nuclear Cut3 to wild-type cells, suggesting that overall CDK activity was not increased , the buffer might relate to cell-size-related factors, such as the accumulation of cyclin–CDK complex over the cell cycle, or CDK activity regulators such as DNA concentration (ppa1 and ppa2) encoding the PP2A catalytic subunit. However, although removal of Clp1 has the greatest effect on the CDK activity buffer on its own, compensation between the two redundant PP2A catalytic subunits may mask the true effect of PP2A activity on the CDK activity buffer. Given that deletion of Clp1 has no effect on the phosphorylation of the model reporter substrate we used, this influence on the CDK activity buffer is likely to work through reducing the CDK activity threshold needed for the final substrate essential for mitosis.The molecular mechanisms behind the buffer are unclear, and while unlikely to arise from CDK inhibitory phosphorylation (ntration . HoweverS. pombe media and standard methods are as previously described (cdc2(as) were performed with the addition of 1 μM 1-NmPP1  for one cell cycle unless stated otherwise. Strains used in this work were authenticated by PCR and sequencing, and their full genotypes are listed in Table S1.escribed . Experimescribed . To shut6 cells/ml, which corresponds to the leftmost dilution of all dilution assays, followed by repeated 1:10 dilutions plated from left to right. For each spot, 4 μl of cell suspension was plated on EMM4S or YE4S. Plates were incubated for 3 days at 32°C unless otherwise stated.Cells were taken from a culture of exponentially growing cells at a density of 5×10Most imaging was performed using a Deltavision Elite (Applied Precision) microscope – an Olympus IX71 widefield inverted fluorescence microscope with a PLAN APO 60× oil, 1.42 NA objective and a Photometrics CoolSNAP HQ2 camera. The microscope was controlled using the SoftWoRx software. Widefield imaging of synCut3–mCherry strains was conducted using a Nikon Ti2 inverted microscope with Perfect Focus System and Okolab environmental chamber, and a Prime sCMOS camera (Photometrics). The microscope was controlled using Micro-Manager v2.0 software (Open Imaging). Fluorescence excitation was performed using a SpectraX LED light engine (Lumencor) fitted with a 575/25 filter for imaging mCherry. To maintain specified temperatures during imaging, an IMSOL incubator environment control system and an objective heater were used.6 cells/ml culture. The pellet was then resuspended in 3.5 μl of medium before 1.5 μl of this suspension was applied to a glass slide and covered with a coverslip. Fields of view (FOV) were avoided if they contained dead cells due to compression by the coverslip, but were otherwise sampled at random. For each slide, 10–20 FOV were acquired. FOV were imaged for <15 min to avoid imaging any perturbations resulting from slide-based acquisition. Imaging was optimised for signal intensity, as each FOV was only used once, and thus photobleaching and photo-toxicity were not of concern. Where Cut3–GFP or Cut3–tdTomato was used, CDK activity was assayed using the mean of the top 15% of pixel intensities within a cell body as an approximation of nuclear concentration. For images where Polo kinase activation was studied, the top eight pixel intensities within a cell body were used as a measure. Cell cycle stage assignment during widefield imaging was based on the nuclear:cytoplasmic (N:C) ratio of Cut3 by comparing the highest 15% of pixel intensities within a cell body with the lowest 85%, as an approximation of nuclear:cytoplasmic concentration. Cells possessing an N:C ratio of <0.95 were categorised as G2 cells. Cells possessing N:C ratios of 0.95–1.05 were categorised as G2-M cells. Where synCut3–mCherry was used, CDK activity was assayed by N:C ratio of the top 15% to bottom 85% of pixel intensities. Fiji was used for all image analysis . Cell pellets were then resuspended in 1 ml of release medium (this point is defined as the time of release) in a new microcentrifuge tube before pelleting (2100 g for 30 s) and resuspension in 5 μl of medium. This concentrated cell suspension was then applied to the centre of the MatTek dish and allowed to settle for ∼5 s. The dish was then washed forcefully with 1 ml of release medium three times. The dish was then filled with 3 ml of release medium before rapid imaging. In general, the wash process required 1.5 min, and the imaging setup required 5 min for acquisition of ∼8 FOV.MatTek glass-bottom dishes were used for time-lapse imaging applications. Dishes were pre-treated with soybean lectin to permit cell adherence. Before addition of cells, MatTek dishes were pre-warmed on a heat block at 32°C. Cells were grown and blocked in liquid culture before 2 ml of the culture was pelleted (2100


Tridacna in the Gulf of Oman and review its stratigraphic distribution in the Persian Gulf to provide new insights on the connectivity of coral reef habitats around southern Arabia under changing climate and ocean conditions. Fossil shells were carbon-14 dated and employed as sclerochronological proxy archives. This reveals that the Omani population represents a last glacial colonization event during the Marine Isotope Stage 3 interstadial under colder-than-present temperatures and variable upwelling intensity linked to Dansgaard-Oeschger climate oscillations. It was favored by temperatures just above the lower threshold for the habitat-forming reef coral communities and instability of the upwelling barrier. We conclude that the distribution of Tridacna in the northern Arabian Sea is generally limited by either strong upwelling or cool sea surface temperature under gradually changing climate conditions at the interglacial-glacial scale. Opportunities for dispersal and temporary colonization existed only when there was a simultaneous attenuation of both limiting factors due to high-frequency climate variability. The OUZ will unlikely become a future climate change refuge for giant clams because they will be exposed either to thermal stress by rapid anthropogenic Indian Ocean warming or to unfavorable upwelling conditions.The Oman upwelling zone (OUZ) creates an unfavorable environment and a major biogeographic barrier for many coral reef species, such as giant clams, thus promoting and maintaining faunal differences among reefs on the east and west side of the Arabian Peninsula. We record the former existence of  Because of their great importance for community dynamics, stability and diversity, conservation and preservation of such “key(stone)” species have shifted into the focus of recent conservation strategies2.Giant clams (Cardiidae: Tridacninae) are iconic animals of Indo-Pacific coral reefs, which help to maintain the overall reef biodiversity and functionality by contributing to the reef carbonate structure and serving various ecosystem functions4. These symbionts contribute significantly to the energy budget of the host and encourage the large shell growth4, with the largest shells reaching > 1 m length5. Light availability is thus essential for giant clam growth and survival4. When ambient seawater gets too warm for too long, giant clams release their symbionts thereby turning white and starving, a process known as bleaching5. Additionally, elevated temperature reduces fertilization success in giant clams6. High pCO2 seawater conditions can also alter the symbiont density and photosynthetic yield that affect the biomineralization process5. Giant clams are therefore expected to particularly suffer from human-caused ocean warming and acidification6.Unlike most other bivalves, but similar to most reef-building corals, the species of the Tridacninae host symbiotic dinoflagellate photoautotrophs of the Symbiodinaceae family5  cause the ocean currents to flow clockwise14  giant clam species dwells in reefs and lagoons, rarely beyond a depth of 10 m owing to its strong reliance on light5. It has the most widespread distribution of the Tridacninae today, encompassing almost the entire geographical range of all other species5  in two dated shells. The aims are to understand: (1) the timing of the ancient range expansion of giant clams into the Gulf of Oman, and (2) the underlying environmental conditions in the range-edge habitat. Such knowledge about range-edge dynamics of “key(stone)” species in the past may help predicting when, where and to what extent species’ distributions can be expected to shift in the face of continuing anthropogenic climate change and thus can contribute towards identifying potential refuges for biodiversity conservation.We describe a fossil range-edge population of Tridacna maxima shells have been found between 4 and 9 m above sea level  on raised marine terraces at four localities in northeastern Oman , which are located within a distance of 75 km , pebbly calcarenite or calcarenitic conglomerate  are to be found within the light growth bands of the fossil Tridacna shells from Oman  and one from Fins locality (T-Fins), were selected for geochemical analyses. Both sites are only 6 km apart  sea-level highstand ca. 50–35 ka ago 7. Tridacna from Kish Island in southern Iran were dated by U-Th to 126.1 ± 0.6 ka BP (MIS5e), 100.3 ± 0.8 ka BP (MIS5d) and 80.9 ± 0.5 ka BP (MIS5a)22. Thus, the finds in northeastern Oman 23  is considered as the dominating factor for changing distribution and diversity patterns of tropical coral reefs during the Quaternary climate cycles27 and upwelling is the main constraint for shallow-marine biogeographic connectivity around the Arabian Peninsula today14. Specific information on the seasonal and longer-term variability of SST and upwelling in the range-edge habitat is thus required for understanding the last glacial colonization event of the Gulf of Oman by Tridacna.The occurrence of Tridacna shell is secreted in close-to-isotopic equilibrium with ambient seawater, cyclic oxygen isotope variations display changes in temperature and precipitation/evaporation, with shell isotopic composition becoming more positive with decreasing temperature and/or increasing evaporation 17. Based on the absence of speleothem growths in the Al Hajar Mountains and a fluvio-lacustrine sediment record from Wadi Mistal  and strongest plankton blooms (high turbidity) in the Gulf of Oman32, we deduce that the seasonal δ13C drops in the shell carbonate display decreased rates of symbiont photosynthesis and increases in heterotrophy15. As the growth rate of Tridacna depends on water temperature and photosynthesis of the symbionts33, periods of cool temperature and low light levels result in decreases of the growth rate and the formation of dark (dense) growth bands15. Consistently, dark growth bands in T-Tiwi1 and T-Fins  in winter. Accordingly, minor peaks in summer  δ13C composition of T-Fins compared to T-Tiwi1  and T-Fins (0.009 ± 0.005 mmol/mol), the Ba/Ca winter maximum peaks are almost fivefold higher in T-Tiwi1 (mean 0.026 ± 0.01 mmol/mol) compared to T-Fins  events, which occur with a frequency of 2–3 ka and amplitudes close to the glacial-interglacial changes37 Fig. b. AlthouTridacna shell carbonate41. We apply Eq.  in VPDB. The combined measurement of Sr/Ca or Mg/Ca in biogenic calcite and aragonite and stable oxygen isotopes on the same material have been widely employed to calculate the δ18Opalaeoseawater by subtracting the SST contribution inferred from Sr/Ca or Mg/Ca from δ18O43. However, there is no significant correlation between Sr/Ca and δ18O in both records  may thus be a better approximation for T-Tiwi1, whereas a δ18Oseawater of 0.3‰ (SMOW) is better suited to approximate absolute water temperatures during lifetime of T-Fins. The δ18Oseawater reported in SMOW is corrected for conversion to VPDB as follows:C is a constant that is equal to 0.2 following Elliot et al.16. The temperatures thus obtained for the last glacial Tridacna are cooler than the coastal sea surface temperature at Tiwi today, whereas T-Tiwi1  indicates lower temperatures than T-Fins 47  within the oxygen minimum zone (i.e. upwelling) as well as for terrigenous sediment input (i.e. precipitation), indicate a weaker monsoon during the MIS2 and MIS4 stadials of the last glaciation compared to the last and present interglacials, whereas maximum monsoon strengths during the MIS3 interstadial and the interglacials were comparable  and following (MIS2) stadials  remained even slightly cooler than the pre-industrial average52. This would mean less heat stress for Indian Ocean coral reefs during the last interglacial warmth than during the actual temperature rise. We therefore conclude that SST was not a limitation for the distribution of giant clams in the Arabian Sea during the last interglacial. Even though D-O climate oscillations only go back to the beginning of last glacial37 22 strikingly coincides with a prominent outburst flooding event from the melting Laurentide Ice Sheet at around 126 ka BP54. For this time, the Arabian Sea monsoon stacks49 show a marked decline in the strength of SWM  climate and strengthening (weakening) upwelling over a interglacial–glacial cycle because of the adverse effects of strong upwelling and low temperature on the performance of the bivalve-dinoflagellate holobiont, habitat availability and biogeographic connectivity. Superimposed high frequency climate variability, however, had temporarily opened up narrow environmental windows that allowed 57, putting increasing stress on the local coral reef ecosystems58. With advancing climate change, the OUZ zone is proposed as refuge area for coral reefs in the warming Indian Ocean because upwelling and heat threat coincide, and this overlap reduces the magnitude, frequency and duration of thermal disturbances59. But what is not considered in that respect is that the intensity of the Oman upwelling is expected to change under current global warming scenario, even if there is no consensus about the direction of this change. Results from climate modelling indicate an intensification of upwelling in response to the ongoing temperature rise60. This would not only further impair the population connectivity between coral reef habitats in the Gulf of Oman ecoregion and the Gulf of Aden and Red Sea ecoregions but could also amplify ocean acidification and its negative impacts on the mineralization and survival of calcifying marine organisms due to the low aragonite saturation state and pH of the upwelled high pCO2 water61. In contrast, δ18O and Sr/Ca records of modern and fossil corals from the OUZ zone provide evidence that the Arabian Sea upwelling intensity was very stable over the last millennium and unprecedently declines during the current warming trend43. While a reduction of upwelling improves coral reef connectivity within the Somali-Arabian Seas biogeographic province, it lessens the positive cooling effect on the sea surface during summer heat. The coastal waters of Oman will thus not become a suitable climate-change refuge for giant clams and many other calcifying reef species looking to migrate to cooler areas with continuing ocean warming, regardless whether the upwelling strengthens or weakens in future.SSTs over the Indian Ocean have shown a rapid warming trend over the last several decades14C ages were calibrated to calendar ages with BetaCal 3.21, which uses the High Probability Density Range Method and Marine13 calibration, and a reservoir correction (Delta-R) of 256 ± 68 as calculated from the website http://calib.org/marine13/ based on local values of Southon et al.62. 256 ± 68 is the calculated average of two Delta-R values (297 ± 51 and 200 ± 60) given for the same locality .Shell samples from a total of nine specimens  in regular 0.5 mm intervals (drilling depth 0.5 mm) the shell slabs were mounted on a manually operated three-axis precision stage beneath a Proxxon MF 70 micro-mill equipped with a 0.6 mm drill bit. Stable isotope analyses were performed at the Institute of Geophysics and Geology of the Leipzig University in Germany. Carbonate powders were reacted with 105% phosphoric acid at 70 °C using a Kiel IV online carbonate preparation line connected to a MAT 253 isotope ratio mass spectrometer. All δ18O and δ13C values for carbonate are reported in per mil (‰) relative to the PDB standard, for water against SMOW. Reproducibility was checked by replicate analysis of laboratory standards  and was always better than ± 0.08‰ (1σ) for oxygen isotopes (δ18O) and better than ± 0.06‰ (1σ) for carbon isotopes (δ13C).To obtain powder samples for isotopic analysis  to calculate the element concentrations of the sample measurements. During each session, basaltic USGS BCR-2G, synthetic carbonate USGS MACS-3 and biogenic carbonate  were analyzed repeatedly as quality control materials to monitor precision and accuracy of the measurements as well as calibration strategy. All reference materials were analyzed at the beginning and at the end of a sequence and after ca. 40 spots on the samples. For all materials 43Ca was used as internal standard applying for the USGS BCR-2G and MACS-3 the preferred values reported in the GeoReM database, for JCp-1-NP a Ca content of 38.18 wt%66, for JCt-1-NP 38.90 wt%67, and 39 wt% for the samples. Resulting element concentrations for the control materials together with reference values are provided in Supplementary Table During two analytical sessions, element concentrations for Sr, Mg, Ba and B were determined at the Institute of Geosciences, Johannes Gutenberg University Mainz (Germany), using an Agilent 7500ce inductively coupled plasma-mass spectrometer (ICP-MS) coupled to an ESI NWR193 ArF excimer laser ablation (LA) system equipped with a TwoVol2 ablation cell. The ArF LA system was operated at a pulse repetition rate of 10 Hz and an energy density of ca. 3 J cm68. A simple linear regression was then applied to describe the relationships between the geochemical proxies (Supplementary Table 17.Corresponding stable isotope and trace element records were matched by using the growth bands as a guide (Supplementary Fig. Supplementary Information 1.Supplementary Information 2.


Preclinical evidence suggests a cross-regulation between adenosine A2A and dopamine D2 receptors in this region and that it is linked to the sensitization of the dopamine system. However, the relationship between A2A receptor availability and schizophrenia has not been directly examined in vivo in patients with this disorder. To investigate, using positron emission tomography (PET), the availability of A2A receptors in patients diagnosed with schizophrenia in comparison to matched healthy controls. A2A receptor availability was measured using the PET tracer [11C]SCH442416. Twelve male patients with chronic schizophrenia were compared to 13 matched healthy subjects. All patients were medicated with antipsychotics and none presented with any motor or extrapyramidal symptoms. Binding potential (BPND), a ratio measure between specific and non-specific tracer uptake, were compared between the groups for the caudate, putamen, accumbens and globus pallidum. There was no differences between A2A receptor binding potential (BPND) of schizophrenia patients in the caudate (p = 0.16), putamen (p = 0.86), accumbens (p = 0.44) and globus pallidum (p = 0.09) to that of matched healthy subjects. There was also no significant correlation between [11C]SCH442416 binding and severity of psychotic symptoms (p = 0.2 to 0.82) or antipsychotic dosage (p = 0.13 to 0.34). By showing that A2A receptor availability in medicated patients with chronic male schizophrenia is not different than in healthy controls, this study does not support the primary role of this receptor in the pathogenesis of schizophrenia.Adenosine A Schizophrenia is a chronic mental health disorder that affects approximately 1% of the population. Although the precise neurobiological mechanisms underlying schizophrenia are still unknown, several lines of evidence show that striatal abnormalities are a core component of the pathophysiology of this disorder  in the A2A receptor (A2AR) in the basal ganglia of patients with this disorder when compared to healthy controls (Kurumaji and Toru n = 12) were recruited from the South London and Maudsley Foundation NHS Trust, and were compared to matched healthy controls (n = 13). All participants gave written informed consent to participate after receiving a full description of the study. The study protocol was approved by the National Research Ethics Service (NRES) and permission to administer radioactive substances was granted by the Administration of Radioactive Substances Advisory Committee (ARSAC), UK. All subjects had a physical, psychiatric and neurological examination. Diagnosis was performed by reviewing patients medical notes and the use of the Structured Clinical Interview for DSM-5 (SCID)  = 2300 ms, time echo (TE) = 2.98 ms, flip angle of 9°, time to inversion (TI) = 900 ms, matrix = 240 × 256) for co-registration with the PET images; fast GM T1 inversion recovery  (REF) and fluid and WM suppression  sequences for improving delineation of subcortical brain regions. All sequences used a 1-mm11C]SCH442416. Subjects were positioned supine with their transaxial planes parallel to the line intersecting the anterior–posterior commissure line. Head position was maintained with the help of individualised foam holders, monitored by video and subjects were repositioned if movement was detected. Subjects were in a resting state with low light. Intrascan notes for participant’s movement were acquired during scanning. Dynamic emission data were acquired continuously for 90 min following the injection of [11C]SCH442416. The dynamic images were reconstructed into 26 frames , using a filtered back projection algorithm (direct inversion Fourier transform) with a 128 × 128 matrix, zoom of 2.6, producing images with isotropic voxel size of 2 × 2 × 2 mm3 and a 5-mm isotropic Gaussian filter. Corrections were applied for attenuation, randoms and scatter. Arterial plasma samples were collected during the scan to enable metabolite corrected input functions to be determined.PET scans were acquired on a Siemens Biograph Hi-Rez 6 PET-CT scanner  following the injection of an intravenous bolus of SCH442416 BPND in the primary regions of interest ) and putamen ) or in secondary regions of interest ) and nucleus accumbens ) when compared to healthy controls , putamen , globus pallidus (r = 0.21 p = 0.28), nucleus accumbens ) or with severity of total psychotic symptoms , putamen , globus pallidus , nucleus accumbens ). There was also no significant correlation between [11C]SCH442416 BPND and severity of positive , putamen , globus pallidus , nucleus accumbens ), negative , putamen , globus pallidus , nucleus accumbens ) or general psychotic symptoms , putamen , globus pallidus , nucleus accumbens ). Finally, there was also no significant correlation between [11C]SCH442416 BPND and duration of illness , putamen , globus pallidus , nucleus accumbens ).There were no differences in the injected dose, injected mass and specific activity between patients with schizophrenia and healthy controls Table . PatientTo our knowledge, this is the first study to assess the A2A receptor in vivo in patients with schizophrenia. We found no difference in striatal A2A binding potential between patients diagnosed with schizophrenia and healthy controls in any of the basal ganglia regions examined.11C]-TMSX, but these showed several problems such as a variable affinity at striatal and non-striatal A2AR, and hence non-xanthine tracers are preferred . Another approach has been to use spectral analysis to identify the different components of the signal (Subrata et al. ND data from the study using the spectral analysis approach are lower than we obtained using our approach. However, it should be recognised that both our approach and spectral analysis approach for this tracer require further validation. Alternatively, future studies could make use of more recent radiotracers for imaging A2A with better kinetics properties, such as [18F]-MNI-444 (Barret et al. Several potential limitations affecting the interpretation of the results need to be considered. First, the challenges for quantifying A2AR with current in vivo ligands. The first measures of A2A receptors in healthy controls were made using xanthine tracers such as [H]-CGS21680 (Kurumaji and Toru Our results do not support the existing post-mortem imaging findings in schizophrenia. While Villar-Menéndez showed a reduction in A2AR binding, Kurmanji and Toru found an increase in approximately 35 to 55% in A2A receptor specific binding in the caudate and putamen of post-mortem brains, as measured by [3In conclusion, we showed for the first time in vivo that there is no significant differences in A2A receptor expression between patients and healthy controls, not supporting the adenosine hypofunction hypothesis of schizophrenia.


However, DNA mutations alone do not fully explain malignant transformation7. Here we investigate the co-evolution of the genome and epigenome of colorectal tumours at single-clone resolution using spatial multi-omic profiling of individual glands. We collected 1,370 samples from 30 primary cancers and 8 concomitant adenomas and generated 1,207 chromatin accessibility profiles, 527 whole genomes and 297 whole transcriptomes. We found positive selection for DNA mutations in chromatin modifier genes and recurrent somatic chromatin accessibility alterations, including in regulatory regions of cancer driver genes that were otherwise devoid of genetic mutations. Genome-wide alterations in accessibility for transcription factor binding involved CTCF, downregulation of interferon and increased accessibility for SOX and HOX transcription factor families, suggesting the involvement of developmental genes during tumourigenesis. Somatic chromatin accessibility alterations were heritable and distinguished adenomas from cancers. Mutational signature analysis showed that the epigenome in turn influences the accumulation of DNA mutations. This study provides a map of genetic and epigenetic tumour heterogeneity, with fundamental implications for understanding colorectal cancer biology.Colorectal malignancies are a leading cause of cancer-related death A study maps genetic and epigenetic heterogeneity of primary colorectal adenomas and cancers at single-clone resolution through spatial multi-omic profiling of individual glands and adjacent normal tissue. Much is known about the genetic evolution and intra-tumour heterogeneity of colorectal malignancies10. Although genetic heterogeneity is widespread11, epigenetic changes are also responsible for phenotypic variation between cancer cells7. Epigenetic profiling of chromatin accessibility in colon cancer has been performed in seminal studies in cell lines12 and human samples14. However, current investigations are limited to single-bulk samples and some also lack normal controls14. Moreover, how cancer genomes and epigenomes concomitantly evolve and shape intra-tumour genetic and epigenetic heterogeneity remains unexplored.Clonal evolution, fuelled by intra-tumour heterogeneity, drives tumour initiation, progression and treatment resistance15. Normal crypts are tube-like invaginations where cell proliferation is driven by a relatively small number of stem cells at the base19, and cancer glands are thought to have the same architecture20. This implies that all cells within a gland share a recent common ancestor and are a few cell divisions apart: thus, glands are largely clonal populations that, through cell proliferation, copy DNA with relatively high fidelity. Ultimately, the gland can be thought of as a natural whole-genome amplification machine that can be exploited to perform multi-omics at single-clone resolution. Indeed, single-crypt and single-gland genomic profiling has long been used to study clonal dynamics in both normal23 and cancer cells29. We developed a method to concomitantly profile single nucleotide variants (SNVs), copy-number alterations (CNAs), chromatin accessibility with transposase-accessible chromatin sequencing (ATAC-seq)30 and full transcriptomes with RNA sequencing (RNA-seq) from the same individual gland or crypt. Here we present the results of multi-region single-gland multi-omics of 1,370 samples from 38 lesions arising in 30 patients, with 21–55 tumour samples per patient (median = 42).Measuring genome–epigenome co-evolution in a quantitative manner is possible by multi-omic profiling at single-clone resolution and accurate spatial sampling of human neoplasms, as well as matched normal tissue. Colorectal cancers (CRCs) are organized into glandular structures, reminiscent of the crypts in the normal intestinal epithelium2  located close to the tumour edge, one distant region of normal epithelium (region E) and concomitant adenomas if present . A bulk sample was collected from each region and was spatially annotated in the original resection specimen Fig. . Each pin = 370/1,370), both WGS and ATAC-seq data were available  cases exhibiting high aneuploidy and MSI cases being largely diploid  and MYC (one case). Recurrent cancer driver mutation events in CRCs were recapitulated in this dataset, with stereotypical mutations in APC, KRAS and TP53  and KRAS  mutation frequencies. We observed a slightly larger incidence of TP53 mutations in our study . Adenoma mutation frequencies were similar to another previous study3 .We first assessed the landscape of genetic alterations in our cohort. Six cases were characterized by microsatellite instability MSI; , as repon/dS  and compared each peak size in the tumour versus a pool of normal samples, while normalizing for the effect of CNAs (see figures at 10.6084/m9.figshare.19849789), to identify significant SCAAs  in our cohort. We identified peaks in the ATAC-seq data for each region of a cancer using MACS2  DNA mutations present in all samples of the tumour and assessed the frequency of these variants in the reads from ATAC-seq to obtain accurate estimates of sample purity . SamplesJAK3 promoter gain of accessibility in 11/24 cancers  and an associated enhancer region (3/24 cancers); see, for example, case C524 in Supplementary Fig. CCDC6 are infrequent in CRC  in Supplementary Fig. Among the recurrently altered and almost invariably clonal epigenetic changes, we found a ers Fig. , as well14 and the ENCODE normal colon tissue dataset37. Moreover, the average peak sizes correlated strongly when reanalysed with our pipeline  in C561, which was present in each region of the cancer but not in any of the concomitant two adenomas  with available concomitant adenomas, only 113 (17.0%) were also detected in the matched adenoma, suggesting that most SCAAs probably occurred at the onset of malignant transformation, hence after neoplastic growth initiation but before subclonal diversification . Such events are exemplified by the gain of accessibility of a To gain more insight on the origins of SCAAs, we investigated chromatin changes in the normal colon by comparing each normal crypt against the pool of normals from the other patients. We found very few SCAAs in individual normal crypts, supporting the idea that the SCAAs we observed in the tumours were indeed somatic alterations originated during tumourigenesis, rather than during the normal process of epigenetic ageing of colon crypts. A small subset of SCAAs was detected in multiple crypts of the same patient , but SCAAs in normal crypts were not recurrent  and did not overlap with SCAAs observed in tumours . Plausibly germline genetic variation could cause some of the chromatin accessibility alterations in normal tissue we observe.trans regulation. To further probe the impact of somatic mutations on SCAAs, we analysed SNVs that we found were associated with changes in cis gene expression in our associated article38 and found that some of these SNVs co-occurred with a change in chromatin accessibility at the locus (figures at 10.6084/m9.figshare.19857274).We assessed the impact of SCAAs on gene expression using matched RNA-seq  binding sites for 870 TFsP = 0.012, Fisher’s exact test).Unsupervised clustering of somatic TF binding signatures produced three main clusters. The first main cluster  genes was significantly reduced in both MSS and MSI cancers with respect to normal samples Fig.  consiste42. This cluster was significantly enriched in cases showing truncating mutations in chromatin modifier genes , consistent with previously reported regulation of epithelial-to-mesenchymal transition by chromatin modulators43. We cannot exclude that there could be further subgroups of patients with distinct TF accessibility patterns beyond the CTCF subgroup (blue cluster); further studies with more patients are needed.We also noted a small cluster characterized by increased accessibility at the TF-binding sites of SNAI1 and SNAI2, two TFs involved in epithelial-to-mesenchymal transition44. We tested whether SCAAs identified at TF-binding sites , as well as typically expressed and not expressed genes identified with the RNA-seq data.Mutations in chromatin modifier genes, or alterations in TF-binding sites, can determine the characteristics of the epigenome. Conversely, chromatin architecture determines how the cancer genome accumulates mutations owing to its effect on different mutational processes and activity of DNA repair genes54). The activity of the mismatch repair signature in MSI cases was more uniformly distributed across the genome  was 2–4-fold higher in closed chromatin regions of the genome (inactive promoters and enhancers) for both clonal and subclonal mutations, consistent with the need for methyl cytosine (enriched in inactivated regulatory regions) to be present for it to become deaminated and produce the associated mutational signature Fig. . Analogo40. We selected CTCF sites with somatic mutations that were predicted by deltaSVM55 to cause significant loss or gain of binding and assessed the relative contribution of each mutational signature to these mutations in the CTCF-binding site across the five mutational signature clusters. In MSS cancers (cluster 1), mutations predicted to cause loss of binding had a signature that was consistent with the background mutational signature acting on the genome , identifying the direct (cis) functional effects on the chromatin caused by DNA variants remains difficult. Our multi-omic dataset provides some clear examples of a genome–epigenome relationship: we observed somatic mutations associated with both changed cis gene expression and changed chromatin accessibility. Follow-up work is required to explore the functional impact of epigenetic alterations in cancer driver genes and other loci.The contribution of epigenetic events to cancer evolution is recognized as highly significant3. Moreover, there is no validated prognostic genetic alteration that predicts recurrence in CRC. Others have shown that chromatin topology changes over time in ageing colon tissue, including in transformed tissues, and that a link between altered chromatin patterns and patient outcomes exists60. This is consistent with our finding of a decisive role for SCAAs in cancer biology. We acknowledge that our multi-omic analysis was based on the analysis of tumour glands, and it possible that the biology could differ in the rare CRCs that completely lack glands.We also observed that the epigenomes of adenomas and carcinomas are distinct. The lower prevalence of SCAAs in adenomas and, at the same time, the clonality of most SCAAs in carcinomas suggest that many cancer SCAAs may occur at the onset of malignant transformation. This is important because, besides broad CNAs, mostly non-focal chromosomal arm gains or losses of unknown significance, there is little difference in driver alterations between benign adenomas and malignant carcinomas54 as an enabler of growth and adaption. We identified a group of TFs with decreased accessibility that were related to interferon signalling. On the other hand, we also found a group of TFs that had increased accessibility and was enriched with homeobox genes  that are directly involved in early development. We speculate that we may detect biological processes that aim at reprogramming cell fate through reactivation of developmental genes. Further functional work is warranted.One of the most intriguing results was the evidence of reactivation of developmental genes during tumorigenesis. Those genes are usually silenced in somatic tissue, and the reactivation of the genes in these families and their involvement in tumorigenesis has been postulated before in the context of glioblastoma tumorigenesisOverall, our spatially resolved multi-omic analysis of primary colorectal cancers shows non-genetic determinants of cancer cell biology and clonal evolution.Primary tumour tissue and matched blood samples were prospectively collected from patients undergoing curatively intentioned surgery at University College London Hospital (UCLH). All patients gave informed consent for collection of their materials to the UCLH Cancer Biobank . Four regions of each primary cancer were sampled by punch biopsy or scalpel dissection, at notionally 12, 3, 6 and 9 o’clock positions around the tumour periphery. Each region was further cut into four pieces and slow-frozen to −80 °C, using a Mr Frosty Freezing Container (Thermo Fisher), in 1 ml of a buffered medium  in a 1.8-ml Nunc Cryotube (Sigma-Aldrich), and immersed in isopropanol to preserve chromatin structure. All investigators were blinded to patient data related to outcome, gender and other clinicopathological information.2, 0.1% IGEPAL CA-630 supplemented with protease inhibitors  and RNASE inhibitor 1 U µl−1  and incubated on ice for 10–45 min. Bulk samples were collected in a final volume of 100 µl cell lysis buffer. We found that longer or warmer incubations decreased the RNA quality and yields and negatively affected chromatin structure. In selecting the 30 cases included in our study, we rejected only a single further case owing to being unable to isolate any glands, confirming that retention of glandular structures is pervasive in CRC.A clean glass slide was placed into a 10-cm Petri dish and 500 μl PBS supplemented with RNAse and protease inhibitors was pipetted on top of the slide. The Petri dish was then transferred to the stage of a dissecting microscope. Tissue pieces were manually dissociated under the microscope using two 16G needles, with individual glands being pulled away from the tissue mass. For every specimen, a further epithelial minibulk sample that comprised a total of approximately 10–20 crypts or glands was collected. Each gland was transferred to a 1.5-ml Eppendorf tube containing a total volume of 50 µl cell lysis buffer  or 90μl (bulks) of the supernatant was transferred into a new tube containing 300 µl of TRIzol (taking care not to disturb the pellet). TRIzol lysates were stored at −20 °C if not processed immediately or at −80 °C for long-term storage. For extraction of nuclear material, the pellet of nuclei was resuspended in residual cell lysis buffer. A 2.5 µl volume of suspension was transferred into another tube for subsequent DNA extraction, which was frozen if required. The remaining suspension was immediately used for preparation of ATAC-seq libraries, as we found subsequent handling or storage compromised library quality.Each tube containing an individual gland or bulk was lightly vortexed, transferred to a pre-chilled centrifuge and spun at 500Tubes containing the suspension of nuclei (2.5 µl for glands and 7.5 µl for minibulks) were kept on wet ice. A 2.5 µl volume of 2× TD buffer and 0.25 µl of Tn5 transposes (Illumina) was added to each gland tube and 25 µl 2x TD buffer, 2.5 µl Tn5 transposes and 15 µl of DNAseq/RNase free water was added to each minibulk tube before incubation at 37 °C for 30 min. Purification was performed using AMpure XP SPRI beads (Beckman Coulter); 10 µl (2× sample volume) of room-temperature beads was added to each tube and mixed by pipetting 10 times, before incubation at room temperature for 1 min. The tube was placed on a magnetic plate, and beads were allowed to settle for 3 min. Once clear the supernatant was discarded. With the tube still on the magnetic plate, 200 μl of 80% ethanol was added and incubated at room temperature for 30 s, the ethanol supernatant was discarded. The tube was removed from the magnetic plate and 10 µl of 10 mM Tris buffer was added to each tube and mixed. The tubes were placed on a magnetic plate, and the beads were allowed to settle for 3 min. Once clear, 10 µl of supernatant containing purified transposed DNA fragments was transferred to a fresh tube for immediate library preparation or stored at −20 °C for later use.For library preparation, the transposed sample was supplemented with 1 µl of 10 µM Nextera i7 PCR primer, 1 µl of 10 µM Nextera i5 PCR primer (Illumina) and 12.5 µl of NEBNext Q5 High-Fidelity 2× PCR Master Mix (New England Biolabs). PCR amplification was performed, with initial elongation at 72 °C for 5 min, then initial denaturation at 98 °C for 30 s, and then 14 cycles (for glands) or 10 cycles (for bulks) of the following: 10 s of denaturation at 98 °C, annealing step at 63 °C for 30 s followed by 72 °C for 1 min.Following amplification, samples were purified with 2× SPRI beads and eluted in 20–30 µl of 10 mM Tris buffer, pH 8. Samples were screened using the Agilent Tapestation 4200 and HSD1000 screentapes. Only those that showed a fragment size distribution with peaks at multiples of about 147 base pairs (bp), indicating intact nucleosomal structure within the nuclei, were sent for sequencing.DNA fractions were extracted using the Zymo QuickDNA Microprep plus kit according to the manufacturer’s instructions. Only samples with a total DNA yield higher than 10 ng were taken forwards for WGS library preparation. Libraries were prepared using the NEBNext Ultra II FS kit according to the manufacturer’s instructions. A short enzymatic fragmentation step of 5 min was performed, and five PCR cycles were used for library enrichment. After purification, libraries were quantified by Qubit and run on the Agilent Tapestation using HSD1000 screentapes. Samples with sufficient library DNA yield and characteristic fragment size distribution (about 200–500 bp) were further subjected to either low-pass (about 1× coverage) or deep (about 35× coverage) WGS.The cytoplasmic fractions of each sample in the form of TRIzol lysates were used for RNA extraction using the Directzol kit (Zymo R2052). Modifications to the manufacturer’s protocol were introduced to increase the total RNA yields. First, we passed the initial TRIzol and ethanol mix twice through the spin column. Second, we eluted the RNA using two 25 µl volumes of water instead of just one 50 µl elution. The optional DNase step was used.Agilent Tapestation quality control showed low RNA integrity number scores (<3) for most samples and so was not used to exclude samples for library preparation. Libraries were prepared using the Illumina TruSeq RNA Exome kit  according to the manufacturer’s instructions.DNA methylation array analyses were carried out on selected bulk samples with sufficient DNA yield. Genomic DNA was bisulfite-converted using the Zymo EZ DNA Methylation kit. A 50-µl reaction containing 2.5–100 ng of DNA was incubated in the dark using a modified conversion protocol: 95 °C for 30 s and then 50 °C for 60 min, for 16 cycles and then holding at 4 °C. The full 8 µl eluate of converted DNA was repaired using the Infinium HD FFPE Restore Kit (Illumina). All 8 µl of the bisulfite-converted DNA for each sample was analysed on the lllumina Human MethylationEPIC BeadChip (Illumina). Processing was carried out by the University College London Genomics Core Facility according to a standard protocol.Sequence libraries were multiplexed and sequenced on an Illumina NovaSeq, typically using S2 flow cells. Read length and depth were varied as required by library composition. Sequencing was performed by the Institute of Cancer Research Tumour Profiling Unit.62). Adapter sequences were 5′-AGATCGGAAGAGC-3′ and 5′-ACGCTCTTCCGATCT-3′, with a maximum error rate of 0.1, a minimum mean quality value of 10 and a minimum read length of 35 after trimming using the options -l 35 -r 0.1 -Q 10 -n. The trimmed and filtered reads from each sequencing run and library were separately aligned to the GRCh38 reference assembly of the human genome63 using the BWA-MEM algorithm v0.7.17 (ref. 64). Following the GATK best practices and the associated set of tools v4.1.4.1 (refs. 67), reads were sorted by coordinates (GATK SortSam), independent sequencing runs or libraries generated from the same tissue sample were merged and duplicate reads were marked using GATK’s MarkDuplicates. The structure of the final bam files was verified using GATK’s ValidateSamFile.Contaminating adapter sequences were removed using Skewer v0.2.2 (ref. 62) using the following full-length adapter sequences with the option ‘-m any’: 5′-CTGTCTCTTATACACATCTCCGAGCCCACGAGACNNNNNNNNATCTCGTATGCCGTCTTCTGCTTG-3′ 5′-CTGTCTCTTATACACATCTGACGCTGCCGACGANNNNGTGTAGATCTCGGTGGTCGCCGTATCATT-3′.Adapter sequences were removed with Skewer v0.2.2 (ref. 68) with the options ‘--very-sensitive -X 2000’ set. After sorting the reads with SAMtools v1.9 (ref. 69), those mapping to non-canonical chromosomes and mitochondria (chrM) were removed (GATK PrintReads followed by RevertSam and SortSam). After merging independent libraries for each sample, we removed duplicate reads using GATK’s MarkDuplicates and removed all reads mapping to several locations (multi-mappers). The final bam files were validated with GATK’s ValidateSamFile.The reads of each sequencing run and library were aligned to the GRCh38 reference genome using Bowtie2 v2.3.4.3  using known germline variant annotations from build 146 of the dbSNP database71 separately for each chromosome. Resulting VCF files were then merged with GATK MergeVcfs. Variant recalibration was performed with GATK’s VariantRecalibrator with options set according to GATK best practices74 and applied to VCF files using GATK ApplyVQSR with the options ‘-mode SNP -ts-filter-level 99.0’ and ‘-mode INDEL -ts-filter-level 99.0’, respectively. All germline variant calls marked as PASS were retained.HaplotypeCaller v4.1.4.1 with the GATK package75. For virtually all high-purity samples without extensive loss of heterozygosity, we were able to confirm that the samples were obtained from the expected patient. A few samples with high amount of LOH and high purity fingerprinting did not confirm the sample-patient match; for these we instead inspected copy-number profiles (see below) to confirm that these matched the remaining samples of the corresponding patient.For all samples, we excluded the possibility of sample mismatch by comparing germline variants identified in normal tissue to neoplasia samples of a given patient. The reads of each read group were extracted with SAMtools view using the options ‘-bh {input_bam} -r {read_group_id}’, and GATK’s CheckFingerprint tool was applied to extract statistics on sample–patient matches76) and binned in non-overlapping windows of 106 bp. B-allele frequencies of germline mutations determined with the GATK HaplotypeCaller (see above) for each patient were added to these binned files. Joint segmentation on B-allele frequencies and depth ratios across all samples from a given tumour were used to determine a set of breakpoints to use for the subsequent analysis. Specifically, GC content bias correction was applied using the gc.norm method from Sequenza v2.1.2, and positions with non-unique mappability , as determined by the approach of QDNAseq v3.8 (ref. 77), in windows of 50 bp were removed. Piecewise constant curves were fitted for each chromosome arm using the multipcf function (gamma = 80) from the copynumber v1.22.0 package for R (ref. 78). The per-patient set of breakpoints, binned depth ratio and B-allele frequency data were then inputted into the Sequenza algorithm (v2.1.2) to determine allele-specific copy numbers, ploidy Ψ and purity ρ estimates76. The initial parameter space searched was restricted to {ρ | 0.1 ≤ ρ ≤ 1} and {Ψ | 1 ≤ Ψ ≤ 7}. On manual review of the results, we identified several samples with unreasonable fits . For these samples, we manually identified alternative solutions consistent with the other samples and somatic variant calls.Coverage values for genomic loci relative to matched normal tissue samples  were extracted with methods provided in the Sequenza v2.1.2 package for R (ref. 77 to count reads in 500-kilobase (kb) bins across the autosomes of hg38 and convert read counts into log2-ratios. Data normalization was performed in accordance with the QDNAseq workflow, except for outlier smoothing (smoothOutlierBins function), which was seen to artificially depress the signal from highly amplified bins. Bins for hg38 were also generated according to QDNAseq instructions. log2[ratio] values in each bin were normalized by subtracting the median log2[ratio] from all log2[ratio] values per sample. Samples from a patient were segmented jointly using the multipcf function in the R package copynumber (gamma = 10)78, and the mean segment log2[ratio] was calculated across the bins.Low-pass WGS bam files were processed using QDNAseq79. Using the ASCAT equation to describe log2[R ratio] values, we took an integer ploidy value Ψt in the tumour t as determined by paired deep WGS in each case and searched a range of purities from 0.1 to 1 (and assumed gamma was 1 as is the case in sequencing data). For each purity (ρ) value, we calculated the continuous copy-number status of each bin and calculated the sum of squared differences of these values to the nearest positive integer of the modulus. Purity estimates were given by local minima  across the purity range considered. The absolute copy-number state for each bin was taken as the closest integer value calculated using this purity. If no local minimum was found the purity was assumed to be 1. If the best solution produced negative copy-number states at some loci, these were set to have a copy number of zero to avoid impossible copy-number states. In two patients, per sample ploidies were determined by manual adjustment owing to integer ploidy values producing poor fits.Absolute copy-number status was calculated using the approach taken by ASCAT80). Variants detected in any tumour sample  were merged into a single list of candidate mutations. The multi-sample caller Platypus v0.8.1.1 (ref. 81) was then used to recall variants at each candidate mutation position in all samples of the patient. In practice, this meant that the pipeline leveraged information across samples to improve the sensitivity of variant calling. The Platypus output of joint variant calls was then filtered to keep only high-quality variants with the flags PASS, alleleBias, QD or Q20, in canonical chromosomes , a minimum number of reads NR > 5 in all samples, a genotyping quality GQ > 10 in all samples, a reference genotype  in the normal reference and a non-reference genotype  in at least one tumour sample.Somatic mutations were first called for each tumour sample separately against matched blood derived buffycoats or adjacent normal tissue samples with Mutect2 (v4.1.4.1) using the options ‘--af-of-alleles-not-in resource 0.0000025 --germline-resource af-onlygnomad.hg38.vcf.gz’  to which we did not apply the second step of filtering.To alleviate concerns of false-negative calls of mutations in important driver alterations, we generated a second set of variant calls for the identification of known driver mutations and d3 and IntOGen82 as well as pan-cancer driver genes reported by refs. 83 were filtered with the Variant Effect Predictor v93.2 (ref. 84).Somatic variants were annotated and candidate driver genes of CRCs reported by ref. 85). We first determined the position of microsatellite sites by applying the MSIsensor scan method to the GRCh38 reference assembly and subsetting the identified microsatellites to those located on the first chromosome. In a second step, we identified the fraction of mutated microsatellites in each sample using the MSIsensor msi method with default options. Generally, in known MSI cases , more than 30% of microsatellites were mutated, and we used this as a critical value to classify cases as MSS and MSI. One exception was C562, in which the low purity of the samples led to a low MSIsensor score. However, this case was clinically classified as MSI by pathological reports, and it had a relatively high indel burden leading to the conclusion that it was MSI.The identification of MSI CRCs was performed with MSIsensor v0.2  was used for dn/dS analysis. Per-patient variant calls were obtained from the VCF files86 and lifted over to the hg19 reference genome using the rtracklayer package for R (ref. 87). Variants were divided into clonal mutations  and subclonal mutations  present in the cancer and a set of mutations present in any of the adenoma samples. MSI and MSS cases were treated separately. dndscv was applied separately to each of the four sets  . Further, dn/dS values for a set of 167 chromatin modifier genes were extracted.The dndscv package for R . For this reason, as in previous studies, ATAC-seq reads were divided into a set of nucleosome-free reads (insertion size ≤ 100) and a set of nucleosome-associated reads (180 ≤ insertion size ≤ 620).For the detection of cut sites (hereafter ‘peaks’ where read density was high), bed files of ATAC-seq cut sites were produced. Aligned reads were sorted by read name using SAMtools sort -n{bam}, and all proper reads pairs  were isolated using SAMtools view -bf 0x2 and finally converted to the bed format using bedtools bamtobed -bedpe -mate1 -i{bam}. As in ref. 89) using ‘macs2 callpeak -f BED -g hs --shift --75 --extsize 150 --nomodel --call-summits --keep-dup all -p 0.01’ with the concatenated and sorted bed read files of nucleosome-free cut sites of all samples as input. A set of normal peaks (across patients) was called using the concatenated normal sample bed files  as input. Per-adenoma peaks were  called using all adenoma bulk samples as input.Peaks were called separately for each tumour region using MACS2 v2.21  as well as across all cancer samples and pan-patient normal peak calls (pan-patient peak set). The iterative merging resulted in a total of n= 343,240 peaks, of which n = 67,215 peaks called in >2 tumour regions or the panel of normal were retained. The ChIPseeker v2.14.0 package for R (ref. 90) was used in combination with the TxDb.Hsapiens.UCSC.hg38.knownGene package v3.10.0 for R to annotate peaks on the basis of their genomic location. For peaks that were not proximal to known promoter regions , overlaps with known enhancer elements reported in the double-elite annotations of the GeneHancer database were examined91.Per region peak calls were filtered for those having a 92 using: ‘bedtools coverage -a bed peaks -b bed cut sites -split -counts -sorted’.Read counts for each peak in the final set were collated using bedtoolsci and mutation multiplicity mi of each variant site i were obtained from the WGS data. For a mutation at site i covered by ns,i reads in sample s, the number of reads ki containing the alternative allele is expected to follow a binomial distribution with the pdfps,i is a function of the sample purity ρs’ the number of mutated alleles in the tumour cells ms,i, the total copy number of the mutated site in the tumour cells csi, and the copy number in contaminating normal cells cn = 2Clonal variants identified by paired WGS sequencing  were used to estimate sample-specific ATAC-seq purity. First, variants in intervals with identical  copy-number states  and regions of closed chromatin were identified from WGS data. Copy-number values ps was obtained by minimizing the negative-log-likelihood A maximum-likelihood estimate of the sample purity SN is the signal of the reference allele, S weighted by the total number of reads across samples. Indeed, CNAs were affecting the read depth at the locus (see the figures at 10.6084/m9.figshare.19849789 for details).To account for the influence of CNAs on the read counts, the signal observed at a locus should be given by However, it is important to consider that, in general, CNAs are causing relatively small changes in the ATAC-seq signals compared to those of bona fide SCAAs. This was demonstrated by the strong correlation of the recurrence number in the model with copy-number adjustment versus the one without. This approach was most relevant in the identification of lost chromatin accessibility in regions with a copy-number gain and gained chromatin accessibility in regions with a copy-number loss.91. An overdispersed Poisson model was fitted to each peak using edgeR v3.30.3 (refs. 94), per-sample set normalization factors were calculated using the TMMwsp method95, a global dispersion estimate was estimated across sets from all cancers and each set of pure glands (per patient) was compared against a large pool of normal tissue ATAC-seq samples. Recurrently altered peaks were identified as those that were significantly altered at a level of P ≤ 0.01 in at least 4/26  of cases.Analysis was restricted to samples with purity 38. A subset of 27,699 peaks that were either adjacent to a known TSS of a gene96 or overlapped a previously characterized enhancer element described in the GeneHancer database91 were identified. Of these 456/27,699 (≈1.65%) were recurrently altered. Changes in expression of genes associated with these sites were tested for using DESeq2 (ref. 97) to compare coefficients of the fitted β-binomial regression model  with the contrast argument being a list of vectors containing the significant and non-significant patient sets.The basic processing of matched RNA-seq data is described in the associated manuscriptP values from all tests were adjusted for multiple-hypothesis testing using FDR method98 associations at FDR < 0.1% where reported. For the visualization of gene expression values, the average variance stabilised log-transformed gene expression was calculated across samples of all each cancer and across all normal samples.For promoters, a one-tailed hypothesis test was applied by setting the altHypothesis argument to ‘less’ (for closed peaks) or ‘greater’ (for opened peaks). For enhancers, a two-tailed hypothesis test on all associated genes was applied by setting the altHypothesis argument to ‘greaterAbs’. Subclonality was assessed only for a set of recurrent somatic accessibility changes, comprising recurrent events affecting driver genes and the top 25 most recurrent in each of the 4 categories: gained promoter, lost promoter, gained enhancer and lost enhancer .P value was below 0.05 and if the direction of log[fold change] from analysis of matched bulk tissues was correlated with that observed in individual samples. In the case of gained events, subclonal events were filtered out if MACS peak-calling (see above) had not called a peak within 500 bp of the location of the putative gain event (this removed 33 sites). For losses, 5/45 subclonal events were removed as the log[fold change] was in the wrong direction.Our previous analyses recognized that sample purity was highly correlated with tumour piece (regions A–D). To distinguish subclonal chromatin accessibility alterations from variability in ploidy, regression to account for purity was performed. Specifically, a log ratio test from DESeq2 was used to compare a ‘full model’ ~purity + region to a reduced model ~purity. Samples from the same region were used as biological replicates. Events were considered putatively subclonal when the adjusted 99 and Gviz100.For visualization of peaks, coverage per region was calculated 1 kb upstream and 1 kb downstream from the centre of the peak. Coverage was normalized per million reads in peaks and was plotted using functions from GenomicRanges101), a reimplementation of the C++ library MOODS103, was used to identify binding sites for all human TF motifs defined in a curated version of the CIS-BP database104. The list of predicted binding sites was filtered using a minimum significance value of P ≤ 10−6, followed by removal of binding sites in centromeric regions and non-autosomal  chromosomes. After this initial filtering, predicted binding sites were split into six distinct groups on the basis of their distance to the next TSS  and whether they overlapped with a peak observed in the ATAC-seq data. For a number of TFs, homotypic clustering of binding sites in specific intervals was observed; to account for this, binding sites that were closer than d ≤ 1,000 bp to the next predicted binding site of the same TF were removed.The motifmatchr package for R (ref. 99 were tabulated. The insertion sizes (rows) were binned into intervals of 5 bp and divided by the total count of reads with an equivalent size in the entire genome. After this, the background signal was estimated to be theaverage number of insertions 1,000 bp–750 bp from the centre of the TF-binding site per insertion size and subtracted from the counts. The difference between these normalized and background-corrected TF signals in each sample and a pool of normal samples was calculated and integrated across the central region of the TF-binding sites  as a summary statistic. Linear regression analysis was used to identify associations with purity estimates, and in this context, signals were found to correlate with TSS enrichment . For thisreason, a further term was added to the regression model of each TF to correct for this effect: signal ≈ tsse*tssenf + purity:patient , in which tsse and tssenf are the differences in TSSe between the sample and the pooled normal samples, and each observation was weighted by the square root of the number of reads in the sample. A second linear model in which a region-specific effect of the purity  was considered was also fitted to the data. For both models, the statistical significance of the purity coefficient was determined. The estimates of the coefficients were also used as a patient-specific summary for subsequent analysis.For each of the TF sets described above, the counts of insertions around the centre of the TF-binding site  as well as the insertion size of the read pair  for each sample105 to identify significantly overrepresented pathways.The analysis was focused on the 150 TFs for which a significant association with the tumour cell content  and TF signal was most frequently observed. With the aim to identify general patterns in these data, a clustering analysis was conducted . This method identified three main groups of TFs, each of which was analysed with STRINGdb106 that included normal tissue sampled adjacent to CRCs that was profiled using the HumanMethylation450 BeadChip array (Illumina).A reference normal methylation array dataset was downloaded from ref. Here, eight bulk samples from four cases  were profiled using the MethylationEPIC BeadChip (Infinium) microarray according to the manufacturer’s instructions.107 was used to analyse the methylation bead array data. Probes that had a detection P > 0.01 and probes with <3 beads in at least 5% of samples per probe, probes that were on the X or Y chromosome, all probes associated with single nucleotide polymorphisms and all multi-hit probes were removed. Subset-within-array normalization was used to correct for biases resulting from type 1 and type 2 probes on the array. After quality control and normalization, β-values were calculated for further comparison.The ChAMP R package pipelineTo compare the methylation patterns between our samples and the reference normal dataset, the overlapped probes of all samples located distal to the TSS, close to the TSS and proximal to the TSS, both on the ATAC peak and not on the ATAC peak were compared.https://github.com/s-andrews/FastQC) and default adapter trimming with Skewer62, paired-end reads were aligned to the GRCh38 reference genome and v28 of the Gencode GTF annotation using the STAR two-pass method108. Read groups were added with Picard v2.5.0 (http://broadinstitute.github.io/picard). Per-gene read counts were produced with htseq-count, which is incorporated in the STAR pipeline108.After initial quality control with FastQC . If samples had fewer than 5 million of these ‘usable’ reads, they were resequenced to improve coverage. When possible, the same library preparation pool was sent again for sequencing. These ‘top-ups’ proved to be true technical replicates, as the resulting gene expression of the resequenced samples clustered very closely to their original samples on both a sample–sample heatmap and a principal component analysis. It was therefore determined that the FASTQs of these samples could simply be merged at the start of the pipeline. In cases in which resequencing was required but insufficient library remained, a new library was prepared, and the sequencing run that produced the highest read was used in subsequent analysis. For eight samples, the sequencing of the second library contained too few reads to enable downstream analysis. Six of eight samples showed per-gene read counts that were very similar between libraries 1 and 2  and so read counts were combined across libraries; the two remaining samples were discarded. Samples were also discarded if matched DNA sequencing revealed a tumour purity of less than 0.1.97).The number of non-ribosomal protein-coding genes on the 23 canonical chromosome pairs used for quality control was 19,671. Raw read counts uniquely assigned to these genes were converted into both transcripts per million and variance-stabilizing transformed (vst) counts using DESeq2  was determined by filtering out genes for which less than 5% of tumour samples had at least 10 transcripts per million. To concentrate on tumour epithelial cell gene expression, genes were further filtered out if they negatively correlated with purity as estimated from matched DNA-sequencing data. Specifically, for the 157 tumour samples that had matched DNA sequencing and therefore accurate purity estimates, a linear mixed-effects model of exp(vst) ≈ Purity + (1|Patient) was compared using a chi-squared test to exp ≈ (1|Patient). Genes that had a negative coefficient for purity in the first model and an FDR-adjusted P value less than 0.05, suggesting that purity significantly affected the expression, were filtered out. This led to a filtered list of 11,401 expressed genes.A list of expressed genes . It implements a procedure based on bi-cross-validation110 to select the best values for both the regularization parameter λ and the number of signatures. Deconvolution using a maximum of 10 signatures was performed and values of λ of 0.000, 0.025, 0.050 and 0.100 were tested. Optimal parameters were selected on the basis of the median bi-cross-validation error estimated over 1,000 iterations, resulting in an optimal estimate with minimum cross-validation median error when 6 signatures were fitted and λ = 0.025. A second analysis with SigProfiler111, with default parameters and a total of 1,000 iterations, confirmed the existence of these signatures.Mutational signature analysis was performed with SparseSignaturesk-means with six clusters explaining all of the variance. Although from a statistical perspective clusters C3 and C4 are defined by a small number of samples , from the biological perspective, we have evidence that in these patients the distribution of mutations resembles very different signatures and mutational processes . For each iteration, the mutations in each genomic region were used to compute a data matrix normalized against the trinucleotide content (across the 96 channels) in the whole genome versus region-specific counts, and signature assignments were then performed on the normalized data by LASSO109. Finally, relative signature activities estimated over the 100 jackknife samples were normalized on the basis of the total size of each region. Moreover, as clusters C3 and C4 represent rare and very distinct mutational patterns, we excluded these samples from the estimation of mutational processes in the epigenetic regions by jackknife, and instead we focused on MSS (cluster 1) versus MSI (clusters 2 and 5) tumour, as the samples in clusters C3 and C4 would probably have biased the jackknife estimation for these two groups.Mutational signature exposures were also analysed across epigenetic regions. Mutations were first grouped as clonal or subclonal across the whole genome and then in different genomic regions (as described above). Signature activities in each region were estimated by jackknife samplingFurther information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-022-05202-1.Supplementary InformationThis file contains Supplementary Figs. 1–19.Reporting SummarySupplementary Table 1Clinicopathological annotation of the analysed tissue samples.Supplementary Table 2Annotation of ATAC-seq-profiled tissue samples.Supplementary Table 3Annotation of WGS-profiled tissue samples.Supplementary Table 4Annotation of RNA-seq profiled tissue samples.Supplementary Table 5List of chromatin modifier genes.Supplementary Table 6List of driver genes.Supplementary Table 7Summary of the recurrence of SCAAs and their associated gene expression changes.


DOHMH intervention resulted in a decrease in blood lead levels for all family members (range = 1–6 µg/dL at last measurement dates).The New York City (NYC) Department of Health and Mental Hygiene (DOHMH) receives blood lead test results for NYC residents and conducts investigations of child and adult lead poisoning cases . Shortly thereafter, the mother and father received elevated blood lead test results .In November 2020, the child’s blood was retested for lead, and, as encouraged by the family physician and DOHMH, blood samples from the child’s two adult siblings were also tested; all three had blood lead levels at or above 5 , DOHMH learned that the family was using traditional ceramic ware purchased in Mexico for cooking, storing meals, and making coffee. DOHMH screened the ceramic ware using an X-ray fluorescence device (Viken Detection). The glazed interior measured 15.7 mg of lead per cm2, a level with the potential to leach substantial amounts of lead, particularly when used for cooking (µg/dL within 3–4 months and to 1–6 µg/dL after 14–16 months.During follow-up risk assessment interviewsLead can cause serious health effects in both children and adults; therefore, exposure to known lead sources should be avoided. Traditional ceramic ware from around the world has been found to contain lead at levels thousands of times higher than regulatory limits in the United States (