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International audience; The persistence of cancer cells resistant to therapy remains a major clinical challenge. In triple-negative breast cancer, resistance to chemotherapy results in the highest recurrence risk among breast cancer subtypes. The drug-tolerant state seems largely defined by nongenetic features, but the underlying mechanisms are poorly understood. Here, by monitoring epigenomes, transcriptomes and lineages with single-cell resolution, we show that the repressive histone mark H3K27me3 (trimethylation of histone H3 at lysine 27) regulates cell fate at the onset of chemotherapy. We report that a persister expression program is primed with both H3K4me3 (trimethylation of histone H3 at lysine 4) and H3K27me3 in unchallenged cells, with H3K27me3 being the lock to its transcriptional activation. We further demonstrate that depleting H3K27me3 enhances the potential of cancer cells to tolerate chemotherapy. Conversely, preventing H3K27me3 demethylation simultaneously to chemotherapy inhibits the transition to a drug-tolerant state, and delays tumor recurrence in vivo. Our results highlight how chromatin landscapes shape the potential of cancer cells to respond to initial therapy.

BackgroundSingle-cell histone post translation modification (scHPTM) assays such as scCUT&Tag or scChIP-seq allow single-cell mapping of diverse epigenomic landscapes within complex tissues, and are likely to unlock our understanding of various epigenetic mechanisms involved in development or diseases. Running an scHTPM experiment and analyzing the data produced remains, however, a challenging task since few consensus guidelines exist currently regarding good practices for experimental design and data analysis pipelines.MethodsWe perform a computational benchmark to assess the impact of experimental parameters and of the data analysis pipeline on the ability of the cell representation produced to recapitulate known biological similarities. We run more than ten thousands experiments to systematically study the impact of coverage and number of cells, of the count matrix construction method, of feature selection and normalization, and of the dimension reduction algorithm used.ResultsThe analysis of the benchmark results allows us to identify key experimental parameters and computational choices to obtain a good representation of single-cell HPTM data. We show in particular that the count matrix construction step has a strong influence on the quality of the representation, and that using fixed-size bin counts outperforms annotation-based binning; that dimension reduction methods based on latent semantic indexing outperform others; and that feature selection is detrimental, while keeping only high-quality cells has little influence on the final representation as long as enough cells are analyzed.

SUMMARYDefects in double-strand repair mechanisms - both through germline or somatic inactivation of repair genes - is a hallmark of basal-like breast cancers. In this genetically-unstable context, a recurrent shift in cell identity occurs within the mammary epithelium. Basal-like tumors have indeed been proposed to originate from luminal progenitor (LP) cells yet tumor-initiating events remain poorly understood. Here, we map state transitions at the onset of basal-like tumorigenesis, using a Brca-1 deficient mouse model launching tumorigenesis in multiple LP cells. Combining single-cell transcriptomics to spatial multiplex imaging, we identify a population of cycling p16-expressing cells, emerging from the luminal progenitor compartment, undergoing partial epithelial-to-mesenchymal transition and losing luminal identity. Pseudo-temporal analyses position these cells as a transitory pre-tumoral state between aberrant Brca1-deficient luminal progenitors and growing tumor cells. In patients, the p16 pre-tumoral signature is found in early stage basal-like tumors, that rarely recur. Concomitant to p16 activation, we show that LP cells undergo an epigenomic crisis attested by the general re-organization of their heterochromatin. They accumulate multiple H3K27me3 micro-foci - reminiscent of the formation of senescence-associated heterochromatin foci (SAHFs) - and lose their inactive X (Xi). Both p16 activation and heterochromatin reorganization are hallmarks of human basal-like breast tumors; we propose that these events occur during initial LP transformation and are scars of an initial transitory senescent-like state.

SummaryTriple-negative breast cancer is associated with the worst prognosis and the highest risk of recurrence among all breast cancer subtypes1. Residual disease, formed by cancer cells persistent to chemotherapy, remains one of the major clinical challenges towards full cure2,3. There is now consensus that non-genetic processes contribute to chemoresistance in various tumor types, notably through the initial emergence of a reversible chemotolerant state4–6. Understanding non-genetic tumor evolution stands now as a prerequisite for the design of relevant combinatorial approaches to delay recurrence. Here we show that the repressive histone mark H3K27me3 is a determinant of cell fate under chemotherapy exposure, monitoring epigenomes, transcriptomes and lineage with single-cell resolution. We identify a reservoir of persister basal cells with EMT markers and activated TGF-β pathway leading to multiple chemoresistance phenotypes. We demonstrate that, in unchallenged cells, H3K27 methylation is a lock to the expression program of persister cells. Promoters are primed with both H3K4me3 and H3K27me3, and removing H3K27me3 is sufficient for their transcriptional activation. Leveraging lineage barcoding, we show that depleting H3K27me3 alters tumor cell fate under chemotherapy insult – a wider variety of tumor cells tolerate chemotherapy. Our results highlight how chromatin landscapes shape the potential of unchallenged cancer cells to respond to therapeutic stress.

Chromatin modifications orchestrate the dynamic regulation of gene expression during development and in disease. Bulk approaches have characterized the wide repertoire of histone modifications across cell types, detailing their role in shaping cell identity. However, these population-based methods do not capture cell-to-cell heterogeneity of chromatin landscapes, limiting our appreciation of the role of chromatin in dynamic biological processes. Recent technological developments enable the mapping of histone marks at single-cell resolution, opening up perspectives to characterize the heterogeneity of chromatin marks in complex biological systems over time. Yet, existing tools used to analyze bulk histone modifications profiles are not fit for the low coverage and sparsity of single-cell epigenomic datasets. Here, we present ChromSCape, a user-friendly interactive Shiny/R application distributed as a Bioconductor package, that processes single-cell epigenomic data to assist the biological interpretation of chromatin landscapes within cell populations. ChromSCape analyses the distribution of repressive and active histone modifications as well as chromatin accessibility landscapes from single-cell datasets. Using ChromSCape, we deconvolve chromatin landscapes within the tumor micro-environment, identifying distinct H3K27me3 landscapes associated with cell identity and breast tumor subtype., Bulk approaches fail to capture the cell-to-cell heterogeneity of chromatin landscapes, while single-cell approaches provide low coverage datasets. Here, the authors present ChromSCape, a user-friendly interactive application that processes single-cell epigenomic data to assist the biological interpretation of chromatin landscapes within cell populations, as demonstrated in the context of cancer.

Assessing chromatin profiles at single-cell resolution is now feasible thanks to recently published experimental methods such as single cell chromatin immunoprecipitation followed by sequencing (scChIP-seq) (Grosselin et al., 2019; Rotem et al., 2015) and single-cell assay for transposase-accessibility chromatin (scATAC-seq) (Buenrostro et al., 2015; Chen et al., 2018; Cusanovich et al., 2015; Lareau et al., 2019). With these methods, we can detect the heterogeneity of epigenomic profiles within complex biological samples. Yet, existing tools used to analyze bulk epigenomic experiments are not fit for the low coverage and sparsity of single-cell epigenomic datasets. Here, we present ChromSCape: a user-friendly Shiny/R application that processes single-cell epigenomic data to help the biological interpretation of epigenomic landscapes within cell populations. The user can identify different sub-populations within heterogeneous samples, find differentially enriched regions between subpopulations and identify associated genes and pathways. ChromSCape accepts multiple samples to allow comparisons of cell populations between and within samples. ChromSCape source code is written in Shiny/R, works as a stand-alone application and is freely downloadable at https://github.com/vallotlab/ChromSCape. Here, using ChromSCape on multiple H3K27me3 scChIP-seq datasets, we deconvolve chromatin landscapes within the tumor microenvironment, identifying distinct H3K27me3 landscapes associated to cell identity and tumor subtype.Contactpacome.prompsy@curie.fr; celine.vallot@curie.fr