Biology subjects

Vallot, C.

Publications and source records attributed to Vallot, C..

13 recordsLinked to original sources

bioRxiv2026

The Pentose Phosphate Pathway Regulates Myelo-Lymphoid Lineage Specification

Following infection, hematopoietic stem and progenitor cells (HSPCs) support immunity by increasing the rate of innate immune cell production but the metabolic cues that guide this process are unknown. To address this question, we combined in situ RNA barcoding and metabolomics approaches to perform metabolic state-fate mapping in vivo. This approach revealed a subset of myeloid-biased HSPCs that express a distinct set of metabolic enzymes and transporters as well as the surface marker CD62L. Metabolically, CD62Lhigh HSPCs have differential activity of the pentose phosphate pathway (PPP), OXPHOS and translation, as well as differential levels of S-adenosylmethionine (SAM) cycle metabolites associated with epigenetic modifications. Inhibition of the PPP skews HSPC lineage fate decisions by disrupting myeloid associated enhancers, while simultaneously increasing enhancer activity for the master B-lymphoid regulator Ikaros. In vivo, overexpression of glucose-6-phosphate dehydrogenase, a rate limiting enzyme of the PPP, skewed HSPC output from B-lymphocytes. In summary, our data shows that HSPCs undergo significant metabolic changes to facilitate the bioenergetic and epigenetic demands of myeloid versus lymphoid lineage specification. We highlight a key role for the pentose phosphate pathway which modulates myeloid rather than lymphoid commitment by shaping the HSPC enhancer landscape, providing proof of principle that HSPC metabolism can be targeted to modulate immune system dynamics.

immunology↗

bioRxiv2026

Routine FFPE sections support clinically compatible single-nucleus transcriptomics across six human cancer types

Tumor cellular composition--including malignant cell states, immune populations, and stromal populations--is increasingly recognized as a determinant of therapeutic response and resistance to anti-cancer agents, yet comprehensive cellular profiling remains largely confined to research settings. Here, we present a clinically compatible sample-to-report workflow for tumor composition profiling from routine formalin-fixed paraffin-embedded (FFPE) clinical specimens. By combining low-input single-nucleus RNA sequencing with foundation model- based automated cell annotation, this workflow enables prospective sample-by-sample analysis without dedicated research material or cohort-based processing. Across 116 clinical specimens representing six cancer types, we generated reproducible measurements of cellular composition and cell-type-specific gene expression, demonstrated high technical reproducibility, and showed concordance with pathological assessment of immune infiltration. The workflow was similarly applicable to archival FFPE material and ultra-low-input biopsy specimens. Together, these findings establish a practical framework for routine single-cell profiling from standard pathology specimens and open the perspective of prospective evaluation of cellular composition as a clinical biomarker in precision oncology.

cancer biology↗

bioRxiv2026

Single-cell trajectories in metastatic urothelial carcinoma reveal tumor-immune reprogramming and macrophage-driven resistance to PD-(L)1 blockade

Immune checkpoint inhibitors (ICI) improved outcomes in metastatic urothelial carcinoma (mUC), but primary and acquired resistance remain poorly understood. We performed single-nuclei RNA sequencing on sequential metastatic biopsies from ICI-treated mUC patients. Tumor cells showed transcriptomic heterogeneity within individual lesions, basal cells being associated with increased immune infiltration and response. Myeloid and lymphoid compartments exhibited features of immune dysfunction in non-responders. Longitudinal analyses revealed convergent adaptive resistance mechanisms, dominated by polarization toward pro-tumoral macrophage states, but also including downregulation of the antigen presentation machinery in tumor cells, increased checkpoint expression with loss of cytotoxicity in T cells. Individual trajectories point to distinct evolutionary routes under ICI pressure. Across pivotal ICI trials, bulk expression of the M2-like macrophage marker HES1 predicted ICI resistance. Our study provides the first single-cell longitudinal atlas of ICI-treated mUC, revealing macrophage reprogramming as a dominant driver of resistance, establishing a framework for individualized immunotherapy strategies.

cancer biology↗

bioRxiv2026

Matched single-cell chromatin, transcriptome, and surface marker profiling captures in vivo epigenomic reprogramming during basal-to-luminal transition in the mammary gland

Single-cell multi-omics methods enable simultaneous mapping of chromatin states and transcriptomes, offering deep insights into gene regulation. Yet, the full potential of these approaches remains untapped for rare cell populations, as most methods require thousands of cells and are limited in their ability to capture multiple molecular layers comprehensively within the same cell. Here, we introduce OneCell CUT&Tag a user-friendly method that provides matched high-resolution epigenome, full-transcriptome, and surface marker quantification from every cell, with input as low as one cell. Using this approach, we uncovered epigenomic priming of basal cells in the mammary gland and captured the dynamics of basal-to-luminal transdifferentiation. We identified a transitional cell population with intermediate epigenomic profiles--absent in reference populations--and demonstrated a continuous epigenomic progression from basal to luminal states, while transcriptomes exhibited a binary switch. Adaptable to diverse samples and tissues, this method also revealed the role of H3K27me3 in shaping zygotic expression programs. By matching multiple layers of molecular information at single-cell resolution, OneCell CUT&Tag dissects the complementary roles of each omics layer in shaping cellular identity and function, opening new avenues to study rare and complex biological systems.

genomics↗

bioRxiv2025

A single-nucleus multimodal framework reveals epigenomic priming of chemoresistant states in ovarian cancer

Non-genetic intratumor heterogeneity (ITH) drives therapeutic failure in cancer, yet its clinical monitoring remains challenging. We develop a multimodal single-nucleus framework that simultaneously profiles transcriptional and histone modification landscapes from frozen biopsies. Applied to longitudinal samples from 16 patients with high-grade serous ovarian cancer (HGSOC), this approach reveals reproducible tumor evolution under chemotherapy: proliferative and interferon-responsive states are lost, while those associated with TNF- and epithelial-mesenchymal transition (EMT) expand. Chromatin profiling shows that these chemoresistant programs are epigenetically primed through H3K4me1 marks before treatment and nominates transcription-factor drivers, including ZBTB7A. Non-genetic baseline tumor composition predicts survival, and fibroblast remodeling parallels malignant adaptation. These findings establish a clinically scalable strategy for mapping functional ITH and identify epigenomic priming as a determinant of therapeutic failure. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=181 HEIGHT=200 SRC="FIGDIR/small/692102v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@432524org.highwire.dtl.DTLVardef@3b7843org.highwire.dtl.DTLVardef@54a2eforg.highwire.dtl.DTLVardef@95273d_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Identification of a shared persistence program in triple-negative breast cancer across treatments and patients

Acquisition of resistance to anti-cancer therapies is a multistep process, which initiates with the survival of drug persister cells. Understanding the mechanisms driving the emergence of persister cells remains challenging, primarily because of their limited accessibility in patients. Here, using mouse models to isolate persister cells from patient tumors, we determine the identity features of persister cells from eight patients with triple-negative breast cancer (TNBC). Combining over 80 transcriptome studies, we reveal hallmarks of the persister state across patient models and treatment modalities: high expression of basal keratins together with activation of a stress response and inflammation pathways. Patient-derived persister cells are transcriptionally plastic and return to a common treatment-naive like state upon relapse, regardless of the treatment they have been exposed to. Leveraging gene regulatory networks, we identify AP-1, NFKB and IRF/STAT as the key drivers of this hallmark persister state. As a proof of concept, we show that FOSL1 - an AP-1 member - is sufficient to drive cells to the persister state by binding enhancers and reprogramming the transcriptome of cancer cells. On the contrary, cancer cells without FOSL1 have a decreased ability to reach the persister state. By defining hallmarks of drug persistence to multiple therapies of the standard of care, our study provides a resource to design novel combination therapeutic strategies to limit resistance.

cancer biology↗

bioRxiv2022

Best practices for single-cell histone modification analysis

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.

bioinformatics↗

bioRxiv2022

Multi-modal quantification of pathway activity with MAYA

Signaling pathways can be activated through various cascades of genes depending on cell identity and biological context. Single-cell atlases now provide the opportunity to inspect such complexity in health and disease. Yet, existing reference tools for pathway scoring resume activity of each pathway to one unique common metric across cell types. Here, we present MAYA a computational method that enables the automatic detection and scoring of the diverse modes of activation of biological pathways across cell populations. MAYA improves the granularity of pathway analysis by detecting subgroups of genes within reference pathways, each characteristic of a cell population and how it activates a pathway. Using multiple single-cell datasets, we demonstrate the biological relevance of identified modes of activation, the robustness of MAYA to noisy pathway lists and batch effect. MAYA can also predict cell types starting from lists of reference markers in a cluster-free manner. Finally, we show that MAYA reveals common modes of pathway activation in tumor cells across patients, opening the perspective to discover shared therapeutic vulnerabilities.

bioinformatics↗

bioRxiv2022

Luminal progenitors undergo partial epithelial-to-mesenchymal transition at the onset of basal-like breast tumorigenesis

Defects 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.

cancer biology↗

bioRxiv2021

Machine learning for single cell genomics data analysis

Single-cell omics technologies produce large quantities of data describing the genomic, transcriptomic or epigenomic profiles of many individual cells in parallel. In order to infer biological knowledge and develop predictive models from these data, machine learning (ML)-based model are increasingly used due to their flexibility, scalability, and impressive success in other fields. In recent years, we have seen a surge of new ML-based method development for low-dimensional representations of single-cell omics data, batch normalization, cell type classification, trajectory inference, gene regulatory network inference or multimodal data integration. To help readers navigate this fast-moving literature, we survey in this review recent advances in ML approaches developed to analyze single-cell omics data, focusing mainly on peer-reviewed publications published in the last two years (2019-2020).

bioinformatics↗

bioRxiv2021

H3K27me3 is a determinant of chemotolerance in triple-negative breast cancer

Triple-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-{beta} 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.

cancer biology↗

bioRxiv2020

The importance and difficulty to tune parameters of methods for single-cell RNA-seq dimensionality reduction

BackgroundMany computational methods have been developed recently to analyze single-cell RNA-seq (scRNA-seq) data. Several benchmark studies have compared these methods on their ability for dimensionality reduction, clustering or differential analysis, often relying on default parameters. Yet given the biological diversity of scRNA-seq datasets, parameter tuning might be essential for the optimal usage of methods, and determining how to tune parameters remains an unmet need. ResultsHere, we propose a benchmark to assess the performance of five methods, systematically varying their tunable parameters, for dimension reduction of scRNA-seq data, a common first step to many downstream applications such as cell type identification or trajectory inference. We run a total of 1.5 million experiments to assess the influence of parameter changes on the performance of each method, and propose two strategies to automatically tune parameters for methods that need it. ConclusionsWe find that principal component analysis (PCA)-based methods like scran and Seurat are competitive with default parameters but do not benefit much from parameter tuning, while more complex models like ZinbWave, DCA and scVI can reach better performance but after parameter tuning.

bioinformatics↗

bioRxiv2019

ChromSCape : an R/Shiny application for interactive analysis of single-cell chromatin profiles

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.\n\nContactpacome.prompsy@curie.fr; celine.vallot@curie.fr

bioinformatics↗