Biology subjects

Bertorello, J.

Publications and source records attributed to Bertorello, J..

3 recordsLinked to original sources

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

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↗