Biology subjects

Richer, M.

Publications and source records attributed to Richer, M..

4 recordsLinked to original sources

bioRxiv2025

Establishment of human glioblastoma cell culture collection

Glioblastoma (GBM) is a highly aggressive primary brain cancer with poor prognosis (<15 months), highlighting the urgent need for more effective therapies. As current treatments are not effective, the need for a deeper understanding of the biology of GBM cells, including how they reprogram their metabolism to support their aberrant and uncontrolled growth, is critical. To this end, we established a collection of 41 human glioma cell lines derived from freshly resected tumour tissues from 99 patients. We characterized 12 of these cell lines by combining histologic, genetic, stem cell derivation and self-renewal, and metabolomic analyses. Histological and genetic profiles included IDH mutation status, Ki-67 proliferation index, ATRX status, mutant TP53 expression, chromosome 10q loss, EGFR amplification, and MGMT promoter methylation. Of these, only p53 mutation expression status showed weak segregation of the cell lines into 2 separate metabolic groups based on amino acid levels, but none showed an effect on stem cell derivation or self-renewal. Further characterization of these 12 cell lines revealed significant metabolic and phenotypic differences when comparing mesenchymal versus proneural gene expression subtyping. We show significant increases in TCA cycle metabolites in mesenchymal-like GBM cells and higher overall metabolic activity compared to proneural-like cells. These findings highlight the complexity of GBM and the need for personalized treatments that consider the metabolome of each subtype as a potential therapeutic avenue.

cancer biology↗

bioRxiv2024

The TRX assay for triplet repeat expansions

Expansion mutations of triplet repeat sequences cause numerous inherited neurological diseases. In some diseases, affected individuals display somatic expansions in affected tissues which have been linked to accelerated disease onset and progression. There is currently considerable interest in developing therapies to slow somatic repeat expansions to delay or block disease onset. In vitro assays are particularly important to evaluate potential therapeutic interventions. Current assays typically use physical methods to monitor triplet repeat lengths within a population of cells. While useful, most of these assays are relatively slow ([~]six weeks) and are somewhat limited in sensitivity to rare events. Here, a new assay, called TRX, is described to monitor CAG*CTG triplet repeat expansions more rapidly and with better sensitivity. TRX uses human tissue culture cells expressing two fluorescent proteins. Red fluorescent protein TagRFP658 is constitutively expressed and serves as an internal control. GFP is expressed in a CAG*CTG repeat length-dependent manner, with longer repeat lengths predicted to give higher green fluorescence intensity. Standard flow cytometry allows quantification of changes in fluorescent signal as a simple readout with <2% sensitivity. Two independently derived cell lines with 63 or 59 CAG repeats yielded similar rates of TRX activity. Cells with increased green fluorescence were observed within one to two weeks of culture, with longer times leading to additional signal. The appearance of green fluorescence was partly dependent on MutS{beta}, the DNA MSH2-MSH3 complex, based on siRNA knockdown of MSH3. However, physical analysis of the CAG*CTG repeat tracts by MiSeq deep sequencing or capillary electrophoresis showed limited changes in the length of the repeat tracts. We conclude that the TRX assay is a promising new tool for monitoring CAG*CTG repeat expansions but that further development of the assay is needed to make it fully useful.

genetics↗

bioRxiv2024

A type 1 immune-stromal cell network mediates disease tolerance and barrier protection against intestinal infection

Type 1 immunity mediates host defense through pathogen elimination, but whether this pathway also impacts tissue function is unknown. Here we demonstrate that rapid induction of IFN{gamma} signaling coordinates a multi-cellular response that is critical to limit tissue damage and maintain gut motility following infection of mice with a tissue-invasive helminth. IFN{gamma} production is initiated by antigen-independent activation of lamina propria CD8+ T cells following MyD88-dependent recognition of the microbiota during helminth-induced barrier invasion. IFN{gamma} acted directly on intestinal stromal cells to recruit neutrophils that limited parasite-induced tissue injury. IFN{gamma} sensing also limited the expansion of smooth muscle actin-expressing cells to prevent pathological gut dysmotility. Importantly, this tissue-protective response had limited impact on parasite burden, indicating that IFN{gamma} supports a disease tolerance defense strategy. Our results have important implications for managing the pathophysiological sequelae of post-infectious gut dysfunction and chronic inflammatory diseases associated with stromal remodelling. HIGHLIGHTSO_LIType 1 immunity is required for disease tolerance to tissue-invasive infection. C_LIO_LIGut-resident CD8+ T cells produce IFN{gamma} in an antigen-independent, yet microbiota-dependent manner. C_LIO_LIIFN{gamma} signaling recruits neutrophils in a cell-extrinsic manner to limit helminth-induced tissue injury. C_LIO_LIDirect sensing of IFN{gamma} by intestinal stroma is essential to limit tissue damage and maintain gut motility during infection. C_LI

immunology↗

bioRxiv2024

PHARAOH: A collaborative crowdsourcing platform for PHenotyping And Regional Analysis Of Histology

Deep learning has proven to be capable of automating key aspects of histopathologic analysis, but its continual reliance on large expert-annotated training datasets hinders widespread adoption. Here, we present an online collaborative portal that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/). PHARAOH uses a weakly supervised active learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched human annotation. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Both custom model design and feature extraction pipelines are amenable to crowdsourcing making PHARAOH a fully scalable systems-level solution for the systematic expansion and cataloging of computational pathology applications.

bioinformatics↗