Biology subjects

Chadoutaud, L.

Publications and source records attributed to Chadoutaud, L..

3 recordsLinked to original sources

bioRxiv2024

sCellST: a Multiple Instance Learning approach to predict single-cell gene expression from H&E images using spatial transcriptomics

Advancing our understanding of tissue organization and its disruptions in disease remains a key focus in biomedical research. Histological slides stained with Hematoxylin and Eosin (H&E) provide an abundant source of morphological information, while Spatial Transcriptomics (ST) enables detailed, spatiallyresolved gene expression (GE) analysis, though at a high cost and with limited clinical accessibility. Predicting GE directly from H&E images using ST as a reference has thus become an attractive objective; however, current patch-based approaches lack single-cell resolution. Here, we present sCellST, a multipleinstance learning model that predicts GE by leveraging cell morphology alone, achieving remarkable predictive accuracy. When tested on a pancreatic ductal adenocarcinoma dataset, sCellST outperformed traditional methods, underscoring the value of basing predictions on single-cell images rather than tissue patches. Additionally, we demonstrate that sCellST can detect subtle morphological differences among cell types by utilizing marker genes in ovarian cancer samples. Our findings suggest that this approach could enable single-cell level GE predictions across large cohorts of H&E-stained slides, providing an innovative means to valorize this abundant resource in biomedical research.

bioinformatics↗

bioRxiv2022

transmorph: a unifying computational framework for single-cell data integration

Data integration of single-cell data describes the task of embedding datasets obtained from different sources into a common space, so that cells with similar cell type or state end up close from one another in this representation independently from their dataset of origin. Data integration is a crucial early step in most data analysis pipelines involving multiple batches and allows informative data visualization, batch effect reduction, high resolution clustering, accurate label transfer and cell type inference. Many tools have been proposed over the last decade to tackle data integration, and some of them are routinely used today within data analysis workflows. Despite constant endeavors to conduct exhaustive benchmarking studies, a recent surge in the number of these methods has made it difficult to choose one objectively for a given use case. Furthermore, these tools are generally provided as rigid pieces of software allowing little to no user agency on their internal parameters and algorithms, which makes it hard to adapt them to a variety of use cases. In an attempt to address both of these issues at once we introduce transmorph, an ambitious unifying framework for data integration. It allows building complex data integration pipelines by combining existing and original algorithmic modules, and is supported by a rich software ecosystem to easily benchmark modules, analyze and report results. We demonstrate transmorph capabilities and the value of its expressiveness by solving a variety of practical single-cell applications including supervised and unsupervised joint datasets embedding, RNA-seq integration in gene space and label transfer of cell cycle phase within cell cycle genes space. We provide transmorph as a free, open source and computationally efficient python library, with a particular effort to make it compatible with the other state-of-the-art tools and workflows.

bioinformatics↗

bioRxiv2022

Clonal evolution during metastatic spread in high-risk neuroblastoma

High-risk neuroblastoma is generally metastatic and often lethal. Using genomic profiling of 470 sequential and spatially separated samples from 283 patients, we characterize subtype-specific genetic evolutionary trajectories from diagnosis, through progression and end-stage metastatic disease. Clonal tracing timed disease initiation to embryogenesis. Continuous acquisition of structural variants at disease defining loci (MYCN, TERT, MDM2-CDK4) followed by convergent evolution of mutations targeting shared pathways emerged as the predominant feature of progression. At diagnosis metastatic clones were already established at distant sites where they could stay dormant, only to cause relapses years later and spread via metastasis-to-metastasis and polyclonal seeding after therapy.

genomics↗