Biology subjects

Cabeli, V.

Publications and source records attributed to Cabeli, V..

5 recordsLinked to original sources

bioRxiv2024

Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients.

Over the last decade, Hippo signaling has emerged as a major tumor-suppressing pathway. Its dysregulation is associated with abnormal expression of YAP1 and TEAD-family genes. Recent works have highlighted the role of YAP1/TEAD activity in several cancers and its potential therapeutic implications. Therefore, identifying patients with a dysregulated Hippo pathway is key to enhancing treatment impact. Although recent studies have derived RNAseq-based signatures, there remains a need for a reproducible and cost-effective method to measure the pathway activation. In recent years, deep learning applied to histology slides have emerged as an effective way to predict molecular information from a data modality available in clinical routine. Here, we trained models to predict YAP1/TEAD activity from H&E-stained histology slides in multiple cancers. The robustness of our approach was assessed in seven independent validation cohorts. Finally, we showed that histological markers of disease aggressiveness were associated with dysfunctional Hippo signaling.

bioinformatics↗

bioRxiv2024

CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data

Live-cell microscopy routinely provides massive amount of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell-cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on-chip cellular ecosystem under therapeutically relevant conditions. In particular, we find that cancer associated fibroblasts directly inhibit cancer cell apoptosis, independently from anti-cancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications.

systems biology↗

bioRxiv2024

Robust Evaluation of Deep Learning-based Representation Methods for Survival and Gene Essentiality Prediction on Bulk RNA-seq Data

Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-seq data in cancer research. However, there is no consensus regarding the impact of design choices of DL approaches on the performance of the learned representation, including the model architecture, the training methodology and the various hyperparameters. To address this problem, we evaluate the performance of various design choices of DL representation learning methods using TCGA and DepMap pan-cancer datasets, and assess their predictive power for survival and gene essentiality predictions. We demonstrate that non DL-based baseline methods achieve comparable or superior performance compared to more complex models on survival predictions tasks. DL representation methods, however, are the most efficient to predict the gene essentiality of cell lines. We show that auto-encoders (AE) are consistently improved by techniques such as masking and multi-head training. Our results suggest that the impact of DL representations and of pre-training are highly task- and architecture-dependent, highlighting the need for adopting rigorous evaluation guidelines. These guidelines for robust evaluation are implemented in a pipeline made available to the research community.

bioinformatics↗

bioRxiv2023

Metabolically Primed Multipotent Hematopoietic Progenitors Fuel Innate Immunity

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 developed MetaFate, a method to trace the metabolic expression state and developmental fate of single cells in vivo. Using MetaFate we identified a gene expression program of metabolic enzymes and transporters that confers differences in myeloid differentiation potential in a subset of HSPCs that express CD62L. Using single-cell metabolic profiling, we confirmed that CD62Lhigh myeloid-biased HSPCs have an increased dependency on oxidative phosphorylation and glucose metabolism. Importantly, metabolism actively regulates immune-cell production, with overexpression of the glucose-6-phosphate dehydrogenase enzyme of the pentose phosphate pathway skewing MPP output from B-lymphocytes towards the myeloid lineages, and expansion of CD62Lhigh HSPCs occurring to support emergency myelopoiesis. Collectively, our data reveal the metabolic cues that instruct innate immune cell development, highlighting a key role for the pentose phosphate pathway. More broadly, our results show that HSPC metabolism can be manipulated to alter the cellular composition of the immune system.

immunology↗

bioRxiv2022

PyDESeq2: a python package for bulk RNA-seq differential expression analysis

SummaryWe present PyDESeq2, a python implementation of the DESeq2 workflow for differential expression analysis on bulk RNA-seq data. This implementation achieves better precision, allows speed improvements on large datasets, as shown in experiments on TCGA data, and can be more easily interfaced with modern python-based data science tools. Availability and ImplementationPyDESeq2 is released as an open-source software under the MIT license. The source code is available on GitHub at https://github.com/owkin/PyDESeq2. Contactboris.muzellec@owkin.com

bioinformatics↗