Biology subjects

Coullomb, A.

Publications and source records attributed to Coullomb, A..

4 recordsLinked to original sources

bioRxiv2023

mosna reveals different types of cellular interactionspredictive of response to immunotherapies in cancer

Spatially resolved omics enable the discovery of tissue organization of biological or clinical importance. Despite the existence of several methods, performing a rational analysis including multiple algorithms while integrating different conditions such as clinical data is still not trivial. To make such investigations more accessible, we developed mosna, a Python package to analyze spatial omics data in integration with clinical or biological data, providing insight on cell interaction patterns or tissue architecture. mosna is compatible with all spatial omics techniques, it leverages tysserand to build accurate spatial networks, and is compatible with Squidpy. It proposes an analysis pipeline, in which increasingly complex features computed at each step with either the mosna- algorithms or others can be explored in integration with clinical data. The approach produces easy-to-use descriptive statistics and data visualization, while seamlessly training machine learning models and identifying variables with the most predictive power. mosna can take as input any dataset produced by spatial omics methods, including subcellular resolved transcriptomics (MERFISH, seqFISH, Xenium) and proteomics (CODEX, MIBI-TOF, low-plex immuno-fluorescence), as well as spot-based spatial transcriptomics (10x Visium, Slide-seq, Stereo-seq). Integration with experimental metadata or clinical data is adapted to binary conditions, such as biological treatments or response status of patients, and to survival data. We demonstrate the proposed analysis pipeline on two spatially resolved proteomic datasets and a spatial transcriptomics dataset containing either binary response to immunotherapy or survival data, and we assess the performance of the proposed niche discovering method in a manually annotated spatial transcriptomic dataset. mosna identifies features describing cellular composition and spatial patterns that can provide biological insight regarding factors that affect response to immunotherapies or survival. Availability and implementationmosna is made publicly available to the community, together with relevant documentation at https://mosna-documentation.readthedocs.io/en/latest/index.html and tutorials implemented as Jupyter notebooks to reproduce the result at https://github.com/AlexCoul/mosna

bioinformatics↗

bioRxiv2021

GEM-DeCan: Improving tumor immune microenvironment profiling by the integration of novel gene expression and DNA methylation deconvolution signatures

Quantifying the proportion of the different cell types present in tumor biopsies remains a priority in cancer research. So far, a number of deconvolution methods have emerged for estimating cell composition using reference signatures, either based on gene expression or on DNA methylation from purified cells. These two deconvolution approaches could be complementary to each other, leading to even more performant signatures, in cases where both data types are available. However, the potential relationship between signatures based on gene expression and those based on DNA methylation remains underexplored. Here we present five new deconvolution signature matrices, based RNAseq data or on DNA methylation, which can estimate the proportion of immune cells and cancer cells in a tumour sample. We test these signature matrices on available datasets for in-silico and in-vitro mixtures, peripheral blood, cancer samples from TCGA, and a single-cell melanoma dataset. Cell proportions estimates based on deconvolution performed using our signature matrices, implemented within the EpiDISH framework, show comparable or better correlation with FACS measurements of immune cell-type abundance and with various estimates of cancer sample purity and composition than existing methods. Using publicly available data of 3D chromatin structure in haematopoietic cells, we expanded the list of genes to be included in the RNAseq signature matrices by considering the presence of methylated CpGs in gene promoters or in genomic regions which are in 3D contact with these promoters. Our expanded signature matrices have improved performance compared to our initial RNAseq signature matrix. Finally, we show the value of our signatures in predicting patient response to immune checkpoint inhibitors in three melanoma cancer cohorts, based on bulk tumour sample gene expression. We also provide GEM-DeCan: a snakemake pipeline, able to run an analysis from raw sequencing data to deconvolution based on various gene expression signature matrices, both for bulk RNASeq and DNA methylation data.

bioinformatics↗

bioRxiv2020

Tysserand - Fast reconstruction of spatial networks from bioimages

SummaryNetworks provide a powerful framework to analyze spatial omics experiments. However, we lack tools that integrate several methods to easily reconstruct networks for further analyses with dedicated libraries. In addition, choosing the appropriate method and parameters can be challenging. We propose tysserand, a Python library to reconstruct spatial networks from spatially resolved omics experiments. It is intended as a common tool to which the bioinformatics community can add new methods to reconstruct networks, choose appropriate parameters, clean resulting networks and pipe data to other libraries. Availability and implementationtysserand software and tutorials with a Jupyter notebook to reproduce the results are available at https://github.com/VeraPancaldiLab/tysserand Supplementary informationSupplementary data are available at Bioarxiv online.

bioinformatics↗

bioRxiv2017

Magneto-active substrates for local mechanical stimulation of living cells

Cells are able to sense and react to their physical environment by translating a mechanical cue into an intracellular biochemical signal that triggers biological and mechanical responses. This process, called mechanotransduction, controls essential cellular functions such as proliferation and migration. The cellular response to an external mechanical stimulation has been investigated with various static and dynamic systems, so far limited to global deformations or to local stimulation through discrete substrates. To apply local and dynamic mechanical constraints at the single cell scale through a continuous surface, we have developed and modelled magneto-active substrates made of magnetic micro-pillars embedded in an elastomer. Constrained and unconstrained substrates are analysed to map surface stress resulting from the magnetic actuation of the micro-pillars and the adherent cells. These substrates have a rigidity in the range of cell matrices, and the magnetic micro-pillars generate local forces in the range of cellular forces, both in traction and compression. As an application, we followed the protrusive activity of cells subjected to dynamic stimulations. Our magneto-active substrates thus represent a new tool to study mechanotransduction in single cells, and complement existing techniques by exerting a local and dynamic stimulation, traction and compression, through a continuous soft substrate.

biophysics↗