Biology subjects

Trussart, M.

Publications and source records attributed to Trussart, M..

6 recordsLinked to original sources

bioRxiv2026

Fast, accurate and scalable normalization for RNA sequencing data with the RUVprps software package

Unwanted variation refers to any source of variability in the data that can compromise down-stream analysis. Effective removal of such variation from gene expression data is essential to derive accurate and meaningful biological results. We refer to this process as normalization. Data may come from a single study or from multiple studies with different sources of unwanted variation. We have previously developed the RUV-III method for normalizing omics data with a strong focus on transcriptomics. Initially, we introduced RUV-III for the normalization of Nanostring nCounter gene expression data, utilizing genuine technical replicates and pseudo-replicates as control samples. Subsequently, we proposed RUV-III with pseudo-replicates of pseudo-samples (PRPS), and which demonstrated its potential in mitigating the effects of different sources of unwanted variation in large and complex RNA-seq studies. To enhance accessibility and performance of this method, we present a new comprehensive R package named RUVprps. The package offers over 100 functions including ones for assessing variation in both biological and unwanted variables, an automated RUV-III normalization process, and metrics for evaluating the effectiveness of the resulting normalizations. Further, it introduces several new features such as ways of identifying unknown sources of unwanted variation, strategies to identify suitable negative control genes, and methods for generating PRPS when information on the biological and unwanted variation is unavailable. The package also implements a faster approach to RUV-III normalization, streamlining its application to large RNA-seq datasets. Our freely available R package and normalization assessment pipeline can help find effective data normalization methods for new data and help benchmark new methods.

bioinformatics↗

bioRxiv2024

Early cytokine-driven adaptation of survival pathways in lymphoid cells during targeted therapies

Venetoclax, a first-in-class BH3 mimetic drug targeting BCL-2, has improved outcomes for patients with chronic lymphocytic leukemia (CLL). Early measurements of the depth of the venetoclax treatment response, assessed by minimal residual disease, are strong predictors of long-term clinical outcomes. Yet, there are limited data concerning the early changes induced by venetoclax treatment that might inform strategies to improve responses. To address this gap, we conducted longitudinal mass cytometric profiling of blood cells from patients with CLL during the first two months of venetoclax monotherapy. At baseline, we resolved CLL heterogeneity at the single-cell level to define multiple subpopulations in all patients distinguished by proliferative, metabolic and cell survival proteins. Venetoclax induced significant reduction in all CLL subpopulations coincident with rapid upregulation of pro-survival BCL-2, BCL-XL and MCL-1 proteins in surviving cells, which had reduced sensitivity to the drug. Mouse models recapitulated the venetoclax-induced elevation of survival proteins in B cells and CLL-like cells that persisted in vivo, with genetic models demonstrating that extensive apoptosis and access to the B cell cytokine, BAFF, were essential. Accordingly, analysis of patients with CLL that were treated with a different targeted therapy, the anti-CD20 antibody obinutuzumab, also exhibited marked elevation of BAFF and increased pro-survival proteins in leukemic cells that persisted. Overall, these data highlight the rapid adaptation of CLL cells to targeted therapies via homeostatic factors and support co-targeting of cytokine signals to achieve deeper and more durable long-term responses. Key pointsO_LILeukaemic cells rapidly adapt to targeted therapy by elevating pro-survival protein expression. C_LIO_LICell attrition and increased bioavailability of homeostatic cytokines drive this heightened survival, highlighting avenues for more potent combination therapies. C_LI

cancer biology↗

bioRxiv2022

Robust differential composition and variability analysis for multisample cell omics

Cell omics such as single-cell genomics, proteomics and microbiomics allow the characterisation of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to unveiling markers of disease progression such as cancer and pathogen infection. For cell omic data, no method for differential variability analysis exists, and methods for differential composition analysis only take a few fundamental data properties into account. Here we introduce sccomp, a generalised method for differential composition and variability analyses able to jointly model data count distribution, compositionality, group-specific variability and proportion mean-variability association, with awareness against outliers. Sccomp is an extensive analysis framework that allows realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies showing the inadequacy of the very popular Dirichlet-multinomial modelling and provide mandatory principles for differential variability analysis. We show that sccomp accurately fits experimental data, with a 50% incremental improvement over state-of-the-art algorithms. Using sccomp, we identified novel differential constraints and composition in the microenvironment of primary breast cancer. Significance statementDetermining the composition of cell populations is made possible by technologies like single-cell transcriptomics, CyTOF and microbiome sequencing. Such analyses are now widespread across fields (~800 publications/month, Scopus). However, existing methods for differential abundance do not model all data features, and cell-type/taxa specific differential variability is not yet possible. Increase in the variability of tissue composition and microbial communities is a well-known indicator of loss of homeostasis and disease. A suitable statistical method would enable new types of analyses to identify component-specific loss of homeostasis for the first time. This and other innovations are now possible through our discovery of the mean-variability association for compositional data. Based on this fundamental observation, we have developed a new statistical model, sccomp, that enables differential variability analysis for composition data, improved differential abundance analyses, with cross-sample information borrowing, outlier identification and exclusion, realistic data simulation, based on experimental datasets, cross-study knowledge transfer.

bioinformatics↗

bioRxiv2021

Early immune pressure imposed by tissue resident memory T cells sculpts tumour evolution in non-small cell lung cancer

Tissue-resident memory T cells (TRM) provide immune defence against local infection and can inhibit cancer progression. However, it is unclear to what extent chronic inflammation impacts TRM activation and how the immune pressure exerted by TRM affects developing tumours in humans. We performed deep profiling of lung cancers arising in never-smokers (NS) and ever-smokers (ES), finding evidence of enhanced TRM immunosurveillance in ES lung. Only tumours arising in ES patients underwent clonal immune escape, even when evaluating cancers with similar tumour mutational burden to NS patients, suggesting that the timing of immune pressure exerted by TRM is a critical factor in the evolution of tumour immune evasion. Tumours grown in T cell quiescent NS lungs displayed little evidence of immune evasion and had fewer neoantigens with low diversity, paradoxically making them amenable to treatment with agonist of the costimulatory molecule, ICOS. These data demonstrate local environmental insults enhance TRM immunosurveillance of human tissue, shape the evolution of tumour immunogenicity and that this interplay informs effective immunotherapeutic modalities.

cancer biology↗

bioRxiv2020

CytofRUV: Removing unwanted variation to integrate multiple CyTOF datasets

Mass cytometry (CyTOF) is a technology that has revolutionised single cell biology. One illuminating application of CyTOF has been in understanding the mechanisms of blood cancer resistance to therapy. Longitudinal studies of clinical cohorts during drug treatment provide a deeper understanding of the molecular changes that underlie sensitivity or resistance to treatment in each patient. However, understanding the biological impact of a cancer drug in such studies necessitates the integration of multiple CyTOF batches. To date, the integration of CyTOF datasets remains a challenge due to technical differences arising in multiple batches. To overcome this limitation, we developed an approach called CytofRUV for analysing multiple CyTOF batches which includes an R-Shiny application with diagnostics plots. CytofRUV can correct for batch effects and integrate data from large numbers of patients and conditions across batches, to confidently compare cellular changes and correlate these with clinically relevant outcomes.

bioinformatics↗

bioRxiv2018

Long-range chromatin interactions on the inactive X and at Hox clusters are regulated by the non-canonical SMC protein Smchd1.

The regulation of higher order chromatin structure is complex and dynamic; however we do not yet understand the full suite of mechanisms governing architecture. Here we reveal the non-canonical SMC protein Smchd1 as a novel regulator of long-range chromatin interactions, and add it to the canon of epigenetic proteins required for Hox gene regulation. The effect of losing Smchd1-dependent chromatin interactions has varying outcomes dependent on chromatin context. At autosomal targets transcriptionally sensitive to Smchd1 deletion, we find increased short-range interactions and ectopic enhancer activation. By contrast, the inactive X chromosome is transcriptionally refractive to Smchd1 ablation, despite chromosome-wide increases in short-range interactions. There we observe spreading of H3K27me3 domains into regions not normally decorated by this mark. Together these data suggest Smchd1 has the capacity to insulate the chromatin, thereby limiting access to other chromatin modifying proteins.

molecular biology↗