Biology subjects

Chazarra-Gil, R.

Publications and source records attributed to Chazarra-Gil, R..

3 recordsLinked to original sources

bioRxiv2026

Single-cell atlas of transcript usage remodelling in antiviral immune responses across human populations

Humans exhibit substantial interindividual variation in their immune responses to infection, yet the contribution of transcript usage --the relative abundance of gene isoforms-- to this variation remains poorly understood. Here, we generate the first single-cell atlas of transcript usage variation during early responses to influenza A virus and SARS-CoV-2 across 160 individuals of African and European ancestry. We show that viral stimulation induces widespread remodelling of transcript usage across all major immune lineages, with changes that are largely lineage-restricted and frequently undetected at the gene expression level. We further find that ancestry-associated effects on transcript usage are predominantly cell type-specific and contribute to population differences in antiviral responses. In addition, the genetic regulation of transcript usage during stimulation differs between influenza A and SARS-CoV-2, pointing to virus-dependent regulatory architectures. Together, our findings establish transcript usage as a dynamic regulatory layer shaping responses to viral infection across immune cell types and human populations, providing new insights into the molecular basis of variation in human antiviral immunity.

immunology↗

bioRxiv2021

The sum of two halves may be different from the whole. Effects of splitting sequencing samples across lanes.

The advances in high throughput sequencing (HTS) enabled the characterisation of biological processes at an unprecedented level of detail; the majority of hypotheses in molecular biology rely on analyses of HTS data. However, achieving increased robustness and reproducibility of results remains one of the main challenges. Although variability in results may be introduced at various stages, e.g. alignment, summarisation or detection of differences in expression, one source of variability was systematically omitted: the sequencing design which propagates through analyses and may introduce an additional layer of technical variation. We illustrate qualitative and quantitative differences arising from splitting samples across lanes, on bulk and single-cell sequencing. For bulk mRNAseq data, we focus on differential expression and enrichment analyses; for bulk ChIPseq data, we investigate the effect on peak calling, and peaks properties. At single-cell level, we concentrate on identifying cell subpopulations. We rely on markers used for assigning cell identities; both smartSeq and 10x data are presented. The observed reduction in the number of unique sequenced fragments reduces the level of detail on which the different prediction approaches depend. Further, the sequencing stochasticity adds in a weighting bias corroborated with variable sequencing depths and (yet unexplained) sequencing bias.

bioinformatics↗

bioRxiv2020

Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench

As the cost of single-cell RNA-seq experiments has decreased, an increasing number of datasets are now available. Combining newly generated and publicly accessible datasets is challenging due to non-biological signals, commonly known as batch effects. Although there are several computational methods available that can remove batch effects, evaluating which method performs best is not straightforward. Here we present BatchBench (https://github.com/cellgeni/batchbench), a modular and flexible pipeline for comparing batch correction methods for single-cell RNA-seq data. We apply BatchBench to eight methods, highlighting their methodological differences and assess their performance and computational requirements through a compendium of well-studied datasets. This systematic comparison guides users in the choice of batch correction tool, and the pipeline makes it easy to evaluate other datasets.

bioinformatics↗