bioRxiv Science⌕ Search

Biology subjects

Garcia-Prieto, C.

Publications and source records attributed to Garcia-Prieto, C..

2 recordsLinked to original sources

Spatial transcriptomics unveils the in situ cellular and molecular hallmarks of the lung in fatal COVID-19

Severe Coronavirus disease 2019 (COVID-19) induces heterogeneous and progressive diffuse alveolar damage (DAD) highly disrupting lung tissue architecture and homeostasis, hampering disease management leading to fatal outcomes. Characterizing DAD pathophysiology across disease progression is of ultimate importance to better understand the molecular and cellular features driving different DAD patterns and to optimize treatment strategies. To contextualize the interplay between cell types and assess their distribution, spatial transcriptomics (ST) techniques have emerged, allowing unprecedented resolution to investigate spatial architecture of tissues. To this end, post-mortem lung tissue provides valuable insights into cellular composition and their spatial relationships at the time of death. Here, we have leveraged VisumST technology in post-mortem COVID-19 induced acute and proliferative DAD lungs including control samples with normal morphological appearance, to unravel the immunopathological mechanisms underlying DAD, providing novel insights into cellular and molecular communication events driving DAD progression in fatal COVID-19. We report a progressive loss of endothelial cell types, pneumocytes type I and natural killer cells coupled with a continuous increase of myeloid and stromal cells, mostly peribronchial fibroblasts, over disease progression. Spatial organization analysis identified variable cellular compartments, ranging from major compartments defined by cell type lineages in control lungs to increased and more specific compartmentalization including immune-specific clusters across DAD spectrum. Importantly, spatially informed ligand-receptor interaction (LRI) analysis revealed an intercellular communication signature defining COVID-19 induced DAD lungs. Transcription factor (TF) activity enrichment analysis identified TGF-B pathway as DAD driver, highlighting SMAD3 and SMAD7 TFs activity role during lung fibrosis. Integration of deregulated LRIs and TFs activity allowed us to propose a downstream intracellular signaling pathway in peribronchial fibroblasts, suggesting potential novel therapeutic targets. Finally, spatio-temporal trajectories analysis provided insights into the alveolar epithelium regeneration program, characterizing markers of pneumocytes type II differentiation towards pneumocytes type I. In conclusion, we provide a spatial characterization of lung tissue architecture upon COVID-19 induced DAD progression, identifying molecular and cellular hallmarks that may help optimize treatment and patient management.

pathology↗

The consequences of variant calling decisions in secondary analyses of cancer sequencing data

The analysis of cancer genomes provides fundamental information about its aetiology, the processes driving cell transformation or potential treatments. The first crucial step in the analysis of any tumor genome is the identification of somatic genetic variants that cancer cells have acquired during their evolution. For that purpose, a wide range of somatic variant callers have been developed in recent years. While there have been some efforts to benchmark somatic variant calling tools and strategies, the extent to which variant calling decisions impact the results of downstream analyses of tumor genomes remains unknown. Here we present a study to elucidate whether different variant callers (MuSE, MuTect2, SomaticSniper, VarScan2) and strategies to combine them (Consensus and Union) lead to different results in these three important downstream analyses of cancer genomics data: identification of cancer driver genes, quantification of mutational signatures and detection of clinically actionable variants. To this end, we tested how the results of these three analyses varied depending on the somatic mutation caller in five different projects from The Cancer Genome Atlas (TCGA). Our results show that variant calling decisions have a significant impact on these downstream analyses, creating important differences in driver genes identification and mutational processes attribution among variant call sets, as well as in the detection of clinically actionable targets. More importantly, it seems that Consensus, a very widely used strategy by the research community, is not the optimal strategy, as it can lead to the loss of some cancer driver genes and actionable mutations. On the other hand, the Union seems to be a legit strategy for some downstream analyses with a robust performance overall. Contact: eduard.porta@bsc.es; alfonso.valencia@bsc.es

bioinformatics↗