Biology subjects

Becavin, C.

Publications and source records attributed to Becavin, C..

7 recordsLinked to original sources

bioRxiv2024

scMusketeers: Addressing imbalanced cell type annotation and batch effect reduction with a modular autoencoder

The growing number of single-cell gene expression atlases available offers a conceptual framework for improving our understanding of physio-pathological processes. To take full advantage of this revolution, data integration and cell annotation strategies need to be improved, in particular to better detect rare cell types and by better controlling batch effects in experiments. scMusketeers is a deep learning model that optimises the representation of latent data and solves both challenges. scMusketeers features three modules: (1) an autoencoder for noise and dimensionality reductions; (2) a focal loss classifier to enhance rare cell type predictions; and (3) an adversarial domain adaptation (DANN) module for batch effect correction. Benchmarking against state-of-the-art tools, including the UCE foundation model, showed that scMusketeers performs on par or better, particularly in identifying rare cell types. It also allows to transfer cell labels from single-cell RNA sequencing to spatial transcriptomics. With its modular and adaptable design, scMusketeers offers a versatile framework that can be generalized to other large-scale biological projects requiring deep learning approaches, establishing itself as a valuable tool for single-cell data integration and analysis.

bioinformatics↗

bioRxiv2022

Immunopeptidomics-based design of highly effective mRNA vaccine formulations against Listeria monocytogenes

Listeria monocytogenes is a foodborne intracellular bacterial pathogen leading to human listeriosis. Despite a high mortality rate and increasing antibiotic resistance no clinically approved vaccine against Listeria is available. Attenuated Listeria strains offer protection and are tested as antitumor vaccine vectors, but would benefit from a better knowledge on immunodominant vector antigens. To identify novel antigens, we screened for Listeria epitopes presented on the surface of infected human cell lines by mass spectrometry-based immunopeptidomics. In between more than 15,000 human self-peptides, we detected 68 Listeria epitopes from 42 different bacterial proteins, including several known antigens. Peptide epitopes presented on different cell lines were often derived from the same bacterial surface proteins, classifying these antigens as potential vaccine candidates. Encoding these highly presented antigens in lipid nanoparticle mRNA vaccine formulations resulted in specific CD8+ T-cell responses and high levels of protection in vaccination challenge experiments in mice. Our results pave the way for the development of a clinical mRNA vaccine against Listeria and aid to improve attenuated Listeria vaccines and vectors, demonstrating the power of immunopeptidomics for next-generation bacterial vaccine development.

immunology↗

bioRxiv2022

An integrated cell atlas of the human lung in health and disease

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

cell biology↗

bioRxiv2020

Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, creates an urgent need for identifying molecular mechanisms that mediate viral entry, propagation, and tissue pathology. Cell membrane bound angiotensin-converting enzyme 2 (ACE2) and associated proteases, transmembrane protease serine 2 (TMPRSS2) and Cathepsin L (CTSL), were previously identified as mediators of SARS-CoV2 cellular entry. Here, we assess the cell type-specific RNA expression of ACE2, TMPRSS2, and CTSL through an integrated analysis of 107 single-cell and single-nucleus RNA-Seq studies, including 22 lung and airways datasets (16 unpublished), and 85 datasets from other diverse organs. Joint expression of ACE2 and the accessory proteases identifies specific subsets of respiratory epithelial cells as putative targets of viral infection in the nasal passages, airways, and alveoli. Cells that co-express ACE2 and proteases are also identified in cells from other organs, some of which have been associated with COVID-19 transmission or pathology, including gut enterocytes, corneal epithelial cells, cardiomyocytes, heart pericytes, olfactory sustentacular cells, and renal epithelial cells. Performing the first meta-analyses of scRNA-seq studies, we analyzed 1,176,683 cells from 282 nasal, airway, and lung parenchyma samples from 164 donors spanning fetal, childhood, adult, and elderly age groups, associate increased levels of ACE2, TMPRSS2, and CTSL in specific cell types with increasing age, male gender, and smoking, all of which are epidemiologically linked to COVID-19 susceptibility and outcomes. Notably, there was a particularly low expression of ACE2 in the few young pediatric samples in the analysis. Further analysis reveals a gene expression program shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues, including genes that may mediate viral entry, subtend key immune functions, and mediate epithelial-macrophage cross-talk. Amongst these are IL6, its receptor and co-receptor, IL1R, TNF response pathways, and complement genes. Cell type specificity in the lung and airways and smoking effects were conserved in mice. Our analyses suggest that differences in the cell type-specific expression of mediators of SARS-CoV-2 viral entry may be responsible for aspects of COVID-19 epidemiology and clinical course, and point to putative molecular pathways involved in disease susceptibility and pathogenesis.

bioinformatics↗

bioRxiv2019

An RNA-binding protein secreted by Listeria monocytogenes activates RIG-I signaling

Recent studies have reported on the presence of bacterial RNA within or outside extracellular membrane vesicles, possibly as ribonucleoprotein complexes. Proteins that bind and stabilize bacterial RNAs in the extracellular environment have not been reported. Here, we show that the bacterial pathogen Listeria monocytogenes secretes a small RNA binding protein that we named Zea. We show that Zea binds and stabilizes a subset of L. monocytogenes RNAs causing their accumulation in the extracellular medium. Furthermore, Zea binds RIG-I, the vertebrate non-self-RNA innate immunity sensor and potentiates RIG-I-signaling leading to interferon {beta} production. By performing in vivo infection, we finally show that Zea modulates L. monocytogenes virulence. Together, this study reveals that bacterial extracellular RNAs and RNA binding proteins can affect the host-pathogen crosstalk.

cell biology↗

bioRxiv2018

A role for gut microbiota in m6A epitranscriptomic mRNA modifications in different host tissues

The intestinal microbiota modulates host physiology and gene expression via mechanisms that are not fully understood. A recently discovered layer of gene expression regulation is N6-methyladenosine (m6A) modification of mRNA. To unveil if this epitranscriptomic mark in part mediates the impact of the gut microbiota on the host, we analyzed m6A-modifications in transcripts of mice displaying either a conventional, or a modified, or no gut flora. We discovered that the microbiota has a strong influence on m6A-modifications in the cecum, and also, albeit to a lesser extent, in the liver. We furthermore show that a single commensal bacterium, Akkermansia muciniphila, can affect specific m6A modifications. Together, we report here epitranscriptomic modifications as an unexpected level of interaction in the complex interplay between commensal bacteria and their host.

molecular biology↗

bioRxiv2018

Genome-wide CRISPR-Cas9 screen in E. coli identifies design rules for efficient targeting

The main outcome of efficient CRISPR-Cas9 cleavage in the chromosome of bacteria is cell death. This can be conveniently used to eliminate specific genotypes from a mixed population of bacteria, which can be achieved both in vitro, e.g. to select mutants, or in vivo as an antimicrobial strategy. The efficiency with which Cas9 kills bacteria has been observed to be quite variable depending on the specific target sequence, but little is known about the sequence determinants and mechanisms involved. Here we performed a genome-wide screen of Cas9 cleavage in the chromosome of E. coli to determine the efficiency with which each guide RNA kills the cell. Surprisingly we observed a large-scale pattern where guides targeting some regions of the chromosome are more rapidly depleted than others. Unexpectedly, this pattern arises from the influence of degrading specific chromosomal regions on the copy number of the plasmid carrying the guide RNA library. After taking this effect into account, it is possible to train a neural network to predict Cas9 efficiency based on the target sequence. We show that our model learns different features than previous models trained on Eukaryotic CRISPR-Cas9 knockout libraries. Our results highlight the need for specific models to design efficient CRISPR-Cas9 tools in bacteria.

synthetic biology↗