Biology subjects

Banchero, M.

Publications and source records attributed to Banchero, M..

3 recordsLinked to original sources

bioRxiv2023

Interpretable Inflammation Landscape of Circulating Immune cells

Inflammation is a biological phenomenon involved in a wide variety of physiological and pathological processes. Although a controlled inflammatory response is beneficial for restoring homeostasis, it can become unfavorable if dysregulated. In recent years, major progress has been made in characterizing acute and chronic inflammation in specific diseases. However, a global, holistic understanding of inflammation is still elusive. This is particularly intriguing, considering the crucial function of inflammation for human health and its potential for modern medicine if fully deciphered. Here, we leverage advances in the field of single-cell genomics to delineate the full spectrum of circulating immune cell activation underlying inflammatory processes during infection, immune-mediated inflammatory diseases and cancer. Our single-cell atlas of >6.5 million peripheral blood mononuclear cells from 1047 patients and 19 diseases allowed us to learn a comprehensive model of inflammation in circulating immune cells. The atlas expanded our current knowledge of the biology of inflammation of immune-mediated diseases, acute and chronic inflammatory diseases, infection and solid tumors, and laid the foundation to develop a precision medicine framework using unsupervised as well as explainable machine learning. Beyond a disease-centered analysis, we charted altered activity of inflammatory molecules in peripheral blood cells, depicting discriminative inflammation-related genes to further understand mechanisms of inflammation. Finally, we have laid the groundwork for developing precision medicine diagnostic tools for patients experiencing pathologic inflammation by learning a classifier for inflammatory diseases, presenting cells in circulation as a powerful resource for patient diagnosis.

immunology↗

bioRxiv2023

Airway epithelial response to RSV is impaired in multiciliated and goblet cells in asthma

In patients with asthma, respiratory syncytial virus (RSV) infections can cause disease exacerbations by infecting the epithelial layer of the airways, inducing an innate and adaptive immune response. The type-I interferon antiviral response of epithelial cells upon RSV infection is found to be reduced in asthma in most -but not all-studies. Moreover, the molecular mechanisms that cause the differences in the asthmatic bronchial epithelium in response to viral infection are poorly understood. Here, we investigated the transcriptional response to RSV infection of primary bronchial epithelial cells (pBECs) from asthma patients(n=8) and healthy donors(n=8). The pBECs obtained from bronchial brushes were differentiated in air-liquid interface conditions and infected with RSV. After three days, cells were processed for single-cell RNA sequencing. A strong antiviral response to RSV was observed for all cell types present, from both asthma patients and healthy donors. Most differentially regulated genes following RSV infection were found in cells transitioning from basal to secretory. Goblet cells from asthma patients showed lower expression of genes involved in the interferon response. In multiciliated cells, an impairment of the signaling pathways involved in the response to RSV in asthma was observed, including no enrichment of the type-III interferon response. Our results highlight that the response to RSV infection of the bronchial epithelium in asthma and healthy airways was largely similar. However, in asthma, the response of goblet and the multiciliated cells was impaired, highlighting the need for studying airway epithelial cells at high resolution in the context of asthma exacerbations. What is already know on this topicThe airway epithelium response to RSV is altered in asthma. However, literature remains conflicted about the exact changes in the antiviral response, and the mechanisms causing these changes are yet to be found. What this study addsThis study describes extensively the response of the bronchial epithelial cells (BECs) to RSV for both healthy subjects and asthma patients, at a single-cell resolution. It highlights the major overlap between healthy and asthma in the antiviral response to RSV. It allows the identification of specific genes and cell types that show a different behavior in response to RSV in asthma compared to healthy. How this study might affect research, practice or policyOur study indicates that goblet and multiciliated cells are the most relevant BECs to further investigate in the context of drug development for RSV-induced asthma exacerbation. It also suggests that focusing research on the cross-talk between the epithelial and the immune cells, or into investigating a potential delayed response in asthma would be the best way forward into understanding the mechanisms involved in the asthma response to RSV.

molecular biology↗

bioRxiv2022

FastCAR: Fast Correction for Ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets

Cell type-specific differential gene expression analyses based on single-cell transcriptome datasets are sensitive to the presence of cell-free mRNA in the droplets containing single cells. This so-called ambient RNA contamination may differ between samples obtained from patients and healthy controls. Current ambient RNA correction methods were not developed specifically for single-cell differential gene expression (sc-DGE) analyses and might therefore not sufficiently correct for ambient RNA-derived signals. Here, we show that ambient RNA levels are highly sample-specific. We found that without ambient RNA correction, sc-DGE analyses erroneously identify transcripts originating from ambient RNA as cell type-specific disease-associated genes. We therefore developed a computationally lean and intuitive correction method, Fast Correction for Ambient RNA (FastCAR), optimized for sc-DGE analysis of scRNA-Seq datasets generated by droplet-based methods including the 10XGenomics Chromium platform. FastCAR uses the profile of transcripts observed in libraries that likely represent empty droplets to determine the level of ambient RNA in each individual sample, and then corrects for these ambient RNA gene expression values. FastCAR can be applied as part of the data pre-processing and QC in sc-DGE workflows comparing scRNA-Seq data in a health versus disease experimental design. We compared FastCAR with two methods previously developed to remove ambient RNA, SoupX and CellBender. All three methods identified additional genes in sc-DGE analyses that were not identified in the absence of ambient RNA correction. However, we show that FastCAR performs better at correcting gene expression values attributed to ambient RNA, resulting in a lower frequency of false-positive observations. Moreover, the use of FastCAR in a sc-DGE workflow increases the cell-type specificity of sc-DGE analyses across disease conditions.

bioinformatics↗