Biology subjects

Rizzetto, S.

Publications and source records attributed to Rizzetto, S..

7 recordsLinked to original sources

bioRxiv2023

A transcriptome-wide meta-analysis reveals lack of cancer-cell intrinsic determinants of response to immune checkpoint blockade

Immune-checkpoint therapy (ICB) has conferred significant and durable clinical benefit to some cancer patients. However, most patients do not respond to ICB, and reliable biomarkers of ICB response are needed to improve patient stratification. Here, we performed a transcriptome-wide meta-analysis across 1,486 tumors from ICB-treated patients and tumors with expected ICB outcomes based on microsatellite status. Using a robust transcriptome deconvolution approach, we inferred cancer and stroma-specific gene expression differences and identified cell-type specific features of ICB response across cancer types. Consistent with current knowledge, stromal expression of CXCL9, CXCL13, and IFNG were the top determinants of favorable ICB response. In addition, we identified a group of potential immune-suppressive genes, including FCER1A, associated with poor response to ICB. Strikingly, PD-L1 expression in stromal cells, but not cancer cells, is correlated with ICB response across cancer types. Furthermore, the unbiased transcriptome-wide analysis failed to identify cancer-cell intrinsic features of ICB response conserved across tumor types. Overall, our results challenge the prevailing dogma that cancer cells present tissue-agnostic molecular markers that modulate immune activity and ICB treatment response. These results have implications for the development of improved ICB treatments and diagnostics.

cancer biology↗

bioRxiv2021

Single cell multi-omics reveals early elevated function and multiple fates within human progenitor exhausted CD8+ T cells

T-cell exhaustion is a hallmark of hepatitis C virus (HCV) infection and limits protective immunity in chronic viral infections and cancer. Limited knowledge exists of the initial viral and immune dynamics that characterise exhaustion in humans. We studied longitudinal blood samples from a unique cohort of subjects with primary infection using single cell multi-omics to identify the functions and phenotypes of HCV-specific CD8+ T cells. Early elevated IFN-{gamma} response against the transmitted virus was associated with the rate of immune escape, larger clonal expansion, and early onset of exhaustion. Irrespective of disease outcome we discovered progenitors of early-exhaustion with intermediate expression of PD-1. Intra clonal analysis revealed distinct trajectories with multiple fates suggesting evolutionary plasticity of precursor cells. These findings challenge current paradigm on the contribution of CD8+ T cells to HCV disease outcome and provide data for future studies on T-cell differentiation in human infections. One sentence summaryProgenitors of T-cell exhaustion in acute HCV infection

immunology↗

bioRxiv2021

A distinct CD115- erythro-myeloid precursor present at the maternal-embryonic interface and in the bone marrow of adult mice

During ontogeny, macrophages develop from CD115+ precursors, including erythro-myeloid progenitors (EMP). EMP arise in the embryonic yolk sac, the primary site of early haematopoiesis. In adults, CD115+ bone marrow-derived monocytes represent essential macrophage precursors. Herein, we identify a CD115- macrophage precursor within the adult bone marrow that is unrelated to the classical monocyte lineage but rather shares transcriptomic and functional characteristics of embryonic EMP. These EMPROR (for Erythro Myeloid Precursor) cells are capable of efficiently generating macrophages in disease settings. During early development, EMPROR cells were largely absent from the yolk sac but were instead found at the embryonic-maternal interface in the uterine wall. Unexpectedly, the latter site contains robust haematopoietic activity and harbours defined embryonic haematopoietic progenitor cells, including classical CD115+ EMP. Our data suggest the existence of an alternative pathway of macrophage generation in the adult. Further, we uncover a hitherto unknown site of earliest blood cell development.

immunology↗

bioRxiv2020

The persistence of Nontypeable Haemophilus influenzae fuels airway inflammation

*Nontypeable Haemophilus influenzae (NTHi) is commonly isolated from airway of cystic fibrosis (CF) patients. However, to what extent NTHi persistence contributes to the lung inflammatory burden during CF chronic airway disease is controversial. Here, we aimed at determining the pathological role of NTHi persistence in a cohort of CF patients and in a newly generated mouse model of NTHi persistence. In our study cohort, we found that CF patients chronically colonized by NTHi had significantly higher levels of IL-8 and CXCL1 than those who were not colonized. To better define the impact of NTHi persistence in fuelling inflammatory response, we developed a new mouse model using both laboratory and CF clinical strains. NTHi persistence was associated with chronic inflammation of the lung, characterized by recruitment of neutrophils and cytokine release (KC, G-CFS, IL-6 and IL-17A) at 2 and 14 days postinfection. An increased burden of T cell mediated response (CD4+ and {gamma}{delta} cells) and higher levels of matrix metalloproteinase 9, known to be associated with tissue remodelling, were observed at 14 days post-infection. Of note we found that both CD4+IL-17+ cells and levels of IL-17 cytokine were enriched in mice at advanced stage of NTHi chronic infection. Moreover, by immunohistochemistry we found CD3+, B220+ and CXCL-13+ cells localized in bronchus-associated lymphoid tissue-like structures at day 14. Our results demonstrate that NTHi persistence exerts a pro-inflammatory activity in the human and murine lung, and could therefore contribute to the exaggerated burden of lung inflammation in CF patients.

microbiology↗

bioRxiv2019

Single cell multi-modal analysis with VDJPuzzle identifies clonality and gene expression profiles of T and B cells

BackgroundSingle cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.\n\nResultsWe developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.\n\nConclusionsVDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview.

bioinformatics↗

bioRxiv2017

B-cell receptor reconstruction from single-cell RNA-seq with VDJPuzzle

The B-cell receptor (BCR) performs essential functions for the adaptive immune system including recognition of pathogen-derived antigens. Cell-to-cell variability of BCR sequences due to V(D)J recombination and somatic hypermutation (SHM) necessitates single-cell characterization of BCR sequences. Single-cell RNA sequencing (scRNA-seq) presents the opportunity for simultaneous capture of the BCR sequence and transcriptomic signature for a detailed understanding of the dynamics of an immune response.\n\nWe developed VDJPuzzle 2.0, a bioinformatic tool that reconstructs productive, full-length B-cell receptor sequences of both heavy and light chains. VDJPuzzle successfully reconstructs BCRs from 98.3% (n=117) of human and 96.5% (n=200) from murine B cells. 92.0% of clonotypes and 90.3% of mutations were concordant with single-cell Sanger sequencing of the immunoglobulin chains. VDJPuzzle is available at https://bitbucket.org/kirbyvisp/vdjpuzzle2

bioinformatics↗

bioRxiv2017

Impact Of Sequencing Depth And Read Length On Single Cell RNA Sequencing Data: Lessons From T Cells

Single cell RNA sequencing (scRNA-seq) has shown great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant sub-populations of T cells, and notably the identification of the full length T cell receptor (TCR{beta}), which defines the specificity against cognate antigens. Several factors, such as RNA library capture, cell quality, and sequencing output have been suggested to affect the quality of scRNA-seq data, but these factors have not been systematically examined.\n\nWe studied the effect of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCR{beta} reconstruction, utilising 1,305 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (<50 bp), but these featured higher technical variability compared to profiles from longer reads. TCR{beta} were detected in 1,027 cells (79%), with a success rate between 81% and 100% for datasets with at least 250,000 (PE) reads of length >50 bp.\n\nSufficient read length and sequencing depth can control technical noise to enable accurate identification of TCR{beta} and gene expression profiles from scRNA-seq data of T cells.

bioinformatics↗