Automated and reproducible cell identification in mass cytometry using neural networks.

The principal use of mass cytometry is to identify distinct cell types and changes in their composition, phenotype and function in different samples and conditions. Combining data from different studies has the potential to increase the power of these discoveries in diverse fields such as immunology, oncology and infection. However, current tools are lacking in scalable, reproducible and automated methods to integrate and study data sets from mass cytometry that often use heterogenous approaches to study similar samples. To address these limitations, we present two novel developments: (1) a pre-trained cell identification model named Immunopred that allows automated identification of immune cells without user-defined prior knowledge of expected cell types and (2) a fully automated cytometry meta-analysis pipeline built around Immunopred. We evaluated this pipeline on six COVID-19 study data sets comprising 270 unique samples and uncovered novel significant phenotypic changes in the wider immune landscape of COVID-19 that were not identified when each study was analyzed individually. Applied widely, our approach will support the discovery of novel findings in research areas where cytometry data sets are available for integration.

Single-cell phenotypes of peripheral blood immune cells in early and late stages of non-alcoholic fatty liver disease.

BACKGROUND/AIMS: Immune and inflammatory cells respond to multiple pathological hits in the development of nonalcoholic steatohepatitis (NASH) and fibrosis. Relatively little is known about how their type and function change through the non-alcoholic fatty liver disease (NAFLD) spectrum. Here we used multi-dimensional mass cytometry and a tailored bioinformatic approach to study circulating immune cells sampled from healthy individuals and people with NAFLD. METHODS: Cytometry by time of flight using 36 metal-conjugated antibodies was applied to peripheral blood mononuclear cells (PBMCs) from biopsy-proven NASH fibrosis (late disease), steatosis (early disease), and healthy patients. Supervised and unsupervised analyses were used, findings confirmed, and mechanisms assessed using independent healthy and disease PBMC samples. RESULTS: Of 36 PBMC clusters, 21 changed between controls and disease samples. Significant differences were observed between diseases stages with changes in T cells and myeloid cells throughout disease and B cell changes in late stages. Semi-supervised gating and re-clustering showed that disease stages were associated with fewer monocytes with active signalling and more inactive NK cells; B and T cells bearing activation markers were reduced in late stages, while B cells bearing co-stimulatory molecules were increased. Functionally, disease states were associated with fewer activated mucosal-associated invariant T cells and reduced toll-like receptor-mediated cytokine production in late disease. CONCLUSION: A range of innate and adaptive immune changes begin early in NAFLD, and disease stages are associated with a functionally less active phenotype compared to controls. Further study of the immune response in NAFLD spectrum may give insight into mechanisms of disease with potential clinical application.