Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning.

Quantitative or qualitative differences in immunity may drive clinical severity in COVID-19. Although longitudinal studies to record the course of immunological changes are ample, they do not necessarily predict clinical progression at the time of hospital admission. Here we show, by a machine learning approach using serum pro-inflammatory, anti-inflammatory and anti-viral cytokine and anti-SARS-CoV-2 antibody measurements as input data, that COVID-19 patients cluster into three distinct immune phenotype groups. These immune-types, determined by unsupervised hierarchical clustering that is agnostic to severity, predict clinical course. The identified immune-types do not associate with disease duration at hospital admittance, but rather reflect variations in the nature and kinetics of individual patient's immune response. Thus, our work provides an immune-type based scheme to stratify COVID-19 patients at hospital admittance into high and low risk clinical categories with distinct cytokine and antibody profiles that may guide personalized therapy.

High-Dimensional Immunophenotyping with 37-Color Panel Using Full-Spectrum Cytometry.

A comprehensive study of the cellular components of the immune system demands both deep and broad immunophenotyping of numerous cell subsets in an effective and practical way. Novel full-spectrum technology reveals the complete emission spectrum of each dye maximizing the amount of information that can be obtained on a single sample regarding conventional flow cytometry and provide an expanded knowledge of biological processes. In this chapter, we describe a 37-color protocol that allows to identify more than 45 different cell populations on whole blood samples of SARS-CoV-2-infected patients.