Understanding health and disease with multidimensional single-cell methods

Abstract

Current efforts in the biomedical sciences and related interdisciplinary fields are focused ongaining a molecular understanding of health and disease, which is a problem of dauntingcomplexity that spans many orders of magnitude in characteristic length scales, from smallmolecules that regulate cell function to cell ensembles that form tissues and organs workingtogether as an organism. In order to uncover the molecular nature of the emergent properties of acell, it is essential to measure multiple cell components simultaneously in the same cell. In turn,cell heterogeneity requires multiple cells to be measured in order to understand health and diseasein the organism. This review summarizes current efforts towards a data-driven framework thatleverages single-cell technologies to build robust signatures of healthy and diseased phenotypes.While some approaches focus on multicolor flow cytometry data and other methods are designedto analyze high-content image-based screens, we emphasize the so-called Supercell/SVMparadigm (recently developed by the authors of this review and collaborators) as a unifiedframework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond theirspecific contributions to basic and translational biomedical research, these efforts illustrate, from alarger perspective, the powerful synergy that might be achieved from bringing together methodsand ideas from statistical physics, data mining, and mathematics to solve the most pressingproblems currently facing the life sciences.