bioRxivImmunology
Interpretable Inflammation Landscape of Circulating Immune cells
L. Jimenez-Gracia, D. Maspero, S. Aguilar-Fernandez, F. Craighero, M. Boulougouri, M. Ruiz, D. Marchese, G. Caratu, J. Linares-Blanco, M. Berasategi, R. O. Ramirez Flores, A. Sanzo-Machuca, A. M. Corraliza, H. A. Tran, R. Normand, J. Nestor, Y. Hong, T. Kole, P. van der Velde, F. Alleblas, F. Pedretti, A. Aterido, M. Banchero, G. Soriano, E. Roman, , Na
Abstract
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.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: deep learning
- approach: machine learning (classical)
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