Contributors: Rybakowska,P; Alarcón-Riquelme,ME; Marañón,C GENYO, Centre for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Spain. Alarcón-Riquelme,ME Institute for Environmental Medicine, Karolinska Institute, Stockholm, Sweden.; PR acknowledges support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n° 115565 , resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution (MEAR as PI) and in particular the in-cash support from Sanofi/Genzyme to PR. CM was supported by Instituto de Salud Carlos III (Miguel Servet II program, CPII16/00028). The authors also acknowledge support from Instituto de Salud Carlos III (PI18/00082) partly supported by European FEDER funds.
نبذة مختصرة : High-dimensional, single-cell cell technologies revolutionized the way to study biological systems, and polychromatic flow cytometry (FC) and mass cytometry (MC) are two of the drivers of this revolution. As up to 30-50 dimensions respectively can be measured per single-cell, they allow deep phenotyping combined with cellular functions studies, like cytokine production or protein phosphorylation. In parallel, the bioinformatics field develops algorithms that are able to process incoming data and extract the most useful and meaningful biological information. However, the success of automated analysis tools depends on the generation of high-quality data. In this review we present the most recent FC and MC computational approaches that are used to prepare, process and interpret high-content cytometry data. We also underscore proper experimental design as a key step for obtaining good quality data. ; Yes
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