Publication:
Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry

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2020-03-31

Authors

Rybakowska, Paulina
Alarcón-Riquelme, Marta E.
Marañón, Concepción

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Elsevier
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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.

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Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Cytological Techniques::Cytophotometry::Flow Cytometry
Medical Subject Headings::Disciplines and Occupations::Natural Science Disciplines::Biological Science Disciplines::Biology::Computational Biology
Medical Subject Headings::Phenomena and Processes::Chemical Phenomena::Biochemical Phenomena::Biochemical Processes::Phosphorylation
Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Methods::Research Design
Medical Subject Headings::Phenomena and Processes::Mathematical Concepts::Algorithms
Medical Subject Headings::Information Science::Information Science::Computing Methodologies::Algorithms
Medical Subject Headings::Anatomy::Cells

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Flow cytometry, Mass cytometry, Bioinformatics, Computational tools, Single-cell proteomics, Data accuracy, Citometría de flujo, Biología computacional, Algoritmos, Células, Exactitud de los datos

Citation

Rybakowska P, Alarcón-Riquelme ME, Marañón C. Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry. Comput Struct Biotechnol J. 2020 Mar 31;18:874-886