Single-Calibration Cell Size Measurement With Flow Cytometry.

IF 2.5 4区 生物学 Q3 BIOCHEMICAL RESEARCH METHODS
Philip Davies, Massimo Cavallaro, Daniel Hebenstreit
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引用次数: 0

Abstract

Measuring the size of individual cells in high-throughput experiments is often important in biomedical research and applications. Nevertheless, popular tools for high-throughput single-cell biology, such as flow cytometers, only offer proxies of a cell's size, typically reported in arbitrary scales and often subject to changes in the instrument's settings as selected by multiple users. In this paper, we demonstrate that it is possible to calibrate flowcytometry laser scatter signals with accurate measures of cell diameter from separate devices and that the calibration can be conserved upon changes in the laser settings. We demonstrate our approach based on flow cytometric sorting of cells of a mammalian cell line according to a selection of scatter parameters, followed by cell size determination with a Coulter counter. A straightforward procedure is presented that relates the flow cytometric scatter parameters to the absolute size measurements using linear models, along with a linear transformation that converts between different instrument settings on the flow cytometer. Our method makes it possible to record on a flow cytometer a cell's size in absolute units and correlate it with other features that are recorded in parallel in the fluorescence detection channels.

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来源期刊
Cytometry Part A
Cytometry Part A 生物-生化研究方法
CiteScore
8.10
自引率
13.50%
发文量
183
审稿时长
4-8 weeks
期刊介绍: Cytometry Part A, the journal of quantitative single-cell analysis, features original research reports and reviews of innovative scientific studies employing quantitative single-cell measurement, separation, manipulation, and modeling techniques, as well as original articles on mechanisms of molecular and cellular functions obtained by cytometry techniques. The journal welcomes submissions from multiple research fields that fully embrace the study of the cytome: Biomedical Instrumentation Engineering Biophotonics Bioinformatics Cell Biology Computational Biology Data Science Immunology Parasitology Microbiology Neuroscience Cancer Stem Cells Tissue Regeneration.
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