Single-cell analysis avoids sample processing bias

Sergey N. Krylov, Edgar Arriaga, Zheru Zhang, Nora W C Chan, Monica M. Palcic, Norman J. Dovichi

Research output: Contribution to journalArticle

50 Scopus citations

Abstract

Microscale separation tools such as capillary chromatography and capillary electrophoresis (CE) allow the study of metabolism in individual cells. In this work, we demonstrate that single-cell analysis describes metabolism more accurately than analysis of cellular extracts. We incubated HT29 cells (human colon adenocarcinoma) with a fluorescently labeled metabolic probe. This disaccharide, LacNAc, was labeled with a fluorescent dye, tetramethylrhodamine (TMR). The probe was taken up by the cells and metabolized to a number of products that retained the fluorescent label. We then split the cells into two batches. A cellular extract was prepared from one batch and analyzed by CE with laser-induced fluorescence (LIF) detection. The cells from the second batch were used for single-cell analysis by CE-LIF. Separation and detection conditions were identical for extract and single- cell analyses. We found that the electropherogram obtained by averaging the results from a number of single cells differed significantly from the cell extract electropherogram. Differences were due to sample processing during extract preparation. Disruption of the cells liberated enzymes that were compartmentalized within the cell, which allowed non-metabolic reactions to proceed. The accumulation of these non-metabolic products introduced a bias in the cell extract assay. During single-cell analysis, cells were lysed inside the capillary and the separation voltage was applied immediately to separate the enzymes from their substrates and prevent non-metabolic reactions. This paper is the first to report that CE analysis of single cells provides more accurate metabolic information than the CE analysis of a cellular extract. (C) 2000 Elsevier Science B.V.

Original languageEnglish (US)
Pages (from-to)31-35
Number of pages5
JournalJournal of Chromatography B: Biomedical Sciences and Applications
Volume741
Issue number1
DOIs
StatePublished - Apr 28 2000

Keywords

  • Single-cell analysis

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