Xiaoshan Shi, Woodrow E. Lomas III, Aaron Middlebrook, Wei Fan, Louise M. D'Cruz, Vishnu Ramani, Stephanie J. Widmann, Aaron J. Tyznik
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引用次数: 0
Abstract
Single-cell sorting (index sorting) is a widely used method to isolate one cell at a time using fluorescence-activated cell sorting (FACS) for downstream applications such as single-cell sequencing or single-cell expansion. Despite widespread use, few assays are available to evaluate the proteomic features of the sorted single cell and further confirm the accuracy of single-cell sorting. With this caveat, we developed a novel assay to confirm the protein expression of sorted single cells by co-staining cells with the same marker using both antibody-derived tags (ADTs) and fluorescent antibodies. After single-cell sorting, we amplified the oligo of the ADT reagent as a surrogate signal for the protein expression using multiplex TaqMan™ qPCR on sorted cells. This assay is not only useful for confirming the identity of a sorted single cell but also an efficient method to profile proteomic features at the single-cell level. Finally, we applied this assay to characterize protein expression on whole cell lysate. Because of the sensitivity of the TaqMan™ qPCR, we can detect protein expression from a small number of cells. In summary, the ADT-based qPCR assay developed here can be utilized to confirm single-cell sorting accuracy and characterizing protein expression on both single cells and whole cell lysate.
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.