Flow cytometry analysis of protein expression using antibody-derived tags followed by CITE-Seq
Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) is a single-cell phenotyping method that uses antibody-derived tags (ADTs) to quantitatively detect cell surface protein expression and generate transcriptomic data at the single-cell level. Despite the increased popularity of this technique to study cellular heterogeneity and dynamics, detailed methods on how to choose ADT markers and ensuring reagent performance in biological relevant systems prior to sequencing is not available. Here we describe a novel and easy-to-use multiplex flow proxy assay in which multiple protein markers can be measured simultaneously using a combination of ADT reagents and dye-oligo conjugates by flow cytometry. Using dye-oligo conjugates with sequences complementary to the ADT reagents, we can achieve specific binding and evaluate protein marker expression in a multiplex way. This quality control assay is useful for guiding ADT marker choice and confirming protein expression prior to sequencing. Importantly, the labeled cells can be directly isolated based on the specific fluorescence from dye-oligo conjugates using a flow cytometry cell sorter and processed for downstream single-cell multiomics. Using this streamlined workflow, we sorted natural killer cells and T cells efficiently using only ADT and dye-oligo reagents, avoiding the possibility of decreased marker resolution from co-staining cells with ADT and fluorescent antibodies. This novel workflow provides a viable option for improving ADT marker choice and cell sorting efficiency, allowing subsequent CITE-Seq. Keywords: CITE-Seq; antibody-derived tags; dye-oligo conjugate; flow proxy assay.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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