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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., (© 2024 Becton Dickinson Co. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.)

The dual interrogation of the transcriptome and proteome with single-cell resolution provides exquisite insights into immune mechanisms in health and disease. Here, we describe a cutting-edge protocol wherein we combine Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq), a technique utilizing antibody-oligonucleotide conjugates (AOCs), with fluorescence-activated cell sorting to enrich rare cell populations. Our protocol incorporates co-staining of cells with both fluorescent antibodies and AOCs simultaneously for subsequent input into the cell sorting and CITE-Seq pipeline. For complete details on the use and execution of this protocol, please refer to Mair et al. (2020)., Competing Interests: All authors are employees of BD Biosciences., (© 2021 Becton, Dickenson and Company.)

By including oligonucleotide-labeled antibodies into high-throughput single-cell RNA-sequencing protocols, combined transcript and protein expression data can be acquired on the single-cell level. Here, we describe a protocol for the combined analysis of over 40 proteins and 400 genes on over 10 4 cells using the nano-well based Rhapsody platform. We also include a workflow for sample multiplexing, which uniquely identifies the initial source of cells (such as tissue type or donor) in the downstream analysis after upstream pooling. For complete information on the use and execution of this protocol, please refer to Mair et al. (2020)., Competing Interests: DECLARATION OF INTERESTS J.R.E., F.M. G.B., and M.P. declare no competing interests. J.M., A.J.T., S.M., and M.N. are employees of BD Biosciences.

High-throughput single-cell RNA sequencing (scRNA-seq) has become a frequently used tool to assess immune cell heterogeneity. Recently, the combined measurement of RNA and protein expression was developed, commonly known as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq). Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcripts but also nearly doubles the sequencing read depth required per single cell. Furthermore, there is still a paucity of analysis tools to visualize combined transcript-protein datasets. Here, we describe a targeted transcriptomics approach that combines an analysis of over 400 genes with simultaneous measurement of over 40 proteins on 2 × 10 4 cells in a single experiment. This targeted approach requires only about one-tenth of the read depth compared to a whole-transcriptome approach while retaining high sensitivity for low abundance transcripts. To analyze these multi-omic datasets, we adapted one-dimensional soli expression by nonlinear stochastic embedding (One-SENSE) for intuitive visualization of protein-transcript relationships on a single-cell level., Competing Interests: Declaration of Interests A.J.T. and J.M. are employees of BD Biosciences (manuscript approval by BD Biosciences was not required, and BD Biosciences had no influence regarding data analysis, data interpretation, and discussion). R.G. has received support from Juno Therapeutics and Janssen Pharma; has consulted for Takeda Vaccines, Juno Therapeutics, and Infotech Soft; and has ownership interest in CellSpace Bio. E.W.N. is a cofounder, shareholder, and advisor for Immunoscape Pte. Ltd. and an advisor for Neogene Therapeutics., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Recent technological advances have greatly diversified the platforms that are available for high-dimensional single-cell immunophenotyping, including mass cytometry, single-cell RNA sequencing, and fluorescent-based flow cytometry. The latter is currently the most commonly used approach, and modern instrumentation allows for the measurement of up to 30 parameters, revealing deep insights into the complexity of the immune system.Here, we provide a practical guidebook for the successful design and execution of complex fluorescence-based immunophenotyping panels. We address common misconceptions and caveats, and also discuss challenges that are associated with the quality control and analysis of these data sets.