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A flow-cytometry-based protocol using diverse cell types for detecting autoantibodies from human plasma and serum samples

ABSTRACT: Summary Here, we describe a protocol for cell-based detection of autoantibodies from human plasma and serum samples using a standard flow cytometer. The protocol allows detection of autoantibodies against a wide array of extracellular antigens. Antigen coverage is limited to the cell types tested, and researchers will need to further determine if autoantibody-positive samples correlate with cytotoxic or clinical outcomes. This protocol is less expensive and faster to perform when compared to protein microarrays and requires no prior knowledge of potential targets. For complete details on the use and execution of this protocol, please refer to Wong et al. (2021). Graphical abstract Highlights • A flow-cytometry-based protocol to detect the presence of autoantibodies• Uses input cells to screen plasma or serum samples and identify hits• Hits are samples reactive with cellular surface antigens, and may be studied further Here, we describe a protocol for cell-based detection of autoantibodies from human plasma and serum samples using a standard flow cytometer. The protocol allows detection of autoantibodies against a wide array of extracellular antigens. Antigen coverage is limited to the cell types tested, and researchers will need to further determine if autoantibody-positive samples correlate with cytotoxic or clinical outcomes. This protocol is less expensive and faster to perform when compared to protein microarrays and requires no prior knowledge of potential targets.

SUBMITTER: Wong A

PROVIDER: S-EPMC8567435 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less). Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment, and the ability to align cell subsets across multiple data samples for comparative analysis. In this manuscript, we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model (DPGMM) approach we have previously described for cell subset identification, and show that the hierarchical DPGMM (HDPGMM) naturally generates an aligned data model that captures both commonalities and variations across multiple samples. HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously. We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell (PBMC) samples with known frequencies of antigen-specific T cells. These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding. We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations. We show that hierarchical modeling is a useful probabilistic approach that can provide a consistent labeling of cell subsets and increase the sensitivity of rare event detection in the context of quantifying antigen-specific immune responses.

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A flow-cytometry-based protocol using diverse cell types for detecting autoantibodies from human plasma and serum samples

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