Cell Reports: Methods (Oct 2023)
A robust pipeline for high-content, high-throughput immunophenotyping reveals age- and genetics-dependent changes in blood leukocytes
Abstract
Summary: High-dimensional flow cytometry is the gold standard to study the human immune system in large cohorts. However, large sample sizes increase inter-experimental variation because of technical and experimental inaccuracies introduced by batch variability. Our high-throughput sample processing pipeline in combination with 28-color flow cytometry focuses on increased throughput (192 samples/experiment) and high reproducibility. We implemented quality control checkpoints to reduce technical and experimental variation. Finally, we integrated FlowSOM clustering to facilitate automated data analysis and demonstrate the reproducibility of our pipeline in a study with 3,357 samples. We reveal age-associated immune dynamics in 2,300 individuals, signified by decreasing T and B cell subsets with age. In addition, by combining genetic analyses, our approach revealed unique immune signatures associated with a single nucleotide polymorphism (SNP) that abrogates CD45 isoform splicing. In summary, we provide a versatile and reliable high-throughput, flow cytometry-based pipeline for immune discovery and exploration in large cohorts. Motivation: Flow cytometry-based immunophenotyping studies are crucial in human immunology research. However, large sample sizes cause non-biological data variation, which impacts precision. We developed a sample processing and analysis pipeline for high-dimensional flow cytometry that focuses on sample throughput and incorporates stringent instrument standardization, staining protocols, quality controls, and unsupervised data analysis. These measures mitigate batch effect and experimental errors and increase data precision. With our pipeline we measured 3,357 samples in 19 experiments and obtained minimal non-biological variation, showcasing its usability for large immunophenotyping studies.
