Journal
AMERICAN JOURNAL OF REPRODUCTIVE IMMUNOLOGY
Volume 79, Issue 1, Pages -Publisher
WILEY
DOI: 10.1111/aji.12774
Keywords
adaptive immunity; decidua; dendritic cells; flow cytometry; machine learning; pregnancy immunology; T lymphocytes
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Funding
- Eunice Kennedy Shriver National Institute of Child Health and Human Development [K12HD000849-28]
- National Institute of General Medical Sciences [R25 GM083252]
- March of Dimes Foundation
- American Association of Immunologists
- UW SciMed GRS Fellowship
- WISE Summer Research Grant
- UWCCC Flow Core Grant [1S100OD018202-01]
- Burroughs Wellcome Fund
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ProblemDecidual immune dysregulation is thought to underlie major pregnancy disorders; however, incomplete understanding of the decidual immune interface has hampered the mechanistic investigation. Method of studyHuman term decidua was collected, and single-cell phenotypic information was acquired by highly polychromatic flow cytometry. Cellular identity analysis was performed with t-distributed stochastic neighbor embedding, DensVM clustering, and matched to CellOntology database. ResultsTraditional analytical methods validated known cellular T and dendritic cell subsets in human term decidua. Computational analysis revealed a complex and tissue-specific decidual immune signature in both the innate and adaptive immune compartments. ConclusionPolychromatic flow cytometry with a streamlined computational analysis pipeline is a feasible approach to comprehensive immunome mapping of human term decidua. As an unbiased, standardized method of investigation, computational flow cytometry promises to unravel the immune pathology of pregnancy disorders.
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