Unknown

Dataset Information

0

Computational flow cytometry analysis reveals a unique immune signature of the human maternal-fetal interface.


ABSTRACT: PROBLEM:Decidual immune dysregulation is thought to underlie major pregnancy disorders; however, incomplete understanding of the decidual immune interface has hampered the mechanistic investigation. METHOD OF STUDY:Human 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. RESULTS:Traditional 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. CONCLUSION:Polychromatic 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.

SUBMITTER: Vazquez J 

PROVIDER: S-EPMC5725254 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

altmetric image

Publications

Computational flow cytometry analysis reveals a unique immune signature of the human maternal-fetal interface.

Vazquez Jessica J   Chavarria Melina M   Li Yan Y   Lopez Gladys E GE   Stanic Aleksandar K AK  

American journal of reproductive immunology (New York, N.Y. : 1989) 20171014 1


<h4>Problem</h4>Decidual immune dysregulation is thought to underlie major pregnancy disorders; however, incomplete understanding of the decidual immune interface has hampered the mechanistic investigation.<h4>Method of study</h4>Human 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 databa  ...[more]

Similar Datasets

| S-EPMC3913911 | biostudies-literature
| S-EPMC9257341 | biostudies-literature
| S-EPMC7879580 | biostudies-literature
| S-EPMC9681661 | biostudies-literature
| S-EPMC7649376 | biostudies-literature
| S-EPMC10579943 | biostudies-literature
| S-EPMC7526739 | biostudies-literature
| S-EPMC7080858 | biostudies-literature
| S-EPMC7820666 | biostudies-literature
| S-EPMC8404883 | biostudies-literature