Human bone marrow assessment by single-cell RNA sequencing, mass cytometry, and flow cytometry

New techniques for single-cell analysis have led to insights into hematopoiesis and the immune system, but the ability of these techniques to cross-validate and reproducibly identify the biological variation in diverse human samples is currently unproven. We therefore performed a comprehensive asses...

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Bibliographic Details
Published in:JCI insight 2018-12, Vol.3 (23)
Main Authors: Oetjen, Karolyn A, Lindblad, Katherine E, Goswami, Meghali, Gui, Gege, Dagur, Pradeep K, Lai, Catherine, Dillon, Laura W, McCoy, J Philip, Hourigan, Christopher S
Format: Article
Language:English
Subjects:
Adaptive Immunity
Adult
Aged
Aged, 80 and over
Bone Marrow - immunology
Bone Marrow - metabolism
Bone Marrow Cells - immunology
Cell Differentiation
Female
Flow Cytometry - methods
Hematopoiesis
Humans
Immunophenotyping
Killer Cells, Natural
Male
Middle Aged
Reproducibility of Results
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
T-Lymphocytes
Young Adult
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Summary:New techniques for single-cell analysis have led to insights into hematopoiesis and the immune system, but the ability of these techniques to cross-validate and reproducibly identify the biological variation in diverse human samples is currently unproven. We therefore performed a comprehensive assessment of human bone marrow cells using both single-cell RNA sequencing and multiparameter flow cytometry from 20 healthy adult human donors across a broad age range. These data characterize variation between healthy donors as well as age-associated changes in cell population frequencies. Direct comparison of techniques revealed discrepancy in the quantification of T lymphocyte and natural killer cell populations. Orthogonal validation of immunophenotyping using mass cytometry demonstrated a strong correlation with flow cytometry. Technical replicates using single-cell RNA sequencing matched robustly, while biological replicates showed variation. Given the increasing use of single-cell technologies in translational research, this resource serves as an important reference data set and highlights opportunities for further refinement.
ISSN:2379-3708
DOI:10.1172/jci.insight.124928