Quantitative single-cell proteomics as a tool to characterize cellular hierarchies
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Quantitative single-cell proteomics as a tool to characterize cellular hierarchies. / Schoof, Erwin M; Furtwängler, Benjamin; Üresin, Nil; Rapin, Nicolas; Savickas, Simonas; Gentil, Coline; Lechman, Eric; Keller, Ulrich Auf dem; Dick, John E; Porse, Bo T.
In: Nature Communications, Vol. 12, No. 1, 07.06.2021, p. 3341.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Quantitative single-cell proteomics as a tool to characterize cellular hierarchies
AU - Schoof, Erwin M
AU - Furtwängler, Benjamin
AU - Üresin, Nil
AU - Rapin, Nicolas
AU - Savickas, Simonas
AU - Gentil, Coline
AU - Lechman, Eric
AU - Keller, Ulrich Auf dem
AU - Dick, John E
AU - Porse, Bo T
PY - 2021/6/7
Y1 - 2021/6/7
N2 - Large-scale single-cell analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems, but have largely been limited to RNA-based technologies. Here we present a comprehensive benchmarked experimental and computational workflow, which establishes global single-cell mass spectrometry-based proteomics as a tool for large-scale single-cell analyses. By exploiting a primary leukemia model system, we demonstrate both through pre-enrichment of cell populations and through a non-enriched unbiased approach that our workflow enables the exploration of cellular heterogeneity within this aberrant developmental hierarchy. Our approach is capable of consistently quantifying ~1000 proteins per cell across thousands of individual cells using limited instrument time. Furthermore, we develop a computational workflow (SCeptre) that effectively normalizes the data, integrates available FACS data and facilitates downstream analysis. The approach presented here lays a foundation for implementing global single-cell proteomics studies across the world.
AB - Large-scale single-cell analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems, but have largely been limited to RNA-based technologies. Here we present a comprehensive benchmarked experimental and computational workflow, which establishes global single-cell mass spectrometry-based proteomics as a tool for large-scale single-cell analyses. By exploiting a primary leukemia model system, we demonstrate both through pre-enrichment of cell populations and through a non-enriched unbiased approach that our workflow enables the exploration of cellular heterogeneity within this aberrant developmental hierarchy. Our approach is capable of consistently quantifying ~1000 proteins per cell across thousands of individual cells using limited instrument time. Furthermore, we develop a computational workflow (SCeptre) that effectively normalizes the data, integrates available FACS data and facilitates downstream analysis. The approach presented here lays a foundation for implementing global single-cell proteomics studies across the world.
KW - Humans
KW - Leukemia, Myeloid, Acute
KW - Mass Spectrometry
KW - Neoplastic Stem Cells
KW - Proteome/metabolism
KW - Proteomics/methods
KW - RNA
KW - Single-Cell Analysis/methods
KW - Workflow
U2 - 10.1038/s41467-021-23667-y
DO - 10.1038/s41467-021-23667-y
M3 - Journal article
C2 - 34099695
VL - 12
SP - 3341
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
IS - 1
ER -
ID: 274273501