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Proliferation tracing with single-cell mass cytometry optimizes generation of stem cell memory-like T cells.

ABSTRACT: Selective differentiation of naive T cells into multipotent T cells is of great interest clinically for the generation of cell-based cancer immunotherapies. Cellular differentiation depends crucially on division state and time. Here we adapt a dye dilution assay for tracking cell proliferative history through mass cytometry and uncouple division, time and regulatory protein expression in single naive human T cells during their activation and expansion in a complex ex vivo milieu. Using 23 markers, we defined groups of proteins controlled predominantly by division state or time and found that undivided cells account for the majority of phenotypic diversity. We next built a map of cell state changes during naive T-cell expansion. By examining cell signaling on this map, we rationally selected ibrutinib, a BTK and ITK inhibitor, and administered it before T cell activation to direct differentiation toward a T stem cell memory (TSCM)-like phenotype. This method for tracing cell fate across division states and time can be broadly applied for directing cellular differentiation.

SUBMITTER: Good Z

PROVIDER: S-EPMC6521980 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Proliferation tracing with single-cell mass cytometry optimizes generation of stem cell memory-like T cells.

Good Zinaida Z Borges Luciene L Vivanco Gonzalez Nora N Sahaf Bita B Samusik Nikolay N Tibshirani Robert R Nolan Garry P GP Bendall Sean C SC

Nature biotechnology 20190211 3

Selective differentiation of naive T cells into multipotent T cells is of great interest clinically for the generation of cell-based cancer immunotherapies. Cellular differentiation depends crucially on division state and time. Here we adapt a dye dilution assay for tracking cell proliferative history through mass cytometry and uncouple division, time and regulatory protein expression in single naive human T cells during their activation and expansion in a complex ex vivo milieu. Using 23 marker ...[more]

PMID: 30742126

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Project description:Mass cytometry by time-of-flight (CyTOF) is a valuable technology for high-dimensional analysis at the single cell level. Identification of different cell populations is an important task during the data analysis. Many clustering tools can perform this task, which is essential to identify "new" cell populations in explorative experiments. However, relying on clustering is laborious since it often involves manual annotation, which significantly limits the reproducibility of identifying cell-populations across different samples. The latter is particularly important in studies comparing different conditions, for example in cohort studies. Learning cell populations from an annotated set of cells solves these problems. However, currently available methods for automatic cell population identification are either complex, dependent on prior biological knowledge about the populations during the learning process, or can only identify canonical cell populations. We propose to use a linear discriminant analysis (LDA) classifier to automatically identify cell populations in CyTOF data. LDA outperforms two state-of-the-art algorithms on four benchmark datasets. Compared to more complex classifiers, LDA has substantial advantages with respect to the interpretable performance, reproducibility, and scalability to larger datasets with deeper annotations. We apply LDA to a dataset of ~3.5 million cells representing 57 cell populations in the Human Mucosal Immune System. LDA has high performance on abundant cell populations as well as the majority of rare cell populations, and provides accurate estimates of cell population frequencies. Further incorporating a rejection option, based on the estimated posterior probabilities, allows LDA to identify previously unknown (new) cell populations that were not encountered during training. Altogether, reproducible prediction of cell population compositions using LDA opens up possibilities to analyze large cohort studies based on CyTOF data. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Project description:Understanding the unique phenotypes and complex signaling pathways of leukemia stem cells (LSCs) will provide insights and druggable targets that can be used to eradicate acute myeloid leukemia (AML). Current work on AML LSCs is limited by the number of parameters that conventional flow cytometry (FCM) can analyze because of cell autofluorescence and fluorescent dye spectral overlap. Single-cell mass cytometry (CyTOF) substitutes rare earth elements for fluorophores to label antibodies, which allows measurements of up to 120 parameters in single cells without correction for spectral overlap. The aim of this study was the evaluation of intracellular signaling in antigen-defined stem/progenitor cell subsets in primary AML. CyTOF and conventional FCM yielded comparable results on LSC phenotypes defined by CD45, CD34, CD38, CD123, and CD99. Intracellular phosphoprotein responses to ex vivo cell signaling inhibitors and cytokine stimulation were assessed in myeloid leukemia cell lines and one primary AML sample. CyTOF and conventional FCM results were confirmed by western blotting. In the primary AML sample, we investigated the cell responses to ex vivo stimulation with stem cell factor and BEZ235-induced inhibition of PI3K and identified activation patterns in multiple PI3K downstream signaling pathways including p-4EBP1, p-AKT, and p-S6, particularly in CD34(+) subsets. We evaluated multiple signaling pathways in antigen-defined subpopulations in primary AML cells with FLT3-ITD mutations. The data demonstrated the heterogeneity of cell phenotype distribution and distinct patterns of signaling activation across AML samples and between AML and normal samples. The mTOR targets p-4EBP1 and p-S6 were exclusively found in FLT3-ITD stem/progenitor cells, but not in their normal counterparts, suggesting both as novel targets in FLT3 mutated AML. Our data suggest that CyTOF can identify functional signaling pathways in antigen-defined subpopulations in primary AML, which may provide a rationale for designing therapeutics targeting LSC-enriched cell populations.

Dataset Information

Proliferation tracing with single-cell mass cytometry optimizes generation of stem cell memory-like T cells.

Publications

Proliferation tracing with single-cell mass cytometry optimizes generation of stem cell memory-like T cells.

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