当前位置： SCI文献检索 > CYTOTHERAPY期刊下所有文献 > Morphological profiling using machine learning reveals emergent subpopulations of interferon-γ-stimulated mesenchymal stromal cells that predict immunosuppression.

Morphological profiling using machine learning reveals emergent subpopulations of interferon-γ-stimulated mesenchymal stromal cells that predict immunosuppression.

Abstract：

BACKGROUND:Although a preponderance of pre-clinical data demonstrates the immunosuppressive potential of mesenchymal stromal cells (MSCs), significant heterogeneity and lack of critical quality attributes (CQAs) based on immunosuppressive capacity likely have contributed to inconsistent clinical outcomes. This heterogeneity exists not only between MSC lots derived from different donors, tissues and manufacturing conditions, but also within a given MSC lot in the form of functional subpopulations. We therefore explored the potential of functionally relevant morphological profiling (FRMP) to identify morphological subpopulations predictive of the immunosuppressive capacity of MSCs derived from multiple donors, manufacturers and passages. METHODS:We profiled the single-cell morphological response of MSCs from different donors and passages to the functionally relevant inflammatory cytokine interferon (IFN)-γ. We used the machine learning approach visual stochastic neighbor embedding (viSNE) to identify distinct morphological subpopulations that could predict suppression of activated CD4+ and CD8+ T cells in a multiplexed quantitative assay. RESULTS:Multiple IFN-γ-stimulated subpopulations significantly correlated with the ability of MSCs to inhibit CD4+ and CD8+ T-cell activation and served as effective CQAs to predict the immunosuppressive capacity of additional manufactured MSC lots. We further characterized the emergence of morphological heterogeneity following IFN-γ stimulation, which provides a strategy for identifying functional subpopulations for future single-cell characterization and enrichment techniques. DISCUSSION:This work provides a generalizable analytical platform for assessing functional heterogeneity based on single-cell morphological responses that could be used to identify novel CQAs and inform cell manufacturing decisions.

journal_name

Cytotherapy

journal_title

Cytotherapy

authors

Marklein RA,Klinker MW,Drake KA,Polikowsky HG,Lessey-Morillon EC,Bauer SR

doi

10.1016/j.jcyt.2018.10.008

subject

Has Abstract

pub_date

2019-01-01 00:00:00

pages

17-31

issue

eissn

1465-3249

issn

1477-2566

pii

S1465-3249(18)30646-7

journal_volume

pub_type

杂志文章

在线工具

Morphological profiling using machine learning reveals emergent subpopulations of interferon-γ-stimulated mesenchymal stromal cells that predict immunosuppression.