Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging

Abstract Mesenchymal stromal cells (MSCs) are multipotent cells that have great potential for regenerative medicine, tissue repair, and immunotherapy. Unfortunately, the outcomes of MSC-based research and therapies can be highly inconsistent and difficult to reproduce, largely due to the inherently...

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Autores principales:	Sara Imboden, Xuanqing Liu, Brandon S. Lee, Marie C. Payne, Cho-Jui Hsieh, Neil Y. C. Lin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5e949a3d6ebc4534863c66b5c3625123
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spelling	oai:doaj.org-article:5e949a3d6ebc4534863c66b5c36251232021-12-02T16:35:56ZInvestigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging10.1038/s41598-021-85905-z2045-2322https://doaj.org/article/5e949a3d6ebc4534863c66b5c36251232021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85905-zhttps://doaj.org/toc/2045-2322Abstract Mesenchymal stromal cells (MSCs) are multipotent cells that have great potential for regenerative medicine, tissue repair, and immunotherapy. Unfortunately, the outcomes of MSC-based research and therapies can be highly inconsistent and difficult to reproduce, largely due to the inherently significant heterogeneity in MSCs, which has not been well investigated. To quantify cell heterogeneity, a standard approach is to measure marker expression on the protein level via immunochemistry assays. Performing such measurements non-invasively and at scale has remained challenging as conventional methods such as flow cytometry and immunofluorescence microscopy typically require cell fixation and laborious sample preparation. Here, we developed an artificial intelligence (AI)-based method that converts transmitted light microscopy images of MSCs into quantitative measurements of protein expression levels. By training a U-Net+ conditional generative adversarial network (cGAN) model that accurately (mean $$r_s$$ r s = 0.77) predicts expression of 8 MSC-specific markers, we showed that expression of surface markers provides a heterogeneity characterization that is complementary to conventional cell-level morphological analyses. Using this label-free imaging method, we also observed a multi-marker temporal-spatial fluctuation of protein distributions in live MSCs. These demonstrations suggest that our AI-based microscopy can be utilized to perform quantitative, non-invasive, single-cell, and multi-marker characterizations of heterogeneous live MSC culture. Our method provides a foundational step toward the instant integrative assessment of MSC properties, which is critical for high-throughput screening and quality control in cellular therapies.Sara ImbodenXuanqing LiuBrandon S. LeeMarie C. PayneCho-Jui HsiehNeil Y. C. LinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sara Imboden Xuanqing Liu Brandon S. Lee Marie C. Payne Cho-Jui Hsieh Neil Y. C. Lin Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging
description	Abstract Mesenchymal stromal cells (MSCs) are multipotent cells that have great potential for regenerative medicine, tissue repair, and immunotherapy. Unfortunately, the outcomes of MSC-based research and therapies can be highly inconsistent and difficult to reproduce, largely due to the inherently significant heterogeneity in MSCs, which has not been well investigated. To quantify cell heterogeneity, a standard approach is to measure marker expression on the protein level via immunochemistry assays. Performing such measurements non-invasively and at scale has remained challenging as conventional methods such as flow cytometry and immunofluorescence microscopy typically require cell fixation and laborious sample preparation. Here, we developed an artificial intelligence (AI)-based method that converts transmitted light microscopy images of MSCs into quantitative measurements of protein expression levels. By training a U-Net+ conditional generative adversarial network (cGAN) model that accurately (mean $$r_s$$ r s = 0.77) predicts expression of 8 MSC-specific markers, we showed that expression of surface markers provides a heterogeneity characterization that is complementary to conventional cell-level morphological analyses. Using this label-free imaging method, we also observed a multi-marker temporal-spatial fluctuation of protein distributions in live MSCs. These demonstrations suggest that our AI-based microscopy can be utilized to perform quantitative, non-invasive, single-cell, and multi-marker characterizations of heterogeneous live MSC culture. Our method provides a foundational step toward the instant integrative assessment of MSC properties, which is critical for high-throughput screening and quality control in cellular therapies.
format	article
author	Sara Imboden Xuanqing Liu Brandon S. Lee Marie C. Payne Cho-Jui Hsieh Neil Y. C. Lin
author_facet	Sara Imboden Xuanqing Liu Brandon S. Lee Marie C. Payne Cho-Jui Hsieh Neil Y. C. Lin
author_sort	Sara Imboden
title	Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging
title_short	Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging
title_full	Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging
title_fullStr	Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging
title_full_unstemmed	Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging
title_sort	investigating heterogeneities of live mesenchymal stromal cells using ai-based label-free imaging
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5e949a3d6ebc4534863c66b5c3625123
work_keys_str_mv	AT saraimboden investigatingheterogeneitiesoflivemesenchymalstromalcellsusingaibasedlabelfreeimaging AT xuanqingliu investigatingheterogeneitiesoflivemesenchymalstromalcellsusingaibasedlabelfreeimaging AT brandonslee investigatingheterogeneitiesoflivemesenchymalstromalcellsusingaibasedlabelfreeimaging AT mariecpayne investigatingheterogeneitiesoflivemesenchymalstromalcellsusingaibasedlabelfreeimaging AT chojuihsieh investigatingheterogeneitiesoflivemesenchymalstromalcellsusingaibasedlabelfreeimaging AT neilyclin investigatingheterogeneitiesoflivemesenchymalstromalcellsusingaibasedlabelfreeimaging
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Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging

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