Exploring the social life of mesenchymal stromal cells by label-free tracking of individual and collective cell behaviour

AbstractHuman mesenchymal stromal cells (hMSCs) have demonstrated, in various preclinical settings, consistent ability in promoting tissue healing and improving outcomes in animal disease models. However, translation from the preclinical model into clinical practice has proven to be considerably more difficult. One key challenge being the inability to perform in situ assessment of the hMSCs in continuous culture, where the accumulation of the senescent cells impairs the culture’s viability, differentiation potential and ultimately leads to reduced therapeutic efficacies. Histochemical $$\upbeta $$ β -galactosidase staining is the current standard for measuring hMSC senescence, but this method is destructive and not label-free. In this study, we have investigated alternatives in quantification of hMSCs senescence, which included flow cytometry methods that are based on a combination of cell size measurements and fluorescence detection of SA-$$\upbeta $$ β -galactosidase activity using the fluorogenic substrate, C$${_{12}}$$ 12 FDG; and autofluorescence methods that measure fluorescence output from endogenous fluorophores including lipopigments. For identification of senescent cells in the hMSC batches produced, the non-destructive and label-free methods could be a better way forward as they involve minimum manipulations of the cells of interest, increasing the final output of the therapeutic-grade hMSC cultures. In this work, we have grown hMSC cultures over a period of 7 months and compared early and senescent hMSC passages using the advanced flow cytometry and autofluorescence methods, which were benchmarked with the current standard in $$\upbeta $$ β -galactosidase staining. Both the advanced methods demonstrated statistically significant values, (r = 0.76, p $$\le $$ ≤ 0.001 for the fluorogenic C$${_{12}}$$ 12 FDG method, and r = 0.72, p $$\le $$ ≤ 0.05 for the forward scatter method), and good fold difference ranges (1.120–4.436 for total autofluorescence mean and 1.082–6.362 for lipopigment autofluorescence mean) between early and senescent passage hMSCs. Our autofluroescence imaging and spectra decomposition platform offers additional benefit in label-free characterisation of senescent hMSC cells and could be further developed for adoption for future in situ cellular senescence evaluation by the cell manufacturers.

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

Scientific Reports ◽

10.1038/s41598-021-85905-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Sara Imboden ◽

Xuanqing Liu ◽

Brandon S. Lee ◽

Marie C. Payne ◽

Cho-Jui Hsieh ◽

...

Keyword(s):

Mesenchymal Stromal Cells ◽

Stromal Cells ◽

High Throughput Screening ◽

Imaging Method ◽

Significant Heterogeneity ◽

Label Free ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Integrative Assessment ◽

Conventional Cell

AbstractMesenchymal 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.

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Biomolecular phenotyping and heterogeneity assessment of mesenchymal stromal cells using label-free Raman spectroscopy

Scientific Reports ◽

10.1038/s41598-021-81991-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

R. A. Rocha ◽

J. M. Fox ◽

P. G. Genever ◽

Y. Hancock

Keyword(s):

Raman Spectroscopy ◽

Cell Fate ◽

Mesenchymal Stromal Cells ◽

Stromal Cells ◽

Complex Disease ◽

Dermal Fibroblasts ◽

Statistical Sampling ◽

Label Free ◽

Cell Level ◽

Phenotypic Characterisation

AbstractEasy, quantitative measures of biomolecular heterogeneity and high-stratified phenotyping are needed to identify and characterise complex disease processes at the single-cell level, as well as to predict cell fate. Here, we demonstrate how Raman spectroscopy can be used in the difficult-to-assess case of clonal, bone-derived mesenchymal stromal cells (MSCs) to identify MSC lines and group these according to biological function (e.g., differentiation capacity). Biomolecular stratification is achieved using high-precision measures obtained from representative statistical sampling that also enable quantified heterogeneity assessment. Application to primary MSCs and human dermal fibroblasts shows use of these measures as a label-free assay to classify cell sub-types within complex heterogeneous cell populations, thus demonstrating the potential for therapeutic translation, and broad application to the phenotypic characterisation of other cells.

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