High Content Imaging of Early Morphological Signatures Predicts Long Term Mineralization Capacity of Human Mesenchymal Stem Cells upon Osteogenic Induction

Microglia morphological response to mesenchymal stromal cell extracellular vesicles demonstrates EV therapeutic potential for modulating neuroinflammation

BACKGROUND

Mesenchymal stromal cell derived extracellular vesicles (MSC-EVs) are a promising therapeutic for neuroinflammation. MSC-EVs can interact with microglia, the resident immune cells of the brain, to exert their immunomodulatory effects. In response to inflammatory cues, such as cytokines, microglia undergo phenotypic changes indicative of their function e.g. morphology and secretion. However, these changes in response to MSC-EVs are not well understood. Additionally, no disease-relevant screening tools to assess MSC-EV bioactivity exist, which has further impeded clinical translation. Here, we developed a quantitative, high throughput morphological profiling approach to assess the response of microglia to neuroinflammation- relevant signals and whether this morphological response can be used to indicate the bioactivity of MSC-EVs.

RESULTS

Using an immortalized human microglia cell-line, we observed increased size (perimeter, major axis length) and complexity (form factor) upon stimulation with interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α). Upon treatment with MSC-EVs, the overall morphological score (determined using principal component analysis) shifted towards the unstimulated morphology, indicating that MSC-EVs are bioactive and modulate microglia. The morphological effects of MSC-EVs in TNF-α /IFN-γ stimulated cells were concomitant with reduced secretion of 14 chemokines/cytokines (e.g. CXCL6, CXCL9) and increased secretion of 12 chemokines/cytokines (e.g. CXCL8, CXCL10). Proteomic analysis of cell lysates revealed significant increases in 192 proteins (e.g. HIBADH, MEAK7, LAMC1) and decreases in 257 proteins (e.g. PTEN, TOM1, MFF) with MSC-EV treatment. Of note, many of these proteins are involved in regulation of cell morphology and migration. Gene Set Variation Analysis revealed upregulation of pathways associated with immune response, such as regulation of cytokine production, immune cell infiltration (e.g. T cells, NK cells) and morphological changes (e.g. Semaphorin, RHO/Rac signaling). Additionally, changes in microglia mitochondrial morphology were measured suggesting that MSC-EV modulate mitochondrial metabolism.

CONCLUSION

This study comprehensively demonstrates the effects of MSC-EVs on human microglial morphology, cytokine secretion, cellular proteome, and mitochondrial content. Our high-throughput, rapid, low-cost morphometric approach enables screening of MSC-EV batches and manufacturing conditions to enhance EV function and mitigate EV functional heterogeneity in a disease relevant manner. This approach is highly generalizable and can be further adapted and refined based on selection of the disease-relevant signal, target cell, and therapeutic product.

Microglia Morphological Response to Mesenchymal Stromal Cell Extracellular Vesicles Demonstrates EV Therapeutic Potential for Modulating Neuroinflammation

Background

Mesenchymal stromal cell derived extracellular vesicles (MSC-EVs) are a promising therapeutic for neuroinflammation. MSC-EVs can interact with microglia, the resident immune cells of the brain, to exert their immunomodulatory effects. In response to inflammatory cues, such as cytokines, microglia undergo phenotypic changes indicative of their function e.g. morphology and secretion. However, these changes in response to MSC-EVs are not well understood. Additionally, no disease-relevant screening tools to assess MSC-EV bioactivity exist, which has further impeded clinical translation. Here, we developed a quantitative, high throughput morphological profiling approach to assess the response of microglia to neuroinflammation-relevant signals and whether this morphological response can be used to indicate the bioactivity of MSC-EVs.

Results

Using an immortalized human microglia cell-line, we observed increased size (perimeter, major axis length) and complexity (form factor) upon stimulation with interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α). Upon treatment with MSC-EVs, the overall morphological score (determined using principal component analysis) shifted towards the unstimulated morphology, indicating that MSC-EVs are bioactive and modulate microglia. The morphological effects of MSC-EVs in TNF-γ/IFN-α stimulated cells were concomitant with reduced secretion of 14 chemokines/cytokines (e.g. CXCL6, CXCL9) and increased secretion of 12 chemokines/cytokines (e.g. CXCL8, CXCL10). Proteomic analysis of cell lysates revealed significant increases in 192 proteins (e.g. HIBADH, MEAK7, LAMC1) and decreases in 257 proteins (e.g. PTEN, TOM1, MFF) with MSC-EV treatment. Of note, many of these proteins are involved in regulation of cell morphology and migration. Gene Set Variation Analysis revealed upregulation of pathways associated with immune response, such as regulation of cytokine production, immune cell infiltration (e.g. T cells, NK cells) and morphological changes (e.g. Semaphorin, RHO/Rac signaling). Additionally, changes in microglia mitochondrial morphology were measured suggesting that MSC-EV modulate mitochondrial metabolism.

Conclusion

This study comprehensively demonstrates the effects of MSC-EVs on human microglial morphology, cytokine secretion, cellular proteome, and mitochondrial content. Our high-throughput, rapid, low-cost morphological approach enables screening of MSC-EV batches and manufacturing conditions to enhance EV function and mitigate EV functional heterogeneity in a disease relevant manner. This approach is highly generalizable and can be further adapted and refined based on selection of the disease-relevant signal, target cell, and therapeutic product.

High throughput screening of mesenchymal stromal cell morphological response to inflammatory signals for bioreactor-based manufacturing of extracellular vesicles that modulate microglia

Due to their immunomodulatory function, mesenchymal stromal cells (MSCs) are a promising therapeutic with the potential to treat neuroinflammation associated with neurodegenerative diseases. This function is mediated by secreted extracellular vesicles (MSC-EVs). Despite established safety, MSC clinical translation has been unsuccessful due to inconsistent clinical outcomes resulting from functional heterogeneity. Current approaches to mitigate functional heterogeneity include 'priming' MSCs with inflammatory signals to enhance function. However, comprehensive evaluation of priming and its effects on MSC-EV function has not been performed. Furthermore, clinical translation of MSC-EV therapies requires significant manufacturing scale-up, yet few studies have investigated the effects of priming in bioreactors. As MSC morphology has been shown to predict their immunomodulatory function, we screened MSC morphological response to an array of priming signals and evaluated MSC-EV identity and potency in response to priming in flasks and bioreactors. We identified unique priming conditions corresponding to distinct morphologies. These conditions demonstrated a range of MSC-EV preparation quality and lipidome, allowing us to discover a novel MSC-EV manufacturing condition, as well as gain insight into potential mechanisms of MSC-EV microglia modulation. Our novel screening approach and application of priming to MSC-EV bioreactor manufacturing informs refinement of larger-scale manufacturing and enhancement of MSC-EV function.

Spherical harmonics analysis reveals cell shape-fate relationships in zebrafish lateral line neuromasts

Cell shape is a powerful readout of cell state, fate and function. We describe a custom workflow to perform semi-automated, 3D cell and nucleus segmentation, and spherical harmonics and principal components analysis to distill cell and nuclear shape variation into discrete biologically meaningful parameters. We apply these methods to analyze shape in the neuromast cells of the zebrafish lateral line system, finding that shapes vary with cell location and identity. The distinction between hair cells and support cells accounted for much of the variation, which allowed us to train classifiers to predict cell identity from shape features. Using transgenic markers for support cell subpopulations, we found that subtypes had different shapes from each other. To investigate how loss of a neuromast cell type altered cell shape distributions, we examined atoh1a mutants that lack hair cells. We found that mutant neuromasts lacked the cell shape phenotype associated with hair cells, but did not exhibit a mutant-specific cell shape. Our results demonstrate the utility of using 3D cell shape features to characterize, compare and classify cells in a living developing organism.

Deep Learning Model for Predicting Airway Organoid Differentiation

BACKGROUND

Organoids are self-organized three-dimensional culture systems and have the advantages of both in vitro and in vivo experiments. However, each organoid has a different degree of self-organization, and methods such as immunofluorescence staining are required for confirmation. Therefore, we established a system to select organoids with high tissue-specific similarity using deep learning without relying on staining by acquiring bright-field images in a non-destructive manner.

METHODS

We identified four biomarkers in RNA extracted from airway organoids. We also predicted biomarker expression by image-based analysis of organoids by convolution neural network, a deep learning method.

RESULTS

We predicted airway organoid-specific marker expression from bright-field images of organoids. Organoid differentiation was verified by immunofluorescence staining of the same organoid after predicting biomarker expression in bright-field images.

CONCLUSION

Our study demonstrates the potential of imaging and deep learning to distinguish organoids with high human tissue similarity in disease research and drug screening.

Application and progress of highcontent imaging in molecular biology

Humans have adopted many different methods to explore matter imaging, among which high content imaging (HCI) could conduct automated imaging analysis of cells while maintaining its structural and functional integrity. Meanwhile, as one of the most important research tools for diagnosing human diseases, HCI is widely used in the frontier of medical research, and its future application has attracted researchers' great interests. Here, the meaning of HCI was briefly explained, the history of optical imaging and the birth of HCI were described, and the experimental methods of HCI were described. Furthermore, the directions of the application of HCI were highlighted in five aspects: protein localization changes, gene identification, chemical and genetic analysis, microbiology, and drug discovery. Most importantly, some challenges and future directions of HCI were discussed, and the application and optimization of HCI were expected to be further explored.

Morphology-Based Prediction of Proliferation and Differentiation Potencies of Porcine Muscle Stem Cells for Cultured Meat Production

Inconsistent efficiency of cell production caused by cellular quality variations has become a significant problem in the cultured meat industry. In our study, morphological information on passages 5-9 of porcine muscle stem cells (pMuSCs) from three lots was analyzed and used as input data in prediction models. Cell proliferation and differentiation potencies were measured by cell growth rate and average stained area of the myosin heavy chain. Analysis of PCA and heatmap showed that the morphological parameters could be used to discriminate the differences of passages and lots. Various morphological parameters were analyzed, which revealed that accumulating time-course information regarding morphological heterogeneity in cell populations is crucial to predicting the potencies. Based on the 36 and 60 h morphological profiles, the best proliferation potency prediction model (R2 = 0.95, RMSE = 1.1) and differentiation potency prediction model (R2 = 0.74, RMSE = 1.2) were explored. Correlation analysis demonstrated that morphological parameters selected in models are related to the quality of porcine muscle stem cells.

High throughput screening of mesenchymal stromal cell morphological response to inflammatory signals for bioreactor-based manufacturing of extracellular vesicles that modulate microglia

Due to their immunomodulatory function, mesenchymal stromal cells (MSCs) are a promising therapeutic with the potential to treat neuroinflammation associated with neurodegenerative diseases. This function can be mediated by secreted extracellular vesicles (MSC-EVs). Despite established safety, MSC clinical translation has been unsuccessful due to inconsistent clinical outcomes resulting from functional heterogeneity. Current approaches to mitigate functional heterogeneity include 'priming' MSCs with inflammatory signals to enhance function. However, comprehensive evaluation of priming and its effects on MSC-EV function has not been performed. Clinical translation of MSC-EV therapies requires significant manufacturing scale-up, yet few studies have investigated the effects of priming in bioreactors. As MSC morphology has been shown to predict their immunomodulatory function, we screened MSC morphological response to an array of priming signals and evaluated MSC-EV identity and potency in response to priming in flasks and bioreactors. We identified unique priming conditions corresponding to distinct morphologies. These conditions demonstrated a range of MSC-EV preparation quality and lipidome, allowing us to discover a novel MSC-EV manufacturing condition, as well as gain insight into potential mechanisms of MSC-EV microglia modulation. Our novel screening approach and application of priming to MSC-EV bioreactor manufacturing informs refinement of larger-scale manufacturing and enhancement of MSC-EV function.

Effects of hydroxyapatite-coated porous titanium scaffolds functionalized by exosomes on the regeneration and repair of irregular bone

As a traditional bone implant material, titanium (Ti) and its alloys have the disadvantages of lack of biological activity and susceptibility to stress shielding effect. Adipose stem cells (ADSCs) and exosomes were combined with the scaffold material in the current work to effectively create a hydroxyapatite (HA) coated porous titanium alloy scaffold that can load ADSCs and release exosomes over time. The composite made up for the drawbacks of traditional titanium alloy materials with higher mechanical characteristics and a quicker rate of osseointegration. Exosomes (Exos) are capable of promoting the development of ADSCs in porous titanium alloy scaffolds with HA coating, based on experimental findings from in vitro and in vivo research. Additionally, compared to pure Ti implants, the HA scaffolds loaded with adipose stem cell exosomes demonstrated improved bone regeneration capability and bone integration ability. It offers a theoretical foundation for the combined use of stem cell treatment and bone tissue engineering, as well as a design concept for the creation and use of novel clinical bone defect repair materials.

Biology and therapeutic potential of mesenchymal stem cell extracellular vesicles in axial spondyloarthritis

Axial spondyloarthritis (AxSpA) is a chronic, inflammatory, autoimmune disease that predominantly affects the joints of the spine, causes chronic pain, and, in advanced stages, may result in spinal fusion. Recent developments in understanding the immunomodulatory and tissue-differentiating properties of mesenchymal stem cell (MSC) therapy have raised the possibility of applying such treatment to AxSpA. The therapeutic effectiveness of MSCs has been shown in numerous studies spanning a range of diseases. Several studies have been conducted examining acellular therapy based on MSC secretome. Extracellular vesicles (EVs) generated by MSCs have been proven to reproduce the impact of MSCs on target cells. These EVs are associated with immunological regulation, tissue remodeling, and cellular homeostasis. EVs' biological effects rely on their cargo, with microRNAs (miRNAs) integrated into EVs playing a particularly important role in gene expression regulation. In this article, we will discuss the impact of MSCs and EVs generated by MSCs on target cells and how these may be used as unique treatment strategies for AxSpA.

Rejuvenation of Mesenchymal Stem Cells to Ameliorate Skeletal Aging

Advanced age is a shared risk factor for many chronic and debilitating skeletal diseases including osteoporosis and periodontitis. Mesenchymal stem cells develop various aging phenotypes including the onset of senescence, intrinsic loss of regenerative potential and exacerbation of inflammatory microenvironment via secretory factors. This review elaborates on the emerging concepts on the molecular and epigenetic mechanisms of MSC senescence, such as the accumulation of oxidative stress, DNA damage and mitochondrial dysfunction. Senescent MSCs aggravate local inflammation, disrupt bone remodeling and bone-fat balance, thereby contributing to the progression of age-related bone diseases. Various rejuvenation strategies to target senescent MSCs could present a promising paradigm to restore skeletal aging.

Cell morphology as a biological fingerprint of chondrocyte phenotype in control and inflammatory conditions

Introduction

Little is known how inflammatory processes quantitatively affect chondrocyte morphology and how single cell morphometric data could be used as a biological fingerprint of phenotype.

Methods

We investigated whether trainable high-throughput quantitative single cell morphology profiling combined with population-based gene expression analysis can be used to identify biological fingerprints that are discriminatory of control vs. inflammatory phenotypes. The shape of a large number of chondrocytes isolated from bovine healthy and human osteoarthritic (OA) cartilages was quantified under control and inflammatory (IL-1β) conditions using a trainable image analysis technique measuring a panel of cell shape descriptors (area, length, width, circularity, aspect ratio, roundness, solidity). The expression profiles of phenotypically relevant markers were quantified by ddPCR. Statistical analysis, multivariate data exploration, and projection-based modelling were used for identifying specific morphological fingerprints indicative of phenotype.

Results

Cell morphology was sensitive to both cell density and IL-1β. In both cell types, all shape descriptors correlated with expression of extracellular matrix (ECM)- and inflammatory-regulating genes. A hierarchical clustered image map revealed that individual samples sometimes responded differently in control or IL-1β conditions than the overall population. Despite these variances, discriminative projection-based modeling revealed distinct morphological fingerprints that discriminated between control and inflammatory chondrocyte phenotypes: the most essential morphological characteristics attributable to non-treated control cells was a higher cell aspect ratio in healthy bovine chondrocytes and roundness in OA human chondrocytes. In contrast, a higher circularity and width in healthy bovine chondrocytes and length and area in OA human chondrocytes indicated an inflammatory (IL-1β) phenotype. When comparing the two species/health conditions, bovine healthy and human OA chondrocytes exhibited comparable IL-1β-induced morphologies in roundness, a widely recognized marker of chondrocyte phenotype, and aspect ratio.

Discussion

Overall, cell morphology can be used as a biological fingerprint for describing chondrocyte phenotype. Quantitative single cell morphometry in conjunction with advanced methods for multivariate data analysis allows identifying morphological fingerprints that can discriminate between control and inflammatory chondrocyte phenotypes. This approach could be used to assess how culture conditions, inflammatory mediators, and therapeutic modulators regulate cell phenotype and function.

Advanced Technologies for Potency Assay Measurement

Crucial for their application, cell products need to be well-characterized in the cell manufacturing facilities and conform to regulatory approval criteria before infusion into the patients. Mesenchymal Stromal Cells (MSCs) are the leading cell therapy candidate in clinical trials worldwide. Early phase clinical trials have demonstrated that MSCs display an excellent safety profile and are well tolerated. However, MSCs have also exhibited contradictory efficacy in later-phase clinical trials with reasons for this discrepancy including poorly understood mechanism of MSC therapeutic action. With likelihood that a number of attributes are involved in MSC derived clinical benefit, an assay that measures a single quality of may not adequately reflect potency, thus a combination of bioassays and analytical methods, collectively called "assay matrix" are favoured for defining the potency of MSC more adequately. This chapter highlights advanced technologies and targets that can achieve quantitative measurement for a range of MSC attributes, including immunological, genomic, secretome, phosphorylation, morphological, biomaterial, angiogenic and metabolic assays.

Sensor technologies for quality control in engineered tissue manufacturing

The use of engineered cells, tissues, and organs has the opportunity to change the way injuries and diseases are treated. Commercialization of these groundbreaking technologies has been limited in part by the complex and costly nature of their manufacture. Process-related variability and even small changes in the manufacturing process of a living product will impact its quality. Without real-time integrated detection, the magnitude and mechanism of that impact are largely unknown. Real-time and non-destructive sensor technologies are key for in-process insight and ensuring a consistent product throughout commercial scale-up and/or scale-out. The application of a measurement technology into a manufacturing process requires cell and tissue developers to understand the best way to apply a sensor to their process, and for sensor manufacturers to understand the design requirements and end-user needs. Furthermore, sensors to monitor component cells' health and phenotype need to be compatible with novel integrated and automated manufacturing equipment. This review summarizes commercially relevant sensor technologies that can detect meaningful quality attributes during the manufacturing of regenerative medicine products, the gaps within each technology, and sensor considerations for manufacturing.

High throughput screening of mesenchymal stem cell lines using deep learning

Mesenchymal stem cells (MSCs) are increasingly used as regenerative therapies for patients in the preclinical and clinical phases of various diseases. However, the main limitations of such therapies include functional heterogeneity and the lack of appropriate quality control (QC) methods for functional screening of MSC lines; thus, clinical outcomes are inconsistent. Recently, machine learning (ML)-based methods, in conjunction with single-cell morphological profiling, have been proposed as alternatives to conventional in vitro/vivo assays that evaluate MSC functions. Such methods perform in silico analyses of MSC functions by training ML algorithms to find highly nonlinear connections between MSC functions and morphology. Although such approaches are promising, they are limited in that extensive, high-content single-cell imaging is required; moreover, manually identified morphological features cannot be generalized to other experimental settings. To address these limitations, we propose an end-to-end deep learning (DL) framework for functional screening of MSC lines using live-cell microscopic images of MSC populations. We quantitatively evaluate various convolutional neural network (CNN) models and demonstrate that our method accurately classifies in vitro MSC lines to high/low multilineage differentiating stress-enduring (MUSE) cells markers from multiple donors. A total of 6,120 cell images were obtained from 8 MSC lines, and they were classified into two groups according to MUSE cell markers analyzed by immunofluorescence staining and FACS. The optimized DenseNet121 model showed area under the curve (AUC) 0.975, accuracy 0.922, F1 0.922, sensitivity 0.905, specificity 0.942, positive predictive value 0.940, and negative predictive value 0.908. Therefore, our DL-based framework is a convenient high-throughput method that could serve as an effective QC strategy in future clinical biomanufacturing processes.

An Appearance Data-Driven Model Visualizes Cell State and Predicts Mesenchymal Stem Cell Regenerative Capacity

Mesenchymal stem cells (MSCs) are widely used in treating various diseases. However, lack of a reliable evaluation approach to characterize the potency of MSCs has dampened their clinical applications. Here, a function-oriented mathematical model is established to evaluate and predict the regenerative capacity (RC) of MSCs. Processed by exhaustive testing, the model excavates four optimal fitted indices, including nucleus roundness, nucleus/cytoplasm ratio, side-scatter height, and ERK1/2 from the given index combinations. Notably, three of them except ERK1/2 are cell appearance-associated features. The predictive power of the model is validated via screening experiments of these indices by predicting the RC of newly enrolled and chemical inhibitor-treated MSCs. Further RNA-sequencing analysis reveals that cell appearance-based indices may serve as major indicators to visualize the results of integration-weighted signals in and out of cells and reflect MSC stemness. In general, this study proposes an appearance data-driven predictive model for the RC and stemness of MSCs.

Chemokine Assay Matrix Defines the Potency of Human Bone Marrow Mesenchymal Stromal Cells

Potency analysis of mesenchymal stromal cells (MSCs) is required for their use in advanced clinical trials. Assay matrix strategy evaluating more than a single property of MSCs is an emerging strategy in potency analysis. Here we developed an assay matrix approach focusing on the secretory chemokine responses of MSCs using multiplex analytical method. MSCs’ innate fitness in secreting matrix of chemokines is correlated with their metabolic fitness in differential degrees. In addition, innately secreting chemokines are correlated among themselves in a unique pattern. MSC’s matrix chemokine responses to exogenous stimulation of IFNγ and/or TNFα are distinct. However, the combination of IFNγ and TNFα is superior than individual stimulations in eliciting robust and broad matrix chemokine responses of MSCs. Correlation matrix analysis has identified that chemokine responses to IFNγ and/or TNFα display unique correlative secretion patterns. MSC and peripheral blood mononuclear cells coculture analysis has identified the correlation matrix responses of chemokines that predicted immune suppression. In addition, MSC-mediated blocking of T-cell proliferation predominantly correlates with chemokines in an inverse manner. Knockdown of chemokines has demonstrated that MSC-sourced inherent chemokines do not actively play a role in T-cell suppression and thus are the bystander predictors of T-cell suppression. The present analysis of MSC’s matrix chemokine responses can be deployed in the advanced potency analysis of MSCs.

An array-based nanosensor for detecting cellular responses in macrophages induced by femtomolar levels of pesticides

Environmental agents can induce cellular responses at concentrations far below the limits of detection for current viability and biomarker-based cell sensing platforms. Hypothesis-free cell sensor platforms can be engineered to maximize sensitivity to phenotypic changes, providing a tool for lowering the threshold for detecting cellular changes. Pesticides are one of the most prevalent sources of chemical exposure due to their use in food and agriculture fields. We report here a FRET-based nanosensor array engineered to maximize responses to changes at cell surfaces after pesticide exposure. This sensor array robustly detected macrophage responses to femtomolar concentrations of common pesticides-orders of magnitude lower concentrations than traditional toxicological and biomarker-based strategies. Significantly, this platform was able to classify these responses by pesticide class, demonstrating the ability to distinguish between changes induced by these different agents. Taken together, hypothesis-free cell surface sensing is a promising tool for detecting the effects of ultra-trace environmental chemicals on human health, as well as detecting threshold responses for use in drug discovery and diagnostics.

Preservation of the naïve features of mesenchymal stromal cells in vitro: Comparison of cell- and bone-derived decellularized extracellular matrix

The fate and behavior of bone marrow mesenchymal stem/stromal cells (BM-MSC) is bidirectionally influenced by their microenvironment, the stem cell niche, where a magnitude of biochemical and physical cues communicate in an extremely orchestrated way. It is known that simplified 2D in vitro systems for BM-MSC culture do not represent their naïve physiological environment. Here, we developed four different 2D cell-based decellularized matrices (dECM) and a 3D decellularized human trabecular-bone scaffold (dBone) to evaluate BM-MSC behavior. The obtained cell-derived matrices provided a reliable tool for cell shape-based analyses of typical features associated with osteogenic differentiation at high-throughput level. On the other hand, exploratory proteomics analysis identified native bone-specific proteins selectively expressed in dBone but not in dECM models. Together with its architectural complexity, the physico-chemical properties of dBone triggered the upregulation of stemness associated genes and niche-related protein expression, proving in vitro conservation of the naïve features of BM-MSC.

Morphological heterogeneity description enabled early and parallel non-invasive prediction of T-cell proliferation inhibitory potency and growth rate for facilitating donor selection of human mesenchymal stem cells

Background

Within the extensively developed therapeutic application of mesenchymal stem cells (MSCs), allogenic immunomodulatory therapy is among the promising categories. Although donor selection is a critical early process that can maximize the production yield, determining the promising candidate is challenging owing to the lack of effective biomarkers and variations of cell sources. In this study, we developed the morphology-based non-invasive prediction models for two quality attributes, the T-cell proliferation inhibitory potency and growth rate.

Methods

Eleven lots of mixing bone marrow-derived and adipose-derived MSCs were analyzed. Their morphological profiles and growth rates were quantified by image processing by acquiring 6 h interval time-course phase-contrast microscopic image acquisition. T-cell proliferation inhibitory potency was measured by employing flow cytometry for counting the proliferation rate of peripheral blood mononuclear cells (PBMCs) co-cultured with MSCs. Subsequently, the morphological profile comprising 32 parameters describing the time-course transition of cell population distribution was used for explanatory parameters to construct T-cell proliferation inhibitory potency classification and growth rate prediction models. For constructing prediction models, the effect of machine learning methods, parameter types, and time-course window size of morphological profiles were examined to identify those providing the best performance.

Results

Unsupervised morphology-based visualization enabled the identification of anomaly lots lacking T-cell proliferation inhibitory potencies. The best performing machine learning models exhibited high performances of predictions (accuracy > 0.95 for classifying risky lots, and RMSE < 1.50 for predicting growth rate) using only the first 4 days of morphological profiles. A comparison of morphological parameter types showed that the accumulated time-course information of morphological heterogeneity in cell populations is important for predicting the potencies.

Conclusions

To enable more consistent cell manufacturing of allogenic MSC-based therapeutic products, this study indicated that early non-invasive morphology-based prediction can facilitate the lot selection process for effective cell bank establishment. It was also found that morphological heterogeneity description is important for such potency prediction. Furthermore, performances of the morphology-based prediction models trained with data consisting of origin-different MSCs demonstrated the effectiveness of sharing morphological data between different types of MSCs, thereby complementing the data limitation issue in the morphology-based quality prediction concept.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41232-021-00192-5.

Morphology-Based Deep Learning Approach for Predicting Osteogenic Differentiation

Early, high-throughput, and accurate recognition of osteogenic differentiation of stem cells is urgently required in stem cell therapy, tissue engineering, and regenerative medicine. In this study, we established an automatic deep learning algorithm, i.e., osteogenic convolutional neural network (OCNN), to quantitatively measure the osteogenic differentiation of rat bone marrow mesenchymal stem cells (rBMSCs). rBMSCs stained with F-actin and DAPI during early differentiation (day 0, 1, 4, and 7) were captured using laser confocal scanning microscopy to train OCNN. As a result, OCNN successfully distinguished differentiated cells at a very early stage (24 h) with a high area under the curve (AUC) (0.94 ± 0.04) and correlated with conventional biochemical markers. Meanwhile, OCNN exhibited better prediction performance compared with the single morphological parameters and support vector machine. Furthermore, OCNN successfully predicted the dose-dependent effects of small-molecule osteogenic drugs and a cytokine. OCNN-based online learning models can further recognize the osteogenic differentiation of rBMSCs cultured on several material surfaces. Hence, this study initially demonstrated the foreground of OCNN in osteogenic drug and biomaterial screening for next-generation tissue engineering and stem cell research.

Morphological landscapes from high content imaging reveal cytokine priming strategies that enhance mesenchymal stromal cell immunosuppression

Successful clinical translation of mesenchymal stromal cell (MSC) products has not been achieved in the United States and may be in large part due to MSC functional heterogeneity. Efforts have been made to identify "priming" conditions that produce MSCs with consistent immunomodulatory function; however, challenges remain with predicting and understanding how priming impacts MSC behavior. The purpose of this study was to develop a high throughput, image-based approach to assess MSC morphology in response to combinatorial priming treatments and establish morphological profiling as an effective approach to screen the effect of manufacturing changes (i.e., priming) on MSC immunomodulation. We characterized the morphological response of multiple MSC lines/passages to an array of Interferon-gamma (IFN-γ) and tumor necrosis factor-⍺ (TNF-⍺) priming conditions, as well as the effects of priming on MSC modulation of activated T cells and MSC secretome. Although considerable functional heterogeneity, in terms of T-cell suppression, was observed between different MSC lines and at different passages, this heterogeneity was significantly reduced with combined IFN-γ/TNF-⍺ priming. The magnitude of this change correlated strongly with multiple morphological features and was also reflected by MSC secretion of immunomodulatory factors, for example, PGE2, ICAM-1, and CXCL16. Overall, this study further demonstrates the ability of priming to enhance MSC function, as well as the ability of morphology to better understand MSC heterogeneity and predict changes in function due to manufacturing.

Engineering microenvironments for manufacturing therapeutic cells

There are a growing number of globally approved products and clinical trials utilizing autologous and allogeneic therapeutic cells for applications in regenerative medicine and immunotherapies. However, there is a need to develop rapid and cost-effective methods for manufacturing therapeutically effective cells. Furthermore, the resulting manufactured cells may exhibit heterogeneities that result in mixed therapeutic outcomes. Engineering approaches that can provide distinct microenvironmental cues to these cells may be able to enhance the growth and characterization of these cell products. This mini-review describes strategies to potentially enhance the expansion of therapeutic cells with biomaterials and bioreactors, as well as to characterize the cell products with microphysiological systems. These systems can provide distinct cues to maintain the quality attributes of the cells and evaluate their function in physiologically relevant conditions.

A New Predictive Technology for Perinatal Stem Cell Isolation Suited for Cell Therapy Approaches

The use of stem cells for regenerative applications and immunomodulatory effect is increasing. Amniotic epithelial cells (AECs) possess embryonic-like proliferation ability and multipotent differentiation potential. Despite the simple isolation procedure, inter-individual variability and different isolation steps can cause differences in isolation yield and cell proliferation ability, compromising reproducibility observations among centers and further applications. We investigated the use of a new technology as a diagnostic tool for quality control on stem cell isolation. The instrument label-free separates cells based on their physical characteristics and, thanks to a micro-camera, generates a live fractogram, the fingerprint of the sample. Eight amniotic membranes were processed by trypsin enzymatic treatment and immediately analysed. Two types of profile were generated: a monomodal and a bimodal curve. The first one represented the unsuccessful isolation with all recovered cell not attaching to the plate; while for the second type, the isolation process was successful, but we discovered that only cells in the second peak were alive and resulted adherent. We optimized a Quality Control (QC) method to define the success of AEC isolation using the fractogram generated. This predictive outcome is an interesting tool for laboratories and cell banks that isolate and cryopreserve fetal annex stem cells for research and future clinical applications.

Early time-point cell morphology classifiers successfully predict human bone marrow stromal cell differentiation modulated by fiber density in nanofiber scaffolds

Nanofiber scaffolds can induce osteogenic differentiation and cell morphology alterations of human bone marrow stromal cells (hBMSCs) without introduction of chemical cues. In this study, we investigate the predictive power of day 1 cell morphology, quantified by a machine learning based method, as an indicator of osteogenic differentiation modulated by nanofiber density. Nanofiber scaffolds are fabricated via electrospinning. Microscopy, quantitative image processing and clustering analysis are used to systematically quantify scaffold properties as a function of fiber density. hBMSC osteogenic differentiation potential is evaluated after 14 days using osteogenic marker gene expression and after 50 days using calcium mineralization, showing enhanced osteogenic differentiation with an increase in nanofiber density. Cell morphology measurements at day 1 successfully predict differentiation potential when analyzed with the support vector machine (SVM)/supercell tools previously developed and trained on cells from a different donor. A correlation is observed between differentiation potential and cell morphology, demonstrating sensitivity of the morphology measurement to varying degrees of differentiation potential. To further understand how nanofiber density determines hBMSC morphology, both full 3-D morphology measurements as well as other measurements of the 2-D projected morphology are investigated in this study. To achieve predictive power on hBMSC osteogenic differentiation, at least two morphology metrics need to be considered together for each cell, with the majority of metric pairs including one 3-D morphology metric. Analysis of the local nanofiber structure surrounding each cell reveals a correlation with single-cell morphology and indicates that the osteogenic differentiation phenotype may be predictive at the single-cell level.

Cell shape-based chemical screening reveals an epigenetic network mediated by focal adhesions

Adapter proteins CRK and CRKL participate in a variety of signaling pathways, including cell adhesion, and fate regulation of mammalian cells. However, the molecular functions of CRK/CRKL in epigenetic regulation remain largely unknown. Here, we developed a pipeline to evaluate cell morphology using high-content image analysis combined with chemical screening of kinase and epigenetic modulators. We found that CRK/CRKL modulates gene regulatory networks associated with cell morphology through epigenetic alteration in mouse embryonic fibroblasts. Integrated epigenome and transcriptome analyses revealed that CRK/CRKL is involved in super-enhancer activity and upregulation of Cdt1, Rin1, and Spp1 expression for the regulation of cell morphology. Screening of a library of 80 epigenetic inhibitors showed that histone H3 modifiers, euchromatic histone methyltransferase 2 and mitogen- and stress-activated kinase 1, may be important for CRK/CRKL-mediated morphological changes. Taken together, our results indicate that CRK/CRKL plays a critical role in gene regulatory networks through epigenetic modification. DATABASES: Chromatin immunoprecipitation sequencing and RNA sequencing data were deposited in the DNA Data Bank of Japan under DRA011080 and DRA011081 accession numbers, respectively.

RNA-seq reveals correlations between cytoskeleton-related genes and the osteogenic activity of mesenchymal stem cells on strontium loaded titania nanotube arrays

Strontium loaded titania nanotube arrays (NTSr), as well as titania nanotube arrays (NT), have been regarded as effective coatings to promote bone regeneration on titanium implants, but an understanding of the full extent of early processes affected by such surface modifications is absent. To address this limitation, we performed RNA sequencing (RNA-seq) of Sprague-Dawley rat bone marrow mesenchymal stem cells (rBMMSCs) cultured on unmodified titanium sheets (Con), NT and NTSr specimens. By pairwise comparisons we found that NT and NTSr shared a majority of differentially expressed genes. The Gene Ontology (GO) analysis revealed that NT and NTSr up-regulated a bunch of genes that are annotated to the cytoskeleton. The results were supported by immunofluorescent, transmission electron microscopy (TEM) and western blotting assays. By inhibiting the cytoskeleton through pharmacological agents, the activities of alkaline phosphatase (ALP) on NT and NTSr were also suppressed. Informed by these results, we concluded that NT and NTSr specimens reorganized the cytoskeleton of cultured cells that may play a crucial role in osteogenic lineage commitment.

Automated mesenchymal stem cell segmentation and machine learning-based phenotype classification using morphometric and textural analysis

Purpose: Mesenchymal stem cells (MSCs) have demonstrated clinically relevant therapeutic effects for treatment of trauma and chronic diseases. The proliferative potential, immunomodulatory characteristics, and multipotentiality of MSCs in monolayer culture is reflected by their morphological phenotype. Standard techniques to evaluate culture viability are subjective, destructive, or time-consuming. We present an image analysis approach to objectively determine morphological phenotype of MSCs for prediction of culture efficacy. Approach: The algorithm was trained using phase-contrast micrographs acquired during the early and mid-logarithmic stages of MSC expansion. Cell regions are localized using edge detection, thresholding, and morphological operations, followed by cell marker identification using H-minima transform within each region to differentiate individual cells from cell clusters. Clusters are segmented using marker-controlled watershed to obtain single cells. Morphometric and textural features are extracted to classify cells based on phenotype using machine learning. Results: Algorithm performance was validated using an independent test dataset of 186 MSCs in 36 culture images. Results show 88% sensitivity and 86% precision for overall cell detection and a mean Sorensen-Dice coefficient of 0.849 ± 0.106 for segmentation per image. The algorithm exhibited an area under the curve of 0.816 (CI 95 = 0.769 to 0.886) and 0.787 (CI 95 = 0.716 to 0.851) for classifying MSCs according to their phenotype at early and mid-logarithmic expansion, respectively. Conclusions: The proposed method shows potential to segment and classify low and moderately dense MSCs based on phenotype with high accuracy and robustness. It enables quantifiable and consistent morphology-based quality assessment for various culture protocols to facilitate cytotherapy development.

Exosomes Enhance Adhesion and Osteogenic Differentiation of Initial Bone Marrow Stem Cells on Titanium Surfaces

Successful osseointegration involves the biological behavior of bone marrow stem cells (BMSCs) on an implant surface; however, the role of BMSC-derived extracellular vesicles (EVs)/exosomes in osseointegration is little known. This study aimed to: (i) explore the interaction force between exosomes (Exo) and cells on a titanium surface; (ii) discuss whether the morphology and biological behavior of BMSCs are affected by exosomes; and (iii) preliminarily investigate the mechanism by which exosomes regulate cells on Ti surface. Exosomes secreted by rat BMSCs were collected by ultracentrifugation and analyzed using transmission electron microscopy and nanoparticle tracking analysis. Confocal fluorescence microscopy, scanning electron microscopy, Cell Counting Kit-8 (CCK-8), quantitative real-time polymerase chain reaction techniques, and alkaline phosphatase bioactivity, Alizarin Red staining, and quantification were used to investigate the exosomes that adhere to the Ti plates under different treatments as well as the morphological change, adhesion, spread, and differentiation of BMSCs. We found that exosomes were efficiently internalized and could regulate cell morphology and promoted the adhesion, spreading, and osteogenic differentiation of BMSCs. These were achieved partly by activating the RhoA/ROCK signaling pathway. Our discovery presents a new insight into the positive regulatory effect of exosomes on the biological behaviors of BMSCs on Ti surface and provides a novel route to modify the surface of a Ti implant.

Predicting quality decay in continuously passaged mesenchymal stem cells by detecting morphological anomalies

With rapid advances in cell therapy, technologies enabling both consistency and efficiency in cell manufacturing are becoming necessary. Morphological monitoring allows practical quality maintenance in cell manufacturing facilities, but relies heavily on human skill. For more reproducible and data-driven quality evaluation, image-based morphological analysis provides multiple advantages over manual observation. Our group has investigated the performance of multiple morphological parameters obtained from time-course images to non-invasively and quantitatively predict cellular quality using machine learning algorithms. Although such morphology-based computational models succeeded in early cell quality predictions, it was difficult to introduce our approach in cell manufacturing facilities owing to data variation issues. Since manufacturing facilities have fixed their protocol to minimize anomalies as much as possible, most accumulated data are normal, and anomalies are scarce. Thus, our morphological analysis had to adapt to such practical situation where it was difficult to observe a wide range of data variations, including both normal samples and anomalies, which is typically essential to improve most machine learning models' performance. In the present study, we introduce a practical morphological analysis concept by investigating the performance of anomalous quality decay discrimination during the continuous passaging of human mesenchymal stem cells (hMSCs). Combining the visualization method and asymmetric statistic discrimination, we describe an effective morphology-based, in-process quality monitoring concept to detect quality anomalies throughout cell culture process. Our results showed that the use of morphological parameters to reflect cellular population heterogeneity can predict hMSC quality decay within 6 h after seeding.

Characterization of heterogeneous primary human cartilage-derived cell population using non-invasive live-cell phase-contrast time-lapse imaging

Reliable and reproducible cell therapy strategies to treat osteoarthritis demand an improved characterization of the cell and heterogeneous cell population resident in native cartilage tissue. Using live-cell phase-contrast time-lapse imaging (PC-TLI), this study investigates the morphological attributes and biological performance of the three primary biological objects enzymatically isolated from primary human cartilage: connective tissue progenitors (CTPs), non-progenitors (NPs) and multi-cellular structures (MCSs). The authors' results demonstrated that CTPs were smaller in size in comparison to NPs (P < 0.001). NPs remained part of the adhered cell population throughout the cell culture period. Both NPs and CTP progeny on day 8 increased in size and decreased in circularity in comparison to their counterparts on day 1, although the percent change was considerably less in CTP progeny (P < 0.001). PC-TLI analyses indicated three colony types: single-CTP-derived (29%), multiple-CTP-derived (26%) and MCS-derived (45%), with large heterogeneity with respect to cell morphology, proliferation rate and cell density. On average, clonal (CL) (P = 0.009) and MCS (P = 0.001) colonies exhibited higher cell density (cells per colony area) than multi-clonal (MC) colonies; however, it is interesting to note that the behavior of CL (less cells per colony and less colony area) and MCS (high cells per colony and high colony area) colonies was quite different. Overall effective proliferation rate (EPR) of the CTPs that formed CL colonies was higher than the EPR of CTPs that formed MC colonies (P = 0.02), most likely due to CTPs with varying EPR that formed the MC colonies. Finally, the authors demonstrated that lag time before first cell division of a CTP (early attribute) could potentially help predict its proliferation rate long-term. Quantitative morphological characterization using non-invasive PC-TLI serves as a reliable and reproducible technique to understand cell heterogeneity. Size and circularity parameters can be used to distinguish CTP from NP populations. Morphological cell and colony features can also be used to reliably and reproducibly identify CTP subpopulations with preferred proliferation and differentiation potentials in an effort to improve cell manufacturing and therapeutic outcomes.

The importance of scoring recognition fitness in spheroid morphological analysis for robust label-free quality evaluation

Because of the growing demand for human cell spheroids as functional cellular components for both drug development and regenerative therapy, the technology to non-invasively evaluate their quality has emerged. Image-based morphology analysis of spheroids enables high-throughput screening of their quality. However, since spheroids are three-dimensional, their images can have poor contrast in their surface area, and therefore the total spheroid recognition by image processing is greatly dependent on human who design the filter-set to fit for their own definition of spheroid outline. As a result, the reproducibility of morphology measurement is critically affected by the performance of filter-set, and its fluctuation can disrupt the subsequent morphology-based analysis. Although the unexpected failure derived from the inconsistency of image processing result is a critical issue for analyzing large image data for quality screening, it has been tackled rarely. To achieve robust analysis performances using morphological features, we investigated the influence of filter-set's reproducibility for various types of spheroid data. We propose a new scoring index, the "recognition fitness deviation (RFD)," as a measure to quantitatively and comprehensively evaluate how reproductively a designed filter-set can work with data variations, such as the variations in replicate samples, in time-course samples, and in different types of cells (a total of six normal or cancer cell types). Our result shows that RFD scoring from 5000 images can automatically rank the best robust filter-set for obtaining the best 6-cell type classification model (94% accuracy). Moreover, the RFD score reflected the differences between the worst and the best classification models for morphologically similar spheroids, 60% and 89% accuracy respectively. In addition to RFD scoring, we found that using the time-course of morphological features can augment the fluctuations in spheroid recognitions leading to robust morphological analysis.

Senescence in Mesenchymal Stem Cells: Functional Alterations, Molecular Mechanisms, and Rejuvenation Strategies

Mesenchymal stem cells (MSCs) are multipotent cells capable of self-renewal and differentiation. There is increasing evidence of the therapeutic value of MSCs in various clinical situations, however, these cells gradually lose their regenerative potential with age, with a concomitant increase in cellular dysfunction. Stem cell aging and replicative exhaustion are considered as hallmarks of aging and functional attrition in organisms. MSCs do not proliferate infinitely but undergo only a limited number of population doublings before becoming senescent. This greatly hinders their clinical application, given that cultures must be expanded to obtain a sufficient number of cells for cell-based therapy. Here, we review the current knowledge of the phenotypic and functional characteristics of senescent MSCs, molecular mechanisms underlying MSCs aging, and strategies to rejuvenate senescent MSCs, which can broaden their range of therapeutic applications.

Single-cell RNA-seq highlights heterogeneity in human primary Wharton's jelly mesenchymal stem/stromal cells cultured in vitro

BACKGROUND

Mesenchymal stem/stromal cells (MSCs) are multipotent cells with a promising application potential in regenerative medicine and immunomodulation. However, MSCs cultured in vitro exhibit functional heterogeneity. The underlying molecular mechanisms that define MSC heterogeneity remain unclear.

METHODS

We investigated the gene expression profile via single-cell RNA sequencing (scRNA-seq) of human primary Wharton's jelly-derived MSCs (WJMSCs) cultured in vitro from three donors. We also isolated CD142+ and CD142- WJMSCs based on scRNA-seq data and compared their proliferation capacity and "wound healing" potential in vitro. Meanwhile, we analyzed publicly available adipose-derived MSC (ADMSCs) scRNA-seq data and performed transcriptome comparison between WJMSCs and ADMSCs at the single-cell level.

RESULTS

GO enrichment analysis of highly variable genes (HVGs) obtained from WJMSCs revealed that these genes are significantly enriched in extracellular region with binding function, involved in developmental process, signal transduction, cell proliferation, etc. Pathway analysis showed that these HVGs are associated with functional characteristics of classic MSCs, such as inflammation mediated by chemokine and cytokine signaling, integrin signaling, and angiogenesis. After regressing out the batch and cell cycle effects, these HVGs were used for dimension reduction and clustering analysis to identify candidate subpopulations. Differentially expressed gene analysis revealed the existence of several distinct subpopulations of MSCs that exhibit diverse functional characteristics related to proliferation, development, and inflammation response. In line with our data, sorted CD142+ and CD142- WJMSCs showed distinct proliferation capacity as well as "wound healing" potential. Although WJMSCs and ADMSCs were derived from different tissues and were displaying different differentiation potencies, their HVGs were largely overlapped and had similar functional enrichment.

CONCLUSION

HVGs identified in MSCs are associated with classic MSC function. Regarding therapeutic potential, these genes are associated with functional characteristics, on which the MSC clinical application were theoretically based, such as development and inflammation response. Altogether, these HVGs hold the potential to be used as candidate markers for further potency association studies.

The Implication of Spatial Statistics in Human Mesenchymal Stem Cell Response to Nanotubular Architectures

INTRODUCTION

In recent years there has been ample interest in nanoscale modifications of synthetic biomaterials to understand fundamental aspects of cell-surface interactions towards improved biological outcomes. In this study, we aimed at closing in on the effects of nanotubular TiO2 surfaces with variable nanotopography on the response on human mesenchymal stem cells (hMSCs). Although the influence of TiO2 nanotubes on the cellular response, and in particular on hMSC activity, has already been addressed in the past, previous studies overlooked critical morphological, structural and physical aspects that go beyond the simple nanotube diameter, such as spatial statistics.

METHODS

To bridge this gap, we implemented an extensive characterization of nanotubular surfaces generated by anodization of titanium with a focus on spatial structural variables including eccentricity, nearest neighbour distance (NND) and Voronoi entropy, and associated them to the hMSC response. In addition, we assessed the biological potential of a two-tiered honeycomb nanoarchitecture, which allowed the detection of combinatory effects that this hierarchical structure has on stem cells with respect to conventional nanotubular designs. We have combined experimental techniques, ranging from Scanning Electron (SEM) and Atomic Force (AFM) microscopy to Raman spectroscopy, with computational simulations to characterize and model nanotubular surfaces. We evaluated the cell response at 6 hrs, 1 and 2 days by fluorescence microscopy, as well as bone mineral deposition by Raman spectroscopy, demonstrating substrate-induced differential biological cueing at both the short- and long-term.

RESULTS

Our work demonstrates that the nanotube diameter is not sufficient to comprehensively characterize nanotubular surfaces and equally important parameters, such as eccentricity and wall thickness, ought to be included since they all contribute to the overall spatial disorder which, in turn, dictates the overall bioactive potential. We have also demonstrated that nanotubular surfaces affect the quality of bone mineral deposited by differentiated stem cells. Lastly, we closed in on the integrated effects exerted by the superimposition of two dissimilar nanotubular arrays in the honeycomb architecture.

DISCUSSION

This work delineates a novel approach for the characterization of TiO2 nanotubes which supports the incorporation of critical spatial structural aspects that have been overlooked in previous research. This is a crucial aspect to interpret cellular behaviour on nanotubular substrates. Consequently, we anticipate that this strategy will contribute to the unification of studies focused on the use of such powerful nanostructured surfaces not only for biomedical applications but also in other technology fields, such as catalysis.

Native-Osteoarthritic Joint Resident Stem and Progenitor Cells for Cartilage Cell-Based Therapies: A Quantitative Comparison With Respect to Concentration and Biological Performance

BACKGROUND

Cell-based therapy for cartilage repair is a promising approach and is becoming an established technique. Yet, there is no consensus on the optimal cell source.

PURPOSE

To provide a donor-matched quantitative comparison of the connective tissue progenitors (CTPs) derived from cartilage (Outerbridge grade 1-3 [G1-2-3]), bone marrow aspirate concentrate (BMC), infrapatellar fat pad (IPFP), synovium, and periosteum with respect to (1) cell concentration ([Cell], cells/mL), (2) CTP prevalence (P_CTP, colonies per million cells), and (3) biological performance based on in vitro proliferation potential (cells per colony) colony density, and differentiation potential (expression of negatively charged extracellular matrix: glycosaminoglycan-rich extra cellular matrix [GAG-ECM]).

STUDY DESIGN

Descriptive laboratory study.

METHODS

Tissues were obtained from 10 patients undergoing total knee arthroplasty (mean age, 59 years; women, n = 6). Automated quantitative colony-forming unit analysis was used to compare [Cell], P_CTP, and CTP biological performance across tissue sources.

RESULTS

[Cell] was highest in grade 3 cartilage (P = .002) and BMC (P = .001). Median P_CTP was highest in IPFP (P = .001), synovium (P = .003), and G1-2 cartilage (P = .02). Proliferation was highest in synovium-derived CTPs (P < .001). Median colony density was highest in G1-2-3 (P < .001). Median GAG-ECM was highest in G1-2-3 (P < .001). Within each patient, CTPs derived from all tissues were highly heterogeneous in biological performance as determined by cells per colony, density, and GAG-ECM.

CONCLUSION

Tissue sources differ in [Cell], P_CTP, and biological attributes. The data presented in this study suggest that cartilage (G1-2-3) is the preferred tissue source for cartilage repair based on P_CTP and GAG-ECM, followed by synovium, IPFP, BMC, and periosteum. However, due to the heterogeneous mixture of CTPs within each tissue source, there exists a subset of CTPs with biological performance similar to G1-2-3 cartilage, particularly in synovium and IPFP. Performance-based clonal selection and expansion of preferred CTPs and their progeny will potentially lead to improved cell population with predictive future.

CLINICAL RELEVANCE

Optimal tissue regeneration strategies will require informed decisions regarding which of the available tissue sources to use. Optimizing cell sourcing in any tissue may require separation of CTPs with preferred attributes from those with less desirable attributes. The heterogeneity manifest in the early stage of colony formation represents an opportunity for performance-based clone selection for clinical cell processing and manufacturing.

Single-cell high-content imaging parameters predict functional phenotype of cultured human bone marrow stromal stem cells

Cultured human bone marrow stromal (mesenchymal) stem cells (hBM-MSCs) are heterogenous cell populations exhibiting variable biological properties. Quantitative high-content imaging technology allows identification of morphological markers at a single cell resolution that are determinant for cellular functions. We determined the morphological characteristics of cultured primary hBM-MSCs and examined their predictive value for hBM-MSC functionality. BM-MSCs were isolated from 56 donors and characterized for their proliferative and differentiation potential. We correlated these data with cellular and nuclear morphological features determined by Operetta; a high-content imaging system. Cell area, cell geometry, and nucleus geometry of cultured hBM-MSCs exhibited significant correlation with expression of hBM-MSC membrane markers: ALP, CD146, and CD271. Proliferation capacity correlated negatively with cell and nucleus area and positively with cytoskeleton texture features. In addition, in vitro differentiation to osteoblasts as well as in vivo heterotopic bone formation was associated with decreased ratio of nucleus width to length. Multivariable analysis applying a stability selection procedure identified nuclear geometry and texture as predictors for hBM-MSCs differentiation potential to osteoblasts or adipocytes. Our data demonstrate that by employing a limited number of cell morphological characteristics, it is possible to predict the functional phenotype of cultured hBM-MSCs and thus can be used as a screening test for "quality" of hBM-MSCs prior their use in clinical protocols.

Integration of Mesenchymal Stem Cells into a Novel Micropillar Confinement Assay

Mechanical cues such as stiffness have been shown to influence cell gene expression, protein expression, and cell behaviors critical for tissue engineering. The mechanical cue of confinement is also a pervasive parameter affecting cells in vivo and in tissue-engineered constructs. Despite its prevalence, the mechanical cue of confinement lacks assays that provide precise control over the degree of confinement induced on cells, yield a large sample size, enable long-term culture, and enable easy visualization of cells over time. In this study, we developed a process to systematically confine cells using micropillar arrays. Using photolithography and polydimethylsiloxane (PDMS) molding, we created PDMS arrays of micropillars that were 5, 10, 20, or 50 μm in spacing and between 13 and 17 μm in height. The tops of micropillars were coated with Pluronic F127 to inhibit cell adhesion, and we observed that mesenchymal stem cells (MSCs) robustly infiltrated into the micropillar arrays. MSC and nucleus morphology were altered by narrowing the micropillar spacing, and cytoskeletal elements within MSCs appeared to become more diffuse with increasing confinement. Specifically, MSCs exhibited a ring of actin around their periphery and punctate focal adhesions. MSC migration speed was reduced by narrowing micropillar spacing, and distinct migration behaviors of MSCs emerged in the presence of micropillars. MSCs continued to proliferate within micropillar arrays after 3 weeks in culture, displaying our assay's capability for long-term studies. Our assay also has the capacity to provide adequate cell numbers for quantitative assays to investigate the effect of confinement on gene and protein expression. Through deeper understanding of cell mechanotransduction in the context of confinement, we can modify tissue-engineered constructs to be optimal for a given purpose. Impact Statement In this study, we developed a novel process to systematically confine cells using micropillar arrays. Our assay provides insight into cell behavior in response to mechanical confinement. Through deeper understanding of how cells sense and respond to confinement, we can fine tune tissue-engineered constructs to be optimal for a given purpose. By combining confinement with other physical cues, we can harness mechanical properties to encourage or inhibit cell migration, direct cells down a particular lineage, induce cell secretion of specific cytokines or extracellular vesicles, and ultimately direct cells to behave in a way conducive to tissue engineering.

Influence of Cell Morphology on Mesenchymal Stem Cell Transfection

Gene transfection has broad applications in bioengineering and biomedical fields. Although many gene carrier materials and transfection methods have been developed, it remains unclear how cell morphology including cell spreading and elongation affects gene transfection. In this study, human bone marrow-derived mesenchymal stem cells (hMSCs) were cultured on micropatterns and transfected with cationic pAcGFP1-N1 plasmid complexes. The relationship between the cell morphology of hMSCs and gene transfection was investigated using micropatterning techniques. Spreading and elongation of hMSCs were precisely controlled by micropatterned surfaces. The results showed that well-spread and elongated hMSCs had high transfection efficiency. Analysis of the uptake of exogenous genes and DNA synthesis activity indicated that the well-spread and elongated cell morphology promoted gene transfection through enhanced uptake of the cationic complexes and accelerated DNA synthesis. The results should provide useful information for understanding of cell morphology on gene transfection and development of efficient gene transfection methods.

In-process evaluation of culture errors using morphology-based image analysis

INTRODUCTION

Advancing industrial-scale manufacture of cells as therapeutic products is an example of the wide applications of regenerative medicine. However, one bottleneck in establishing stable and efficient cell manufacture is quality control. Owing to the lack of effective in-process measurement technology, analyzing the time-consuming and complex cell culture process that essentially determines cellular quality is difficult and only performed by manual microscopic observation. Our group has been applying advanced image-processing and machine-learning modeling techniques to construct prediction models that support quality evaluations during cell culture. In this study, as a model of errors during the cell culture process, intentional errors were compared to the standard culture and analyzed based only on the time-course morphological information of the cells.

METHODS

Twenty-one lots of human mesenchymal stem cells (MSCs), including both bone-marrow-derived MSCs and adipose-derived MSCs, were cultured under 5 conditions (one standard and 4 types of intentional errors, such as clear failure of handlings and machinery malfunctions). Using time-course microscopic images, cell morphological profiles were quantitatively measured and utilized for visualization and prediction modeling. For visualization, modified principal component analysis (PCA) was used. For prediction modeling, linear regression analysis and the MT method were applied.

RESULTS

By modified PCA visualization, the differences in cellular lots and culture conditions were illustrated as traits on a morphological transition line plot and found to be effective descriptors for discriminating the deviated samples in a real-time manner. In prediction modeling, both the cell growth rate and error condition discrimination showed high accuracy (>80%), which required only 2 days of culture. Moreover, we demonstrated the applicability of different concepts of machine learning using the MT method, which is effective for manufacture processes that mostly collect standard data but not a large amount of failure data.

CONCLUSIONS

Morphological information that can be quantitatively acquired during cell culture has great potential as an in-process measurement tool for quality control in cell manufacturing processes.

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

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.

The effect of S53P4-based borosilicate glasses and glass dissolution products on the osteogenic commitment of human adipose stem cells

Despite the good performance of silicate bioactive glasses in bone regeneration, there is considerable potential to enhance their properties by chemical modifications. In this study, S53P4-based borosilicate glasses were synthesized and their dissolution profile was studied in simulated body fluid by assessing pH change, ion release and conversion to hydroxyapatite. The viability, proliferation, attachment, osteogenesis and endothelial marker expression of human adipose stem cells (hASCs) was evaluated upon direct culture on glass discs and in the extract medium. This is the first study evaluating cell behavior in response to borosilicate glasses based on S53P4 (commercially available as BonAlive®). Replacing silicate with borate in S53P4 increased the glass reactivity. Despite the good viability of hASCs under all conditions, direct culture of cells on borosilicate discs and in undiluted extract medium reduced cell proliferation. This was accompanied with changes in cell morphology. Regarding osteogenic commitment, alkaline phosphatase activity was significantly reduced by the borosilicate glass discs and extracts, whereas the expression of osteogenic markers RUNX2a, OSTERIX, DLX5 and OSTEOPONTIN was upregulated. There was also a borosilicate glass-induced increase in osteocalcin protein production. Moreover, osteogenic supplements containing borosilicate extracts significantly increased the mineral production in comparison to the osteogenic medium control. Interestingly, borosilicate glasses stimulated the expression of endothelial markers vWF and PECAM-1. To conclude, our results reveal that despite reducing hASC proliferation, S53P4-based borosilicate glasses and their dissolution products stimulate osteogenic commitment and upregulate endothelial markers, thus supporting their further evaluation for regenerative medicine.

Functional Profiling of Chondrogenically Induced Multipotent Stromal Cell Aggregates Reveals Transcriptomic and Emergent Morphological Phenotypes Predictive of Differentiation Capacity

Multipotent stromal cells (MSCs) are an attractive cell source for bone and cartilage tissue repair strategies. However, the functional heterogeneity of MSCs derived from different donors and manufacturing conditions has limited clinical translation, emphasizing the need for improved methods to assess MSC chondrogenic capacity. We used functionally relevant morphological profiling to dynamically monitor emergent morphological phenotypes of chondrogenically induced MSC aggregates to identify morphological features indicative of MSC chondrogenesis. Toward this goal, we characterized the morphology of chondrogenically stimulated MSC aggregates from eight different human cell-lines at multiple passages and demonstrated that MSC aggregates exhibited unique morphological dynamics that were both cell line- and passage-dependent. This variation in 3D morphology was shown to be informative of long-term MSC chondrogenesis based on multiple quantitative functional assays. We found that the specific morphological features of spheroid area, radius, minimum feret diameter, and minor axis length to be strongly correlated with MSC chondrogenic synthetic activity but not gene expression as early as day 4 in 3D culture. Our high-throughput, nondestructive approach could potentially serve as a tool to identify MSC lines with desired chondrogenic capacity toward improving manufacturing strategies for MSC-based cellular products for cartilage tissue repair. Stem Cells Translational Medicine 2018;1-12.

Novel Lipid Signaling Mediators for Mesenchymal Stem Cell Mobilization during Bone Repair

Introduction

Mesenchymal stem and progenitor cells (MSCs), which normally reside in the bone marrow, are critical to bone health and can be recruited to sites of traumatic bone injury, contributing to new bone formation. The ability to control the trafficking of MSCs provides therapeutic potential for improving traumatic bone healing and therapy for genetic bone diseases such as hypophosphatasia.

Methods

In this study, we explored the sphingosine-1-phosphate (S1P) signaling axis as a means to control the mobilization of MSCs into blood and possibly to recruit MSCs enhancing bone growth.

Results

Loss of S1P receptor 3 (S1PR3) leads to an increase in circulating CD45-/CD29+/CD90+/Sca1 putative mesenchymal progenitor cells, suggesting that blocking S1PR3 may stimulate MSCs to leave the bone marrow. Antagonism of S1PR3 with the small molecule VPC01091 stimulated acute migration of CD45-/CD29+/CD90+/Sca1+ MSCs into the blood as early as 1.5 hours after treatment. VPC01091 administration also increased ectopic bone formation induced by BMP-2 and significantly increased new bone formation in critically sized rat cranial defects, suggesting that mobilized MSCs may home to injuries to contribute to healing. We also explored the possibility of combining S1P manipulation of endogenous host cell occupancy with exogenous MSC transplantation for potential use in combination therapies. Importantly, reducing niche occupancy of host MSCs with VPC01091 does not impede engraftment of exogenous MSCs.

Conclusions

Our studies suggest that MSC mobilization through S1PR3 antagonism is a promising strategy for endogenous tissue engineering and improving MSC delivery to treat bone diseases.

FDA and NIST collaboration on standards development activities supporting innovation and translation of regenerative medicine products

The development of standards for the field of regenerative medicine has been noted as a high priority by several road-mapping activities. Additionally, the U.S. Congress recognizes the importance of standards in the 21st Century Cure Act. Standards will help to accelerate and streamline cell and gene therapy product development, ensure the quality and consistency of processes and products, and facilitate their regulatory approval. Although there is general agreement for the need of additional standards for regenerative medicine products, a shared understanding of standards is required for real progress toward the development of standards to advance regenerative medicine. Here, we describe the roles of standards in regenerative medicine as well as the process for standards development and the interactions of different entities in the standards development process. Highlighted are recent coordinated efforts between the U.S. Food and Drug Administration and the National Institute of Standards and Technology to facilitate standards development and foster science that underpins standards development.

Polycaprolactone-chitosan nanofibers influence cell morphology to induce early osteogenic differentiation

Osteogenic differentiation is highly correlated with cell morphology. Morphological changes are a stimulus as well as a consequence of the differentiation process. Besides, geometrical, biochemical and mechanical properties of a substrate can modulate cell adhesion and morphology. Therefore, in the current study, nanofibrous substrate properties were used to implement necessary changes in cell morphology which induced osteogenic differentiation without biological supplements. A polycaprolactone-chitosan nanofiber substrate had been fabricated with an average diameter of ∼75 nm and an appropriate ratio of polymers that balances surface biocompatibility as well as mechanical strength. DSC and wide-angle XRD analysis revealed miscibility between polymers; whereas a degradation study confirmed the structural integrity of nanofibers. Nanofibers did not cause any cytotoxicity to MC3T3-E1 cells as confirmed by Live/Dead® staining. Morphological studies by SEM and confocal microscopy showed significant changes in terms of cell shape, area, compactness, aspect ratio and nucleus area in cells grown on nanofibers which indicated the osteogenic differentiation inducing potential of nanofibers. This was further confirmed by enhanced mineral deposition and alkaline phosphatase activity up to three weeks. In summary, polycaprolactone-chitosan nanofibers could induce early osteogenic differentiation in MC3T3-E1 pre-osteoblasts without any biological supplements by modulating cell morphology. Moreover, cell morphological features can be used as a predictive and informative approach at the early stages of differentiation experiments.