High-content imaging-based screening of microenvironment-induced changes to 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 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.

Morphology-based deep learning approach for predicting adipogenic and osteogenic differentiation of human mesenchymal stem cells (hMSCs)

Human mesenchymal stem cells (hMSCs) are multipotent progenitor cells with the potential to differentiate into various cell types, including osteoblasts, chondrocytes, and adipocytes. These cells have been extensively employed in the field of cell-based therapies and regenerative medicine due to their inherent attributes of self-renewal and multipotency. Traditional approaches for assessing hMSCs differentiation capacity have relied heavily on labor-intensive techniques, such as RT-PCR, immunostaining, and Western blot, to identify specific biomarkers. However, these methods are not only time-consuming and economically demanding, but also require the fixation of cells, resulting in the loss of temporal data. Consequently, there is an emerging need for a more efficient and precise approach to predict hMSCs differentiation in live cells, particularly for osteogenic and adipogenic differentiation. In response to this need, we developed innovative approaches that combine live-cell imaging with cutting-edge deep learning techniques, specifically employing a convolutional neural network (CNN) to meticulously classify osteogenic and adipogenic differentiation. Specifically, four notable pre-trained CNN models, VGG 19, Inception V3, ResNet 18, and ResNet 50, were developed and tested for identifying adipogenic and osteogenic differentiated cells based on cell morphology changes. We rigorously evaluated the performance of these four models concerning binary and multi-class classification of differentiated cells at various time intervals, focusing on pivotal metrics such as accuracy, the area under the receiver operating characteristic curve (AUC), sensitivity, precision, and F1-score. Among these four different models, ResNet 50 has proven to be the most effective choice with the highest accuracy (0.9572 for binary, 0.9474 for multi-class) and AUC (0.9958 for binary, 0.9836 for multi-class) in both multi-class and binary classification tasks. Although VGG 19 matched the accuracy of ResNet 50 in both tasks, ResNet 50 consistently outperformed it in terms of AUC, underscoring its superior effectiveness in identifying differentiated cells. Overall, our study demonstrated the capability to use a CNN approach to predict stem cell fate based on morphology changes, which will potentially provide insights for the application of cell-based therapy and advance our understanding of regenerative medicine.

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.

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.

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.

Plant Tissue Parenchyma and Vascular Bundles Selectively Regulate Stem Cell Mechanosensing and Differentiation

Introduction

Plant tissues are plentiful, diverse, and due to convergent evolution are structurally similar to many animal tissues. Decellularized plant tissues feature microtopographies that resemble cancellous bone (porous parenchyma) and skeletal muscle (fibrous vascular bundles). However, the use of plant tissues as an inexpensive and abundant biomaterial for controlling stem cell behavior has not been widely explored.

Methods

Celery plant tissues were cut cross-sectionally (porous parenchyma) or longitudinally (fibrous vascular bundles) and decellularized. Human mesenchymal stem cells (MSCs) were then cultured atop plant tissues and confocal imaging of single cells was used to evaluate the early effects of microtopography on MSC adhesion, morphology, cytoskeletal alignment, Yes-associated protein (YAP) signaling, and downstream lineage commitment to osteogenic or myogenic phenotypes.

Results

Microtopography was conserved post plant tissue decellularization and MSCs attached and proliferated on plant tissues. MSCs cultured on porous parenchyma spread isotropically along the periphery of plant tissue pores. In contrast, MSCs cultured on vascular bundles spread anisotropically and aligned in the direction of fibrous vascular bundles. Differences in microtopography also influenced MSC nuclear YAP localization and actin anisotropy, with higher values observed on fibrous tissues. When exposed to osteogenic or myogenic culture medium, MSCs on porous parenchyma had a higher percentage of cells stain positive for bone biomarker alkaline phosphatase, whereas myoblast determination protein 1 (MyoD) was significantly upregulated for MSCs on fibrous vascular bundles.

Conclusions

Together, these results show that plant tissues are an abundant biomaterial with defined microarchitecture that can reproducibly regulate MSC morphology, mechanosensing, and differentiation.

Supplementary Information

The online version of this article contains supplementary material available 10.1007/s12195-022-00737-9.

Injectable hydrogel with immobilized BMP-2 mimetic peptide for local bone regeneration

Osteoporosis is a disease characterized by a decrease in bone mineral density, thereby increasing the risk of sustaining a fragility fracture. Most medical therapies are systemic and do not restore bone in areas of need, leading to undesirable side effects. Injectable hydrogels can locally deliver therapeutics with spatial precision, and this study reports the development of an injectable hydrogel containing a peptide mimic of bone morphogenetic protein-2 (BMP-2). To create injectable hydrogels, hyaluronic acid was modified with norbornene (HANor) or tetrazine (HATet) which upon mixing click into covalently crosslinked Nor-Tet hydrogels. By modifying HANor macromers with methacrylates (Me), thiolated BMP-2 mimetic peptides were immobilized to HANor via a Michael addition reaction, and coupling was confirmed with 1H NMR spectroscopy. BMP-2 peptides presented in soluble and immobilized form increased alkaline phosphatase (ALP) expression in MSCs cultured on 2D and encapsulated in 3D Nor-Tet hydrogels. Injection of bioactive Nor-Tet hydrogels into hollow intramedullary canals of Lewis rat femurs showed a local increase in trabecular bone density as determined by micro-CT imaging. The presented work shows that injectable hydrogels with immobilized BMP-2 peptides are a promising biomaterial for the local regeneration of bone tissue and for the potential local treatment of osteoporosis.

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.

Long-term and label-free monitoring for osteogenic differentiation of mesenchymal stem cells using force sensor and impedance measurement

Stem cells have attracted increasing research interest in the field of regenerative medicine due to their unique abilities to differentiate into multiple cell lineages. Label-free, real-time, and long-term monitoring for stem cell differentiation is requisite in studying directional differentiation and development mechanisms for tissue engineering applications, but a great challenge because of the rigorous demands for sensitivity, stability and biocompatibility of devices. In this article, a label-free and real-time monitoring approach using a zinc oxide (ZnO) nanorod field effect transistor (FET) is proposed to detect cell traction forces (CTFs) exerted by cells on underlying substrates. The ZnO nanorod FET with the approach of difference-frequency lock-in detection achieves high sensitivity, good stability, and excellent biocompatibility, by which real-time and long-term (over 20 days) monitoring of cellular mechanical changes in osteogenic differentiation of mesenchymal stem cells (MSCs) is successfully achieved. We also employ electrical impedance monitoring using microelectrode array chips and microscopic observation to investigate cell migration and nodular aggregation behaviors of MSCs in osteogenic differentiation. Various biochemical assays including alkaline phosphatase (ALP), osteopontin expression and alizarin red staining are utilized to verify osteogenic differentiation of MSCs. We propose a combination of cell traction force measurement, impedance measurement and microscopic observation to provide multimodal profiling of cell morphology, and cellular biomechanical and electrophysiological phenotypes, which can track cellular dynamics in stem cell development and help to deeply understand the mechanism of osteogenic differentiation.

Microchip with Single-Cell Impedance Measurements for Monitoring Osteogenic Differentiation of Mesenchymal Stem Cells under Electrical Stimulation

Effective induction methods and in situ monitoring are essential for studying the mechanism of biological responses in stem cell differentiation. This article proposes an induction method incorporating electrical stimulation under an inhomogeneous field with single-cell impedance monitoring for studying osteogenic differentiation of mesenchymal stem cells (MSCs) using a microchip. The microchip contains an array of sextupole-electrode units for implementing a combination of controllable electrical stimulation and single-cell impedance measurements. MSCs are inducted to osteogenic differentiation under electrical stimulation using quadrupole electrodes and single-cell impedances are monitored in situ using a pair of microelectrodes at each unit center. The proposed microchip adopts an array design to monitor a number of MSCs in parallel, which improves measurement throughput and facilitates to carry out statistic tests. We perform osteogenic differentiation of MSCs on the microchip with and without electrical stimulation meanwhile monitoring single-cell impedance in real time for 21 days. The recorded impedance results show the detailed characteristic change of MSCs at the single-cell level during osteogenic differentiation, which demonstrates a significant difference between the conditions with and without electrical stimulation. The cell morphology and various staining analyses are also used to validate osteogenesis and correlate with the impedance expression. Correlation analysis of the impedance measurement, cell morphology, and various staining assays proves the great acceleration effect of the proposed electrical stimulation on osteogenic differentiation of MSCs. The proposed impedance method can monitor the dynamic process of cell development and study heterogeneity of stem cell differentiation at the single-cell level.

High-content image informatics of the structural nuclear protein NuMA parses trajectories for stem/progenitor cell lineages and oncogenic transformation

Stem and progenitor cells that exhibit significant regenerative potential and critical roles in cancer initiation and progression remain difficult to characterize. Cell fates are determined by reciprocal signaling between the cell microenvironment and the nucleus; hence parameters derived from nuclear remodeling are ideal candidates for stem/progenitor cell characterization. Here we applied high-content, single cell analysis of nuclear shape and organization to examine stem and progenitor cells destined to distinct differentiation endpoints, yet undistinguishable by conventional methods. Nuclear descriptors defined through image informatics classified mesenchymal stem cells poised to either adipogenic or osteogenic differentiation, and oligodendrocyte precursors isolated from different regions of the brain and destined to distinct astrocyte subtypes. Nuclear descriptors also revealed early changes in stem cells after chemical oncogenesis, allowing the identification of a class of cancer-mitigating biomaterials. To capture the metrology of nuclear changes, we developed a simple and quantitative "imaging-derived" parsing index, which reflects the dynamic evolution of the high-dimensional space of nuclear organizational features. A comparative analysis of parsing outcomes via either nuclear shape or textural metrics of the nuclear structural protein NuMA indicates the nuclear shape alone is a weak phenotypic predictor. In contrast, variations in the NuMA organization parsed emergent cell phenotypes and discerned emergent stages of stem cell transformation, supporting a prognosticating role for this protein in the outcomes of nuclear functions.

Quantitative multivariate analysis of dynamic multicellular morphogenic trajectories

Interrogating fundamental cell biology principles that govern tissue morphogenesis is critical to better understanding of developmental biology and engineering novel multicellular systems. Recently, functional micro-tissues derived from pluripotent embryonic stem cell (ESC) aggregates have provided novel platforms for experimental investigation; however elucidating the factors directing emergent spatial phenotypic patterns remains a significant challenge. Computational modelling techniques offer a unique complementary approach to probe mechanisms regulating morphogenic processes and provide a wealth of spatio-temporal data, but quantitative analysis of simulations and comparison to experimental data is extremely difficult. Quantitative descriptions of spatial phenomena across multiple systems and scales would enable unprecedented comparisons of computational simulations with experimental systems, thereby leveraging the inherent power of computational methods to interrogate the mechanisms governing emergent properties of multicellular biology. To address these challenges, we developed a portable pattern recognition pipeline consisting of: the conversion of cellular images into networks, extraction of novel features via network analysis, and generation of morphogenic trajectories. This novel methodology enabled the quantitative description of morphogenic pattern trajectories that could be compared across diverse systems: computational modelling of multicellular structures, differentiation of stem cell aggregates, and gastrulation of cichlid fish. Moreover, this method identified novel spatio-temporal features associated with different stages of embryo gastrulation, and elucidated a complex paracrine mechanism capable of explaining spatiotemporal pattern kinetic differences in ESC aggregates of different sizes.

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

Human bone marrow-derived multipotent mesenchymal stromal cells, often referred to as mesenchymal stem cells (MSCs), represent an attractive cell source for many regenerative medicine applications due to their potential for multi-lineage differentiation, immunomodulation, and paracrine factor secretion. A major complication for current MSC-based therapies is the lack of well-defined characterization methods that can robustly predict how they will perform in a particular in vitro or in vivo setting. Significant advances have been made with identifying molecular markers of MSC quality and potency using multivariate genomic and proteomic approaches, and more recently with advanced techniques incorporating high content imaging to assess high-dimensional single cell morphological data. We sought to expand upon current methods of high dimensional morphological analysis by investigating whether short term cell and nuclear morphological profiles of MSCs from multiple donors (at multiple passages) correlated with long term mineralization upon osteogenic induction. Using the combined power of automated high content imaging followed by automated image analysis, we demonstrated that MSC morphology after 3 days was highly correlated with 35 day mineralization and comparable to other methods of MSC osteogenesis assessment (such as alkaline phosphatase activity). We then expanded on this initial morphological characterization and identified morphological features that were highly predictive of mineralization capacities (>90% accuracy) of MSCs from additional donors and different manufacturing techniques using linear discriminant analysis. Together, this work thoroughly demonstrates the predictive power of MSC morphology for mineralization capacity and motivates further studies into MSC morphology as a predictive marker for additional in vitro and in vivo responses.

Organizational metrics of interchromatin speckle factor domains: integrative classifier for stem cell adhesion & lineage signaling

Stem cell fates on biomaterials are influenced by the complex confluence of microenvironmental cues emanating from soluble growth factors, cell-to-cell contacts, and biomaterial properties. Cell-microenvironment interactions influence the cell fate by initiating a series of outside-in signaling events that traverse from the focal adhesions to the nucleus via the cytoskeleton and modulate the sub-nuclear protein organization and gene expression. Here, we report a novel imaging-based framework that highlights the spatial organization of sub-nuclear proteins, specifically the splicing factor SC-35 in the nucleoplasm, as an integrative marker to distinguish between minute differences of stem cell lineage pathways in response to stimulatory soluble factors, surface topologies, and microscale topographies. This framework involves the high resolution image acquisition of SC-35 domains and imaging-based feature extraction to obtain quantitative nuclear metrics in tandem with machine learning approaches to generate a predictive cell state classification model. The acquired SC-35 metrics led to >90% correct classification of emergent human mesenchymal stem cell (hMSC) phenotypes in populations of hMSCs exposed for merely 3 days to basal, adipogenic, or osteogenic soluble cues, as well as varying levels of dexamethasone-induced alkaline phosphatase (ALP) expression. Early osteogenic cellular responses across a series of surface patterns, fibrous scaffolds, and micropillars were also detected and classified using this imaging-based methodology. Complex cell states resulting from inhibition of RhoGTPase, β-catenin, and FAK could be classified with >90% sensitivity on the basis of differences in the SC-35 organizational metrics. This indicates that SC-35 organization is sensitively impacted by adhesion-related signaling molecules that regulate osteogenic differentiation. Our results show that diverse microenvironment cues affect different attributes of the SC-35 organizational metrics and lead to distinct emergent organizational patterns. Taken together, these studies demonstrate that the early organization of SC-35 domains could serve as a "fingerprint" of the intracellular mechanotransductive signaling that governs growth factor- and topography-responsive stem cell states.

Human pluripotent stem cells on artificial microenvironments: a high content perspective

Self-renewing stem cell populations are increasingly considered as resources for cell therapy and tools for drug discovery. Human pluripotent stem (hPS) cells in particular offer a virtually unlimited reservoir of homogeneous cells and can be differentiated toward diverse lineages. Many diseases show impairment in self-renewal or differentiation, abnormal lineage choice or other aberrant cell behavior in response to chemical or physical cues. To investigate these responses, there is a growing interest in the development of specific assays using hPS cells, artificial microenvironments and high content analysis. Several hurdles need to be overcome that can be grouped into three areas: (i) availability of robust, homogeneous, and consistent cell populations as a starting point; (ii) appropriate understanding and use of chemical and physical microenvironments; (iii) development of assays that dissect the complexity of cell populations in tissues while mirroring specific aspects of their behavior. Here we review recent progress in the culture of hPS cells and we detail the importance of the environment surrounding the cells with a focus on synthetic material and suitable high content analysis approaches. The technologies described, if properly combined, have the potential to create a paradigm shift in the way diseases are modeled and drug discovery is performed.

Label-free morphology-based prediction of multiple differentiation potentials of human mesenchymal stem cells for early evaluation of intact cells

Precise quantification of cellular potential of stem cells, such as human bone marrow-derived mesenchymal stem cells (hBMSCs), is important for achieving stable and effective outcomes in clinical stem cell therapy. Here, we report a method for image-based prediction of the multiple differentiation potentials of hBMSCs. This method has four major advantages: (1) the cells used for potential prediction are fully intact, and therefore directly usable for clinical applications; (2) predictions of potentials are generated before differentiation cultures are initiated; (3) prediction of multiple potentials can be provided simultaneously for each sample; and (4) predictions of potentials yield quantitative values that correlate strongly with the experimental data. Our results show that the collapse of hBMSC differentiation potentials, triggered by in vitro expansion, can be quantitatively predicted far in advance by predicting multiple potentials, multi-lineage differentiation potentials (osteogenic, adipogenic, and chondrogenic) and population doubling potential using morphological features apparent during the first 4 days of expansion culture. In order to understand how such morphological features can be effective for advance predictions, we measured gene-expression profiles of the same early undifferentiated cells. Both senescence-related genes (p16 and p21) and cytoskeleton-related genes (PTK2, CD146, and CD49) already correlated to the decrease of potentials at this stage. To objectively compare the performance of morphology and gene expression for such early prediction, we tested a range of models using various combinations of features. Such comparison of predictive performances revealed that morphological features performed better overall than gene-expression profiles, balancing the predictive accuracy with the effort required for model construction. This benchmark list of various prediction models not only identifies the best morphological feature conversion method for objective potential prediction, but should also allow clinicians to choose the most practical morphology-based prediction method for their own purposes.

A high content imaging-based approach for classifying cellular phenotypes

Current methods to characterize cell-biomaterial interactions are population-based and rely on imaging or biochemical analysis of end-point biological markers. The analysis of stem cells in cultures is further challenged by the heterogeneous nature and divergent fates of stem cells, especially in complex, engineered microenvironments. Here, we describe a high content imaging-based platform capable of identifying cell subpopulations based on cell phenotype-specific morphological descriptors. This method can be utilized to identify microenvironment-responsive morphological descriptors, which can be used to parse cells from a heterogeneous cell population based on emergent phenotypes at the single-cell level and has been successfully deployed to forecast long-term cell lineage fates and screen regenerative phenotype-prescriptive biomaterials.