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

Biodegradable Honeycomb-Mimic Scaffolds Consisting of Nanofibrous Walls

The scaffold is a porous three-dimensional (3D) material that supports cell growth and tissue regeneration. Such 3D structures should be generated with simple techniques and nontoxic ingredients to mimic bio-environment and facilitate tissue regeneration. In this work, simple but powerful techniques are demonstrated for the fabrication of lamellar and honeycomb-mimic scaffolds with poly(L-lactic acid). The honeycomb-mimic scaffolds with tunable pore size ranging from 70 to 160 µm are fabricated by crystal needle-guided thermally induced phase separation in a directional freezing apparatus. The compressive modulus of the honeycomb-mimic scaffold is ≈4 times higher than that of scaffold with randomly oriented pore structure. The fabricated honeycomb-mimic scaffold exhibits a hierarchical structure from nanofibers to micro-/macro-tubular structures. Pre-osteoblast MC3T3-E1 cells cultured on the honeycomb-mimic nanofibrous scaffolds exhibit an enhanced osteoblastic phenotype, with elevated expression levels of osteogenic marker genes, than those on either porous lamellar scaffolds or porous scaffolds with randomly oriented pores. The advanced techniques for the fabrication of the honeycomb-mimic structure may potentially be used for a wide variety of advanced functional materials.

Development of an integrin αv-based universal marker, capable of both prediction and direction of stem cell fate

Integrin-mediated focal adhesion (FA) and subsequent cytoskeletal reorganization influence cell morphology, migration, and ultimately cell fate. Previous studies have used various patterned surfaces with defined macroscopic cell shapes or nanoscopic FA distributions to explore how different substrates affect the fate of human bone marrow mesenchymal stem cells (BMSCs). However, there is currently no straightforward relationship between BMSC cell fates induced by patterned surfaces and FA distribution substrates. In this study, we conducted single-cell image analysis of integrin αv-mediated FA and cell morphological features of BMSCs during biochemically induced differentiation. This enabled the identification of distinct FA features that can discriminate between osteogenic and adipogenic differentiation, demonstrating that integrin αv-mediated focal adhesion (FA) can be used as a non-invasive biomarker for real time observation. Based on these results, we developed an organized microscale fibronectin (FN) patterned surface where the fate of BMSC could be precisely manipulated by these FA features. Notably, even in the absence of any biochemical inducers, such as those contained in the differentiation medium, BMSCs cultured on these FN patterned surfaces exhibited upregulation of differentiation markers comparable to BMSCs cultured using conventional differentiation methods. Therefore, the present study reveals the application of these FA features as universal markers not only for predicting differentiation status, but also for regulating cell fate by precisely controlling the FA features with a new cell culture platform. STATEMENT OF SIGNIFICANCE: Although the effects of material physiochemical properties on cell morphology and subsequent cell fate decisions have been extensively studied, a simple yet intuitive correlation between cellular features and differentiation remains unavailable. We present a single cell image-based strategy for predicting and directing stem cell fate. By using a specific integrin isoform, integrin αv, we identified distinct geometric features that can be used as a marker for discriminating between osteogenic and adipogenic differentiation in real-time. From these data, new cell culture platforms capable of regulating cell fate by precisely controlling FA features and cell area can be developed.

Osteoblast derived extracellular vesicles induced by dexamethasone: A novel biomimetic tool for enhancing osteogenesis in vitro

Extracellular vesicles (EVs) are newly appreciated communicators involved in intercellular crosstalk, and have emerged as a promising biomimetic tool for bone tissue regeneration, overcoming many of the limitations associated with cell-based therapies. However, the significance of osteoblast-derived extracellular vesicles on osteogenesis has not been fully established. In this present study, we aim to investigate the therapeutic potential of extracellular vesicles secreted from consecutive 14 days of dexamethasone-stimulated osteoblasts (OB-EVDex) to act as a biomimetic tool for regulating osteogenesis, and to elucidate the underlying mechanisms. OB-EVdex treated groups are compared to the clinically used osteo-inductor of BMP-2 as control. Our findings revealed that OB-EVDex have a typical bilayer membrane nanostructure of, with an average diameter of 178 ± 21 nm, and that fluorescently labeled OB-EVDex were engulfed by osteoblasts in a time-dependent manner. The proliferation, attachment, and viability capacities of OB-EVDex-treated osteoblasts were significantly improved when compared to untreated cells, with the highest proliferative rate observed in the OB-EVDex + BMP-2 group. Notably, combinations of OB-EVDex and BMP-2 markedly promoted osteogenic differentiation by positively upregulating osteogenesis-related gene expression levels of RUNX2, BGLAP, SPP1, SPARC, Col 1A1, and ALPL relative to BMP-2 or OB-EVDex treatment alone. Mineralization assays also showed greater pro-osteogenic potency after combined applications of OB-EVDex and BMP-2, as evidenced by a notable increase in mineralized nodules (calcium deposition) revealed by Alkaline Phosphatase (ALP), Alizarin Red Alizarin Red staining (ARS), and von Kossa staining. Therefore, our findings shed light on the potential of OB-EVDex as a new therapeutic option for enhancing osteogenesis.

Formation of low-density electrospun fibrous network integrated mesenchymal stem cell sheet

Cell sheets combined with electrospun fibrous mats represent an attractive approach for the repair and regeneration of injured tissues. However, the conventional dense electrospun mats as supportive substrates in forming "cell sheet on fiber mat" complexes suffer from problems of limiting the cellular function and eliciting a host response upon implantation. To give full play to the role of electrospun biomimicking fibers in forming quality cell sheets, this study proposed to develop a cell-fiber integrated sheet (CFIS) featuring a spatially homogeneous distribution of cells within the fiber structure by using a low-density fibrous network for cell sheet formation. A low-density electrospun polycaprolactone (PCL) fibrous network at a density of 103.8 ± 16.3 μg cm^-2 was produced by controlling the fiber deposition for a short period of 1 min and subsequently transferred onto polydimethylsiloxane rings for facilitating cell sheet formation, in which rat bone marrow-derived mesenchymal cells were used. Using a dense electrospun PCL fibrous mat (481.5 ± 7.5 μg cm^-2) as the control, it was found that cells on the low-density fibrous network (L-G) exhibited improved capacities in spreading, proliferation, stemness maintenance and matrix-remodeling during the process of CFIS formation. Structurally, the CFIS constructs revealed strong integration between the cells and the fibrous network, thus providing excellent cohesion and physical integrity to enable strengthening of the formed cell sheet. By contrast, the cell sheet formed on the dense fibrous mat (D-G) showed a two-layer (biphasic) structure due to the limitation of cellular invasion. Moreover, such engineered CFIS was identified with enhanced immunomodulatory effects by promoting LPS-stimulated macrophages towards an M2 phenotype in vitro. Our results suggest that the CFIS may be used as a native tissue equivalent "cell sheet" for improving the efficacy of the tissue engineering approach for the repair and regeneration of impaired tissues.

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.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Design and characterization of Persian gum/polyvinyl alcohol electrospun nanofibrous scaffolds for cell culture applications

Biocompatible electrospun nanofiber scaffolds were fabricated in this study using Persian gum (PG) and poly (vinyl alcohol) (PVA) to build an artificial extracellular matrix for cell growth. The preparation procedure involves mixing various ratios of PG/PVA to be electrospun and seeded with L929 fibroblasts. Upon addition of PG up to 60% to the solutions, a 30% decrease to around 240 μs·cm^-1 is found in electrical conductivity which is in the range of semi-conductive polymers, whereas the surface tension is increased to around 3%. The fabricated scaffolds were characterized by morphological, chemical, thermal and structural analyses including SEM, FTIR spectroscopy, DSC, XRD, and tensile stress. The results showed that incorporation of 50% PG to the polymer solutions causes the formation of nanofibers with the least bead-shaped segments. All ratios of nanofibers containing PG showed significant biocompatibility with the cultured cells, which is presumably due to the radical scavenging feature of PG. The MTT and SEM analyses demonstrated that the scaffolds containing 50% PG possess the optimal cell compatibility, adhesion and proliferation properties. The fabricated PG/PVA cell culture scaffolds are potentially appropriate for wound dressing and cell culture applications in biomedicine.

Fibrous Structure and Stiffness of Designer Protein Hydrogels Synergize to Regulate Endothelial Differentiation of Bone Marrow Mesenchymal Stem Cells

Matrix stiffness and fibrous structure provided by the native extracellular matrix have been increasingly appreciated as important cues in regulating cell behaviors. Recapitulating these physical cues for cell fate regulation remains a challenge due to the inherent difficulties in making mimetic hydrogels with well-defined compositions, tunable stiffness, and structures. Here, we present two series of fibrous and porous hydrogels with tunable stiffness based on genetically engineered resilin-silk-like and resilin-like protein polymers. Using these hydrogels as substrates, the mechanoresponses of bone marrow mesenchymal stem cells to stiffness and fibrous structure were systematically studied. For both hydrogel series, increasing compression modulus from 8.5 to 14.5 and 23 kPa consistently promoted cell proliferation and differentiation. Nonetheless, the promoting effects were more pronounced on the fibrous gels than their porous counterparts at all three stiffness levels. More interestingly, even the softest fibrous gel (8.5 kPa) allowed the stem cells to exhibit higher endothelial differentiation capability than the toughest porous gel (23 kPa). The predominant role of fibrous structure on the synergistic regulation of endothelial differentiation was further explored. It was found that the stiffness signal activated Yes-associated protein (YAP), the main regulator of endothelial differentiation, via spreading of focal adhesions, whereas fibrous structure reinforced YAP activation by promoting the maturation of focal adhesions and associated F-actin alignment. Therefore, our results shed light on the interplay of physical cues in regulating stem cells and may guide the fabrication of designer proteinaceous matrices toward regenerative medicine.

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.