Self-assembled nanofiber coatings for controlling cell responses

Biomaterials based on hyaluronic acid, collagen and peptides for three-dimensional cell culture and their application in stem cell differentiation

In recent decades, three-dimensional (3D) cell culture technologies have been developed rapidly in the field of tissue engineering and regeneration, and have shown unique advantages and great prospects in the differentiation of stem cells. Herein, the article reviews the progress and advantages of 3D cell culture technologies in the field of stem cell differentiation. Firstly, 3D cell culture technologies are divided into two main categories: scaffoldless and scaffolds. Secondly, the effects of hydrogels scaffolds and porous scaffolds on stem cell differentiation in the scaffold category were mainly reviewed. Among them, hydrogels scaffolds are divided into natural hydrogels and synthetic hydrogels. Natural materials include polysaccharides, proteins, and their derivatives, focusing on hyaluronic acid, collagen and polypeptides. Synthetic materials mainly include polyethylene glycol (PEG), polyacrylic acid (PAA), polyvinyl alcohol (PVA), etc. In addition, since the preparation techniques have a large impact on the properties of porous scaffolds, several techniques for preparing porous scaffolds based on different macromolecular materials are reviewed. Finally, the future prospects and challenges of 3D cell culture in the field of stem cell differentiation are reviewed. This review will provide a useful guideline for the selection of materials and techniques for 3D cell culture in stem cell differentiation.

Effects of nanofibers on mesenchymal stem cells: environmental factors affecting cell adhesion and osteogenic differentiation and their mechanisms

Nanofibers can mimic natural tissue structure by creating a more suitable environment for cells to grow, prompting a wide application of nanofiber materials. In this review, we include relevant studies and characterize the effect of nanofibers on mesenchymal stem cells, as well as factors that affect cell adhesion and osteogenic differentiation. We hypothesize that the process of bone regeneration in vitro is similar to bone formation and healing in vivo, and the closer nanofibers or nanofibrous scaffolds are to natural bone tissue, the better the bone regeneration process will be. In general, cells cultured on nanofibers have a similar gene expression pattern and osteogenic behavior as cells induced by osteogenic supplements in vitro. Genes involved in cell adhesion (focal adhesion kinase (FAK)), cytoskeletal organization, and osteogenic pathways (transforming growth factor-β (TGF-β)/bone morphogenic protein (BMP), mitogen-activated protein kinase (MAPK), and Wnt) are upregulated successively. Cell adhesion and osteogenesis may be influenced by several factors. Nanofibers possess certain physical properties including favorable hydrophilicity, porosity, and swelling properties that promote cell adhesion and growth. Moreover, nanofiber stiffness plays a vital role in cell fate, as cell recruitment for osteogenesis tends to be better on stiffer scaffolds, with associated signaling pathways of integrin and Yes-associated protein (YAP)/transcriptional co-activator with PDZ-binding motif (TAZ). Also, hierarchically aligned nanofibers, as well as their combination with functional additives (growth factors, HA particles, etc.), contribute to osteogenesis and bone regeneration. In summary, previous studies have indicated that upon sensing the stiffness of the nanofibrous environment as well as its other characteristics, stem cells change their shape and tension accordingly, regulating downstream pathways followed by adhesion to nanofibers to contribute to osteogenesis. However, additional experiments are needed to identify major signaling pathways in the bone regeneration process, and also to fully investigate its supportive role in fabricating or designing the optimum tissue-mimicking nanofibrous scaffolds.

Use of high-content analysis and machine learning to characterize complex microbial samples via morphological analysis

High Content Analysis (HCA) has become a cornerstone of cellular analysis within the drug discovery industry. To expand the capabilities of HCA, we have applied the same analysis methods, validated in numerous mammalian cell models, to microbiology methodology. Image acquisition and analysis of various microbial samples, ranging from pure cultures to culture mixtures containing up to three different bacterial species, were quantified and identified using various machine learning processes. These HCA techniques allow for faster cell enumeration than standard agar-plating methods, identification of “viable but not plate culturable” microbe phenotype, classification of antibiotic treatment effects, and identification of individual microbial strains in mixed cultures. These methods greatly expand the utility of HCA methods and automate tedious and low-throughput standard microbiological methods.