How Not To Drown in Data:A Guide for Biomaterial Engineers

High-throughput assays that produce hundreds of measurements per sample are powerful tools for quantifying cell–material interactions. With advances in automation and miniaturization in material fabrication, hundreds of biomaterial samples can be rapidly produced, which can then be characterized using these assays. However, the resulting deluge of data can be overwhelming. To the rescue are computational methods that are well suited to these problems. Machine learning techniques provide a vast array of tools to make predictions about cell–material interactions and to find patterns in cellular responses. Computational simulations allow researchers to pose and test hypotheses and perform experiments in silico. This review describes approaches from these two domains that can be brought to bear on the problem of analyzing biomaterial screening data

The Galapagos Chip Platform for High-Throughput Screening of Cell Adhesive Chemical Micropatterns

In vivo cells reside in a complex extracellular matrix (ECM) that presents spatially distributed biochemical and ‑physical cues at the nano- to micrometer scales. Chemical micropatterning is successfully used to generate adhesive islands to control where and how cells attach and restore cues of the ECM in vitro. Although chemical micropatterning has become a powerful tool to study cell–material interactions, only a fraction of the possible micropattern designs was covered so far, leaving many other possible designs still unexplored. Here, a high-throughput screening platform called “Galapagos chip” is developed. It contains a library of 2176 distinct subcellular chemical patterns created using mathematical algorithms and a straightforward UV-induced two-step surface modification. This approach enables the immobilization of ligands in geometrically defined regions onto cell culture substrates. To validate the system, binary RGD/polyethylene glycol patterns are prepared on which human mesenchymal stem cells are cultured, and the authors observe how different patterns affect cell and organelle morphology. As proof of concept, the cells are stained for the mechanosensitive YAP protein, and, using a machine-learning algorithm, it is demonstrated that cell shape and YAP nuclear translocation correlate. It is concluded that the Galapagos chip is a versatile platform to screen geometrical aspects of cell–ECM interaction

Mechanotransduction is a context-dependent activator of TGF-β signaling in mesenchymal stem cells

We previously found that surface topographies induce the expression of the Scxa gene, encoding Scleraxis in tenocytes. Because Scxa is a TGF-β responsive gene, we investigated the link between mechanotransduction and TGF-β signaling. We discovered that mesenchymal stem cells exposed to both micro-topographies and TGF-β2 display synergistic induction of SMAD phosphorylation and transcription of the TGF-β target genes SCX, a-SMA, and SOX9. Pharmacological perturbations revealed that Rho/ROCK/SRF signaling is required for this synergistic response. We further found an activation of the early response genes SRF and EGR1 during the early adaptation phase on micro-topographies, which coincided with higher expression of the TGF-β type-II receptor gene. Of interest, PKC activators Prostratin and Ingenol-3, known for inducing actin reorganization and activation of serum response elements, were able to mimic the topography-induced TGF-β response. These findings provide novel insights into the convergence of mechanobiology and TGF-β signaling, which can lead to improved culture protocols and therapeutic applications

Immune Modulation by Design: Using Topography to Control Human Monocyte Attachment and Macrophage Differentiation

© 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Macrophages play a central role in orchestrating immune responses to foreign materials, which are often responsible for the failure of implanted medical devices. Material topography is known to influence macrophage attachment and phenotype, providing opportunities for the rational design of “immune-instructive” topographies to modulate macrophage function and thus foreign body responses to biomaterials. However, no generalizable understanding of the inter-relationship between topography and cell response exists. A high throughput screening approach is therefore utilized to investigate the relationship between topography and human monocyte–derived macrophage attachment and phenotype, using a diverse library of 2176 micropatterns generated by an algorithm. This reveals that micropillars 5–10µm in diameter play a dominant role in driving macrophage attachment compared to the many other topographies screened, an observation that aligns with studies of the interaction of macrophages with particles. Combining the pillar size with the micropillar density is found to be key in modulation of cell phenotype from pro to anti-inflammatory states. Machine learning is used to successfully build a model that correlates cell attachment and phenotype with a selection of descriptors, illustrating that materials can potentially be designed to modulate inflammatory responses for future applications in the fight against foreign body rejection of medical devices

Designed Surface Topographies Control ICAM-1 Expression in Tonsil-Derived Human Stromal Cells

Fibroblastic reticular cells (FRCs), the T-cell zone stromal cell subtype in the lymph nodes, create a scaffold for adhesion and migration of immune cells, thus allowing them to communicate. Although known to be important for the initiation of immune responses, studies about FRCs and their interactions have been impeded because FRCs are limited in availability and lose their function upon culture expansion. To circumvent these limitations, stromal cell precursors can be mechanotranduced to form mature FRCs. Here, we used a library of designed surface topographies to trigger FRC differentiation from tonsil-derived stromal cells (TSCs). Undifferentiated TSCs were seeded on a TopoChip containing 2176 different topographies in culture medium without differentiation factors, then monitored cell morphology and the levels of ICAM-1, a marker of FRC differentiation. We identified 112 and 72 surfaces that upregulated and downregulated, respectively, ICAM-1 expression. By monitoring cell morphology, and expression of the FRC differentiation marker ICAM-1 via image analysis and machine learning, we discovered correlations between ICAM-1 expression, cell shape and design of surface topographies and confirmed our findings by using flow cytometry. Our findings confirmed that TSCs are mechano-responsive cells and identified particular topographies that can be used to improve FRC differentiation protocols

Dynamic adaptation of mesenchymal stem cell physiology upon exposure to surface micropatterns

Human mesenchymal stem (hMSCs) are defined as multi-potent colony-forming cells expressing a specific subset of plasma membrane markers when grown on flat tissue culture polystyrene. However, as soon as hMSCs are used for transplantation, they are exposed to a 3D environment, which can strongly impact cell physiology and influence proliferation, differentiation and metabolism. Strategies to control in vivo hMSC behavior, for instance in stem cell transplantation or cancer treatment, are skewed by the un-physiological flatness of the standard well plates. Even though it is common knowledge that cells behave differently in vitro compared to in vivo, only little is known about the underlying adaptation processes. Here, we used micrometer-scale defined surface topographies as a model to describe the phenotype of hMSCs during this adaptation to their new environment. We used well established techniques to compare hMSCs cultured on flat and topographically enhanced polystyreneand observed dramatically changed cell morphologies accompanied by shrinkage of cytoplasm and nucleus, a decreased overall cellular metabolism, and slower cell cycle progression resulting in a lower proliferation rate in cells exposed to surface topographies. We hypothesized that this reduction in proliferation rate effects their sensitivity to certain cancer drugs, which was confirmed by higher survival rate of hMSCs cultured on topographies exposed to paclitaxel. Thus, micro-topographies can be used as a model system to mimic the natural cell micro-environment, and be a powerful tool to optimize cell treatment in vitro

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.Peer reviewe

How not to drown in data:a guide for biomaterial engineers

High-throughput assays that produce hundreds of measurements per sample are powerful tools for quantifying cell–material interactions. With advances in automation and miniaturization in material fabrication, hundreds of biomaterial samples can be rapidly produced, which can then be characterized using these assays. However, the resulting deluge of data can be overwhelming. To the rescue are computational methods that are well suited to these problems. Machine learning techniques provide a vast array of tools to make predictions about cell–material interactions and to find patterns in cellular responses. Computational simulations allow researchers to pose and test hypotheses and perform experiments in silico. This review describes approaches from these two domains that can be brought to bear on the problem of analyzing biomaterial screening data

In silico clinical trials for pediatric orphan diseases

\u3cp\u3eTo date poor treatment options are available for patients with congenital pseudarthrosis of the tibia (CPT), a pediatric orphan disease. In this study we have performed an in silico clinical trial on 200 virtual subjects, generated from a previously established model of murine bone regeneration, to tackle the challenges associated with the small, pediatric patient population. Each virtual subject was simulated to receive no treatment and bone morphogenetic protein (BMP) treatment. We have shown that the degree of severity of CPT is significantly reduced with BMP treatment, although the effect is highly subject-specific. Using machine learning techniques we were also able to stratify the virtual subject population in adverse responders, non-responders, responders and asymptomatic. In summary, this study shows the potential of in silico medicine technologies as well as their implications for other orphan diseases.\u3c/p\u3

Scalable topographies to support proliferation and Oct4 expression by human induced pluripotent stem cells

It is well established that topographical features modulate cell behaviour, including cell morphology, proliferation and differentiation. To define the effects of topography on human induced pluripotent stem cells (iPSC), we plated cells on a topographical library containing over 1000 different features in medium lacking animal products (xeno-free). Using high content imaging, we determined the effect of each topography on cell proliferation and expression of the pluripotency marker Oct4 24 h after seeding. Features that maintained Oct4 expression also supported proliferation and cell-cell adhesion at 24 h, and by 4 days colonies of Oct4-positive, Sox2-positive cells had formed. Computational analysis revealed that small feature size was the most important determinant of pluripotency, followed by high wave number and high feature density. Using this information we correctly predicted whether any given topography within our library would support the pluripotent state at 24 h. This approach not only facilitates the design of substrates for optimal human iPSC expansion, but also, potentially, identification of topographies with other desirable characteristics, such as promoting differentiation