This Review covers the steps required to create high-quality image-based profiles from high-throughput microscopy images.

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 5–10 µm 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.

Stem cells respond to the physicochemical parameters of the substrate on which they grow. Quantitative material activity relationships - the relationships between substrate parameters and the phenotypes they induce - have so far poorly predicted the success of bioactive implant surfaces. In this report, we screened a library of randomly selected designed surface topographies for those inducing osteogenic differentiation of bone marrow-derived mesenchymal stem cells. Cell shape features, surface design parameters, and osteogenic marker expression were strongly correlated in vitro. Furthermore, the surfaces with the highest osteogenic potential in vitro also demonstrated their osteogenic effect in vivo: these indeed strongly enhanced bone bonding in a rabbit femur model. Our work shows that by giving stem cells specific physicochemical parameters through designed surface topographies, differentiation of these cells can be dictated.

A growing body of evidence suggests that a loss of chromosome 9 open reading frame 72 (C9ORF72) expression, formation of dipeptide-repeat proteins, and generation of RNA foci contribute to disease pathogenesis in amyotrophic lateral sclerosis and frontotemporal dementia. Although the levels of C9ORF72 transcripts and dipeptide-repeat proteins have already been examined thoroughly, much remains unknown about the role of RNA foci in C9ORF72-linked diseases. As such, we performed a comprehensive RNA foci study in an extensive pathological cohort of C9ORF72 expansion carriers (n = 63). We evaluated two brain regions using a newly developed computer-automated pipeline allowing recognition of cell nuclei and RNA foci (sense and antisense) supplemented by manual counting. In the frontal cortex, the percentage of cells with sense or antisense RNA foci was 26 or 12%, respectively. In the cerebellum, 23% of granule cells contained sense RNA foci and 1% antisense RNA foci. Interestingly, the highest percentage of cells with RNA foci was observed in cerebellar Purkinje cells (~70%). In general, more cells contained sense RNA foci than antisense RNA foci; however, when antisense RNA foci were present, they were usually more abundant. We also observed that an increase in the percentage of cells with antisense RNA foci was associated with a delayed age at onset in the frontal cortex (r = 0.43, p = 0.003), whereas no other associations with clinico-pathological features were seen. Importantly, our large-scale study is the first to provide conclusive evidence that RNA foci are not the determining factor of the clinico-pathological variability observed in C9ORF72 expansion carriers and it emphasizes that the distribution of RNA foci does not follow the pattern of neurodegeneration, stressing the complex interplay between different aspects of C9ORF72-related diseases.Electronic supplementary materialThe online version of this article (doi:10.1007/s00401-017-1725-7) contains supplementary material, which is available to authorized users.

The complex interaction of cells with biomaterials (i.e., materiobiology) plays an increasingly pivotal role in the development of novel implants, biomedical devices, and tissue engineering scaffolds to treat diseases, aid in the restoration of bodily functions, construct healthy tissues, or regenerate diseased ones. However, the conventional approaches are incapable of screening the huge amount of potential material parameter combinations to identify the optimal cell responses and involve a combination of serendipity and many series of trial-and-error experiments. For advanced tissue engineering and regenerative medicine, highly efficient and complex bioanalysis platforms are expected to explore the complex interaction of cells with biomaterials using combinatorial approaches that offer desired complex microenvironments during healing, development, and homeostasis. In this review, we first introduce materiobiology and its high-throughput screening (HTS). Then we present an in-depth of the recent progress of 2D/3D HTS platforms (i.e., gradient and microarray) in the principle, preparation, screening for materiobiology, and combination with other advanced technologies. The Compendium for Biomaterial Transcriptomics and high content imaging, computational simulations, and their translation toward commercial and clinical uses are highlighted. In the final section, current challenges and future perspectives are discussed. High-throughput experimentation within the field of materiobiology enables the elucidation of the relationships between biomaterial properties and biological behavior and thereby serves as a potential tool for accelerating the development of high-performance biomaterials.