Volkova, A. | Search bioRxiv

bioRxiv2025

Vascular-Perfusable Human 3D Brain-on-Chip

Development and delivery of treatments for neurological diseases are limited by the tight and selective human blood-brain barrier (BBB). Although animal models have been important research and preclinical tools, the rodent BBB exhibits species differences and fails to capture the complexity of human genetics. Microphysiological systems incorporating human-derived cells hold great potential for modeling disease and therapeutic development, with advantages in screening throughput, real-time monitoring, and tunable genetic backgrounds when combined with induced pluripotent stem cell (iPSC) technology. Existing 3D BBB-on-chip systems have incorporated iPSC-derived endothelial cells but not the other major brain cell types from iPSCs, each of which contributes to brain physiology and disease. Here we developed a 3D Brain-Chip system incorporating endothelial cells, pericytes, astrocytes, neurons, microglia, and oligodendroglia from iPSCs. To enable this multicellular 3D co-culture in-chip, we designed a GelChip microfluidic platform using a 3D printing-based approach and dextran-based engineered hydrogel. Leveraging this platform, we co-cultured and characterized iPSC-derived brain-on-chips and modeled the brain microvasculature of APOE4, the strongest known genetic risk factor for sporadic Alzheimers disease. These 3D brain-on-chips provide a versatile system to assess BBB vascular morphology and function, investigate downstream neurological effects in disease, and screen therapeutics to optimize delivery to the brain. Significance StatementThe blood-brain barrier (BBB) is both a contributing factor to neurological disease and a major obstacle to its treatment, yet human-relevant models remain limited. Most existing brain-on-chip systems incorporate only subsets of BBB cell types and cannot capture the full cellular complexity of the human neurovascular unit. Here, we establish a vascular-perfusable 3D Brain-Chip using human induced pluripotent stem cell-derived brain cells including endothelial cells, pericytes, astrocytes, neurons, microglia, and oligodendroglia. This system enables systematic analysis of human genetic risk factors, such as APOE4 in Alzheimers disease, and provides a powerful platform to investigate BBB function and dysfunction and accelerate the development of more effective neurological therapies.

neuroscience↗

bioRxiv2019

Predictive Metagenomic Analysis of Autoimmune Disease Identifies Robust Autoimmunity and Disease Specific Signatures

Within the last decade, numerous studies have demonstrated changes in the gut microbiome associated with specific autoimmune diseases. Due to differences in study design, data quality control, analysis and statistical methods, many results of these studies are inconsistent and incomparable. To better understand the relationship between the intestinal microbiome and autoimmunity, we have completed a comprehensive re-analysis of 42 studies focusing on the gut microbiome in twelve autoimmune diseases to identify a microbial signature predictive of multiple sclerosis (MS), inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and general autoimmune disease using both 16S rRNA sequencing data and shotgun metagenomics data. To do this, we used four machine learning algorithms, random forest, eXtreme Gradient Boosting (XGBoost), ridge regression and support vector machine with radial kernel and recursive feature elimination to rank disease predictive taxa comparing disease vs. healthy participants and pairwise comparisons of each disease. Comparing the performance of these models, we found XGBoost and random forest, tree-based methods capable of handling sparse multidimensional data, to consistently produce the best results. Through this modeling, we identified a number of taxa consistently identified as dysregulated in a general autoimmune disease model including Odoribacter, Lachnospiraceae Clostridium and Mogibacteriaceae implicating all as potential factors connecting the gut microbiome and to autoimmune response. Further, we computed pairwise comparison models to identify disease specific taxa signatures highlighting a role for Peptostreptococcaceae and Ruminococcaceae Gemmiger in IBD and Akkermansia, Butyricicoccus and Mogibacteriaceae in MS. We then connected a subset of these taxa with potential metabolic alterations based on metagenomic/metabolomic correlation analysis, identifying 250 metabolites associated with autoimmunity-predictive taxa.

systems biology↗