Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.

Background Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs. Methods The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs. Results We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this hypothesis in a large-scale study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases, as well as a therapeutic study of the mouse arthritis model. Conclusions Overall, our results support that our strategy has the potential to help prioritize diagnostic and therapeutic targets in human disease. Electronic supplementary material The online version of this article (10.1186/s13073-019-0657-3) contains supplementary material, which is available to authorized users.

Early regulators of disease may increase understanding disease mechanisms, and serve as markers for pre-symptomatic diagnosis and treatment. However, early regulators are difficult to identify because patients generally present after they are symptomatic. We hypothesized that early regulators of T-cell associated diseases could be found by identifying upstream transcription factors (TFs) in T-cell differentiation, and by prioritizing hub TFs that were enriched for disease associated This analytical strategy to identify early regulators of disease by combining gene regulatory networks with GWAS may be generally applicable for functional and clinical studies of early disease development.

5-methylcytosine (5mC) is converted to 5-hydroxymethylcytosine (5hmC) by the TET family of enzymes as part of a recently discovered active DNA de-methylation pathway. 5hmC plays important roles in regulation of gene expression and differentiation and has been implicated in T cell malignancies and autoimmunity. Here, we report early and widespread 5mC/5hmC remodeling during human CD4(+) T cell differentiation ex vivo at genes and cell-specific enhancers with known T cell function. We observe similar DNA de-methylation in CD4(+) memory T cells in vivo, indicating that early remodeling events persist long term in differentiated cells. Underscoring their important function, 5hmC loci were highly enriched for genetic variants associated with T cell diseases and T-cell-specific chromosomal interactions. Extensive functional validation of 22 risk variants revealed potentially pathogenic mechanisms in diabetes and multiple sclerosis. Our results support 5hmC-mediated DNA de-methylation as a key component of CD4(+) T cell biology in humans, with important implications for gene regulation and lineage commitment.

Multiple sclerosis (MS) is a chronic inflammatory disease of the CNS and has a varying disease course as well as variable response to treatment. Biomarkers may therefore aid personalized treatment. We tested whether in vitro activation of MS patient-derived CD4+ T cells could reveal potential biomarkers. The dynamic gene expression response to activation was dysregulated in patient-derived CD4+ T cells. By integrating our findings with genome-wide association studies, we constructed a highly connected MS gene module, disclosing cell activation and chemotaxis as central components. Changes in several module genes were associated with differences in protein levels, which were measurable in cerebrospinal fluid and were used to classify patients from control individuals. In addition, these measurements could predict disease activity after 2 years and distinguish low and high responders to treatment in two additional, independent cohorts. While further validation is needed in larger cohorts prior to clinical implementation, we have uncovered a set of potentially promising biomarkers.