A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets.

BACKGROUND: Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery. METHODS: We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases. RESULTS: Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs. CONCLUSIONS: We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.

Generation of CD34 Fluorescent Reporter Human Induced Pluripotent Stem Cells for Monitoring Hematopoietic Differentiation.

Hematopoietic stem and progenitor cells (HSPCs) derived from human induced pluripotent stem cells (hiPSCs) hold great promise for disease modeling, drug screens, and eventually cell therapy approaches. During in vitro differentiation of hiPSCs into hematoendothelial progenitors, the emergence of CD34-positive cells indicates a critical step of lineage specification. To facilitate the monitoring of hematopoietic differentiation of hiPSCs, we established fluorescent reporter cells for the stem and progenitor cell marker CD34. An IRES-GFP (internal ribosome entry site green fluorescent protein) construct was introduced by CRISPR/Cas9 into the 3' untranslated region of one endogenous CD34 allele. Single-cell clones were generated after excision of the floxed puromycin resistance cassette by Cre recombination and correct insertion was confirmed by genotyping polymerase chain reaction and Southern blot. To validate their functionality, the reporter hiPSCs were in vitro differentiated toward CD34+ cells using the STEMdiff Hematopoietic Kit combined with short-term inhibition of GSK3 (glycogen synthase kinase 3). All cells expressing nuclear GFP were positive for cell surface CD34, thus allowing the direct monitoring of the differentiation of hiPSCs into CD34+ cells either by flow cytometry or confocal microscopy. After fluorescence-activated cell sorting, cells displaying high GFP expression exhibited increased colony-forming potential in the MethoCult colony-forming unit assays as compared with CD34+ cells obtained by magnetic-activated cell sorting. In summary, we have generated functional CD34 GFP reporter hiPSCs, which not only permit label-free separation of HSPCs, but also tracing of the emergence and fate of CD34+ progenitors at the single-cell level.