Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom.

5-Aminolevulinic acid (5-ALA) is a delta amino acid naturally present in every living cell of the human body. 5-ALA is produced in the mitochondria as the first product of the porphyrin synthesis pathway and composes heme; exogenously supplemented 5-ALA helps in upregulating mitochondrial functions. Mitochondrial dysfunction has been associated with the pathophysiology of diabetes mellitus. Thus, in this review, we evaluate the mechanisms of action and adverse effects of common medications used to treat type 2 diabetes mellitus as well as 5-ALA including its mechanism and possible use in diabetes management.

Strong phenotype-genotype associations have been reported across brain diseases. However, understanding underlying gene regulatory mechanisms remains challenging, especially at the cellular level. To address this, we integrated the multi-omics data at the cellular resolution of the human brain: cell-type chromatin interactions, epigenomics and single cell transcriptomics, and predicted cell-type gene regulatory networks linking transcription factors, distal regulatory elements and target genes (e.g., excitatory and inhibitory neurons, microglia, oligodendrocyte). Using these cell-type networks and disease risk variants, we further identified the cell-type disease genes and regulatory networks for schizophrenia and Alzheimer's disease. The celltype regulatory elements (e.g., enhancers) in the networks were also found to be potential pleiotropic regulatory loci for a variety of diseases. Further enrichment analyses including gene ontology and KEGG pathways revealed potential novel cross-disease and disease-specific molecular functions, advancing knowledge on the interplays among genetic, transcriptional and epigenetic risks at the cellular resolution between neurodegenerative and neuropsychiatric diseases. Finally, we summarized our computational analyses as a general-purpose pipeline for predicting gene regulatory networks via multi-omics data.Recent analyses have also revealed that brain disease risk variants are located in non-coding regulatory elements (e.g., enhancers) and that the risk genes likely have cell-type specific effects including neuronal and non-neuronal types [25,26]. In addition, recent single-cell studies 68 TFs,

IntroductionMetabolic reprogramming of cancer and immune cells occurs during tumorigenesis and has a significant impact on cancer progression. Unfortunately, current techniques to measure tumor and immune cell metabolism require sample destruction and/or cell isolations that remove the spatial context. Two-photon fluorescence lifetime imaging microscopy (FLIM) of the autofluorescent metabolic coenzymes nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD) provides in vivo images of cell metabolism at a single cell level.MethodsHere, we report an immunocompetent mCherry reporter mouse model for immune cells that express CD4 either during differentiation or CD4 and/or CD8 in their mature state and perform in vivo imaging of immune and cancer cells within a syngeneic B78 melanoma model. We also report an algorithm for single cell segmentation of mCherry-expressing immune cells within in vivo images.ResultsWe found that immune cells within B78 tumors exhibited decreased FAD mean lifetime and an increased proportion of bound FAD compared to immune cells within spleens. Tumor infiltrating immune cell size also increased compared to immune cells from spleens. These changes are consistent with a shift towards increased activation and proliferation in tumor infiltrating immune cells compared to immune cells from spleens. Tumor infiltrating immune cells exhibited increased FAD mean lifetime and increased protein-bound FAD lifetime compared to B78 tumor cells within the same tumor. Single cell metabolic heterogeneity was observed in both immune and tumor cells in vivo.DiscussionThis approach can be used to monitor single cell metabolic heterogeneity in tumor cells and immune cells to study promising treatments for cancer in the native in vivo context.