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Abstract Background Single-cell (sc) sequencing performs unbiased profiling of individual cells and enables evaluation of less prevalent cellular populations, often missed using bulk sequencing. However, the scale and the complexity of the sc datasets poses a great challenge in its utility and this problem is further exacerbated when working with larger datasets typically generated by consortium efforts. As the scale of single cell datasets continues to increase exponentially, there is an unmet technological need to develop database platforms that can evaluate key biological hypotheses by querying extensive single-cell datasets. Large single-cell datasets like Human Cell Atlas and COVID-19 cell atlas (collection of annotated sc datasets from various human organs) are excellent resources for profiling target genes involved in human diseases and disorders ranging from oncology, auto-immunity, as well as infectious diseases like COVID-19 caused by SARS-CoV-2 virus. SARS-CoV-2 infections have led to a worldwide pandemic with massive loss of lives, infections exceeding 7 million cases. The virus uses ACE2 and TMPRSS2 as key viral entry associated proteins expressed in human cells for infections. Evaluating the expression profile of key genes in large single-cell datasets can facilitate testing for diagnostics, therapeutics, and vaccine targets, as the world struggles to cope with the on-going spread of COVID-19 infections. Main body In this manuscript we describe REVEAL: SingleCell, which enables storage, retrieval, and rapid query of single-cell datasets inclusive of millions of cells. The array native database described here enables selecting and analyzing cells across multiple studies. Cells can be selected using individual metadata tags, more complex hierarchical ontology filtering, and gene expression threshold ranges, including co-expression of multiple genes. The tags on selected cells can be further evaluated for testing biological hypotheses. One such example includes identifying the most prevalent cell type annotation tag on returned cells. We used REVEAL: SingleCell to evaluate the expression of key SARS-CoV-2 entry associated genes, and queried the current database (2.2 Million cells, 32 projects) to obtain the results in

Aims: Dermatomyositis (DM) is a progressive, idiopathic inflammatory myopathy with poorly understood pathogenesis. A hallmark of DM is an increased risk for developing breast, ovarian, and lung cancer. Since autoantibodies against anti-TIF-1-γ, a member of the tripartite motif (TRIM) proteins, has a strong association with malignancy, we examined expression of the TRIM gene family to identify pathways that may be contributing to DM pathogenesis. Materials and methods: We employed the Search Tag Analyze Resource for GEO platform to search the NCBI Gene Expression Omnibus to elucidate TRIM family gene expression as well as oncogenic drivers in DM pathology. We conducted meta-analysis of the data from human skin (60 DM vs 34 healthy) and muscle (71 DM vs 22 healthy). Key findings: We identified genes involved in innate immunity, antigen presentation, metabolism, and other cellular processes as facilitators of DM disease activity and confirmed previous observations regarding the presence of a robust interferon signature. Moreover, analysis of DM muscle samples revealed upregulation of TRIM14, TRIM22, TRIM25, TRIM27, and TRIM38. Likewise, analysis of DM skin samples showed upregulation of TRIM5, TRIM6, TRIM 14, TRIM21, TRIM34, and TRIM38 and downregulation of TRIM73. Additionally, we noted upregulation of oncogenes IGLC1, IFI44, POSTN, MYC, NPM1, and IDO1 and related this change to interferon signaling. While the clinical data associated with genetic data that was analyzed did not contain clinical data regarding malignancy in these cohorts, the observed genetic changes may be associated with homeostatic and signaling changes that relate to the increased risk in malignancy in DM. Significance: Our results implicate previously unknown genes as potential drivers of DM pathology and suggest certain TRIM family members may have disease-specific roles with potential diagnostic and therapeutic implications.

To identify genetic and environmental factors contributing to the pathogenesis of non-alcoholic fatty liver disease, we examined liver steatosis and related clinical and molecular traits in more than 100 unique inbred mouse strains, which were fed a diet rich in fat and carbohydrates. A >30-fold variation in hepatic TG accumulation was observed among the strains. Genome-wide association studies revealed three loci associated with hepatic TG accumulation. Utilizing transcriptomic data from the liver and adipose tissue, we identified several high-confidence candidate genes for hepatic steatosis, including Gde1, a glycerophosphodiester phosphodiesterase not previously implicated in triglyceride metabolism. We confirmed the role of Gde1 by in vivo hepatic over-expression and shRNA knockdown studies. We hypothesize that Gde1 expression increases TG production by contributing to the production of glycerol-3-phosphate. Our multi-level data, including transcript levels, metabolite levels, and gut microbiota composition, provide a framework for understanding genetic and environmental interactions underlying hepatic steatosis.

The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.