Significance The vast majority of autoimmune diseases are polygenic, and causal loci uncovered by genetic-mapping studies explain only a minority of the heritable contribution to trait variation. Multiple explanations for this missing heritability include rare meaningful variants, rare copy number variations or deletions, epistasis, epigenetics, disease heterogeneity, and rare or infrequent variants that segregate within individual families (even within monozygotic twins). Here we demonstrate that experimental models of spontaneous autoimmune diseases may be invaluable tools to map rare germline variants impacting disease susceptibility traits. We identified a variant of the dual-specificity phosphatase 10 encoding gene that accelerates disease in an autoimmune type 1 diabetes model, the nonobese diabetic mouse.
Abstract Background Spatially resolved profiling technologies to quantify transcriptomes, epigenomes, and proteomes have been emerging as groundbreaking methods for comprehensive molecular characterizations. Dimensionality reduction and visualization is an essential step to analyze and interpret spatially resolved profiling data. However, state-of-the-art dimensionality reduction methods for single-cell sequencing data, such as the t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), were not tailored for spatially resolved profiling data. Results Here we developed a spatially resolved t-SNE (SpaSNE) method to integrate both spatial and molecular information. We applied it to a variety of public spatially resolved profiling datasets that were generated from 3 experimental platforms and consisted of cells from different diseases, tissues, and cell types. To compare the performances of SpaSNE, t-SNE, and UMAP, we applied them to 4 spatially resolved profiling datasets obtained from 3 distinct experimental platforms (Visium, STARmap, and MERFISH) on both diseased and normal tissues. Comparisons between SpaSNE and these state-of-the-art approaches reveal that SpaSNE achieves more accurate and meaningful visualization that better elucidates the underlying spatial and molecular data structures. Conclusions This work demonstrates the broad application of SpaSNE for reliable and robust interpretation of cell types based on both molecular and spatial information, which can set the foundation for many subsequent analysis steps, such as differential gene expression and trajectory or pseudotime analysis on the spatially resolved profiling data.
The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.
Significance Human IFN-inducible protein-16 (IFI16) is an essential intracellular foreign DNA receptor of innate immunity and also implicated in several autoimmune disorders. However, little is known about molecular mechanisms that underlie its function. We show here that IFI16 cooperatively assembles into filaments on dsDNA. IFI16 thus oligomerizes even in the presence of excess DNA, and it is the non–DNA-binding pyrin domain of IFI16 that drives the filament assembly. These results provide unifying mechanistic explanations for several previous in vivo observations regarding IFI16 and also suggest that assembling filaments on foreign nucleic acids is a broad host defense strategy.
Metastasis is the principal cause of cancer related deaths. Tumor invasion is essential for metastatic spread. However, determinants of invasion are poorly understood. We addressed this knowledge gap by leveraging a unique attribute of kidney cancer. Renal tumors invade into large vessels forming tumor thrombi (TT) that migrate extending sometimes into the heart. Over a decade, we prospectively enrolled 83 ethnically-diverse patients undergoing surgical resection for grossly invasive tumors at UT Southwestern Kidney Cancer Program. In this study, we perform comprehensive histological analyses, integrate multi-region genomic studies, generate in vivo models, and execute functional studies to define tumor invasion and metastatic competence. We find that invasion is not always associated with the most aggressive clone. Driven by immediate early genes, invasion appears to be an opportunistic trait attained by subclones with diverse oncogenomic status in geospatial proximity to vasculature. We show that not all invasive tumors metastasize and identify determinants of metastatic competency. TT associated with metastases are characterized by higher grade, mTOR activation and a particular immune contexture. Moreover, TT grade is a better predictor of metastasis than overall tumor grade, which may have implications for clinical practice.
In in situ capturing-based spatial transcriptomics, spots of the same size and printed at fixed locations cannot precisely capture the randomly-located single cells, therefore inherently failing to profile transcriptome at the single-cell level. To this end, we present STIE, an Expectation Maximization algorithm that aligns the spatial transcriptome to its matched histology image-based nuclear morphology and recovers missing cells from ~70% gap area, thereby achieving the real single-cell level and whole-slide scale deconvolution, convolution, and clustering for both low- and high-resolution spots. STIE characterizes cell-type-specific gene expression and demonstrates outperforming concordance with true cell-type-specific transcriptomic signatures than the other spot- and subspot-level methods. Furthermore, STIE reveals the single-cell level insights, for instance, lower actual spot resolution than its reported spot size, unbiased evaluation of cell type colocalization, superior power of high-resolution spot in distinguishing nuanced cell types, and spatial cell-cell interactions at the single-cell level other than spot level.
Breast cancer is the most commonly diagnosed cancer in women, with 10% of disease attributed to hereditary factors. Although BRCA1 and BRCA2 account for a high percentage of hereditary cases, there are more than 25 susceptibility genes that differentially impact the risk for breast cancer. Traditionally, germline testing for breast cancer was performed by Sanger dideoxy terminator sequencing in a reflexive manner, beginning with BRCA1 and BRCA2. The introduction of next-generation sequencing (NGS) has enabled the simultaneous testing of all genes implicated in breast cancer resulting in diagnostic labs offering large, comprehensive gene panels. However, some physicians prefer to only test for those genes in which established surveillance and treatment protocol exists. The NGS based BRCAplus test utilizes a custom tiled PCR based target enrichment design and bioinformatics pipeline coupled with array comparative genomic hybridization (aCGH) to identify mutations in the six high-risk genes: BRCA1, BRCA2, PTEN, TP53, CDH1, and STK11. Validation of the assay with 250 previously characterized samples resulted in 100% detection of 3,025 known variants and analytical specificity of 99.99%. Analysis of the clinical performance of the first 3,000 BRCAplus samples referred for testing revealed an average coverage greater than 9,000X per target base pair resulting in excellent specificity and the sensitivity to detect low level mosaicism and allele-drop out. The unique design of the assay enabled the detection of pathogenic mutations missed by previous testing. With the abundance of NGS diagnostic tests being released, it is essential that clinicians understand the advantages and limitations of different test designs.
Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.
