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Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease. One of the basic mechanisms in this disease is the autoimmune response against the myelin sheet leading to axonal damage. There is strong evidence showing that this response is regulated by both genetic and environmental factors. In addition, the role of viruses has been extensively studied, especially in the case of human endogenous retroviruses (HERVs). However, although several associations with MS susceptibility, especially in the case of HERV-W family have been observed, the pathogenic mechanisms have remained enigmatic. To clarify these HERV-mediated mechanisms as well as the responsible HERV-W loci, we utilized RNA sequencing data obtained from the white matter of the brain of individuals with and without MS. CIBERSORTx tool was applied to estimate the proportions of neuronal, glial, and endothelial cells in the brain. In addition, the transcriptional activity of 215 HERV-W loci were analyzed. The results indicated that 65 HERV-W loci had detectable expression, of which 14 were differentially expressed between MS and control samples. Of these, 12 HERV-W loci were upregulated in MS. Expression levels of the 8 upregulated HERV-W loci had significant negative correlation with estimated oligodendrocyte proportions, suggesting that they are associated with the dynamics of oligodendrocyte generation and/or maintenance. Furthermore, Gene Set Enrichment Analysis (GSEA) results indicated that expression levels of three upregulated HERV-W loci: 2p16.2, 2q13, and Xq13.3, are associated with suppression of oligodendrocyte development and myelination. Taken together, these data suggest new HERV-W loci candidates that might take part in MS pathogenesis. [ABSTRACT FROM AUTHOR]

Schizophrenia (SCZ) is a severe mental disorder that may result in hallucinations, delusions, and extremely disordered thinking. How each cell type in the brain contributes to SCZ occurrence is still unclear. Here, we leveraged the human dorsolateral prefrontal cortex bulk RNA-seq data, then used the RNA-seq deconvolution algorithm CIBERSORTx to generate SCZ brain single-cell RNA-seq data for a comprehensive analysis to understand SCZ-associated brain cell types and gene expression changes. Firstly, we observed that the proportions of brain cell types in SCZ differed from normal samples. Among these cell types, astrocyte, pericyte, and PAX6 cells were found to have a higher proportion in SCZ patients (astrocyte: SCZ = 0.163, control = 0.145, P.adj = 4.9 × 10−4, effect size = 0.478; pericyte: SCZ = 0.057, control = 0.066, P.adj = 1.1 × 10−4, effect size = 0.519; PAX6: SCZ = 0.014, control = 0.011, P.adj = 0.014, effect size = 0.377), while the L5/6_IT_CAR3 cells and LAMP5 cells are the exact opposite (L5/6_IT_Car3: SCZ = 0.102, control = 0.108, P.adj = 0.016, effect size = 0.369; LAMP5: SCZ = 0.057, control = 0.066, P.adj = 2.2 × 10−6, effect size = 0.617). Next, we investigated gene expression in cell types and functional pathways in SCZ. We observed chemical synaptic transmission dysregulation in two types of GABAergic neurons (PVALB and LAMP5), and immune reaction involvement in GABAergic neurons (SST) and non-neuronal cell types (endothelial and oligodendrocyte). Furthermore, we observed that some differential expression genes from bulk RNA-seq displayed cell-type-specific abnormalities in the expression of molecules in SCZ. Finally, the cell types with the SCZ-related transcriptomic changes could be considered to belong to the same module since we observed two major similar coordinated transcriptomic changes across these cell types. Together, our results offer novel insights into cellular heterogeneity and the molecular mechanisms underlying SCZ. [ABSTRACT FROM AUTHOR]

The tumor micro-environment (TME) plays an important role in various cancers, including gliomas. We estimated immune cell type-specific gene expression profiles in 3 large clinically annotated glioma datasets using CIBERSORTx and LM22/LM10 blood-based immune signatures and found that the proportions and estimated gene expression patterns of specific immune cells significantly varied according to IDH mutation status. When IDH-WT and IDH-MUT tumors were considered separately, cluster-of-cluster analyses of immune cell gene expression identified groups with distinct survival outcomes. We confirmed and extended these findings by applying a signature matrix derived from single-cell RNA-sequencing data derived from 19 glioma tumor samples to the bulk profiling data, validating findings from the LM22/LM10 results. To link immune cell signatures with outcomes in checkpoint therapy, we then showed a significant association of monocytic lineage cell gene expression clusters with patient survival and with mesenchymal gene expression scores. Integrating immune cell-based gene expression with previously described malignant cell states in glioma demonstrated that macrophage M0 abundance significantly correlated with mesenchymal state in IDH-WT gliomas, with evidence of a previously implicated role of the Oncostatin-M receptor and macrophages in the mesenchymal state. Among IDH-WT tumors that were enriched for the mesenchymal cell state, the estimated M0 macrophage expression signature coordinately also trended to a mesenchymal signature. We also examined IDH-MUT tumors stratified by 1p/19q status, showing that a mesenchymal gene expression signature the M0 macrophage fraction was enriched in IDH-MUT, non-codeleted tumors. Overall, these results highlight the biological and clinical significance of the immune cell environment related to IDH mutation status, patient prognosis and the mesenchymal state in diffuse gliomas. [ABSTRACT FROM AUTHOR]

Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell-state co-occurrence patterns, we discovered ten clinically distinct multicellular communities with unexpectedly strong conservation, including three with myeloid and stromal elements linked to adverse survival, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue. [Display omitted] • EcoTyper enables large-scale profiling of cell states and multicellular ecosystems • Applicable to bulk, single-cell, and spatially resolved gene expression data • A reference atlas of 69 cell states and 10 ecosystems across 16 types of carcinoma • Carcinoma ecosystems have distinct biology, clinical outcomes, and spatial topology EcoTyper, a machine learning framework for identifying and characterizing cell states and ecosystems from gene expression data, yields insights into the cellular landscape and community structure of human carcinoma, the leading cause of cancer-related mortality. [ABSTRACT FROM AUTHOR]

Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly driven by cell-of-origin subtypes and associated genomic lesions, but also by diverse cell types and cell states in the tumor microenvironment (TME). However, dissecting these cell states and their clinical relevance at scale remains challenging. Here, we implemented EcoTyper, a machine-learning framework integrating transcriptome deconvolution and single-cell RNA sequencing, to characterize clinically relevant DLBCL cell states and ecosystems. Using this approach, we identified five cell states of malignant B cells that vary in prognostic associations and differentiation status. We also identified striking variation in cell states for 12 other lineages comprising the TME and forming cell state interactions in stereotyped ecosystems. While cell-of-origin subtypes have distinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existing subtypes and extend beyond cell-of-origin and genotypic classes. These results resolve the DLBCL microenvironment at systems-level resolution and identify opportunities for therapeutic targeting (https://ecotyper.stanford.edu/lymphoma). [Display omitted] • Large-scale profiling of cell states & cellular ecosystems in hematologic malignancies • Atlas of malignant B cell states and 12 cell types in the DLBCL tumor microenvironment • Nine DLBCL cellular ecosystems & their relationships to molecular subtypes and survival • Candidate cellular biomarkers of response to bortezomib in DLBCL Steen et al. implement EcoTyper, a machine-learning approach for dissecting cellular heterogeneity in the most common blood cancer, diffuse large B cell lymphoma (DLBCL). Forty-four cell states spanning malignant cells and the microenvironment are defined, uncovering a rich landscape of cellular ecosystems that extend beyond traditional DLBCL classifications, revealing new opportunities for therapy selection. [ABSTRACT FROM AUTHOR]

• IDO1 was demonstrated to be a diagnostic and prognostic biomarker for DN. • 22 infiltrating immune cell types were identified in DN using CIBERSORTx. • IDO1 expression was positively correlated with M1 macrophage/monocyte infiltration. • Targeted inhibition of IDO1 reduced expression of inflammatory factors in vitro. • Targeted IDO1 immunotherapy may improve the prognosis of DN patients. Indoleamine 2,3-dioxygenase 1(IDO1) has complicated roles in immune-inflammatory response regulation, but its correlation with immune cell infiltration in diabetic nephropathy (DN) remains unknown. Gene expression data were extracted from the GEO database. Differentially expressed genes (DEGs) were identified and functional correlation analysis was performed. The immune hub gene was screened using Maximal Clique Centrality, and verified in DN model mice via western blotting, immunohistochemistry, and immunofluorescence analysis. CIBERSORTx was used to assign values to immune cell infiltration in DN and determine a correlation with the hub gene. The prognostic significance of the hub gene was then validated. The 330 screened DEGs from the GEO dataset were most enriched in GO functions and KEGG pathways associated with immune inflammation. IDO1 was identified as a hub immune gene, with upregulated expression in DN model mice. IDO1 expression was positively correlated with M1 macrophages (R = 0.58, P < 0.001) and monocytes (R = 0.44, P = 0.049), and was negatively correlated with resting memory CD4 T cells (R = -0.51, P = 0.019). IDO1 expression was upregulated in peritoneal macrophages after high glucose stimulation, and inflammatory factor production was reversed by IDO1 inhibition. Higher IDO1 expression was associated with worse prognosis in DN patients via multivariate survival analysis (P < 0.001). IDO1 was identified as a diagnostic and prognostic biomarker for DN and shown to play a vital role in immune cell infiltration in DN, ascertained using microarray data and CIBERSORTx for the first time. [ABSTRACT FROM AUTHOR]

Hyperbaric oxygen (HBO) is widely applied to treat several hypoxia-related diseases. Previous studies have focused on the immediate effect of HBO-exposure induced oxidative stress on the lungs, but knowledge regarding the chronic effects from repetitive HBO exposure is limited, especially at the gene expression level. We found that repetitive HBO exposure did not alter the morphology of murine lungs. However, by deconvolution of RNA-seq from those mice lungs using CIBERSORTx and the expression profile matrices of 8 mesenchymal cell subtypes obtained from bleomycin-treated mouse lungs, we identify several mesenchymal cell subtype changes. These include increases in Col13a1 matrix fibroblasts, mesenchymal progenitors and mesothelial cell populations and decreases in lipofibroblasts, endothelial and Pdgfrb high cell populations. Our data suggest that repetitive HBO exposure may affect biological processes in the lungs such as response to wounding, extracellular matrix, vasculature development and immune response. [ABSTRACT FROM AUTHOR]