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UBTD1 Drives Ovarian Cancer Progression via Mutation-Associated Alterations, Stromal Microenvironment Remodeling, and TNF/AP-1 Signaling.
BACKGROUND: The ubiquitin domain-containing protein 1 (UBTD1) is involved in protein homeostasis and cell cycle regulation, and emerging evidence suggests its role in tumor biology. However, its function in ovarian cancer (OC) remains unclear. OC is highly lethal due to late diagnosis, metastasis, and platinum resistance. This study is aimed at investigating the role of UBTD1 in OC by integrating single-cell transcriptomics, mutation and HRD-related analyses, tumor microenvironment evaluation, and experimental validation. METHODS: We examined UBTD1 expression, prognosis, somatic mutation features, and immune microenvironment characteristics utilizing public databases, such as the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Single-cell RNA-seq data were analyzed using Seurat, hdWGCNA, and CopyKAT to characterize UBTD1-associated malignant-cell states and inferred CNV burden. Based on the median UBTD1 expression, TCGA-OV samples were divided into high and low expression groups to compare mutation spectra, DNA repair pathway activity, stromal scores, and immune cell infiltration. Functional assays were performed in A2780 and SK-OV-3 cell lines following lentiviral shRNAs-mediated UBTD1 knockdown. RNA sequencing and rescue experiments were used to explore downstream pathways. RESULTS: Integrated single-cell and TCGA-OV analyses revealed that UBTD1-low tumors were enriched for genomic instability-related features, including increased inferred CNV burden and higher tumor mutation burden. By comparison, UBTD1-high tumors showed increased stromal scores and modest enrichment of selected immune-cell populations, including macrophages and neutrophils. Experimentally, UBTD1 was upregulated in OC and associated with poor prognosis. UBTD1 knockdown inhibited malignant phenotypes, enhanced cisplatin sensitivity, promoted apoptosis, and suppressed TNF/AP-1/FOS-related signaling. FOS overexpression partially reversed the effects of UBTD1 silencing. CONCLUSIONS: UBTD1 expression defines distinct mutation and microenvironmental states in OC and functionally promotes malignant phenotypes, potentially through TNF/AP-1/FOS-related signaling.
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Application of CIBERSORTx and BayesPrism to deconvolution of bulk RNA-seq data from human myocardium and skeletal muscle.
RNA-sequencing (RNA-seq) is an important tool to explore molecular mechanisms of disease. Technological advances mean this can be performed at the single-cell level, but the large sample sizes needed in clinical studies are currently prohibitively expensive and complex. Deconvolution of bulk RNA-seq offers an opportunity to bridge this gap by defining the cell lineage composition of samples. This approach is widely used in immunology studies, but currently there are no validated pipelines for researchers analysing human myocardium or skeletal muscle. Here, we describe the application and in silico validation of two pipelines to deconvolute human right atrium, left ventricle and skeletal muscle bulk RNA-seq data. Specifically, we have defined the major cell lineages of these tissues using single cell/nucleus RNA-seq data from the Heart Cell Atlas, which are then applied during deconvolution using the CIBERSORTx or BayesPrism deconvolution packages. Both pipelines gave robust estimates of the proportion of all major cell lineages in these tissues. We demonstrate their value in defining age- and sex-differences in tissue composition using bulk RNA-seq data from the GTEx consortium. Our validated pipelines can be rapidly applied by researchers working with existing or novel bulk RNA-seq of myocardium or skeletal muscle to gain novel insights.
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Standardizing single-cell approaches to osteoarthritis: Toward a comprehensive cellular atlas.
Osteoarthritis (OA) is a degenerative joint disease marked by progressive cartilage degradation and complex cellular heterogeneity. In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for dissecting the cellular composition of the osteoarthritic joint. However, constructing a complete and coherent picture of the OA single-cell landscape remains challenging, akin to assembling a puzzle from multiple sets, each with pieces of varying shapes and sizes. This editorial outlines the next steps in advancing single-cell research in OA, emphasizing the need for standardized cell type annotations and comprehensive, integrative access to both historical and newly generated datasets.
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Genetic and cellular architecture of breast cancer risk across ancestries.
BACKGROUND: Breast cancer genome-wide association studies (GWAS) have identified more than 200 susceptibility loci, but most studies are dominated by European and East Asian populations. METHODS: We analyzed breast cancer GWAS summary statistics from African (AFR), East Asian (EAS), European (EUR), and Hispanic/Latina (H/L) samples (159,297 cases and 212,102 controls). We estimated logit-scale SNP-based heritability, polygenicity, and cross-ancestry genetic correlation, partitioned heritability across functional annotations, and integrated GWAS results with the Tabula Sapiens single-cell atlas using scDRS+. RESULTS: The logit-scale heritability of breast cancer ranged from h2=0.47 (SE = 0.07) in EAS to AFR h2=0.61 (SE = 0.10), with no significant differences across ancestries (p = 0.63). The model-implied number of non-null susceptibility SNPs in the sparse normal-mixture effect-size model also varied from 4,446 (SE = 3,100) in EAS to 8,308 (SE = 2,751) in AFR, but differences were not significant across ancestries (p = 0.55). Cross-sample genetic correlations varied, with the strongest correlation between EUR and EAS (ρ=0.79, SE = 0.08) and weakest between AFR and H/L (ρ=0.26, SE = 0.24). Regulatory annotations were enriched for breast cancer heritability across samples. Integration with single-cell expression profiles implicated ancestry-shared associations with innate immune, secretory epithelial, and stromal cell types. CONCLUSION: These results indicate substantial cross-ancestry sharing of breast cancer polygenic architecture, highlight a consistent contribution of regulatory variation, and identify convergent cellular contexts that motivate functional follow-up and inform expectations for the transferability and attainable performance of common-variant risk prediction across populations.
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Harmonized cross-species cell atlases of trigeminal and dorsal root ganglia.
Sensory neurons in the dorsal root ganglion (DRG) and trigeminal ganglion (TG) are specialized to detect and transduce diverse environmental stimuli to the central nervous system. Single-cell RNA sequencing has provided insights into the diversity of sensory ganglia cell types in rodents, nonhuman primates, and humans, but it remains difficult to compare cell types across studies and species. We thus constructed harmonized atlases of the DRG and TG that describe and facilitate comparison of 18 neuronal and 11 non-neuronal cell types across six species and 31 datasets. We then performed single-cell/nucleus RNA sequencing of DRG from both human and the highly regenerative axolotl and found that the harmonized atlas also improves cell type annotation, particularly of sparse neuronal subtypes. We observed that the transcriptomes of sensory neuron subtypes are broadly similar across vertebrates, but the expression of functionally important neuropeptides and channels can vary notably. The resources presented here can guide future studies in comparative transcriptomics, simplify cell-type nomenclature differences across studies, and help prioritize targets for future analgesic development.
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Novel insights into adipose tissue heterogeneity.
When normalized to volume, adipose tissue is comprised mainly of large lipid metabolizing and storing cells called adipocytes. Strikingly, the numerical representation of non-adipocytes, composed of a wide variety of cell types found in the so-called stromal vascular fraction (SVF), outnumber adipocytes by far. Besides its function in energy storage, adipose tissue has emerged as a versatile organ that regulates systemic metabolism and has therefore constituted an attractive target for the treatment of metabolic diseases. Recent high-resolution single cells/nucleus RNA seq data exemplify an intriguingly profound diversity of both adipocytes and SVF cells in all adipose depots, and the current data, while limited, demonstrate the significance of the intra-tissue cell composition in shaping the overall functionality of this tissue. Due to the complexity of adipose tissue, our understanding of the biological relevance of this heterogeneity and plasticity is fractional. Therefore, establishing atlases of adipose tissue cell heterogeneity is the first step towards generating an understanding of these functionalities. In this review, we will describe the current knowledge on adipose tissue cell composition and the heterogeneity of single-cell RNA sequencing, including the technical limitations.
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Comprehensive single-cell transcriptional profiling of a multicellular organism.
To resolve cellular heterogeneity, we developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial indexing RNA sequencing). We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 larval stage, which provided >50-fold "shotgun" cellular coverage of its somatic cell composition. From these data, we defined consensus expression profiles for 27 cell types and recovered rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. We integrated these profiles with whole-animal chromatin immunoprecipitation sequencing data to deconvolve the cell type-specific effects of transcription factors. The data generated by sci-RNA-seq constitute a powerful resource for nematode biology and foreshadow similar atlases for other organisms.
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Pan-cancer analysis implicates novel insights of lactate metabolism into immunotherapy response prediction and survival prognostication.
BACKGROUND: Immunotherapy has emerged as a potent clinical approach for cancer treatment, but only subsets of cancer patients can benefit from it. Targeting lactate metabolism (LM) in tumor cells as a method to potentiate anti-tumor immune responses represents a promising therapeutic strategy. METHODS: Public single-cell RNA-Seq (scRNA-seq) cohorts collected from patients who received immunotherapy were systematically gathered and scrutinized to delineate the association between LM and the immunotherapy response. A novel LM-related signature (LM.SIG) was formulated through an extensive examination of 40 pan-cancer scRNA-seq cohorts. Then, multiple machine learning (ML) algorithms were employed to validate the capacity of LM.SIG for immunotherapy response prediction and survival prognostication based on 8 immunotherapy transcriptomic cohorts and 30 The Cancer Genome Atlas (TCGA) pan-cancer datasets. Moreover, potential targets for immunotherapy were identified based on 17 CRISPR datasets and validated via in vivo and in vitro experiments. RESULTS: The assessment of LM was confirmed to possess a substantial relationship with immunotherapy resistance in 2 immunotherapy scRNA-seq cohorts. Based on large-scale pan-cancer data, there exists a notably adverse correlation between LM.SIG and anti-tumor immunity as well as imbalance infiltration of immune cells, whereas a positive association was observed between LM.SIG and pro-tumorigenic signaling. Utilizing this signature, the ML model predicted immunotherapy response and prognosis with an AUC of 0.73/0.80 in validation sets and 0.70/0.87 in testing sets respectively. Notably, LM.SIG exhibited superior predictive performance across various cancers compared to published signatures. Subsequently, CRISPR screening identified LDHA as a pan-cancer biomarker for estimating immunotherapy response and survival probability which was further validated using immunohistochemistry (IHC) and spatial transcriptomics (ST) datasets. Furthermore, experiments demonstrated that LDHA deficiency in pancreatic cancer elevated the CD8+ T cell antitumor immunity and improved macrophage antitumoral polarization, which in turn enhanced the efficacy of immunotherapy. CONCLUSIONS: We unveiled the tight correlation between LM and resistance to immunotherapy and further established the pan-cancer LM.SIG, holds the potential to emerge as a competitive instrument for the selection of patients suitable for immunotherapy.
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Exploring the utility of snRNA-seq in profiling human bladder tissue: A comprehensive comparison with scRNA-seq.
Single cell sequencing technologies have revolutionized our understanding of biology by mapping cell diversity and gene expression in healthy and diseased tissues. While single-cell RNA sequencing (scRNA-seq) has been widely used, interest in single-nucleus RNA sequencing (snRNA-seq) is growing due to its benefits, including the ability to analyze archival tissues and capture rare cell types that are challenging to dissociate. However, comparative studies across tissues have yielded mixed results, with some reporting enhanced cell type retention using snRNA-seq while others finding cell type identification to be challenging in snRNA-seq data. The GUDMAP consortium aims to construct a molecular atlas of the lower urinary tract (LUT); thus, we set out to determine the strengths and limitations of each approach in characterizing LUT cell types. Using the human bladder, we determined that scRNA-seq offered more discriminative gene sets for identification while snRNA-seq could facilitate capture of previously underrepresented cell types.
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An Atlas of Thyroid Hormone Receptors' Target Genes in Mouse Tissues.
We gathered available RNA-seq and ChIP-seq data in a single database to better characterize the target genes of thyroid hormone receptors in several cell types. This database can serve as a resource to analyze the mode of action of thyroid hormone (T3). Additionally, it is an easy-to-use and convenient tool to obtain information on specific genes regarding T3 regulation or to extract large gene lists of interest according to the users' criteria. Overall, this atlas is a unique compilation of recent sequencing data focusing on T3, its receptors, modes of action, targets and roles, which may benefit researchers within the field. A preliminary analysis indicates extensive variations in the repertoire of target genes where transcription is upregulated by chromatin-bound nuclear receptors. Although it has a major influence, chromatin accessibility is not the only parameter that determines the cellular selectivity of the hormonal response.