A large proportion of heritability for prostate cancer risk remains unknown. Transcriptome‐wide association study combined with validation comparing overall levels will help to identify candidate ...genes potentially playing a role in prostate cancer development. Using data from the Genotype‐Tissue Expression Project, we built genetic models to predict normal prostate tissue gene expression using the statistical framework PrediXcan, a modified version of the unified test for molecular signatures and Joint‐Tissue Imputation. We applied these prediction models to the genetic data of 79 194 prostate cancer cases and 61 112 controls to investigate the associations of genetically determined gene expression with prostate cancer risk. Focusing on associated genes, we compared their expression in prostate tumor vs normal prostate tissue, compared methylation of CpG sites located at these loci in prostate tumor vs normal tissue, and assessed the correlations between the differentiated genes' expression and the methylation of corresponding CpG sites, by analyzing The Cancer Genome Atlas (TCGA) data. We identified 573 genes showing an association with prostate cancer risk at a false discovery rate (FDR) ≤ 0.05, including 451 novel genes and 122 previously reported genes. Of the 573 genes, 152 showed differential expression in prostate tumor vs normal tissue samples. At loci of 57 genes, 151 CpG sites showed differential methylation in prostate tumor vs normal tissue samples. Of these, 20 CpG sites were correlated with expression of 11 corresponding genes. In this TWAS, we identified novel candidate susceptibility genes for prostate cancer risk, providing new insights into prostate cancer genetics and biology.
What's new?
Although an estimated 58 percent of prostate cancers are linked to familial factors, most heritable risk variants for prostate cancer remain unidentified. In this transcriptome‐wide association study, data from the Genotype‐Tissue Expression Project was used to construct models for investigating associations between genetically determined gene expression and prostate cancer risk. In total, 573 candidate genes for prostate cancer risk were identified, including 451 newly linked to risk. Of candidate genes, 152 exhibited differential expression between tumor and adjacent normal tissue, while CpG sites in some genes showed differences in methylation. Additional evidence suggests that several genes identified influence prostate tumorigenesis.
Summary
The advent of full‐length transcriptome sequencing technologies has accelerated the discovery of novel splicing isoforms. However, existing alternative splicing (AS) tools are either tailored ...for short‐read RNA‐Seq data or designed for human and animal studies. The disparities in AS patterns between plants and animals still pose a challenge to the reliable identification and functional exploration of novel isoforms in plants.
Here, we developed integrated full‐length alternative splicing analysis (iFLAS), a plant‐optimized AS toolkit that introduced a semi‐supervised machine learning method known as positive‐unlabeled (PU) learning to accurately identify novel isoforms. iFLAS also enables the investigation of AS functions from various perspectives, such as differential AS, poly(A) tail length, and allele‐specific AS (ASAS) analyses.
By applying iFLAS to three full‐length transcriptome sequencing datasets, we systematically identified and functionally characterized maize (Zea mays) AS patterns. We found intron retention not only introduces premature termination codons, resulting in lower expression levels of isoforms, but may also regulate the length of 3′UTR and poly(A) tail, thereby affecting the functional differentiation of isoforms. Moreover, we observed distinct ASAS patterns in two genes within heterosis offspring, highlighting their potential value in breeding.
These results underscore the broad applicability of iFLAS in plant full‐length transcriptome‐based AS research.
The therapeutic application of human induced pluripotent stem cells (hiPSCs) for cartilage regeneration is largely hindered by the low yield of chondrocytes accompanied by unpredictable and ...heterogeneous off-target differentiation of cells during chondrogenesis. Here, we combine bulk RNA sequencing, single cell RNA sequencing, and bioinformatic analyses, including weighted gene co-expression analysis (WGCNA), to investigate the gene regulatory networks regulating hiPSC differentiation under chondrogenic conditions. We identify specific WNTs and MITF as hub genes governing the generation of off-target differentiation into neural cells and melanocytes during hiPSC chondrogenesis. With heterocellular signaling models, we further show that WNT signaling produced by off-target cells is responsible for inducing chondrocyte hypertrophy. By targeting WNTs and MITF, we eliminate these cell lineages, significantly enhancing the yield and homogeneity of hiPSC-derived chondrocytes. Collectively, our findings identify the trajectories and molecular mechanisms governing cell fate decision in hiPSC chondrogenesis, as well as dynamic transcriptome profiles orchestrating chondrocyte proliferation and differentiation.
Large-scale sequencing of RNA from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states
. However, current short-read single-cell RNA-sequencing ...methods have limited ability to count RNAs at allele and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells
. Here we introduce Smart-seq3, which combines full-length transcriptome coverage with a 5' unique molecular identifier RNA counting strategy that enables in silico reconstruction of thousands of RNA molecules per cell. Of the counted and reconstructed molecules, 60% could be directly assigned to allelic origin and 30-50% to specific isoforms, and we identified substantial differences in isoform usage in different mouse strains and human cell types. Smart-seq3 greatly increased sensitivity compared to Smart-seq2, typically detecting thousands more transcripts per cell. We expect that Smart-seq3 will enable large-scale characterization of cell types and states across tissues and organisms.
There is a growing appreciation of the extent of transcriptome variation across individual cells of the same cell type. While expression variation may be a byproduct of, for example, dynamic or ...homeostatic processes, here we consider whether single‐cell molecular variation per se might be crucial for population‐level function. Under this hypothesis, molecular variation indicates a diversity of hidden functional capacities within an ensemble of “identical” cells, and this functional diversity facilitates collective behavior that would be inaccessible to a homogenous population. In reviewing this topic, we explore possible functions that might be carried by a heterogeneous ensemble of cells; however, this question has proven difficult to test, both because methods to manipulate molecular variation are limited and because it is complicated to define, and measure, population‐level function. We consider several possible methods to further pursue the hypothesis that “variation is function” through the use of comparative analysis and novel experimental techniques.
Exploration of the genes with abnormal expression during the development of breast cancer is essential to provide a deeper understanding of the mechanisms involved. Transcriptome sequencing and ...bioinformatics analysis of invasive ductal carcinoma and paracancerous tissues from the same patient were performed to identify the key genes and signaling pathways related to breast cancer development.
Samples of breast tumor tissue and paracancerous breast tissue were obtained from 6 patients. Sequencing used the Illumina HiSeq platform. All. Only perfectly matched clean reads were mapped to the reference genome database, further analyzed and annotated based on the reference genome information. Differentially expressed genes (DEGs) were identified using the DESeq R package (1.10.1) and DEGSeq R package (1.12.0). Using KOBAS software to execute the KEGG bioinformatics analyses, enriched signaling pathways of DEGs involved in the occurrence of breast cancer were determined. Subsequently, quantitative real time PCR was used to verify the accuracy of the expression profile of key DEGs from the RNA-seq result and to explore the expression patterns of novel cancer-related genes on 8 different clinical individuals.
The transcriptomic sequencing results showed 937 DEGs, including 487 upregulated and 450 downregulated genes in the breast cancer specimens. Further quantitative gene expression analysis was performed and captured 252 DEGs (201 downregulated and 51 upregulated) that showed the same differential expression pattern in all libraries. Finally, 6 upregulated DEGs (CST2, DRP2, CLEC5A, SCD, KIAA1211, DTL) and 6 downregulated DEGs (STAC2, BTNL9, CA4, CD300LG, GPIHBP1 and PIGR), were confirmed in a quantitative real time PCR comparison of breast cancer and paracancerous breast tissues from 8 clinical specimens. KEGG analysis revealed various pathway changes, including 20 upregulated and 21 downregulated gene enrichment pathways. The extracellular matrix-receptor (ECM-receptor) interaction pathway was the most enriched pathway: all genes in this pathway were DEGs, including the THBS family, collagen and fibronectin. These DEGs and the ECM-receptor interaction pathway may perform important roles in breast cancer.
Several potential breast cancer-related genes and pathways were captured, including 7 novel upregulated genes and 76 novel downregulated genes that were not found in other studies. These genes are related to cell proliferation, movement and adhesion. They may be important for research into breast cancer mechanisms, particularly CST2 and CA4. A key signaling pathway, the ECM-receptor interaction signal pathway, was also identified as possibly involved in the development of breast cancer.
Key message
Single-cell transcriptomic techniques have emerged as powerful tools in plant biology, offering high-resolution insights into gene expression at the individual cell level. This review ...highlights the rapid expansion of single-cell technologies in plants, their potential in understanding plant development, and their role in advancing plant biotechnology research.
Single-cell techniques have emerged as powerful tools to enhance our understanding of biological systems, providing high-resolution transcriptomic analysis at the single-cell level. In plant biology, the adoption of single-cell transcriptomics has seen rapid expansion of available technologies and applications. This review article focuses on the latest advancements in the field of single-cell transcriptomic in plants and discusses the potential role of these approaches in plant development and expediting plant biotechnology research in the near future. Furthermore, inherent challenges and limitations of single-cell technology are critically examined to overcome them and enhance our knowledge and understanding.
Triple-negative breast cancer (TNBC) is characterized by a more aggressive clinical course with extensive inter- and intra-tumour heterogeneity. Combination of single-cell and bulk tissue ...transcriptome profiling allows the characterization of tumour heterogeneity and identifies the association of the immune landscape with clinical outcomes. We identified inter- and intra-tumour heterogeneity at a single-cell resolution. Tumour cells shared a high correlation amongst stemness, angiogenesis, and EMT in TNBC. A subset of cells with concurrent high EMT, stemness and angiogenesis was identified at the single-cell level. Amongst tumour-infiltrating immune cells, M2-like tumour-associated macrophages (TAMs) made up the majority of macrophages and displayed immunosuppressive characteristics. CIBERSORT was applied to estimate the abundance of M2-like TAM in bulk tissue transcriptome file from The Cancer Genome Atlas (TCGA). M2-like TAMs were associated with unfavourable prognosis in TNBC patients. A TAM-related gene signature serves as a promising marker for predicting prognosis and response to immunotherapy. Two commonly used machine learning methods, random forest and SVM, were applied to find the genes that were mostly associated with M2-like TAM densities in the gene signature. A neural network-based deep learning framework based on the TAM-related gene signature exhibits high accuracy in predicting the immunotherapy response.
Trophoblast cells play an essential role in the interactions between the fetus and mother. Mouse trophoblast stem (TS) cells have been derived and used as the best in vitro model for molecular and ...functional analysis of mouse trophoblast lineages, but attempts to derive human TS cells have so far been unsuccessful. Here we show that activation of Wingless/Integrated (Wnt) and EGF and inhibition of TGF-β, histone deacetylase (HDAC), and Rho-associated protein kinase (ROCK) enable long-term culture of human villous cytotrophoblast (CT) cells. The resulting cell lines have the capacity to give rise to the three major trophoblast lineages, which show transcriptomes similar to those of the corresponding primary trophoblast cells. Importantly, equivalent cell lines can be derived from human blastocysts. Our data strongly suggest that the CT- and blastocyst-derived cell lines are human TS cells, which will provide a powerful tool to study human trophoblast development and function.
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•Human TS cells have the capacity to give rise to the three major trophoblast lineages•Human TS and primary trophoblast cells have similar transcriptomes and methylomes•Human TS cells injected into mice mimic trophoblast invasion during implantation•Signaling pathways maintaining human and mouse TS cells are substantially different
Trophoblast cells are specialized cells in the placenta that mediate the interactions between the fetus and mother. Okae et al. report the derivation of human trophoblast stem cells from blastocysts and early placentas, which will provide a powerful tool to study human placental development and function.
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