RESUMEN
The Tripartite motif-containing 28 (TRIM28) transcriptional cofactor is significantly upregulated in high-grade and metastatic prostate cancers. To study the role of TRIM28 in prostate cancer progression in vivo, we generated a genetically-engineered mouse model, combining prostate-specific inactivation of Trp53, Pten and Trim28. Trim28 inactivated NPp53T mice developed an inflammatory response and necrosis in prostate lumens. By conducting single-cell RNA sequencing, we found that NPp53T prostates had fewer luminal cells resembling proximal luminal lineage cells, which are cells with progenitor activity enriched in proximal prostates and prostate invagination tips in wild-type mice with analogous populations in human prostates. However, despite increased apoptosis and reduction of cells expressing proximal luminal cell markers, we found that NPp53T mouse prostates evolved and progressed to invasive prostate carcinoma with a shortened overall survival. Altogether, our findings suggest that TRIM28 promotes expression of proximal luminal cell markers in prostate tumor cells and provides insights into TRIM28 function in prostate tumor plasticity.
Asunto(s)
Plasticidad de la Célula , Neoplasias de la Próstata , Humanos , Masculino , Ratones , Animales , Neoplasias de la Próstata/patología , Proteína 28 que Contiene Motivos Tripartito/genética , Proteína 28 que Contiene Motivos Tripartito/metabolismo , Próstata/patología , Modelos Animales de Enfermedad , Células Madre Neoplásicas/patologíaRESUMEN
Currently, the detection of single nucleotide variants (SNVs) from 10 x Genomics single-cell RNA sequencing data (scRNA-seq) is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gaining of information regarding SNV assessments from individual cell scRNA-seq data, wherein the alignments are split by cellular barcode prior to the variant call. We also reanalyze publicly available data on the MCF7 cell line during anticancer treatment. We assessed SNV calls by three variant callers-GATK, Strelka2, and Mutect2, in combination with a method for the cell-level tabulation of the sequencing read counts bearing variant alleles-SCReadCounts (single-cell read counts). Our analysis shows that variant calls on individual cell alignments identify at least a two-fold higher number of SNVs as compared to the pooled scRNA-seq; these SNVs are enriched in novel variants and in stop-codon and missense substitutions. Our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes the need for cell-level variant detection approaches and tools, which can contribute to the understanding of the cellular heterogeneity and the relationships to phenotypes, and help elucidate somatic mutation evolution and functionality.
Asunto(s)
Polimorfismo de Nucleótido Simple , RNA-Seq/métodos , Análisis de la Célula Individual/métodos , Humanos , Células MCF-7 , Alineación de Secuencia/métodosRESUMEN
BACKGROUND: Recently, pioneering expression quantitative trait loci (eQTL) studies on single cell RNA sequencing (scRNA-seq) data have revealed new and cell-specific regulatory single nucleotide variants (SNVs). Here, we present an alternative QTL-related approach applicable to transcribed SNV loci from scRNA-seq data: scReQTL. ScReQTL uses Variant Allele Fraction (VAFRNA) at expressed biallelic loci, and corelates it to gene expression from the corresponding cell. RESULTS: Our approach employs the advantage that, when estimated from multiple cells, VAFRNA can be used to assess effects of SNVs in a single sample or individual. In this setting scReQTL operates in the context of identical genotypes, where it is likely to capture RNA-mediated genetic interactions with cell-specific and transient effects. Applying scReQTL on scRNA-seq data generated on the 10 × Genomics Chromium platform using 26,640 mesenchymal cells derived from adipose tissue obtained from three healthy female donors, we identified 1272 unique scReQTLs. ScReQTLs common between individuals or cell types were consistent in terms of the directionality of the relationship and the effect size. Comparative assessment with eQTLs from bulk sequencing data showed that scReQTL analysis identifies a distinct set of SNV-gene correlations, that are substantially enriched in known gene-gene interactions and significant genome-wide association studies (GWAS) loci. CONCLUSION: ScReQTL is relevant to the rapidly growing source of scRNA-seq data and can be applied to outline SNVs potentially contributing to cell type-specific and/or dynamic genetic interactions from an individual scRNA-seq dataset. AVAILABILITY: https://github.com/HorvathLab/NGS/tree/master/scReQTL.
Asunto(s)
ARN Citoplasmático Pequeño , Femenino , Expresión Génica , Perfilación de la Expresión Génica , Estudio de Asociación del Genoma Completo , Humanos , Análisis de Secuencia de ARN , Análisis de la Célula Individual , Programas InformáticosRESUMEN
With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAFRNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3'-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics.