Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets
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ABSTRACT:
ORGANISM(S): Mus musculus Homo sapiens
PROVIDER: GSE147203 | GEO | 2020/03/19
REPOSITORIES: GEO
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Project description:BackgroundSingle-cell RNA-seq (scRNA-seq) is emerging as a powerful tool to dissect cell-specific effects of drug treatment in complex tissues. This application requires high levels of precision, robustness, and quantitative accuracy-beyond those achievable with existing methods for mainly qualitative single-cell analysis. Here, we establish the use of standardized reference cells as spike-in controls for accurate and robust dissection of single-cell drug responses.ResultsWe find that contamination by cell-free RNA can constitute up to 20% of reads in human primary tissue samples, and we show that the ensuing biases can be removed effectively using a novel bioinformatics algorithm. Applying our method to both human and mouse pancreatic islets treated ex vivo, we obtain an accurate and quantitative assessment of cell-specific drug effects on the transcriptome. We observe that FOXO inhibition induces dedifferentiation of both alpha and beta cells, while artemether treatment upregulates insulin and other beta cell marker genes in a subset of alpha cells. In beta cells, dedifferentiation and insulin repression upon artemether treatment occurs predominantly in mouse but not in human samples.ConclusionsThis new method for quantitative, error-correcting, scRNA-seq data normalization using spike-in reference cells helps clarify complex cell-specific effects of pharmacological perturbations with single-cell resolution and high quantitative accuracy.
| S-EPMC7201533 | biostudies-literature
Project description:Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool to dissect cell-specific effects of drug treatment in complex tissues. This application requires high levels of precision, robustness, and quantitative accuracy beyond those achievable with current methods for qualitative single cell characterization. Here, we establish the use of standardized reference cells as spike-in controls for accurate and robust dissection of single-cell drug responses. We find that contamination by cell-free RNA can constitute up to 20% of reads in human primary tissue samples, and show that the ensuing biases can effectively be removed by a novel bioinformatics method. Applying this method to both human and mouse pancreatic islets treated ex vivo, we obtain an accurate and quantitative assessment of cell-specific drug effects on the transcriptome. We observe that FOXO inhibition induces dedifferentiation of both alpha and beta cells, while artemether treatment upregulates insulin and other beta cell marker genes in a subset of alpha cells. In beta cells, dedifferentiation and insulin repression upon artemether treatment occurs predominantly in mouse but not in human samples. This new method for quantitative, error-correcting, scRNA-seq data normalization using spike-in reference cells allows to clarify the complexities of the cell-specific effects of pharmacological perturbations with single-cell resolution and high quantitative accuracy.
2020-03-19 | GSE142465 | GEO
Project description:Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool to dissect cell-specific effects of drug treatment in complex tissues. Here we treated human islet ex vivo with drugs to study the changes in their cellular identity. Artemether treatment upregulated insulin and other beta cell marker genes in a subset of alpha cells.
2020-03-20 | GSE147202 | GEO
Project description:Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets
| PRJNA613399 | ENA
Project description:Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets [10X]
| PRJNA597064 | ENA
Project description:Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets [Drop-seq]
| PRJNA613402 | ENA
Project description:The current world-wide epidemic of diabetes has prompted attempts to generate new sources of insulin-producing cells for cell replacement therapy. An inherent challenge in many of these strategies is the lack of cell-surface markers permitting isolation and characterization of specific cell types from differentiating stem cell populations. Here we introduce an iterative proteomics procedure allowing tag-free isolation of cell types based on their function. Our method detects and associates specific cell-surface markers with particular cell functionality by coupling cell capture on antibody arrays with immunofluorescent labeling. Using this approach in an iterative manner, we discovered marker combinations capable of enriching for discrete pancreatic cell subtypes from human islets of Langerhans: insulin-producing beta cells (CD9high/CD56+), glucagon-producing alpha cells (CD9-/CD56+) and trypsin-producing acinar cells (CD9-/CD56-). This strategy may assist future beta cell research and the development of diagnostic tools for diabetes. It can also be applied more generally for function-based purification of desired cell types from other limited and heterogeneous biological samples.
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Project description:Pancreatic islets are comprised of multiple endocrine cell types that produce hormones required for glucose homeostasis, and islet dysfunction is a major factor in the development of type 1 and type 2 diabetes (T1D, T2D). Numerous studies have generated gene expression profiles in individual islet cell types using single cell assays. However, there is no canonical reference of gene expression in islet cell types in both health and disease that is also easily accessible for researchers to access, query, and use in bioinformatics pipelines. Here we present an integrated reference map of islet cell type-specific gene expression from 192,203 cells derived from single cell RNA-seq assays of 65 non-diabetic, T1D autoantibody positive (Aab+), T1D, and T2D donors from the Human Pancreas Analysis Program. We identified 10 endocrine and non-endocrine cell types as well as sub-populations of several cell types, and defined sets of marker genes for each cell type and sub-population. We tested for differential expression within each cell type in T1D Aab+, T1D, and T2D states, and identified 1,701 genes with significant changes in expression in any cell type. Most changes were observed in beta cells in T1D, and, by comparison, there were almost no genes with changes in T1D Aab+. To facilitate user interaction with this reference, we provide the data using several single cell visualization and reference mapping tools as well as open-access analytical pipelines used to create this reference. The results will serve as a valuable resource to investigators studying islet biology and diabetes.
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Project description:Quantitative mass spectrometry (MS) is a key technique in many research areas (1), including proteomics, metabolomics, glycomics, and lipidomics. Because all of the corresponding molecules can be described by chemical formulas, universal quantification tools are highly desirable. Here, we present pyQms, an open-source software for accurate quantification of all types of molecules measurable by MS. pyQms uses isotope pattern matching that offers an accurate quality assessment of all quantifications and the ability to directly incorporate mass spectrometer accuracy. pyQms is, due to its universal design, applicable to every research field, labeling strategy, and acquisition technique. This opens ultimate flexibility for researchers to design experiments employing innovative and hitherto unexplored labeling strategies. Importantly, pyQms performs very well to accurately quantify partially labeled proteomes in large scale and high throughput, the most challenging task for a quantification algorithm.
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Project description:Heterozygous HNF1A mutations cause pancreatic-islet beta-cell dysfunction and monogenic diabetes (MODY3). Hnf1alpha is known to regulate numerous hepatic genes, yet knowledge of its function in pancreatic islets is more limited. We now show that Hnf1a deficiency in mice leads to highly tissue-specific changes in the expression of genes involved in key functions of both islets and liver. To gain insights into the mechanisms of tissue-specific Hnf1alpha regulation, we integrated expression studies of Hnf1a-deficient mice with identification of direct Hnf1alpha targets. We demonstrate that Hnf1alpha can bind in a tissue-selective manner to genes that are expressed only in liver or islets. We also show that Hnf1alpha is essential only for the transcription of a minor fraction of its direct-target genes. Even among genes that were expressed in both liver and islets, the subset of targets showing functional dependence on Hnf1alpha was highly tissue specific. This was partly explained by the compensatory occupancy by the paralog Hnf1beta at selected genes in Hnf1a-deficient liver. In keeping with these findings, the biological consequences of Hnf1a deficiency were markedly different in islets and liver. Notably, Hnf1a deficiency led to impaired large-T-antigen-induced growth and oncogenesis in beta cells yet enhanced proliferation in hepatocytes. Collectively, these findings show that Hnf1alpha governs broad, highly tissue-specific genetic programs in pancreatic islets and liver and reveal key consequences of Hnf1a deficiency relevant to the pathophysiology of monogenic diabetes.
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