Blog
About

16
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          Hormone-secreting cells within pancreatic islets of Langerhans play important roles in metabolic homeostasis and disease. However, their transcriptional characterization is still incomplete. Here, we sequenced the transcriptomes of thousands of human islet cells from healthy and type 2 diabetic donors. We could define specific genetic programs for each individual endocrine and exocrine cell type, even for rare δ, γ, ε, and stellate cells, and revealed subpopulations of α, β, and acinar cells. Intriguingly, δ cells expressed several important receptors, indicating an unrecognized importance of these cells in integrating paracrine and systemic metabolic signals. Genes previously associated with obesity or diabetes were found to correlate with BMI. Finally, comparing healthy and T2D transcriptomes in a cell-type resolved manner uncovered candidates for future functional studies. Altogether, our analyses demonstrate the utility of the generated single-cell gene expression resource.

          Graphical Abstract

          Highlights

          • Single-cell RNA-seq enabled molecular profiling of rare human pancreatic cells
          • Subpopulations were identified within endocrine and exocrine cell types
          • Genes associated with obesity or diabetes had correlating expression with BMI
          • Transcriptional alterations found in type 2 diabetic individuals

          Abstract

          Segerstolpe et al. use single-cell transcriptomics to generate transcriptional profiles of individual pancreatic endocrine and exocrine cells of healthy and type 2 diabetic donors. They revealed cell-type-specific gene expression and novel subpopulations, as well as gene correlations to BMI and gene expression alterations in diabetes.

          Related collections

          Most cited references 54

          • Record: found
          • Abstract: found
          • Article: not found

          Mechanisms linking obesity to insulin resistance and type 2 diabetes.

          Obesity is associated with an increased risk of developing insulin resistance and type 2 diabetes. In obese individuals, adipose tissue releases increased amounts of non-esterified fatty acids, glycerol, hormones, pro-inflammatory cytokines and other factors that are involved in the development of insulin resistance. When insulin resistance is accompanied by dysfunction of pancreatic islet beta-cells - the cells that release insulin - failure to control blood glucose levels results. Abnormalities in beta-cell function are therefore critical in defining the risk and development of type 2 diabetes. This knowledge is fostering exploration of the molecular and genetic basis of the disease and new approaches to its treatment and prevention.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Full-length RNA-seq from single cells using Smart-seq2.

            Emerging methods for the accurate quantification of gene expression in individual cells hold promise for revealing the extent, function and origins of cell-to-cell variability. Different high-throughput methods for single-cell RNA-seq have been introduced that vary in coverage, sensitivity and multiplexing ability. We recently introduced Smart-seq for transcriptome analysis from single cells, and we subsequently optimized the method for improved sensitivity, accuracy and full-length coverage across transcripts. Here we present a detailed protocol for Smart-seq2 that allows the generation of full-length cDNA and sequencing libraries by using standard reagents. The entire protocol takes ∼2 d from cell picking to having a final library ready for sequencing; sequencing will require an additional 1-3 d depending on the strategy and sequencer. The current limitations are the lack of strand specificity and the inability to detect nonpolyadenylated (polyA(-)) RNA.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Smart-seq2 for sensitive full-length transcriptome profiling in single cells.

              Single-cell gene expression analyses hold promise for characterizing cellular heterogeneity, but current methods compromise on either the coverage, the sensitivity or the throughput. Here, we introduce Smart-seq2 with improved reverse transcription, template switching and preamplification to increase both yield and length of cDNA libraries generated from individual cells. Smart-seq2 transcriptome libraries have improved detection, coverage, bias and accuracy compared to Smart-seq libraries and are generated with off-the-shelf reagents at lower cost.
                Bookmark

                Author and article information

                Contributors
                Journal
                Cell Metab
                Cell Metab
                Cell Metabolism
                Cell Press
                1550-4131
                1932-7420
                11 October 2016
                11 October 2016
                : 24
                : 4
                : 593-607
                S1550-4131(16)30436-3
                10.1016/j.cmet.2016.08.020
                5069352
                27667667
                © 2016 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                Categories
                Resource

                Cell biology

                Comments

                Comment on this article