Two single-cell sequencing papers published

Bock C, Farlik M, Sheffield NC (2016). Multi-omics of Single Cells: Strategies and Applications. Trends in Biotechnology, doi:10.1016/j.tibtech.2016.04.004.

Abstract: Most genome-wide assays provide averages across large numbers of cells, but recent technological advances promise to overcome this limitation. Pioneering single-cell assays are now available for genome, epigenome, transcriptome, proteome, and metabolome profiling. Here, we describe how these different dimensions can be combined into multi-omics assays that provide comprehensive profiles of the same cell. (PDF)

-> This review paper describes new experimental and computational technologies that will make it possible to establish comprehensive genomes, epigenomes, transcriptomes, proteomes, and metabolomes from the same single cell.

Li J, Klughammer J#, Farlik M#, Penz T#, Spittler A, Barbieux C, Berishvili E, Bock C*, Kubicek S* (2016). Single-cell transcriptomes reveal characteristic features of human pancreatic islet cell types. EMBO Reports 17, 178-187.

Abstract: Pancreatic islets of Langerhans contain several specialized endocrine cell types, which are commonly identified by the expression of single marker genes. However, the established marker genes cannot capture the complete spectrum of cellular heterogeneity in human pancreatic islets, and existing bulk transcriptome datasets provide averages across several cell populations. To dissect the cellular composition of the human pancreatic islet and to establish transcriptomes for all major cell types, we performed single-cell RNA sequencing on 70 cells sorted from human primary tissue. We used this dataset to validate previously described marker genes at the single-cell level and to identify specifically expressed transcription factors for all islet cell subtypes. All data are available for browsing and download, thus establishing a useful resource of single-cell expression profiles for endocrine cells in human pancreatic islets. (PDF)

-> This paper describes an experimental and computational approach for reconstructing complex human tissues from single-cell RNA-seq data, and it provides the first high-resolution maps of the transcriptome of all cell types in the human pancreatic islet, constituting a relevant resource for diabetes research.

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