Single cell transcriptome profiling of mouse and hESC-derived pancreatic progenitors

Krentz, Nicole A.J.; Lee, Michelle Ying Ya; Xu, Eric E.; Sasaki, Shugo; Lynn, Francis C.

doi:10.1101/289470

Cited by 32 publications

(60 citation statements)

References 71 publications

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“…The unknown cell population is enriched in genes such as CXCL14, CA3, CRABP2, S100A11, ARHGAP29, NR2F2, TFAP2B, PDGFC, etc. [61]. Endocrine cells, which made up 74.3% of the total population, expressed INS, GCG or SST, while some EP cells also expressed INS.…”

Section: Human Pluripotent Stem Cell (Hpsc)-derived Isletsmentioning

confidence: 99%

“…Off-target cells include non-islet cells that are either endocrine or non-endocrine cell types [54]. Four studies have sequenced stem cell derived islets from both embryonic and induced pluripotent stem cells across various stages of differentiation (Stage 3 to Stage 7pancreatic progenitor to islet cells) [45,54,59,61]. Previous studies have sequenced cells prior to Stage 3 pancreatic progenitors, including pluripotent stem cells (Stage 0) [62,63] and definitive endoderm stage cells (Stage 1) [64,65].…”

Section: Human Pluripotent Stem Cell (Hpsc)-derived Isletsmentioning

confidence: 99%

“…Previous studies have sequenced cells prior to Stage 3 pancreatic progenitors, including pluripotent stem cells (Stage 0) [62,63] and definitive endoderm stage cells (Stage 1) [64,65]. This section will discuss the analyses performed on Stage 6 or Stage 7 cells generated from the three studies that have sequenced hPSC-derived islets or islet progenitors [54,59,61].…”

Section: Human Pluripotent Stem Cell (Hpsc)-derived Isletsmentioning

confidence: 99%

“…In Krentz,et al 4,462 Stage 6 day 1 (S6d1) cells, generated from the hES cell line CyT49 (Rezania protocol) passed QC for scRNA-seq analysis [61]. The S6d1 cells sequenced by Krentz formed 9 clusters that were classified into 5 cell types: endocrine (Endo), endocrine progenitors (EP) and off-targets including duct, liver, and an unknown cell type.…”

Section: Human Pluripotent Stem Cell (Hpsc)-derived Isletsmentioning

confidence: 99%

See 3 more Smart Citations

Single Cell Transcriptomic Analysis of Pancreatic β Cell Development and Differentiation from Pluripotent Stem Cells

Xie¹,

Ye²,

Poh³

et al. 2019

obm transplant

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Single cell genomics is a powerful tool to study cellular heterogeneity and discover novel cell types. Recent studies used single cell RNA sequencing (scRNA-seq) to analyze the transcriptomes of individual pancreatic islet cells. Islets are a complex mixture of endocrine cells and therefore represent an ideal tissue type for single cell transcriptomic analysis. Adult human islets consist of five known endocrine cell types (α, β, δ, γ, ε) and multiple less welldefined non-endocrine cells. In this review, we discuss the scRNA-seq studies performed on human fetal, adult, diseased and pluripotent stem cell-derived islets in recent years. Since 2015, ~30,000 adult human islet cells have been analyzed using several scRNA-seq technologies. Studies provide a complete catalogue of all islet cell types and subtypes found throughout human development from fetus to adulthood. Islets from patients with Type 2 diabetes have also been analyzed with scRNA-seq unraveling multiple mechanisms of islet dysfunction. Advances in stem cell differentiation protocols and cell therapy manufacturing are bringing stem cell-derived islets (SC-Islets) closer to clinical trials. In 2018, more than 60,000 SC-Islet cells were analyzed using scRNA-seq technologies. Lessons learned include SC-Islet cell populations, lineage trajectories and comparative analyses to adult human islet cell transcriptomes. Studies have also identified and characterized the non-islet, off-target cell populations revealing potential strategies for their elimination.

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Section: Human Pluripotent Stem Cell (Hpsc)-derived Isletsmentioning

confidence: 99%

Section: Human Pluripotent Stem Cell (Hpsc)-derived Isletsmentioning

confidence: 99%

Section: Human Pluripotent Stem Cell (Hpsc)-derived Isletsmentioning

confidence: 99%

Section: Human Pluripotent Stem Cell (Hpsc)-derived Isletsmentioning

confidence: 99%

See 2 more Smart Citations

Single Cell Transcriptomic Analysis of Pancreatic β Cell Development and Differentiation from Pluripotent Stem Cells

Xie¹,

Ye²,

Poh³

et al. 2019

obm transplant

View full text Add to dashboard Cite

show abstract

“…Often, researchers rely on curated marker genes and expert knowledge to identify cells co-expressing markers of distinct cell types as putative doublets (e.g., (Wang et al, 2016;Ibarra-Soria et al, 2018;Rosenberg et al, 2018)). Based on the assumption that doublets would have higher total RNA content, another approach is to use a measure for overall expression signal (total counts, for example) as a means for classifying cells as doublets (Bach et al, 2017;Ziegenhain et al, 2017;Krentz et al, 2018). However, given that marker gene information and expert knowledge is not always available (and not always objective), and that doublets may not necessarily have high total counts, in the last year a number of computational doublet detection/annotation methods have been proposed that do not rely on markers or total counts alone (Lun et al, 2016;Shor and Gayoso, 2019;DePasquale et al, 2018), see Table 4.…”

Section: Introductionmentioning

confidence: 99%

scds: Computational Annotation of Doublets in Single Cell RNA Sequencing Data

Bais

Kostka

2019

Preprint

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Motivation: Single cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. Specifically, high-throughput approaches that employ micro-fluidics in combination with unique molecular identifiers (UMIs) are capable of assaying many thousands of cells per experiment and are rapidly becoming commonplace. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a study's conclusions, and therefore computational strategies for the identification of doublets are needed. Here we present single cell doublet scoring (scds), a software tool for the in silico identification of doublets in scRNA-seq data.Results: With scds, we propose two new and complementary approaches for doublet identification: Co-expression based doublet scoring (cxds) and binary classification based doublet scoring (bcds). The co-expression based approach, cxds, utilizes binarized (absence/presence) gene expression data and employs a binomial model for the co-expression of pairs of genes and yields interpretable doublet annotations. bcds, on the other hand, uses a binary classification approach to discriminate artificial doublets from the original data. We apply our methods and existing doublet identification approaches to four data sets with experimental doublet annotations and find that our methods perform at least as well as the state of the art, but at comparably little computational cost. We also find appreciable differences between methods and across data sets, that no approach dominates all others, and we believe there is room for improvement in computational doublet identification as more data with experimental annotations becomes available. In the meanwhile, scds presents a scalable, competitive approach that allows for doublet annotations in thousands of cells in a matter of seconds.Availability and Implementation: scds is implemented as an R package and freely available at https://github.com/ kostkalab/scds.

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Single‐cell RNA sequencing technologies and applications: A brief overview

Jovic

Liang

Zeng

et al. 2022

Clinical & Translational Med

507

204

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Single‐cell RNA sequencing (scRNA‐seq) technology has become the state‐of‐the‐art approach for unravelling the heterogeneity and complexity of RNA transcripts within individual cells, as well as revealing the composition of different cell types and functions within highly organized tissues/organs/organisms. Since its first discovery in 2009, studies based on scRNA‐seq provide massive information across different fields making exciting new discoveries in better understanding the composition and interaction of cells within humans, model animals and plants. In this review, we provide a concise overview about the scRNA‐seq technology, experimental and computational procedures for transforming the biological and molecular processes into computational and statistical data. We also provide an explanation of the key technological steps in implementing the technology. We highlight a few examples on how scRNA‐seq can provide unique information for better understanding health and diseases. One important application of the scRNA‐seq technology is to build a better and high‐resolution catalogue of cells in all living organism, commonly known as atlas, which is key resource to better understand and provide a solution in treating diseases. While great promises have been demonstrated with the technology in all areas, we further highlight a few remaining challenges to be overcome and its great potentials in transforming current protocols in disease diagnosis and treatment.

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Single cell transcriptome profiling of mouse and hESC-derived pancreatic progenitors

Cited by 32 publications

References 71 publications

Single Cell Transcriptomic Analysis of Pancreatic β Cell Development and Differentiation from Pluripotent Stem Cells

Single Cell Transcriptomic Analysis of Pancreatic β Cell Development and Differentiation from Pluripotent Stem Cells

scds: Computational Annotation of Doublets in Single Cell RNA Sequencing Data

Single‐cell RNA sequencing technologies and applications: A brief overview

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