FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2913525504> ?p ?o ?g. }

• W2913525504 abstract "Resource8 February 2019free access Transparent process Defining multistep cell fate decision pathways during pancreatic development at single-cell resolution Xin-Xin Yu orcid.org/0000-0002-4477-7589 Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China Search for more papers by this author Wei-Lin Qiu orcid.org/0000-0002-6649-0378 Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China PKU-Tsinghua-NIBS Graduate Program, Peking University, Beijing, China Search for more papers by this author Liu Yang Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China Search for more papers by this author Yu Zhang Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China Search for more papers by this author Mao-Yang He Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China PKU-Tsinghua-NIBS Graduate Program, Peking University, Beijing, China Search for more papers by this author Lin-Chen Li orcid.org/0000-0002-7791-8958 Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China Search for more papers by this author Cheng-Ran Xu Corresponding Author [email protected] orcid.org/0000-0002-0583-4464 Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China Search for more papers by this author Xin-Xin Yu orcid.org/0000-0002-4477-7589 Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China Search for more papers by this author Wei-Lin Qiu orcid.org/0000-0002-6649-0378 Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China PKU-Tsinghua-NIBS Graduate Program, Peking University, Beijing, China Search for more papers by this author Liu Yang Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China Search for more papers by this author Yu Zhang Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China Search for more papers by this author Mao-Yang He Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China PKU-Tsinghua-NIBS Graduate Program, Peking University, Beijing, China Search for more papers by this author Lin-Chen Li orcid.org/0000-0002-7791-8958 Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China Search for more papers by this author Cheng-Ran Xu Corresponding Author [email protected] orcid.org/0000-0002-0583-4464 Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China Search for more papers by this author Author Information Xin-Xin Yu1,2,‡, Wei-Lin Qiu1,3,‡, Liu Yang1,2, Yu Zhang1,2, Mao-Yang He1,3, Lin-Chen Li1,2 and Cheng-Ran Xu *,1 1Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China 2Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China 3PKU-Tsinghua-NIBS Graduate Program, Peking University, Beijing, China ‡These authors contributed equally to this work *Corresponding author. Tel: +86 10 6275 7119; E-mail: [email protected] EMBO J (2019)38:e100164https://doi.org/10.15252/embj.2018100164 See also: Z Liu & JB Sneddon (April 2019) PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract The generation of terminally differentiated cell lineages during organogenesis requires multiple, coordinated cell fate choice steps. However, this process has not been clearly delineated, especially in complex solid organs such as the pancreas. Here, we performed single-cell RNA-sequencing in pancreatic cells sorted from multiple genetically modified reporter mouse strains at embryonic stages E9.5–E17.5. We deciphered the developmental trajectories and regulatory strategies of the exocrine and endocrine pancreatic lineages as well as intermediate progenitor populations along the developmental pathways. Notably, we discovered previously undefined programs representing the earliest events in islet α- and β-cell lineage allocation as well as the developmental pathway of the “first wave” of α-cell generation. Furthermore, we demonstrated that repressing ERK pathway activity is essential for inducing both α- and β-lineage differentiation. This study provides key insights into the regulatory mechanisms underlying cell fate choice and stepwise cell fate commitment and can be used as a resource to guide the induction of functional islet lineage cells from stem cells in vitro. Synopsis Combining genetic lineage tracing and single-cell gene expression analyses, this resource deciphers the multistep trajectories of endocrine and exocrine lineages and intermediate progenitor populations during early murine pancreas development. Single-cell analysis of cell lineages from fetal pancreas at E9.5–E17.5 identifies differentiation steps and pathways from multipotent progenitors to endocrine and exocrine lineages. Fifteen cell clusters delineate all pancreatic cell types and four steps of cell fate decisions from progenitors to islet lineages. Developmental pathways involved in the “first wave” of alpha-cell generation differ from intermediate progenitor and population specific markers. Endocrine progenitors undergo four transient stages. The MAPK/ERK pathway restrains early endocrine specification. Introduction In complex organs, the generation of a single lineage usually involves multiple steps of cell fate choice. Comprehensively, understanding the pathways of cell lineage differentiation during in vivo development, especially regulatory strategies at points of cell lineage segregation, is critical for directing stem cell differentiation into the desired cell types for regenerative medicine. However, deciphering the precise pathways of multiple-step cell fate choices and the regulatory logic underlying the generation of complex organs requires further investigation. The pancreas is a digestive organ with both exocrine and endocrine functions. The exocrine compartment consists of acinar and ductal cells, and the endocrine portion includes β, α, δ, ε, and PP cells clustered in the pancreatic islets. During embryogenesis, all pancreatic lineages arise from multipotent progenitor (MP) cells. The developmental potential of these progenitors is restricted in a stepwise manner, ultimately resulting in the generation of the exocrine and islet endocrine lineages (Pan & Wright, 2011; Shih et al, 2013; Bastidas-Ponce et al, 2017; Larsen & Grapin-Botton, 2017). In mice, the pancreatic anlagen is detected in a dorsal definitive endoderm domain as early as embryonic day 8.5 (E8.5) and in a ventral endoderm domain approximately half a day later (Gittes, 2009). The early MP cells express the key transcription factors (TFs), PDX1 and PTF1A (Burlison et al, 2008), which are regulated by other TFs such as SOX9, HNF1β, and FOXA1/2 (Haumaitre et al, 2005; Lynn et al, 2007; Gao et al, 2008). The development and maintenance of MP cells are regulated by several cell signaling pathways, including the Notch, FGF10, and BMP pathways (Bhushan et al, 2001; Tiso et al, 2002; Hald et al, 2003; Murtaugh et al, 2003; Norgaard et al, 2003). After the pancreatic anlagen is established, the rapid growth of MP cells and cell shape changes drive the formation of nascent pancreatic buds at E9.5–E10.5. Thereafter, the pancreatic buds undergo dramatic morphological changes, resulting in multiple branched protrusions. The cells in the “tip” domain (“tip” cells) express the marker genes Ptf1a, c-Myc, and Cpa1 (Zhou et al, 2007; Pan et al, 2013). The inner cells constituting the “trunk” domain (“trunk” cells) are marked by Sox9, Nkx6.1, Hes1, Hnf1β, Hnf6, and Glis3 expression (Jacquemin et al, 2000; Solar et al, 2009; Schaffer et al, 2010; Kopinke et al, 2011; Kopp et al, 2011; Kang et al, 2016). Genetic tracing studies have revealed that tip cells are quickly restricted into an acinar lineage, whereas trunk cells are bipotent progenitors of ductal and endocrine progenitor cells (Zhou et al, 2007; Solar et al, 2009; Kopinke et al, 2011; Kopp et al, 2011; Pan et al, 2013). Two reciprocally repressive TFs, PTF1A and NKX6.1, are master regulators of tip–trunk segregation. PTF1A promotes tip fate by repressing trunk formation, whereas NKX6.1 induces trunk fate by inhibiting the generation of tip domains (Schaffer et al, 2010). The Notch signaling pathway is critical in promoting the trunk program (Horn et al, 2012). Starting from approximately E12.0, endocrine progenitors are specified from the bipotent trunk epithelium. Neurogenin 3 (Ngn3) is a master regulator of endocrine lineage specification, and all endocrine lineages are generated from Ngn3-expressing progenitors (Gu et al, 2002; Schonhoff et al, 2004). A first wave of Ngn3-expressing cells can be detected in the dorsal endoderm from E9.0 to E11.0, and the first differentiated endocrine cells at this stage are mainly glucagon-producing α cells (first wave of α cell, also named α-1st; Larsson, 1998). A second wave of Ngn3+ cell specification occurs in both the dorsal and ventral pancreas beginning at approximately E12.0, peaking at E15.5, and decreasing abruptly thereafter (Gradwohl et al, 2000; Schwitzgebel et al, 2000). A high level of Notch signaling represses Ngn3 expression but enhances ductal cell differentiation. Wnt, sphingosine-1-phosphate (S1p), and epidermal growth factor receptor (EGFR) positively regulate endocrine specification (Baumgartner et al, 2014; Lof-Ohlin et al, 2017; Serafimidis et al, 2017). The early-specified endocrine progenitors express low level of Ngn3 (Ngn3low cell). The expression of Ngn3 subsequently increases in these cells (Ngn3high cell), resulting in cell cycle exit (Gu et al, 2002; Desgraz & Herrera, 2009; Miyatsuka et al, 2011; Azzarelli et al, 2017; Krentz et al, 2017), delamination from the epithelium, and activation of key TFs for endocrine development, such as NeuroD1, Pax4, Arx, Isl1, Rfx6, Insm1, and Myt1 (Petri et al, 2006; Pan & Wright, 2011; Mastracci & Sussel, 2012). The cells expressing these factors progressively differentiate into a specific islet cell lineage. Two TFs, PAX4 and ARX, play reciprocally repressive roles in regulating β- vs α-lineage diversification, respectively (Collombat et al, 2003). Many factors and cell signaling pathways that regulate various stages of pancreatic development have been revealed using classic genetic approaches. However, due to the technical difficulty of purifying the intermediate progenitor cells and their direct progeny at each juncture of the cascade of lineage choices from developing pancreatic tissue, the underlying regulatory logic has remained elusive. Here, using single-cell transcriptomic analysis and various genetically labeled mouse strains, we comprehensively studied the precise developmental pathways of the early pancreatic lineages in vivo. Results Isolation of cell lineages from fetal pancreas for sc-RNA-seq To comprehensively define a fate map of pancreatic lineage differentiation, we isolated pancreatic cells from different transgenic or knock-in mouse strains from E9.5 to E17.5 via fluorescence-activated cell sorting (FACS) and performed single-cell RNA-sequencing (sc-RNA-seq) analysis using Smart-seq2 technology (Picelli et al, 2014). We employed the Pdx1-GFP transgenic mouse line (Gu et al, 2004) to purify pancreatic progenitor cells from E9.5 to E11.5 (Figs 1A and EV1A and B, and Dataset EV1). In our previous study, we sequenced 126 single Pdx1-GFP+ cells from E9.5 or E10.5 dorsal pancreas using Smart-seq2 (Li et al, 2018), and the data were remined here (Figs 1A and EV1A, and Dataset EV1). We used a Pdx1-Cre; Rosa-RFP line (Hingorani et al, 2003) to isolate all pancreatic lineages on each day from E10.5 to E15.5 (Figs 1A and EV1A and B, and Dataset EV1). To avoid the omission of cell types caused by genetic labeling, our analysis included recently published datasets of unbiased enriched pancreatic lineage cells with negative selection using a blood cell marker Tie2 and an endothelial marker CD45 (Tie2−CD45−) at E13.25 or E15.25 (Sznurkowska et al, 2018; Figs 1A and EV1A, and Dataset EV1). Because the percentage of endocrine cells in the pancreas is relatively low, to enrich for endocrine lineages, we used an Ngn3-GFP knock-in mouse strain (Lee et al, 2002) to sort cells expressing GFP at lower (Ngn3-GFPlow) and higher (Ngn3-GFPhigh) levels from E13.5 to E15.5 (Figs 1A and EV1A and B, and Dataset EV1). The single-cell datasets generated in our previous study (108 single Ngn3-GFPlow and Ngn3-GFPhigh cells at E13.5; Yu et al, 2018) were also included in this study (Figs 1A and EV1A, and Dataset EV1). At an earlier developmental stage (E12.5) and later stages (E16.5 and E17.5), because Ngn3-GFPlow and Ngn3-GFPhigh cells could not be effectively separated by FACS (Fig EV1B), we generally sorted Ngn3+ cells for single-cell analyses (Figs 1A and EV1A and B, and Dataset EV1). Figure 1. Identification of fetal pancreatic cell types Overview of the 2,702 fetal pancreatic cells analyzed in this study. The numbers show the cell counts from the indicated mouse strains at different developmental time points. The mouse strains (cell sources) are numbered with the circled numbers. * represents the indicated population including the cells from published resources (see Fig EV1A for details). The t-SNE plot shows 15 distinct cell types. Each dot represents a single cell. Cell counts are labeled in brackets. The t-SNE plots show the enriched cell source (left), the circled number indicating the cell source labeled in (A), and developmental time (right). Expression levels of marker genes are projected onto t-SNE plots. The colors ranging from blue to red indicate low to high relative gene expression levels. The violin plot under the t-SNE plot shows the expression level (TPM) of the indicated gene in each cell type. The dot within each violin plot indicates the median of expression levels. Heat map of cell type-enriched genes. Each column represents a single cell, and each row represents one gene. Cell cycle-related genes were extracted as group-0. TFs of each gene group are labeled. The bolded TFs are known to be important for pancreas development. The colors ranging from blue to red indicate low to high relative gene expression levels. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. The strategy for enriching pancreatic lineages from mouse embryos Overview of the 2,702 fetal pancreatic cells analyzed in this study. The numbers separated by slashes represent the number of cells produced in each experiment. § represents the cells from Pdx1-GFP highly expressing cells, *represents the single-cell datasets from published resources (GEO: GSE86225; Li et al, 2018), **represents the single-cell datasets from published resources (GEO: GSE84324; Yu et al, 2018), and ***represents the single-cell datasets from published resources (GEO: GSE89798; Sznurkowska et al, 2018). FACS gating strategies for purifying pancreatic cells at multiple developmental stages from various mouse strains. The pancreatic tissues from WT embryos were used as negative controls (upper panel). The dashed line in the “E10.5 Pdx1-GFP” gating plot separates the cells with higher GFP expression from cells with lower expression. Generation of the Gcg-P2A-GFP strain by inserting P2A and GFP DNA sequences before the stop codon. Immunofluorescence staining of the GCG in 8-week-old mouse islets verified the high quality of the Gcg-P2A-GFP strain. Scale bars: 50 μm. PCA plot of single-cell transcriptomes of E17.5 GFP+ cells from the Gcg-P2A-GFP mouse strain and α cells from published data (GEO: GSE87375; Qiu et al, 2017a). Each dot represents a single cell. Download figure Download PowerPoint To obtain differentiated α cells, we generated a Gcg-P2A-GFP mouse strain (Fig EV1C) in which α-cell identity was validated by immunostaining of glucagon in islets (Fig EV1D) and by sc-RNA-seq of E17.5 GFP+ cells from this strain. Principal component analysis (PCA) showed that these cells clustered with the E17.5 α cells sequenced in our previous study (Qiu et al, 2017a; Fig EV1E). Therefore, this Gcg-P2A-GFP mouse strain can be used to label α cells. From Gcg-P2A-GFP embryos, we sorted the early-stage α-2nd cells at E15.5. We employed an Ins1-RFP line (Piccand et al, 2014) to sort the early-stage differentiated β cells at E15.5 (Figs 1A and EV1A and B, and Dataset EV1). We enriched pancreatic ductal cells using Sox9-CreER; Rosa-RFP mice (Kopp et al, 2011) and collected RFP+ cells at E16.5 and E17.5 (Figs 1A and EV1A and B, and Dataset EV1). To investigate the characteristics of the earlier-differentiated endocrine cells, also known as “first wave” of endocrine cells, we purified Ngn3-GFP+ cells at E10.5 and the descendants of Ngn3-expressing cells by sorting Ngn3-Cre; Rosa-RFP+ cells at E11.5 (Schonhoff et al, 2004), when Ngn3 expression was turned off (Figs 1A and EV1A and B, and Dataset EV1). To estimate the technical noise in the sc-RNA-seq experiments, we applied an ERCC spike-in control (Brennecke et al, 2013). A total of 2,702 transcriptomes of single cells passed the quality control criteria. On average, 6,000–9,000 genes were detected in each cell, with > 0.2 million mapped reads (Fig EV2A–D and Dataset EV1). The cells at each time point were pooled from multiple embryos, and at least two biological replicates were performed to generate all single-cell transcriptomic data (Fig EV1A and Dataset EV1). The batch effects between these replicates were not obvious (Fig EV2E). Click here to expand this figure. Figure EV2. Quality control analysis of sc-RNA-seq data generated by Smart-seq2 A. Saturation analysis indicated that 0.2 million mapped reads were sufficient to cover most of the genes detected in a single cell. Each point represents the mean of four independent subsamplings. Error bars: SEM. B, C. Statistical analysis of the alignment (B) and detected genes (C) for available single-cell samples. Each bar represents one sample. D. Correlations between the number of mRNA molecules and TPM values. Each point represents an ERCC spike-in. Point size reflects transcript length. E. The t-SNE plots showed no obvious batch effects of replicated experiments. * indicates cells from our recently published work (Li et al, 2018; Yu et al, 2018). F. t-SNE analysis identified nonpancreatic and pancreatic epithelial lineage cells. The color code, marker gene, and cell count for each cell type are provided next to the t-SNE plot. G. Expression patterns of marker genes in (F). H. The overview of cell type distribution in certain mouse strains at various time points. The circled number indicates the cell source labeled in Fig 1A. The colors denote the cell types shown in Fig 1B. I. Cells expressing Sst, Ghrl and Ppy (TPM > 10,000) are projected onto the t-SNE plot of Fig 1B. Download figure Download PowerPoint Identification of cell types in fetal pancreatic development To identify cell types among the sequenced single cells, we performed a t-distributed stochastic neighbor-embedding (t-SNE) analysis (Satija et al, 2015). After excluding cells expressing the macrophage marker gene Fcgr1 (Gautier et al, 2012), the enteric neuron marker gene Ascl1 (Memic et al, 2016), the mesenchymal cell marker gene Col3a1 (Byrnes et al, 2018), and the extrahepatic bile ductal cell marker gene Sox17 (Spence et al, 2009; Fig EV2F and G, and Dataset EV1), we identified fifteen distinct cell types (clusters 1–15) among the remaining 2,282 cells (Figs 1B and C, and EV2H, and Dataset EV1), which generally expressed the pancreatic marker genes Pdx1 and/or Prox1 (Figs 1D and EV2G). The cells in cluster-1 mainly consisted of cells from E9.5 Pdx1-GFP embryos and expressed pancreatic progenitor feature genes such as Pdx1, Sox9, and Hnf1β, but not the endocrine cell marker gene NeuroD1 (Fig 1B–D). Therefore, the cells in cluster-1 were considered MP-early cells. The cluster-2 cells primarily included E10.5 and E11.5 pancreatic cells from Pdx1-GFP or Pdx1-Cre; Rosa-RFP embryos and expressed Ptf1a, Sox9, and Hnf1β at high levels (Fig 1B–D). Therefore, the cells in cluster-2 were MP-late cells. The cluster-3 cells were primarily obtained at E11.5–E14.5 and expressed Ptf1a, Rbpjl, and Cpa1 at high levels but not the acinar marker gene Amy1, which indicated that the cluster-3 cells were tip cells (Fig 1B–D). The cluster-4 cells were RFP+ cells from Pdx1-Cre; Rosa-RFP embryos and Ngn3-GFPlow cells at E12.5-E16.5. These cells showed high expression levels of Sox9, Nkx6.1, and Hes1 but not Ptf1a expression. Based on this information, we concluded that the cluster-4 cells were trunk cells (Fig 1B–D). The cluster-5 cells mainly contained Ngn3-GFPlow and a fraction of Ngn3-GFPhigh cells, and the cluster-6 and cluster-7 cells mainly consisted of Ngn3-GFPhigh cells at E13.5–E15.5 (Fig 1B and C). Ngn3 expression was upregulated in the cluster-5 cells and peaked in the cluster-6 and cluster-7 cells (Fig 1D). We concluded that the cells in cluster-5, cluster-6, and cluster-7 represented continuous developmental stages of endocrine precursor (EP) cells. We therefore named these populations EP1, EP2, and EP3, respectively. In cluster-8, Ngn3 expression was decreased, whereas the expression levels of the Ngn3 downstream genes NeuroD1 and Pax6 were maintained at high levels. In addition, islet hormone genes were not expressed in these cells (Fig 1B–D). Taken together, the results supported the identification of the cluster-8 cells as late-stage EP cells (EP4). The cluster-9 cells expressed the endocrine and α-cell feature TFs NeuroD1 and Arx but not Gcg and included cells at E9.5–E10.5 (Fig 1B–D), indicating that these cells were early-specified “first wave” of α cells (pre-α-1st cells). The cluster-10 and cluster-11 cells expressed the α-cell markers Gcg and Arx and included many E10.5–E13.5 cells (Fig 1B–D), indicating that these cells were differentiated α-1st cells but at different developmental stages. We therefore named the cluster-10 and cluster-11 cells α-1st-early and α-1st-late, respectively. Notably, a fraction of α-1st cells also expressed Ins1 but at a much lower level than in later differentiated β cells (Fig 1D). This result is consistent with a previous finding that the earliest endocrine cells express polyhormones (Larsson, 1998). The other Gcg+Arx+ cells were located in cluster-12, and because E15.5 Gcg-GFP+ cells were found in this cluster (Fig 1B–D), we referred to these cells as “second wave” of α cells (α-2nd cells). The cluster-13 cells were clearly β cells because they included E15.5 Ins1-RFP+ cells and expressed a high level of the Ins1 gene (Fig 1B–D). The cluster-14 cells were acinar cells because they exclusively expressed the differentiated acinar markers Amy1 and showed a high expression level of Rbpjl and Ptf1a (Fig 1B–D). The cluster-15 cells were primarily isolated from E17.5 Sox9-CreER; Rosa-RFP embryos, in which Cre recombinase expression was induced by tamoxifen injection 1.5 days before embryo harvest. After E15.5, Sox9 expression is gradually restricted to differentiated ductal cells in the pancreas (Fig 1B–D). Therefore, the cluster-15 cells were identified as ductal cells. In the EP4, α-2nd, and β-cell populations, we also detected some cells expressing Sst (18 cells), Ghrl (16 cells), Ppy (6 cells), or polyhormonal genes (28 cells; Fig EV2I). However, due to the insufficient cell number, these cells were not identified as specific cell clusters. To characterize the transcriptomic features of each cluster, we identified the genes showing enriched expression in each cell cluster and extracted cell cycle-related genes (group-0 in Fig 1E) through hierarchical clustering analysis. These genes were used to distinguish proliferative cells (Fig 1E and Dataset EV2). Collectively, these comprehensive single-cell transcriptomic analyses identified all pancreatic lineages and their major intermediate progenitors. The cell throughput of the Smart-seq2 method was typically lower than that of the most recent droplet-based sc-RNA-seq technologies, such as 10X Genomics, which can sample thousands of cells on a microfluidic chip (Pijuan-Sala et al, 2018). To verify whether the number of cells in our study was sufficient to identify the cell subpopulations of pancreatic lineages and the differentially expressed genes, we performed sc-RNA-seq using the 10X Genomics platform on E14.5 whole mouse dorsal pancreas. After filtering of low-quality cells (Fig EV3A), 10,904 cells were retained for further analyses. On average, more than 2,000 genes were detected in each cell, with ~3 × 104 mapped reads (Fig EV3B and C, and Dataset EV1). After t-SNE analysis, groups expressing markers of mesenchymal cells, erythrocytes, endothelial cells, immune cells, or neurons were excluded from downstream analyses (Fig EV3D and E). Finally, a cluster containing 3,251 cells that generally expressed Prox1 was identified as pancreatic epithelial lineage cells (Fig EV3D and E). Focusing on this cluster, we performed another round of t-SNE analysis and identified four groups representing tip cells (1,280 cells), trunk cells (793 cells), the early stage of EP cells (EP-early) (551 cells), and the late stage of EP cells (EP-late)/endocrine cells (627 cells) based on the expression patterns of the TFs Ptf1a, Cpa1, Sox9, Ngn3, NeuroD1, and Pax4 and the hormone genes Gcg and Ins1 (Fig EV3F and G). From 3,251 single-cell datasets produced by 10X Genomics, we identified 327 genes specifically enriched in each cell cluster (Fig EV3H and Dataset EV3). We also included the datasets of E14.5 pancreata generated using the 10X Genomics platform from a recently published work (Byrnes et al, 2018). Although the number of cells increased, the number of differentially expressed genes detected between the four cell populations did not increase (Fig EV3D, F, H, and Dataset EV3), indicating that for 10X Genomics, a cell number of 2,000–3,000 should be “saturated” for identifying differentially expressed gene sets. Similarly, t-SNE analysis of the E14.5 sequencing datasets generated by Smart-seq2 (296 cells) was performed and revealed four cell clusters representing tip (32 cells), trunk (80 cells), EP-early (86 cells), and EP-late/endocrine (98 cells) cells (Fig EV3I). However, 1,794 cluster-enriched genes were identified in the 296 Smart-seq2 single-cell datasets (Fig EV3J and Dataset EV3). Notably, approximately 80% of the differentially expressed genes identified by 10X Genomics overlapped with the differentially expressed genes identified by Smart-seq2 (Fig EV3K). Therefore, our analyses using Smart-seq2 efficiently identified more cell population-specific expressed genes, thereby enabling us to characterize the features and map the trajectory of pancreatic lineage development. Click here to expand this figure. Figure EV3. Comparison of E14.5 pancreatic sc-RNA-seq data between analyses using Smart-seq2 and the 10X Genomics platform A. Barcode-labeled cells (green) were identified using the cellranger program (10X Genomics). The y-axis represents the count of UMI (unique molecular identifiers) associated with each individual barcode. B, C. Statistical analyses of mapped reads (B) and detected genes (C) for available 10X Genomics samples. D. t-SNE analysis identified pancreatic epithelial cells and nonpancreatic l" @default.
• W2913525504 created "2019-02-21" @default.
• W2913525504 creator A5024173271 @default.
• W2913525504 creator A5038460718 @default.
• W2913525504 creator A5043318315 @default.
• W2913525504 creator A5046538435 @default.
• W2913525504 creator A5061726266 @default.
• W2913525504 creator A5071773009 @default.
• W2913525504 creator A5072751506 @default.
• W2913525504 date "2019-02-08" @default.
• W2913525504 modified "2023-10-15" @default.
• W2913525504 title "Defining multistep cell fate decision pathways during pancreatic development at single‐cell resolution" @default.
• W2913525504 cites W1721336579 @default.
• W2913525504 cites W1966116114 @default.
• W2913525504 cites W1967327758 @default.
• W2913525504 cites W1981362835 @default.
• W2913525504 cites W1982327008 @default.
• W2913525504 cites W1988363662 @default.
• W2913525504 cites W1990826732 @default.
• W2913525504 cites W1990893332 @default.
• W2913525504 cites W2005331344 @default.
• W2913525504 cites W2006017090 @default.
• W2913525504 cites W2011710385 @default.
• W2913525504 cites W2023830997 @default.
• W2913525504 cites W2025828346 @default.
• W2913525504 cites W2027836023 @default.
• W2913525504 cites W2030398384 @default.
• W2913525504 cites W2031337142 @default.
• W2913525504 cites W2038616760 @default.
• W2913525504 cites W2041026739 @default.
• W2913525504 cites W2047563941 @default.
• W2913525504 cites W2052234382 @default.
• W2913525504 cites W2066135299 @default.
• W2913525504 cites W2068767807 @default.
• W2913525504 cites W2068949232 @default.
• W2913525504 cites W2069013746 @default.
• W2913525504 cites W2069089843 @default.
• W2913525504 cites W2075406901 @default.
• W2913525504 cites W2077039092 @default.
• W2913525504 cites W2086262311 @default.
• W2913525504 cites W2086300652 @default.
• W2913525504 cites W2089328941 @default.
• W2913525504 cites W2089688272 @default.
• W2913525504 cites W2107414039 @default.
• W2913525504 cites W2107890914 @default.
• W2913525504 cites W2110447217 @default.
• W2913525504 cites W2110760195 @default.
• W2913525504 cites W2110982118 @default.
• W2913525504 cites W2115482726 @default.
• W2913525504 cites W2116010045 @default.
• W2913525504 cites W2116100337 @default.
• W2913525504 cites W2118430243 @default.
• W2913525504 cites W2126354094 @default.
• W2913525504 cites W2134526812 @default.
• W2913525504 cites W2136075462 @default.
• W2913525504 cites W2136188258 @default.
• W2913525504 cites W2137987484 @default.
• W2913525504 cites W2138255346 @default.
• W2913525504 cites W2138294489 @default.
• W2913525504 cites W2140729960 @default.
• W2913525504 cites W2141421937 @default.
• W2913525504 cites W2142201990 @default.
• W2913525504 cites W2142486892 @default.
• W2913525504 cites W2145639699 @default.
• W2913525504 cites W2148078342 @default.
• W2913525504 cites W2149689427 @default.
• W2913525504 cites W2153947402 @default.
• W2913525504 cites W2155869778 @default.
• W2913525504 cites W2156314428 @default.
• W2913525504 cites W2160400743 @default.
• W2913525504 cites W2169745228 @default.
• W2913525504 cites W2171298096 @default.
• W2913525504 cites W2171814352 @default.
• W2913525504 cites W2203339336 @default.
• W2913525504 cites W2336732042 @default.
• W2913525504 cites W2410665730 @default.
• W2913525504 cites W2576205103 @default.
• W2913525504 cites W2592495410 @default.
• W2913525504 cites W2607879901 @default.
• W2913525504 cites W2608406261 @default.
• W2913525504 cites W2610510965 @default.
• W2913525504 cites W2613078538 @default.
• W2913525504 cites W2744337003 @default.
• W2913525504 cites W2747877289 @default.
• W2913525504 cites W2750106311 @default.
• W2913525504 cites W2755419006 @default.
• W2913525504 cites W2766001106 @default.
• W2913525504 cites W2791795220 @default.
• W2913525504 cites W2792633644 @default.
• W2913525504 cites W2792717465 @default.
• W2913525504 cites W2794521141 @default.
• W2913525504 cites W2801803962 @default.
• W2913525504 cites W2886217103 @default.
• W2913525504 cites W2886222702 @default.
• W2913525504 cites W2889745339 @default.
• W2913525504 cites W2951799803 @default.
• W2913525504 doi "https://doi.org/10.15252/embj.2018100164" @default.
• W2913525504 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6463266" @default.
• W2913525504 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30737258" @default.
• W2913525504 hasPublicationYear "2019" @default.

• next