- Single-cell RNA-Seq analysis reveals. - Background: The differentiation and maturation trajectories of fetal liver stem/progenitor cells (LSPCs) are not fully understood at single-cell resolution, and a priori knowledge of limited biomarkers could restrict trajectory tracking.. - Results: We employed marker-free single-cell RNA-Seq to characterize comprehensive transcriptional profiles of 507 cells randomly selected from seven stages between embryonic day 11.5 and postnatal day 2.5 during mouse liver development, and also 52 Epcam-positive cholangiocytes from postnatal day 3.25 mouse livers. - Single-cell resolution dynamic developmental trajectories of LSPCs exhibited contiguous but discrete genetic control through transcription factors and signaling pathways. - Keywords: Liver stem/progenitor cells, Single-cell RNA-Seq, Developmental trajectory, Cholangiocyte, Fate decision. - Full list of author information is available at the end of the article. - 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0. - observation of the trajectory of cell lineage differenti- ation and maturation of these naïve LSPCs at single-cell resolution.. - Recently, the development of single-cell sequencing- based technology has provided a unique chance to ad- dress many longstanding questions, such as cell lineage relationships and heterogeneity in a given cell popula- tion [12–14]. - Single-cell transcriptomic analysis, such as RNA-Seq, would supplant the coarse notions of the marker-based cell types and uncover new cell types by the unbiased sampling of single cells [15]. - For liver re- search, the gene expression profiles of zonation and spatial division of hepatic lobule in adult mouse liver were revealed at single-cell resolution [16], and multi- lineage communication is also shown to be important for human liver bud development [17]. - Currently, the definition and molecular state of LSPCs during liver de- velopment are still obscure, and the developmental trajectories of LSPCs, including self-renewal, differenti- ation and maturation, are not fully understood at single-cell resolution.. - To address the above questions, in this study, we ap- plied single-cell RNA-Seq and quantitative RT-PCR (qPCR) to analyze ~ 800 single cells from eight different stages during mouse liver development. - The transcrip- tomic analysis of LSPCs reconstructed their stepwise dif- ferentiation and maturation process at single-cell resolution. - To further understand the fate decision and differentiation process of LSPCs, we also analyzed the single-cell transcriptomic profiles of Epcam-positive cholangiocytes. - Our single-cell transcriptomic analysis of LSPCs during mouse liver de- velopment provides insights into the transcriptional con- trol of their self-renewal, differentiation and maturation and is a useful resource for future research, including re- search on isolation methods designed for LSPCs.. - Overview of single-cell qPCR and RNA-Seq of developing mouse livers. - To comprehensively understand the transcriptional pro- gram during liver development, we carried out single- cell transcriptomic analysis, including qPCR, on 722 cells and RNA-Seq on 559 cells derived from mouse fetal livers at eight developmental stages, including embryonic day (E and postnatal day (P) 2.5 and P3.25 (Fig. - We first randomly selected 467 single cells and then assessed them via single-cell qPCR with genes related to cell types and liver develop- ment (Fig. - E11.5 to E16.5 livers, which were later identified as hepatoblasts. - Here, the median correlation coefficients between single-cell RNA-Seq and qPCR were approxi- mately 0.9 for all stages (Additional file 1: Figure S2c).. - Limited markers may lead to the incorrect identification of cell populations, and single-cell transcriptomic profil- ing facilitates ab initio cell-type characterization.. - S1, S5), we focused on single cells from E11.5 to E16.5 for cell type identification. - One of the remaining two groups belonged to hepato- blasts that express hepatic markers such as Afp and Alb,. - 1 Overview of single-cell analysis of developing mouse fetal livers. - b Statistics of the single cells analyzed in this study.. - c Single-cell qPCR analysis of mouse fetal liver cells, with E12.5 as an example. - 2 Decomposition of the constituent cell types in mouse fetal livers. - d Comparison of the gene expression profiles between hepatoblasts and mesenchymal cells with selected marker genes. - e Expression of Dlk1 and vimentin in E11.5 mouse liver shown by immunofluorescence assay. - f The temporal changes in the proportions of the six cell types. - Our single-cell RNA-Seq data showed that Epcam expression was de- tected in 1 of 2 cells of E11.5 hepatoblasts and 5 of 43 cells of E11.5 mesenchymal cells, not detected in E12.5. - This temporal change of Epcam expression indicated our single-cell RNA-seq data was consistent with the previous observation via immunofluorescence assay or flow cytometric analysis [7]. - As single-cell RNA-Seq may suffer drop-out issue, here we selected the 8 cells with Epcam transcript and 8 cells without Epcam transcript form E11.5 and E16.5 for further qPCR validation. - The comparison of the two approaches indicated that our single-cell RNA-Seq data at the current sequen- cing depth was more sensitive than qPCR in detecting low expressed genes, which also showed consistence with the results in Additional file 1: Figure S2c. - We then characterized the distribution of the six groups at each stage. - Interestingly, the fetal livers from E11.5 to E16.5 all had six cell types despite different pro- portions (Fig. - The proportion of erythrocytes in- creased from E11.5 to E14.5 and then decreased at E16.5, and a stepwise decrease in mesenchymal cells and a slight increase in hepatoblasts from E11.5 to E16.5 were observed.. - Dynamic developmental process of LSPCs at single-cell resolution. - The gene expression pattern of hepatoblasts from E12.5 to E14.5 had no statistically meaningful differences, but the comparison of E14.5 and E16.5 hepatoblasts revealed that the cell cycle and. - This finding suggested that the transition from E14.5 to E16.5 may be the critical differentiation switch for hepatoblasts via cell division.. - We then analyzed all hepatoblasts from E11.5 to E16.5 livers to construct the landscape of the dynamic devel- opmental processes. - Theoretically, hepatoblasts will differentiate into hepa- tocytes and cholangiocytes, but the hepatoblasts from E11.5 to E16.5 only exhibited stepwise increased expres- sion of hepatic-related proteins without expressing bil- iary markers. - Here, our data indicated that hepato- blasts in E11.5 ~ E16.5 are authentic hepatoblasts.. - LSPCs are generally believed to co-express hep- atocyte and cholangiocyte markers, and we checked such a possibility in these single liver cells from E11.5 to E16.5. - Only 4 cells from E11.5, E12.5 and E14.5 livers. - We thus employed flow cytometry to enrich the relative rare mouse cholangiocytes using the well-known marker Epcam which will enable single-cell transcrip- tomic comparison between cholangiocytes and hepato- blasts and facilitate understanding the fate-decision stage for differentiation into cholangiocytes. - expression was maintained in all Epcam + cells, whilst Afp was only expressed in about half of the Epcam + cells. - The expression of Krt7 or Krt19 emerged in some of the Epcam + cells, but not in E11.5 ~ E16.5 hepatoblasts. - 3 Dynamic developmental process of mouse LSPCs at single-cell resolution. - However, this single-cell genomics-derived model still needs further validation, especially solid evidence from well-designed lineage tracing experiments employing reliable markers.. - Comparison of the gene expression profiles of P3.25 cholangiocytes and hepatoblasts from different stages by HC (c) and t-SNE plot (d) are shown. - 5a and Additional file 1:. - As our single cells were randomly selected from fetal livers, systematic assessment of the sensitivity. - Cdh1 exhibited the best sensitivity and specificity for hepatoblast isolation from E12.5 to E16.5 livers (Fig. - Dlk1 was expressed in both hepatoblasts and mesenchymal cells at early stage, where its specificity gradually increased from E11.5 to E16.5 for hepatoblasts and decreased from E11.5 to E14.5 for mesenchymal cells (Fig. - The specificity of Igdcc4 for hepatoblasts gradually increased from E11.5 to E16.5, despite its decreased sensitivity (Additional file 1: Figure S7c). - Our data provide a systematic evaluation of the isolation markers for LSPCs, which is helpful for evaluating the reliability of previously isolated LSPCs and for predicting isolation markers for further validation. - 5e and Additional file 1: Figure S8a-b), consistent with our single-cell RNA-Seq data. - Flow cytometric analysis also revealed that only a portion of the Prom1 + cells from E14.5 and E16.5 fetal livers were Dlk1-positive (Fig.. - In general, both single-cell RNA-Seq and flow cytometry supported E-cadherin, Anpep and Dlk1 as appropriate biomarkers for isolation of LSPCs, while Prom1 may slightly differ from the above three markers (Figs. - Single- cell analysis thus facilitates a more accurate identifica- tion of gene expression changes related to LSPCs. - In this study, we employed single-cell qPCR and RNA-Seq to systematically re-visit the developmental process of mouse fetal livers, and the marker-free approach based on global transcriptional profiles enabled more reliable identification of the constituent cell types in fetal livers.. - Our data support the systematic assessment and predic- tion of markers for LSPC isolation, and reconstructed the developmental track of hepatoblasts at single-cell resolution.. - Our single-cell data showed that the changes in hepatoblasts during this period are mainly related to stepwise increased hepatic. - Our single-cell transcriptomic analysis thus provides insights about the fate decision stage of hepatoblasts.. - In summary, our data provides a useful resource describ- ing LSPCs at single-cell resolution during mouse liver development. - 6 The proposed schematic diagram of the fate decision and differentiation of LSPCs. - From E11.5 to neonatal, the increased expression of hepatic- or biliary-related genes indicates an elevated hepatocyte or cholangiocyte signature in LSPCs. - Single-cell qPCR and RNA-Seq. - The quality of cDNA li- braries was checked by single-cell qPCR using selected genes on a BioMark HD system (Fluidigm), and the qPCR primers are shown in Additional file 5: Table S4.. - Single-cell libraries were pooled and sequenced by NextSeq 500 (Illumina), with 2 × 151 bp or 2 × 76 bp sequencing modes. - To check the expression levels of Epcam, single-cell qPCR was carried out using SYBR® Premix Ex Taq™ (Clontech) on the StepOnePlus system (Applied Biosystems).. - Epcam + cells were sorted from P3.25 fetal liver for single-cell RNA-Seq.. - bcl2fastq2 Conversion Software (Illumina) was used for de-multiplexing, adaptor trimming and generation of FASTQ files for each single cell according to the unique barcode combinations of Nextera XT Index kit. - should not include zero, which guarantees the overall expression of the given transcript is large enough to surpass noise interfer- ence. - (2) σ 2 of the transcript should be larger than the. - Single-cell data analysis. - For grouping of all fetal liver cells from E11.5 to E16.5, un-supervised HC of all the liver cells with the top 400 genes ranked by PCA scores was performed and then identified two groups. - Performance assessment of the genes for isolation of hepatoblasts. - Whether a marker is suitable for isolation of LSPCs can be assessed by two factors: (1) sensitivity, which refers to whether the marker-positive cells include most of the LSPCs. - and (2) specificity, which refers to whether the purity of the marker-positive cells excludes other types of cells. - Single-cell qPCR analysis of mouse fetal liver cells. - Quality control of single-cell RNA-Seq analysis of mouse fetal liver cells. - Grouping of fetal liver cells from E11.5 to E16.5. - Validation of single-cell RNA- Seq results by immunofluorescence and qPCR. - Dynamic developmental process of mouse LSPCs at single-cell resolution. - Comparison of the gene expression patterns of some marker genes. - Top 30 ANOVA ranked genes for E11.5 ~ E16.5 hepatoblast. - Primers used for single-cell qPCR. - The funding bodies didn ’ t involve in the design of the study, collection, analysis, interpretation of data, or writing the manuscript.. - All authors read and approved final version of the manuscript.. - Organogenesis and development of the liver. - mRNA-Seq whole-transcriptome analysis of a single cell. - Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. - Tracing haematopoietic stem cell formation at single-cell resolution.. - Single-cell spatial reconstruction reveals global division of labour in the mammalian liver.. - Activation of the sonic hedgehog signaling pathway occurs in the CD133 positive cells of mouse liver cancer Hepa 1-6 cells. - Accounting for technical noise in single-cell RNA-seq experiments. - Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq
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