scholarly journals Characterizing the Emergence of Liver and Gallbladder from the Embryonic Endoderm through Single-Cell RNA-Seq

2019 ◽  
Author(s):  
Tianhao Mu ◽  
Liqin Xu ◽  
Yu Zhong ◽  
Xinyu Liu ◽  
Zhikun Zhao ◽  
...  
Keyword(s):  
Single Cell ◽  
Primitive Streak ◽  
Developmental Trajectory ◽  
Visceral Endoderm ◽  
Rna Seq ◽  
Internal Organs ◽  
Factors Associated ◽  
Complex Signaling ◽  
Foregut Endoderm ◽  
Mesenchymal Epithelial Transition

AbstractThe liver and gallbladder are among the most important internal organs derived from the endoderm. Several inductive signals regulate liver development, yet the pure nascent hepatic and gallbladder cells are unable to be isolated due to limited cell markers and cell numbers. The transcriptome networks of the hepatic lineage in the endoderm, and how the gallbladder differentiates from the adjacent endoderm population, are not fully understood. Using a transgenic Foxa2eGFP reporter mouse line, we performed deep single-cell RNA sequencing on 1,966 individual cells, including nascent hepatic and gallbladder cells, isolated from the endoderm and hepatic regions from ten embryonic stages, ranging from day E7.5 to E15.5. We identified the embryonic liver developmental trajectory from primitive streak to hepatoblasts and characterized the transcriptome of the hepatic lineage. During pre-hepatogenesis (5-6 somite stage), we have identified two groups of foregut endoderm cells, one derived from visceral endoderm and the second derived from primitive streak via a mesenchymal-epithelial transition (MET). During the liver specification stages, liver primordium was identified to share both foregut and liver features. We also documented dynamic gene expression during the epithelial-hepatic transition (EHT). Six gene groups were found to switch on or off at different stages during liver specification. Importantly, we found that RXR complex signaling and newly identified transcription factors associated with liver specification. Moreover, we revealed the gallbladder primordium cells at E9.5 and found genes that transcriptionally distinguish them from the liver primordium. The present data provides a high-resolution resource and critical insights for understanding the emergence of the endoderm, liver and gallbladder development.

scholarly journals Embryonic liver developmental trajectory revealed by single-cell RNA sequencing in the Foxa2eGFP mouse

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Tianhao Mu ◽  
Liqin Xu ◽  
Yu Zhong ◽  
Xinyu Liu ◽  
Zhikun Zhao ◽  
...  
Keyword(s):  
Single Cell ◽  
Transcriptional Profiling ◽  
Developmental Trajectory ◽  
Embryonic Liver ◽  
Internal Organs ◽  
Reporter Mouse ◽  
Mrna Sequencing ◽  
Hepatic Cells ◽  
Single Cell Rna Sequencing ◽  
Gut Endoderm

AbstractThe liver and gallbladder are among the most important internal organs derived from the endoderm, yet the development of the liver and gallbladder in the early embryonic stages is not fully understood. Using a transgenic Foxa2eGFP reporter mouse line, we performed single-cell full-length mRNA sequencing on endodermal and hepatic cells isolated from ten embryonic stages, ranging from E7.5 to E15.5. We identified the embryonic liver developmental trajectory from gut endoderm to hepatoblasts and characterized the transcriptome of the hepatic lineage. More importantly, we identified liver primordium as the nascent hepatic progenitors with both gut and liver features and documented dynamic gene expression during the epithelial-hepatic transition (EHT) at the stage of liver specification during E9.5–11.5. We found six groups of genes switched on or off in the EHT process, including diverse transcripitional regulators that had not been previously known to be expressed during EHT. Moreover, we identified and revealed transcriptional profiling of gallbladder primordium at E9.5. The present data provides a high-resolution resource and critical insights for understanding the liver and gallbladder development.

scholarly journals Time-course single-cell RNA sequencing reveals transcriptional dynamics and heterogeneity of limbal stem cells derived from human pluripotent stem cells

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Changbin Sun ◽  
Hailun Wang ◽  
Qiwang Ma ◽  
Chao Chen ◽  
Jianhui Yue ◽  
...  
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Cell Fate ◽  
Time Course ◽  
Embryonic Stem ◽  
Developmental Trajectory ◽  
Rna Seq ◽  
Limbal Stem Cells ◽  
Developmental Origin

Abstract Background Human pluripotent stem cell-derived limbal stem cells (hPSC-derived LSCs) provide a promising cell source for corneal transplants and ocular surface reconstruction. Although recent efforts in the identification of LSC markers have increased our understanding of the biology of LSCs, much more remains to be characterized in the developmental origin, cell fate determination, and identity of human LSCs. The lack of knowledge hindered the establishment of efficient differentiation protocols for generating hPSC-derived LSCs and held back their clinical application. Results Here, we performed a time-course single-cell RNA-seq to investigate transcriptional heterogeneity and expression changes of LSCs derived from human embryonic stem cells (hESCs). Based on current protocol, expression heterogeneity of reported LSC markers were identified in subpopulations of differentiated cells. EMT has been shown to occur during differentiation process, which could possibly result in generation of untargeted cells. Pseudotime trajectory analysis revealed transcriptional changes and signatures of commitment of hESCs-derived LSCs and their progeny—the transit amplifying cells. Conclusion Single-cell RNA-seq revealed time-course expression changes and significant transcriptional heterogeneity during hESC-derived LSC differentiation in vitro. Our results demonstrated candidate developmental trajectory and several new candidate markers for LSCs, which could facilitate elucidating the identity and developmental origin of human LSCs in vivo.

scholarly journals Exploring Additional Valuable Information From Single-Cell RNA-Seq Data

2020 ◽  
Vol 8 ◽  
Author(s):  
Yunjin Li ◽  
Qiyue Xu ◽  
Duojiao Wu ◽  
Geng Chen
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Large Scale ◽  
Cell Communication ◽  
Cell Types ◽  
Copy Number Variations ◽  
Developmental Trajectory ◽  
Cellular Heterogeneity ◽  
Circular Rnas ◽  
Rna Seq

Single-cell RNA-seq (scRNA-seq) technologies are broadly applied to dissect the cellular heterogeneity and expression dynamics, providing unprecedented insights into single-cell biology. Most of the scRNA-seq studies mainly focused on the dissection of cell types/states, developmental trajectory, gene regulatory network, and alternative splicing. However, besides these routine analyses, many other valuable scRNA-seq investigations can be conducted. Here, we first review cell-to-cell communication exploration, RNA velocity inference, identification of large-scale copy number variations and single nucleotide changes, and chromatin accessibility prediction based on single-cell transcriptomics data. Next, we discuss the identification of novel genes/transcripts through transcriptome reconstruction approaches, as well as the profiling of long non-coding RNAs and circular RNAs. Additionally, we survey the integration of single-cell and bulk RNA-seq datasets for deconvoluting the cell composition of large-scale bulk samples and linking single-cell signatures to patient outcomes. These additional analyses could largely facilitate corresponding basic science and clinical applications.

scholarly journals Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data

2020 ◽  
Author(s):  
Kevin Z. Lin ◽  
Jing Lei ◽  
Kathryn Roeder
Keyword(s):  
Single Cell ◽  
Exponential Family ◽  
Developmental Trajectories ◽  
Dimensional Space ◽  
Developmental Trajectory ◽  
Rna Seq ◽  
Product Model ◽  
Non Linear ◽  
Linear Embedding ◽  
Embedding Methods

AbstractScientists often embed cells into a lower-dimensional space when studying single-cell RNA-seq data for improved downstream analyses such as developmental trajectory analyses, but the statistical properties of such non-linear embedding methods are often not well understood. In this article, we develop the eSVD (exponential-family SVD), a non-linear embedding method for both cells and genes jointly with respect to a random dot product model using exponential-family distributions. Our estimator uses alternating minimization, which enables us to have a computationally-efficient method, prove the identifiability conditions and consistency of our method, and provide statistically-principled procedures to tune our method. All these qualities help advance the single-cell embedding literature, and we provide extensive simulations to demonstrate that the eSVD is competitive compared to other embedding methods.We apply the eSVD via Gaussian distributions where the standard deviations are proportional to the means to analyze a single-cell dataset of oligodendrocytes in mouse brains (Marques et al., 2016). Using the eSVD estimated embedding, we then investigate the cell developmental trajectories of the oligodendrocytes. While previous results are not able to distinguish the trajectories among the mature oligodendrocyte cell types, our diagnostics and results demonstrate there are two major developmental trajectories that diverge at mature oligodendrocytes.

Sign in / Sign up

Export Citation Format

Share Document