scholarly journals Topographer Reveals Dynamic Mechanisms of Cell Fate Decisions from Single-Cell Transcriptomic Data

2018 ◽  
Author(s):  
Jiajun Zhang ◽  
Qing Nie ◽  
Tianshou Zhou
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Branch Point ◽  
State Transition ◽  
Transition Probabilities ◽  
Marker Gene ◽  
Complex Cell ◽  
Cell Fate Decisions ◽  
Transcriptomic Data ◽  
Cell State

AbstractCell fate decisions play a pivotal role in development but technologies for dissecting them are limited. We developed a multifunction new method, Topographer to construct a ‘quantitative’ Waddington’s landscape of single-cell transcriptomic data. This method is able to identify complex cell-state transition trajectories and to estimate complex cell-type dynamics characterized by fate and transition probabilities. It also infers both marker gene networks and their dynamic changes as well as dynamic characteristics of transcriptional bursting along the cell-state transition trajectories. Applying this method to single-cell RNA-seq data on the differentiation of primary human myoblasts, we not only identified three known cell types but also estimated both their fate probabilities and transition probabilities among them. We found that the percent of genes expressed in a bursty manner is significantly higher at (or near) the branch point (∼97%) than before or after branch (below 80%), and that both gene-gene and cell-cell correlation degrees are apparently lower near the branch point than away from the branching. Topographer allows revealing of cell fate mechanisms in a coherent way at three scales: cell lineage (macroscopic), gene network (mesoscopic) and gene expression (microscopic).

scholarly journals Integrated single-cell transcriptomic and epigenetic analyses of cell-state transition and lineage commitment in the embryonic mouse cerebellum

2021 ◽  
Author(s):  
Khouri Farah-Nagham ◽  
Qiuxia Guo ◽  
Kerry Morgan ◽  
Jihye Shin ◽  
James Y.H. Li
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Regulatory Networks ◽  
State Transition ◽  
Control Cell ◽  
Cellular Heterogeneity ◽  
Embryonic Mouse ◽  
Molecular Control ◽  
Cell State

Recent studies using single-cell RNA-seq have revealed cellular heterogeneity in the developing mammalian cerebellum, yet the regulatory logic underlying this cellular diversity remains to be elucidated. Using integrated single-cell RNA and ATAC analyses, we resolved developmental trajectories of cerebellar progenitors and identified putative trans- and cis-elements that control cell state transition. We reverse-engineered gene regulatory networks (GRNs) of each cerebellar cell type. Through in silico simulations and in vivo experiments, we validated the efficacy of GRN analyses and uncovered the molecular control of a newly identified stem zone, the posterior transitory zone (PTZ), which contains multipotent progenitors for granule neurons, Bergmann glia, and choroid plexus epithelium. Importantly, we showed that perturbing cell fate specification of PTZ progenitors causes posterior cerebellar vermis hypoplasia, the most common cerebellar birth defect in humans. Our study provides a foundation for comprehensive studies of developmental programs of the mammalian cerebellum.

scholarly journals Single cell RNA-seq and ATAC-seq indicate critical roles of Isl1 and Nkx2-5 for cardiac progenitor cell transition states and lineage settlement

2017 ◽  
Author(s):  
Guangshuai Jia ◽  
Jens Preussner ◽  
Stefan Guenther ◽  
Xuejun Yuan ◽  
Michail Yekelchyk ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Progenitor Cell ◽  
Marker Gene ◽  
Transitional Cell ◽  
Developmental Trajectory ◽  
Open Chromatin ◽  
Cardiac Progenitor Cell ◽  
Cell Fate Decisions

SUMMARYFormation and segregation of cell lineages building the vertebrate heart have been studied extensively by genetic cell tracing techniques and by analysis of single marker gene expression but the underlying gene regulatory networks driving cell fate transitions during early cardiogenesis are only partially understood. Here, we comprehensively characterized mouse cardiac progenitor cells (CPC) marked by Nkx2-5 and Isl1 expression from E7.5 to E9.5 using single-cell RNA sequencing. By leveraging on cell-to-cell heterogeneity, we identified different previously unknown cardiac sub-populations. Reconstruction of the developmental trajectory revealed that Isl1+ CPC represent a transitional cell population maintaining a prolonged multipotent state, whereas extended expression of Nkx2-5 commits CPC to a unidirectional cardiomyocyte fate. Furthermore, we show that CPC fate transitions are associated with distinct open chromatin states, which critically depend on Isl1 and Nkx2-5. Our data provide a model of transcriptional and epigenetic regulations during cardiac progenitor cell fate decisions at single-cell resolution.

scholarly journals DensityPath: an algorithm to visualize and reconstruct cell state-transition path on density landscape for single-cell RNA sequencing data

2018 ◽  
Vol 35 (15) ◽  
pp. 2593-2601 ◽  
Author(s):  
Ziwei Chen ◽  
Shaokun An ◽  
Xiangqi Bai ◽  
Fuzhou Gong ◽  
Liang Ma ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
State Transition ◽  
Supplementary Information ◽  
Transition Path ◽  
Cell Fate Decisions ◽  
Cell State ◽  
Topological Features ◽  
Representative Cell ◽  
Single Cell Rna Sequencing

Abstract Motivation Visualizing and reconstructing cell developmental trajectories intrinsically embedded in high-dimensional expression profiles of single-cell RNA sequencing (scRNA-seq) snapshot data are computationally intriguing, but challenging. Results We propose DensityPath, an algorithm allowing (i) visualization of the intrinsic structure of scRNA-seq data on an embedded 2-d space and (ii) reconstruction of an optimal cell state-transition path on the density landscape. DensityPath powerfully handles high dimensionality and heterogeneity of scRNA-seq data by (i) revealing the intrinsic structures of data, while adopting a non-linear dimension reduction algorithm, termed elastic embedding, which can preserve both local and global structures of the data; and (ii) extracting the topological features of high-density, level-set clusters from a single-cell multimodal density landscape of transcriptional heterogeneity, as the representative cell states. DensityPath reconstructs the optimal cell state-transition path by finding the geodesic minimum spanning tree of representative cell states on the density landscape, establishing a least action path with the minimum-transition-energy of cell fate decisions. We demonstrate that DensityPath can ably reconstruct complex trajectories of cell development, e.g. those with multiple bifurcating and trifurcating branches, while maintaining computational efficiency. Moreover, DensityPath has high accuracy for pseudotime calculation and branch assignment on real scRNA-seq, as well as simulated datasets. DensityPath is robust to parameter choices, as well as permutations of data. Availability and implementation DensityPath software is available at https://github.com/ucasdp/DensityPath. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst

2010 ◽  
Vol 18 (4) ◽  
pp. 675-685 ◽  
Author(s):  
Guoji Guo ◽  
Mikael Huss ◽  
Guo Qing Tong ◽  
Chaoyang Wang ◽  
Li Li Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Cell Fate Decisions ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development

2014 ◽  
Vol 31 (7) ◽  
pp. 1060-1066 ◽  
Author(s):  
Haifen Chen ◽  
Jing Guo ◽  
Shital K. Mishra ◽  
Paul Robson ◽  
Mahesan Niranjan ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Transcriptional Analysis ◽  
Mouse Development ◽  
Cell Fate Decisions ◽  
Regulatory Circuits ◽  
Early Mouse

scholarly journals Basal stem cell fate specification is mediated by SMAD signaling in the developing human lung

2018 ◽  
Author(s):  
Alyssa J. Miller ◽  
Qianhui Yu ◽  
Michael Czerwinski ◽  
Yu-Hwai Tsai ◽  
Renee F. Conway ◽  
...  
Keyword(s):  
Single Cell ◽  
Progenitor Cells ◽  
Cell Fate ◽  
Basal Cell ◽  
Human Lung ◽  
Branching Morphogenesis ◽  
Basal Cells ◽  
Smad Signaling ◽  
Cell Fate Decisions ◽  
Cell Transition

AbstractBasal stem cells (basal cells), located in the bronchi and trachea of the human lung epithelium, play a critical role in normal airway homeostasis and repair, and have been implicated in the development of diseases such as cancer1-4. Additionally, basal-like cells contribute to alveolar regeneration and fibrosis following severe injury5-8. However, the developmental origin of basal cells in humans is unclear. Previous work has shown that specialized progenitor cells exist at the tips of epithelial tubes during lung branching morphogenesis, and in mice, give rise to all alveolar and airway lineages9,10. These ‘bud tip progenitor cells’ have also been described in the developing human lung11-13, but the mechanisms controlling bud tip differentiation into specific cell lineages, including basal cells, are unknown. Here, we interrogated the bud tip-to-basal cell transition using human tissue specimens, bud tip progenitor organoid cultures11, and single-cell transcriptomics. We used single-cell mRNA sequencing (scRNAseq) of developing human lung specimens from 15-21 weeks gestation to identify molecular signatures and cell states in the developing human airway epithelium. We then inferred differentiation trajectories during bud tip-to-airway differentiation, which revealed a previously undescribed transitional cell state (‘hub progenitors’) and implicated SMAD signaling as a regulator of the bud tip-to-basal cell transition. We used bud tip progenitor organoids to show that TGFT1 and BMP4 mediated SMAD signaling robustly induced the transition into functional basal-like cells, and these in vitro-derived basal cells exhibited clonal expansion, self-renewal and multilineage differentiation. This work provides a framework for deducing and validating key regulators of cell fate decisions using single cell transcriptomics and human organoid models. Further, the identification of SMAD signaling as a critical regulator of newly born basal cells in the lung may have implications for regenerative medicine, basal cell development in other organs, and understanding basal cell misregulation in disease.

scholarly journals scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data

2018 ◽  
Vol 34 (12) ◽  
pp. 2077-2086 ◽  
Author(s):  
Suoqin Jin ◽  
Adam L MacLean ◽  
Tao Peng ◽  
Qing Nie
Keyword(s):  
Single Cell ◽  
Transition Probabilities ◽  
Energy Landscape ◽  
Control Cell ◽  
Robust Inference ◽  
Supplementary Information ◽  
Myoblast Differentiation ◽  
State Transitions ◽  
Marker Genes ◽  
Cell State

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for studying cellular decision-making processes. Robust inference of cell state transition paths and probabilities is an important yet challenging step in the analysis of these data. Results Here we present scEpath, an algorithm that calculates energy landscapes and probabilistic directed graphs in order to reconstruct developmental trajectories. We quantify the energy landscape using ‘single-cell energy’ and distance-based measures, and find that the combination of these enables robust inference of the transition probabilities and lineage relationships between cell states. We also identify marker genes and gene expression patterns associated with cell state transitions. Our approach produces pseudotemporal orderings that are—in combination—more robust and accurate than current methods, and offers higher resolution dynamics of the cell state transitions, leading to new insight into key transition events during differentiation and development. Moreover, scEpath is robust to variation in the size of the input gene set, and is broadly unsupervised, requiring few parameters to be set by the user. Applications of scEpath led to the identification of a cell-cell communication network implicated in early human embryo development, and novel transcription factors important for myoblast differentiation. scEpath allows us to identify common and specific temporal dynamics and transcriptional factor programs along branched lineages, as well as the transition probabilities that control cell fates. Availability and implementation A MATLAB package of scEpath is available at https://github.com/sqjin/scEpath. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract 39: Fate Of Renin Cell Progenitors In The Developing Kidney Vasculature

Hypertension ◽  
2021 ◽  
Vol 78 (Suppl_1) ◽  
Author(s):  
Alexandre Martini ◽  
Ariel R Gomez ◽  
Maria Luisa Sequeira Lopez
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Fluorescent Protein ◽  
Enrichment Analysis ◽  
Cell Fate Decisions ◽  
Mural Cells ◽  
Developing Kidney ◽  
Green Fluorescent ◽  
Old Mice ◽  
Accessible Chromatin

The unique spatial arregement of the kidney arterioles is an essential event for its development. However, the mechanisms that govern this process are still poorly understood. During nephrogenesis, a group of stromal cells expressing the Forkhead Box D1 ( FoxD1 ) transcription factor (TF) will give rise to the metanephric progenitors for the mural cells of the kidneys arteries and arterioles. We aim to identify the core TFs involved in the cell fate along the differentiaton pathways of the developing kidney vasculature. Therefore, we generated Foxd1-cre; mTmG mice, whose Foxd1 derivative cells are labeled with green fluorescent protein (GFP). GFP+ cells were isolated from 5 (P5) or 30 (P30) days old mice kidneys, and processed either for single-cell RNA-Seq (scRNA-Seq) or for single-cell Assay for Transposase-Accessible Chromatin (scATAC-Seq ). The top5 highly expressed TFs on scRNA-Seq at P5 are: Tcf21, Zeb2, Meis2, Cebpd and Nme3 (p_adjusted_value(padj)= 0, 3.8E-187, 3.9E-180, 4E-172, 4.1E-172 and 3.2E-154, respectively). They are involved in developmental processes and cell proliferation. At P30, the top5 highly expressed TFs are: Atf3, klf2, Fos, Nr4a2 and Junb (padj= 4.2E-294, 2.1E-200, 3.5E-182, 1.7E-52 and 0.2E-24, respectively). They are implicated with calcium-signaling pathway and inflammation. Additionally, scATAC-Seq identifies regions of accessible chromatin for pontential TFs binding, leading to changes in gene expression content and cell identity. At P30, scATAC-Seq showed differential accessible regions with subsequent putative motif enrichment analysis for the TF N4a2 (padj: 4E-297). This is in accordance with our scRNA-Seq results and might play a role in the Foxd1 progenitors cell fate decisions. Our results tracks the fate of the Foxd1+ cells during the kidney vasculature assembly and suggest a new transcription factors network that might play a role to orchestrate cell fate decisions during kidney vascular development.

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