scholarly journals Palantir characterizes cell fate continuities in human hematopoiesis

2018 ◽  
Author(s):  
Manu Setty ◽  
Vaidotas Kiseliovas ◽  
Jacob Levine ◽  
Adam Gayoso ◽  
Linas Mazutis ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Transcription Factors ◽  
High Resolution ◽  
Single Cell ◽  
Cell Fate ◽  
Terminal State ◽  
Rna Seq ◽  
Hematopoietic Differentiation ◽  
Cell Plasticity ◽  
Probabilistic Process

AbstractRecent studies using single cell RNA-seq (scRNA-seq) data derived from differentiating systems have raised fundamental questions regarding the discrete vs continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells, which treats cell-fate as a probabilistic process, and leverages entropy to measure the changing nature of cell plasticity along the differentiation trajectory. Palantir generates a high resolution pseudotime ordering of cells, and assigns each cell state with its probability to differentiate into each terminal state. We apply Palantir to human bone marrow scRNA-seq data and detect key landmarks of hematopoietic differentiation. Palantir’s resolution enables identification of key transcription factors driving lineage fate choices, as these TFs closely track when cells lose plasticity. We demonstrate that Palantir is generalizable to diverse tissue types and well-suited to resolve less studied differentiating systems.

scholarly journals Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

2019 ◽  
Author(s):  
Ning Wang ◽  
Andrew E. Teschendorff
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Large Scale ◽  
Single Cells ◽  
Differential Expression Analysis ◽  
Dropout Rate ◽  
Rna Seq ◽  
Regulatory Activity

AbstractInferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

scholarly journals Single-cell epigenomic tracing of lifelong endothelial cell plasticity across mouse organs

2021 ◽  
Author(s):  
Xianhong Yu ◽  
Yaxi Liu ◽  
Xiaoge Liu ◽  
Haiqing Xiong ◽  
Aibin He
Keyword(s):  
Endothelial Cells ◽  
Transcription Factors ◽  
Endothelial Cell ◽  
Single Cell ◽  
Cell Fate ◽  
Developmental Origins ◽  
Cellular Plasticity ◽  
Cell Plasticity ◽  
Mesenchymal Transition ◽  
Endothelial To Mesenchymal Transition

Endothelial cells (ECs) across ages and tissues are highly heterogeneous in developmental origins, structures, functions, and cellular plasticity. Here, we applied CoBATCH for single-cell epigenomic tracing of dynamic EC lineage histories in five mouse organs from development to ageing. Our analyses showed that epigenomic memory reflects both developmental origins and tissue-restricted specialization of EC sublineages but with varying time lengths across organs. To gain insights into cellular plasticity of ECs, we identified bivalent chromatin occupancy of otherwise mutually exclusive EC- (ERG) and mesenchymal-specific (TWIST1/SNAI1) transcription factors promoting endothelial-to-mesenchymal transition. We further revealed that pseudotime trajectories by histone modifications H3K36me3 and H3K27ac faithfully recapitulate short- and long-range EC fate change over senescence, respectively. Together, our data provide a unique exploration of chromatin-level cell fate regulation of organotypic EC lineages across the lifespan.

scholarly journals Comprehensive network modeling from single cell RNA sequencing of human and mouse reveals well conserved transcription regulation of hematopoiesis

BMC Genomics ◽  
2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Shouguo Gao ◽  
Zhijie Wu ◽  
Xingmin Feng ◽  
Sachiko Kajigaya ◽  
Xujing Wang ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Transcription Factors ◽  
Single Cell ◽  
Rna Sequencing ◽  
Transcription Regulation ◽  
Regulatory Networks ◽  
Stem Cell Differentiation ◽  
Hematopoietic Stem ◽  
Single Cell Rna Sequencing ◽  
Human And Mouse

Abstract Background Presently, there is no comprehensive analysis of the transcription regulation network in hematopoiesis. Comparison of networks arising from gene co-expression across species can facilitate an understanding of the conservation of functional gene modules in hematopoiesis. Results We used single-cell RNA sequencing to profile bone marrow from human and mouse, and inferred transcription regulatory networks in each species in order to characterize transcriptional programs governing hematopoietic stem cell differentiation. We designed an algorithm for network reconstruction to conduct comparative transcriptomic analysis of hematopoietic gene co-expression and transcription regulation in human and mouse bone marrow cells. Co-expression network connectivity of hematopoiesis-related genes was found to be well conserved between mouse and human. The co-expression network showed “small-world” and “scale-free” architecture. The gene regulatory network formed a hierarchical structure, and hematopoiesis transcription factors localized to the hierarchy’s middle level. Conclusions Transcriptional regulatory networks are well conserved between human and mouse. The hierarchical organization of transcription factors may provide insights into hematopoietic cell lineage commitment, and to signal processing, cell survival and disease initiation.

scholarly journals Single-Cell RNA-Seq of Bone Marrow-Derived Mesenchymal Stem Cells Reveals Unique Profiles of Lineage Priming

PLoS ONE ◽  
2015 ◽  
Vol 10 (9) ◽  
pp. e0136199 ◽  
Author(s):  
Brian T. Freeman ◽  
Jangwook P. Jung ◽  
Brenda M. Ogle
Keyword(s):  
Stem Cells ◽  
Bone Marrow ◽  
Mesenchymal Stem Cells ◽  
Single Cell ◽  
Rna Seq ◽  
Lineage Priming

scholarly journals Exploring the Changing Landscape of Cell-to-Cell Variation After CTCF Knockdown via Single Cell RNA-seq

2019 ◽  
Author(s):  
Wei Wang ◽  
Gang Ren ◽  
Ni Hong ◽  
Wenfei Jin
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Single Cell ◽  
Zinc Finger ◽  
Ctcf Binding ◽  
Rna Seq ◽  
Expression Noise ◽  
Genome Wide ◽  
Cell Variation ◽  
Variable Genes

Abstract Background: CCCTC-Binding Factor (CTCF), also known as 11-zinc finger protein, participates in many cellular processes, including insulator activity, transcriptional regulation and organization of chromatin architecture. Based on single cell flow cytometry and single cell RNA-FISH analyses, our previous study showed that deletion of CTCF binding site led to a significantly increase of cellular variation of its target gene. However, the effect of CTCF on genome-wide landscape of cell-to-cell variation is unclear. Results: We knocked down CTCF in EL4 cells using shRNA, and conducted single cell RNA-seq on both wild type (WT) cells and CTCF-Knockdown (CTCF-KD) cells using Fluidigm C1 system. Principal component analysis of single cell RNA-seq data showed that WT and CTCF-KD cells concentrated in two different clusters on PC1, indicating gene expression profiles of WT and CTCF-KD cells were systematically different. Interestingly, GO terms including regulation of transcription, DNA binding, Zinc finger and transcription factor binding were significantly enriched in CTCF-KD-specific highly variable genes, indicating tissue-specific genes such as transcription factors were highly sensitive to CTCF level. The dysregulation of transcription factors potentially explain why knockdown of CTCF lead to systematic change of gene expression. In contrast, housekeeping genes such as rRNA processing, DNA repair and tRNA processing were significantly enriched in WT-specific highly variable genes, potentially due to a higher cellular variation of cell activity in WT cells compared to CTCF-KD cells. We further found cellular variation-increased genes were significantly enriched in down-regulated genes, indicating CTCF knockdown simultaneously reduced the expression levels and increased the expression noise of its regulated genes. Conclusions: To our knowledge, this is the first attempt to explore genome-wide landscape of cellular variation after CTCF knockdown. Our study not only advances our understanding of CTCF function in maintaining gene expression and reducing expression noise, but also provides a framework for examining gene function.

scholarly journals Improved detection of tumor suppressor events in single-cell RNA-Seq data

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Andrew E. Teschendorff ◽  
Ning Wang
Keyword(s):  
Transcription Factors ◽  
Tumor Suppressor ◽  
Single Cell ◽  
Cancer Cells ◽  
Dropout Rate ◽  
Lung Epithelial Cells ◽  
Rna Seq ◽  
Regulatory Activity ◽  
Tissue Specific ◽  
Early Tumor

Abstract Tissue-specific transcription factors are frequently inactivated in cancer. To fully dissect the heterogeneity of such tumor suppressor events requires single-cell resolution, yet this is challenging because of the high dropout rate. Here we propose a simple yet effective computational strategy called SCIRA to infer regulatory activity of tissue-specific transcription factors at single-cell resolution and use this tool to identify tumor suppressor events in single-cell RNA-Seq cancer studies. We demonstrate that tissue-specific transcription factors are preferentially inactivated in the corresponding cancer cells, suggesting that these are driver events. For many known or suspected tumor suppressors, SCIRA predicts inactivation in single cancer cells where differential expression does not, indicating that SCIRA improves the sensitivity to detect changes in regulatory activity. We identify NKX2-1 and TBX4 inactivation as early tumor suppressor events in normal non-ciliated lung epithelial cells from smokers. In summary, SCIRA can help chart the heterogeneity of tumor suppressor events at single-cell resolution.

scholarly journals Resolving Cell Fate Decisions during Somatic Cell Reprogramming by Single-Cell RNA-Seq

2019 ◽  
Vol 73 (4) ◽  
pp. 815-829.e7 ◽  
Author(s):  
Lin Guo ◽  
Lihui Lin ◽  
Xiaoshan Wang ◽  
Mingwei Gao ◽  
Shangtao Cao ◽  
...  
Keyword(s):  
Somatic Cell ◽  
Single Cell ◽  
Cell Fate ◽  
Cell Reprogramming ◽  
Rna Seq ◽  
Cell Fate Decisions ◽  
Somatic Cell Reprogramming

Single Cell RNA seq for Analysis of Cell Fate Decisions

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. SCI-20-SCI-20
Author(s):  
H. Leighton Grimes ◽  
Singh Harinder ◽  
Andre Olsson ◽  
Nathan Salomonis ◽  
Bruce J. Aronow ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Clustering Analysis ◽  
Conflicts Of Interest ◽  
Regulatory Gene ◽  
Rna Seq ◽  
Regulatory State ◽  
Cell Fate Decisions ◽  
General Utility ◽  
Developmental Hierarchy

Abstract Single-cell RNA-Seq has the potential to become a dominant approach in probing diverse and complex developmental compartments. Its unbiased and comprehensive nature could enable developmental ordering of cellular and regulatory gene hierarchies without prior knowledge. To test general utility we performed single-cell RNA-seq of murine hematopoietic progenitors focusing on the myeloid developmental hierarchy. Using novel unsupervised clustering analysis, ICDS, we correctly ordered known hierarchical states as well as revealed rare intermediates. Regulatory state analysis suggested that the transcription factors Gfi1 and Irf8 function antagonistically to control homeostatic neutrophil and macrophage production, respectively. This prediction was validated by complementary genetic and genomic experiments in granulocyte-macrophage progenitors. Using knock-in reporters for Gfi1 and Irf8 and clonogenic analyses coupled with single-cell RNA-seq we distinguished regulatory states of bi-potential progenitors from their lineage specifying or committed progeny. Thus single-cell RNA-Seq is a powerful developmental tool to characterize hierarchical and rare cellular states along with the regulators that control their dynamics. Disclosures No relevant conflicts of interest to declare.

scholarly journals FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data

2018 ◽  
Vol 15 (5) ◽  
pp. 379-386 ◽  
Author(s):  
Josip S Herman ◽  
Sagar ◽  
Dominic Grün
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Rna Seq ◽  
Multipotent Progenitors

scholarly journals Combined analysis of single cell RNA-Seq and ATAC-Seq data reveals putative regulatory toggles operating in native and iPS-derived retina

2020 ◽  
Author(s):  
Anouk Georges ◽  
Haruko Takeda ◽  
Arnaud Lavergne ◽  
Michiko Mandai ◽  
Fanny Lepiemme ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Retinal Development ◽  
Amacrine Cells ◽  
Regulatory Elements ◽  
Precursor Cells ◽  
Retinal Cell ◽  
Rna Seq ◽  
Combined Analysis ◽  
Temporal Sampling

AbstractBackgroundIt has recently become possible to recapitulate retinal development from induced pluripotent stem cells, opening new investigative and therapeutic opportunities. Single cell RNA sequencing allows comparison of transcriptome unfolding during in vivo and in vitro development at single cell resolution, which can be integrated with information about accessible regulatory elements identified by ATAC-Seq.ResultsWe report the generation and analysis of single-cell RNA-Seq data (> 38,000 cells) from native and iPSC-derived murine retina at four matched developmental stages spanning the emergence of the major retinal cell types. We combine information from temporal sampling, visualization of 3D UMAP manifolds, and RNA velocity to show that iPSC-derived 3D retinal aggregates broadly recapitulate the native developmental trajectories with evidence supporting re-specification from amacrine cells to horizontal and photoreceptor precursor cells, as well as a direct differentiation of Tbr1+ retinal ganglion cells from neuro-epithelium cells. We show relaxation of spatial and temporal transcriptome control, premature emergence and dominance of photoreceptor precursor cells, and susceptibility of dynamically regulated pathways and transcription factors to culture conditions in iPSC-derived retina. We generate bulk ATAC-Seq data for native and iPSC-derived murine retina identifying ∼125,000 peaks. We combine single-cell RNA-Seq with ATAC-Seq information and obtain evidence that approximately halve the transcription factors that are dynamically regulated during retinal development may act as repressors rather than activators. We propose that sets of activators and repressors with cell-type specific expression control “regulatory toggles” that lock cells in distinct transcriptome states underlying differentiation, with subtle but noteworthy differences between native and iPSC-derived retina.ConclusionsCombined analysis of single-cell RNA-Seq and ATAC-Seq information has refined the comparison of native and iPS-derived retinal development.

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