scholarly journals IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data

2021 ◽  
Author(s):  
Tiam Heydari ◽  
Matthew A. Langley ◽  
Cynthia Fisher ◽  
Daniel Aguilar-Hidalgo ◽  
Shreya Shukla ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Regulatory Networks ◽  
Developmental Trajectories ◽  
A Priori ◽  
Developmental Trajectory ◽  
Developmental Systems ◽  
Dynamic Simulations ◽  
Versatile Tool ◽  
Gene Perturbations

The increasing availability of single-cell RNA-sequencing (scRNA-seq) data from various developmental systems provides the opportunity to infer gene regulatory networks (GRNs) directly from data. Herein we describe IQCELL , a platform to infer, simulate, and study executable logical GRNs directly from scRNA-seq data. Such executable GRNs provide an opportunity to inform fundamental hypotheses in developmental programs and help accelerate the design of stem cell-based technologies. We first describe the architecture of IQCELL. Next, we apply IQCELL to a scRNA-seq dataset of early mouse T-cell development and show that it can infer a priori over 75% of causal gene interactions previously reported via decades of research. We will also show that dynamic simulations of the derived GRN qualitatively recapitulate the effects of the known gene perturbations on the T-cell developmental trajectory. IQCELL is applicable to many developmental systems and offers a versatile tool to infer, simulate, and study GRNs in biological systems. (https://gitlab.com/stemcellbioengineering/iqcell)

scholarly journals Developmental trajectory of prehematopoietic stem cell formation from endothelium

Blood ◽  
2020 ◽  
Vol 136 (7) ◽  
pp. 845-856 ◽  
Author(s):  
Qin Zhu ◽  
Peng Gao ◽  
Joanna Tober ◽  
Laura Bennett ◽  
Changya Chen ◽  
...  
Keyword(s):  
Single Cell ◽  
Fetal Liver ◽  
Developmental Trajectories ◽  
Cell Formation ◽  
Intermediate Stage ◽  
Chromatin Accessibility ◽  
Small Population ◽  
Developmental Trajectory ◽  
Mammalian Embryo ◽  
Hematopoietic Stem

Abstract Hematopoietic stem and progenitor cells (HSPCs) in the bone marrow are derived from a small population of hemogenic endothelial (HE) cells located in the major arteries of the mammalian embryo. HE cells undergo an endothelial to hematopoietic cell transition, giving rise to HSPCs that accumulate in intra-arterial clusters (IAC) before colonizing the fetal liver. To examine the cell and molecular transitions between endothelial (E), HE, and IAC cells, and the heterogeneity of HSPCs within IACs, we profiled ∼40 000 cells from the caudal arteries (dorsal aorta, umbilical, vitelline) of 9.5 days post coitus (dpc) to 11.5 dpc mouse embryos by single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin sequencing. We identified a continuous developmental trajectory from E to HE to IAC cells, with identifiable intermediate stages. The intermediate stage most proximal to HE, which we term pre-HE, is characterized by increased accessibility of chromatin enriched for SOX, FOX, GATA, and SMAD motifs. A developmental bottleneck separates pre-HE from HE, with RUNX1 dosage regulating the efficiency of the pre-HE to HE transition. A distal candidate Runx1 enhancer exhibits high chromatin accessibility specifically in pre-HE cells at the bottleneck, but loses accessibility thereafter. Distinct developmental trajectories within IAC cells result in 2 populations of CD45+ HSPCs; an initial wave of lymphomyeloid-biased progenitors, followed by precursors of hematopoietic stem cells (pre-HSCs). This multiomics single-cell atlas significantly expands our understanding of pre-HSC ontogeny.

scholarly journals Single-cell transcriptomics dissects hematopoietic cell destruction and T cell engagement in aplastic anemia

Blood ◽  
2021 ◽  
Author(s):  
Caiying Zhu ◽  
Yu Lian ◽  
Chenchen Wang ◽  
Peng Wu ◽  
Xuan Li ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Aplastic Anemia ◽  
Single Cell ◽  
Regulatory Networks ◽  
Bone Marrow Failure ◽  
Autoimmune Disorder ◽  
Transcriptional Regulatory Networks ◽  
Dna Damage And Repair ◽  
Hematopoietic Stem

Aplastic anemia (AA) is a T cell-mediated autoimmune disorder of the hematopoietic system manifested by severe depletion of the hematopoietic stem and progenitor cells (HSPCs). Nonetheless our understanding of the complex relationship between HSPCs and T cells is still obscure, mainly limited by techniques and the sparsity of HSPCs in the context of bone marrow failure. Here we performed single-cell transcriptome analysis of residual HSPCs and T cells to identify the molecular players from AA patients. We observed that residual HSPCs in AA exhibited lineage-specific alterations in gene expression and transcriptional regulatory networks, indicating a selective disruption of distinct lineage-committed progenitor pools. In particular, HSPCs displayed frequently altered alternative splicing events and skewed patterns of polyadenylation in transcripts related to DNA damage and repair, suggesting a likely role in AA progression to myelodysplastic syndromes. We further identified cell-type-specific ligand-receptor interactions as potential mediators for ongoing HSPCs destruction by T cells. By tracking patients after immunosuppressive therapy (IST), we showed that hematopoiesis remission was incomplete accompanied by IST insensitive interactions between HSPCs and T cells as well as sustained abnormal transcription state. These data collectively constitute the transcriptomic landscape of disrupted hematopoiesis in AA at single-cell resolution, providing new insights into the molecular interactions of engaged T cells with residual HSPCs and render novel therapeutic opportunities for AA.

scholarly journals Single-cell analysis reveals the origins and intrahepatic development of liver-resident IFN-γ-producing γδ T cells

2021 ◽  
Vol 18 (4) ◽  
pp. 954-968
Author(s):  
Yuan Hu ◽  
Keke Fang ◽  
Yanan Wang ◽  
Nan Lu ◽  
Haoyu Sun ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Adoptive Transfer ◽  
Γδ T Cells ◽  
Developmental Trajectory ◽  
Γδ T Cell ◽  
Developmental Pathway ◽  
Hematopoietic Progenitor ◽  
Ifn Γ

Abstractγδ T cells are heterogeneous lymphocytes located in various tissues. However, a systematic and comprehensive understanding of the origins of γδ T cell heterogeneity and the extrathymic developmental pathway associated with liver γδ T cells remain largely unsolved. In this study, we performed single-cell RNA sequencing (scRNA-seq) to comprehensively catalog the heterogeneity of γδ T cells derived from murine liver and thymus samples. We revealed the developmental trajectory of γδ T cells and found that the liver contains γδ T cell precursors (pre-γδ T cells). The developmental potential of hepatic γδ T precursor cells was confirmed through in vitro coculture experiments and in vivo adoptive transfer experiments. The adoptive transfer of hematopoietic progenitor Lin−Sca-1+Mac-1+ (LSM) cells from fetal or adult liver samples to sublethally irradiated recipients resulted in the differentiation of liver LSM cells into pre-γδ T cells and interferon-gamma+ (IFN-γ+) but not interleukin-17a+ (IL-17a+) γδ T cells in the liver. Importantly, thymectomized mouse models showed that IFN-γ-producing γδ T cells could originate from liver LSM cells in a thymus-independent manner. These results suggested that liver hematopoietic progenitor LSM cells were able to differentiate into pre-γδ T cells and functionally mature γδ T cells, which implied that these cells are involved in a distinct developmental pathway independent of thymus-derived γδ T cells.

scholarly journals Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thoma Itoh ◽  
Takashi Makino
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Rna Seq ◽  
Gene Deletions ◽  
Functional Compensation ◽  
Downstream Effects ◽  
New Strategy ◽  
Single Deletion ◽  
Network Estimation ◽  
Gene Perturbations

AbstractRecent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in “transport”, which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.

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 Defining developmental diversification of diencephalon neurons through single-cell gene expression profiling

2018 ◽  
Author(s):  
Qiuxia Guo ◽  
James Y. H. Li
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Regulatory Networks ◽  
Neuronal Cell ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Form Complex ◽  
Molecular Features ◽  
Close Relationship ◽  
Cell Groups

ABSTRACTThe embryonic diencephalon gives rise to diverse neuronal cell types, which form complex integration centers and intricate relay stations of the vertebrate forebrain. Prior anecdotal gene expression studies suggest several developmental compartments within the developing diencephalon. In the current study, we utilized single-cell RNA sequencing to profile transcriptomes of dissociated cells from the diencephalon of E12.5 mouse embryos. Through analysis of unbiased transcriptional data, we identified the divergence of different progenitors, intermediate progenitors, and emerging neuronal cell types. After mapping the identified cell groups to their spatial origins, we were able to characterize the molecular features across different cell types and cell states, arising from various diencephalic compartments. Furthermore, we reconstructed the developmental trajectory of different cell lineages within the diencephalon. This allowed the identification of the genetic cascades and gene regulatory networks underlying the progression of the cell cycle, neurogenesis, and cellular diversification. The analysis provides new insights into the molecular mechanism underlying the specification and amplification of thalamic progenitor cells. In addition, the single-cell-resolved trajectories not only confirm a close relationship between the rostral thalamus and prethalamus, but also uncover an unexpected close relationship between the caudal thalamus, epithalamus and rostral pretectum. Our data provide a useful resource for the systematic study of cell heterogeneity and differentiation kinetics within the developing diencephalon.

scholarly journals Reversed graph embedding resolves complex single-cell developmental trajectories

2017 ◽  
Author(s):  
Xiaojie Qiu ◽  
Qi Mao ◽  
Ying Tang ◽  
Li Wang ◽  
Raghav Chawla ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Developmental Trajectories ◽  
Single Cells ◽  
Graph Embedding ◽  
Developmental Trajectory ◽  
Loss Of Function ◽  
Cell Fate Decisions ◽  
Principal Graph ◽  
Cell Trajectories

AbstractOrganizing single cells along a developmental trajectory has emerged as a powerful tool for understanding how gene regulation governs cell fate decisions. However, learning the structure of complex single-cell trajectories with two or more branches remains a challenging computational problem. We present Monocle 2, which uses reversed graph embedding to reconstruct single-cell trajectories in a fully unsupervised manner. Monocle 2 learns an explicit principal graph to describe the data, greatly improving the robustness and accuracy of its trajectories compared to other algorithms. Monocle 2 uncovered a new, alternative cell fate in what we previously reported to be a linear trajectory for differentiating myoblasts. We also reconstruct branched trajectories for two studies of blood development, and show that loss of function mutations in key lineage transcription factors diverts cells to alternative branches on the a trajectory. Monocle 2 is thus a powerful tool for analyzing cell fate decisions with single-cell genomics.

scholarly journals An Arabidopsis root phloem pole cell atlas reveals PINEAPPLE genes as transitioners to autotrophy

2021 ◽  
Author(s):  
Sofia Otero ◽  
Iris Sevilem ◽  
Pawel Roszak ◽  
Yipeng Lu ◽  
Valerio Di Vittori ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Holistic View ◽  
Arabidopsis Root ◽  
Single Cell Sequencing ◽  
Gene Regulatory ◽  
Dof Transcription Factors

AbstractSingle cell sequencing has recently allowed the generation of exhaustive root cell atlases. However, some cell types are elusive and remain underrepresented. Here, we use a second- generation single cell approach, where we zoom in on the root transcriptome sorting with specific markers to profile the phloem poles at an unprecedented resolution. Our data highlight the similarities among the developmental trajectories and gene regulatory networks communal to protophloem sieve element (PSE) adjacent lineages in relation to PSE enucleation, a key event in phloem biology.As a signature for early PSE-adjacent lineages, we have identified a set of DNA-binding with one finger (DOF) transcription factors, the PINEAPPLEs (PAPL), that act downstream of PHLOEM EARLY DOF (PEAR) genes, and are important to guarantee a proper root nutrition in the transition to autotrophy.Our data provide a holistic view of the phloem poles that act as a functional unit in root development.

scholarly journals Temporal mixture modelling of single-cell RNA-seq data resolves a CD4+ T cell fate bifurcation

2016 ◽  
Author(s):  
Tapio Lönnberg ◽  
Valentine Svensson ◽  
Kylie R James ◽  
Daniel Fernandez-Ruiz ◽  
Ismail Sebina ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Developmental Trajectories ◽  
Computational Modelling ◽  
Computational Approach ◽  
Cell Populations ◽  
Cell Fates ◽  
Inflammatory Monocytes

AbstractDifferentiation of naïve CD4+ T cells into functionally distinct T helper subsets is crucial for the orchestration of immune responses. Due to multiple levels of heterogeneity and multiple overlapping transcriptional programs in differentiating T cell populations, this process has remained a challenge for systematic dissection in vivo. By using single-cell RNA transcriptomics and computational modelling of temporal mixtures, we reconstructed the developmental trajectories of Th1 and Tfh cell populations during Plasmodium infection in mice at single-cell resolution. These cell fates emerged from a common, highly proliferative and metabolically active precursor. Moreover, by tracking clonality from T cell receptor sequences, we infer that ancestors derived from the same naïve CD4+ T cell can concurrently populate both Th1 and Tfh subsets. We further found that precursor T cells were coached towards a Th1 but not a Tfh fate by monocytes/macrophages. The integrated genomic and computational approach we describe is applicable for analysis of any cellular system characterized by differentiation towards multiple fates.One Sentence SummaryUsing single-cell RNA sequencing and a novel unsupervised computational approach, we resolve the developmental trajectories of two CD4+ T cell fates in vivo, and show that uncommitted T cells are externally influenced towards one fate by inflammatory monocytes.

scholarly journals Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast

2021 ◽  
Author(s):  
Thoma Itoh ◽  
Takashi Makino
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Rna Seq ◽  
Gene Deletions ◽  
Functional Compensation ◽  
Downstream Effects ◽  
New Strategy ◽  
Single Deletion ◽  
Network Estimation ◽  
Gene Perturbations

Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in 'transport', which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.

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