scholarly journals The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution

Science ◽  
2018 ◽  
Vol 360 (6392) ◽  
pp. eaar5780 ◽  
Author(s):  
James A. Briggs ◽  
Caleb Weinreb ◽  
Daniel E. Wagner ◽  
Sean Megason ◽  
Leonid Peshkin ◽  
...  

Time series of single-cell transcriptome measurements can reveal dynamic features of cell differentiation pathways. From measurements of whole frog embryos spanning zygotic genome activation through early organogenesis, we derived a detailed catalog of cell states in vertebrate development and a map of differentiation across all lineages over time. The inferred map recapitulates most if not all developmental relationships and associates new regulators and marker genes with each cell state. We find that many embryonic cell states appear earlier than previously appreciated. We also assess conflicting models of neural crest development. Incorporating a matched time series of zebrafish development from a companion paper, we reveal conserved and divergent features of vertebrate early developmental gene expression programs.

2021 ◽  
Author(s):  
Sanshiro Kanazawa ◽  
Hironori Hojo ◽  
Shinsuke Ohba ◽  
Junichi Iwata ◽  
Makoto Komura ◽  
...  

Abstract Although multiple studies have investigated the mesenchymal stem and progenitor cells (MSCs) that give rise to mature bone marrow, high heterogeneity in their morphologies and properties causes difficulties in molecular separation of their distinct populations. In this study, by taking advantage of the resolution of the single cell transcriptome, we analyzed Sca-1 and PDGFR-α fraction in the mouse bone marrow tissue. The single cell transcriptome enabled us to further classify the population into seven populations according to their gene expression profiles. We then separately obtained the seven populations based on candidate marker genes, and specified their gene expression properties and epigenetic landscape by ATAC-seq. Our findings will enable to elucidate the stem cell niche signal in the bone marrow microenvironment, reconstitute bone marrow in vitro, and shed light on the potentially important role of identified subpopulation in various clinical applications to the treatment of bone- and bone marrow-related diseases.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 47-48
Author(s):  
Yiqing Cai ◽  
Xiangxiang Zhou ◽  
Juan Yang ◽  
Jiarui Liu ◽  
Yi Zhao ◽  
...  

Introduction Cancer immunotherapy and targeted therapy have yielded impressive clinical efficacy in acute B lymphoblastic leukemia (B-ALL). Despite the initial high complete remission (CR) rate following first-line therapy, treatment refractoriness and disease relapse remain are correlated with dismal survival. By the time the malignant cells generate, they are accompanied by a rich network of stromal cells and cytokines in bone marrow (BM). This tumor microenvironment (TME) represents an important feature of the biology of B-ALL but also shapes the clinical behavior of the disease. It remains to be confirmed whether the cellular composition and transcriptional heterogeneity impacts the clinical effects of B-ALL. Herein, we analyzed the immune cell infiltration features and related marker genes for B-ALL based on single cell RNA sequencing (scRNA-seq) data, which would be of significance for the development of novel immunotherapies. Methods ScRNA-seq data of 11373 BM cells from 3 B-ALL patients were obtained from the Gene Expression Omnibus (GEO, GSE153358). After quality control and data normalization, cell filtration and marker genes extraction were performed by the Seurat package. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were then applied to cluster cells, following with cell types' definition and gene expression profiles in total subsets. Cell clustering was demonstrated using t-SNE-1 and t-SNE-2. In order to determine the cellular characteristics of TME cells mainly mediated by STING pathway, dendritic cell (DC) and B cell were extracted and further plotted gene expression including immunosuppressive molecules and STING pathway, respectively. The pseudo-time analysis was finally performed by Monocle package to display B cell development trajectory and gene expressions over time. Results 9 cell subsets in B-ALL BM, including naïve and memory CD4+ T cell, CD14+ monocyte, B cell, CD8+ T cell, FCGRA3+monocyte, nature killer (NK) cell, DC, and platelet, were identified based on t-SNE analysis (Fig.1A). Top 10 marker genes in each cell cluster were presented in heat map (Fig.1B). Through analyzing differentially expressed genes, we found that BM B cells hardly expressed PD-L1 (CD274), but partially carried TMEM173 (STING), NFKB1 (NF-κB) and GSDMD. In addition, immune cells in BM TME broadly distributed and highly expressed STING and NF-κB, indicating the potential response to type I innate immune response and higher sensitivity to STING agonists than PD-1 antibody (Fig.2A and B). Previous studies had revealed that STING pathway participated in the activation of DC following with production of type I IFNs. We further isolated DC from 9 subsets and profiled the gene expression features. T-SNE analysis revealed 3 subtypes of DC in BM, marker genes comparison further identified as monocyte derived DC, CD1C-CD14-DC and myeloid conventional DC. Cyclic GMP-AMP synthase (cGAS), STING and NF-κB were highly expressed in each type in compared with PDCD1 (PD-1) and highest existed on myeloid conventional DC (Fig.3). We then explored B cell subsets to determine whether STING pathway could induce cell pyroptosis in BM B cells. Different subgroup of B cells shared similar marker genes, companying with higher expression of NF-κB and GDSMD (Fig.4). Furthermore, pseudo-time analysis plotted the development trajectory of malignant B cells. The results showed that GSDMD gradually increased along with cell development, suggesting that STING agonist would be sensitive to mature B cells (Fig.5). Subsets analysis shown that anti-tumor immune response of DC and pyroptosis of B cell might be triggered through STING pathway activation. Conclusion Our study profiled for the first time the expression of STING pathway in BM DC and B cell from B-ALL patients based on single-cell transcriptome. Combination of STING agonist and conventional immunotherapy had been shown prospects in antitumor therapy. STING agonist is expected to be an adjuvant drug for B-ALL immunotherapies in the future. Keywords: Single-cell RNA sequencing; acute B lymphoblastic leukemia; STING; PD-1; immunotherapy. Disclosures No relevant conflicts of interest to declare.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sanshiro Kanazawa ◽  
Hiroyuki Okada ◽  
Hironori Hojo ◽  
Shinsuke Ohba ◽  
Junichi Iwata ◽  
...  

AbstractAlthough multiple studies have investigated the mesenchymal stem and progenitor cells (MSCs) that give rise to mature bone marrow, high heterogeneity in their morphologies and properties causes difficulties in molecular separation of their distinct populations. In this study, by taking advantage of the resolution of the single cell transcriptome, we analyzed Sca-1 and PDGFR-α fraction in the mouse bone marrow tissue. The single cell transcriptome enabled us to further classify the population into seven populations according to their gene expression profiles. We then separately obtained the seven populations based on candidate marker genes, and specified their gene expression properties and epigenetic landscape by ATAC-seq. Our findings will enable to elucidate the stem cell niche signal in the bone marrow microenvironment, reconstitute bone marrow in vitro, and shed light on the potentially important role of identified subpopulation in various clinical applications to the treatment of bone- and bone marrow-related diseases.


2020 ◽  
Author(s):  
Grace H.T. Yeo ◽  
Sachit D. Saksena ◽  
David K. Gifford

SummaryExisting computational methods that use single-cell RNA-sequencing for cell fate prediction either summarize observations of cell states and their couplings without modeling the underlying differentiation process, or are limited in their capacity to model complex differentiation landscapes. Thus, contemporary methods cannot predict how cells evolve stochastically and in physical time from an arbitrary starting expression state, nor can they model the cell fate consequences of gene expression perturbations. We introduce PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative modeling framework that learns an underlying differentiation landscape from single-cell time-series gene expression data. Our generative model framework provides insight into the process of differentiation and can simulate differentiation trajectories for arbitrary gene expression progenitor states. We validate our method on a recently published experimental lineage tracing dataset that provides observed trajectories. We show that this model is able to predict the fate biases of progenitor cells in neutrophil/macrophage lineages when accounting for cell proliferation, improving upon the best-performing existing method. We also show how a model can predict trajectories for cells not found in the model’s training set, including cells in which genes or sets of genes have been perturbed. PRESCIENT is able to accommodate complex perturbations of multiple genes, at different time points and from different starting cell populations. PRESCIENT models are able to recover the expected effects of known modulators of cell fate in hematopoiesis and pancreatic β cell differentiation.


2021 ◽  
Author(s):  
Teresa Rayon ◽  
Rory J. Maizels ◽  
Christopher Barrington ◽  
James Briscoe

AbstractThe spinal cord receives input from peripheral sensory neurons and controls motor output by regulating muscle innervating motor neurons. These functions are carried out by neural circuits comprising molecularly and physiologically distinct neuronal subtypes that are generated in a characteristic spatial-temporal arrangement from progenitors in the embryonic neural tube. The systematic mapping of gene expression in mouse embryos has provided insight into the diversity and complexity of cells in the neural tube. For human embryos, however, less information has been available. To address this, we used single cell mRNA sequencing to profile cervical and thoracic regions in four human embryos of Carnegie Stages (CS) CS12, CS14, CS17 and CS19 from Gestational Weeks (W) 4-7. In total we recovered the transcriptomes of 71,219 cells. Analysis of progenitor and neuronal populations from the neural tube, as well as cells of the peripheral nervous system, in dorsal root ganglia adjacent to the neural tube, identified dozens of distinct cell types and facilitated the reconstruction of the differentiation pathways of specific neuronal subtypes. Comparison with existing mouse datasets revealed the overall similarity of mouse and human neural tube development while highlighting specific features that differed between species. These data provide a catalogue of gene expression and cell type identity in the developing neural tube that will support future studies of sensory and motor control systems and can be explored at https://shiny.crick.ac.uk/scviewer/neuraltube/.


2018 ◽  
Vol 29 (8) ◽  
pp. 2060-2068 ◽  
Author(s):  
Nikos Karaiskos ◽  
Mahdieh Rahmatollahi ◽  
Anastasiya Boltengagen ◽  
Haiyue Liu ◽  
Martin Hoehne ◽  
...  

Background Three different cell types constitute the glomerular filter: mesangial cells, endothelial cells, and podocytes. However, to what extent cellular heterogeneity exists within healthy glomerular cell populations remains unknown.Methods We used nanodroplet-based highly parallel transcriptional profiling to characterize the cellular content of purified wild-type mouse glomeruli.Results Unsupervised clustering of nearly 13,000 single-cell transcriptomes identified the three known glomerular cell types. We provide a comprehensive online atlas of gene expression in glomerular cells that can be queried and visualized using an interactive and freely available database. Novel marker genes for all glomerular cell types were identified and supported by immunohistochemistry images obtained from the Human Protein Atlas. Subclustering of endothelial cells revealed a subset of endothelium that expressed marker genes related to endothelial proliferation. By comparison, the podocyte population appeared more homogeneous but contained three smaller, previously unknown subpopulations.Conclusions Our study comprehensively characterized gene expression in individual glomerular cells and sets the stage for the dissection of glomerular function at the single-cell level in health and disease.


Author(s):  
Justin Lakkis ◽  
David Wang ◽  
Yuanchao Zhang ◽  
Gang Hu ◽  
Kui Wang ◽  
...  

AbstractRecent development of single-cell RNA-seq (scRNA-seq) technologies has led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effect, which is inevitable in studies involving human tissues. Most existing methods remove batch effect in a low-dimensional embedding space. Although useful for clustering, batch effect is still present in the gene expression space, leaving downstream gene-level analysis susceptible to batch effect. Recent studies have shown that batch effect correction in the gene expression space is much harder than in the embedding space. Popular methods such as Seurat3.0 rely on the mutual nearest neighbor (MNN) approach to remove batch effect in the gene expression space, but MNN can only analyze two batches at a time and it becomes computationally infeasible when the number of batches is large. Here we present CarDEC, a joint deep learning model that simultaneously clusters and denoises scRNA-seq data, while correcting batch effect both in the embedding and the gene expression space. Comprehensive evaluations spanning different species and tissues showed that CarDEC consistently outperforms scVI, DCA, and MNN. With CarDEC denoising, those non-highly variable genes offer as much signal for clustering as the highly variable genes, suggesting that CarDEC substantially boosted information content in scRNA-seq. We also showed that trajectory analysis using CarDEC’s denoised and batch corrected expression as input revealed marker genes and transcription factors that are otherwise obscured in the presence of batch effect. CarDEC is computationally fast, making it a desirable tool for large-scale scRNA-seq studies.


2021 ◽  
Author(s):  
Chaohao Gu ◽  
Zhandong Liu

Abstract Spatial gene-expression is a crucial determinant of cell fate and behavior. Recent imaging and sequencing-technology advancements have enabled scientists to develop new tools that use spatial information to measure gene-expression at close to single-cell levels. Yet, while Fluorescence In-situ Hybridization (FISH) can quantify transcript numbers at single-cell resolution, it is limited to a small number of genes. Similarly, slide-seq was designed to measure spatial-expression profiles at the single-cell level but has a relatively low gene-capture rate. And although single-cell RNA-seq enables deep cellular gene-expression profiling, it loses spatial information during sample-collection. These major limitations have stymied these methods’ broader application in the field. To overcome spatio-omics technology’s limitations and better understand spatial patterns at single-cell resolution, we designed a computation algorithm that uses glmSMA to predict cell locations by integrating scRNA-seq data with a spatial-omics reference atlas. We treated cell-mapping as a convex optimization problem by minimizing the differences between cellular-expression profiles and location-expression profiles with an L1 regularization and graph Laplacian based L2 regularization to ensure a sparse and smooth mapping. We validated the mapping results by reconstructing spatial- expression patterns of well-known marker genes in complex tissues, like the mouse cerebellum and hippocampus. We used the biological literature to verify that the reconstructed patterns can recapitulate cell-type and anatomy structures. Our work thus far shows that, together, we can use glmSMA to accurately assign single cells to their original reference-atlas locations.


2019 ◽  
Author(s):  
Dylan R. Farnsworth ◽  
Lauren Saunders ◽  
Adam C. Miller

ABSTRACTThe ability to define cell types and how they change during organogenesis is central to our understanding of animal development and human disease. Despite the crucial nature of this knowledge, we have yet to fully characterize all distinct cell types and the gene expression differences that generate cell types during development. To address this knowledge gap, we produced an Atlas using single-cell RNA-sequencing methods to investigate gene expression from the pharyngula to early larval stages in developing zebrafish. Our single-cell transcriptome Atlas encompasses transcriptional profiles from 44,102 cells across four days of development using duplicate experiments that confirmed high reproducibility. We annotated 220 identified clusters and highlighted several strategies for interrogating changes in gene expression associated with the development of zebrafish embryos at single-cell resolution. Furthermore, we highlight the power of this analysis to assign new cell-type or developmental stage-specific expression information to many genes, including those that are currently known only by sequence and/or that lack expression information altogether. The resulting Atlas is a resource of biologists to generate hypotheses for genetic (mutant) or functional analysis, to launch an effort to define the diversity of cell-types during zebrafish organogenesis, and to examine the transcriptional profiles that produce each cell type over developmental time.


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