scholarly journals Single cell response landscape of graded Nodal signaling in zebrafish explants

2021 ◽  
Author(s):  
Tao Cheng ◽  
Yanyi Xing ◽  
Yunfei Li ◽  
Cong Liu ◽  
Ying Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Cell Response ◽  
Expression Patterns ◽  
Cell Types ◽  
Nodal Signaling ◽  
Animal Pole ◽  
Fate Decision ◽  
Single Cell Response

Nodal, as a morphogen, plays important roles in cell fate decision, pattern formation and organizer function. But because of the complex context in vivo and technology limitations, systematic studying of genes, cell types and patterns induced by Nodal alone is still missing. Here, by using a relatively simplified model, the zebrafish blastula animal pole explant avoiding additional instructive signals and prepatterns, we constructed a single cell response landscape of graded Nodal signaling, identified 105 Nodal immediate targets and depicted their expression patterns. Our results show that Nodal signaling is sufficient to induce anterior-posterior patterned axial mesoderm and head structure. Surprisingly, the endoderm induced by Nodal alone is mainly the anterior endoderm which gives rise to the pharyngeal pouch only, but not internal organs. Among the 105 Nodal targets, we identified 14 genes carrying varying levels of axis induction capability. Overall, our work provides new insights for understanding of the Nodal function and a valuable resource for future studies of patterning and morphogenesis induced by it.

scholarly journals Stochastic antagonism between two proteins governs a bacterial cell fate switch

Science ◽  
2019 ◽  
Vol 366 (6461) ◽  
pp. 116-120 ◽  
Author(s):  
Nathan D. Lord ◽  
Thomas M. Norman ◽  
Ruoshi Yuan ◽  
Somenath Bakshi ◽  
Richard Losick ◽  
...  
Keyword(s):  
Cell Fate ◽  
Regulatory Networks ◽  
Cell Types ◽  
Feedback Loops ◽  
Cell Fate Determination ◽  
Cell Fate Decision ◽  
A Cell ◽  
Fate Decision ◽  
Antagonistic Interactions

Cell fate decision circuits must be variable enough for genetically identical cells to adopt a multitude of fates, yet ensure that these states are distinct, stably maintained, and coordinated with neighboring cells. A long-standing view is that this is achieved by regulatory networks involving self-stabilizing feedback loops that convert small differences into long-lived cell types. We combined regulatory mutants and in vivo reconstitution with theory for stochastic processes to show that the marquee features of a cell fate switch in Bacillus subtilis—discrete states, multigenerational inheritance, and timing of commitments—can instead be explained by simple stochastic competition between two constitutively produced proteins that form an inactive complex. Such antagonistic interactions are commonplace in cells and could provide powerful mechanisms for cell fate determination more broadly.

scholarly journals A single cell brain atlas in human Alzheimer’s disease

2019 ◽  
Author(s):  
Alexandra Grubman ◽  
Gabriel Chew ◽  
John F. Ouyang ◽  
Guizhi Sun ◽  
Xin Yi Choo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Single Cell ◽  
Cell Fate ◽  
Expression Patterns ◽  
Cell Types ◽  
Gene Expression Patterns ◽  
Cell Type ◽  
Web Resource ◽  
Cell Type Specific

AbstractAlzheimer’s disease (AD) is a heterogeneous disease that is largely dependent on the complex cellular microenvironment in the brain. This complexity impedes our understanding of how individual cell types contribute to disease progression and outcome. To characterize the molecular and functional cell diversity in the human AD brain we utilized single nuclei RNA- seq in AD and control patient brains in order to map the landscape of cellular heterogeneity in AD. We detail gene expression changes at the level of cells and cell subclusters, highlighting specific cellular contributions to global gene expression patterns between control and Alzheimer’s patient brains. We observed distinct cellular regulation of APOE which was repressed in oligodendrocyte progenitor cells (OPCs) and astrocyte AD subclusters, and highly enriched in a microglial AD subcluster. In addition, oligodendrocyte and microglia AD subclusters show discordant expression of APOE. Integration of transcription factor regulatory modules with downstream GWAS gene targets revealed subcluster-specific control of AD cell fate transitions. For example, this analysis uncovered that astrocyte diversity in AD was under the control of transcription factor EB (TFEB), a master regulator of lysosomal function and which initiated a regulatory cascade containing multiple AD GWAS genes. These results establish functional links between specific cellular sub-populations in AD, and provide new insights into the coordinated control of AD GWAS genes and their cell-type specific contribution to disease susceptibility. Finally, we created an interactive reference web resource which will facilitate brain and AD researchers to explore the molecular architecture of subtype and AD-specific cell identity, molecular and functional diversity at the single cell level.HighlightsWe generated the first human single cell transcriptome in AD patient brainsOur study unveiled 9 clusters of cell-type specific and common gene expression patterns between control and AD brains, including clusters of genes that present properties of different cell types (i.e. astrocytes and oligodendrocytes)Our analyses also uncovered functionally specialized sub-cellular clusters: 5 microglial clusters, 8 astrocyte clusters, 6 neuronal clusters, 6 oligodendrocyte clusters, 4 OPC and 2 endothelial clusters, each enriched for specific ontological gene categoriesOur analyses found manifold AD GWAS genes specifically associated with one cell-type, and sets of AD GWAS genes co-ordinately and differentially regulated between different brain cell-types in AD sub-cellular clustersWe mapped the regulatory landscape driving transcriptional changes in AD brain, and identified transcription factor networks which we predict to control cell fate transitions between control and AD sub-cellular clustersFinally, we provide an interactive web-resource that allows the user to further visualise and interrogate our dataset.Data resource web interface:http://adsn.ddnetbio.com

scholarly journals High-throughput single-cell transcriptomics reveals the female germline differentiation trajectory in Arabidopsis thaliana

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zhimin Hou ◽  
Yanhui Liu ◽  
Man Zhang ◽  
Lihua Zhao ◽  
Xingyue Jin ◽  
...  
Keyword(s):  
Arabidopsis Thaliana ◽  
Single Cell ◽  
Cell Fate ◽  
Single Cells ◽  
Expression Patterns ◽  
Cell Types ◽  
Developmental Time ◽  
Developmental Trajectory ◽  
Germline Differentiation ◽  
Female Germline

AbstractFemale germline cells in flowering plants differentiate from somatic cells to produce specialized reproductive organs, called ovules, embedded deep inside the flowers. We investigated the molecular basis of this distinctive developmental program by performing single-cell RNA sequencing (scRNA-seq) of 16,872 single cells of Arabidopsis thaliana ovule primordia at three developmental time points during female germline differentiation. This allowed us to identify the characteristic expression patterns of the main cell types, including the female germline and its surrounding nucellus. We then reconstructed the continuous trajectory of female germline differentiation and observed dynamic waves of gene expression along the developmental trajectory. A focused analysis revealed transcriptional cascades and identified key transcriptional factors that showed distinct expression patterns along the germline differentiation trajectory. Our study provides a valuable reference dataset of the transcriptional process during female germline differentiation at single-cell resolution, shedding light on the mechanisms underlying germline cell fate determination.

scholarly journals Decision tree models and cell fate choice

2020 ◽  
Author(s):  
Ivan Croydon Veleslavov ◽  
Michael P.H. Stumpf
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Cell Types ◽  
Lineage Tree ◽  
Tree Models ◽  
Fate Decision ◽  
Average Gene ◽  
Lineage Trees ◽  
Cell Data

AbstractSingle cell transcriptomics has laid bare the heterogeneity of apparently identical cells at the level of gene expression. For many cell-types we now know that there is variability in the abundance of many transcripts, and that average transcript abun-dance or average gene expression can be a unhelpful concept. A range of clustering and other classification methods have been proposed which use the signal in single cell data to classify, that is assign cell types, to cells based on their transcriptomic states. In many cases, however, we would like to have not just a classifier, but also a set of interpretable rules by which this classification occurs. Here we develop and demonstrate the interpretive power of one such approach, which sets out to establish a biologically interpretable classification scheme. In particular we are interested in capturing the chain of regulatory events that drive cell-fate decision making across a lineage tree or lineage sequence. We find that suitably defined decision trees can help to resolve gene regulatory programs involved in shaping lineage trees. Our approach combines predictive power with interpretabilty and can extract logical rules from single cell data.

scholarly journals Compositional perturbation autoencoder for single-cell response modeling

2021 ◽  
Author(s):  
Mohammad Lotfollahi ◽  
Anna Klimovskaia ◽  
Carlo De Donno ◽  
Yuge Ji ◽  
Ignacio L. Ibarra ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Response ◽  
In Silico ◽  
Linear Models ◽  
Cell Types ◽  
Drug Combinations ◽  
Genetic Interactions ◽  
Learning Approaches ◽  
Response Modeling ◽  
Single Cell Response

Recent advances in multiplexing single-cell transcriptomics across experiments are enabling the high throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible, so computational methods are needed to predict, interpret and prioritize perturbations. Here, we present the Compositional Perturbation Autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA encodes and learns transcriptional drug response across different cell types, doses, and drug combinations. The model produces easy-to-interpret embeddings for drugs and cell types, allowing drug similarity analysis and predictions for unseen dosages and drug combinations. We show CPA accurately models single-cell perturbations across compounds, dosages, species, and time. We further demonstrate that CPA predicts combinatorial genetic interactions of several types, implying it captures features that distinguish different interaction programs. Finally, we demonstrate CPA allows in-silico generation of 5,329 missing combinations (97.6% of all possibilities) with diverse genetic interactions. We envision our model will facilitate efficient experimental design by enabling in silico response prediction at the single-cell level.

scholarly journals Single-cell analysis of human retina identifies evolutionarily conserved and species-specific mechanisms controlling development

2019 ◽  
Author(s):  
Yufeng Lu ◽  
Fion Shiau ◽  
Wenyang Yi ◽  
Suying Lu ◽  
Qian Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Single Cell Analysis ◽  
Retinal Development ◽  
Cell Types ◽  
Retinal Cell ◽  
Transcriptional Networks ◽  
Species Specific

SummaryThe development of single-cell RNA-Sequencing (scRNA-Seq) has allowed high resolution analysis of cell type diversity and transcriptional networks controlling cell fate specification. To identify the transcriptional networks governing human retinal development, we performed scRNA-Seq over retinal organoid and in vivo retinal development, across 20 timepoints. Using both pseudotemporal and cross-species analyses, we examined the conservation of gene expression across retinal progenitor maturation and specification of all seven major retinal cell types. Furthermore, we examined gene expression differences between developing macula and periphery and between two distinct populations of horizontal cells. We also identify both shared and species-specific patterns of gene expression during human and mouse retinal development. Finally, we identify an unexpected role for ATOH7 expression in regulation of photoreceptor specification during late retinogenesis. These results provide a roadmap to future studies of human retinal development, and may help guide the design of cell-based therapies for treating retinal dystrophies.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals Single-cell mapping of focused ultrasound-transfected brain

Gene Therapy ◽  
2021 ◽  
Author(s):  
A. S. Mathew ◽  
C. M. Gorick ◽  
R. J. Price
Keyword(s):  
Single Cell ◽  
Focused Ultrasound ◽  
Cell Types ◽  
Brain Cell ◽  
Therapeutic Modality ◽  
Full Potential ◽  
Stress Genes ◽  
Multiple Cell ◽  
Differential Gene

AbstractGene delivery via focused ultrasound (FUS) mediated blood-brain barrier (BBB) opening is a disruptive therapeutic modality. Unlocking its full potential will require an understanding of how FUS parameters (e.g., peak-negative pressure (PNP)) affect transfected cell populations. Following plasmid (mRuby) delivery across the BBB with 1 MHz FUS, we used single-cell RNA-sequencing to ascertain that distributions of transfected cell types were highly dependent on PNP. Cells of the BBB (i.e., endothelial cells, pericytes, and astrocytes) were enriched at 0.2 MPa PNP, while transfection of cells distal to the BBB (i.e., neurons, oligodendrocytes, and microglia) was augmented at 0.4 MPa PNP. PNP-dependent differential gene expression was observed for multiple cell types. Cell stress genes were upregulated proportional to PNP, independent of cell type. Our results underscore how FUS may be tuned to bias transfection toward specific brain cell types in vivo and predict how those cells will respond to transfection.

scholarly journals Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tracy M. Yamawaki ◽  
Daniel R. Lu ◽  
Daniel C. Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Data Interpretation ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

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