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

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.

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A single cell brain atlas in human Alzheimer’s disease

10.1101/628347 ◽

2019 ◽

Cited By ~ 4

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

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Single cell response landscape of graded Nodal signaling in zebrafish explants

10.1101/2021.04.25.441305 ◽

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.

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Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis

Nature Biotechnology ◽

10.1038/s41587-021-01006-2 ◽

2021 ◽

Author(s):

T. Lohoff ◽

S. Ghazanfar ◽

A. Missarova ◽

N. Koulena ◽

N. Pierson ◽

...

Keyword(s):

Single Cell ◽

Cell Fate ◽

Target Genes ◽

Single Cells ◽

Cell Types ◽

Spatial Context ◽

Cell Fate Decisions ◽

Spatially Resolved ◽

Regulatory Processes ◽

Cell Transcriptome

AbstractMolecular profiling of single cells has advanced our knowledge of the molecular basis of development. However, current approaches mostly rely on dissociating cells from tissues, thereby losing the crucial spatial context of regulatory processes. Here, we apply an image-based single-cell transcriptomics method, sequential fluorescence in situ hybridization (seqFISH), to detect mRNAs for 387 target genes in tissue sections of mouse embryos at the 8–12 somite stage. By integrating spatial context and multiplexed transcriptional measurements with two single-cell transcriptome atlases, we characterize cell types across the embryo and demonstrate that spatially resolved expression of genes not profiled by seqFISH can be imputed. We use this high-resolution spatial map to characterize fundamental steps in the patterning of the midbrain–hindbrain boundary (MHB) and the developing gut tube. We uncover axes of cell differentiation that are not apparent from single-cell RNA-sequencing (scRNA-seq) data, such as early dorsal–ventral separation of esophageal and tracheal progenitor populations in the gut tube. Our method provides an approach for studying cell fate decisions in complex tissues and development.

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Reversed graph embedding resolves complex single-cell developmental trajectories

10.1101/110668 ◽

2017 ◽

Cited By ~ 36

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.

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Combined aptamer and transcriptome sequencing of single cells

10.1101/228338 ◽

2017 ◽

Cited By ~ 2

Author(s):

Cyrille L. Delley ◽

Leqian Liu ◽

Maen F. Sarhan ◽

Adam R. Abate

Keyword(s):

Single Cell ◽

Single Cells ◽

Expression Patterns ◽

Cell Types ◽

Single Cell Protein ◽

Protein Characterization ◽

Cell Protein ◽

Surface Binding ◽

Distinct Cell

AbstractThe transcriptome and proteome encode distinct information that is important for characterizing heterogeneous biological systems. We demonstrate a method to simultaneously characterize the transcriptomes and proteomes of single cells at high throughput using aptamer probes and droplet-based single cell sequencing. With our method, we differentiate distinct cell types based on aptamer surface binding and gene expression patterns. Aptamers provide advantages over antibodies for single cell protein characterization, including rapid, in vitro, and high-purity generation via SELEX, and the ability to amplify and detect them with PCR and sequencing.

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Cell Dissociation from Butterfly Pupal Wing Tissues for Single-Cell RNA Sequencing

Methods and Protocols ◽

10.3390/mps3040072 ◽

2020 ◽

Vol 3 (4) ◽

pp. 72

Author(s):

Anupama Prakash ◽

Antónia Monteiro

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Single Cells ◽

Expression Patterns ◽

Cell Types ◽

Rna Seq ◽

Bicyclus Anynana ◽

Single Cell Sequencing ◽

Pupal Wing

Butterflies are well known for their beautiful wings and have been great systems to understand the ecology, evolution, genetics, and development of patterning and coloration. These color patterns are mosaics on the wing created by the tiling of individual units called scales, which develop from single cells. Traditionally, bulk RNA sequencing (RNA-seq) has been used extensively to identify the loci involved in wing color development and pattern formation. RNA-seq provides an averaged gene expression landscape of the entire wing tissue or of small dissected wing regions under consideration. However, to understand the gene expression patterns of the units of color, which are the scales, and to identify different scale cell types within a wing that produce different colors and scale structures, it is necessary to study single cells. This has recently been facilitated by the advent of single-cell sequencing. Here, we provide a detailed protocol for the dissociation of cells from Bicyclus anynana pupal wings to obtain a viable single-cell suspension for downstream single-cell sequencing. We outline our experimental design and the use of fluorescence-activated cell sorting (FACS) to obtain putative scale-building and socket cells based on size. Finally, we discuss some of the current challenges of this technique in studying single-cell scale development and suggest future avenues to address these challenges.

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TBIO-15. MODELING DEVELOPMENTAL GENE EXPRESSION DYNAMICS AT CELLULAR RESOLUTION TO INTERPRET PEDIATRIC BRAIN TUMOR TRANSCRIPTIONAL PROGRAMS

Neuro-Oncology ◽

10.1093/neuonc/noaa222.842 ◽

2020 ◽

Vol 22 (Supplement_3) ◽

pp. iii469-iii469

Author(s):

Selin Jessa ◽

Nisha Kabir ◽

Maria Vladoiu ◽

Steven Hébert ◽

Michael D Taylor ◽

...

Keyword(s):

Brain Tumor ◽

Single Cell ◽

Time Course ◽

Expression Patterns ◽

Pediatric Brain Tumor ◽

Cell Types ◽

Pediatric Brain Tumors ◽

Developmental Time ◽

Genetic Alterations ◽

Pediatric Brain

Abstract A central challenge in understanding the biology of pediatric brain tumors is defining the cellular and molecular context where oncogenesis occurs. We hypothesize that spatiotemporally restricted cell types are uniquely susceptible to specific genetic alterations, which alter normal neurodevelopmental programs and ultimately lead to oncogenesis. The resulting tumors retain some transcriptomic features of their lineage of origin. To delineate these origins, we assembled a densely sampled developmental time course of the mouse forebrain and pons, doubling our recently published single-cell atlas. This dataset comprises >100,000 cells at 9 timepoints from E10-P6. However, while single cell transcriptomics reveal rich gene dynamics during cell differentiation, interpretation of individual genes can be challenging due to data sparsity. Leveraging this time-series, we present strategies to model and visualize the expression of a given gene across differentiation of distinct lineages. We demonstrate an interactive web app to interrogate the expression of genes or gene sets during brain development, extract temporally correlated genes, and search active transcription factor regulatory modules. Finally, we profile the expression of core transcriptional programs of several pediatric brain tumor entities during development. Our analyses reveal genes with restricted expression patterns that elucidate tumor etiology. More broadly, these resources harness single cell data to enable exploration of neurodevelopmental gene programs with great relevance to pediatric brain tumor oncogenesis.

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An unsupervised learning method for reconstructing cell spatial organization with application to the DREAM Single Cell Transcriptomics Challenge

F1000Research ◽

10.12688/f1000research.20446.1 ◽

2020 ◽

Vol 9 ◽

pp. 124

Author(s):

Yang Chen ◽

Disheng Mao ◽

Yuping Zhang ◽

Zhengqing Ouyang

Keyword(s):

Gene Expression ◽

Unsupervised Learning ◽

Single Cell ◽

Spatial Organization ◽

Single Cells ◽

Expression Patterns ◽

Cell Types ◽

Learning Method ◽

Driver Genes ◽

Clustering Approach

Single cell RNA sequencing (scRNA-seq) data analysis is important for building a global transcription landscape of all cell types in tissues, tracing cell lineages, and reconstructing cell spatial organizations. In this article, we propose an unsupervised learning method to predict spatial positions and gene expression of individual cells in Drosophila embryos using a small number of driver genes. Specifically, we develop a two-stage clustering approach, and compute a probability matrix of the spatial positions of single cells. This method is applied to dataset in the DREAM Single Cell Transcriptomics Challenge. The comparison with the “gold standard” suggests that our method is effective in reconstructing the cell positions and gene expression patterns in spatial tissues.

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

BMC Bioinformatics ◽

10.1186/s12859-020-03873-z ◽

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.

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Single-cell map of diverse immune phenotypes in the acute myeloid leukemia microenvironment

Biomarker Research ◽

10.1186/s40364-021-00265-0 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Rongqun Guo ◽

Mengdie Lü ◽

Fujiao Cao ◽

Guanghua Wu ◽

Fengcai Gao ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Single Cell ◽

Myeloid Leukemia ◽

Immune Cell ◽

Immune Surveillance ◽

Cell Types ◽

Developmental Trajectory ◽

Significant Difference ◽

Cell Phenotypes ◽

Acute Myeloid

Abstract Background Knowledge of immune cell phenotypes, function, and developmental trajectory in acute myeloid leukemia (AML) microenvironment is essential for understanding mechanisms of evading immune surveillance and immunotherapy response of targeting special microenvironment components. Methods Using a single-cell RNA sequencing (scRNA-seq) dataset, we analyzed the immune cell phenotypes, function, and developmental trajectory of bone marrow (BM) samples from 16 AML patients and 4 healthy donors, but not AML blasts. Results We observed a significant difference between normal and AML BM immune cells. Here, we defined the diversity of dendritic cells (DC) and macrophages in different AML patients. We also identified several unique immune cell types including T helper cell 17 (TH17)-like intermediate population, cytotoxic CD4+ T subset, T cell: erythrocyte complexes, activated regulatory T cells (Treg), and CD8+ memory-like subset. Emerging AML cells remodels the BM immune microenvironment powerfully, leads to immunosuppression by accumulating exhausted/dysfunctional immune effectors, expending immune-activated types, and promoting the formation of suppressive subsets. Conclusion Our results provide a comprehensive AML BM immune cell census, which can help to select pinpoint targeted drug and predict efficacy of immunotherapy.

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