scholarly journals STEEL enables high-resolution delineation of spatiotemporal transcriptomic data

2022 ◽  
Author(s):  
Yamao Chen ◽  
Shengyu Zhou ◽  
Ming Li ◽  
Fangqing Zhao ◽  
Ji Qi
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Detection Efficiency ◽  
Early Stage ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Spatial Distance ◽  
Transcriptomic Data ◽  
Different Cell Types

Abstract Advances in spatial transcriptomics enlarge the use of single cell technologies to unveil the expression landscape of the tissues with valuable spatial context. However, computational tools developed for single-cell transcriptomics have great limits in dealing with spatial transcriptomic data with high noise on detected transcript signals. Here we propose an unsupervised and manifold learning-based algorithm, STEEL, which identifies different cell types from spatial transcriptome by clustering cells/beads exhibiting both highly similar gene expression profiles and close spatial distance in the manner of graphs. Comprehensive evaluation of STEEL on various spatial transcriptomic datasets from 10X Visium platform demonstrates that it not only achieves a high resolution to characterize fine structures of mouse brain, but also enables the integration of multiple tissue slides individually analyzed into a larger one. STEEL outperforms previous methods to effectively distinguish different cell types of various tissues on Slide-seq datasets, featuring in higher bead density but lower transcript detection efficiency. Application of STEEL on spatial transcriptomes of early-stage mouse embryos (E9.5 to E12.5) successfully delineates a progressive development landscape of tissues from ectoderm, mesoderm and endoderm layers, and futher profiles dynamic changes on cell differentiation in heart and other organs. With the advancement of spatial transcriptome technologies, our method will have great applicability in high-resolution cell type identification and unbiased spatiotemporal data integration.

scholarly journals Molecular, spatial and projection diversity of neurons in primary motor cortex revealed by in situ single-cell transcriptomics

2020 ◽  
Author(s):  
Meng Zhang ◽  
Stephen W. Eichhorn ◽  
Brian Zingg ◽  
Zizhen Yao ◽  
Hongkui Zeng ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Resolution ◽  
Single Cell ◽  
Primary Motor Cortex ◽  
Expression Profiles ◽  
Neuronal Cell ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Different Cell Types

AbstractA mammalian brain is comprised of numerous cell types organized in an intricate manner to form functional neural circuits. Single-cell RNA sequencing provides a powerful approach to identify cell types based on their gene expression profiles and has revealed many distinct cell populations in the brain1-3. Single-cell epigenomic profiling4,5 further provides information on gene-regulatory signatures of different cell types. Understanding how different cell types contribute to brain function, however, requires knowledge of their spatial organization and connectivity, which is not preserved in sequencing-based methods that involve cell dissociation3,6. Here, we used an in situ single-cell transcriptome-imaging method, multiplexed error-robust fluorescence in situ hybridization (MERFISH)7, to generate a molecularly defined and spatially resolved cell atlas of the mouse primary motor cortex (MOp). We profiled ∼300,000 cells in the MOp, identified 95 neuronal and non-neuronal cell clusters, and revealed a complex spatial map in which not only excitatory neuronal clusters but also most inhibitory neuronal clusters adopted layered organizations. Notably, intratelencephalic (IT) cells, the largest branch of neurons in the MOp, formed a continuous spectrum of cells with gradual changes in both gene expression profiles and cortical depth positions in a highly correlated manner. Furthermore, we integrated MERFISH with retrograde tracing to probe the projection targets for different MOp neuronal cell types and found that projections of MOp neurons to other cortical regions formed a many-to-many network with each target region receiving input preferentially from a different composition of IT clusters. Overall, our results provide a high-resolution spatial and projection map of molecularly defined cell types in the MOp. We anticipate that the imaging platform described here can be broadly applied to create high-resolution cell atlases of a wide range of systems.

scholarly journals Single-cell atlas of the first intra-mammalian developmental stage of the human parasite Schistosoma mansoni

2019 ◽  
Author(s):  
Carmen Lidia Diaz Soria ◽  
Jayhun Lee ◽  
Tracy Chong ◽  
Avril Coghlan ◽  
Alan Tracey ◽  
...  
Keyword(s):  
Schistosoma Mansoni ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Mammalian Development ◽  
Transcriptional Dynamics ◽  
Swimming Water ◽  
Different Cell Types

AbstractOver 250 million people suffer from schistosomiasis, a tropical disease caused by parasitic flatworms known as schistosomes. Humans become infected by free-swimming, water-borne larvae, which penetrate the skin. The earliest intra-mammalian stage, called the schistosomulum, undergoes a series of developmental transitions. These changes are critical for the parasite to adapt to its new environment as it navigates through host tissues to reach its niche, where it will grow to reproductive maturity. Unravelling the mechanisms that drive intra-mammalian development requires knowledge of the spatial organisation and transcriptional dynamics of different cell types that comprise the schistomulum body. To fill these important knowledge gaps, we performed single-cell RNA sequencing on two-day old schistosomula of Schistosoma mansoni. We identified likely gene expression profiles for muscle, nervous system, tegument, parenchymal/primordial gut cells, and stem cells. In addition, we validated cell markers for all these clusters by in situ hybridisation in schistosomula and adult parasites. Taken together, this study provides a comprehensive cell-type atlas for the early intra-mammalian stage of this devastating metazoan parasite.

scholarly journals SC2disease: a manually curated database of single-cell transcriptome for human diseases

2020 ◽  
Vol 49 (D1) ◽  
pp. D1413-D1419 ◽  
Author(s):  
Tianyi Zhao ◽  
Shuxuan Lyu ◽  
Guilin Lu ◽  
Liran Juan ◽  
Xi Zeng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Cellular Level ◽  
Human Diseases ◽  
Cell Type ◽  
Cell Type Specific ◽  
Different Cell Types

Abstract SC2disease (http://easybioai.com/sc2disease/) is a manually curated database that aims to provide a comprehensive and accurate resource of gene expression profiles in various cell types for different diseases. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering cellular heterogeneity of different tissues for different diseases has become feasible by profiling transcriptomes across cell types at the cellular level. In particular, comparing gene expression profiles between different cell types and identifying cell-type-specific genes in various diseases offers new possibilities to address biological and medical questions. However, systematic, hierarchical and vast databases of gene expression profiles in human diseases at the cellular level are lacking. Thus, we reviewed the literature prior to March 2020 for studies which used scRNA-seq to study diseases with human samples, and developed the SC2disease database to summarize all the data by different diseases, tissues and cell types. SC2disease documents 946 481 entries, corresponding to 341 cell types, 29 tissues and 25 diseases. Each entry in the SC2disease database contains comparisons of differentially expressed genes between different cell types, tissues and disease-related health status. Furthermore, we reanalyzed gene expression matrix by unified pipeline to improve the comparability between different studies. For each disease, we also compare cell-type-specific genes with the corresponding genes of lead single nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS) to implicate cell type specificity of the traits.

scholarly journals Single-cell atlas of the first intra-mammalian developmental stage of the human parasite Schistosoma mansoni

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Carmen Lidia Diaz Soria ◽  
Jayhun Lee ◽  
Tracy Chong ◽  
Avril Coghlan ◽  
Alan Tracey ◽  
...  
Keyword(s):  
Schistosoma Mansoni ◽  
Single Cell ◽  
Expression Profiles ◽  
In Situ Hybridisation ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Mammalian Development ◽  
Transcriptional Dynamics ◽  
Swimming Water ◽  
Different Cell Types

AbstractOver 250 million people suffer from schistosomiasis, a tropical disease caused by parasitic flatworms known as schistosomes. Humans become infected by free-swimming, water-borne larvae, which penetrate the skin. The earliest intra-mammalian stage, called the schistosomulum, undergoes a series of developmental transitions. These changes are critical for the parasite to adapt to its new environment as it navigates through host tissues to reach its niche, where it will grow to reproductive maturity. Unravelling the mechanisms that drive intra-mammalian development requires knowledge of the spatial organisation and transcriptional dynamics of different cell types that comprise the schistomulum body. To fill these important knowledge gaps, we perform single-cell RNA sequencing on two-day old schistosomula of Schistosoma mansoni. We identify likely gene expression profiles for muscle, nervous system, tegument, oesophageal gland, parenchymal/primordial gut cells, and stem cells. In addition, we validate cell markers for all these clusters by in situ hybridisation in schistosomula and adult parasites. Taken together, this study provides a comprehensive cell-type atlas for the early intra-mammalian stage of this devastating metazoan parasite.

scholarly journals Aortic heterogeneity across segments and under high fat/salt/glucose conditions at the single-cell level

2020 ◽  
Vol 7 (5) ◽  
pp. 881-896 ◽  
Author(s):  
Dongxu He ◽  
Aiqin Mao ◽  
Chang-Bo Zheng ◽  
Hao Kan ◽  
Ka Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
The Body ◽  
Dietary Salt ◽  
High Fat ◽  
High Blood Glucose ◽  
Thoracic And Abdominal

Abstract The aorta, with ascending, arch, thoracic and abdominal segments, responds to the heartbeat, senses metabolites and distributes blood to all parts of the body. However, the heterogeneity across aortic segments and how metabolic pathologies change it are not known. Here, a total of 216 612 individual cells from the ascending aorta, aortic arch, and thoracic and abdominal segments of mouse aortas under normal conditions or with high blood glucose levels, high dietary salt, or high fat intake were profiled using single-cell RNA sequencing. We generated a compendium of 10 distinct cell types, mainly endothelial (EC), smooth muscle (SMC), stromal and immune cells. The distributions of the different cells and their intercommunication were influenced by the hemodynamic microenvironment across anatomical segments, and the spatial heterogeneity of ECs and SMCs may contribute to differential vascular dilation and constriction that were measured by wire myography. Importantly, the composition of aortic cells, their gene expression profiles and their regulatory intercellular networks broadly changed in response to high fat/salt/glucose conditions. Notably, the abdominal aorta showed the most dramatic changes in cellular composition, particularly involving ECs, fibroblasts and myeloid cells with cardiovascular risk factor-related regulons and gene expression networks. Our study elucidates the nature and range of aortic cell diversity, with implications for the treatment of metabolic pathologies.

scholarly journals Self-reporting transposons enable simultaneous readout of gene expression and transcription factor binding in single cells

2019 ◽  
Author(s):  
Arnav Moudgil ◽  
Michael N. Wilkinson ◽  
Xuhua Chen ◽  
June He ◽  
Alex J. Cammack ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Single Cell ◽  
Binding Sites ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Specific Cell

AbstractIn situ measurements of transcription factor (TF) binding are confounded by cellular heterogeneity and represent averaged profiles in complex tissues. Single cell RNA-seq (scRNA-seq) is capable of resolving different cell types based on gene expression profiles, but no technology exists to directly link specific cell types to the binding pattern of TFs in those cell types. Here, we present self-reporting transposons (SRTs) and their use in single cell calling cards (scCC), a novel assay for simultaneously capturing gene expression profiles and mapping TF binding sites in single cells. First, we show how the genomic locations of SRTs can be recovered from mRNA. Next, we demonstrate that SRTs deposited by the piggyBac transposase can be used to map the genome-wide localization of the TFs SP1, through a direct fusion of the two proteins, and BRD4, through its native affinity for piggyBac. We then present the scCC method, which maps SRTs from scRNA-seq libraries, thus enabling concomitant identification of cell types and TF binding sites in those same cells. As a proof-of-concept, we show recovery of cell type-specific BRD4 and SP1 binding sites from cultured cells. Finally, we map Brd4 binding sites in the mouse cortex at single cell resolution, thus establishing a new technique for studying TF biology in situ.

scholarly journals Reclassification of classic T cell subsets and identification of novel cell types by single-cell RNA sequencing

2020 ◽  
Author(s):  
Xiangru Shen ◽  
Xuefei Wang ◽  
Shan Chen ◽  
Hongyi Liu ◽  
Ni Hong ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
T Cell Activation ◽  
Cell Activation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
T Cell Subsets ◽  
Single Cell Rna Sequencing

Abstract Single cell RNA sequencing (scRNA-seq) clusters cells using genome-wide gene expression profiles. The relationship between scRNA-seq Clustered-Populations (scCPops) and cell surface marker-defined classic T cell subsets is unclear. Here, we interrogated 6 bead-enriched T cell subsets with 62,235 single cell transcriptomes and re-grouped them into 9 scCPops. Bead-enriched CD4 Naïve, CD8 Naïve and CD4 memory were mainly clustered into their scCPop counterparts, while the other T cell subsets were clustered into multiple scCPops including unexpected mucosal-associated invariant T cell and natural killer T cell. Most interestingly, we discovered a new T cell type that highly expressed Interferon Signaling Associated Genes (ISAGs), namely IFNhi T. We further enriched IFNhi T for scRNA-seq analyses. IFNhi T cluster disappeared on tSNE after removing ISAGs, and IFNhi T cluster showed up by tSNE analyses of ISAGs alone, indicating ISAGs are the major contributor of IFNhi T cluster. BST2+ cells and BST2- cells showing different efficiencies of T cell activation indicates high ISAGs may contribute to quick immune responses.

scholarly journals Semi-soft Clustering of Single Cell Data

2018 ◽  
Author(s):  
Lingxue Zhu ◽  
Jing Lei ◽  
Bernie Devlin ◽  
Kathryn Roeder
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Pairwise Comparison ◽  
Cell Types ◽  
Intermediate Cell ◽  
Soft Clustering ◽  
Membership Matrix ◽  
Cell Data

AbstractMotivated by the dynamics of development, in which cells of recognizable types, or pure cell types, transition into other types over time, we propose a method of semi-soft clustering that can classify both pure and intermediate cell types from data on gene expression or protein abundance from individual cells. Called SOUP, for Semi-sOft clUstering with Pure cells, this novel algorithm reveals the clustering structure for both pure cells, which belong to one single cluster, as well as transitional cells with soft memberships. SOUP involves a two-step process: identify the set of pure cells and then estimate a membership matrix. To find pure cells, SOUP uses the special block structure the K cell types form in a similarity matrix, devised by pairwise comparison of the gene expression profiles of individual cells. Once pure cells are identified, they provide the key information from which the membership matrix can be computed. SOUP is applicable to general clustering problems as well, as long as the unrestrictive modeling assumptions hold. The performance of SOUP is documented via extensive simulation studies. Using SOUP to analyze two single cell data sets from brain shows it produce sensible and interpretable results.

scholarly journals Reconstructing complex lineage trees from scRNA-seq data using MERLoT

2018 ◽  
Author(s):  
R. Gonzalo Parra ◽  
Nikolaos Papadopoulos ◽  
Laura Ahumada-Arranz ◽  
Jakob El Kholtei ◽  
Noah Mottelson ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Cell Fates ◽  
Tree Structures ◽  
Temporal Gene Expression ◽  
Transcriptomics Data ◽  
User Friendly ◽  
Lineage Trees

AbstractAdvances in single-cell transcriptomics techniques are revolutionizing studies of cellular differentiation and heterogeneity. Consequently, it becomes possible to track the trajectory of thousands of genes across the cellular lineage trees that represent the temporal emergence of cell types during dynamic processes. However, reconstruction of cellular lineage trees with more than a few cell fates has proved challenging. We present MERLoT (https://github.com/soedinglab/merlot), a flexible and user-friendly tool to reconstruct complex lineage trees from single-cell transcriptomics data and further impute temporal gene expression profiles along the reconstructed tree structures. We demonstrate MERLoT’s capabilities on various real cases and hundreds of simulated datasets.

scholarly journals Characterization of hormone-producing cell types in the teleost pituitary gland using single-cell RNA-seq

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Khadeeja Siddique ◽  
Eirill Ager-Wick ◽  
Romain Fontaine ◽  
Finn-Arne Weltzien ◽  
Christiaan V. Henkel
Keyword(s):  
Single Cell ◽  
Stress Responses ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Peptide Hormone ◽  
Cell Types ◽  
Specific Cell ◽  
Endocrine Gland ◽  
Hormone Production ◽  
Male Adult

AbstractThe pituitary is the vertebrate endocrine gland responsible for the production and secretion of several essential peptide hormones. These, in turn, control many aspects of an animal’s physiology and development, including growth, reproduction, homeostasis, metabolism, and stress responses. In teleost fish, each hormone is presumably produced by a specific cell type. However, key details on the regulation of, and communication between these cell types remain to be resolved. We have therefore used single-cell sequencing to generate gene expression profiles for 2592 and 3804 individual cells from the pituitaries of female and male adult medaka (Oryzias latipes), respectively. Based on expression profile clustering, we define 15 and 16 distinct cell types in the female and male pituitary, respectively, of which ten are involved in the production of a single peptide hormone. Collectively, our data provide a high-quality reference for studies on pituitary biology and the regulation of hormone production, both in fish and in vertebrates in general.

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