scholarly journals HMGA2 Promotes Colorectal Cancer Angiogenesis via Dual Regulation of Sema3A and VEGFA

2021 ◽  
Author(s):  
Cancan Wang ◽  
Yi Long ◽  
Miaomiao Tao ◽  
Hongbo Ma ◽  
Yanyan Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Epithelial Cells ◽  
Single Cell ◽  
Transcriptional Activity ◽  
Specific Cell ◽  
Cell Type ◽  
Invasion And Metastasis ◽  
Mesenchymal Tumors ◽  
Online Tools ◽  
Cell Data

Background: HMGA2 encodes a small non histone chromatin-associated protein that has no intrinsic transcriptional activity, but can modulate transcription by altering the chromatin architecture. HMGA2 was found overexpressed in a variety of epithelial and mesenchymal tumors and promoted invasion and metastasis in most malignant epithelial tumors. A recent study showed that P53 inhibited CRC progression by targeting HMGA2. However, the mechanism by which HMGA2 affect angiogenesis in CRC has not been clarified. Methods: The expression of HMGA2 was analyzed by IHC, WB and bio infomatic analysis. Cbioportal and mexpress online tools were applied to explore the CNV and methylation of HMGA2 in CRC patients. Single cell data from GEO was used to examine the specific cell type that contribute to the high HMGA2 expression in CRC. Lentivirus was used to knock down HMGA2 in CRC cells and HUVECs was used to study angiogenesis. Results: In the current study, we first detected the expression pattern of HMGA2 in CRC patients and evaluated its clinical values and CNV amplification could possibly contribute to the up regulation of HMGA2 in CRC patients. By analyzing CRC single cell data we found that HMGA2 was specifically up regulated in the colorectal epithelial cells. Furthermore, knocking down of HMGA2 suppresses angiogenesis via dual regulation of VEGF-A and SEMA3A in CRC through inactivating VEGRR2 pathway in HUVECs. Conclusions: HMGA2 might be a promising prognostic marker and target for treating advanced CRC patients.

scholarly journals Single-Cell Sequencing Reveals Lineage-Specific Dynamic Genetic Regulation of Gene Expression During Human Cardiomyocyte Differentiation

2021 ◽  
Author(s):  
Reem Elorbany ◽  
Joshua M Popp ◽  
Katherine Rhodes ◽  
Benjamin J Strober ◽  
Kenneth Barr ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Lines ◽  
Specific Gene ◽  
Specific Cell ◽  
Cardiomyocyte Differentiation ◽  
Cell Type ◽  
Dynamic Effects ◽  
Regulatory Changes ◽  
Gene Regulatory ◽  
Cell Data

Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation.

scholarly journals Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells

2020 ◽  
Author(s):  
Christoph Muus ◽  
Malte D. Luecken ◽  
Gokcen Eraslan ◽  
Avinash Waghray ◽  
Graham Heimberg ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Single Cell ◽  
Viral Entry ◽  
Integrated Analysis ◽  
Target Cells ◽  
Specific Cell ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTThe COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, creates an urgent need for identifying molecular mechanisms that mediate viral entry, propagation, and tissue pathology. Cell membrane bound angiotensin-converting enzyme 2 (ACE2) and associated proteases, transmembrane protease serine 2 (TMPRSS2) and Cathepsin L (CTSL), were previously identified as mediators of SARS-CoV2 cellular entry. Here, we assess the cell type-specific RNA expression of ACE2, TMPRSS2, and CTSL through an integrated analysis of 107 single-cell and single-nucleus RNA-Seq studies, including 22 lung and airways datasets (16 unpublished), and 85 datasets from other diverse organs. Joint expression of ACE2 and the accessory proteases identifies specific subsets of respiratory epithelial cells as putative targets of viral infection in the nasal passages, airways, and alveoli. Cells that co-express ACE2 and proteases are also identified in cells from other organs, some of which have been associated with COVID-19 transmission or pathology, including gut enterocytes, corneal epithelial cells, cardiomyocytes, heart pericytes, olfactory sustentacular cells, and renal epithelial cells. Performing the first meta-analyses of scRNA-seq studies, we analyzed 1,176,683 cells from 282 nasal, airway, and lung parenchyma samples from 164 donors spanning fetal, childhood, adult, and elderly age groups, associate increased levels of ACE2, TMPRSS2, and CTSL in specific cell types with increasing age, male gender, and smoking, all of which are epidemiologically linked to COVID-19 susceptibility and outcomes. Notably, there was a particularly low expression of ACE2 in the few young pediatric samples in the analysis. Further analysis reveals a gene expression program shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues, including genes that may mediate viral entry, subtend key immune functions, and mediate epithelial-macrophage cross-talk. Amongst these are IL6, its receptor and co-receptor, IL1R, TNF response pathways, and complement genes. Cell type specificity in the lung and airways and smoking effects were conserved in mice. Our analyses suggest that differences in the cell type-specific expression of mediators of SARS-CoV-2 viral entry may be responsible for aspects of COVID-19 epidemiology and clinical course, and point to putative molecular pathways involved in disease susceptibility and pathogenesis.

scholarly journals Mapping single-cell atlases throughout Metazoa unravels cell type evolution

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Alexander J Tarashansky ◽  
Jacob M Musser ◽  
Margarita Khariton ◽  
Pengyang Li ◽  
Detlev Arendt ◽  
...  
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Cell Types ◽  
The Self ◽  
Cell Type ◽  
Germ Layers ◽  
Animal Evolution ◽  
Self Assembling ◽  
Animal Phyla ◽  
Cell Data

Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning mouse to sponge, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution.

scholarly journals Axes of inter-sample variability among transcriptional neighborhoods reveal disease associated cell states in single-cell data

2021 ◽  
Author(s):  
Yakir A Reshef ◽  
Laurie Rumker ◽  
Joyce B Kang ◽  
Aparna Nathan ◽  
Megan B Murray ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Single Cell ◽  
Active Tuberculosis ◽  
Cell Populations ◽  
Cell Type ◽  
Accurate Identification ◽  
Shared Function ◽  
Statistical Approaches ◽  
Cell Data ◽  
Notch Activation

As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes like clinical phenotypes. Current statistical approaches typically map cells to cell-type clusters and examine sample differences through that lens alone. Here we present covarying neighborhood analysis (CNA), an unbiased method to identify cell populations of interest with greater flexibility and granularity. CNA characterizes dominant axes of variation across samples by identifying groups of very small regions in transcriptional space, termed neighborhoods, that covary in abundance across samples, suggesting shared function or regulation. CNA can then rigorously test for associations between any sample-level attribute and the abundances of these covarying neighborhood groups. We show in simulation that CNA enables more powerful and accurate identification of disease-associated cell states than a cluster-based approach. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, redefines monocyte populations expanded in sepsis, and identifies a previously undiscovered T-cell population associated with progression to active tuberculosis.

scholarly journals ESCO: single cell expression simulation incorporating gene co-expression

2020 ◽  
Author(s):  
Jinjin Tian ◽  
Jiebiao Wang ◽  
Kathryn Roeder
Keyword(s):  
Single Cell ◽  
R Package ◽  
Brain Cell ◽  
Gene Interactions ◽  
Cell Type ◽  
Imputation Methods ◽  
Biological Interest ◽  
A Cell ◽  
Cell Expression ◽  
Cell Data

AbstractMotivationGene-gene co-expression networks (GCN) are of biological interest for the useful information they provide for understanding gene-gene interactions. The advent of single cell RNA-sequencing allows us to examine more subtle gene co-expression occurring within a cell type. Many imputation and denoising methods have been developed to deal with the technical challenges observed in single cell data; meanwhile, several simulators have been developed for benchmarking and assessing these methods. Most of these simulators, however, either do not incorporate gene co-expression or generate co-expression in an inconvenient manner.ResultsTherefore, with the focus on gene co-expression, we propose a new simulator, ESCO, which adopts the idea of the copula to impose gene co-expression, while preserving the highlights of available simulators, which perform well for simulation of gene expression marginally. Using ESCO, we assess the performance of imputation methods on GCN recovery and find that imputation generally helps GCN recovery when the data are not too sparse, and the ensemble imputation method works best among leading methods. In contrast, imputation fails to help in the presence of an excessive fraction of zero counts, where simple data aggregating methods are a better choice. These findings are further verified with mouse and human brain cell data.AvailabilityThe ESCO implementation is available as R package SplatterESCO (https://github.com/JINJINT/SplatterESCO)[email protected]

The Immunohistochemical Characterisation of an SV40-immortalised Human Corneal Epithelial Cell Line

2003 ◽  
Vol 31 (4) ◽  
pp. 409-417 ◽  
Author(s):  
Anne Huhtala ◽  
Sami K. Nurmi ◽  
Hanna Tähti ◽  
Lotta Salminen ◽  
Päivi Alajuuma ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Single Cell ◽  
Cell Line ◽  
Cell Cultures ◽  
Corneal Epithelium ◽  
Culture Medium ◽  
Corneal Epithelial Cell ◽  
Cell Type ◽  
Corneal Epithelial ◽  
Single Cell Type

Alternatives to the Draize rabbit eye irritation test are currently being investigated. Because of morphological and biochemical differences between the rabbit and the human eye, continuous human cell lines have been proposed for use in ocular toxicology studies. Single cell-type monolayer cultures in culture medium have been used extensively in ocular toxicology. In the present study, an SV40-immortalised human corneal epithelial (HCE) cell line was characterised immunohistochemically, by using 13 different monoclonal antibodies to cytokeratins (CKs), ranging from CK3 to CK20. The results from the monolayer HCE cell cultures were compared with those from the corneal epithelium of human corneal cryostat sections. Previous studies have shown that the morphology of the HCE cell is similar to that of primary cultured human corneal epithelial cells, and that the cells express the cornea-specific CK3. In the study reported here, we show that the cell line also expresses CKs 7, 8, 18 and 19. These CKs are typically expressed by simple epithelial cells, and are not found in the human cornea in vivo. Therefore, the monolayer HCE cell line grown in culture medium does not express the CK pattern that is typical of human corneal epithelium. This should be taken into consideration when using HCE cell cultures in similar single cell-type experiments for ocular toxicology.

scholarly journals Ensemble learning for classifying single-cell data and projection across reference atlases

2020 ◽  
Vol 36 (11) ◽  
pp. 3585-3587
Author(s):  
Lin Wang ◽  
Francisca Catalan ◽  
Karin Shamardani ◽  
Husam Babikir ◽  
Aaron Diaz
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Status Quo ◽  
Supplementary Information ◽  
Published Data ◽  
Supplementary Data ◽  
Cell Type ◽  
Low Sensitivity ◽  
Project Data ◽  
Cell Data

Abstract Summary Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across diverse platforms. Availability and implementation https://github.com/diazlab/ELSA Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Single-Cell Transcriptomic Map of the Human and Mouse Bladders

2019 ◽  
Vol 30 (11) ◽  
pp. 2159-2176 ◽  
Author(s):  
Zhenyuan Yu ◽  
Jinling Liao ◽  
Yang Chen ◽  
Chunlin Zou ◽  
Haiying Zhang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Epithelial Cells ◽  
Single Cell ◽  
Cell Types ◽  
Specific Cell ◽  
Human Bladder ◽  
Healthy Human ◽  
Bladder Cell ◽  
Bladder Cells ◽  
Human And Mouse

BackgroundHaving a comprehensive map of the cellular anatomy of the normal human bladder is vital to understanding the cellular origins of benign bladder disease and bladder cancer.MethodsWe used single-cell RNA sequencing (scRNA-seq) of 12,423 cells from healthy human bladder tissue samples taken from patients with bladder cancer and 12,884 cells from mouse bladders to classify bladder cell types and their underlying functions.ResultsWe created a single-cell transcriptomic map of human and mouse bladders, including 16 clusters of human bladder cells and 15 clusters of mouse bladder cells. The homology and heterogeneity of human and mouse bladder cell types were compared and both conservative and heterogeneous aspects of human and mouse bladder evolution were identified. We also discovered two novel types of human bladder cells. One type is ADRA2A+ and HRH2+ interstitial cells which may be associated with nerve conduction and allergic reactions. The other type is TNNT1+ epithelial cells that may be involved with bladder emptying. We verify these TNNT1+ epithelial cells also occur in rat and mouse bladders.ConclusionsThis transcriptomic map provides a resource for studying bladder cell types, specific cell markers, signaling receptors, and genes that will help us to learn more about the relationship between bladder cell types and diseases.

scholarly journals Integrative Analysis of Spatial Transcriptome with Single-cell Transcriptome and Single-cell Epigenome in Mouse Lungs after Immunization

2021 ◽  
Author(s):  
Zhongli Xu ◽  
Xinjun Wang ◽  
Li Fan ◽  
Fujing Wang ◽  
Jiebiao Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
T Helper Cells ◽  
Integrative Analysis ◽  
Cytokine Signaling ◽  
Specific Cell ◽  
Cell Type ◽  
T Helper ◽  
Communication Analysis ◽  
Helper Cells ◽  
Lung Immunity

Immunological memory is key to productive adaptive immunity. An unbiased, high through-put gene expression profiling of tissue-resident memory T cells residing in various anatomical location within the lung is fundamental to understand lung immunity but still lacking. In this study, using a well-established model on Klebsiella pneumoniae, we performed an integrative analysis of spatial transcriptome with single-cell RNA-seq and single-cell ATAC-seq on lung cells from mice after Immunization using the 10x Genomics Chromium and Visium platform. We employed several deconvolution algorithms and established an optimized deconvolution pipeline to accurately decipher specific cell-type composition by location. We identified and located 12 major cell types by scRNA-seq and spatial transcriptomic analysis. Integrating scATAC-seq data from the same cells processed in parallel with scRNA-seq, we found epigenomic profiles provide more robust cell type identification, especially for lineage-specific T helper cells. When combining all three data modalities, we observed a dynamic change in the location of T helper cells as well as their corresponding chemokines for chemotaxis. Furthermore, cell-cell communication analysis of spatial transcriptome provided evidence of lineage-specific T helper cells receiving designated cytokine signaling. In summary, our first-in-class study demonstrated the power of multi-omics analysis to uncover intrinsic spatial- and cell-type-dependent molecular mechanisms of lung immunity. Our data provides a rich research resource of single cell multi-omics data as a reference for understanding spatial dynamics of lung immunization.

scholarly journals An analytical framework for interpretable and generalizable ‘quasilinear’ single-cell data analysis

2020 ◽  
Author(s):  
Jian Zhou ◽  
Olga G. Troyanskaya
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Analytical Framework ◽  
Data Representation ◽  
Neuronal Population ◽  
Specific Cell ◽  
Nonlinear Methods ◽  
Order Of Magnitude ◽  
Representational Power ◽  
Cell Data

AbstractScaling single-cell data exploratory analysis with the rapidly growing diversity and quantity of single-cell omics datasets demands more interpretable and robust data representation that is generalizable across datasets. To address this challenge, here we developed a novel ‘quasilinear’ framework that combines the interpretability and transferability of linear methods with the representational power of nonlinear methods. Within this framework, we introduce a data representation and visualization method, GraphDR, and a structure discovery method, StructDR, that unifies cluster, trajectory, and surface estimation and allows their confidence set inference. We applied both methods to diverse single-cell RNA-seq datasets from whole embryos and tissues. Unlike PCA and t-SNE, GraphDR and StructDR generated representations that both distinguished highly specific cell types and were comparable across datasets. In addition, GraphDR is at least an order of magnitude faster than commonly used nonlinear methods. Our visualizations of scRNA-seq data from developing zebrafish and Xenopus embryos revealed extruding branches of lineages from a continuum of cell states, suggesting that the current branch view of cell specification may be oversimplified. Moreover, StructDR identified a novel neuronal population using scRNA-seq data from mouse hippocampus. An open-source python library and a user-friendly graphical interface for 3D data visualization and analysis with these methods are available at https://github.com/jzthree/quasildr.

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