Identification of EMT signaling cross-talk and gene regulatory networks by single-cell RNA sequencing

The epithelial-to-mesenchymal transition (EMT) plays a critical role during normal development and in cancer progression. EMT is induced by various signaling pathways, including TGF-β, BMP, Wnt–β-catenin, NOTCH, Shh, and receptor tyrosine kinases. In this study, we performed single-cell RNA sequencing on MCF10A cells undergoing EMT by TGF-β1 stimulation. Our comprehensive analysis revealed that cells progress through EMT at different paces. Using pseudotime clustering reconstruction of gene-expression profiles during EMT, we found sequential and parallel activation of EMT signaling pathways. We also observed various transitional cellular states during EMT. We identified regulatory signaling nodes that drive EMT with the expression of important microRNAs and transcription factors. Using a random circuit perturbation methodology, we demonstrate that the NOTCH signaling pathway acts as a key driver of TGF-β–induced EMT. Furthermore, we demonstrate that the gene signatures of pseudotime clusters corresponding to the intermediate hybrid EMT state are associated with poor patient outcome. Overall, this study provides insight into context-specific drivers of cancer progression and highlights the complexities of the EMT process.

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Single-Cell Transcriptome Analysis Reveals Embryonic Endothelial Heterogeneity at Spatiotemporal Level and Multifunctions of MicroRNA-126 in Mice

Arteriosclerosis Thrombosis and Vascular Biology ◽

10.1161/atvbaha.121.317093 ◽

2022 ◽

Author(s):

Fang-Hao Guo ◽

Ya-Na Guan ◽

Jun-Jun Guo ◽

Lu-Jun Zhang ◽

Jing-Jing Qiu ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Knockout Mice ◽

Critical Role ◽

Proliferative Potential ◽

Metabolic Dysfunction ◽

Mesenchymal Transition ◽

Intussusceptive Angiogenesis ◽

Single Cell Rna Sequencing

Background: Endothelial cells (ECs) play a critical role in angiogenesis and vascular remodeling. The heterogeneity of ECs has been reported at adult stages, yet it has not been fully investigated. This study aims to assess the transcriptional heterogeneity of developmental ECs at spatiotemporal level and to reveal the changes of embryonic ECs clustering when endothelium-enriched microRNA-126 (miR-126) was specifically knocked out. Methods: C57BL/6J mice embryos at day 14.5 were harvested and digested, followed by fluorescence-activated cell sorting to enrich ECs. Then, single-cell RNA sequencing was applied to enriched embryonic ECs. Tie2 (Tek receptor tyrosine kinase)-cre–mediated ECs-specific miR-126 knockout mice were constructed, and ECs from Tie2-cre–mediated ECs-specific miR-126 knockout embryos were subjected to single-cell RNA sequencing. Results: Embryonic ECs were clustered into 11 groups corresponding to anatomic characteristics. The vascular bed (arteries, capillaries, veins, lymphatics) exhibited transcriptomic similarity across the developmental stage. Embryonic ECs had higher proliferative potential than adult ECs. Integrating analysis showed that 3 ECs populations (hepatic, mesenchymal transition, and pulmonary ECs) were apparently disorganized after miR-126 being knocked out. Gene ontology analysis revealed that disrupted ECs were mainly related to hypoxia, glycometabolism, and vascular calcification. Additionally, in vivo experiment showed that Tie2-cre–mediated ECs-specific miR-126 knockout mice exhibited excessive intussusceptive angiogenesis; reductive glucose and pyruvate tolerance; and excessive accumulation of calcium. Agonist miR-126-3p agomir significantly rescued the phenotype of glucose metabolic dysfunction in Tie2-cre–mediated ECs-specific miR-126 knockout mice. Conclusions: The heterogeneity of ECs is established as early as the embryonic stage. The deficiency of miR-126 disrupts the differentiation and diversification of embryonic ECs, suggesting that miR-126 plays an essential role in the maintenance of ECs heterogeneity.

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MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data

10.1101/111591 ◽

2017 ◽

Cited By ~ 43

Author(s):

David van Dijk ◽

Juozas Nainys ◽

Roshan Sharma ◽

Pooja Kaithail ◽

Ambrose J. Carr ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Regulatory Networks ◽

Missing Values ◽

Cluster Structure ◽

Gene Interactions ◽

Specific Gene ◽

Sequencing Data ◽

Mesenchymal Transition ◽

Single Cell Rna Sequencing

ABSTRACTSingle-cell RNA-sequencing is fast becoming a major technology that is revolutionizing biological discovery in fields such as development, immunology and cancer. The ability to simultaneously measure thousands of genes at single cell resolution allows, among other prospects, for the possibility of learning gene regulatory networks at large scales. However, scRNA-seq technologies suffer from many sources of significant technical noise, the most prominent of which is ‘dropout’ due to inefficient mRNA capture. This results in data that has a high degree of sparsity, with typically only ~10% non-zero values. To address this, we developed MAGIC (Markov Affinity-based Graph Imputation of Cells), a method for imputing missing values, and restoring the structure of the data. After MAGIC, we find that two- and three-dimensional gene interactions are restored and that MAGIC is able to impute complex and non-linear shapes of interactions. MAGIC also retains cluster structure, enhances cluster-specific gene interactions and restores trajectories, as demonstrated in mouse retinal bipolar cells, hematopoiesis, and our newly generated epithelial-to-mesenchymal transition dataset.

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Comprehensive network modeling from single cell RNA sequencing of human and mouse reveals well conserved transcription regulation of hematopoiesis

BMC Genomics ◽

10.1186/s12864-020-07241-2 ◽

2020 ◽

Vol 21 (S11) ◽

Author(s):

Shouguo Gao ◽

Zhijie Wu ◽

Xingmin Feng ◽

Sachiko Kajigaya ◽

Xujing Wang ◽

...

Keyword(s):

Bone Marrow ◽

Transcription Factors ◽

Single Cell ◽

Rna Sequencing ◽

Transcription Regulation ◽

Regulatory Networks ◽

Stem Cell Differentiation ◽

Hematopoietic Stem ◽

Single Cell Rna Sequencing ◽

Human And Mouse

Abstract Background Presently, there is no comprehensive analysis of the transcription regulation network in hematopoiesis. Comparison of networks arising from gene co-expression across species can facilitate an understanding of the conservation of functional gene modules in hematopoiesis. Results We used single-cell RNA sequencing to profile bone marrow from human and mouse, and inferred transcription regulatory networks in each species in order to characterize transcriptional programs governing hematopoietic stem cell differentiation. We designed an algorithm for network reconstruction to conduct comparative transcriptomic analysis of hematopoietic gene co-expression and transcription regulation in human and mouse bone marrow cells. Co-expression network connectivity of hematopoiesis-related genes was found to be well conserved between mouse and human. The co-expression network showed “small-world” and “scale-free” architecture. The gene regulatory network formed a hierarchical structure, and hematopoiesis transcription factors localized to the hierarchy’s middle level. Conclusions Transcriptional regulatory networks are well conserved between human and mouse. The hierarchical organization of transcription factors may provide insights into hematopoietic cell lineage commitment, and to signal processing, cell survival and disease initiation.

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Single cell sequencing reveals endothelial plasticity with transient mesenchymal activation after myocardial infarction

Nature Communications ◽

10.1038/s41467-021-20905-1 ◽

2021 ◽

Vol 12 (1) ◽

Cited By ~ 2

Author(s):

Lukas S. Tombor ◽

David John ◽

Simone F. Glaser ◽

Guillermo Luxán ◽

Elvira Forte ◽

...

Keyword(s):

Myocardial Infarction ◽

Endothelial Cells ◽

Endothelial Cell ◽

Single Cell ◽

Rna Sequencing ◽

Endothelial Cell Migration ◽

Metabolic Adaptation ◽

Endothelial Cell Proliferation ◽

Mesenchymal Transition ◽

Single Cell Rna Sequencing

AbstractEndothelial cells play a critical role in the adaptation of tissues to injury. Tissue ischemia induced by infarction leads to profound changes in endothelial cell functions and can induce transition to a mesenchymal state. Here we explore the kinetics and individual cellular responses of endothelial cells after myocardial infarction by using single cell RNA sequencing. This study demonstrates a time dependent switch in endothelial cell proliferation and inflammation associated with transient changes in metabolic gene signatures. Trajectory analysis reveals that the majority of endothelial cells 3 to 7 days after myocardial infarction acquire a transient state, characterized by mesenchymal gene expression, which returns to baseline 14 days after injury. Lineage tracing, using the Cdh5-CreERT2;mT/mG mice followed by single cell RNA sequencing, confirms the transient mesenchymal transition and reveals additional hypoxic and inflammatory signatures of endothelial cells during early and late states after injury. These data suggest that endothelial cells undergo a transient mes-enchymal activation concomitant with a metabolic adaptation within the first days after myocardial infarction but do not acquire a long-term mesenchymal fate. This mesenchymal activation may facilitate endothelial cell migration and clonal expansion to regenerate the vascular network.

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Single cell RNA sequencing of the Strongylocentrotus purpuratus larva reveals the blueprint of major cell types and nervous system of a non-chordate deuterostome

eLife ◽

10.7554/elife.70416 ◽

2021 ◽

Vol 10 ◽

Author(s):

Periklis Paganos ◽

Danila Voronov ◽

Jacob M Musser ◽

Detlev Arendt ◽

Maria Ina Arnone

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Sea Urchin ◽

Regulatory Networks ◽

Sea Urchins ◽

Neuronal Cell ◽

Cell Types ◽

Molecular Fingerprint ◽

Larval Body ◽

Single Cell Rna Sequencing

Identifying the molecular fingerprint of organismal cell types is key for understanding their function and evolution. Here, we use single cell RNA sequencing (scRNA-seq) to survey the cell types of the sea urchin early pluteus larva, representing an important developmental transition from non-feeding to feeding larva. We identify 21 distinct cell clusters, representing cells of the digestive, skeletal, immune, and nervous systems. Further subclustering of these reveal a highly detailed portrait of cell diversity across the larva, including the identification of neuronal cell types. We then validate important gene regulatory networks driving sea urchin development and reveal new domains of activity within the larval body. Focusing on neurons that co-express Pdx-1 and Brn1/2/4, we identify an unprecedented number of genes shared by this population of neurons in sea urchin and vertebrate endocrine pancreatic cells. Using differential expression results from Pdx-1 knockdown experiments, we show that Pdx1 is necessary for the acquisition of the neuronal identity of these cells. We hypothesize that a network similar to the one orchestrated by Pdx1 in the sea urchin neurons was active in an ancestral cell type and then inherited by neuronal and pancreatic developmental lineages in sea urchins and vertebrates.

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Localization of migraine susceptibility genes in human brain by single-cell RNA sequencing

Cephalalgia ◽

10.1177/0333102418762476 ◽

2018 ◽

Vol 38 (13) ◽

pp. 1976-1983 ◽

Cited By ~ 5

Author(s):

William Renthal

Keyword(s):

Human Brain ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Cell Types ◽

Susceptibility Genes ◽

Brain Cell ◽

Cell Type ◽

Single Cell Rna Sequencing ◽

Brain Cell Types

Background Migraine is a debilitating disorder characterized by severe headaches and associated neurological symptoms. A key challenge to understanding migraine has been the cellular complexity of the human brain and the multiple cell types implicated in its pathophysiology. The present study leverages recent advances in single-cell transcriptomics to localize the specific human brain cell types in which putative migraine susceptibility genes are expressed. Methods The cell-type specific expression of both familial and common migraine-associated genes was determined bioinformatically using data from 2,039 individual human brain cells across two published single-cell RNA sequencing datasets. Enrichment of migraine-associated genes was determined for each brain cell type. Results Analysis of single-brain cell RNA sequencing data from five major subtypes of cells in the human cortex (neurons, oligodendrocytes, astrocytes, microglia, and endothelial cells) indicates that over 40% of known migraine-associated genes are enriched in the expression profiles of a specific brain cell type. Further analysis of neuronal migraine-associated genes demonstrated that approximately 70% were significantly enriched in inhibitory neurons and 30% in excitatory neurons. Conclusions This study takes the next step in understanding the human brain cell types in which putative migraine susceptibility genes are expressed. Both familial and common migraine may arise from dysfunction of discrete cell types within the neurovascular unit, and localization of the affected cell type(s) in an individual patient may provide insight into to their susceptibility to migraine.

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Single-cell RNA sequencing confirms IgG transcription and limited diversity of VHDJH rearrangements in proximal tubular epithelial cells

Scientific Reports ◽

10.1038/s41598-020-75013-9 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Zhenling Deng ◽

Xinyao Wang ◽

Yue Liu ◽

Xinyu Tian ◽

Shaohui Deng ◽

...

Keyword(s):

Epithelial Cells ◽

Single Cell ◽

Rna Sequencing ◽

Mesangial Cells ◽

Gene Segment ◽

Tubular Epithelial Cells ◽

Mesenchymal Transition ◽

Single Cell Rna Sequencing ◽

Proximal Tubular Epithelial Cells ◽

Proximal Tubular

AbstractIncreasing evidence has confirmed that immunoglobulins (Igs) can be expressed in non-B cells. Our previous work demonstrated that mesangial cells and podocytes express IgA and IgG, respectively. The aim of this work was to reveal whether proximal tubular epithelial cells (PTECs) express Igs. High-throughput single-cell RNA sequencing (scRNA-seq) detected Igs in a small number of PTECs, and then we combined nested PCR with Sanger sequencing to detect the transcripts and characterize the repertoires of Igs in PTECs. We sorted PTECs from the normal renal cortex of two patients with renal cancer by FACS and further confirmed their identify by LRP2 gene expression. Only the transcripts of the IgG heavy chain were successfully amplified in 91/111 single PTECs. We cloned and sequenced 469 VHDJH transcripts from 91 single PTECs and found that PTEC-derived IgG exhibited classic VHDJH rearrangements with nucleotide additions at the junctions and somatic hypermutations. Compared with B cell-derived IgG, PTEC-derived IgG displayed less diversity of VHDJH rearrangements, predominant VH1-24/DH2-15/JH4 sequences, biased VH1 usage, centralized VH gene segment location at the 3′ end of the genome and non-Gaussian distribution of the CDR3 length. These results demonstrate that PTECs can express a distinct IgG repertoire that may have implications for their role in the renal tubular epithelial-mesenchymal transition.

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Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽

10.1126/science.aba5257 ◽

2020 ◽

Vol 371 (6531) ◽

pp. eaba5257 ◽

Cited By ~ 2

Author(s):

Anna Kuchina ◽

Leandra M. Brettner ◽

Luana Paleologu ◽

Charles M. Roco ◽

Alexander B. Rosenberg ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Single Cell Analysis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Growth Stages ◽

High Throughput Analysis ◽

Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

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A map of tumor–host interactions in glioma at single-cell resolution

GigaScience ◽

10.1093/gigascience/giaa109 ◽

2020 ◽

Vol 9 (10) ◽

Cited By ~ 3

Author(s):

Francesca Pia Caruso ◽

Luciano Garofano ◽

Fulvio D'Angelo ◽

Kai Yu ◽

Fuchou Tang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cross Talk ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Sequencing Data ◽

Host Interaction ◽

Receptor Interactions ◽

Single Cell Rna Sequencing

ABSTRACT Background Single-cell RNA sequencing is the reference technique for characterizing the heterogeneity of the tumor microenvironment. The composition of the various cell types making up the microenvironment can significantly affect the way in which the immune system activates cancer rejection mechanisms. Understanding the cross-talk signals between immune cells and cancer cells is of fundamental importance for the identification of immuno-oncology therapeutic targets. Results We present a novel method, single-cell Tumor–Host Interaction tool (scTHI), to identify significantly activated ligand–receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand–receptor interactions in glioma using 6 publicly available human glioma datasets encompassing 57,060 gene expression profiles from 71 patients. By leveraging this large-scale collection we show that unexpected cross-talk partners are highly conserved across different datasets in the majority of the tumor samples. This suggests that shared cross-talk mechanisms exist in glioma. Conclusions Our results provide a complete map of the active tumor–host interaction pairs in glioma that can be therapeutically exploited to reduce the immunosuppressive action of the microenvironment in brain tumor.

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Characterization of Diabetic and Non-Diabetic Foot Ulcers Using Single-Cell RNA-Sequencing

Micromachines ◽

10.3390/mi11090815 ◽

2020 ◽

Vol 11 (9) ◽

pp. 815

Author(s):

Michael Januszyk ◽

Kellen Chen ◽

Dominic Henn ◽

Deshka S. Foster ◽

Mimi R. Borrelli ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Chronic Wounds ◽

Expression Profiles ◽

Foot Ulcers ◽

Cold Ischemia Time ◽

Cold Ischemia ◽

Ischemia Time ◽

Extracellular Matrix Components ◽

Single Cell Rna Sequencing

Background: Recent advances in high-throughput single-cell sequencing technologies have led to their increasingly widespread adoption for clinical applications. However, challenges associated with tissue viability, cell yield, and delayed time-to-capture have created unique obstacles for data processing. Chronic wounds, in particular, represent some of the most difficult target specimens, due to the significant amount of fibrinous debris, extracellular matrix components, and non-viable cells inherent in tissue routinely obtained from debridement. Methods: Here, we examined the feasibility of single cell RNA sequencing (scRNA-seq) analysis to evaluate human chronic wound samples acquired in the clinic, subjected to prolonged cold ischemia time, and processed without FACS sorting. Wound tissue from human diabetic and non-diabetic plantar foot ulcers were evaluated using an optimized 10X Genomics scRNA-seq platform and analyzed using a modified data pipeline designed for low-yield specimens. Cell subtypes were identified informatically and their distributions and transcriptional programs were compared between diabetic and non-diabetic tissue. Results: 139,000 diabetic and non-diabetic wound cells were delivered for 10X capture after either 90 or 180 min of cold ischemia time. cDNA library concentrations were 858.7 and 364.7 pg/µL, respectively, prior to sequencing. Among all barcoded fragments, we found that 83.5% successfully aligned to the human transcriptome and 68% met the minimum cell viability threshold. The average mitochondrial mRNA fraction was 8.5% for diabetic cells and 6.6% for non-diabetic cells, correlating with differences in cold ischemia time. A total of 384 individual cells were of sufficient quality for subsequent analyses; from this cell pool, we identified transcriptionally-distinct cell clusters whose gene expression profiles corresponded to fibroblasts, keratinocytes, neutrophils, monocytes, and endothelial cells. Fibroblast subpopulations with differing fibrotic potentials were identified, and their distributions were found to be altered in diabetic vs. non-diabetic cells. Conclusions: scRNA-seq of clinical wound samples can be achieved using minor modifications to standard processing protocols and data analysis methods. This simple approach can capture widespread transcriptional differences between diabetic and non-diabetic tissue obtained from matched wound locations.

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