Examining the Development of Chronic Thromboembolic Pulmonary Hypertension at the Single-Cell Level

ABSTRACTInnate regeneration following digit tip amputation is one of the few examples of epimorphic regeneration in mammals. Digit tip regeneration is mediated by the blastema, the same structure invoked during limb regeneration in some lower vertebrates. By genetic lineage analyses in mice, the digit tip blastema has been defined as a population of heterogeneous, lineage restricted progenitor cells. These previous studies, however, do not comprehensively evaluate blastema heterogeneity or address lineage restriction of closely related cell types. In this report we present single cell RNA sequencing of over 38,000 cells from mouse digit tip blastemas and unamputated control digit tips and generate an atlas of the cell types participating in digit tip regeneration. We define the differentiation trajectories of vascular, monocytic, and fibroblastic lineages over regeneration, and while our data confirm broad lineage restriction of progenitors, our analysis reveals an early blastema fibroblast population expressing a novel regeneration-specific gene, Mest.

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In situelectro-sequencing in three-dimensional tissues

10.1101/2021.04.22.440941 ◽

2021 ◽

Author(s):

Qiang Li ◽

Zuwan Lin ◽

Ren Liu ◽

Xin Tang ◽

Jiahao Huang ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Three Dimensional ◽

Cell Types ◽

Developmental Trajectory ◽

Single Cell Level ◽

Cell Level ◽

Cell Gene Expression ◽

Cell Gene

AbstractPairwise mapping of single-cell gene expression and electrophysiology in intact three-dimensional (3D) tissues is crucial for studying electrogenic organs (e.g., brain and heart)1–5. Here, we introducein situelectro-sequencing (electro-seq), combining soft bioelectronics within situRNA sequencing to stably map millisecond-timescale cellular electrophysiology and simultaneously profile a large number of genes at single-cell level across 3D tissues. We appliedin situelectro-seq to 3D human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) patches, precisely registering the CM gene expression with electrophysiology at single-cell level, enabling multimodalin situanalysis. Such multimodal data integration substantially improved the dissection of cell types and the reconstruction of developmental trajectory from spatially heterogeneous tissues. Using machine learning (ML)-based cross-modal analysis,in situelectro-seq identified the gene-to-electrophysiology relationship over the time course of cardiac maturation. Further leveraging such a relationship to train a coupled autoencoder, we demonstrated the prediction of single-cell gene expression profile evolution using long-term electrical measurement from the same cardiac patch or 3D millimeter-scale cardiac organoids. As exemplified by cardiac tissue maturation,in situelectro-seq will be broadly applicable to create spatiotemporal multimodal maps and predictive models in electrogenic organs, allowing discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.

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Single Cell Gene Transcriptome Analysis of Ovarian Mature Teratomas

Pathology & Oncology Research ◽

10.3389/pore.2021.604228 ◽

2021 ◽

Vol 27 ◽

Author(s):

Sun Shin ◽

Youn Jin Choi ◽

Seung-Hyun Jung ◽

Yeun-Jun Chung ◽

Sug Hyung Lee

Keyword(s):

Single Cell ◽

Germ Cells ◽

Expression Profiles ◽

Cell Types ◽

Cell Tumor ◽

Single Cell Level ◽

Cell Level ◽

Normal Cells ◽

Single Cell Rna Sequencing ◽

Cell Gene

Teratoma is a type of germ cell tumor that originates from totipotential germ cells that are present in gonads, which can differentiate into any of the cell types found in adult tissues. Ovarian teratomas are usually mature cystic teratomas (OMCTs, also known as dermoid cysts). Chromosome studies in OMCTs show that the chromosomes are uniformly homozygous with karyotype of 46, XX, indicating that they may be parthenogenic tumors that arise from a single ovum after thefirst meiotic division. However, the tissues in OMCTs have been known to be morphologically and immunophenotypically identical to the orthotopic tissues. Currently, expression profiles of tissue components in OMCTs are not known. To identify whether OMCT tissues are expressionally similar to or different from the orthotopic tissues, we adopted single-cell RNA-sequencing (scRNA-seq), and analyzed transcriptomes of individual cells in heterogenous tissues of two OMCTs. We found that transcriptome profiles of the OMCTs at single cell level were not significantly different from those of normal cells in orthotopic locations. The present data suggest that parthenogeneticlly altered OMCTs may not alter expression profiles of inrivirual tissue components in OMCTs.

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Single cell transcriptomics view of cell lineage, cell fate and cellular differentiation

10.7287/peerj.preprints.3193 ◽

2017 ◽

Author(s):

Wenfa Ng

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Fate ◽

Cellular Differentiation ◽

Cell Types ◽

Rna Degradation ◽

Single Cell Level ◽

Cell Level ◽

Single Cell Rna Sequencing ◽

A Cell

Single cell studies increasing reveal myriad cellular subtypes beyond those postulated or observed through optical and fluorescence microscopy as well as DNA sequencing studies. While gene sequencing at the single cell level offer a path towards illuminating, in totality, the different subtypes of cells present, the technique nevertheless does not offer answers concerning the functional repertoire of the cell, which is defined by the collection of RNA transcribed from the genome. Known as the transcriptome, transcribed RNA defines the function of the cell as proteins or effector RNA molecules, while the genome is the collection of all information endowed in the cell type, expressed or not. Thus, a particular cell state, lineage, cell fate or cellular differentiation is more fully depicted by transcriptomic analysis compared to delineating the genomic context at the single cell level. While conceptually sound and could be analysed by contemporary single cell RNA sequencing technology and data analysis pipelines, the relative instability of RNA in view of RNase in the environment would make sample preparation particularly challenging, where degradation of cellular RNA by extraneous factors could provide a misinterpretation of specific functions available to a cell type. Hence, RNA as the de facto functional molecule of the cell defining the proteomics landscape as well as effector RNA repertoire, meant that RNA transcriptomics at the single cell level is the way forward if the goal is to understand all available cell types, lineage, cell fate and cellular differentiation. Given that a cell state is defined by the functions encoded by functional molecules such as proteins and RNA, single cell RNA sequencing offers a larger contextual basis for understanding cellular decision making and functions, for example, proteins are increasingly known to work in concert with RNA effector molecules in enabling a function. Hence, providing a view of the diverse cell types and lineages present in a body, single cell RNA sequencing is only hampered by the high sensitivity required to analyse the small amount of RNA available in single cells, as well as the perennial problem of RNA studies: how to prevent or reduce RNA degradation by environmental RNase enzymes. Ability to reduce RNA degradation would provide the cell biologist a unique view of the functional landscape of different cells in the body through the language of RNA.

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Single cell transcriptomics view of cell lineage, cell fate and cellular differentiation

10.7287/peerj.preprints.3193v1 ◽

2017 ◽

Author(s):

Wenfa Ng

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Fate ◽

Cellular Differentiation ◽

Cell Types ◽

Rna Degradation ◽

Single Cell Level ◽

Cell Level ◽

Single Cell Rna Sequencing ◽

A Cell

Single cell studies increasing reveal myriad cellular subtypes beyond those postulated or observed through optical and fluorescence microscopy as well as DNA sequencing studies. While gene sequencing at the single cell level offer a path towards illuminating, in totality, the different subtypes of cells present, the technique nevertheless does not offer answers concerning the functional repertoire of the cell, which is defined by the collection of RNA transcribed from the genome. Known as the transcriptome, transcribed RNA defines the function of the cell as proteins or effector RNA molecules, while the genome is the collection of all information endowed in the cell type, expressed or not. Thus, a particular cell state, lineage, cell fate or cellular differentiation is more fully depicted by transcriptomic analysis compared to delineating the genomic context at the single cell level. While conceptually sound and could be analysed by contemporary single cell RNA sequencing technology and data analysis pipelines, the relative instability of RNA in view of RNase in the environment would make sample preparation particularly challenging, where degradation of cellular RNA by extraneous factors could provide a misinterpretation of specific functions available to a cell type. Hence, RNA as the de facto functional molecule of the cell defining the proteomics landscape as well as effector RNA repertoire, meant that RNA transcriptomics at the single cell level is the way forward if the goal is to understand all available cell types, lineage, cell fate and cellular differentiation. Given that a cell state is defined by the functions encoded by functional molecules such as proteins and RNA, single cell RNA sequencing offers a larger contextual basis for understanding cellular decision making and functions, for example, proteins are increasingly known to work in concert with RNA effector molecules in enabling a function. Hence, providing a view of the diverse cell types and lineages present in a body, single cell RNA sequencing is only hampered by the high sensitivity required to analyse the small amount of RNA available in single cells, as well as the perennial problem of RNA studies: how to prevent or reduce RNA degradation by environmental RNase enzymes. Ability to reduce RNA degradation would provide the cell biologist a unique view of the functional landscape of different cells in the body through the language of RNA.

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Identification of putative endothelial progenitor cells (CD34+CD133+Flk-1+) in endarterectomized tissue of patients with chronic thromboembolic pulmonary hypertension

AJP Lung Cellular and Molecular Physiology ◽

10.1152/ajplung.90413.2008 ◽

2009 ◽

Vol 296 (6) ◽

pp. L870-L878 ◽

Cited By ~ 55

Author(s):

Weijuan Yao ◽

Amy L. Firth ◽

Richard S. Sacks ◽

Aiko Ogawa ◽

William R. Auger ◽

...

Keyword(s):

Pulmonary Hypertension ◽

Progenitor Cells ◽

Endothelial Progenitor Cells ◽

Progenitor Cell ◽

Chronic Thromboembolic Pulmonary Hypertension ◽

Cell Types ◽

Vascular Wall ◽

Potential Contribution ◽

Endothelial Progenitor ◽

Thromboembolic Pulmonary Hypertension

Chronic thromboembolic pulmonary hypertension (CTEPH) is characterized by a fibrotic thrombus persisting and obliterating the lumen of pulmonary arteries; its pathogenesis remains poorly defined. This study investigates a potential contribution for progenitor cell types in the development of vascular obliteration and remodeling in CTEPH patients. Endarterectomized tissue from patients undergoing pulmonary thromboendarterectomy was collected and examined for the structure and cellular composition. Our data show an organized fibrin network structure in unresolved thromboemboli and intimal remodeling in vascular wall tissues, characterized by smooth muscle α-actin (SM-αA)-positive cell proliferation in proximal regions (adjacent to thromboemboli) and neoangiogenesis/recanalization in distal regions (downstream from thromboemboli). Cells that are positively stained with CD34 and fetal liver kinase-1 (Flk-1) (CD34+Flk-1+) were identified in both the proximal and distal vascular tissues; a subpopulation of CD34+Flk-1+CD133+cells were further identified by immunostaining. Triple-positive cells are indicative of a population of putative endothelial progenitor cells or potential colony-forming units of endothelial cells. In addition, inflammatory cells (CD45+) and collagen-secreting cells (procollagen-1+) were detected in the proximal vascular wall. Some of the CD34+cells in CTEPH cells isolated from proximal regions were also positive for SM-αA. Our data indicate that putative progenitor cell types are present in the neointima of occluded vessels of CTEPH patients. It is possible that the microenvironment provided by thromboemboli may promote these putative progenitor cells to differentiate and enhance intimal remodeling.

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Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level

10.1101/2020.04.02.022632 ◽

2020 ◽

Cited By ~ 1

Author(s):

Suraj Kannan ◽

Michael Farid ◽

Brian L. Lin ◽

Matthew Miyamoto ◽

Chulan Kwon

Keyword(s):

Stem Cell ◽

Single Cell ◽

Rna Sequencing ◽

Pluripotent Stem Cell ◽

Developmental Trajectory ◽

Single Cell Level ◽

Batch Effects ◽

Cell Level ◽

Single Cell Rna Sequencing

The immaturity of pluripotent stem cell (PSC)-derived tissues has emerged as a universal problem for their biomedical applications. While efforts have been made to generate adult-like cells from PSCs, direct benchmarking of PSC-derived tissues against in vivo development has not been established. Thus, maturation status is often assessed on an ad-hoc basis. Single cell RNA-sequencing (scRNA-seq) offers a promising solution, though cross-study comparison is limited by dataset-specific batch effects. Here, we developed a novel approach to quantify PSC-derived cardiomyocyte (CM) maturation through transcriptomic entropy. Transcriptomic entropy is robust across datasets regardless of differences in isolation protocols, library preparation, and other potential batch effects. With this new model, we analyzed over 45 scRNA-seq datasets and over 52,000 CMs, and established a cross-study, cross-species CM maturation reference. This reference enabled us to directly compare PSC-CMs with the in vivo developmental trajectory and thereby to quantify PSC-CM maturation status. We further found that our entropy-based approach can be used for other cell types, including pancreatic beta cells and hepatocytes. Our study presents a biologically relevant and interpretable metric for quantifying PSC-derived tissue maturation, and is extensible to numerous tissue engineering contexts.Significance StatementThere is significant interest in generating mature cardiomyocytes from pluripotent stem cells. However, there are currently few effective metrics to quantify the maturation status of a single cardiomyocyte. We developed a new metric for measuring cardiomyocyte maturation using single cell RNA-sequencing data. This metric, called entropy score, uses the gene distribution to estimate maturation at the single cell level. Entropy score enables comparing pluripotent stem cell-derived cardiomyocytes directly against endogenously-isolated cardiomyocytes. Thus, entropy score can better assist in development of approaches to improve the maturation of pluripotent stem cell-derived cardiomyocytes.

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