scholarly journals Lineage analysis reveals an endodermal contribution to the vertebrate pituitary

Science ◽  
2020 ◽  
Vol 370 (6515) ◽  
pp. 463-467 ◽  
Author(s):  
Peter Fabian ◽  
Kuo-Chang Tseng ◽  
Joanna Smeeton ◽  
Joseph J. Lancman ◽  
P. Duc Si Dong ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Endocrine Cell ◽  
Time Lapse ◽  
Cell Types ◽  
Lineage Tracing ◽  
Single Cell Rna Sequencing ◽  
Growth Metabolism ◽  
Time Lapse Imaging ◽  
Lineage Analysis

Vertebrate sensory organs arise from epithelial thickenings called placodes. Along with neural crest cells, cranial placodes are considered ectodermal novelties that drove evolution of the vertebrate head. The anterior-most placode generates the endocrine lobe [adenohypophysis (ADH)] of the pituitary, a master gland controlling growth, metabolism, and reproduction. In addition to known ectodermal contributions, we use lineage tracing and time-lapse imaging in zebrafish to identify an endodermal contribution to the ADH. Single-cell RNA sequencing of the adult pituitary reveals similar competency of endodermal and ectodermal epithelia to generate all endocrine cell types. Further, endoderm can generate a rudimentary ADH-like structure in the near absence of ectodermal contributions. The fish condition supports the vertebrate pituitary arising through interactions of an ancestral endoderm-derived proto-pituitary with newly evolved placodal ectoderm.

scholarly journals Cellular basis of omentum activation and expansion revealed by single-cell RNA sequencing using a parabiosis model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kazuhiko Ishigaki ◽  
Keiki Kumano ◽  
Kyohei Fujita ◽  
Hiroo Ueno
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cells ◽  
Physiological Function ◽  
Cell Types ◽  
Lineage Tracing ◽  
Mesenchymal Progenitors ◽  
Fluorescent Markers ◽  
Circulating Cells ◽  
Single Cell Rna Sequencing

AbstractAlthough the physiological function of the omentum remains elusive, it has been proposed that it plays an important role in fat storage, immune regulation, and regeneration of injured tissues and organs. Although the omentum undergoes expansion upon activation, reports on the accurate assessment of increased cell types and the origin of the increased cells remain limited. To investigate this aspect, the omenta of parabiotic mice were subjected to activation using distinct fluorescent markers and single-cell RNA sequencing (scRNA-seq) was performed to identify circulation-derived omental cells. We found that a considerable number of circulating cells contributed to the activation of the omentum. The omental cells derived from circulating cells exhibited morphological features similar to those of fibroblasts. scRNA-seq revealed the existence of a novel cell population that co-expressed macrophage and fibroblast markers in the activated omentum, suggesting that it corresponded to circulating macrophage-derived fibroblast-like cells. Lineage tracing experiments revealed that most fibroblasts in the activated omentum were not derived from WT1-positive mesenchymal progenitors. The cell cluster also expressed various chemokine genes, indicating its role in the activation and recruitment of immune cells. These results provide important information regarding the activation of the omentum.

scholarly journals Single-cell data clustering based on sparse optimization and low-rank matrix factorization

2021 ◽  
Author(s):  
Yinlei Hu ◽  
Bin Li ◽  
Falai Chen ◽  
Kun Qu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Matrix Factorization ◽  
Data Clustering ◽  
Cell Types ◽  
Low Rank ◽  
Sequencing Data ◽  
Rank Matrix ◽  
Single Cell Rna Sequencing ◽  
Low Rank Matrix

Abstract Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

Abstract 305: Single Cell RNA Sequencing of Adult Cardiac c-kit+ Cells in a Murine Lineage Tracing Model

2015 ◽  
Vol 117 (suppl_1) ◽  
Author(s):  
Bryan D Maliken ◽  
Onur Kanisicak ◽  
Jeffery D Molkentin
Keyword(s):  
Single Cell ◽  
Progenitor Cells ◽  
Rna Sequencing ◽  
Cardiac Injury ◽  
Mesenchymal Cells ◽  
Lineage Tracing ◽  
Tyrosine Kinase Receptor ◽  
Early Differentiation ◽  
Gfp Reporter ◽  
Single Cell Rna Sequencing

A subset of adult cardiac resident cells defined by the stem cell factor tyrosine kinase receptor termed c-kit, are believed to have myogenic potential and are now being delivered via intracoronary infusion to presumably promote cardiac regeneration and improve ventricular function after ischemic cardiac injury. However, recent studies have shown that despite these benefits, c-kit+ progenitor cells in the adult murine heart are more inclined to take on an endothelial rather than cardiomyocyte lineage. To better define the factors involved in early differentiation of these resident cardiac progenitor cells and to distinguish distinct cell subpopulations, we performed single cell RNA sequencing on c-kit+ cells from Kit-Cre lineage traced GFP reporter mice versus total mesenchymal cells from the heart that were CD31- and CD45-. Cells were isolated by cardiac digestion and FACS was performed, positively sorting for the c-kit+ lineage while negatively sorting for CD31 and CD45 to eliminate endothelial and leukocyte progenitor contamination, respectively. Following this isolation, cells were examined to determine GFP reporter status and then submitted for single cell RNA sequencing using the Fluidigm A1 system. Clustering of 654 genes from this data identified 6 distinct subpopulations indicating various stages of early differentiation among CD31- and CD45-negative cardiac interstitial cells. Furthermore, comparison of GFP+ c-kit cells to the total non-GFP mesenchymal cells identified 75 differentially expressed transcripts. These unique gene signatures may help parse the genes that underlie cellular plasticity in the heart and define the best molecular lineages for transdifferentiation into cardiac myocytes.

scholarly journals 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 ◽  
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.

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

Cephalalgia ◽  
2018 ◽  
Vol 38 (13) ◽  
pp. 1976-1983 ◽  
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.

scholarly journals A spatially restricted fibrotic niche in pulmonary fibrosis is sustained by M-CSF/M-CSFR signalling in monocyte-derived alveolar macrophages

2019 ◽  
Vol 55 (1) ◽  
pp. 1900646 ◽  
Author(s):  
Nikita Joshi ◽  
Satoshi Watanabe ◽  
Rohan Verma ◽  
Renea P. Jablonski ◽  
Ching-I Chen ◽  
...  
Keyword(s):  
Pulmonary Fibrosis ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Molecule ◽  
Alveolar Macrophages ◽  
Lineage Tracing ◽  
Single Cell Rna Sequencing ◽  
Pharmacological Blockade ◽  
Macrophage Colony ◽  
Druggable Targets

Ontologically distinct populations of macrophages differentially contribute to organ fibrosis through unknown mechanisms.We applied lineage tracing, single-cell RNA sequencing and single-molecule fluorescence in situ hybridisation to a spatially restricted model of asbestos-induced pulmonary fibrosis.We demonstrate that tissue-resident alveolar macrophages, tissue-resident peribronchial and perivascular interstitial macrophages, and monocyte-derived alveolar macrophages are present in the fibrotic niche. Deletion of monocyte-derived alveolar macrophages but not tissue-resident alveolar macrophages ameliorated asbestos-induced lung fibrosis. Monocyte-derived alveolar macrophages were specifically localised to fibrotic regions in the proximity of fibroblasts where they expressed molecules known to drive fibroblast proliferation, including platelet-derived growth factor subunit A. Using single-cell RNA sequencing and spatial transcriptomics in both humans and mice, we identified macrophage colony-stimulating factor receptor (M-CSFR) signalling as one of the novel druggable targets controlling self-maintenance and persistence of these pathogenic monocyte-derived alveolar macrophages. Pharmacological blockade of M-CSFR signalling led to the disappearance of monocyte-derived alveolar macrophages and ameliorated fibrosis.Our findings suggest that inhibition of M-CSFR signalling during fibrosis disrupts an essential fibrotic niche that includes monocyte-derived alveolar macrophages and fibroblasts during asbestos-induced fibrosis.

Identifying cell types to interpret scRNA-seq data: how, why and more possibilities

2020 ◽  
Vol 19 (4) ◽  
pp. 286-291 ◽  
Author(s):  
Ziwei Wang ◽  
Hui Ding ◽  
Quan Zou
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Biological Phenomena ◽  
Single Cell Rna Sequencing ◽  
Numerous Data ◽  
Near Future

Abstract Single-cell RNA sequencing (scRNA-seq) has generated numerous data and renewed our understanding of biological phenomena at the cellular scale. Identification of cell types has been one of the most prevalent means for interpreting scRNA-seq data, based upon which connections are made between the transcriptome and phenotype. Herein, we attempt to review the methods and tools that dedicate to the task regarding their feature and usage and look at the possibilities for scRNA-seq development in the near future.

scholarly journals Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Patrick S. Stumpf ◽  
Xin Du ◽  
Haruka Imanishi ◽  
Yuya Kunisaki ◽  
Yuichiro Semba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bone Marrow ◽  
Single Cell ◽  
Rna Sequencing ◽  
Transfer Learning ◽  
Biomedical Research ◽  
Human Cell ◽  
Cell Types ◽  
Single Cell Rna Sequencing ◽  
Using Data

AbstractBiomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning—the branch of machine learning that concerns passing information from one domain to another—can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research.

scholarly journals Single-cell RNA sequencing reveals cell type- and artery type-specific vascular remodelling in male spontaneously hypertensive rats

2020 ◽  
Author(s):  
Jun Cheng ◽  
Wenduo Gu ◽  
Ting Lan ◽  
Jiacheng Deng ◽  
Zhichao Ni ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Spontaneously Hypertensive Rats ◽  
Cell Types ◽  
Vascular Remodelling ◽  
Cell Type ◽  
Hypertensive Rats ◽  
Spontaneously Hypertensive ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific

Abstract Aims Hypertension is a major risk factor for cardiovascular diseases. However, vascular remodelling, a hallmark of hypertension, has not been systematically characterized yet. We described systematic vascular remodelling, especially the artery type- and cell type-specific changes, in hypertension using spontaneously hypertensive rats (SHRs). Methods and results Single-cell RNA sequencing was used to depict the cell atlas of mesenteric artery (MA) and aortic artery (AA) from SHRs. More than 20 000 cells were included in the analysis. The number of immune cells more than doubled in aortic aorta in SHRs compared to Wistar Kyoto controls, whereas an expansion of MA mesenchymal stromal cells (MSCs) was observed in SHRs. Comparison of corresponding artery types and cell types identified in integrated datasets unravels dysregulated genes specific for artery types and cell types. Intersection of dysregulated genes with curated gene sets including cytokines, growth factors, extracellular matrix (ECM), receptors, etc. revealed vascular remodelling events involving cell–cell interaction and ECM re-organization. Particularly, AA remodelling encompasses upregulated cytokine genes in smooth muscle cells, endothelial cells, and especially MSCs, whereas in MA, change of genes involving the contractile machinery and downregulation of ECM-related genes were more prominent. Macrophages and T cells within the aorta demonstrated significant dysregulation of cellular interaction with vascular cells. Conclusion Our findings provide the first cell landscape of resistant and conductive arteries in hypertensive animal models. Moreover, it also offers a systematic characterization of the dysregulated gene profiles with unbiased, artery type-specific and cell type-specific manners during hypertensive vascular remodelling.

Sign in / Sign up

Export Citation Format

Share Document