scholarly journals A cell atlas of the adult Drosophila midgut

2020 ◽  
Vol 117 (3) ◽  
pp. 1514-1523 ◽  
Author(s):  
Ruei-Jiun Hung ◽  
Yanhui Hu ◽  
Rory Kirchner ◽  
Yifang Liu ◽  
Chiwei Xu ◽  
...  

Studies of the adult Drosophila midgut have led to many insights in our understanding of cell-type diversity, stem cell regeneration, tissue homeostasis, and cell fate decision. Advances in single-cell RNA sequencing provide opportunities to identify new cell types and molecular features. We used single-cell RNA sequencing to characterize the transcriptome of midgut epithelial cells and identified 22 distinct clusters representing intestinal stem cells, enteroblasts, enteroendocrine cells (EEs), and enterocytes. This unbiased approach recovered most of the known intestinal stem cells/enteroblast and EE markers, highlighting the high quality of the dataset, and led to insights on intestinal stem cell biology, cell type-specific organelle features, the roles of new transcription factors in progenitors and regional variation along the gut, 5 additional EE gut hormones, EE hormonal expression diversity, and paracrine function of EEs. To facilitate mining of this rich dataset, we provide a web-based resource for visualization of gene expression in single cells. Altogether, our study provides a comprehensive resource for addressing functions of genes in the midgut epithelium.

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi26-vi27
Author(s):  
Abrar Choudhury ◽  
Martha Cady ◽  
Calixto Lucas ◽  
Brisa Palikuqi ◽  
Ophir Klein ◽  
...  

Abstract BACKGROUND Meningiomas are the most common primary intracranial tumors in humans and dogs, but biologic drivers and cell types underlying meningeal tumorigenesis are incompletely understood. Here we integrate meningioma single-cell RNA sequencing with stem cell approaches to define a perivascular stem cell underlying vertebrate meningeal tumorigenesis. METHODS Single-cell RNA sequencing was performed on 57,114 cells from 8 human meningiomas, 54,607 cells from 3 dog meningiomas, and human meningioma xenografts in mice. Results were validated using immunofluorescence (IF), immunohistochemistry (IHC), and deconvolution of bulk RNA sequencing of 200 human meningiomas. Mechanistic and functional studies were performed using clonogenic and limiting dilution assays, xenografts, and genetically engineered mouse models. RESULTS Copy number variant identification from human meningioma single cells distinguished tumor cells with loss of chr22q from non-tumor cells with intact chr22q. A single cluster distinguished by expression of Notch3 and other cancer stem cell genes had an intermediate level of loss of chr22q, suggesting this cluster may represent meningioma stem cells. In support of this hypothesis, pseudotime trajectory analysis demonstrated transcriptomic progression starting from Notch3+ cells and encompassing all other meningioma cell types. Notch3+ meningioma cells had transcriptomic concordance to mural pericytes, and IF/IHC of prenatal and adult human meninges, as well as lineage tracing using a Notch3-CreERT2 allele in mice, confirmed Notch3+ cells were restricted to the perivascular stem cell niche in mammalian meningeal development and homeostasis. Integrating human and dog meningioma single cells revealed Notch3+ cells in tumor and non-tumor clusters in dog meningiomas. Notch3 IF/IHC and cell-type deconvolution of bulk RNA sequencing showed Notch3+ cells were enriched in high-grade human meningiomas. Notch3 overexpression in human meningioma cells increased clonogenic growth in vitro, and increased tumorigenesis and tumor growth in vivo, decreasing overall survival. CONCLUSIONS Notch3+ stem cells in the perivascular niche underlie vertebrate meningeal tumorigenesis.


2017 ◽  
Author(s):  
Quan H. Nguyen ◽  
Samuel W. Lukowski ◽  
Han Sheng Chiu ◽  
Anne Senabouth ◽  
Timothy J. C. Bruxner ◽  
...  

AbstractHeterogeneity of cell states represented in pluripotent cultures have not been described at the transcriptional level. Since gene expression is highly heterogeneous between cells, single-cell RNA sequencing can be used to identify how individual pluripotent cells function. Here, we present results from the analysis of single-cell RNA sequencing data from 18,787 individual WTC CRISPRi human induced pluripotent stem cells. We developed an unsupervised clustering method, and through this identified four subpopulations distinguishable on the basis of their pluripotent state including: a core pluripotent population (48.3%), proliferative (47.8%), early-primed for differentiation (2.8%) and late-primed for differentiation (1.1%). For each subpopulation we were able to identify the genes and pathways that define differences in pluripotent cell states. Our method identified four transcriptionally distinct predictor gene sets comprised of 165 unique genes that denote the specific pluripotency states; and using these sets, we developed a multigenic machine learning prediction method to accurately classify single cells into each of the subpopulations. Compared against a set of established pluripotency markers, our method increases prediction accuracy by 10%, specificity by 20%, and explains a substantially larger proportion of deviance (up to 3-fold) from the prediction model. Finally, we developed an innovative method to predict cells transitioning between subpopulations, and support our conclusions with results from two orthogonal pseudotime trajectory methods.


2020 ◽  
Vol 36 (12) ◽  
pp. 3825-3832
Author(s):  
Wenming Wu ◽  
Xiaoke Ma

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) profiles transcriptome of individual cells, which enables the discovery of cell types or subtypes by using unsupervised clustering. Current algorithms perform dimension reduction before cell clustering because of noises, high-dimensionality and linear inseparability of scRNA-seq data. However, independence of dimension reduction and clustering fails to fully characterize patterns in data, resulting in an undesirable performance. Results In this study, we propose a flexible and accurate algorithm for scRNA-seq data by jointly learning dimension reduction and cell clustering (aka DRjCC), where dimension reduction is performed by projected matrix decomposition and cell type clustering by non-negative matrix factorization. We first formulate joint learning of dimension reduction and cell clustering into a constrained optimization problem and then derive the optimization rules. The advantage of DRjCC is that feature selection in dimension reduction is guided by cell clustering, significantly improving the performance of cell type discovery. Eleven scRNA-seq datasets are adopted to validate the performance of algorithms, where the number of single cells varies from 49 to 68 579 with the number of cell types ranging from 3 to 14. The experimental results demonstrate that DRjCC significantly outperforms 13 state-of-the-art methods in terms of various measurements on cell type clustering (on average 17.44% by improvement). Furthermore, DRjCC is efficient and robust across different scRNA-seq datasets from various tissues. The proposed model and methods provide an effective strategy to analyze scRNA-seq data. Availability and implementation The software is coded using matlab, and is free available for academic https://github.com/xkmaxidian/DRjCC. Supplementary information Supplementary data are available at Bioinformatics online.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 771-771
Author(s):  
Irene Ganan-Gomez ◽  
Hui Yang ◽  
Feiyang Ma ◽  
Matteo Pellegrini ◽  
Karen Clise-Dwyer ◽  
...  

Myelodysplastic Syndromes (MDS) are a group of heterogeneous stem cell disorders that result in inefficient hematopoiesis. Although the genetic and cytogenetic landscapes of MDS have been well characterized (Papaemmanuil 2013, Sperling 2017), little is known about the differentiation abnormalities that underlie the MDS phenotype. Gaining insights on how different hematopoietic stem and progenitor cell (HSPC) types contribute to MDS is essential for the design of new targeted therapies to supplement the currently limited effective therapeutic options. To understand the contribution of different cell types to the pathogenesis of MDS, we analyzed the expression profile of the Lin-CD34+ HSPC compartment at the single-cell level. Single-cell RNA-sequencing (scRNA-seq) analysis of HSPCs isolated from 2 MDS patients and 2 age-matched healthy donor samples revealed distinct cell clusters driven by the sample type and the differentiation potential of the cells. To annotate the specific subsets of HSPCs in each cluster, we scored them on the basis of previously reported population-specific gene signatures (Laurenti 2013, Psaila 2016, Van Galen 2019). Whereas CD34+ cells from the 2 healthy donor bone marrow (BM) samples largely overlapped with each other and displayed 2 distinct erythroid/megakaryocytic (Er/Mk; cluster 3) and lympho/myeloid (clusters 2, 5) differentiation trajectories in line with the current view of hematopoiesis, CD34+ cells from the 2 MDS BM samples clustered separately and showed predominantly myeloid differentiation routes (Fig a). Importantly, differential expression analysis of the HSPCs from the 2 MDS samples (Fig b) showed that cells residing atop of the HSPC hierarchy retained the transcriptional profile of immature HSCs in one of the samples (clusters 2, 4), while they were characterized by the expression of genes involved in the differentiation of myelo/lympho multipotent progenitor cells (clusters 0, 1) in the other. However, pseudotime analysis of the HSPCs' transcriptional dynamics showed that, despite the distinct differentiation state of the early hematopoietic cells in each group, the differentiation trajectories of those cells converged at the late myeloid progenitor state (clusters 3, 5, 6). These results suggest that, although the earlier HSC architecture is heterogeneous across MDS patients, the more differentiated myeloid progenitor compartment is similarly compromised and is responsible for the clinical phenotypes of MDS. To confirm differential cell-type contributions to the MDS hierarchy, we immunophenotyped BM samples from 123 untreated patients using multicolor flow cytometry. We applied principal component analysis and logistic regression to group samples based on their cellular compositions. Our mathematical classifier stratified patients in 2 groups, which had markedly different cellular repertoires consistent with our scRNA-seq results (Fig c). Patients with different MDS stem cell hierarchies did not present with significantly different clinical characteristics at diagnosis. These data confirm that different abnormal hematopoietic trajectories converge in the myeloid bias typically observed in MDS hematopoiesis. Next, we exome-sequenced mononuclear cells and T-cells from 45 untreated MDS patients and identified high-confidence somatic mutations in known oncogenes and/or leukemia driver genes. The median number of mutations (n=3) was not significantly different between MDS groups 1 and 2. We identified 4 genes that were differentially mutated in the 2 MDS architectures (Fig d), which suggested that certain mutations may predispose for a specific HSPC phenotype. However, mutation specificity could not fully account for the origin of the 2 differentiation architectures, which were independent on the genetic background in most patients. In conclusion, we demonstrated that MDS are sustained by distinct and recurrent abnormal HSPC differentiation hierarchies. Diverse cellular compositions suggest that different cell-type specific signaling pathways maintain the disease in each group of patients. Our work shows that the characterization of the cellular diversity in the hematopoietic compartment can be used as a biomarker to stratify MDS patients, and warrants further studies to predict the intrinsic vulnerabilities of the cells involved in the pathogenesis and maintenance of MDS in a patient-specific manner. Figure Disclosures Garcia-Manero: Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Merck: Research Funding. Colla:IONIS: Other: Intellectual property and research material IONIS); Amgen: Research Funding; Abbvie: Research Funding.


2020 ◽  
Author(s):  
Jingsi Ming ◽  
Zhixiang Lin ◽  
Xiang Wan ◽  
Can Yang ◽  
Angela Ruohao Wu

AbstractSingle-cell RNA-sequencing (scRNA-seq) has now been used extensively to discover novel cell types and reconstruct developmental trajectories by measuring mRNA expression patterns of individual cells. However, datasets collected using different scRNA-seq technology platforms, including the popular SMART-Seq2 (SS2) and 10X platforms, are difficult to compare because of their heterogeneity. Each platform has unique advantages, and integration of these datasets would provide deeper insights into cell biology and gene regulation. Through comprehensive data exploration, we found that accurate integration is often hampered by differences in cell-type compositions. Herein we describe FIRM, an algorithm that addresses this problem and achieves efficient and accurate integration of heterogeneous scRNA-seq datasets across multiple platforms. We applied FIRM to numerous scRNA-seq datasets generated using SS2 and 10X from mouse, mouse lemur, and human, comparing its performance in dataset integration with other state-of-the-art methods. The integrated datasets generated using FIRM show accurate mixing of shared cell type identities and superior preservation of original structure for each dataset. FIRM not only generates robust integrated datasets for downstream analysis, but is also a facile way to transfer cell type labels and annotations from one dataset to another, making it a versatile and indispensable tool for scRNA-seq analysis.


Endocrinology ◽  
2018 ◽  
Vol 159 (12) ◽  
pp. 3910-3924 ◽  
Author(s):  
Leonard Y M Cheung ◽  
Akima S George ◽  
Stacey R McGee ◽  
Alexandre Z Daly ◽  
Michelle L Brinkmeier ◽  
...  

Abstract Transcription factors and signaling pathways that regulate stem cells and specialized hormone-producing cells in the pituitary gland have been the subject of intense study and have yielded a mechanistic understanding of pituitary organogenesis and disease. However, the regulation of stem cell proliferation and differentiation, the heterogeneity among specialized hormone-producing cells, and the role of nonendocrine cells in the gland remain important, unanswered questions. Recent advances in single-cell RNA sequencing (scRNAseq) technologies provide new avenues to address these questions. We performed scRNAseq on ∼13,663 cells pooled from six whole pituitary glands of 7-week-old C57BL/6 male mice. We identified pituitary endocrine and stem cells in silico, as well as other support cell types such as endothelia, connective tissue, and red and white blood cells. Differential gene expression analyses identify known and novel markers of pituitary endocrine and stem cell populations. We demonstrate the value of scRNAseq by in vivo validation of a novel gonadotrope-enriched marker, Foxp2. We present novel scRNAseq data of in vivo pituitary tissue, including data from agnostic clustering algorithms that suggest the presence of a somatotrope subpopulation enriched in sterol/cholesterol synthesis genes. Additionally, we show that incomplete transcriptome annotation can cause false negatives on some scRNAseq platforms that only generate 3′ transcript end sequences, and we use in vivo data to recover reads of the pituitary transcription factor Prop1. Ultimately, scRNAseq technologies represent a significant opportunity to address long-standing questions regarding the development and function of the different populations of the pituitary gland throughout life.


2021 ◽  
Author(s):  
Hani Jieun Kim ◽  
Kevin Wang ◽  
Carissa Chen ◽  
Yingxin Lin ◽  
Patrick PL Tam ◽  
...  

We present Cepo, a method to generate cell-type-specific gene statistics of differentially stable genes from single-cell RNA-sequencing (scRNA-seq) data to define cell identity. Cepo outperforms current methods in assigning cell identity and enhances several cell identification applications such as cell-type characterisation, spatial mapping of single cells, and lineage inference of single cells.


2020 ◽  
Author(s):  
Xuanhua P. Xie ◽  
Dan R. Laks ◽  
Daochun Sun ◽  
Asaf Poran ◽  
Ashley M. Laughney ◽  
...  

AbstractAdult neural stem cells (NSC) serve as a reservoir for brain plasticity and origin for certain gliomas. Lineage tracing and genomic approaches have portrayed complex underlying heterogeneity within the major anatomical location for NSC, the subventricular zone (SVZ). To gain a comprehensive profile of NSC heterogeneity, we utilized a well validated stem/progenitor specific reporter transgene in concert with single cell RNA sequencing to achieve unbiased analysis of SVZ cells from infancy to advanced age. The magnitude and high specificity of the resulting transcriptional data sets allow precise identification of the varied cell types embedded in the SVZ including specialized parenchymal cells (neurons, glia, microglia), and non-central nervous system cells (endothelial, immune). Initial mining of the data delineates four quiescent NSC and three progenitor cell subpopulations formed in a linear progression. Further evidence indicates that distinct stem and progenitor populations reside in different regions of the SVZ. As stem/progenitor populations progress from neonatal to advanced age, they acquire a deficiency in transition from quiescence to proliferation. Further data mining identifies stage specific biological processes, transcription factor networks, and cell surface markers for investigation of cellular identities, lineage relationships, and key regulatory pathways in adult NSC maintenance and neurogenesis.Significance StatementAdult neural stem cells (NSC) are closely related to multiple neurological disorders and brain tumors. Comprehensive investigation of their composition, lineage, and aging will provide new insights that may lead to enhanced patient treatment. This study applies a novel transgene to label and manipulate neural stem/progenitor cells, and monitor their evolution during aging. Together with high-throughput single cell RNA sequencing, we are able to analyze the subventricular zone (SVZ) cells from infancy to advanced age with unprecedented granularity. Diverse new cell states are identified in the stem cell niche, and an aging related NSC deficiency in transition from quiescence to proliferation is identified. The related biological features provide rich resources to inspect adult NSC maintenance and neurogenesis.


2021 ◽  
Author(s):  
Moonyoung Kang ◽  
Yuri Choi ◽  
Hyeonjin Kim ◽  
Sang-Gyu Kim

High-throughput single-cell RNA sequencing (scRNA-seq) identifies distinct cell populations based on cell-to-cell heterogeneity in gene expression. By examining the distribution of the density of gene expression profiles, the metabolic features of each cell population can be observed. Here, we employ the scRNA-seq technique to reveal the entire biosynthetic pathway of a flower volatile. The corolla (petals) of the wild tobacco Nicotiana attenuata emits a bouquet of scents that are composed mainly of benzylacetone (BA), a rare floral volatile. Protoplasts from the N. attenuata corolla were isolated at three different time points, and the transcript levels of >16,000 genes were analyzed in 3,756 single cells. We performed unsupervised clustering analysis to determine which cell clusters were involved in BA biosynthesis. The biosynthetic pathway of BA was uncovered by analyzing gene co-expression in scRNA-seq datasets and by silencing candidate genes in the corolla. In conclusion, the high-resolution spatiotemporal atlas of gene expression provided by scRNA-seq reveals the molecular features underlying cell-type-specific metabolism in a plant.


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