scholarly journals Single cell transcriptomic analysis of the adult mouse pituitary reveals a novel multi-hormone cell cluster and physiologic demand-induced lineage plasticity

2018 ◽  
Author(s):  
Yugong Ho ◽  
Peng Hu ◽  
Michael T. Peel ◽  
Sixing Chen ◽  
Pablo G. Camara ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Mammalian Species ◽  
Cell Lineage ◽  
Somatic Growth ◽  
Supporting Cell ◽  
Cellular Composition ◽  
Hormone Production ◽  
Mouse Pituitary ◽  
Hormone Specificity

AbstractThe anterior pituitary gland drives a set of highly conserved physiologic processes in mammalian species. These hormonally-controlled processes are central to somatic growth, pubertal transformation, fertility, lactation, and metabolism. Current models, largely built upon candidate gene based immuno-histochemical and mRNA analyses, suggest that each of the seven hormones synthesized by the pituitary is produced by a specific and exclusive cell lineage. However, emerging evidence suggests more complex models of hormone specificity and cell plasticity. Here we have applied massively parallel single-cell RNA sequencing (scRNA-seq), in conjunction with a set of orthogonal mRNA and protein imaging studies, to systematically map the cellular composition of adult male and female mouse pituitaries at single-cell resolution and in the setting of major physiologic demands. These analyses reveal sex-specific cellular diversity associated with normal pituitary homeostasis, and identify an array of cells with complex complements of hormone-enrichment as well as a series of non-hormone producing interstitial and supporting cell lineages. These scRNA-seq studies identify a major cell population that is characterized by a unique multi-hormone gene expression profile. The detection of dynamic shifts in cellular representations and transcriptome profiles in response to two well-defined physiologic stresses suggests corresponding roles of a number of these clusters in cellular plasticity within the adult pituitary. These studies point to an unanticipated complexity and plasticity in pituitary cellular composition that expands upon current models and concepts of pituitary gene expression and hormone production.

scholarly journals MON-725 Transcriptome Profiling in Postnatal Pituitary Gland Identifies Cell Type-Driven Sex-Specific Changes

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Huayun Hou ◽  
Cadia Chan ◽  
Liis Uusküla-Reimand ◽  
Kyoko E Yuki ◽  
Dustin Sokolowski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Pituitary Gland ◽  
Single Cell ◽  
Postnatal Development ◽  
Mirna Expression ◽  
Expression Patterns ◽  
Cellular Composition ◽  
Mouse Pituitary ◽  
Pubertal Transition

Abstract The pituitary gland is integral to the regulation of growth, metabolism, puberty, reproduction, and stress responses. Previously, we found that many genes associated with age-at-menarche in genome-wide association studies (GWAS) displayed increasingly sex-biased expression across the pubertal transition in the mouse pituitary. However, whether this trend exists beyond puberty-related genes was not known. In addition, the regulatory mechanisms underlying these gene expression changes remained to be explored. To answer these questions, we profiled the transcriptome, including microRNAs, of mouse pituitary in both sexes across pubertal transition in an unbiased manner and leveraged a recently published pituitary single cell transcriptome to explore cellular composition changes. We found that the most dynamic temporal changes in both mRNA and miRNA expression occur prior to puberty, underscoring a role for regulation of early pituitary postnatal development. We also observed ~900 genes displaying sex-biased expression patterns, arising during early development and becoming increasingly biased across puberty, including known sex-biased genes such as Fshb and Lhb. However, sex differences in miRNA expression are less pronounced, only 13 miRNAs were found to be sex-biased, suggesting lesser contribution of miRNAs to sex-biased gene expression relative to other forms of regulation. To assess whether pituitary cellular composition could underlie changes in gene expression across pubertal transition, we performed single cell deconvolution of our bulk pituitary gland gene expression. Interestingly, we found that sex differences in cell proportions were estimated to emerge across puberty: a greater proportion of lactotropes was found among females, and greater proportions of gonadotropes and somatotropes were found among males. We observed sex-biased expression patterns of marker genes for these cell types, including Prl, Fshb, and Gh. This finding suggests that cell proportion differences between sexes likely contribute to whole pituitary transcriptome changes we observed, however, to what extent remains to be studied. Together our study indicates that miRNAs play a substantial role in regulation of pituitary postnatal development but that differences in cellular composition may contribute more robustly to sex-biased gene expression.

scholarly journals The single-cell transcriptional landscape of lung carcinoid tumors

2021 ◽  
Author(s):  
Philip Bischoff ◽  
Alexandra Trinks ◽  
Jennifer Wiederspahn ◽  
Benedikt Obermayer ◽  
Jan Patrick Pett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Carcinoid Tumor ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Carcinoid Tumors ◽  
Cellular Composition ◽  
Cell Gene Expression ◽  
Cell Gene ◽  
Lung Carcinoid

AbstractLung carcinoid tumors, also referred to as pulmonary neuroendocrine tumors or lung carcinoids, are rare neoplasms of the lung with a more favorable prognosis than other subtypes of lung cancer. Still, some patients suffer from relapsed disease and metastatic spread while no consensus treatment exists for metastasized carcinoids. Several recent single-cell studies have provided detailed insights into the cellular heterogeneity of more common lung cancers, such as adeno- and squamous cell carcinoma. However, the characteristics of lung carcinoids on the single-cell level are yet completely unknown.To study the cellular composition and single-cell gene expression profiles in lung carcinoids, we applied single-cell RNA sequencing to three lung carcinoid tumor samples and normal lung tissue. The single-cell transcriptomes of carcinoid tumor cells reflected intertumoral heterogeneity associated with clinicopathological features, such as tumor necrosis and proliferation index. The immune microenvironment was specifically enriched in noninflammatory monocyte-derived myeloid cells. Tumor-associated endothelial cells were characterized by distinct gene expression profiles. A spectrum of vascular smooth muscle cells and pericytes predominated the stromal microenvironment. We found a small proportion of myofibroblasts exhibiting features reminiscent of cancer-associated fibroblasts. Stromal and immune cells exhibited potential paracrine interactions which may shape the microenvironment via NOTCH, VEGF, TGFβ and JAK/STAT signaling. Moreover, single-cell gene signatures of pericytes and myofibroblasts demonstrated prognostic value in bulk gene expression data.Here, we provide first comprehensive insights into the cellular composition and single-cell gene expression profiles in lung carcinoids, demonstrating the non-inflammatory and vessel-rich nature of their tumor microenvironment, and outlining relevant intercellular interactions which could serve as future therapeutic targets.

scholarly journals Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

2020 ◽  
Author(s):  
Nan Papili Gao ◽  
Olivier Gandrillon ◽  
András Páldi ◽  
Ulysse Herbach ◽  
Rudiyanto Gunawan
Keyword(s):  
Gene Expression ◽  
Cell Differentiation ◽  
Single Cell ◽  
Burst Size ◽  
Mechanistic Model ◽  
Cell Lineage ◽  
Transcriptional Analysis ◽  
Cell Gene Expression ◽  
Branching Point ◽  
Cell Gene

ABSTRACTWe employed our previously-described single-cell gene expression analysis CALISTA (Clustering And Lineage Inference in Single-Cell Transcriptional Analysis) to evaluate transcriptional uncertainty at the single-cell level using a stochastic mechanistic model of gene expression. We reconstructed a transcriptional uncertainty landscape during cell differentiation by visualizing single-cell transcriptional uncertainty surface over a two dimensional representation of the single-cell gene expression data. The reconstruction of transcriptional uncertainty landscapes for ten publicly available single-cell gene expression datasets from cell differentiation processes with linear, single or multi-branching cell lineage, reveals universal features in the cell differentiation trajectory that include: (i) a peak in single-cell uncertainty during transition states, and in systems with bifurcating differentiation trajectories, each branching point represents a state of high transcriptional uncertainty; (ii) a positive correlation of transcriptional uncertainty with transcriptional burst size and frequency; (iii) an increase in RNA velocity preceeding the increase in the cell transcriptional uncertainty. Finally, we provided biological interpretations of the universal rise-then-fall profile of the transcriptional uncertainty landscape, including a link with the Waddington’s epigenetic landscape, that is generalizable to every cell differentiation system.

scholarly journals Robust Lineage Reconstruction from High-Dimensional Single-Cell Data

2016 ◽  
Author(s):  
Gregory Giecold ◽  
Eugenio Marco ◽  
Lorenzo Trippa ◽  
Guo-Cheng Yuan
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Quantitative Estimate ◽  
Cell Lineage ◽  
Computational Method ◽  
Expression Data ◽  
Cell Gene Expression ◽  
Cell Data ◽  
Cell Gene

Single-cell gene expression data provide invaluable resources for systematic characterization of cellular hierarchy in multi-cellular organisms. However, cell lineage reconstruction is still often associated with significant uncertainty due to technological constraints. Such uncertainties have not been taken into account in current methods. We present ECLAIR, a novel computational method for the statistical inference of cell lineage relationships from single-cell gene expression data. ECLAIR uses an ensemble approach to improve the robustness of lineage predictions, and provides a quantitative estimate of the uncertainty of lineage branchings. We show that the application of ECLAIR to published datasets successfully reconstructs known lineage relationships and significantly improves the robustness of predictions. In conclusion, ECLAIR is a powerful bioinformatics tool for single-cell data analysis. It can be used for robust lineage reconstruction with quantitative estimate of prediction accuracy.

scholarly journals Single-cell roadmap of human gonadal development

2021 ◽  
Author(s):  
Roser Vento-Tormo ◽  
Luz Garcia-Alonso ◽  
Valentina Lorenzi ◽  
Cecilia Mazzeo ◽  
Carmen Sancho-Serra ◽  
...  
Keyword(s):  
Single Cell ◽  
Granulosa Cells ◽  
Mammalian Species ◽  
Supporting Cell ◽  
Gonadal Development ◽  
Chromatin Accessibility ◽  
In Vitro Models ◽  
Second Trimester ◽  
Germ Cell Differentiation

Abstract Gonadal development is a complex process that involves sex determination followed by divergent maturation into ovaries or testes. Historically, limited tissue accessibility and lack of reliable in vitro models have impeded our understanding of human gonadogenesis, despite its importance in gonadal pathologies and infertility. Here, we generated a comprehensive map of first- and second-trimester gonadal development using a combination of single-cell and spatial transcriptomics, chromatin accessibility assays and imaging. Using this approach, we identified novel transcription factors and cell states in human germ and supporting cell lineages. We compared them with other mammalian species and found primate-specific regulatory programmes. Our data identified cell context–specific interactions shaping sex specification and development of human germ cells. We defined a novel bipotent progenitor cell (LGR5+, TSPAN8+) in late embryos that can differentiate into early Sertoli in males or pre-granulosa cells in females. In fetal ovaries, we defined two subsets of pre-granulosa cells supporting germ-cell differentiation and distributed across the cortico-medullary axis. We also found a subset of developing granulosa cells appearing during the second trimester of pregnancy that is involved in follicular assembly. In fetal testes, we defined a novel supporting population (sPAX8 cells) located at the poles of the developing testis cords. We also found two tissue-resident myeloid populations that we named microglia-like and SIGLEC15+ fetal testicular macrophages. This study provides an unprecedented spatiotemporal map of human gonadal differentiation that can be utilised as a blueprint for in vitro gametogenesis.

scholarly journals Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

Science ◽  
2021 ◽  
Vol 371 (6532) ◽  
pp. eabc1944 ◽  
Author(s):  
Jeffrey J. Quinn ◽  
Matthew G. Jones ◽  
Ross A. Okimoto ◽  
Shigeki Nanjo ◽  
Michelle M. Chan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Mouse Model ◽  
Single Cell ◽  
Cancer Cells ◽  
Cancer Progression ◽  
Cell Lineage ◽  
Metastatic Dissemination ◽  
Cell Lineages ◽  
Metastatic Capacity

Detailed phylogenies of tumor populations can recount the history and chronology of critical events during cancer progression, such as metastatic dissemination. We applied a Cas9-based, single-cell lineage tracer to study the rates, routes, and drivers of metastasis in a lung cancer xenograft mouse model. We report deeply resolved phylogenies for tens of thousands of cancer cells traced over months of growth and dissemination. This revealed stark heterogeneity in metastatic capacity, arising from preexisting and heritable differences in gene expression. We demonstrate that these identified genes can drive invasiveness and uncovered an unanticipated suppressive role for KRT17. We also show that metastases disseminated via multidirectional tissue routes and complex seeding topologies. Overall, we demonstrate the power of tracing cancer progression at subclonal resolution and vast scale.

scholarly journals Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

2020 ◽  
Author(s):  
Jeffrey J. Quinn ◽  
Matthew G. Jones ◽  
Ross A. Okimoto ◽  
Shigeki Nanjo ◽  
Michelle M. Chan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Single Cell ◽  
Cancer Progression ◽  
Cell Lineage ◽  
Lineage Tracing ◽  
Rna Seq ◽  
Disease Etiology ◽  
Cell Lineages ◽  
Transient Events

AbstractCancer progression is characterized by rare, transient events which are nonetheless highly consequential to disease etiology and mortality. Detailed cell phylogenies can recount the history and chronology of these critical events – including metastatic seeding. Here, we applied our Cas9-based lineage tracer to study the subclonal dynamics of metastasis in a lung cancer xenograft mouse model, revealing the underlying rates, routes, and drivers of metastasis. We report deeply resolved phylogenies for tens of thousands of metastatically disseminated cancer cells. We observe surprisingly diverse metastatic phenotypes, ranging from metastasis-incompetent to aggressive populations. These phenotypic distinctions result from pre-existing, heritable, and characteristic differences in gene expression, and we demonstrate that these differentially expressed genes can drive invasiveness. Furthermore, metastases transit via diverse, multidirectional tissue routes and seeding topologies. Our work demonstrates the power of tracing cancer progression at unprecedented resolution and scale.One Sentence SummarySingle-cell lineage tracing and RNA-seq capture diverse metastatic behaviors and drivers in lung cancer xenografts in mice.

scholarly journals Estimation of immune cell content in tumour tissue using single-cell RNA-seq data

2017 ◽  
Author(s):  
Max Schelker ◽  
Sonia Feau ◽  
Jinyan Du ◽  
Nav Ranu ◽  
Edda Klipp ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Solid Tumour ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Reference ◽  
Malignant Cells ◽  
Cellular Composition ◽  
Rna Seq

AbstractAs interactions between the immune system and tumour cells are governed by a complex network of cell-cell interactions, knowing the specific immune cell composition of a solid tumour may be essential to predict a patient’s response to immunotherapy. Here, we analyse in depth how to derive the cellular composition of a solid tumour from bulk gene expression data by mathematical deconvolution, using indication- and cell type-specific reference gene expression profiles (RGEPs) from tumour-derived single-cell RNA sequencing data. We demonstrate that tumour-derived RGEPs are essential for the successful deconvolution and that RGEPs from peripheral blood are insufficient. We distinguish nine major cell types as well as three T cell subtypes. As the ratios of CD4+, CD8+ and regulatory T cells have been shown to predict overall survival, we extended our analysis to include the estimation of prognostic ratios that may enable the application in a clinical setting. Using the tumour derived RGEPs, we can estimate, for the first time, the content of cancer associated fibroblasts, endothelial cells and the malignant cells in a patient sample by a deconvolution approach. In addition, improved tumour cell gene expression profiles can be obtained by this method by computationally removing contamination from non-malignant cells. Given the difficulty around sample preparation and storage to obtain high quality single-cell RNA-seq data in the clinical context, the presented method represents a computational solution to derive the cellular composition of a tissue sample.

scholarly journals Comprehensive evaluation of human brain gene expression deconvolution methods

2020 ◽  
Author(s):  
Gavin J Sutton ◽  
Irina Voineagu
Keyword(s):  
Gene Expression ◽  
Human Brain ◽  
Single Cell ◽  
Brain Cell ◽  
Human Pancreas ◽  
Cellular Composition ◽  
Transcriptome Data ◽  
Cell Type ◽  
Tissue Specific ◽  
Brain Data

AbstractGene expression measurements, similarly to DNA methylation and proteomic measurements, are influenced by the cellular composition of the sample analysed. Deconvolution of bulk transcriptome data aims to estimate the cellular composition of a sample from its gene expression data, which in turn can be used to correct for composition differences across samples. Although a multitude of deconvolution methods have been developed, it is unclear whether their performance is consistent across tissues with different complexities of cellular composition. For example, the human brain is unique in its transcriptomic diversity, and in the complexity of its cellularity, yet a comprehensive assessment of the accuracy of transcriptome deconvolution methods on human brain data is currently lacking.Here we carry out the first comprehensive comparative evaluation of the accuracy of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with transcriptome data from human pancreas.We evaluate 22 transcriptome deconvolution approaches, covering all main classes: 3 partial deconvolution methods, each applied with 6 different categories of cell-type signature data, 2 enrichment methods and 2 complete deconvolution methods. We test the accuracy of cell type estimates using in silico mixtures of single-cell RNA-seq data, mixtures of neuronal and glial RNA, as well as nearly 2,000 human brain samples.Our results bring several important insights into the performance of transcriptome deconvolution: (a) We find that cell-type signature data has a stronger impact on brain deconvolution accuracy than the choice of method. In contrast, cell-type signature only mildly influences deconvolution of pancreas transcriptome data, highlighting the importance of tissue-specific benchmarking. (b) We demonstrate that biological factors influencing brain cell-type signature data (e.g. brain region, in vitro cell culturing), have stronger effects on the deconvolution outcome than technical factors (e.g. RNA sequencing platform). (c) We find that partial deconvolution methods outperform complete deconvolution methods on human brain data. (d) We demonstrate that the impact of cellular composition differences on differential expression analyses is tissue-specific, and more pronounced for brain than for pancreas.To facilitate wider implementation of correction for cellular composition, we develop a novel brain cell-type signature, MultiBrain, which integrates single-cell, immuno-panned, and single-nucleus datasets. We demonstrate that it achieves improved deconvolution accuracy over existing reference signatures. Deconvolution of transcriptome data from autism cases and controls using MultiBrain identified cell-type composition changes replicable across studies, and highlighted novel genes dysregulated in autism.

scholarly journals Single-Cell RNA Sequencing Reveals Compromised Immune Microenvironment in Precursor Stages of Multiple Myeloma

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2603-2603 ◽  
Author(s):  
Oksana Zavidij ◽  
Nicholas Haradhvala ◽  
Tarek H Mouhieddine ◽  
Jihye Park ◽  
Romanos Pistofidis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
T Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Plasma Cells ◽  
Cellular Composition ◽  
Mhc Ii ◽  
Healthy Donors ◽  
Single Cell Rna Sequencing

Abstract Introduction: In multiple myeloma (MM), despite well-characterized precursor states such as monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), there is a lack of sufficient biomarkers to predict mechanisms of disease progression. Most genomic analyses have sought biomarkers by study of the malignant plasma cells, however, cancers form a complex ecosystem with the immune and stromal microenvironment. Thus, to characterize the cellular composition and transcriptional programs of each component of the tumor and microenvironment at different stages of MM progression, we employed a single-cell RNA sequencing on a cohort of 22 patients and 9 healthy donors. Methods: We performed 10X droplet-based single-cell RNA sequencing using CD138-expressing plasma cells and microenvironmental populations isolated from bone marrow (BM) aspirates of patients with MGUS (n=6), low-risk SMM (n=3), high-risk SMM (n=13), newly diagnosed MM (n=8) and from 9 healthy donors (NBM). We collected a total of ~88.8K cells, comprising ~48K CD138+ cells (~36.4 from MM stages) and ~40.8K CD45+/CD138- cells (~30.8 from MM stages).Raw read data was processed using the Cell Ranger pipeline to obtain a gene-by-cell expression matrix, which was used to identify cell types and transcriptional programs by clustering and non-negative matrix factorization. Results: Expression profiles of plasma cells revealed clear tumor-specific differences including known oncogenic drivers in MM (MMSET/FGFR3, CCND1 and MAFB) as well as Lysosome-associated Membrane Protein 5 (LAMP5),Histone Cluster 1 H1 Family Member C (HIST1H1C) and Amphiregulin (AREG) distinguishing them from healthy plasma cells. We identified a subset of cycling plasma cells, observing a range of proliferative activity of the malignant fraction. Furthermore, our approach allowed a unique head-to-head comparison of gene expression changes in normal and malignant plasma cells in the MGUS and SMM patients within an individual, excluding inter-individual variation. We were able to discriminate malignant from non-malignant plasma cells and identify transcriptional alterations including known drivers, genes related to immune modulation (NKBIA) or controlling transcription and differentiation (EID1).Some alterations were patient-specific, while others, such as MHC I overexpression and CD27 loss, were recurrently observed across subsets of the cohort. Analysis of BM microenvironment in several stages of MM progression demonstrated a striking shift in the composition of immune cells with significant infiltration of natural killer cells, non-classical monocytes/macrophages, and T cells, enriched even in the earliest stages of the disease. Further investigation revealed significant upregulation of HLA expression at the mRNA level in CD14+ monocytes/macrophages. Intriguingly, comparison of healthy and patient samples by CyTOF showed downregulation of surface MHC II representation in the corresponding cell type, and moreover, co-culture with MM cell lines induced a sharp decrease of extracellular MHC II. This provided strong evidence for compromised antigen presentation by macrophages in the disease setting, hinting at a mechanism of immune evasion. Additionally, expression signatures in cytotoxic T-cells indicated a substantial skewing towards either granzyme B/H- or granzyme K-expressing memory cell-like transcriptional program. In a subgroup of patients, we found a strong simultaneous enrichment of the anti-viral/anti-bacterial gene expression signature for interferon type-1 activated genes in CD14+ monocytes/macrophages and T cells. Together, our results provide a comprehensive view at the complex interplay of the immune and malignant cells in different stages of the disease. We, for the first time, demonstrate the immune response beginning in premalignant conditions to be heterogeneous, including compromised antigen presentation as well as alterations in cellular composition and signaling. Consideration of the type of immunological response may prove valuable in determination of progression risk, as well as open up potential strategies for therapy. Disclosures Bustoros: Dava Oncology: Honoraria. Ghobrial:Celgene: Consultancy; Janssen: Consultancy; BMS: Consultancy; Takeda: Consultancy.

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