scholarly journals Identification of ASCL1 as a determinant for human iPSC-derived dopaminergic neurons

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aaron M. Earley ◽  
Lena F. Burbulla ◽  
Dimitri Krainc ◽  
Rajeshwar Awatramani
Keyword(s):  
Stem Cells ◽  
Gene Regulation ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Fine Tuning ◽  
Transcriptional Networks ◽  
Single Cell Rna Sequencing

AbstractDuring cellular specification, transcription factors orchestrate cellular decisions through gene regulation. By hijacking these transcriptional networks, human pluripotent stem cells (hPSCs) can be specialized into neurons with different molecular identities for the purposes of regenerative medicine and disease modeling. However, molecular fine tuning cell types to match their in vivo counterparts remains a challenge. Directing cell fates often result in blended or incomplete neuron identities. A better understanding of hPSC to neuron gene regulation is needed. Here, we used single cell RNA sequencing to resolve some of these graded molecular identities during human neurogenesis from hPSCs. Differentiation platforms were established to model neural induction from stem cells, and we characterized these differentiated cell types by 10x single cell RNA sequencing. Using single cell trajectory and co-expression analyses, we identified a co-regulated transcription factor module expressing achaete-scute family basic helix-loop-helix transcription factor 1 (ASCL1) and neuronal differentiation 1 (NEUROD1). We then tested the function of these transcription factors in neuron subtype differentiation by gene knockout in a novel human system that reports the expression of tyrosine hydroxylase (TH), the rate limiting enzyme in dopamine synthesis. ASCL1 was identified as a necessary transcription factor for regulating dopaminergic neurotransmitter selection.

scholarly journals EPCO-32. AN EPIGENETIC SINGLE-CELL ATLAS OF IDH-MUTANT GLIOMA REVEALS THE ROLE OF ATRX IN SHAPING TUMOR COMPOSITION

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii76-ii76
Author(s):  
Husam Babikir ◽  
Lin Wang ◽  
Karin Shamardani ◽  
Sweta Sudhir ◽  
Gary Kohanbash ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Single Cell ◽  
Rna Sequencing ◽  
Myeloid Cell ◽  
Cell Types ◽  
Open Chromatin ◽  
Helix Loop Helix ◽  
Local Immunosuppression ◽  
Single Cell Rna Sequencing

Abstract Recent single-cell RNA-sequencing studies have identified a hierarchy of cell types that is common to all isocitrate dehydrogenase (IDH) -mutant gliomas. This finding is somewhat paradoxical since the genetic differences between IDH-mutant astrocytomas and IDH-mutant oligodendrogliomas are prognostic, predictive of therapeutic response, and correlated with differences in immune infiltrates. To integrate these disparate findings, we constructed a single-cell atlas of 28 human IDH-mutant primary untreated grade-II/III gliomas. All specimens were profiled by single-cell assay for transposase-accessible chromatin, with additional cohorts profiled via single-cell RNA-sequencing and single-cell spatial proteomics. We determined the cell-type specific differences between IDH-mutant gliomas in transcription-factor utilization, associated targeting and cis-regulatory grammars. To elucidate the role of the chromatin remodeler ATRX (inactivated in over 86% of IDH-mutant astrocytomas) in shaping observed differences in open chromatin, we knocked out ATRX in an immunocompetent model of IDH-mutant glioma and subjected murine tumors to single-cell profiling. We found: 1. ATRX-deficient, IDH-mutant human and murine gliomas both upregulate an astrocytic regulatory program driven by Nuclear Factor I genes and downregulate an oligodendrocytic program driven by basic helix-loop-helix transcription factors. 2. Both human and mouse ATRX-deficient, IDH-mutant gliomas up-regulate genes that promote myeloid-cell chemotaxis and both have significantly higher percentages of myeloid-derived immune-suppressive cells than controls; 3. A transcription-factor program is conserved between human and murine ATRX-deficient tumors that shapes glial identity and promotes local immunosuppression. These studies elucidate how IDH-mutant gliomas from different subtypes can have distinct cellular morphologies and tumor micronenvironments despite a common lineage hierarchy.

scholarly journals Targeted single-cell RNA sequencing of transcription factors enhances the identification of cell types and trajectories

2021 ◽  
Author(s):  
Alexandra Pokhilko ◽  
Adam E. Handel ◽  
Fabiola Curion ◽  
Viola Volpato ◽  
Emma S. Whiteley ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Single Cell Rna Sequencing

STEM-27. A PERIVASCULAR STEM CELL UNDERLIES VERTEBRATE MENINGEAL TUMORIGENESIS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi26-vi27
Author(s):  
Abrar Choudhury ◽  
Martha Cady ◽  
Calixto Lucas ◽  
Brisa Palikuqi ◽  
Ophir Klein ◽  
...  
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Tumor Cells ◽  
Rna Sequencing ◽  
Single Cells ◽  
Cell Types ◽  
Human Meningioma ◽  
Single Cell Rna Sequencing ◽  
Human Meningiomas

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.

scholarly journals A comprehensive temporal patterning gene network in Drosophila medulla neuroblasts revealed by single-cell RNA sequencing

2021 ◽  
Author(s):  
Hailun Zhu ◽  
Sihai Dave Zhao ◽  
Alokananda Ray ◽  
Yu Zhang ◽  
Xin Li
Keyword(s):  
Cell Cycle ◽  
Stem Cells ◽  
Transcription Factors ◽  
Neural Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Network ◽  
Temporal Patterning ◽  
Single Cell Rna Sequencing ◽  
High Gradients

During development, neural stem cells are temporally patterned to sequentially generate a variety of neural types before exiting the cell cycle. Temporal patterning is well-studied in Drosophila, where neural stem cells called neuroblasts sequentially express cascades of Temporal Transcription Factors (TTFs) to control the birth-order dependent neural specification. However, currently known TTFs were mostly identified through candidate approaches and may not be complete. In addition, many fundamental questions remain concerning the TTF cascade initiation, progression, and termination. It is also not known why temporal progression only happens in neuroblasts but not in their differentiated progeny. In this work, we performed single-cell RNA sequencing of Drosophila medulla neuroblasts of all ages to study the temporal patterning process with single-cell resolution. Our scRNA-seq data revealed that sets of genes involved in different biological processes show high to low or low to high gradients as neuroblasts age. We also identified a list of novel TTFs, and experimentally characterized their roles in the temporal progression and neural fate specification. Our study revealed a comprehensive temporal gene network that patterns medulla neuroblasts from start to end. Furthermore, we found that the progression and termination of this temporal cascade also require transcription factors differentially expressed along the differentiation axis (neuroblasts -> -> neurons). Lola proteins function as a speed modulator of temporal progression in neuroblasts; while Nerfin-1, a factor required to suppress de-differentiation in post-mitotic neurons, acts at the final temporal stage together with the last TTF of the cascade, to promote the switch to gliogenesis and the cell cycle exit. Our comprehensive study of the medulla neuroblast temporal cascade illustrated mechanisms that might be conserved in the temporal patterning of neural stem cells.

scholarly journals Single-Cell RNA Sequencing Reveals Novel Markers of Male Pituitary Stem Cells and Hormone-Producing Cell Types

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 ◽  
...  
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Pituitary Gland ◽  
Single Cell ◽  
Rna Sequencing ◽  
Clustering Algorithms ◽  
White Blood Cells ◽  
Cell Types ◽  
Single Cell Rna Sequencing

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.

scholarly journals SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yan Zheng ◽  
Yuanke Zhong ◽  
Jialu Hu ◽  
Xuequn Shang
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
Rna Sequencing ◽  
Development Process ◽  
Capture Rate ◽  
Real Data ◽  
Cell Types ◽  
Imputation Method ◽  
Regulation Mechanism ◽  
Single Cell Rna Sequencing

Abstract Background Single-cell RNA sequencing (scRNA-seq) enables the possibility of many in-depth transcriptomic analyses at a single-cell resolution. It’s already widely used for exploring the dynamic development process of life, studying the gene regulation mechanism, and discovering new cell types. However, the low RNA capture rate, which cause highly sparse expression with dropout, makes it difficult to do downstream analyses. Results We propose a new method SCC to impute the dropouts of scRNA-seq data. Experiment results show that SCC gives competitive results compared to two existing methods while showing superiority in reducing the intra-class distance of cells and improving the clustering accuracy in both simulation and real data. Conclusions SCC is an effective tool to resolve the dropout noise in scRNA-seq data. The code is freely accessible at https://github.com/nwpuzhengyan/SCC.

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.

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

BMC Genomics ◽  
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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