scholarly journals Initiation and regulation of vascular tissue identity in theArabidopsisembryo

2019 ◽  
Author(s):  
Margot E. Smit ◽  
Cristina I. Llavata-Peris ◽  
Mark Roosjen ◽  
Henriette van Beijnum ◽  
Daria Novikova ◽  
...  
Keyword(s):  
Large Scale ◽  
Vascular Tissue ◽  
Bzip Transcription Factor ◽  
Marker Genes ◽  
Cell Type ◽  
Auxin Response ◽  
Precursor State ◽  
Cell Embryo ◽  
Gene Regulatory ◽  
Candidate Regulator

AbstractDevelopment of plant vascular tissues involves tissue specification, growth, pattern formation and cell type differentiation. While later steps are understood in some detail, it is still largely unknown how the tissue is initially specified. We have used the early Arabidopsis embryo as a simple model to study this process. Using a large collection of marker genes, we find that vascular identity is established in the 16-cell embryo. After a transient precursor state however, there is no persistent uniform tissue identity. Auxin is intimately connected to vascular tissue development. We find that while AUXIN RESPONSE FACTOR5/MONOPTEROS/ (ARF5/MP)-dependent auxin response is required, it is not sufficient for tissue establishment. We therefore used a large-scale enhanced Yeast One Hybrid assay to identify potential regulators of vascular identity. Network and functional analysis of suggest that vascular identity is under robust, complex control. We found that one candidate regulator, the G-class bZIP transcription factor GBF2, modulates vascular gene expression, along with its homolog GBF1. Furthermore, GBFs bind to MP and modulate its activity. Our work uncovers components of a gene regulatory network that controls the initiation of vascular tissue identity, one of which involves the interaction of MP and GBF2 proteins.

scholarly journals Phenotypic convergence in the brain: distinct transcription factors regulate common terminal neuronal characters

2018 ◽  
Author(s):  
Nikos Konstantinides ◽  
Katarina Kapuralin ◽  
Chaimaa Fadil ◽  
Luendreo Barboza ◽  
Rahul Satija ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Large Scale ◽  
Single Cells ◽  
Deep Understanding ◽  
Cell Types ◽  
Marker Genes ◽  
Cell Type ◽  
Functional Specification ◽  
Phenotypic Convergence

SummaryTranscription factors regulate the molecular, morphological, and physiological characters of neurons and generate their impressive cell type diversity. To gain insight into general principles that govern how transcription factors regulate cell type diversity, we used large-scale single-cell mRNA sequencing to characterize the extensive cellular diversity in the Drosophila optic lobes. We sequenced 55,000 single optic lobe neurons and glia and assigned them to 52 clusters of transcriptionally distinct single cells. We validated the clustering and annotated many of the clusters using RNA sequencing of characterized FACS-sorted single cell types, as well as marker genes specific to given clusters. To identify transcription factors responsible for inducing specific terminal differentiation features, we used machine-learning to generate a ‘random forest’ model. The predictive power of the model was confirmed by showing that two transcription factors expressed specifically in cholinergic (apterous) and glutamatergic (traffic-jam) neurons are necessary for the expression of ChAT and VGlut in many, but not all, cholinergic or glutamatergic neurons, respectively. We used a transcriptome-wide approach to show that the same terminal characters, including but not restricted to neurotransmitter identity, can be regulated by different transcription factors in different cell types, arguing for extensive phenotypic convergence. Our data provide a deep understanding of the developmental and functional specification of a complex brain structure.

scholarly journals ArchR: An integrative and scalable software package for single-cell chromatin accessibility analysis

2020 ◽  
Author(s):  
Jeffrey M. Granja ◽  
M. Ryan Corces ◽  
Sarah E. Pierce ◽  
S. Tansu Bagdatli ◽  
Hani Choudhry ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Cell Type ◽  
Biological Meaning ◽  
Cell Clustering ◽  
Gene Regulatory ◽  
Regulatory Landscapes ◽  
Scalable Software

ABSTRACTThe advent of large-scale single-cell chromatin accessibility profiling has accelerated our ability to map gene regulatory landscapes, but has outpaced the development of robust, scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; www.ArchRProject.com) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses including doublet removal, single-cell clustering and cell type identification, robust peak set generation, cellular trajectory identification, DNA element to gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility, and multi-omic integration with scRNA-seq. Enabling the analysis of over 1.2 million single cells within 8 hours on a standard Unix laptop, ArchR is a comprehensive analytical suite for end-to-end analysis of single-cell chromatin accessibility data that will accelerate the understanding of gene regulation at the resolution of individual cells.

scholarly journals scQuery: a web server for comparative analysis of single-cell RNA-seq data

2018 ◽  
Author(s):  
Amir Alavi ◽  
Matthew Ruffalo ◽  
Aiyappa Parvangada ◽  
Zhilin Huang ◽  
Ziv Bar-Joseph
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Web Server ◽  
Cell Types ◽  
Marker Genes ◽  
Heterogeneous Environments ◽  
Rna Seq ◽  
Cell Type ◽  
Small Set ◽  
Unique Cell

SummarySingle cell RNA-Seq (scRNA-seq) studies often profile upward of thousands of cells in heterogeneous environments. Current methods for characterizing cells perform unsupervised analysis followed by assignment using a small set of known marker genes. Such approaches are limited to a few, well characterized cell types. To enable large scale supervised characterization we developed an automated pipeline to download, process, and annotate publicly available scRNA-seq datasets. We extended supervised neural networks to obtain efficient and accurate representations for scRNA-seq data. We applied our pipeline to analyze data from over 500 different studies with over 300 unique cell types and show that supervised methods greatly outperform unsupervised methods for cell type identification. A case study of neural degeneration data highlights the ability of these methods to identify differences between cell type distributions in healthy and diseased mice. We implemented a web server that compares new datasets to collected data employing fast matching methods in order to determine cell types, key genes, similar prior studies, and more.

scholarly journals STEM-20. A CANCER STEM CELL-SELECTIVE APOPTOSIS-INDUCING SMALL MOLECULE FOR THE TREATMENT OF GBM

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii200-ii200
Author(s):  
Stephen Skirboll ◽  
Natasha Lucki ◽  
Genaro Villa ◽  
Naja Vergani ◽  
Michael Bollong ◽  
...  
Keyword(s):  
Stem Cells ◽  
Cancer Stem Cells ◽  
Small Molecules ◽  
Small Molecule ◽  
Large Scale ◽  
Critical Role ◽  
Tumor Formation ◽  
Cell Type ◽  
Caspase 1 ◽  
Activation Assay

Abstract INTRODUCTION Glioblastoma multiforme (GBM) is the most aggressive form of primary brain cancer. A subpopulation of multipotent cells termed GBM cancer stem cells (CSCs) play a critical role in tumor initiation and maintenance, drug resistance, and recurrence following surgery. New therapeutic strategies for the treatment of GBM have recently focused on targeting CSCs. Here we have used an unbiased large-scale screening approach to identify drug-like small molecules that induce apoptosis in GBM CSCs in a cell type-selective manner. METHODS A luciferase-based survival assay of patient-derived GBM CSC lines was established to perform a large-scale screen of ∼one million drug-like small molecules with the goal of identifying novel compounds that are selectively toxic to chemoresistant GBM CSCs. Compounds found to kill GBM CSC lines as compared to control cell types were further characterized. A caspase activation assay was used to evaluate the mechanism of induced cell death. A xenograft animal model using patient-derived GBM CSCs was employed to test the leading candidate for suppression of in vivo tumor formation. RESULTS We identified a small molecule, termed RIPGBM, from the cell-based chemical screen that induces apoptosis in primary patient-derived GBM CSC cultures. The cell type-dependent selectivity of RIPGBM appears to arise at least in part from redox-dependent formation of a proapoptotic derivative, termed cRIPGBM, in GBM CSCs. cRIPGBM induces caspase 1-dependent apoptosis by binding to receptor-interacting protein kinase 2 (RIPK2) and acting as a molecular switch, which reduces the formation of a prosurvival RIPK2/TAK1 complex and increases the formation of a proapoptotic RIPK2/caspase 1 complex. In an intracranial GBM xenograft mouse model, RIPGBM was found to significantly suppress tumor formation. CONCLUSIONS Our chemical genetics-based approach has identified a small molecule drug candidate and a potential drug target that selectively targets cancer stem cells and provides an approach for the treatment of GBMs.

scholarly journals Epigenetic reprogramming of cell identity: lessons from development for regenerative medicine

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Amitava Basu ◽  
Vijay K. Tiwari
Keyword(s):  
Regenerative Medicine ◽  
Cell Types ◽  
Direct Conversion ◽  
Cellular Reprogramming ◽  
Specific Cell ◽  
Cell Type ◽  
Pathological Conditions ◽  
Gene Regulatory ◽  
Induced Pluripotent ◽  
And Function

AbstractEpigenetic mechanisms are known to define cell-type identity and function. Hence, reprogramming of one cell type into another essentially requires a rewiring of the underlying epigenome. Cellular reprogramming can convert somatic cells to induced pluripotent stem cells (iPSCs) that can be directed to differentiate to specific cell types. Trans-differentiation or direct reprogramming, on the other hand, involves the direct conversion of one cell type into another. In this review, we highlight how gene regulatory mechanisms identified to be critical for developmental processes were successfully used for cellular reprogramming of various cell types. We also discuss how the therapeutic use of the reprogrammed cells is beginning to revolutionize the field of regenerative medicine particularly in the repair and regeneration of damaged tissue and organs arising from pathological conditions or accidents. Lastly, we highlight some key challenges hindering the application of cellular reprogramming for therapeutic purposes.

Large scale gene regulatory network inference with a multi-level strategy

2016 ◽  
Vol 12 (2) ◽  
pp. 588-597 ◽  
Author(s):  
Jun Wu ◽  
Xiaodong Zhao ◽  
Zongli Lin ◽  
Zhifeng Shao
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Large Scale ◽  
Network Inference ◽  
Biological Processes ◽  
Molecular Processes ◽  
Gene Regulatory Network Inference ◽  
Cell Functions ◽  
Multi Level ◽  
Gene Regulatory

Transcriptional regulation is a basis of many crucial molecular processes and an accurate inference of the gene regulatory network is a helpful and essential task to understand cell functions and gain insights into biological processes of interest in systems biology.

scholarly journals Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

2021 ◽  
Author(s):  
Yixuan Qiu ◽  
Jiebiao Wang ◽  
Jing Lei ◽  
Kathryn Roeder
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
R Package ◽  
Supplementary Information ◽  
Marker Genes ◽  
Specific Marker ◽  
Cell Type ◽  
Correlation Pattern ◽  
Tissue Samples ◽  
Bulk Data

Abstract Motivation Marker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. Results To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. Availability and implementation We implement this method as an R package markerpen, hosted on CRAN (https://CRAN.R-project.org/package=markerpen). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Integrative multi-omics analyses identify cell-type disease genes and regulatory networks across schizophrenia and Alzheimer’s disease

2020 ◽  
Author(s):  
Mufang Ying ◽  
Peter Rehani ◽  
Panagiotis Roussos ◽  
Daifeng Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Regulatory Elements ◽  
Disease Genes ◽  
Omics Data ◽  
Cell Type ◽  
Cellular Resolution ◽  
Gene Regulatory

AbstractStrong phenotype-genotype associations have been reported across brain diseases. However, understanding underlying gene regulatory mechanisms remains challenging, especially at the cellular level. To address this, we integrated the multi-omics data at the cellular resolution of the human brain: cell-type chromatin interactions, epigenomics and single cell transcriptomics, and predicted cell-type gene regulatory networks linking transcription factors, distal regulatory elements and target genes (e.g., excitatory and inhibitory neurons, microglia, oligodendrocyte). Using these cell-type networks and disease risk variants, we further identified the cell-type disease genes and regulatory networks for schizophrenia and Alzheimer’s disease. The celltype regulatory elements (e.g., enhancers) in the networks were also found to be potential pleiotropic regulatory loci for a variety of diseases. Further enrichment analyses including gene ontology and KEGG pathways revealed potential novel cross-disease and disease-specific molecular functions, advancing knowledge on the interplays among genetic, transcriptional and epigenetic risks at the cellular resolution between neurodegenerative and neuropsychiatric diseases. Finally, we summarized our computational analyses as a general-purpose pipeline for predicting gene regulatory networks via multi-omics data.

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