scholarly journals Spatiotemporal transcriptome at single-cell resolution reveals key radial glial cell population in axolotl telencephalon development and regeneration

2021 ◽  
Author(s):  
Xiaoyu Wei ◽  
Sulei Fu ◽  
Hanbo Li ◽  
Yang Liu ◽  
Shuai Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Glial Cell ◽  
Cell Population ◽  
Cell Types ◽  
Mammalian Brain ◽  
Regeneration Ability ◽  
Radial Glial Cell ◽  
Cell Dynamics ◽  
Brain Regeneration ◽  
Radial Glial

Brain regeneration requires a precise coordination of complex responses in a time- and region-specific manner. Identifying key cell types and molecules that direct brain regeneration would provide potential targets for the advance of regenerative medicine. However, progress in the field has been hampered largely due to very limited regeneration capacity of the mammalian brain and understanding of the regeneration process at both cellular and molecular level. Here, using axolotl brain with astonishing regeneration ability upon injury, and the Stereo-seq (SpaTial Enhanced REsolution Omics-sequencing), we reconstruct the first architecture of axolotl telencephalon with gene expression profiling at single-cell resolution, and fine cell dynamics maps throughout development and regeneration. Intriguingly, we discover a marked heterogeneity of radial glial cell (RGC) types with distinct behaviors. Of note, one subtype of RGCs is activated since early regeneration stages and proliferates while other RGCs remain dormant. Such RGC subtype appears to be the major cell population involved in early wound healing response and gradually covers the injured area before presumably transformed into the lost neurons. Altogether, our work systematically decodes the complex cellular and molecular dynamics of axolotl telencephalon in development and regeneration, laying the foundation for studying the regulatory mechanism of brain regeneration in future.

scholarly journals C3G regulates cortical neuron migration, preplate splitting and radial glial cell attachment

Development ◽  
2008 ◽  
Vol 135 (12) ◽  
pp. 2139-2149 ◽  
Author(s):  
A. K. Voss ◽  
J. M. Britto ◽  
M. P. Dixon ◽  
B. N. Sheikh ◽  
C. Collin ◽  
...  
Keyword(s):  
Cortical Neuron ◽  
Glial Cell ◽  
Cell Attachment ◽  
Radial Glial Cell ◽  
Neuron Migration ◽  
Radial Glial

scholarly journals IKAP - Identifying K mAjor cell Population groups in single-cell RNA-seq analysis

2019 ◽  
Author(s):  
Yun-Ching Chen ◽  
Abhilash Suresh ◽  
Chingiz Underbayev ◽  
Clare Sun ◽  
Komudi Singh ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Cell Types ◽  
Marker Genes ◽  
Rna Seq ◽  
Population Groups ◽  
Tuning Parameters ◽  
Multiple Datasets ◽  
Cell Groups ◽  
Cell Ontology

AbstractIn single-cell RNA-seq analysis, clustering cells into groups and differentiating cell groups by marker genes are two separate steps for investigating cell identity. However, results in clustering greatly affect the ability to differentiate between cell groups. We develop IKAP – an algorithm identifying major cell groups that improves differentiating by tuning parameters for clustering. Using multiple datasets, we demonstrate IKAP improves identification of major cell types and facilitates cell ontology curation.

scholarly journals Memo1 Tiles the Radial Glial Cell Grid

Neuron ◽  
2019 ◽  
Vol 103 (5) ◽  
pp. 750-752
Author(s):  
Ximena Contreras ◽  
Simon Hippenmeyer
Keyword(s):  
Glial Cell ◽  
Radial Glial Cell ◽  
Radial Glial

scholarly journals The role of the EG5 kinesin in regulating radial glial cell number

2007 ◽  
Vol 306 (1) ◽  
pp. 329-330
Author(s):  
Kristina M. DiPietrantonio ◽  
Alissa Ortman ◽  
Rolf Karlstrom ◽  
Adam Amsterdam ◽  
Nancy Hopkins ◽  
...  
Keyword(s):  
Glial Cell ◽  
Cell Number ◽  
Radial Glial Cell ◽  
Radial Glial

scholarly journals Radial Glial Cell-Neuron Interaction Directs Axon Formation at the Opposite Side of the Neuron from the Contact Site

2015 ◽  
Vol 35 (43) ◽  
pp. 14517-14532 ◽  
Author(s):  
C. Xu ◽  
Y. Funahashi ◽  
T. Watanabe ◽  
T. Takano ◽  
S. Nakamuta ◽  
...  
Keyword(s):  
Glial Cell ◽  
Radial Glial Cell ◽  
Contact Site ◽  
Neuron Interaction ◽  
Radial Glial

Comparative analysis ofTsc1andTsc2single and double radial glial cell mutants

2013 ◽  
Vol 521 (16) ◽  
pp. 3817-3831 ◽  
Author(s):  
Ulrike Mietzsch ◽  
James McKenna ◽  
R. Michelle Reith ◽  
Sharon W. Way ◽  
Michael J. Gambello
Keyword(s):  
Comparative Analysis ◽  
Glial Cell ◽  
Radial Glial Cell ◽  
Radial Glial

scholarly journals Venice: A New Algorithm for Finding Marker Genes in Single-Cell Transcriptomic Data

2020 ◽  
Author(s):  
Hy Vuong ◽  
Thao Truong ◽  
Tan Phan ◽  
Son Pham
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Cell Types ◽  
Marker Genes ◽  
Data Sets ◽  
Interactive Analysis ◽  
Expression Of Genes ◽  
A Cell ◽  
Definition Of ◽  
Cell Data

AbstractMost widely used tools for finding marker genes in single cell data (SeuratT/NegBinom/Poisson, CellRanger, EdgeR, limmatrend) use a conventional definition of differentially expressed genes: genes with different mean expression values. However, in single-cell data, a cell population can be a mixture of many cell types/cell states, hence the mean expression of genes cannot represent the whole population. In addition, these tools assume that gene expression of a population belongs to a specific family of distribution. This assumption is often violated in single-cell data. In this work, we define marker genes of a cell population as genes that can be used to distinguish cells in the population from cells in other populations. Besides log-fold change, we devise a new metric to classify genes into up-regulated, down-regulated, and transitional states. In a benchmark for finding up-regulated and down-regulated genes, our tool outperforms all compared methods, including Seurat, ROTS, scDD, edgeR, MAST, limma, normal t-test, Wilcoxon and Kolmogorov–Smirnov test. Our method is much faster than all compared methods, therefore, enables interactive analysis for large single-cell data sets in BioTuring Browser. Venice algorithm is available within Signac package: https://github.com/bioturing/signac1).

scholarly journals An atlas of gene regulatory elements in adult mouse cerebrum

Nature ◽  
2021 ◽  
Vol 598 (7879) ◽  
pp. 129-136
Author(s):  
Yang Eric Li ◽  
Sebastian Preissl ◽  
Xiaomeng Hou ◽  
Ziyang Zhang ◽  
Kai Zhang ◽  
...  
Keyword(s):  
Glial Cell ◽  
Target Genes ◽  
Adult Mouse ◽  
Cell Types ◽  
Regulatory Elements ◽  
Inhibitory Neurons ◽  
Mammalian Brain ◽  
Regulatory Sequences ◽  
Subcortical Structures ◽  
Gene Regulatory

AbstractThe mammalian cerebrum performs high-level sensory perception, motor control and cognitive functions through highly specialized cortical and subcortical structures1. Recent surveys of mouse and human brains with single-cell transcriptomics2–6 and high-throughput imaging technologies7,8 have uncovered hundreds of neural cell types distributed in different brain regions, but the transcriptional regulatory programs that are responsible for the unique identity and function of each cell type remain unknown. Here we probe the accessible chromatin in more than 800,000 individual nuclei from 45 regions that span the adult mouse isocortex, olfactory bulb, hippocampus and cerebral nuclei, and use the resulting data to map the state of 491,818 candidate cis-regulatory DNA elements in 160 distinct cell types. We find high specificity of spatial distribution for not only excitatory neurons, but also most classes of inhibitory neurons and a subset of glial cell types. We characterize the gene regulatory sequences associated with the regional specificity within these cell types. We further link a considerable fraction of the cis-regulatory elements to putative target genes expressed in diverse cerebral cell types and predict transcriptional regulators that are involved in a broad spectrum of molecular and cellular pathways in different neuronal and glial cell populations. Our results provide a foundation for comprehensive analysis of gene regulatory programs of the mammalian brain and assist in the interpretation of noncoding risk variants associated with various neurological diseases and traits in humans.

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