scholarly journals Single-Cell Transcriptomics Characterizes Cell Types in the Subventricular Zone and Uncovers Molecular Defects Impairing Adult Neurogenesis

Cell Reports ◽  
2018 ◽  
Vol 25 (9) ◽  
pp. 2457-2469.e8 ◽  
Author(s):  
Vera Zywitza ◽  
Aristotelis Misios ◽  
Lena Bunatyan ◽  
Thomas E. Willnow ◽  
Nikolaus Rajewsky
Keyword(s):  
Single Cell ◽  
Adult Neurogenesis ◽  
Subventricular Zone ◽  
Cell Types ◽  
Molecular Defects

scholarly journals Single-cell transcriptomics characterizes cell types in the subventricular zone and uncovers molecular defects underlying impaired adult neurogenesis

2018 ◽  
Author(s):  
Vera Zywitza ◽  
Aristotelis Misios ◽  
Lena Bunatyan ◽  
Thomas E. Willnow ◽  
Nikolaus Rajewsky
Keyword(s):  
Single Cell ◽  
Adult Neurogenesis ◽  
Subventricular Zone ◽  
Single Cells ◽  
Cell Types ◽  
Neural Cell ◽  
Adult Brain ◽  
List Type ◽  
Cell Type ◽  
Cell Type Specific

SUMMARYNeural stem cells (NSCs) contribute to plasticity and repair of the adult brain. Niches harboring NSCs are crucial for regulating stem cell self-renewal and differentiation. We used single-cell RNA profiling to generate an unbiased molecular atlas of all cell types in the largest neurogenic niche of the adult mouse brain, the subventricular zone (SVZ). We characterized > 20 neural and non-neural cell types and gained insights into the dynamics of neurogenesis by predicting future cell states based on computational analysis of RNA kinetics. Furthermore, we apply our single-cell approach to mice lacking LRP2, an endocytic receptor required for SVZ maintenance. The number of NSCs and proliferating progenitors was significantly reduced. Moreover, Wnt and BMP4 signaling was perturbed. We provide a valuable resource for adult neurogenesis, insights into SVZ neurogenesis regulation by LRP2, and a proof-of-principle demonstrating the power of single-cell RNA-seq in pinpointing neural cell type-specific functions in loss-of-function models.HIGHLIGHTSunbiased single-cell transcriptomics characterizes adult NSCs and their nichecell type-specific signatures and marker genes for 22 SVZ cell typesFree online tool to assess gene expression across 9,804 single cellscell type-specific dysfunctions underlying impaired adult neurogenesis

scholarly journals Single-Cell Profiling Resolved Transcriptional Alterations and Lineage Dynamics of Subventricular Zone after Mild Traumatic Brain Injury

2021 ◽  
Author(s):  
Xiameng Chen ◽  
Shuqiang Cao ◽  
Yinji Wang ◽  
Manrui Li ◽  
Yadong Guo ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Single Cell ◽  
Mild Traumatic Brain Injury ◽  
Subventricular Zone ◽  
Cell Types ◽  
Therapeutic Interventions ◽  
Molecular Alterations ◽  
Cell Diversity ◽  
Transcriptional Alterations

Mild traumatic brain injury (mTBI) is the most common form of brain trauma caused by physical impact. The subventricular zone (SVZ) is a neurogenetic niche that contributes to homeostasis and repair after brain injury. It is particularly challenging to fully elucidate the molecular alterations in the SVZ occurring in response to injury due to its cell diversity and the complex network. In this study, we aimed to address this issue using a novel transcriptomic technique- unbiased single-cell RNA sequencing. We resolved previous unknown cell subpopulations harbored in the niche, and uncovered cell type-specific alterations in gene expression, enriched pathways, and cell-cell crosstalk following mTBI. Notably, we also report novel lineage trajectories and molecular hallmarks that govern neurogenesis. This study dissects the delicate transcriptome changes of individual cell types as well as the reprogramming process of cells in the SVZ niche after mTBI, and our findings are expected to facilitate the development of therapeutic interventions or diagnostic tests for mTBI.

scholarly journals Single-cell analysis of the ventricular-subventricular zone reveals signatures of dorsal & ventral adult neurogenesis

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Arantxa Cebrian Silla ◽  
Marcos Assis Assis Nascimento ◽  
Stephanie A Redmond ◽  
Benjamin Mansky ◽  
David Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Adult Neurogenesis ◽  
Subventricular Zone ◽  
Adult Mouse ◽  
Single Cell Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Regulation

The ventricular-subventricular zone (V-SVZ), on the walls of the lateral ventricles, harbors the layrgest neurogenic niche in the adult mouse brain. Previous work has shown that neural steym/progenitor cells (NSPCs) in different locations within the V-SVZ produce different subtypes of new neurons for the olfactory bulb. The molecular signatures that underlie this regional heterogeneity remain largely unknown. Here we present a single-cell RNA-sequencing dataset of the adult mouse V-SVZ revealing two populations of NSPCs that reside in largely non-overlapping domains in either the dorsal or ventral V-SVZ. These regional differences in gene expression were further validated using a single-nucleus RNA-sequencing reference dataset of regionally microdissected domains of the V-SVZ and by immunocytochemistry and RNAscope localization. We also identify two subpopulations of young neurons that have gene expression profiles consistent with a dorsal or ventral origin. Interestingly, a subset of genes are dynamically expressed, but maintained, in the ventral or dorsal lineages. The study provides novel markers and territories to understand the region-specific regulation of adult neurogenesis.

Prioritization of cell types responsive to biological perturbations in single-cell data with Augur

2021 ◽  
Author(s):  
Jordan W. Squair ◽  
Michael A. Skinnider ◽  
Matthieu Gautier ◽  
Leonard J. Foster ◽  
Grégoire Courtine
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cell Data

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals Adult tissue-resident stem cells—fact or fiction?

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Deepa Bhartiya
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Adult Stem Cells ◽  
Cell Types ◽  
Specific Cell ◽  
Tissue Specific ◽  
Cardiac Tissues ◽  
Adult Tissues ◽  
Resident Stem Cells ◽  
Abundant Cytoplasm

AbstractLife-long tissue homeostasis of adult tissues is supposedly maintained by the resident stem cells. These stem cells are quiescent in nature and rarely divide to self-renew and give rise to tissue-specific “progenitors” (lineage-restricted and tissue-committed) which divide rapidly and differentiate into tissue-specific cell types. However, it has proved difficult to isolate these quiescent stem cells as a physical entity. Recent single-cell RNAseq studies on several adult tissues including ovary, prostate, and cardiac tissues have not been able to detect stem cells. Thus, it has been postulated that adult cells dedifferentiate to stem-like state to ensure regeneration and can be defined as cells capable to replace lost cells through mitosis. This idea challenges basic paradigm of development biology regarding plasticity that a cell enters point of no return once it initiates differentiation. The underlying reason for this dilemma is that we are putting stem cells and somatic cells together while processing for various studies. Stem cells and adult mature cell types are distinct entities; stem cells are quiescent, small in size, and with minimal organelles whereas the mature cells are metabolically active and have multiple organelles lying in abundant cytoplasm. As a result, they do not pellet down together when centrifuged at 100–350g. At this speed, mature cells get collected but stem cells remain buoyant and can be pelleted by centrifuging at 1000g. Thus, inability to detect stem cells in recently published single-cell RNAseq studies is because the stem cells were unknowingly discarded while processing and were never subjected to RNAseq. This needs to be kept in mind before proposing to redefine adult stem cells.

scholarly journals Optimizing expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anna S. E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

scholarly journals Single-cell transcriptomic analysis of mIHC images via antigen mapping

2021 ◽  
Vol 7 (10) ◽  
pp. eabc5464
Author(s):  
Kiya W. Govek ◽  
Emma C. Troisi ◽  
Zhen Miao ◽  
Rachael G. Aubin ◽  
Steven Woodhouse ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Patterns ◽  
Cell Types ◽  
Level Of Detail ◽  
Cell Populations ◽  
Sequencing Data ◽  
Spatially Resolved ◽  
Murine Spleen ◽  
Single Cell Rna Sequencing ◽  
Antibody Panel

Highly multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA sequencing data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling the systematic annotation of nuanced cell populations, spatial patterns of transcription, and interactions between cell types. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published cytometry and mIHC data of this organ.

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