scholarly journals Common cell type nomenclature for the mammalian brain

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Jeremy A Miller ◽  
Nathan W Gouwens ◽  
Bosiljka Tasic ◽  
Forrest Collman ◽  
Cindy TJ van Velthoven ◽  
...  
Keyword(s):  
Cell Types ◽  
Data Driven ◽  
Mammalian Brain ◽  
Gene Transcript ◽  
Cell Type ◽  
Sequencing Technologies ◽  
Multiple Organs ◽  
Source Data ◽  
The Common ◽  
Common Cell

The advancement of single-cell RNA-sequencing technologies has led to an explosion of cell type definitions across multiple organs and organisms. While standards for data and metadata intake are arising, organization of cell types has largely been left to individual investigators, resulting in widely varying nomenclature and limited alignment between taxonomies. To facilitate cross-dataset comparison, the Allen Institute created the common cell type nomenclature (CCN) for matching and tracking cell types across studies that is qualitatively similar to gene transcript management across different genome builds. The CCN can be readily applied to new or established taxonomies and was applied herein to diverse cell type datasets derived from multiple quantifiable modalities. The CCN facilitates assigning accurate yet flexible cell type names in the mammalian cortex as a step toward community-wide efforts to organize multi-source, data-driven information related to cell type taxonomies from any organism.

scholarly journals A cluster-aware, weighted ensemble clustering method for cell-type detection

2018 ◽  
Author(s):  
Daphne Tsoucas ◽  
Guo-Cheng Yuan
Keyword(s):  
Single Cell Analysis ◽  
Fano Factor ◽  
Cell Types ◽  
Large Datasets ◽  
Computational Method ◽  
Cell Analysis ◽  
Ensemble Clustering ◽  
Cell Type ◽  
Very Large Datasets ◽  
Common Cell

ABSTRACTSingle-cell analysis is a powerful tool for dissecting the cellular composition within a tissue or organ. However, it remains difficult to detect rare and common cell types at the same time. Here we present a new computational method, called GiniClust2, to overcome this challenge. GiniClust2 combines the strengths of two complementary approaches, using the Gini index and Fano factor, respectively, through a cluster-aware, weighted ensemble clustering technique. GiniClust2 successfully identifies both common and rare cell types in diverse datasets, outperforming existing methods. GiniClust2 is scalable to very large datasets.

scholarly journals DNA Methylation Atlas of the Mouse Brain at Single-Cell Resolution

2020 ◽  
Author(s):  
Hanqing Liu ◽  
Jingtian Zhou ◽  
Wei Tian ◽  
Chongyuan Luo ◽  
Anna Bartlett ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Mouse Brain ◽  
Spatial Organization ◽  
Brain Area ◽  
Cell Types ◽  
Regulatory Elements ◽  
Mammalian Brain ◽  
Open Chromatin ◽  
Cell Type ◽  
Single Nucleus

SummaryMammalian brain cells are remarkably diverse in gene expression, anatomy, and function, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. We carried out a comprehensive assessment of the epigenomes of mouse brain cell types by applying single nucleus DNA methylation sequencing to profile 110,294 nuclei from 45 regions of the mouse cortex, hippocampus, striatum, pallidum, and olfactory areas. We identified 161 cell clusters with distinct spatial locations and projection targets. We constructed taxonomies of these epigenetic types, annotated with signature genes, regulatory elements, and transcription factors. These features indicate the potential regulatory landscape supporting the assignment of putative cell types, and reveal repetitive usage of regulators in excitatory and inhibitory cells for determining subtypes. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. Using this deep dataset, an artificial neural network model was constructed that precisely predicts single neuron cell-type identity and brain area spatial location. Integration of high-resolution DNA methylomes with single-nucleus chromatin accessibility data allowed prediction of high-confidence enhancer-gene interactions for all identified cell types, which were subsequently validated by cell-type-specific chromatin conformation capture experiments. By combining multi-omic datasets (DNA methylation, chromatin contacts, and open chromatin) from single nuclei and annotating the regulatory genome of hundreds of cell types in the mouse brain, our DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse brain.

scholarly journals RNA splicing programs define tissue compartments and cell types at single cell resolution

2021 ◽  
Author(s):  
Julia Eve Olivieri ◽  
Roozbeh Dehghannasiri ◽  
Peter Wang ◽  
SoRi Jang ◽  
Antoine de Morree ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
High Throughput ◽  
Rna Splicing ◽  
Single Cells ◽  
Cell Types ◽  
Mouse Lemur ◽  
Cell Type ◽  
Multiple Organs ◽  
Single Cell Pcr

More than 95% of human genes are alternatively spliced. Yet, the extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach that is agnostic to transcript annotation, to detect cell-type-specific regulated splicing in > 110K carefully annotated single cells from 12 human tissues. Using 10x data for discovery, 9.1% of genes with computable SpliZ scores are cell-type specifically spliced. These results are validated with RNA FISH, single cell PCR, and in high throughput with Smart-seq2. Regulated splicing is found in ubiquitously expressed genes such as actin light chain subunit MYL6 and ribosomal protein RPS24, which has an epithelial-specific microexon. 13% of the statistically most variable splice sites in cell-type specifically regulated genes are also most variable in mouse lemur or mouse. SpliZ analysis further reveals 170 genes with regulated splicing during sperm development using, 10 of which are conserved in mouse and mouse lemur. The statistical properties of the SpliZ allow model-based identification of subpopulations within otherwise indistinguishable cells based on gene expression, illustrated by subpopulations of classical monocytes with stereotyped splicing, including an un-annotated exon, in SAT1, a Diamine acetyltransferase. Together, this unsupervised and annotation-free analysis of differential splicing in ultra high throughput droplet-based sequencing of human cells across multiple organs establishes splicing is regulated cell-type-specifically independent of gene expression.

scholarly journals Decision letter: Common cell type nomenclature for the mammalian brain

2020 ◽  
Author(s):  
Genevieve Konopka ◽  
Fenna Krienen ◽  
Joseph D Dougherty
Keyword(s):  
Mammalian Brain ◽  
Cell Type ◽  
Common Cell

scholarly journals Histological and histochemical characterization of the secretory cells of Choeradoplana iheringi Graff, 1899 (Platyhelminthes: Tricladida: Terricola)

2004 ◽  
Vol 64 (3a) ◽  
pp. 511-522 ◽  
Author(s):  
S. A. de Souza ◽  
A. M. Leal-Zanchet
Keyword(s):  
Secretory Cell ◽  
Basic Protein ◽  
Cell Types ◽  
Secretory Cells ◽  
Mucous Secretion ◽  
Cell Type ◽  
The Common ◽  
First Time ◽  
Histochemical Characterization

The present study aims at providing a detailed description of the histology, as well as the first histochemical characterization, of the secretory cells of the epidermis, pharynx, and copulatory organs of Choeradoplana iheringi, in order to give further support to studies on the physiology of these organs. The secretory cells are distinguished on the basis of secretion morphology and its staining properties, using trichrome methods and histochemical reactions. Four cell types open through the epidermis of Ch. iheringi, three of them secreting basic protein and a fourth containing glycosaminoglycan mucins. The epidermal lining cells store glycogen. In the pharynx, four secretory cell types were distinguished. Two types produce glycoprotein, a third type secretes basic protein, and another one produces glycosaminoglycan mucins. In the male copulatory organs, the prostatic vesicle receives four secretory cell types containing basic protein, except for one type which produces glycoprotein. The two secretory cell types opening into the male atrium secrete, respectively, glycoprotein, and glycosaminoglycan mucins. In the female copulatory organs, the female atrium and its proximal diverticulum, the vagina, receive two types of secretory cells producing, respectively, basic protein and glycosaminoglycan mucins. Another secretory cell type constitutes the so-called shell glands which open into the common glandular duct, secreting basic protein. The lining cells of the male and female atria produce a mucous secretion containing glycosaminoglycans. In addition, the lining epithelium of the female atrium presents an apical secretion of a proteic nature. The occurrence of a kind of spermatophore is reported for the first time for a species of Choeradoplana. This structure is located in the male or female atria in different specimens, and characterized by erythrophil, xanthophil, and/or mixed secretions associated with sperm.

scholarly journals Rapid propagation of membrane tension at a presynaptic terminal

2021 ◽  
Author(s):  
Carolina Gomis Perez ◽  
Natasha R Dudzinski ◽  
Mason Rouches ◽  
Benjamin Machta ◽  
David Zenisek ◽  
...  
Keyword(s):  
Chromaffin Cells ◽  
Cell Types ◽  
Synaptic Activity ◽  
Membrane Tension ◽  
Cell Type ◽  
Membrane Flow ◽  
Common View ◽  
The Common ◽  
Adrenal Chromaffin ◽  
Stimulation Of

Many cellular activities, such as cell migration1, cell division, signaling, infection, phagocytosis and exo-endocytosis, generate membrane tension gradients that in turn regulate them. Moreover, membrane flows, which are driven by tension gradients, can limit exo-endocytosis coupling in space and time, as net membrane flow from exocytic to endocytic sites is required to maintain membrane homeostasis. However, there is controversy over how rapidly plasma membrane flows can relax tension gradients; contrary to the common view, recent work showed membrane tension does not equilibrate in several cell types. Here we show membrane tension can propagate rapidly or slowly, spanning orders of magnitude in speed, depending on cell type. In a neuronal terminal specialized for rapid synaptic vesicle turnover and where exo-endocytosis events occur at distinct loci, membrane tension equilibrates within seconds. By contrast, membrane tension does not propagate in neuroendocrine adrenal chromaffin cells secreting catecholamines. Thus, slow membrane flow and tension equilibration may confine exo- and exocytosis to the same loci. Stimulation of exocytosis causes a rapid, global decrease in the synaptic terminal membrane tension, which recovers slowly due to endocytosis. Our results demonstrate membrane tension propagates rapidly at neuronal terminals and varies during synaptic activity, likely contributing to exo-endocytosis coupling.

scholarly journals DNA methylation atlas of the mouse brain at single-cell resolution

Nature ◽  
2021 ◽  
Vol 598 (7879) ◽  
pp. 120-128 ◽  
Author(s):  
Hanqing Liu ◽  
Jingtian Zhou ◽  
Wei Tian ◽  
Chongyuan Luo ◽  
Anna Bartlett ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Mouse Brain ◽  
Spatial Organization ◽  
Brain Area ◽  
Cell Types ◽  
Regulatory Elements ◽  
Mammalian Brain ◽  
Open Chromatin ◽  
Cell Type ◽  
Single Nucleus

AbstractMammalian brain cells show remarkable diversity in gene expression, anatomy and function, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. Here we carry out a comprehensive assessment of the epigenomes of mouse brain cell types by applying single-nucleus DNA methylation sequencing1,2 to profile 103,982 nuclei (including 95,815 neurons and 8,167 non-neuronal cells) from 45 regions of the mouse cortex, hippocampus, striatum, pallidum and olfactory areas. We identified 161 cell clusters with distinct spatial locations and projection targets. We constructed taxonomies of these epigenetic types, annotated with signature genes, regulatory elements and transcription factors. These features indicate the potential regulatory landscape supporting the assignment of putative cell types and reveal repetitive usage of regulators in excitatory and inhibitory cells for determining subtypes. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. Using this deep dataset, we constructed an artificial neural network model that precisely predicts single neuron cell-type identity and brain area spatial location. Integration of high-resolution DNA methylomes with single-nucleus chromatin accessibility data3 enabled prediction of high-confidence enhancer–gene interactions for all identified cell types, which were subsequently validated by cell-type-specific chromatin conformation capture experiments4. By combining multi-omic datasets (DNA methylation, chromatin contacts, and open chromatin) from single nuclei and annotating the regulatory genome of hundreds of cell types in the mouse brain, our DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse cerebrum.

scholarly journals A Web-Server of Cell Type Discrimination System

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Anyou Wang ◽  
Yan Zhong ◽  
Yanhua Wang ◽  
Qianchuan He
Keyword(s):  
Stem Cells ◽  
Pluripotent Stem Cells ◽  
Embryonic Stem ◽  
Web Server ◽  
Cell Types ◽  
Cell Type ◽  
User Input ◽  
The Common ◽  
Induced Pluripotent ◽  
User Friendly

Discriminating cell types is a daily request for stem cell biologists. However, there is not a user-friendly system available to date for public users to discriminate the common cell types, embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and somatic cells (SCs). Here, we develop WCTDS, a web-server of cell type discrimination system, to discriminate the three cell types and their subtypes like fetal versus adult SCs. WCTDS is developed as a top layer application of our recent publication regarding cell type discriminations, which employs DNA-methylation as biomarkers and machine learning models to discriminate cell types. Implemented by Django, Python, R, and Linux shell programming, run under Linux-Apache web server, and communicated through MySQL, WCTDS provides a friendly framework to efficiently receive the user input and to run mathematical models for analyzing data and then to present results to users. This framework is flexible and easy to be expended for other applications. Therefore, WCTDS works as a user-friendly framework to discriminate cell types and subtypes and it can also be expended to detect other cell types like cancer cells.

scholarly journals Brain Data Standards Ontology: A data-driven ontology of transcriptomically defined cell types in the primary motor cortex

2021 ◽  
Author(s):  
Shawn Zheng Kai Tan ◽  
Huseyin Kir ◽  
Brian Aevermann ◽  
Tom Gillespie ◽  
Michael Hawrylycz ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Single Cell ◽  
Primary Motor Cortex ◽  
Cell Types ◽  
Use Cases ◽  
Data Driven ◽  
Cell Type ◽  
Data Standards ◽  
Brain Data ◽  
The Brain

Large scale single cell omics profiling is revolutionising our understanding of cell types, especially in complex organs like the brain. This presents both an opportunity and a challenge for cell ontologies. Annotation of cell types in single cell 'omics data typically uses unstructured free text, making comparison and mapping of annotation between datasets challenging. Annotation with cell ontologies is key to overcoming this challenge, but this will require meeting the challenge of extending cell ontologies representing classically defined cell types by defining and classifying cell types directly from data. Here we present the Brain Data Standards Ontology (BDSO), a data driven ontology that is built as an extension to the Cell Ontology (CL). It supports two major use cases: cell type annotation, and navigation, search, and organisation of a web application integrating single cell omics datasets for the mammalian primary motor cortex. The ontology is built using a semi-automated pipeline that interlinks cell type taxonomies and necessary and sufficient marker genes, and imports relevant ontology modules derived from external ontologies. Overall, the BDS ontology provides an underlying structure that supports these use cases, while remaining sustainable and extensible through automation as our knowledge of brain cell type expands.

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