scholarly journals Meta-analytic markers reveal a generalizable description of cortical cell types

2021 ◽  
Author(s):  
Stephan Fischer ◽  
Jesse Gillis
Keyword(s):  
Single Cell ◽  
Cortical Cell ◽  
Neuronal Cell ◽  
Cell Types ◽  
Quality Data ◽  
Marker Genes ◽  
Physiological Characterization ◽  
Accessible Information ◽  
The Brain ◽  
Selection Of

Our understanding of neuronal cell types has advanced considerably with the publication of single cell atlases, mapping hundreds of putative cell types across brain areas. Characterizing these cell types is a key step towards understanding how the brain works and dysfunctions. Marker genes play an essential role for experimental validation and computational analyses such as physiological characterization through pathway enrichment, annotation, and deconvolution. However, a framework for quantifying marker replicability and picking replicable markers is currently lacking, particularly for cell types at the finest resolution. Here, using high quality data from the Brain Initiative Cell Census Network (BICCN), we systematically investigate marker replicability for the proposed BICCN cell types. We show that, due to dataset-specific noise, we need to combine 5 datasets to obtain robust differentially expressed (DE) genes, particularly for rare populations and lowly expressed genes. We find that two standard DE statistics, fold change and AUROC, can be combined to select the best markers, ranging from highly sensitive to highly specific. Finally, we show that a meta-analytic selection of markers improves performance in downstream computational tasks: cluster separability, annotation and deconvolution. Based on these tasks, we estimate that 10 to 200 meta-analytic markers provide optimal performance, offering an interpretable and robust description of cell types. Replicable marker lists condense single cell atlases into generalizable and accessible information about cell types, opening avenues for multiple downstream applications, including cell type annotation, selection of gene panels and bulk data deconvolution.

scholarly journals Meta-Analysis of cortical inhibitory interneurons markers landscape and their performances in scRNA-seq studies.

2021 ◽  
Author(s):  
Lorenzo Martini ◽  
Roberta Bardini ◽  
Stefano Di Carlo
Keyword(s):  
Single Cell ◽  
Meta Analysis ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Marker Genes ◽  
Inhibitory Interneurons ◽  
Rna Seq ◽  
Circuit Function ◽  
The Brain

The mammalian cortex contains a great variety of neuronal cells. In particular, GABAergic interneurons, which play a major role in neuronal circuit function, exhibit an extraordinary diversity of cell types. In this regard, single-cell RNA-seq analysis is crucial to study cellular heterogeneity. To identify and analyze rare cell types, it is necessary to reliably label cells through known markers. In this way, all the related studies are dependent on the quality of the employed marker genes. Therefore, in this work, we investigate how a set of chosen inhibitory interneurons markers perform. The gene set consists of both immunohistochemistry-derived genes and single-cell RNA-seq taxonomy ones. We employed various human and mouse datasets of the brain cortex, consequently processed with the Monocle3 pipeline. We defined metrics based on the relations between unsupervised cluster results and the marker expression. Specifically, we calculated the specificity, the fraction of cells expressing, and some metrics derived from decision tree analysis like entropy gain and impurity reduction. The results highlighted the strong reliability of some markers but also the low quality of others. More interestingly, though, a correlation emerges between the general performances of the genes set and the experimental quality of the datasets. Therefore, the proposed method allows evaluating the quality of a dataset in relation to its reliability regarding the inhibitory interneurons cellular heterogeneity study.

scholarly journals A single-cell transcriptomic atlas of the adult Drosophila ventral nerve cord

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Aaron M Allen ◽  
Megan C Neville ◽  
Sebastian Birtles ◽  
Vincent Croset ◽  
Christoph Daniel Treiber ◽  
...  
Keyword(s):  
Single Cell ◽  
Nerve Cord ◽  
Ventral Nerve Cord ◽  
Sensory Information ◽  
Neuronal Cell ◽  
Cell Types ◽  
Distinct Cell ◽  
Old Adult ◽  
And Behavior ◽  
The Brain

The Drosophila ventral nerve cord (VNC) receives and processes descending signals from the brain to produce a variety of coordinated locomotor outputs. It also integrates sensory information from the periphery and sends ascending signals to the brain. We used single-cell transcriptomics to generate an unbiased classification of cellular diversity in the VNC of five-day old adult flies. We produced an atlas of 26,000 high-quality cells, representing more than 100 transcriptionally distinct cell types. The predominant gene signatures defining neuronal cell types reflect shared developmental histories based on the neuroblast from which cells were derived, as well as their birth order. The relative position of cells along the anterior-posterior axis could also be assigned using adult Hox gene expression. This single-cell transcriptional atlas of the adult fly VNC will be a valuable resource for future studies of neurodevelopment and behavior.

scholarly journals A single-cell catalogue of regulatory states in the ageing Drosophila brain

2017 ◽  
Author(s):  
Kristofer Davie ◽  
Jasper Janssens ◽  
Duygu Koldere ◽  
Uli Pech ◽  
Sara Aibar ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Neuronal Cell ◽  
Cell Types ◽  
Disease Mutations ◽  
Drosophila Brain ◽  
Gene Regulatory ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
The Brain

SummaryThe diversity of cell types and regulatory states in the brain, and how these change during ageing, remains largely unknown. Here, we present a single-cell transcriptome catalogue of the entire adult Drosophila melanogaster brain sampled across its lifespan. Both neurons and glia age through a process of “regulatory erosion”, characterized by a strong decline of RNA content, and accompanied by increasing transcriptional and chromatin noise. We identify more than 50 cell types by specific transcription factors and their downstream gene regulatory networks. In addition to neurotransmitter types and neuroblast lineages, we find a novel neuronal cell state driven by datilografo and prospero. This state relates to neuronal birth order, the metabolic profile, and the activity of a neuron. Our single-cell brain catalogue reveals extensive regulatory heterogeneity linked to ageing and brain function and will serve as a reference for future studies of genetic variation and disease mutations.

scholarly journals Parallel RNA and DNA analysis after Deep-sequencing (PRDD-seq) reveals cell type specific lineage patterns in human brain

2020 ◽  
Author(s):  
August Yue Huang ◽  
Pengpeng Li ◽  
Rachel E. Rodin ◽  
Sonia N. Kim ◽  
Yanmei Dou ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Cell Lineage ◽  
Neuronal Cell ◽  
Cell Types ◽  
Inhibitory Neurons ◽  
Cell Type ◽  
Cell Divisions ◽  
Excitatory Neurons ◽  
The Brain

AbstractElucidating the lineage relationships among different cell types is key to understanding human brain development. Here we developed Parallel RNA and DNA analysis after Deep-sequencing (PRDD-seq), which combines RNA analysis of neuronal cell types with analysis of nested spontaneous DNA somatic mutations as cell lineage markers, identified from joint analysis of single cell and bulk DNA sequencing by single-cell MosaicHunter (scMH). PRDD-seq enables the first-ever simultaneous reconstruction of neuronal cell type, cell lineage, and sequential neuronal formation (“birthdate”) in postmortem human cerebral cortex. Analysis of two human brains showed remarkable quantitative details that relate mutation mosaic frequency to clonal patterns, confirming an early divergence of precursors for excitatory and inhibitory neurons, and an “inside-out” layer formation of excitatory neurons as seen in other species. In addition our analysis allows the first estimate of excitatory neuron-restricted precursors (about 10) that generate the excitatory neurons within a cortical column. Inhibitory neurons showed complex, subtype-specific patterns of neurogenesis, including some patterns of development conserved relative to mouse, but also some aspects of primate cortical interneuron development not seen in mouse. PRDD-seq can be broadly applied to characterize cell identity and lineage from diverse archival samples with single-cell resolution and in potentially any developmental or disease condition.Significance StatementStem cells and progenitors undergo a series of cell divisions to generate the neurons of the brain, and understanding this sequence is critical to studying the mechanisms that control cell division and migration in developing brain. Mutations that occur as cells divide are known as the basis of cancer, but have more recently been shown to occur with normal cell divisions, creating a permanent, forensic map of the clonal patterns that define the brain. Here we develop new technology to analyze both DNA mutations and RNA gene expression patterns in single cells from human postmortem brain, allowing us to define clonal patterns among different types of human brain neurons, gaining the first direct insight into how they form.

scholarly journals Fully-automated cell-type identification with specific markers extracted from single-cell transcriptomic data

2019 ◽  
Author(s):  
Aleksandr Ianevski ◽  
Anil K Giri ◽  
Tero Aittokallio
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Computational Method ◽  
Marker Genes ◽  
Cell Type ◽  
Cell Clusters ◽  
Mouse Tissues ◽  
Single Cell Type ◽  
The Individual ◽  
Selection Of

AbstractSingle-cell transcriptomics enables systematic charting of cellular composition of complex tissues. Identification of cell populations often relies on unsupervised clustering of cells based on the similarity of the scRNA-seq profiles, followed by manual annotation of cell clusters using established marker genes. However, manual selection of marker genes for cell-type annotation is a laborious and error-prone task since the selected markers must be specific both to the individual cell clusters and various cell types. Here, we developed a computational method, termed ScType, which enables data-driven selection of marker genes based solely on given scRNA-seq data. Using a compendium of 7 scRNA-seq datasets from various human and mouse tissues, we demonstrate how ScType enables unbiased, accurate and fully-automated single-cell type annotation by guaranteeing the specificity of marker genes both across cell clusters and cell types. The widely-applicable method is implemented as an interactive web-tool (https://sctype.fimm.fi), connected with comprehensive database of specific markers.

scholarly journals Single cell transcriptome atlas of the Drosophila larval brain

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Clarisse Brunet Avalos ◽  
G Larisa Maier ◽  
Rémy Bruggmann ◽  
Simon G Sprecher
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Neural Cells ◽  
Larval Brain ◽  
Cell Diversity ◽  
Transcriptional Changes ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
The Brain

Cell diversity of the brain and how it is affected by starvation, remains largely unknown. Here, we introduce a single cell transcriptome atlas of the entire Drosophila first instar larval brain. We first assigned cell-type identity based on known marker genes, distinguishing five major groups: neural progenitors, differentiated neurons, glia, undifferentiated neurons and non-neural cells. All major classes were further subdivided into multiple subtypes, revealing biological features of various cell-types. We further assessed transcriptional changes in response to starvation at the single-cell level. While after starvation the composition of the brain remains unaffected, transcriptional profile of several cell clusters changed. Intriguingly, different cell-types show very distinct responses to starvation, suggesting the presence of cell-specific programs for nutrition availability. Establishing a single-cell transcriptome atlas of the larval brain provides a powerful tool to explore cell diversity and assess genetic profiles from developmental, functional and behavioral perspectives.

scholarly journals A single-cell transcriptomic atlas of the adult Drosophila ventral nerve cord

2019 ◽  
Author(s):  
Aaron M. Allen ◽  
Megan C. Neville ◽  
Sebastian Birtles ◽  
Vincent Croset ◽  
Christoph D. Treiber ◽  
...  
Keyword(s):  
Single Cell ◽  
Nerve Cord ◽  
Ventral Nerve Cord ◽  
Sensory Information ◽  
Neuronal Cell ◽  
Cell Types ◽  
Distinct Cell ◽  
Old Adult ◽  
And Behavior ◽  
The Brain

AbstractThe Drosophila ventral nerve cord (VNC) receives and processes descending signals from the brain to produce a variety of coordinated locomotor outputs. It also integrates sensory information from the periphery and sends ascending signals to the brain. We used single-cell transcriptomics to generate an unbiased classification of cellular diversity in the VNC of five-day old adult flies. We produced an atlas of 26,000 high-quality cells, representing more than 100 transcriptionally distinct cell types. The predominant gene signatures defining neuronal cell types reflect shared developmental histories based on the neuroblast from which cells were derived, as well as their birth order. Cells could also be assigned to specific neuromeres using adult Hox gene expression. This single-cell transcriptional atlas of the adult fly VNC will be a valuable resource for future studies of neurodevelopment and behavior.

scholarly journals A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex

Nature ◽  
2021 ◽  
Vol 598 (7879) ◽  
pp. 103-110 ◽  
Author(s):  
Zizhen Yao ◽  
Hanqing Liu ◽  
Fangming Xie ◽  
Stephan Fischer ◽  
Ricky S. Adkins ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Neuronal Cell ◽  
Pyramidal Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Marker Genes ◽  
Specific Cell ◽  
Cell Type ◽  
The Brain

AbstractSingle-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1–3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas—containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities—is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.

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