Evolution of cellular diversity in primary motor cortex of human, marmoset monkey, and mouse

AbstractThe primary motor cortex (M1) is essential for voluntary fine motor control and is functionally conserved across mammals. Using high-throughput transcriptomic and epigenomic profiling of over 450,000 single nuclei in human, marmoset monkey, and mouse, we demonstrate a broadly conserved cellular makeup of this region, whose similarity mirrors evolutionary distance and is consistent between the transcriptome and epigenome. The core conserved molecular identity of neuronal and non-neuronal types allowed the generation of a cross-species consensus cell type classification and inference of conserved cell type properties across species. Despite overall conservation, many species specializations were apparent, including differences in cell type proportions, gene expression, DNA methylation, and chromatin state. Few cell type marker genes were conserved across species, providing a short list of candidate genes and regulatory mechanisms responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allowed the Patch-seq identification of layer 5 (L5) corticospinal Betz cells in non-human primate and human and characterization of their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell type diversity in M1 across mammals and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

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Comparative cellular analysis of motor cortex in human, marmoset and mouse

Nature ◽

10.1038/s41586-021-03465-8 ◽

2021 ◽

Vol 598 (7879) ◽

pp. 111-119 ◽

Cited By ~ 1

Author(s):

Trygve E. Bakken ◽

Nikolas L. Jorstad ◽

Qiwen Hu ◽

Blue B. Lake ◽

Wei Tian ◽

...

Keyword(s):

Motor Cortex ◽

Primary Motor Cortex ◽

Neuronal Cell ◽

Cell Types ◽

Morphological Characterization ◽

Fine Motor ◽

Marker Genes ◽

Cell Type ◽

Regulatory Pathways ◽

Cellular Analysis

AbstractThe primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch–seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

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

Nature ◽

10.1038/s41586-021-03500-8 ◽

2021 ◽

Vol 598 (7879) ◽

pp. 103-110 ◽

Cited By ~ 6

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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A multimodal cell census and atlas of the mammalian primary motor cortex

10.1101/2020.10.19.343129 ◽

2020 ◽

Cited By ~ 5

Author(s):

◽

Ricky S. Adkins ◽

Andrew I. Aldridge ◽

Shona Allen ◽

Seth A. Ament ◽

...

Keyword(s):

Motor Cortex ◽

Single Cell ◽

Primary Motor Cortex ◽

Spatial Information ◽

Molecular Genetic ◽

Cell Types ◽

Developmental Trajectory ◽

Open Chromatin ◽

Cell Type ◽

Spatially Resolved

ABSTRACTWe report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

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ExpressHeart: Web Portal to Visualize Transcriptome Profiles of Non-Cardiomyocyte Cells

International Journal of Molecular Sciences ◽

10.3390/ijms22168943 ◽

2021 ◽

Vol 22 (16) ◽

pp. 8943

Author(s):

Gang Li ◽

Changfei Luan ◽

Yanhan Dong ◽

Yifang Xie ◽

Scott C. Zentz ◽

...

Keyword(s):

Heart Development ◽

Heart Diseases ◽

Cell Types ◽

Marker Genes ◽

Specific Marker ◽

Cell Type ◽

Web Portal ◽

Molecular Features ◽

Species Specific ◽

Combine Information

Unveiling the molecular features in the heart is essential for the study of heart diseases. Non-cardiomyocytes (nonCMs) play critical roles in providing structural and mechanical support to the working myocardium. There is an increasing amount of single-cell RNA-sequencing (scRNA-seq) data characterizing the transcriptomic profiles of nonCM cells. However, no tool allows researchers to easily access the information. Thus, in this study, we develop an open-access web portal, ExpressHeart, to visualize scRNA-seq data of nonCMs from five laboratories encompassing three species. ExpressHeart enables comprehensive visualization of major cell types and subtypes in each study; visualizes gene expression in each cell type/subtype in various ways; and facilitates identifying cell-type-specific and species-specific marker genes. ExpressHeart also provides an interface to directly combine information across datasets, for example, generating lists of high confidence DEGs by taking the intersection across different datasets. Moreover, ExpressHeart performs comparisons across datasets. We show that some homolog genes (e.g., Mmp14 in mice and mmp14b in zebrafish) are expressed in different cell types between mice and zebrafish, suggesting different functions across species. We expect ExpressHeart to serve as a valuable portal for investigators, shedding light on the roles of genes on heart development in nonCM cells.

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Multiscale Co-Expression in the Brain

10.1101/2020.03.31.018630 ◽

2020 ◽

Author(s):

Benjamin D. Harris ◽

Megan Crow ◽

Stephan Fischer ◽

Jesse Gillis

Keyword(s):

Motor Cortex ◽

Single Cell ◽

Rna Sequencing ◽

Primary Motor Cortex ◽

Cell Types ◽

Cell Type ◽

Single Cell Rna Sequencing ◽

Cell Type Specific ◽

The Brain ◽

Regulatory Landscape

ABSTRACTSingle-cell RNA-sequencing (scRNAseq) data can reveal co-regulatory relationships between genes that may be hidden in bulk RNAseq due to cell type confounding. Using the primary motor cortex data from the Brain Initiative Cell Census Network (BICCN), we study cell type specific co-expression across 500,000 cells. Surprisingly, we find that the same gene-gene relationships that differentiate cell types are evident at finer and broader scales, suggesting a consistent multiscale regulatory landscape.

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

10.1101/2021.10.10.463703 ◽

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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Cell-Type Specific Responses to Associative Learning in the Primary Motor Cortex

10.1101/2021.08.08.455571 ◽

2021 ◽

Author(s):

Candice Lee ◽

Emerson Harkin ◽

Richard Naud ◽

Simon Chen

Keyword(s):

Motor Cortex ◽

Associative Learning ◽

Primary Motor Cortex ◽

Neuronal Cell ◽

Cell Types ◽

Skill Learning ◽

Cell Type ◽

Movement Initiation ◽

Response Properties ◽

Cell Type Specific

The primary motor cortex (M1) is known to be a critical site for movement initiation and motor learning. Surprisingly, it has also been shown to possess reward-related activity, presumably to facilitate reward-based learning of new movements. However, whether reward-related signals are represented among different cell types in M1, and whether their response properties change after cue-reward conditioning remains unclear. Here, we performed longitudinal in vivo two-photon Ca2+ imaging to monitor the activity of different neuronal cell types in M1 while mice engaged in a classical conditioning task. Our results demonstrate that most of the major neuronal cell types in M1 showed robust but differential responses to both cue and reward stimuli, and their response properties undergo cell-type specific modifications after associative learning. PV-INs' responses became more reliable to the cue stimulus, while VIP-INs' responses became more reliable to the reward stimulus. PNs only showed robust response to the novel reward stimulus, and they habituated to it after associative learning. Lastly, SOM-IN responses emerged and became more reliable to both conditioned cue and reward stimuli after conditioning. These observations suggest that cue- and reward-related signals are represented among different neuronal cell types in M1, and the distinct modifications they undergo during associative learning could be essential in triggering different aspects of local circuit reorganization in M1 during reward-based motor skill learning.

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JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

10.1101/2020.10.06.327601 ◽

2020 ◽

Author(s):

Mohit Goyal ◽

Guillermo Serrano ◽

Ilan Shomorony ◽

Mikel Hernaez ◽

Idoia Ochoa

Keyword(s):

Single Cell ◽

Cell Types ◽

Marker Genes ◽

Specific Marker ◽

Rna Seq ◽

Batch Effects ◽

Cell Type ◽

Latent Space ◽

Cell Type Specific ◽

Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

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Revealing immune responses in the Mycobacterium avium subsp. paratuberculosis-infected THP-1 cells using single cell RNA-sequencing

PLoS ONE ◽

10.1371/journal.pone.0254194 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254194

Author(s):

Hong-Tae Park ◽

Woo Bin Park ◽

Suji Kim ◽

Jong-Sung Lim ◽

Gyoungju Nah ◽

...

Keyword(s):

Crohn’S Disease ◽

Crohn's Disease ◽

Single Cell ◽

Mycobacterium Avium ◽

Expression Patterns ◽

Cell Types ◽

Marker Genes ◽

Specific Marker ◽

Cell Type ◽

Cytokines And Chemokines

Mycobacterium avium subsp. paratuberculosis (MAP) is a causative agent of Johne’s disease, which is a chronic and debilitating disease in ruminants. MAP is also considered to be a possible cause of Crohn’s disease in humans. However, few studies have focused on the interactions between MAP and human macrophages to elucidate the pathogenesis of Crohn’s disease. We sought to determine the initial responses of human THP-1 cells against MAP infection using single-cell RNA-seq analysis. Clustering analysis showed that THP-1 cells were divided into seven different clusters in response to phorbol-12-myristate-13-acetate (PMA) treatment. The characteristics of each cluster were investigated by identifying cluster-specific marker genes. From the results, we found that classically differentiated cells express CD14, CD36, and TLR2, and that this cell type showed the most active responses against MAP infection. The responses included the expression of proinflammatory cytokines and chemokines such as CCL4, CCL3, IL1B, IL8, and CCL20. In addition, the Mreg cell type, a novel cell type differentiated from THP-1 cells, was discovered. Thus, it is suggested that different cell types arise even when the same cell line is treated under the same conditions. Overall, analyzing gene expression patterns via scRNA-seq classification allows a more detailed observation of the response to infection by each cell type.

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A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification

10.1101/247114 ◽

2018 ◽

Cited By ~ 1

Author(s):

Douglas Abrams ◽

Parveen Kumar ◽

R. Krishna Murthy Karuturi ◽

Joshy George

Keyword(s):

Experimental Design ◽

Single Cell ◽

Single Cells ◽

Cell Types ◽

Cell Number ◽

Fold Change ◽

Computational Method ◽

Marker Genes ◽

Cell Type ◽

Estimate Sample Size

AbstractBackgroundThe advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification.ResultsWe have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment.ConclusionsBased on our study, we found that when marker genes are expressed at fold change of 4 or more than the rest of the genes, either Seurat or Simlr algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fC 2 upregulated, choice of the single cell algorithm is dependent on the number of single cells isolated and proportion of rare cell type to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in examining the single cell experimental design.

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