Isoform cell-type specificity in the mouse primary motor cortex

AbstractFull-length SMART-seq1 single-cell RNA sequencing can be used to measure gene expression at isoform resolution, making possible the identification of specific isoform markers for different cell types. Used in conjunction with spatial RNA capture and gene-tagging methods, this enables the inference of spatially resolved isoform expression for different cell types. Here, in a comprehensive analysis of 6,160 mouse primary motor cortex cells assayed with SMART-seq, 280,327 cells assayed with MERFISH2 and 94,162 cells assayed with 10x Genomics sequencing3, we find examples of isoform specificity in cell types—including isoform shifts between cell types that are masked in gene-level analysis—as well as examples of transcriptional regulation. Additionally, we show that isoform specificity helps to refine cell types, and that a multi-platform analysis of single-cell transcriptomic data leveraging multiple measurements provides a comprehensive atlas of transcription in the mouse primary motor cortex that improves on the possibilities offered by any single technology.

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Isoform cell type specificity in the mouse primary motor cortex

10.1101/2020.03.05.977991 ◽

2020 ◽

Author(s):

A. Sina Booeshaghi ◽

Zizhen Yao ◽

Cindy van Velthoven ◽

Kimberly Smith ◽

Bosiljka Tasic ◽

...

Keyword(s):

Gene Expression ◽

Motor Cortex ◽

Single Cell ◽

Primary Motor Cortex ◽

Cell Types ◽

Full Length ◽

Rna Seq ◽

Cell Type Specificity ◽

Gene Level ◽

Isoform Specificity

Full-length SMART-Seq single-cell RNA-seq can be used to measure gene expression at isoform resolution, making possible the identification of isoform markers for cell types and for an isoform atlas. In a comprehensive analysis of 6,160 mouse primary motor cortex cells assayed with SMART-Seq, we find numerous examples of isoform specificity in cell types, including isoform shifts between cell types that are masked in gene-level analysis. These findings can be used to refine spatial gene expression information to isoform resolution. Our results highlight the utility of full-length single-cell RNA-seq when used in conjunction with other single-cell RNA-seq technologies.

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Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH

Nature ◽

10.1038/s41586-021-03705-x ◽

2021 ◽

Vol 598 (7879) ◽

pp. 137-143 ◽

Cited By ~ 4

Author(s):

Meng Zhang ◽

Stephen W. Eichhorn ◽

Brian Zingg ◽

Zizhen Yao ◽

Kaelan Cotter ◽

...

Keyword(s):

Gene Expression ◽

Motor Cortex ◽

Single Cell ◽

Primary Motor Cortex ◽

Cell Types ◽

Mammalian Brain ◽

Imaging Method ◽

Retrograde Labelling ◽

Spatially Resolved ◽

Different Cell Types

AbstractA mammalian brain is composed of numerous cell types organized in an intricate manner to form functional neural circuits. Single-cell RNA sequencing allows systematic identification of cell types based on their gene expression profiles and has revealed many distinct cell populations in the brain1,2. Single-cell epigenomic profiling3,4 further provides information on gene-regulatory signatures of different cell types. Understanding how different cell types contribute to brain function, however, requires knowledge of their spatial organization and connectivity, which is not preserved in sequencing-based methods that involve cell dissociation. Here we used a single-cell transcriptome-imaging method, multiplexed error-robust fluorescence in situ hybridization (MERFISH)5, to generate a molecularly defined and spatially resolved cell atlas of the mouse primary motor cortex. We profiled approximately 300,000 cells in the mouse primary motor cortex and its adjacent areas, identified 95 neuronal and non-neuronal cell clusters, and revealed a complex spatial map in which not only excitatory but also most inhibitory neuronal clusters adopted laminar organizations. Intratelencephalic neurons formed a largely continuous gradient along the cortical depth axis, in which the gene expression of individual cells correlated with their cortical depths. Furthermore, we integrated MERFISH with retrograde labelling to probe projection targets of neurons of the mouse primary motor cortex and found that their cortical projections formed a complex network in which individual neuronal clusters project to multiple target regions and individual target regions receive inputs from multiple neuronal clusters.

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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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An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types

10.1101/2020.02.29.970558 ◽

2020 ◽

Cited By ~ 15

Author(s):

Zizhen Yao ◽

Hanqing Liu ◽

Fangming Xie ◽

Stephan Fischer ◽

A. Sina Booeshaghi ◽

...

Keyword(s):

Motor Cortex ◽

Single Cell ◽

Primary Motor Cortex ◽

Neuronal Cell ◽

Cell Types ◽

Regulatory Elements ◽

Brain Cell ◽

Molecular Signatures ◽

Sequencing Technologies ◽

Cell Transcriptome

AbstractSingle cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp. Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis.

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

Nature ◽

10.1038/s41586-021-03950-0 ◽

2021 ◽

Vol 598 (7879) ◽

pp. 86-102 ◽

Cited By ~ 7

Author(s):

◽

Edward M. Callaway ◽

Hong-Wei Dong ◽

Joseph R. Ecker ◽

Michael J. Hawrylycz ◽

...

Keyword(s):

Motor Cortex ◽

Single Cell ◽

Primary Motor Cortex ◽

Spatial Information ◽

Molecular Genetic ◽

Cortical Cell ◽

Neuronal Cell ◽

Open Chromatin ◽

Cell Type ◽

Spatially Resolved

AbstractHere we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex 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. Our results advance the collective knowledge and understanding of brain cell-type organization1–5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying 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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Spatially mapped single-cell chromatin accessibility

10.1101/815720 ◽

2019 ◽

Cited By ~ 3

Author(s):

Casey A. Thornton ◽

Ryan M. Mulqueen ◽

Andrew Nishida ◽

Kristof A. Torkenczy ◽

Eve G. Lowenstein ◽

...

Keyword(s):

Single Cell ◽

Spatial Information ◽

Three Dimensional ◽

Cell Types ◽

Chromatin Accessibility ◽

Artery Occlusion ◽

Cell Type ◽

Cell Type Specificity ◽

Spatially Resolved ◽

Typical Experiment

AbstractHigh-throughput single-cell epigenomic assays can resolve the heterogeneity of cell types and states in complex tissues, however, spatial orientation within the network of interconnected cells is lost. Here, we present a novel method for highly scalable, spatially resolved, single-cell profiling of chromatin states. We use high-density multiregional sampling to perform single-cell combinatorial indexing on Microbiopsies Assigned to Positions for the Assay for Transposase Accessible Chromatin (sciMAP-ATAC) to produce single-cell data of an equivalent quality to non-spatially resolved single-cell ATAC-seq, where each cell is localized to a three-dimensional position within the tissue. A typical experiment comprises between 96 and 384 spatially mapped tissue positions, each producing 10s to over 100 individual single-cell ATAC-seq profiles, and a typical resolution of 214 cubic microns; with the ability to tune the resolution and cell throughput to suit each target application. We apply sciMAP-ATAC to the adult mouse primary somatosensory cortex, where we profile cortical lamination and demonstrate the ability to analyze data from a single tissue position or compare a single cell type in adjacent positions. We also profile the human primary visual cortex, where we produce spatial trajectories through the cortex. Finally, we characterize the spatially progressive nature of cerebral ischemic infarct in the mouse brain using a model of transient middle cerebral artery occlusion. We leverage the spatial information to identify novel and known transcription factor activities that vary by proximity to the ischemic infarction core with cell type specificity.

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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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Molecular, spatial and projection diversity of neurons in primary motor cortex revealed by in situ single-cell transcriptomics

10.1101/2020.06.04.105700 ◽

2020 ◽

Cited By ~ 9

Author(s):

Meng Zhang ◽

Stephen W. Eichhorn ◽

Brian Zingg ◽

Zizhen Yao ◽

Hongkui Zeng ◽

...

Keyword(s):

Gene Expression ◽

High Resolution ◽

Single Cell ◽

Primary Motor Cortex ◽

Expression Profiles ◽

Neuronal Cell ◽

Gene Expression Profiles ◽

Cell Types ◽

Different Cell Types

AbstractA mammalian brain is comprised of numerous cell types organized in an intricate manner to form functional neural circuits. Single-cell RNA sequencing provides a powerful approach to identify cell types based on their gene expression profiles and has revealed many distinct cell populations in the brain1-3. Single-cell epigenomic profiling4,5 further provides information on gene-regulatory signatures of different cell types. Understanding how different cell types contribute to brain function, however, requires knowledge of their spatial organization and connectivity, which is not preserved in sequencing-based methods that involve cell dissociation3,6. Here, we used an in situ single-cell transcriptome-imaging method, multiplexed error-robust fluorescence in situ hybridization (MERFISH)7, to generate a molecularly defined and spatially resolved cell atlas of the mouse primary motor cortex (MOp). We profiled ∼300,000 cells in the MOp, identified 95 neuronal and non-neuronal cell clusters, and revealed a complex spatial map in which not only excitatory neuronal clusters but also most inhibitory neuronal clusters adopted layered organizations. Notably, intratelencephalic (IT) cells, the largest branch of neurons in the MOp, formed a continuous spectrum of cells with gradual changes in both gene expression profiles and cortical depth positions in a highly correlated manner. Furthermore, we integrated MERFISH with retrograde tracing to probe the projection targets for different MOp neuronal cell types and found that projections of MOp neurons to other cortical regions formed a many-to-many network with each target region receiving input preferentially from a different composition of IT clusters. Overall, our results provide a high-resolution spatial and projection map of molecularly defined cell types in the MOp. We anticipate that the imaging platform described here can be broadly applied to create high-resolution cell atlases of a wide range of systems.

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Accurate and fast cell marker gene identification with COSG

10.1101/2021.06.15.448484 ◽

2021 ◽

Author(s):

Min Dai ◽

Xiaobing Pei ◽

Xiu-Jie Wang

Keyword(s):

Single Cell ◽

Marker Gene ◽

Cell Types ◽

Superior Performance ◽

Gene Identification ◽

Marker Genes ◽

Sequencing Data ◽

Cell Type Specificity ◽

Spatially Resolved ◽

Downstream Analysis

Accurate cell classification is the groundwork for downstream analysis of single-cell sequencing data, yet how to identify marker genes to distinguish different cell types still remains as a big challenge. We developed COSG as a cosine similarity-based method for more accurate and scalable marker gene identification. COSG is applicable to single-cell RNA sequencing data, single-cell ATAC sequencing data and spatially resolved transcriptome data. COSG is fast and scalable for ultra-large datasets of million-scale cells. Application on both simulated and real experimental datasets demonstrates the superior performance of COSG in terms of both accuracy and efficiency as compared with other available methods. Marker genes or genomic regions identified by COSG are more indicative and with greater cell-type specificity.

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