Dataset Information

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain.

ABSTRACT: The mammalian brain consists of millions to billions of cells that are organized into many cell types with specific spatial distribution patterns and structural and functional properties^1-3. Here we report a comprehensive and high-resolution transcriptomic and spatial cell-type atlas for the whole adult mouse brain. The cell-type atlas was created by combining a single-cell RNA-sequencing (scRNA-seq) dataset of around 7 million cells profiled (approximately 4.0 million cells passing quality control), and a spatial transcriptomic dataset of approximately 4.3 million cells using multiplexed error-robust fluorescence in situ hybridization (MERFISH). The atlas is hierarchically organized into 4 nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, to visualize the mouse whole-brain cell-type atlas along with the single-cell RNA-sequencing and MERFISH datasets. We systematically analysed the neuronal and non-neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell-type organization in different brain regions-in particular, a dichotomy between the dorsal and ventral parts of the brain. The dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. Our study also uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types. Finally, we found that transcription factors are major determinants of cell-type classification and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole mouse brain transcriptomic and spatial cell-type atlas establishes a benchmark reference atlas and a foundational resource for integrative investigations of cellular and circuit function, development and evolution of the mammalian brain.

SUBMITTER: Yao Z

PROVIDER: S-EPMC10719114 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain.

Yao Zizhen Z van Velthoven Cindy T J CTJ Kunst Michael M Zhang Meng M McMillen Delissa D Lee Changkyu C Jung Won W Goldy Jeff J Abdelhak Aliya A Aitken Matthew M Baker Katherine K Baker Pamela P Barkan Eliza E Bertagnolli Darren D Bhandiwad Ashwin A Bielstein Cameron C Bishwakarma Prajal P Campos Jazmin J Carey Daniel D Casper Tamara T Chakka Anish Bhaswanth AB Chakrabarty Rushil R Chavan Sakshi S Chen Min M Clark Michael M Close Jennie J Crichton Kirsten K Daniel Scott S DiValentin Peter P Dolbeare Tim T Ellingwood Lauren L Fiabane Elysha E Fliss Timothy T Gee James J Gerstenberger James J Glandon Alexandra A Gloe Jessica J Gould Joshua J Gray James J Guilford Nathan N Guzman Junitta J Hirschstein Daniel D Ho Windy W Hooper Marcus M Huang Mike M Hupp Madie M Jin Kelly K Kroll Matthew M Lathia Kanan K Leon Arielle A Li Su S Long Brian B Madigan Zach Z Malloy Jessica J Malone Jocelin J Maltzer Zoe Z Martin Naomi N McCue Rachel R McGinty Ryan R Mei Nicholas N Melchor Jose J Meyerdierks Emma E Mollenkopf Tyler T Moonsman Skyler S Nguyen Thuc Nghi TN Otto Sven S Pham Trangthanh T Rimorin Christine C Ruiz Augustin A Sanchez Raymond R Sawyer Lane L Shapovalova Nadiya N Shepard Noah N Slaughterbeck Cliff C Sulc Josef J Tieu Michael M Torkelson Amy A Tung Herman H Valera Cuevas Nasmil N Vance Shane S Wadhwani Katherine K Ward Katelyn K Levi Boaz B Farrell Colin C Young Rob R Staats Brian B Wang Ming-Qiang Michael MM Thompson Carol L CL Mufti Shoaib S Pagan Chelsea M CM Kruse Lauren L Dee Nick N Sunkin Susan M SM Esposito Luke L Hawrylycz Michael J MJ Waters Jack J Ng Lydia L Smith Kimberly K Tasic Bosiljka B Zhuang Xiaowei X Zeng Hongkui H

Nature 20231213 7991

The mammalian brain consists of millions to billions of cells that are organized into many cell types with specific spatial distribution patterns and structural and functional properties<sup>1-3</sup>. Here we report a comprehensive and high-resolution transcriptomic and spatial cell-type atlas for the whole adult mouse brain. The cell-type atlas was created by combining a single-cell RNA-sequencing (scRNA-seq) dataset of around 7 million cells profiled (approximately 4.0 million cells passing q ...[more]

PMID: 38092916

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Project description:The mammalian brain is composed of millions to billions of cells that are organized into numerous cell types with specific spatial distribution patterns and structural and functional properties. An essential step towards understanding brain function is to obtain a parts list, i.e., a catalog of cell types, of the brain. Here, we report a comprehensive and high-resolution transcriptomic and spatial cell type atlas for the whole adult mouse brain. The cell type atlas was created based on the combination of two single-cell-level, whole-brain-scale datasets: a single-cell RNA-sequencing (scRNA-seq) dataset of ~7 million cells profiled, and a spatially resolved transcriptomic dataset of ~4.3 million cells using MERFISH. The atlas is hierarchically organized into five nested levels of classification: 7 divisions, 32 classes, 306 subclasses, 1,045 supertypes and 5,200 clusters. We systematically analyzed the neuronal, non-neuronal, and immature neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell type organization in different brain regions, in particular, a dichotomy between the dorsal and ventral parts of the brain: the dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. We also systematically characterized cell-type specific expression of neurotransmitters, neuropeptides, and transcription factors. The study uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types across the brain, suggesting they mediate a myriad of modes of intercellular communications. Finally, we found that transcription factors are major determinants of cell type classification in the adult mouse brain and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole-mouse-brain transcriptomic and spatial cell type atlas establishes a benchmark reference atlas and a foundational resource for deep and integrative investigations of cell type and circuit function, development, and evolution of the mammalian brain.

Project description:The mammalian brain consists of millions to billions of cells that are organized into numerous cell types with specific spatial distribution patterns and structural and functional properties. An essential step towards understanding brain function is to obtain a parts list, i.e., a catalog of cell types, of the brain. Here, we report a comprehensive and high-resolution transcriptomic and spatial cell type atlas for the whole adult mouse brain. The cell type atlas was created based on the combination of two single-cell-level, whole-brain-scale datasets: a single-cell RNA-sequencing (scRNA-seq) dataset of ~7 million cells profiled (~4.0 million cells passing quality control), and a spatially resolved transcriptomic dataset of ~4.3 million cells using MERFISH. The atlas is hierarchically organized into four nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present a newly developed online platform, Allen Brain Cell (ABC) Atlas, to visualize the mouse whole brain cell type taxonomy and atlas along with the scRNA-seq and MERFISH data and metadata sets. We systematically analyzed the neuronal, non-neuronal, and immature neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell type organization in different brain regions, in particular, a dichotomy between the dorsal and ventral parts of the brain: the dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. We also systematically characterized cell-type specific expression of neurotransmitters, neuropeptides, and transcription factors. The study uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types across the brain, suggesting they mediate myriad modes of intercellular communications. Finally, we found that transcription factors are major determinants of cell type classification in the adult mouse brain and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole-mouse-brain transcriptomic and spatial cell type atlas establishes a benchmark reference atlas and a foundational resource for deep and integrative investigations of cellular and circuit function, development, and evolution of the mammalian brain.

Project description:BackgroundThe Allen Brain Atlas (ABA) project systematically profiles three-dimensional high-resolution gene expression in postnatal mouse brains for thousands of genes. By unveiling gene behaviors at both the cellular and molecular levels, ABA is becoming a unique and comprehensive neuroscience data source for decoding enigmatic biological processes in the brain. Given the unprecedented volume and complexity of the in situ hybridization image data, data mining in this area is extremely challenging. Currently, the ABA database mainly serves as an online reference for visual inspection of individual genes; the underlying rich information of this large data set is yet to be explored by novel computational tools. In this proof-of-concept study, we studied the hypothesis that genes sharing similar three-dimensional expression profiles in the mouse brain are likely to share similar biological functions.ResultsIn order to address the pattern comparison challenge when analyzing the ABA database, we developed a robust image filtering method, dubbed histogram-row-column (HRC) algorithm. We demonstrated how the HRC algorithm offers the sensitivity of identifying a manageable number of gene pairs based on automatic pattern searching from an original large brain image collection. This tool enables us to quickly identify genes of similar in situ hybridization patterns in a semi-automatic fashion and consequently allows us to discover several gene expression patterns with expression neighborhoods containing genes of similar functional categories.ConclusionGiven a query brain image, HRC is a fully automated algorithm that is able to quickly mine vast number of brain images and identify a manageable subset of genes that potentially shares similar spatial co-distribution patterns for further visual inspection. A three-dimensional in situ hybridization pattern, if statistically significant, could serve as a fingerprint of certain gene function. Databases such as ABA provide valuable data source for characterizing brain-related gene functions when armed with powerful image querying tools like HRC.

Dataset Information

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain.

Publications

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain.

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