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Wang P, Liu W, Wang J, Liu Y, Li P, Xu P, Cui W, Zhang R, Long Q, Hu Z, Fang C, Dong J, Zhang C, Chen Y, Wang C, Liu G, Xie H, Zhang Y, Xiao M, Chen S, Jiang H, Chen Y, Yang G, Zhang S, Meng Z, Wang X, Feng G, Li X, Zhou Y. scCompass: An Integrated Multi-Species scRNA-seq Database for AI-Ready. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2025:e2500870. [PMID: 40317650 DOI: 10.1002/advs.202500870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2025] [Revised: 03/29/2025] [Indexed: 05/07/2025]
Abstract
Emerging single-cell sequencing technology has generated large amounts of data, allowing analysis of cellular dynamics and gene regulation at the single-cell resolution. Advances in artificial intelligence enhance life sciences research by delivering critical insights and optimizing data analysis processes. However, inconsistent data processing quality and standards remain to be a major challenge. Here scCompass is proposed, which provides a comprehensive resource designed to build large-scale, multi-species, and model-friendly single-cell data collection. By applying standardized data pre-processing, scCompass integrates and curates transcriptomic data from nearly 105 million single cells across 13 species. Using this extensive dataset, it is able to identify stable expression genes (SEGs) and organ-specific expression genes (OSGs) in humans and mice. Different scalable datasets are provided that can be easily adapted for AI model training and the pretrained checkpoints with state-of-the-art single-cell foundation models. In summary, scCompass is highly efficient and scalable database for AI-ready, which combined with user-friendly data sharing, visualization, and online analysis, greatly simplifies data access and exploitation for researchers in single-cell biology (http://www.bdbe.cn/kun).
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Affiliation(s)
- Pengfei Wang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Wenhao Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regenerative Medicine, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
- College of Life Science, Northeast Agricultural University, Harbin, 150030, China
| | - Jiajia Wang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
| | - Yana Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regenerative Medicine, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Pengjiang Li
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Ping Xu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Wentao Cui
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Ran Zhang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Qingqing Long
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Zhilong Hu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Chen Fang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regenerative Medicine, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Jingxi Dong
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regenerative Medicine, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Chunyang Zhang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
| | - Yan Chen
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
| | - Chengrui Wang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
| | - Guole Liu
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Hanyu Xie
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
| | - Yiyang Zhang
- CEMS, NCMIS, HCMS, MDIS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Meng Xiao
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
| | - Shubai Chen
- Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Haiping Jiang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regenerative Medicine, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Yiqiang Chen
- Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Ge Yang
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Shihua Zhang
- CEMS, NCMIS, HCMS, MDIS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Zhen Meng
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Xuezhi Wang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Guihai Feng
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regenerative Medicine, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Xin Li
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regenerative Medicine, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
| | - Yuanchun Zhou
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100864, China
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Liu Y, McDaniel JA, Chen C, Yang L, Kipcak A, Savier EL, Erisir A, Cang J, Campbell JN. Co-Conservation of Synaptic Gene Expression and Circuitry in Collicular Neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.01.23.634521. [PMID: 39896595 PMCID: PMC11785205 DOI: 10.1101/2025.01.23.634521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/04/2025]
Abstract
The superior colliculus (SC), a midbrain sensorimotor hub, is anatomically and functionally similar across vertebrates, but how its cell types have evolved is unclear. Using single-nucleus transcriptomics, we compared the SC's molecular and cellular organization in mice, tree shrews, and humans. Despite over 96 million years of evolutionary divergence, we identified ~30 consensus neuronal subtypes, including Cbln2+ neurons that form the SC-pulvinar circuit in mice and tree shrews. Synapse-related genes were among the most conserved, unlike neocortex, suggesting co-conservation of synaptic genes and circuitry. In contrast, cilia-related genes diverged significantly across species, highlighting the potential importance of the neuronal primary cilium in SC evolution. Additionally, we identified a novel inhibitory SC neuron in tree shrews and humans but not mice. Our findings reveal that the SC has evolved by conserving neuron subtypes, synaptic genes, and circuitry, while diversifying ciliary gene expression and an inhibitory neuron subtype.
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Affiliation(s)
- Yuanming Liu
- Department of Biology, Charlottesville, VA 22904, USA
| | - John A McDaniel
- Department of Psychology University of Virginia, Charlottesville, VA 22904, USA
| | - Chen Chen
- Department of Psychology University of Virginia, Charlottesville, VA 22904, USA
| | - Lu Yang
- Department of Biology, Charlottesville, VA 22904, USA
| | - Arda Kipcak
- Department of Psychology University of Virginia, Charlottesville, VA 22904, USA
| | | | - Alev Erisir
- Department of Psychology University of Virginia, Charlottesville, VA 22904, USA
| | - Jianhua Cang
- Department of Biology, Charlottesville, VA 22904, USA
- Department of Psychology University of Virginia, Charlottesville, VA 22904, USA
| | - John N Campbell
- Department of Biology, Charlottesville, VA 22904, USA
- Lead Contact
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3
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Lahaie SC, Brezner N, Murai KK. Single-cell omics and heterogeneity of neuroglial cells. HANDBOOK OF CLINICAL NEUROLOGY 2025; 209:265-275. [PMID: 40122628 DOI: 10.1016/b978-0-443-19104-6.00013-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/25/2025]
Abstract
Our bodies contain a rich diversity of cell types with unique physiologic properties. Interestingly, cells within our bodies contain the same DNA content, yet they can vary dramatically with respect to their molecular, structural, and functional properties. The need to better understand cellular complexity and diversity in biologic systems has led to a technical revolution in the field through the development of sophisticated single-cell "omic" approaches. This allows the investigation of the genome, epigenome, transcriptome, and proteome of individual cells derived from complex samples or tissues, such as nervous system tissue. These methods are allowing scientists to detect distinct cell populations and cellular states in different species (including rodent and human) and molecular transitions of cell populations across the lifespan. Recent studies have revealed that astrocytes, oligodendrocytes, and microglia exhibit greater molecular and functional heterogeneity than originally thought and innovative single-cell technologies have allowed a more comprehensive and less biased view of this cellular diversity. The chapter begins by providing a primer of single-cell transcriptomic and spatial transcriptomic approaches that have been particularly influential in uncovering single-cell diversity of neuroglial cells in the brain. It then takes a closer look at how these technologies have been pivotal in defining neuroglial cell subtypes and for determining their spatial relationships within the CNS. Then, it concludes with discussion of how the recent technical advances and discoveries have provoked new questions about the origin, organization, and functional purpose of diverse neuroglial cell subtypes.
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Affiliation(s)
- Sylvie C Lahaie
- Centre for Research in Neuroscience, Department of Neurology & Neurosurgery, Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, QC, Canada
| | - Naama Brezner
- Centre for Research in Neuroscience, Department of Neurology & Neurosurgery, Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, QC, Canada
| | - Keith K Murai
- Centre for Research in Neuroscience, Department of Neurology & Neurosurgery, Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, QC, Canada; Quantitative Life Sciences Graduate Program, McGill University, Montreal, QC, Canada.
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7
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Cheung G, Pauler FM, Koppensteiner P, Krausgruber T, Streicher C, Schrammel M, Gutmann-Özgen N, Ivec AE, Bock C, Shigemoto R, Hippenmeyer S. Multipotent progenitors instruct ontogeny of the superior colliculus. Neuron 2024; 112:230-246.e11. [PMID: 38096816 DOI: 10.1016/j.neuron.2023.11.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 10/06/2023] [Accepted: 11/10/2023] [Indexed: 01/21/2024]
Abstract
The superior colliculus (SC) in the mammalian midbrain is essential for multisensory integration and is composed of a rich diversity of excitatory and inhibitory neurons and glia. However, the developmental principles directing the generation of SC cell-type diversity are not understood. Here, we pursued systematic cell lineage tracing in silico and in vivo, preserving full spatial information, using genetic mosaic analysis with double markers (MADM)-based clonal analysis with single-cell sequencing (MADM-CloneSeq). The analysis of clonally related cell lineages revealed that radial glial progenitors (RGPs) in SC are exceptionally multipotent. Individual resident RGPs have the capacity to produce all excitatory and inhibitory SC neuron types, even at the stage of terminal division. While individual clonal units show no pre-defined cellular composition, the establishment of appropriate relative proportions of distinct neuronal types occurs in a PTEN-dependent manner. Collectively, our findings provide an inaugural framework at the single-RGP/-cell level of the mammalian SC ontogeny.
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Affiliation(s)
- Giselle Cheung
- Institute of Science and Technology Austria (ISTA), Am Campus 1, 3400 Klosterneuburg, Austria
| | - Florian M Pauler
- Institute of Science and Technology Austria (ISTA), Am Campus 1, 3400 Klosterneuburg, Austria
| | - Peter Koppensteiner
- Institute of Science and Technology Austria (ISTA), Am Campus 1, 3400 Klosterneuburg, Austria
| | - Thomas Krausgruber
- CeMM Research Center for Molecular Medicine, Austrian Academy of Sciences; 1090 Vienna, Austria; Medical University of Vienna, Institute of Artificial Intelligence, Center for Medical Data Science, 1090 Vienna, Austria
| | - Carmen Streicher
- Institute of Science and Technology Austria (ISTA), Am Campus 1, 3400 Klosterneuburg, Austria
| | - Martin Schrammel
- Institute of Science and Technology Austria (ISTA), Am Campus 1, 3400 Klosterneuburg, Austria
| | - Natalie Gutmann-Özgen
- Institute of Science and Technology Austria (ISTA), Am Campus 1, 3400 Klosterneuburg, Austria
| | - Alexis E Ivec
- Institute of Science and Technology Austria (ISTA), Am Campus 1, 3400 Klosterneuburg, Austria
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine, Austrian Academy of Sciences; 1090 Vienna, Austria; Medical University of Vienna, Institute of Artificial Intelligence, Center for Medical Data Science, 1090 Vienna, Austria
| | - Ryuichi Shigemoto
- Institute of Science and Technology Austria (ISTA), Am Campus 1, 3400 Klosterneuburg, Austria
| | - Simon Hippenmeyer
- Institute of Science and Technology Austria (ISTA), Am Campus 1, 3400 Klosterneuburg, Austria.
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