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Xu B, Ma D, Abruzzi K, Braun R. Detecting Rhythmic Gene Expression in Single-cell Transcriptomics. J Biol Rhythms 2024:7487304241273182. [PMID: 39377613 DOI: 10.1177/07487304241273182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/09/2024]
Abstract
An autonomous, environmentally synchronizable circadian rhythm is a ubiquitous feature of life on Earth. In multicellular organisms, this rhythm is generated by a transcription-translation feedback loop present in nearly every cell that drives daily expression of thousands of genes in a tissue-dependent manner. Identifying the genes that are under circadian control can elucidate the mechanisms by which physiological processes are coordinated in multicellular organisms. Today, transcriptomic profiling at the single-cell level provides an unprecedented opportunity to understand the function of cell-level clocks. However, while many cycling detection algorithms have been developed to identify genes under circadian control in bulk transcriptomic data, it is not known how best to adapt these algorithms to single-cell RNA seq data. Here, we benchmark commonly used circadian detection methods on their reliability and efficiency when applied to single-cell RNA seq data. Our results provide guidance on adapting existing cycling detection methods to the single-cell domain and elucidate opportunities for more robust and efficient rhythm detection in single-cell data. We also propose a subsampling procedure combined with harmonic regression as an efficient strategy to detect circadian genes in the single-cell setting.
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Affiliation(s)
- Bingxian Xu
- Department of Molecular Biosciences, Northwestern University, Evanston, Illinois, USA
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, Illinois, USA
| | - Dingbang Ma
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- Shanghai Key Laboratory of Aging Studies, Shanghai, China
| | - Katharine Abruzzi
- HHMI, Brandeis University, Waltham, Massachusetts, USA
- Department of Biology, Brandeis University, Waltham, Massachusetts, USA
| | - Rosemary Braun
- Department of Molecular Biosciences, Northwestern University, Evanston, Illinois, USA
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, Illinois, USA
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, USA
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois, USA
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Xu B, Ma D, Abruzzi K, Braun R. Detecting Rhythmic Gene Expression in Single Cell Transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.07.570691. [PMID: 38105950 PMCID: PMC10723455 DOI: 10.1101/2023.12.07.570691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
An autonomous, environmentally-synchronizable circadian rhythm is a ubiquitous feature of life on Earth. In multicellular organisms, this rhythm is generated by a transcription-translation feedback loop present in nearly every cell that drives daily expression of thousands of genes in a tissue-dependent manner. Identifying the genes that are under circadian control can elucidate the mechanisms by which physiological processes are coordinated in multicellular organisms. Today, transcriptomic profiling at the single-cell level provides an unprecedented opportunity to understand the function of cell-level clocks. However, while many cycling detection algorithms have been developed to identify genes under circadian control in bulk transcriptomic data, it is not known how best to adapt these algorithms to single-cell RNAseq data. Here, we benchmark commonly used circadian detection methods on their reliability and efficiency when applied to single cell RNAseq data. Our results provide guidance on adapting existing cycling detection methods to the single-cell domain, and elucidate opportunities for more robust and efficient rhythm detection in single-cell data. We also propose a subsampling procedure combined with harmonic regression as an efficient strategy to detect circadian genes in the single-cell setting.
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Affiliation(s)
- Bingxian Xu
- Department of Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL 60208, USA
| | - Dingbang Ma
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
- Shanghai Key Laboratory of Aging Studies, Shanghai 201210, China
| | - Katherine Abruzzi
- HHMI, Brandeis University, Waltham, MA 02453, USA
- Department of Biology, Brandeis University, Waltham, MA 02453
| | - Rosemary Braun
- Department of Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL 60208, USA
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL 60208, USA
- Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208, USA
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Moškon M, Režen T, Juvančič M, Verovšek Š. Integrative Analysis of Rhythmicity: From Biology to Urban Environments and Sustainability. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:764. [PMID: 36613088 PMCID: PMC9819461 DOI: 10.3390/ijerph20010764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/27/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
From biological to socio-technical systems, rhythmic processes are pervasive in our environment. However, methods for their comprehensive analysis are prevalent only in specific fields that limit the transfer of knowledge across scientific disciplines. This hinders interdisciplinary research and integrative analyses of rhythms across different domains and datasets. In this paper, we review recent developments in cross-disciplinary rhythmicity research, with a focus on the importance of rhythmic analyses in urban planning and biomedical research. Furthermore, we describe the current state of the art of (integrative) computational methods for the investigation of rhythmic data. Finally, we discuss the further potential and propose necessary future developments for cross-disciplinary rhythmicity analysis to foster integration of heterogeneous datasets across different domains, as well as guide data-driven decision making beyond the boundaries of traditional intradisciplinary research, especially in the context of sustainable and healthy cities.
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Affiliation(s)
- Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia
| | - Tadeja Režen
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
| | - Matevž Juvančič
- Faculty of Architecture, University of Ljubljana, 1000 Ljubljana, Slovenia
| | - Špela Verovšek
- Faculty of Architecture, University of Ljubljana, 1000 Ljubljana, Slovenia
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