Deciphering Brain Complexity Using Single-cell Sequencing
The human brain contains billions of highly differentiated and interconnected cells that form intricate neural networks and collectively control the physical activities and high-level cognitive functions, such as memory, decision-making, and social behavior. Big data is required to decipher the comp...
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| Format: | Article |
| Language: | English |
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Elsevier
2019-08-01
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| Series: | Genomics, Proteomics & Bioinformatics |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1672022919301275 |
| _version_ | 1797367992315543552 |
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| author | Quanhua Mu Yiyun Chen Jiguang Wang |
| author_facet | Quanhua Mu Yiyun Chen Jiguang Wang |
| author_sort | Quanhua Mu |
| collection | DOAJ |
| description | The human brain contains billions of highly differentiated and interconnected cells that form intricate neural networks and collectively control the physical activities and high-level cognitive functions, such as memory, decision-making, and social behavior. Big data is required to decipher the complexity of cell types, as well as connectivity and functions of the brain. The newly developed single-cell sequencing technology, which provides a comprehensive landscape of brain cell type diversity by profiling the transcriptome, genome, and/or epigenome of individual cells, has contributed substantially to revealing the complexity and dynamics of the brain and providing new insights into brain development and brain-related disorders. In this review, we first introduce the progresses in both experimental and computational methods of single-cell sequencing technology. Applications of single-cell sequencing-based technologies in brain research, including cell type classification, brain development, and brain disease mechanisms, are then elucidated by representative studies. Lastly, we provided our perspectives into the challenges and future developments in the field of single-cell sequencing. In summary, this mini review aims to provide an overview of how big data generated from single-cell sequencing have empowered the advancements in neuroscience and shed light on the complex problems in understanding brain functions and diseases. Keywords: Neuroscience, Single-cell RNA-seq, Cell type, Brain development, Brain diseases |
| first_indexed | 2024-03-08T17:26:06Z |
| format | Article |
| id | doaj.art-e13b42ef977b4a9b99d282d745b1747e |
| institution | Directory Open Access Journal |
| issn | 1672-0229 |
| language | English |
| last_indexed | 2024-03-08T17:26:06Z |
| publishDate | 2019-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Genomics, Proteomics & Bioinformatics |
| spelling | doaj.art-e13b42ef977b4a9b99d282d745b1747e2024-01-02T19:41:07ZengElsevierGenomics, Proteomics & Bioinformatics1672-02292019-08-01174344366Deciphering Brain Complexity Using Single-cell SequencingQuanhua Mu0Yiyun Chen1Jiguang Wang2Department of Chemical and Biological Engineering, Division of Life Science, Center for Systems Biology and Human Health and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong Special Administrative Region, ChinaDepartment of Chemical and Biological Engineering, Division of Life Science, Center for Systems Biology and Human Health and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong Special Administrative Region, ChinaCorresponding author.; Department of Chemical and Biological Engineering, Division of Life Science, Center for Systems Biology and Human Health and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong Special Administrative Region, ChinaThe human brain contains billions of highly differentiated and interconnected cells that form intricate neural networks and collectively control the physical activities and high-level cognitive functions, such as memory, decision-making, and social behavior. Big data is required to decipher the complexity of cell types, as well as connectivity and functions of the brain. The newly developed single-cell sequencing technology, which provides a comprehensive landscape of brain cell type diversity by profiling the transcriptome, genome, and/or epigenome of individual cells, has contributed substantially to revealing the complexity and dynamics of the brain and providing new insights into brain development and brain-related disorders. In this review, we first introduce the progresses in both experimental and computational methods of single-cell sequencing technology. Applications of single-cell sequencing-based technologies in brain research, including cell type classification, brain development, and brain disease mechanisms, are then elucidated by representative studies. Lastly, we provided our perspectives into the challenges and future developments in the field of single-cell sequencing. In summary, this mini review aims to provide an overview of how big data generated from single-cell sequencing have empowered the advancements in neuroscience and shed light on the complex problems in understanding brain functions and diseases. Keywords: Neuroscience, Single-cell RNA-seq, Cell type, Brain development, Brain diseaseshttp://www.sciencedirect.com/science/article/pii/S1672022919301275 |
| spellingShingle | Quanhua Mu Yiyun Chen Jiguang Wang Deciphering Brain Complexity Using Single-cell Sequencing Genomics, Proteomics & Bioinformatics |
| title | Deciphering Brain Complexity Using Single-cell Sequencing |
| title_full | Deciphering Brain Complexity Using Single-cell Sequencing |
| title_fullStr | Deciphering Brain Complexity Using Single-cell Sequencing |
| title_full_unstemmed | Deciphering Brain Complexity Using Single-cell Sequencing |
| title_short | Deciphering Brain Complexity Using Single-cell Sequencing |
| title_sort | deciphering brain complexity using single cell sequencing |
| url | http://www.sciencedirect.com/science/article/pii/S1672022919301275 |
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