Deciphering Brain Complexity Using Single-cell Sequencing.
IF: 6.409
Cited by: 26


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.


Spatial Transcriptomics
Gene Expression
Brain development
Brain diseases
Cell type
Single-cell RNA-seq

MeSH terms

Big Data
Gene Expression Profiling
Genome, Human
High-Throughput Nucleotide Sequencing
Sequence Analysis, RNA
Single-Cell Analysis


Mu, Quanhua
Chen, Yiyun
Wang, Jiguang

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