생화학분자생물학회

	Name: Jungmin Choi ( jungminchoi@korea.ac.kr )
	2019-present	Assistant Professor, Department of Biomedical Sciences, Korea University College of Medicine, Seoul, South Korea
	2018-2019	Research Associate, Laboratory of Human Genetics and Genomics, The Rockefeller University, New York, NY, USA
	2013-2018	Postdoctoral Associate, Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
	2006-2012	Ph.D., Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA

Recent advances in spatially resolved transcriptomics:
challenges and opportunities

Single-cell RNA sequencing (scRNA-seq) has greatly advanced our understanding of cellular heterogeneity by profiling individual cell transcriptomes. However, cell dissociation from the tissue structure causes a loss of spatial information, which hinders the identification of intercellular communication networks and global transcriptional patterns present in the tissue architecture. To overcome this limitation, novel transcriptomic platforms that preserve spatial information have been actively developed. Significant achievements in imaging technologies have enabled in situ targeted transcriptomic profiling in single cells at single molecule resolution. In addition, technologies based on mRNA capture followed by sequencing have made possible profiling of the genome-wide transcriptome at the 55-100 μm resolution. Unfortunately, neither imaging-based technology nor capture based method elucidates a complete picture of the spatial transcriptome in a tissue. Therefore, addressing specific biological questions requires balancing experimental throughput and spatial resolution, mandating the efforts to develop computational algorithms that are pivotal to circumvent technology-specific limitations. In this review, we focus on the current state-of-the-art spatially resolved transcriptomic technologies, describe their applications in a variety of biological domains, and explore recent discoveries demonstrating their enormous potential in biomedical research. We further highlight novel integrative computational methodologies with other data modalities that provide a framework to derive biological insight into heterogeneous and complex tissue organization.


BMB Rep. 2022 Feb 16;5562. Online ahead of print.
https://pubmed.ncbi.nlm.nih.gov/35168703/