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Recent advances in spatially resolved transcriptomics: challenges and opportunities
Jongwon Lee 1 (Research worker), Minsu Yoo1 (Research worker), Jungmin Choi 1,* (Professor)
1Department of Biomedical Sciences, Korea University College of Medicine, Seoul 02841, Republic of Korea,
2Brain Korea 21 Plus Project for Biomedical Science, Korea University College of Medicine, Seoul, Korea,
3Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
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 レm 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.
Abstract, Accepted Manuscript [Submitted on January 19, 2022, Accepted on February 11, 2022]
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