Recent advances in spatially resolved transcriptomics: challenges and opportunities
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Single-cell RNA sequencing (scRNA-seq) has greatly advancedour understanding of cellular heterogeneity by profiling individualcell transcriptomes. However, cell dissociation from the tissuestructure causes a loss of spatial information, which hindersthe identification of intercellular communication networks andglobal transcriptional patterns present in the tissue architecture. To overcome this limitation, novel transcriptomic platforms thatpreserve spatial information have been actively developed. Significantachievements in imaging technologies have enabled insitu targeted transcriptomic profiling in single cells at singlemoleculeresolution. In addition, technologies based on mRNAcapture followed by sequencing have made possible profilingof the genome-wide transcriptome at the 55-100 μm resolution. Unfortunately, neither imaging-based technology nor capturebasedmethod elucidates a complete picture of the spatial transcriptomein a tissue. Therefore, addressing specific biologicalquestions requires balancing experimental throughput and spatialresolution, mandating the efforts to develop computational algorithmsthat are pivotal to circumvent technology-specific limitations. In this review, we focus on the current state-of-the-artspatially resolved transcriptomic technologies, describe their applicationsin a variety of biological domains, and explore recentdiscoveries demonstrating their enormous potential in biomedicalresearch. We further highlight novel integrative computationalmethodologies with other data modalities that provide aframework to derive biological insight into heterogeneous andcomplex tissue organization.
Abstract
Single-cell RNA sequencing (scRNA-seq) has greatly advancedour understanding of cellular heterogeneity by profiling individualcell transcriptomes. However, cell dissociation from the tissuestructure causes a loss of spatial information, which hindersthe identification of intercellular communication networks andglobal transcriptional patterns present in the tissue architecture. To overcome this limitation, novel transcriptomic platforms thatpreserve spatial information have been actively developed. Significantachievements in imaging technologies have enabled insitu targeted transcriptomic profiling in single cells at singlemoleculeresolution. In addition, technologies based on mRNAcapture followed by sequencing have made possible profilingof the genome-wide transcriptome at the 55-100 μm resolution. Unfortunately, neither imaging-based technology nor capturebasedmethod elucidates a complete picture of the spatial transcriptomein a tissue. Therefore, addressing specific biologicalquestions requires balancing experimental throughput and spatialresolution, mandating the efforts to develop computational algorithmsthat are pivotal to circumvent technology-specific limitations. In this review, we focus on the current state-of-the-artspatially resolved transcriptomic technologies, describe their applicationsin a variety of biological domains, and explore recentdiscoveries demonstrating their enormous potential in biomedicalresearch. We further highlight novel integrative computationalmethodologies with other data modalities that provide aframework to derive biological insight into heterogeneous andcomplex tissue organization.
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