Exploring tissue architecture using spatial transcriptomics

By Anjali Rao, Dalia Barkley, Gustavo S. França, Itai Yanai. Nature, 2021

1,390 citations recorded by the source corpus.

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions—including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization. This review describes the state of spatial transcriptomics technologies and analysis tools that are being used to generate biological insights in diverse areas of biology.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bd5ff32f92840ed92e4f40eef7c3fa5b99131f83

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Maksim V. Plikus
2. Qing Nie
3. Min Wu
4. Huan Fu
5. Sohrab P. Shah
6. Kok Siong Ang
7. Zixuan Cang
8. Jinmiao Chen

Citations in the corpus

• Harnessing multimodal data integration to advance precision oncology (2021, methodology): “Deeper assessment of the tumour microenvironment is also possible through characterization of spatial niches derived from multiplexed imaging as well as spatial transcriptomics methods, which can be used to develop biomarkers for precision oncology 41,42 .”
• Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST (2023, positive): “The DCIS/LCIS regions (1, 2) also showed high levels of adjacent normal cell mapping.”
• Screening cell–cell communication in spatial transcriptomics via collective optimal transport (2023, background): “21).”
• Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball patterned arrays (2021, neutral): “Several remarkable methodologies have been developed recently that allow systematic spatially resolved transcriptomic profiling of tissue sections (Rao et al., 2021).”