Exploring tissue architecture using spatial transcriptomics.
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TL;DR: Spatial transcriptomics can also be used for hypothesis testing using experimental designs that compare time points or conditions, including genetic or environmental perturbations as mentioned in this paper, and is naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.
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Abstract: 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.
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Citations
Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays
TL;DR: Pijuan-Sala et al. as discussed by the authors combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq).
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Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer
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TL;DR: This article used GeoMx spatial profiling and single-cell RNA sequencing to identify cell-associated bacteria and the host cells with which they interact, as well as uncovering alterations in transcriptional pathways that are involved in inflammation, metastasis, cell dormancy and DNA repair.
Macrophage diversity in cancer revisited in the era of single-cell omics.
TL;DR: Yang et al. as mentioned in this paper proposed a new nomenclature to identify TAM subsets, and proposed a consensus model of TAM diversity and present avenues for future research, which can summarize the heterogenous nature of TAMs.
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Harnessing multimodal data integration to advance precision oncology.
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Learning the parts of objects by non-negative matrix factorization
TL;DR: An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.
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