Spatially resolved transcriptomics encompasses a growing number of methods developed to enable gene expression profiling of individual cells within a tissue. Different technologies are available and they vary with respect to: the method used to define regions of interest, the method used to assess gene expression, and resolution. Since techniques based on next-generation sequencing are the most prevalent, and provide single-cell resolution, many bioinformatics tools for spatially resolved data are shared with single-cell RNA-seq. The analysis pipelines diverge at the level of quantification matrix, downstream of which spatial techniques require specific tools to answer key biological questions. Those questions include: (i) cell type classification; (ii) detection of genes with specific spatial distribution; (iii) identification of novel tissue regions based on gene expression patterns; (iv) cell–cell interactions. On the other hand, analysis of spatially resolved data is burdened by several specific challenges. Defining regions of interest, e.g. neoplastic tissue, often calls for manual annotation of images, which then poses a bottleneck in the pipeline. Another specific issue is the third spatial dimension and the need to expand the analysis beyond a single slice. Despite the problems, it can be predicted that the popularity of spatial techniques will keep growing until they replace single-cell assays (which will remain limited to specific cases, like blood). As soon as the computational protocol reach the maturity (e.g. bulk RNA-seq), one can foresee the expansion of spatial techniques beyond basic or translational research, even into routine medical diagnostics.
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November 2021
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This special issue of Emerging Topics in Life Sciences (volume 5, issue 5) brings together a collection of perspectives about innovative techniques and ideas from scientists working in the life sciences industry. The cover image is an illustration featured in the piece discussing by ImmTOR™ by Brunn and Kishimoto.
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August 09 2021
Bioinformatics approach to spatially resolved transcriptomics
Ivan Krešimir Lukić
Partek Inc., St. Louis, MO 63005, U.S.A.
Correspondence: Ivan K. Lukic (ilukic@partek.com)
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Publisher: Portland Press Ltd
Received:
April 26 2021
Revision Received:
July 20 2021
Accepted:
July 20 2021
Online ISSN: 2397-8562
Print ISSN: 2397-8554
© 2021 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society and the Royal Society of Biology
2021
Emerg Top Life Sci (2021) 5 (5): 669–674.
Article history
Received:
April 26 2021
Revision Received:
July 20 2021
Accepted:
July 20 2021
Citation
Ivan Krešimir Lukić; Bioinformatics approach to spatially resolved transcriptomics. Emerg Top Life Sci 12 November 2021; 5 (5): 669–674. doi: https://doi.org/10.1042/ETLS20210131
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