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Bridging Histology and Bioinformatics-Computational Analysis of Spatially Resolved Transcriptomics
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Number of Authors: 4
2017 (English)In: Proceedings of the IEEE, ISSN 0018-9219, E-ISSN 1558-2256, Vol. 105, no 3, 530-541 p.Article in journal (Refereed) Published
Abstract [en]

It is well known that cells in tissue display a large heterogeneity in gene expression due to differences in cell lineage origin and variation in the local environment. Traditional methods that analyze gene expression from bulk RNA extracts fail to accurately describe this heterogeneity because of their intrinsic limitation in cellular and spatial resolution. Also, information on histology in the form of tissue architecture and organization is lost in the process. Recently, new transcriptome-wide analysis technologies have enabled the study of RNA molecules directly in tissue samples, thus maintaining spatial resolution and complementing histological information with molecular information important for the understanding of many biological processes and potentially relevant for the clinical management of cancer patients. These new methods generally comprise three levels of analysis. At the first level, biochemical techniques are used to generate signals that can be imaged by different means of fluorescence microscopy. At the second level, images are subject to digital image processing and analysis in order to detect and identify the aforementioned signals. At the third level, the collected data are analyzed and transformed into interpretable information by statistical methods and visualization techniques relating them to each other, to spatial distribution, and to tissue morphology. In this review, we describe state-of-the-art techniques used at all three levels of analysis. Finally, we discuss future perspective in this fast-growing field of spatially resolved transcriptomics.

Place, publisher, year, edition, pages
2017. Vol. 105, no 3, 530-541 p.
Keyword [en]
Biomedical image processing, biomedical signal analysis, computer-aided analysis, genetics, image analysis, image processing
National Category
Biological Sciences Computer and Information Science
Identifiers
URN: urn:nbn:se:su:diva-142712DOI: 10.1109/JPROC.2016.2538562ISI: 000395894900011OAI: oai:DiVA.org:su-142712DiVA: diva2:1093175
Available from: 2017-05-05 Created: 2017-05-05 Last updated: 2017-05-05Bibliographically approved

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Qian, Xiaoyan
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CiteExportLink to record
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