A computational framework for DNA sequencing microscopy

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorHoffecker, Ian T.en_US
dc.contributor.authorYang, Yunshien_US
dc.contributor.authorBernardinelli, Giulioen_US
dc.contributor.authorOrponen, Pekkaen_US
dc.contributor.authorHögberg, Björnen_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.groupauthorProfessorship Orponen P.en
dc.contributor.organizationKarolinska Instituteten_US
dc.date.accessioned2019-09-20T11:16:44Z
dc.date.available2019-09-20T11:16:44Z
dc.date.embargoinfo:eu-repo/date/embargoEnd/2020-03-04en_US
dc.date.issued2019-09-04en_US
dc.description.abstractWe describe a method whereby microscale spatial information such as the relative positions of biomolecules on a surface can be transferred to a sequence-based format and reconstructed into images without conventional optics. Barcoded DNA “polymerase colony” (polony) amplification techniques enable one to distinguish specific locations of a surface by their sequence. Image formation is based on pairwise fusion of uniquely tagged and spatially adjacent polonies. The network of polonies connected by shared borders forms a graph whose topology can be reconstructed from pairs of barcodes fused during a polony cross-linking phase, the sequences of which are determined by recovery from the surface and next-generation (next-gen) sequencing. We developed a mathematical and computational framework for this principle called polony adjacency reconstruction for spatial inference and topology and show that Euclidean spatial data may be stored and transmitted in the form of graph topology. Images are formed by transferring molecular information from a surface of interest, which we demonstrated in silico by reconstructing images formed from stochastic transfer of hypothetical molecular markers. The theory developed here could serve as a basis for an automated, multiplexable, and potentially superresolution imaging method based purely on molecular information.en
dc.description.versionPeer revieweden
dc.format.extent6
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationHoffecker, I T, Yang, Y, Bernardinelli, G, Orponen, P & Högberg, B 2019, ' A computational framework for DNA sequencing microscopy ', Proceedings of the National Academy of Sciences, vol. 116, no. 39, 1821178116, pp. 19282-19287 . https://doi.org/10.1073/pnas.1821178116en
dc.identifier.doi10.1073/pnas.1821178116en_US
dc.identifier.issn0027-8424
dc.identifier.issn1091-6490
dc.identifier.otherPURE UUID: e1498561-c820-4537-a3f5-d3df771c6de5en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/e1498561-c820-4537-a3f5-d3df771c6de5en_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/36646205/dnascopy_2019.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/40355
dc.identifier.urnURN:NBN:fi:aalto-201909205380
dc.language.isoenen
dc.publisherNATL ACAD SCIENCES
dc.relation.ispartofseriesProceedings of the National Academy of Sciencesen
dc.relation.ispartofseriesarticlenumber 1821178116en
dc.rightsopenAccessen
dc.subject.keywordnext-gen sequencingen_US
dc.subject.keywordDNA microscopyen_US
dc.subject.keywordpoloniesen_US
dc.subject.keywordDNA computingen_US
dc.subject.keywordgraph theoryen_US
dc.titleA computational framework for DNA sequencing microscopyen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
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