RT Journal Article
SR Electronic
T1 A computational framework for DNA sequencing microscopy
JF Proceedings of the National Academy of Sciences
JO Proc Natl Acad Sci USA
FD National Academy of Sciences
SP 19282
OP 19287
DO 10.1073/pnas.1821178116
VO 116
IS 39
A1 Hoffecker, Ian T.
A1 Yang, Yunshi
A1 Bernardinelli, Giulio
A1 Orponen, Pekka
A1 Högberg, Björn
YR 2019
UL http://www.pnas.org/content/116/39/19282.abstract
AB Traditional microscopy is based on the propagation of interactions between light and small-scale objects up to larger scales. Such information may be encoded in DNA and transmitted with next-gen sequencing to be later reconstructed and visualized computationally. We provide a mathematical framework and computational proof of concept for a form of DNA-sequencing–based microscopy that may be used to construct whole images without the use of optics. Such an approach can be automated in a parallel and multiplexable way that current optical and scanning-based techniques are unable to achieve.We 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.