From Space to Sequence and Back Again: Iterative DNA Proximity Ligation and its Applications to DNA-Based Imaging

Abstract

We extend the concept of DNA proximity ligation from a single readout per oligonucleotide pair to multiple reversible, iterative ligations re-using the same oligonucleotide molecules. Using iterative proximity ligation (IPL), we can in principle capture multiple ligation events between each oligonucleotide and its various neighbors and thus recover a far richer knowledge about their relative positions than single, irreversible ligation events. IPL would thus act to sample and record local molecular neighborhoods. By integrating a unique DNA barcode into each participating oligonucleotide, we can catalog the individual ligation events and thus capture the positional information contained therein in a high throughput manner using next-generation DNA sequencing. We propose that by interpreting IPL sequencing results in the context of graph theory and by applying spring layout algorithms, we can recover geometric patterns of objects labeled by DNA. Using simulations, we demonstrate that we can in principle recover letter patterns photolithographed onto slide surfaces using only IPL sequencing data, illustrating how our technique maps complex spatial configurations into DNA sequences and thenusing only this sequence informationrecovers them. We complement our theoretical work with an experimental proofof-concept of iterative proximity ligation on an oligonucleotide population.

Bringing Microscopy-By-Sequencing into View

“…Boulgakov and colleagues [26] have recently proposed a DNA microscopy method based on iterative proximity ligation (IPL) (Fig. 2D).…”

“…Empty spaces that separate islands of DNA labeled molecules have to be somehow bridged if the islands are to be properly juxtaposed in the broader reconstruction of a whole sample. Furthermore, empty space can significantly distort reconstructions unless we have additional information [26]. Therefore, it is necessary to consider strategies to bridge such gaps, perhaps by complimenting DNA microscopy data with optical imaging or using a "landmark" approach where we preemptively know the locations of some labels [20].…”

“…While only two papers [20,24] explicitly tackle DNA microscopy in three dimensions and the others explore flat surface patterns, the other approaches are not necessarily confined to the plane. The layout algorithm used by Boulgakov and colleagues, for example, is already implemented in software for the three-dimensional case [26]. As their approach substantially resembles that of Schaus and colleagues [21] in the kind of spatial information obtained and in the use of force-directed layout, both are expected to be computationally tractable in three-dimensions.…”

A computational framework for DNA sequencing microscopy

“…At the time of publication, we are aware of concurrent works whose contributions are complementary to ours on development of DNA-sequencing-based microscopy (25,26). The former work experimentally demonstrates DNA microscopy with images of mRNA in cells using locally confined cDNA amplifications and polymerase extension-based fusion of barcodes to connect spatial patches.…”

A DNA nanoscope that identifies and precisely localizes over a hundred unique molecular features with nanometer accuracy

“…However, they produce monochromatic images and can only discriminate molecular targets when they are resolved to near atomic precision, unachievable for many samples. At the other end of the spectrum, biochemical techniques like Hi-C can discriminate millions of DNA targets on chromosomes by sequencing them, however the contact densities currently obtainable from single nuclei Hi-C experiments preclude synthesizing this information into a structural model of the chromosome, while geometric models synthesized using data from ensemble Hi-C experiments have at best a local resolution of 5 kilobase pairs 6,7 . This fledgling, 'imaging-by-sequencing' field [8][9][10][11][12] has had two main experimental results. Our previous 'auto-cycling proximity recording' (APR) 8 effort demonstrated seven-point reconstructions (spaced ~30 nm apart) from simple, binary proximity data.…”