DNA Microscopy: Optics-free Spatio-genetic Imaging by a Stand-Alone Chemical Reaction.

Experimental assay for DNA microscopy to encode relative positions of molecules in cells To demonstrate DNA microscopy, we aimed to image transcripts belonging to a mixed population of two co-cultured human cell lines, GFP-expressing MDA-MB-231 cells and RFP-expressing BT-549 cells. As an initial proof of concept we aimed to recover images that appear cell-like and where GFP and RFP transcripts are positioned in mutually-exclusive cells, whereas GAPDH and ACTB, expressed in both cell lines, are ubiquitous. In the first step of the experiment, we tag with Unique Molecular Identifiers (UMIs) cDNA synthesized in situ . We designed reaction chambers to both grow cells and perform all reactions ( Figure S1A – C , STAR Methods ). We cultured the cells, and, following fixation and permeabilization, synthesized cDNA by reverse transcription from GFP, RFP, GAPDH, and ACTB gene transcripts ( Tables S1 – S2 ), with primers tagged with 29nt long UMIs ( Figure 1A , Figure S1D ). Notably, we designed the reaction to distinguish two types of UMI-tagged cDNA molecules: “beacons”, synthesized from ACTB (chosen as a universally expressed gene whose sequence would not be analyzed in later stages), and “targets” (everything else). We achieved this distinction between beacon and target amplicons by the artificial sequence-adapters assigned to the primers annealing to each. In the second step of the experiment, we allow beacon-cDNA and target-cDNA molecules, along with the UMIs that tag them, to amplify, diffuse, and concatenate in situ in a manner that generates a new UEI distinct for each concatenation event ( Figure 1B and S1D ) through overlap-extension PCR ( Turchaninova et al., 2013 ). By design, target amplicon-products will only concatenate to beacon amplicon-products, thereby preventing self-reaction. The middle of each overlap-extension primer includes 10 randomized nucleotides, such that each new concatenation event generates a new 20-nucleotide UEI. Paired-end sequencing of the final concatenated products generates reads each containing a beacon UMI, a target UMI, and a UEI associating them ( Figure 1C ). The key to DNA microscopy is that because UEI formation is a second order reaction involving two UMI-tagged PCR amplicons, UEI counts are driven by the co-localization of UMI concentrations, and thus contain information on the proximity between the physical points at which each UMI began to amplify ( Figure 1D ). In particular, as UMI-tagged cDNA amplifies and diffuses in the form of clouds of clonal sequences that overlap to varying extents, the degree of overlap ( Figure 1D , circle intersection) – and thus the probability of concatenation and UEI formation – depends on the proximity of the original (un-amplified) cDNA molecules ( Figure 1D , small dark circles). UMI-diffusion clouds with greater overlap generate more UEIs/concatemers, whereas those clouds with less overlap generate fewer UEIs/concatemers. Although individual diffusion clouds may differ in form, their collective statistical properties will nevertheless allow for original UMI coordinates to be inferred by consensus, given the constraint that positions must occupy the low (two- or three-) dimensionality of physical space. To obtain reliable estimates of UEIs between every pair of UMIs, we must address sources of noise, such as sequencing error. We cluster beacon-UMIs, target-UMIs, and UEIs by separately identifying “peaks” in read-abundances using a log-linear time clustering algorithm ( STAR Methods , Figure S2A ) in a manner analogous to watershed image segmentation, but in the space of sequences. For target UMIs, this allows us to aggregate biological gene sequences originating from single target molecules and achieve low error rates (0.1%−0.3%/bp across ~100 bp) by taking a consensus of the associated reads ( Figure S2B ). We then assign each identified UEI a single consensus beacon-UMI/target-UMI pair based on read-number plurality, and prune the data (by eliminating UMIs associating with only one UEI) to form a sparse matrix whose elements contain integer counts of UEIs pairing each beacon-UMI (matrix rows) and each target-UMI (matrix columns) ( Figure 1E , STAR Methods ). The resulting UEI matrices, containing on the order of 10 5 −10 6 total UMIs among which we estimate