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DNA-PAINT is a powerful and flexible implementation of Stochastic Reconstruction Microscopy (STORM), a super resolution technique that enables researchers to produce images with subresolution accuracy1,2. In its most rudimentary implementation, this imaging system requires two DNA strands: a fluorophore containing imager strand and a docking strand which is anchored to a substrate of interest and is complimentary to the imager strand. The strands are designed in such a manner that they spontaneously hybridize and dehybridize. In the seminal DNA-PAINT publication, it was demonstrated that the rate of detected localizations is directly related to the concentration of the imager strand and independent of the length of the hybridization3. These rates of localizations in turn determine the ‘on-time’ of a localization which is an important parameter to control in order to avoid overlaps. Currently, Picasso is the primary DNA-PAINT simulator that allows one to input custom kinetic parameters such as kon and dark time2. While important parameters to be sure, we hypothesize that these parameters can be computed from the sequences that are to be used as the imager and the docking strands when the problem is articulated in a statistical mechanical framework: What is the probability of observing the micro-state in which the imager and docking strands are hybridized? The Boltzmann distribution is a powerful tool when computing macroscale thermodynamic parameters of chemical systems from its molecular components. Certain formulations of the distribution use three parameters: the number of lattice sites and ligands denoted as Ω and L respectively, and the free energy of a microstate ΔG4. The ΔG of hybridization can be computed using the NUPACK software, while Ω and L can be set by the user5–7. In systems such as DNA-PAINT, Ω >> L as the concentration of the imager strand is dilute. The Boltzmann distribution parameterized by these three parameters can output a probability that in turn parameterizes a Monte Carlo model that simulates an observed localization of the imager strand. Our initial simulations using this sequence informed framework demonstrate that the frequency of localizations and consecutive localizations, indicated by a broad peak in the time-intensity trace diagram, is directly proportional to L when the sequences are complimentary to one another. This is consistent with expected experimental results as STORM necessitates a trace amount of the fluorescent molecule to promote sparse localizations to prevent overlap of adjacent signals. 

Information storage in synthetic DNA oligomers is attractive due to the inherent physical density, stability, and energy efficiency of nucleic acids. Information retention –during writing, storage, and retrieval processes– requires development of efficient encoding/decoding systems. Additionally, potential intrusion of artificial or organic malevolent biologically active molecular machines could potentially cause catastrophic biosecurity concerns. Here we present an improved information storage method that focuses on efficiency and biosecurity. Herein this paper, we have developed and experimentally tested an algorithm to write data in pool of DNA strands by applying a fountain code (rateless erasure code), a Reed Solomon code, and an oligomer mapping code that ensures Bio-Security. We validated our method through wet-lab experiments and wrote, stored, and fully retrieved 105,360 bits of information. We validated the biosecurity aspects of our method through in-silico experimentation using a BLAST-run to compare our generated oligomers to existing genes documented in the public databases, a Plasmidhawk software analysis to determine our oligomers could not be artificially traced to have originated from another lab, and utilized an open-source software to determine whether our oligomers could have expressed any sequences that potentially originate or empower biologically meaningful functions. 

Abstract While the archival digital memory industry approaches its physical limits, the demand is significantly increasing, therefore alternatives emerge. Recent efforts have demonstrated DNA’s enormous potential as a digital storage medium with superior information durability, capacity, and energy consumption. However, the majority of the proposed systems require on-demand de-novo DNA synthesis techniques that produce a large amount of toxic waste and therefore are not industrially scalable and environmentally friendly. Inspired by the architecture of semiconductor memory devices and recent developments in gene editing, we created a molecular digital data storage system called “DNA Mutational Overwriting Storage” (DMOS) that stores information by leveraging combinatorial, addressable, orthogonal, and independent in vitro CRISPR base-editing reactions to write data on a blank pool of greenly synthesized DNA tapes. As a proof of concept, this work illustrates writing and accurately reading of both a bitmap representation of our school’s logo and the title of this study on the DNA tapes. 

Abstract Deoxyribonucleic acid (DNA) is emerging as an alternative archival memory technology. Recent advancements in DNA synthesis and sequencing have both increased the capacity and decreased the cost of storing information in de novo synthesized DNA pools. In this survey, we review methods for translating digital data to and/or from DNA molecules. An emphasis is placed on methods which have been validated by storing and retrieving real-world data via in-vitro experiments.