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A primordial DNA store and compute engine

Nature Nanotechnology volume 19, pages 1654–1664 (2024)Cite this article

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Abstract

Any modern information system is expected to feature a set of primordial features and functions: a substrate stably carrying data; the ability to repeatedly write, read, erase, reload and compute on specific data from that substrate; and the overall ability to execute such functions in a seamless and programmable manner. For nascent molecular information technologies, proof-of-principle realization of this set of primordial capabilities would advance the vision for their continued development. Here we present a DNA-based store and compute engine that captures these primordial capabilities. This system comprises multiple image files encoded into DNA and adsorbed onto ~50-μm-diameter, highly porous, hierarchically branched, colloidal substrate particles comprised of naturally abundant cellulose acetate. Their surface areas are over 200 cm2 mg−1 with binding capacities of over 1012 DNA oligos mg−1, 10 TB mg−1 or 104 TB cm−3. This ‘dendricolloid’ stably holds DNA files better than bare DNA with an extrapolated ability to be repeatedly lyophilized and rehydrated over 170 times compared with 60 times, respectively. Accelerated ageing studies project half-lives of ~6,000 and 2 million years at 4 °C and −18 °C, respectively. The data can also be erased and replaced, and non-destructive file access is achieved through transcribing from distinct synthetic promoters. The resultant RNA molecules can be directly read via nanopore sequencing and can also be enzymatically computed to solve simplified 3 × 3 chess and sudoku problems. Our study establishes a feasible route for utilizing the high information density and parallel computational advantages of nucleic acids.

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Fig. 1: A primordial DNA store and compute engine is enabled by adsorbing DNA onto soft dendritic colloids.
Fig. 2: DNA bound to soft dendritic colloids can be repeatedly transcribed in a microfluidic channel.
Fig. 3: Complex DNA files can be stored, lyophilized and protected from accelerated ageing and repeatedly accessed on soft dendritic colloids.
Fig. 4: Data files can be specifically erased from and reloaded onto soft dendritic colloids.
Fig. 5: RNA nanopore sequencing promotes continuous data processing and reduces skewing of strand distributions.
Fig. 6: Soft dendritic colloids support addressable in-storage computation.

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Data availability

Raw sequencing data, sequences of DNA oligos and source data for all plots are available at https://doi.org/10.5281/zenodo.12169723 (ref. 62) and https://doi.org/10.5281/zenodo.12192541 (ref. 63) and https://github.com/keung-lab/Lin-et-al-2024.git, https://github.com/dna-storage/framed/tree/sdc_nature_submission and https://github.com/dna-storage/framed/releases. All other data are available upon reasonable request. Source data are provided with this paper.

Code availability

The software algorithms we developed to perform the reported analyses are available at https://doi.org/10.5281/zenodo.12169723 (ref. 62), https://github.com/dna-storage/framed/tree/sdc_nature_submission and https://github.com/dna-storage/framed/releases under a permissive open source license with instructions for installation. We implemented code in Python using many standard open-source packages, including biopython, primer3, numpy, scipy, pandas and others. These dependences are documented in the form of a Python requirements.txt file that guides installation of additional dependent software packages.

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Acknowledgements

We thank K. J. Tomek, M. Lee and K. Matange for helpful discussions and R. M. Kelly for use of their vacuum concentrator. We thank the Biomanufacturing Training and Education Center (BTEC) at NCSU for the use of their lyophilizer device. We also thank S. Mukherjee for providing training on the use of the Zetasizer Nano ZSP for measuring the zeta-potential of the SDC–DNA samples. This work was supported by the National Science Foundation (ECCS-2027655 and CSR-1901324). K.N.L. was supported by a Department of Education Graduate Assistance in Areas of Need fellowship, P200A160061. R.E.P. was supported by T32GM133366. Some artwork was created with BioRender.

Author information

Authors and Affiliations

  1. Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA

    Kevin N. Lin, Cyrus Cao, Rachel E. Polak, Andrew S. Clark, Adriana San Miguel, Orlin D. Velev & Albert J. Keung

  2. Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA

    Kevin Volkel & James M. Tuck

  3. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Paul W. Hook & Winston Timp

  4. Genetics Program, North Carolina State University, Raleigh, NC, USA

    Rachel E. Polak, Adriana San Miguel & Albert J. Keung

  5. Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD, USA

    Winston Timp

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Contributions

K.N.L., O.D.V. and A.J.K. conceived the study. K.N.L. planned and performed the wet-lab experiments with guidance from O.D.V. and A.J.K. K.N.L. and K.V. designed the three-file oligo library for SDC. K.V. performed file encoding and decoding simulation with guidance from J.M.T. P.W.H. performed nanopore RNA sequencing with guidance from W.T. K.N.L and R.E.P. planned and performed the simulation for computation library design. C.C. fabricated SDC materials made from cellulose acetate, cellulose and agarose with guidance from O.D.V. K.N.L. and A.S.C. designed the microfluidic system with guidance from A.S.M. K.N.L. performed zeta-potential measurements. K.V. and K.N.L. processed the next-generation sequencing and nanopore sequencing data. K.N.L. and A.J.K. wrote the paper with input from all authors.

Corresponding authors

Correspondence to Orlin D. Velev or Albert J. Keung.

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Competing interests

The authors declare the following competing interests: A.J.K. and J.M.T. are cofounders of DNAli Data Technologies that has potential interest in translating and commercializing DNA-based information systems. A.J.K., K.V., J.M.T. and K.N.L. are inventors of patent WO 2020/096679 which has been licensed to DNAli Data Technologies and from which some of this work is derived. W.T. has two patents (8,748,091 and 8,394,584), licensed to Oxford Nanopore Technologies (ONT), which were used for direct RNA sequencing in this work. W.T. has received travel funds to speak at symposia organized by ONT. The other authors declare no competing interests.

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Extended data

Extended Data Table 1 Maximum binding capacity for dsDNA bound to caSDC, ceSDC, and agSDC
Full size table
Extended Data Table 2 Cost of substrate material that data is bound to
Full size table
Extended Data Table 3 Accuracy of computation for each puzzle
Full size table
Extended Data Table 4 Annotation of direct RNA sequencing by ONT and data processing
Full size table
Extended Data Table 5 Annotation of each sample shown in Extended Data Fig. 4c,d
Full size table

Extended Data Fig. 1 Adsorbing DNA onto SDC can lead to stable RNA generation.

A) Morphology of SDC with (left) and without (right) magnetic nanoparticles. Images were taken using a Nikon Ts2 Inverted microscope. All samples were imaged using the same 10× objective. Samples were imaged using the same microscope settings, and adjusted identically for quantification purposes. B) Equivalent masses of magnetic nanoparticles alone (MNP) or caSDC infused with MNP (MNP-caSDC) were incubated with DNA (dsDNA), washed, and subjected to IVT. An RNA gel indicates no RNA generated from the magnetic nanoparticles. C) Representative strand distribution density (left), skewness (middle), and kurtosis (right) plots for experimental samples of File1. These samples included direct sequencing of the File1 DNA obtained from the DNA synthesis provider (original File1), cDNA obtained after IVT of File1 DNA adsorbed to caSDC (IVT of SDC-File1), and cDNA obtained after IVT of unbound File1 DNA (IVT of File1). D) Representative strand distribution density (left), skewness (middle), and kurtosis (right) plots for experimental samples of File2. These samples included direct sequencing of the File2 DNA obtained from the DNA synthesis provider (original File2), cDNA obtained after IVT of File2 DNA adsorbed to caSDC (‘IVT of SDC-File2’), and cDNA obtained after IVT of unbound File2 DNA (IVT of File2). Plotted values represent the arithmetic mean, and error bars represent the standard deviation of three independent IVT reactions. Statistics was calculated using One-Way ANOVA with Tukey–Kramer post-hoc for panel C and D. a p = 1.77×10−4, b p = 2.92×10−3, c p = 1.67×10−4, d p = 3.01×10−2, e p = 6.12×10−1, f p = 9.51×10−4, g p = 3.76×10−3, h p = 3.18×10−2, i p = 4.28×10−2, j p = 1.54×10−5, k p = 1.63×10−2, l p = 1.29×10−2.

Source data

Extended Data Fig. 2 Complex files adsorbed on SDCs can be repeatedly accessed with robust and stable performance.

A) Skewness (left) and kurtosis (right) plots for strand distribution density of cDNA obtained after each sequential round of IVT to access File1 DNA adsorbed to caSDC. The results demonstrated a consistent strand distribution for sequential rounds of accessing File1 DNA adsorbed to the SDCs. B) Percent error for each DNA sequence position in the cDNA obtained after IVT of File1 DNA adsorbed to caSDC after each sequential round of IVT, and cDNA obtained after IVT of unbound File1 DNA (IVT of File1). The error rate was calculated by dividing the number of errors of a given type occurring at a nucleotide position by the total number of reads for that sequence (Method). C) Number of sequencing reads for strands in cDNA obtained after each sequential round of IVT to access File1 DNA adsorbed to caSDC. D) Percent error for each DNA sequence position in the cDNA obtained from lyophilized File3-SDC after IVT of the first lyophilization (1st round), and after the 3rd and 5th rounds of lyophilization, as obtained by Illumina sequencing. E) Percent error for each DNA sequence position in the cDNA obtained from IVT after 0, 24 and 48 hours of incubation of lyophilized or solubilized File3-SDC at 65 ˚C. The error rate was calculated by dividing the number of errors of a given type occurring at a nucleotide position by the total number of reads for that sequence. F) 300 ng DNA was input into identical IVT reactions either in a microfluidic system placed in an incubator at 37 ˚C or in a PCR tube placed in a PCR machine held at 37 ˚C for overnight. Plotted values represent the arithmetic mean, and error bars represent the standard deviation of three independent IVT reactions. Statistics was calculated using One-Way ANOVA with Tukey–Kramer post-hoc. Comparisons are relative to the first experimental condition in panel C. a p = 3.93×10−1, b p = 6.76×10−1, c p = 8.12×10−1, d p = 3.81×10−1, e p = 3.38×10−1, f p = 1.62×10−1, g p = 1.10×10−2, h p = 6.50×10−3, i p = 8.42×10−3.

Source data

Extended Data Fig. 3 Complex files adsorbed onto SDCs can be specifically erased, and new information can be reloaded onto SDCs.

A) Number of sequencing reads for strands in cDNA obtained from IVT of File DNA adsorbed to caSDC after processed with restriction digestion as file deletion. The values were measured by Illumina sequencing. B) Percent of unique strands of each file found in cDNA after File3 was specifically deleted from the three-file database as measured by Illumina sequencing. The deletion was executed with 1 µL or 5 µL of restriction enzyme. Values were plotted as a percentage of the total unique strands. C) The fraction of all sequencing reads for a targeted file, obtained from cDNA after IVT of File DNA adsorbed to caSDC after reloading. Annotation of each operation is listed in Extended Data Table 5. FileX->FileY indicates FileX was deleted and FileY was then loaded, with IVT of FileY performed, measured, and plotted. Values were measured by Illumina sequencing and plotted as a percentage of the total sequencing reads. D) Skewness (left) and kurtosis (right) plots for strand distribution density of cDNA obtained after IVT of unmodified File DNA (‘Unmod.’), erasing treated File DNA adsorbed to the SDCs (File1+DNaseI) and reloaded new File DNA after each operation on the SDCs. Values were measured by Illumina sequencing. ‘→’ denotes removing current File DNA on SDCs and reloading with new file information. Annotation of each operation is listed in Extended Data Table 5. E) Percent error for each DNA sequence position in the cDNA obtained after incubating File3-SDC under various buffer conditions, followed by IVT. F) Percent error for each DNA sequence position in the cDNA obtained after IVT of unmodified File DNA (Unmod.) and reloaded File DNA. The error rate was calculated by dividing the number of errors of a given type occurring at a nucleotide position by the total number of reads for that sequence. Values were measured by Illumina sequencing and plotted after normalizing to its number of sequencing reads found in untreated File DNA prior to IVT. Plotted values represent the arithmetic mean, and error bars represent the standard deviation of three independent IVT reactions.

Source data

Extended Data Fig. 4 Complex file DNA adsorbed on SDCs can be directly sequenced after IVT using Oxford nanopore sequencing.

A) Violin plots of the strand distributions for experimental samples of File2. These samples include direct sequencing of the File2 DNA obtained from the DNA synthesis provider (Original File2), RNA and cDNA obtained after IVT of File2 DNA adsorbed to caSDC (IVT of SDC-File2), RNA and cDNA obtained after unbound File2 DNA (IVT of File2). B) Violin plots of the strand distributions for experimental samples of File3. These samples include direct sequencing of the File3 DNA obtained from the DNA synthesis provider (Original File3), RNA and cDNA obtained after IVT of File3 DNA adsorbed to caSDC (IVT of SDC-File3), RNA and cDNA obtained after unbound File3 DNA (IVT of File3). C,D (C) Skewness and (D) kurtosis plots for strand distribution density of RNA obtained after IVT of unbound File DNA, File DNA adsorbed to caSDC, and of DNA obtained after direct sequencing of File DNA from synthesis provider. Plotted RNA samples were processed with Oxford nanopore sequencing (ONT) and DNA samples were processed with Illumina Sequencing (Illumina). Each plotted value represents the arithmetic mean, and error bars represent the standard deviation of three independent IVT reactions. Statistics was calculated using One-Way ANOVA with Tukey–Kramer post-hoc for panel A and B. a p = 2.71×10−3, b p = 1.56×10−10, c p = 3.58×10−4, d p = 1.59×10−2, e p = 8.14×10−1, f p = 1.52×10−10, g p = 1.50×10−10, h p = 1.55×10−10.

Source data

Extended Data Fig. 5 Implementation of addressable in-storage computation.

A) Schematic of computation rules for Puzzle1. The payload of each oligo in the DNA library is divided into nine sections, with each section representing the a specific position on the puzzle board. Each position is composed of a specific 20 nt DNA sequence (bit sequence). Combination of these oligos completes the full starting configuration for Puzzle1. In computation, short DNA oligos are used as nucleic acid operators to hybridize to specific puzzle RNA strands which contain information violates puzzle rules. This process triggers endonuclease activities and leaves behind RNA strands representing correct puzzle solutions. These remaining strands are purified and retained for downstream processes and NGS analysis. B) Schematic of correct solutions for Puzzle1, Puzzle2, and Puzzle3, as well as the intentionally incorrect solution for Puzzle3.

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Lin, K.N., Volkel, K., Cao, C. et al. A primordial DNA store and compute engine. Nat. Nanotechnol. 19, 1654–1664 (2024). https://doi.org/10.1038/s41565-024-01771-6

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