DNA barcode (nanopore)
A nanopore DNA barcode is a designed piece of DNA that carries a pattern of bumps along its length. When the molecule is driven through a nanopore, each bump makes an extra drop in the ionic current, and the pattern of drops reads as a binary number. In 2016 N. A. W. Bell and Ulrich Keyser at the University of Cambridge built a library of such carriers in which each bit is a group of hairpins, present or absent, and reported that a three-bit barcode could be assigned with 94% accuracy, and that four different antibodies of the same isotype could be detected at once at nanomolar concentrations.[1] The recordings became the best-known public benchmark for solid-state nanopore signals and the test bed of QuipuNet and of the machine-learning experiments of Molecular Reality Corporation.
Design
The carrier follows the method of DNA origami, in which a long single strand is folded by short staple strands. Rothemund's original used a scaffold of about 7 kilobases and more than 200 staples.[2] The Cambridge carriers use the 7,249-base genome of the phage M13mp18, cut with two restriction enzymes to a linear strand of 7,228 bases and hybridized with 190 oligonucleotides, which gives a double helix 7.2 kilobase pairs long, about 2.5 µm.[3] Where an oligonucleotide is replaced by one that folds back on itself into a dumbbell-shaped hairpin, the double helix has a bump. A bit is a group of such hairpins at a set position, present or absent, and three bits make eight barcodes, written 000 to 111.[1]
The barcode occupies the first half of the carrier. In the second half is a site that binds a target, such as biotin for the protein streptavidin or digoxigenin for an antibody, and the barcode tells which site it is.[4] A protein bound at the site shows as one more drop in the current. A mixture of carriers with different barcodes can then be read in one pore, and each carrier reports its own target. The authors of QuipuNet likened the extra drops to "the knots on a string used in the Inca Quipu system", and a quipu gave the network its name.
Reading
The group makes its pores by pulling quartz glass capillaries, 0.5 mm across outside and 0.2 mm inside, to a tip with a laser puller. The tips measured 15 ± 3 nm across in the work of 2015, and the experiments ran at 600 mV in 4 M lithium chloride, which slows DNA down,[5] with the current filtered at 49.9 kHz and sampled at 250 kHz.[3][6] The pores are glass; see Glass nanopipette. A full-length carrier leaves a charge deficit of about 124 fC, and shorter fragments, which about a fifth of the carriers are, leave less.[3]
An event is hard to read. The authors of QuipuNet list the common causes of error as varying peak magnitude, noise, fluctuating velocities, overlapping peaks, DNA knots and folded molecules.[4] The algorithm of the first paper had twelve steps and more than a dozen hand-tuned parameters, and it could use about a fifth of the events. The neural network of 2018 raised the number of usable events by a factor of five.[4] Its training set of 52,525 events and test set of 3,464 events from independent experiments were not evenly spread among the barcodes.
| Barcode | Training events | Test events |
|---|---|---|
| 000 | 5,593 | 253 |
| 001 | 8,155 | 502 |
| 010 | 2,319 | 101 |
| 011 | 15,178 | 827 |
| 100 | 876 | 83 |
| 101 | 7,251 | 427 |
| 110 | 6,473 | 606 |
| 111 | 6,680 | 665 |
The commonest barcode has seventeen times as many training events as the rarest.
Other uses
The Cambridge group used the carriers to measure protein concentrations in the nanomolar range, to map where short sequence motifs lie along DNA (resolving sites about 200 base pairs apart in a conical quartz pore), and to store data as a changeable pattern on a double helix that is read pore by pore.[6][7][8] Another laboratory grafted aptamers, which recognize proteins, along the carrier and screened several proteins at once in human serum.[9]
At Molecular Reality
The company uses the data as its public test bed. In experiments run by its AI lead, Jade O'Neill, and published in its public repository, a Conformer, a network from speech recognition, scored 85.4% against 81.0% for a retrained QuipuNet network on a test that held out whole experiments, and the README notes that "both models showed a pronounced failure mode on barcode 101".[10] The 81.0% is the company's own run on that stricter split. The 94% and 94.6% figures were obtained by the Cambridge authors on their own test sets. The company's viewer shows 84,156 events in eight classes.[11] Its 2022 overview calls labels and barcodes "bridging strategies" on the way to sensing without labels, the goal that the Holey Grail names.[12]
See also
References
- ^ a b Bell, N. A. W.; Keyser, U. F. (2016). "Digitally encoded DNA nanostructures for multiplexed, single-molecule protein sensing with nanopores". Nature Nanotechnology 11 (7): 645-651. doi:10.1038/nnano.2016.50.
- ^ Rothemund, P. W. K. (2006). "Folding DNA to create nanoscale shapes and patterns". Nature 440 (7082): 297-302. doi:10.1038/nature04586.
- ^ a b c Bell, N. A. W.; Keyser, U. F. (2015). "Specific protein detection using designed DNA carriers and nanopores". Journal of the American Chemical Society 137 (5): 2035-2041. doi:10.1021/ja512521w.
- ^ a b c d Misiunas, K.; Ermann, N.; Keyser, U. F. (2018). "QuipuNet: convolutional neural network for single-molecule nanopore sensing". Nano Letters 18 (6): 4040-4045. doi:10.1021/acs.nanolett.8b01709.
- ^ Kowalczyk, S. W.; Wells, D. B.; Aksimentiev, A.; Dekker, C. (2012). "Slowing down DNA translocation through a nanopore in lithium chloride". Nano Letters 12 (2): 1038-1044. doi:10.1021/nl204273h.
- ^ a b Kong, J.; Bell, N. A. W.; Keyser, U. F. (2016). "Quantifying nanomolar protein concentrations using designed DNA carriers and solid-state nanopores". Nano Letters 16 (6): 3557-3562. doi:10.1021/acs.nanolett.6b00627.
- ^ Chen, K.; et al. (2017). "Ionic current-based mapping of short sequence motifs in single DNA molecules using solid-state nanopores". Nano Letters 17 (8): 5199-5205. doi:10.1021/acs.nanolett.7b01009.
- ^ Chen, K.; Zhu, J.; Bošković, F.; Keyser, U. F. (2020). "Nanopore-based DNA hard drives for rewritable and secure data storage". Nano Letters 20 (5): 3754-3760. doi:10.1021/acs.nanolett.0c00755.
- ^ Sze, J. Y. Y.; Ivanov, A. P.; Cass, A. E. G.; Edel, J. B. (2017). "Single molecule multiplexed nanopore protein screening in human serum using aptamer modified DNA carriers". Nature Communications 8: 1552. doi:10.1038/s41467-017-01584-3.
- ^ "ai-experiments". GitHub. Retrieved October 25, 2026.
- ^ "QuipuNet". Molecular Reality Corporation. July 2026.
- ^ Kemmishtree (March 28, 2026). "2022 sci-tech overview". EQBio-OS.