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Temporal DNA barcodes: A time-based approach for single-molecule imaging

Publication ,  Conference
Shah, S; Reif, J
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2018

In the past decade, single-molecule imaging has opened new opportunities to understand reaction kinetics of molecular systems. DNA-PAINT uses transient binding of DNA strands to perform super-resolution fluorescence imaging. An interesting challenge in DNA nanoscience and related fields is the unique identification of single-molecules. While wavelength multiplexing (using fluorescent dyes of different colors) can be used to increase the number of distinguishable targets, the resultant total number of targets is still limited by the number of dyes with non-overlapping spectra. In this work, we introduce the use of time-domain to develop a DNA-based reporting framework for unique identification of single-molecules. These fluorescent DNA devices undergo a series of conformational transformations that result in (unique) time-changing intensity signals. We define this stochastic temporal intensity trace as the device’s temporal barcode since it can uniquely identify the corresponding DNA device if the collection time is long enough. Our barcodes work with as few as one dye making them easy to design, extremely low-cost, and greatly simplifying the hardware setup. In addition, by adding multiple dyes, we can create a much larger family of uniquely identifiable reporter molecules. Finally, our devices are designed to follow the principle of transient binding and can be imaged using total internal reflection fluorescence (TIRF) microscopes so they are not susceptible to photo-bleaching, allowing us to monitor their activity for extended time periods. We model our devices using continuous-time Markov chains (CTMCs) and simulate their behavior using a stochastic simulation algorithm (SSA). These temporal barcodes are later analyzed and classified in their parameter space. The results obtained from our simulation experiments can provide crucial insights for collecting experimental data.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783030000295

Publication Date

January 1, 2018

Volume

11145 LNCS

Start / End Page

71 / 86

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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MLA
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Shah, S., & Reif, J. (2018). Temporal DNA barcodes: A time-based approach for single-molecule imaging. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11145 LNCS, pp. 71–86). https://doi.org/10.1007/978-3-030-00030-1_5
Shah, S., and J. Reif. “Temporal DNA barcodes: A time-based approach for single-molecule imaging.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11145 LNCS:71–86, 2018. https://doi.org/10.1007/978-3-030-00030-1_5.
Shah S, Reif J. Temporal DNA barcodes: A time-based approach for single-molecule imaging. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2018. p. 71–86.
Shah, S., and J. Reif. “Temporal DNA barcodes: A time-based approach for single-molecule imaging.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11145 LNCS, 2018, pp. 71–86. Scopus, doi:10.1007/978-3-030-00030-1_5.
Shah S, Reif J. Temporal DNA barcodes: A time-based approach for single-molecule imaging. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2018. p. 71–86.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783030000295

Publication Date

January 1, 2018

Volume

11145 LNCS

Start / End Page

71 / 86

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences