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Kinetic fingerprinting to identify and count single nucleic acids

Nature Biotechnology volume 33, pages 730–732 (2015)Cite this article

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Abstract

MicroRNAs (miRNAs) have emerged as promising diagnostic biomarkers. We introduce a kinetic fingerprinting approach called single-molecule recognition through equilibrium Poisson sampling (SiMREPS) for the amplification-free counting of single unlabeled miRNA molecules, which circumvents thermodynamic limits of specificity and virtually eliminates false positives. We demonstrate high-confidence, single-molecule detection of synthetic and endogenous miRNAs in both buffer and minimally treated biofluids, as well as >500-fold discrimination between single nucleotide polymorphisms.

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Figure 1: High-confidence detection of miRNAs with SiMREPS.
Figure 2: Single-molecule mismatch discrimination and detection of RNAs in crude biological matrices.

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Acknowledgements

This work was funded in part by the Department of Defense MURI Award W911NF-12-1-0420 (to N.G.W.) and US National Institutes of Health Transformative R01 grant R01DK085714 (to M.T.). X.S. acknowledges support from the China Scholarship Council. M.D.G. acknowledges initial support from a Rio Hortega Fellowship and later from a Martin Escudero Fellowship. The authors thank A.M. Chinnaiyan, M. Bitzer, A. Sahu, S. Pitchiaya, L.A. Heinicke and M.L. Kahlscheuer for helpful discussions. The authors acknowledge J.D. Hoff and the Single-Molecule Analysis in Real-Time (SMART) Center for instrumentation and assistance in TIRF microscopy measurements.

Author information

Author notes

  1. Alexander Johnson-Buck

    Present address: Present address: Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.,

  2. Alexander Johnson-Buck and Xin Su: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Chemistry, Single Molecule Analysis Group, University of Michigan, Ann Arbor, Michigan, USA

    Alexander Johnson-Buck & Nils G Walter

  2. Beijing National Laboratory for Molecular Sciences, MOE Key Laboratory of Bioorganic Chemistry and Molecular Engineering, College of Chemistry and Molecular Engineering, Peking University, Beijing, China

    Xin Su & Meiping Zhao

  3. Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan, Ann Arbor, Michigan, USA

    Maria D Giraldez & Muneesh Tewari

  4. Department of Internal Medicine, Division of Molecular Medicine and Genetics, University of Michigan, Ann Arbor, Michigan, USA

    Muneesh Tewari

  5. Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA

    Muneesh Tewari

  6. Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA

    Muneesh Tewari

  7. Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan, USA

    Muneesh Tewari

Authors
  1. Alexander Johnson-Buck
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  2. Xin Su
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  3. Maria D Giraldez
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  6. Nils G Walter
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Contributions

N.G.W. and A.J.-B. conceived the idea. A.J.-B., N.G.W. and X.S. designed the experiments. X.S. and A.J.-B. carried out experiments and analyzed the results. A.J.-B., N.G.W., X.S., M.T., M.D.G. and M.Z. interpreted the results and wrote the paper.

Corresponding author

Correspondence to Nils G Walter.

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Competing interests

A US patent application, serial number 14/589,467, was filed by the University of Michigan on technologies described herein, and on the technologies described in the Office of Technology Transfer files 6472 and 6450.

Integrated supplementary information

Supplementary Figure 1 Time-averaged fluorescence image showing signal from both specific and nonspecific binding of the fluorescent probe for miR-141.

Individual fluorescent puncta are analyzed for kinetics of probe binding to distinguish between signal from genuine targets and background binding (Fig. 1a–d).

Supplementary Figure 2 Impact of probe length and Tm on kinetics.

a, There is strong negative correlation between the length of the fluorescent probe and the measured dissociation rate constant (koff) as well as the pseudo-first-order association rate constant (k'on). While an increase in kon for shorter probes is perhaps intuitively surprising, similar trends have been reported in single-molecule studies by other labs within this range of duplex lengths22,23. b, In contrast, no significant correlation is evident between the theoretically predicted Tm of the probe-target duplexes and the rate constants of binding or dissociation. Tm values were predicted using the OligoAnalyzer application from Integrated DNA Technologies using the following parameters: target type = RNA, oligonucleotide concentration = 25 nM, Na+ concentration = 600 mM, Mg++ concentration = 0.

Supplementary Figure 3 increase as

The standard deviation in the number of binding/dissociation transitions per molecule, , is plotted against the mean number of transitions per molecule, , on a log-log scale. Both experimental data for hsa-miR-141 (black circles) and simulations with k⊠on = koff = 6 min−1 (dashed line) are shown for comparison. A linear regression fit to the experimental data yields a slope of ˜2, showing that increases as , as expected for a Poisson process.

Supplementary Figure 4 Optimization of the fluorescent probe for miR-141

While both 10-nucleotide probes yielded poor separation between signal and background, reducing the length of the probe to 9 nt yielded a distinct signal peak.

Supplementary Figure 5 Zero-background detection of diverse miRNAs by SiMREPS

a, Probe and target sequences for the detection of four miRNA targets from H. sapiens and one from C. elegans. The blue sequences represent the miRNA targets, the black sequences represent the locked nucleic acid capture probes, and the red sequences represent the transiently binding fluorescent DNA probes. The underlined letters in the capture probe sequences signify LNA rather than DNA nucleotides. b, Histograms of Nb+d in the absence (black) or presence (red) of miRNA targets. All targets were present at 1 pM except for miR–16, which was present at 500 fM. c, Representative fluorescence-versus-time trajectories for single immobilized miRNAs using the probes shown in (a). d, Mean bound Ton) and unbound (Toff) state lifetimes for the five probe-target pairs shown in (a). The symbols correspond to hsa-miR-141 (red squares), hsa-let-7a (cyan diamonds), hsa-miR-16 (green triangles), hsa-miR-21 (blue circles), and cel-miR-39 (black crosses). Error bars represent one standard deviation; it is noted that the lifetimes of the 9- and 10-nucleotide probes cluster in distinct regions of the plot. e, Specificity of detection of the five miRNA targets as a function of Nb+d threshold. The symbols are the same as in (d).

Supplementary Figure 6 On-state dwell time distributions of Cy5 and Cy3 fluorescent probes in presence and absence of target miRNA

a,b On-state dwell time distributions of fluorescent probes for miR-141 (a, Cy5-labeled probe) and miR-16 (b, Cy3-labeled probe) in the presence (red) and absence (black) of the target miRNA. The dwell time distributions in absence of target (black) likely reflect both photobleaching and dissociation of nonspecifically surface-bound probes, and thus provide lower-bound estimates of the photobleaching time constant for each fluorophore.

Supplementary Figure 7 let-7a/c discrimination and Nb+d histogram of let-7c

a, Discrimination factor Q describing the specificity of let-7a detection over let-7c at varying values of the bound-state lifetime threshold, Ton,min. b, Nb+d histogram for let-7c detected using the fluorescent probe for let-7a. While the shape of the histogram is similar to that for let-7a (Supplementary Fig. 5b), the sensitivity is much lower due to the short binding events, and the two targets can be more completely resolved using their disparate Ton values (panel a and Fig. 2).

Supplementary Figure 8 Protection of miRNA from degradation in serum using proteinase K and SDS

Synthetic miR-16 is completely degraded by the addition of untreated human serum, but is protected by the simultaneous addition of proteinase K (1, 2, 4 correspond to 0.08, 0.16, or 0.32 units/μL) and SDS. Subsequent heating to 90 °C for 2 min in the presence of 20 mM EDTA does not result in significant degradation.

Supplementary Figure 9 Current sample cell designs for SiMREPS using prism-TIRF and objective-TIRF microscopy

a, Closed flow cell constructed for prism-TIRF using double-sided tape sandwiched between a coverslip and a fused silica slide bearing two ~1-mm holes and Tygon tubing for buffer exchange24. b, Taller objective-TIRF sample cell constructed by fastening a cut pipet tip to a coverslip. This sample cell permits a taller column of fluid (~1 cm), resulting in greater sensitivity than with the prism-TIRF flow cell11.

Supplementary Figure 10 Simulated Nb+d histograms from kinetic Monte Carlo simulations of SiMREPS probing

a, Signal (red) and background (black) distributions of Nb+d from simulations in which kon+off,signal = 5 min−1, kon+off,background = 0.5 min−1, and the simulation time is 3.9 min. b, Nb+d distributions from simulations in which kon+off,signal = 5 min−1, kon+off,background = 4 min−1, and the simulation time is 161.5 min. In all cases, histograms were constructed from 1,000 individual simulated trajectories.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10, Supplementary Note 1 and 2 (PDF 2069 kb)

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Johnson-Buck, A., Su, X., Giraldez, M. et al. Kinetic fingerprinting to identify and count single nucleic acids. Nat Biotechnol 33, 730–732 (2015). https://doi.org/10.1038/nbt.3246

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