Research ArticleBIOSENSOR TECHNOLOGY

Fractal circuit sensors enable rapid quantification of biomarkers for donor lung assessment for transplantation

See allHide authors and affiliations

Science Advances  28 Aug 2015:
Vol. 1, no. 7, e1500417
DOI: 10.1126/sciadv.1500417
  • Fig. 1 Lung transplant assessment assay.

    (A) The lung assessment assay workflow. A biopsy is taken from a donated lung, and the cells are homogenized and lysed. The mRNA released from the cells is analyzed using a chip-based method that delivers a gene expression profile predictive of the outcome of a transplant within 20 min of the biopsy. (B) FraCS sensor chip (left) and SEM images of sensors after electrochemical deposition (right). (C) Assay readout [PNA probe (black) and resulting differential pulse voltammetry (DPV) signal (right panel, shown in dark blue), target mRNA (light blue) hybridization, and resulting DPV signal (right panel, shown in red)].

  • Fig. 2 Rapid quantitation of LTx analytes using FraCS.

    (A) Images collected with scanning electron microscopy for FraCS templated with linear apertures compared to those made with smaller round apertures. The scale bar shown on each image corresponds to 20 μm. (B) Mathematical modeling of FraCS (solid line) versus sensors made with circular templates (dashed line) for the current generated by the sensor as a function of DNA concentration. (C and D) Quantitative comparisons of sensors with circular apertures (white bars) and FraCS (black bars) between 1- to 100-nM target (C) and 1- to 10-nM target (D). (E) Hybridization time course for rapid RNA analysis using FraCS. Data represent n = 15 different sensors. Columns represent mean, and error bars correspond to SEM.

  • Fig. 3 Quantitation of RNA markers predictive of lung transplant outcome.

    (A to D) Total RNA titration profiles for (A) IL-6, (B) IL-10, (C) IL-6/IL-10, and (D) ATP11B sensors. Data represent n = 15 different sensors. Columns represent mean, and error bars correspond to SEM.

  • Fig. 4 Lung assessment chip and analysis of lung tissue.

    (A) A multiplexed chip that could accommodate the parallel analysis of the five markers tested as proof of principle was prepared. (B) Correlation of signals obtained from purified RNA from a lung biopsy versus unpurified lysate of the same biopsy (r indicates Pearson’s correlation coefficient). (C) Representative data obtained from a good-outcome lung. (D) Representative data obtained from a poor-outcome lung. The signals are normalized to GAPDH controls, and the nonspecific D. melanogaster signal is shown as a dashed line. Data represent n = 15 different sensors. Columns represent mean, and error bars correspond to SEM. (E to G) Comparison of the FraCS assay response (left y axis) to qPCR expression levels (right y axis) of the same biopsy [PGD0/I (n = 9 to 11), PGDIII+ (n = 9 to 12)] run on both platforms for (E) IL-6, (F) IL-10, and (G) ATP11B.

  • Fig. 5 Relative expression of LTx biomarkers.

    (A to D) Each circle represents the LTx biomarker signal normalized to GAPDH for an individual donor lung, and horizontal lines show the population means of PGD0/I (n = 23) and PGDIII+ (n = 16) donor lungs for (A) IL-6, (B) IL-10, (C) IL-6/IL-10, and (D) ATP11B. Data were analyzed by a two-tailed Mann-Whitney test. The P values of each comparison are as follows: IL-6, 0.0007; IL-10, 0.9833; IL-6/IL-10, 0.0027; and ATP11B, 0.0002.

  • Table 1 PGD predictive value of LTx biomarkers.
    BiomarkerArea under ROC curveP
    IL-60.740.0381
    IL-100.530.8177
    IL-6/IL-100.780.0160
    ATP11B0.840.0003
  • Table 2 The FraCS prediction model.

    The FPM was developed by logistic regression analysis and is expressed by the following equation: loge(PGDIII:PGD0/I) = −5.5669 + 0.0484*IL-6 + 2.4370*ATP11B + 2.0469*IL-6/IL-10.

    GroupArea under
    ROC curve
    P
    Development group
    (n = 32, PGD0/I: n = 23, PGDIII: n = 9)
    0.96<0.0001
      Cross-validation of development group0.88<0.05
    Validation group
    (n = 20, PGD0/I: n = 10, PGDIII: n = 10)
    0.870.0052
    All cases
    (n = 52, PGD0/I: n = 33, PGDIII: n = 19)
    0.88<0.0001
      Cross-validation of all cases0.82<0.05
  • Table 3 Diagnostic characteristics of the FPM.
    SensitivitySpecificityPPVNPV
    FPM73.7% (14/19)90.9% (30/33)82.4%85.7%
  • Table 4
    ParameterValueReference
    Target length20 base pairs
    Spot diameter15 μm
    Line size10 × 100 μm
    Probe density8.7 × 1012 cm−2(50)
    Hybridization efficiency30%(50)
    Ru/Fe turnover45×(51)

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/1/7/e1500417/DC1

    Fig. S1. Validation of LTx probes and FraCS approach with DNA targets.

    Fig. S2. GAPDH validation with RNA.

    Fig. S3. qPCR validation.

    Fig. S4. Bland-Altman analysis of FraCS versus qPCR.

    Table S1. Bland-Altman results of FraCS versus qPCR.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Validation of LTx probes and FraCS approach with DNA targets.
    • Fig. S2. GAPDH validation with RNA.
    • Fig. S3. qPCR validation.
    • Fig. S4. Bland-Altman analysis of FraCS versus qPCR.
    • Table S1. Bland-Altman results of FraCS versus qPCR.

    Download PDF

    Files in this Data Supplement:

Navigate This Article