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

Sage AT, Besant JD, Mahmoudian L, Poudineh M, Bai X, Zamel R, Hsin M, Sargent EH, Cypel M, Liu M, Keshavjee S, Kelley SO - Sci Adv (2015)

Bottom Line: Using fractal circuit sensors (FraCS), three-dimensional metal structures with large surface areas, we were able to rapidly (<20 min) and reproducibly quantify small differences in the expression of interleukin-6 (IL-6), IL-10, and ATP11B mRNA in donor lung biopsies.A proof-of-concept study using 52 human donor lungs was performed to develop a model that was used to predict, with excellent sensitivity (74%) and specificity (91%), the incidence of PGD for a donor lung.This work provides an important step toward bringing rapid diagnostic mRNA profiling to clinical application in lung transplantation.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmaceutical Sciences, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario M5S 3M2, Canada.

ABSTRACT
Biomarker profiling is being rapidly incorporated in many areas of modern medical practice to improve the precision of clinical decision-making. This potential improvement, however, has not been transferred to the practice of organ assessment and transplantation because previously developed gene-profiling techniques require an extended period of time to perform, making them unsuitable in the time-sensitive organ assessment process. We sought to develop a novel class of chip-based sensors that would enable rapid analysis of tissue levels of preimplantation mRNA markers that correlate with the development of primary graft dysfunction (PGD) in recipients after transplant. Using fractal circuit sensors (FraCS), three-dimensional metal structures with large surface areas, we were able to rapidly (<20 min) and reproducibly quantify small differences in the expression of interleukin-6 (IL-6), IL-10, and ATP11B mRNA in donor lung biopsies. A proof-of-concept study using 52 human donor lungs was performed to develop a model that was used to predict, with excellent sensitivity (74%) and specificity (91%), the incidence of PGD for a donor lung. Thus, the FraCS-based approach delivers a key predictive value test that could be applied to enhance transplant patient outcomes. This work provides an important step toward bringing rapid diagnostic mRNA profiling to clinical application in lung transplantation.

No MeSH data available.


Related in: MedlinePlus

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.
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Figure 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.

Mentions: In an effort to maximize the amount of [Ru(NH3)6]3+ that could access the surface of a sensor, we moved to FraCS from the sensors described in our previous work using smaller round templates (Fig. 2A). This would theoretically allow larger electrochemical currents to be generated, and the signals would then exhibit maximal concentration dependence. A mathematical model that compared the total amount of [Ru(NH3)6]3+ (current) on sensors made with round templates to FraCS was constructed by taking into account the electrode size, hybridization efficiency, and diffusional properties of [Ru(NH3)6]3+ (the model is described in “Mathematical modeling” in Materials and Methods) (Fig. 2B). By modeling the amount of ruthenium associated with analyte on the surface of FraCS, we observed that FraCS had a twofold advantage over traditional electrodes. First, the dynamic range for currents produced by FraCS could be greatly enhanced (Fig. 2B), and second, FraCS could reach a saturation current that was significantly higher than traditional electrodes (3.67 versus 0.67 nA; Fig. 2B). We then experimentally tested whether FraCS were superior for nucleic acid quantification compared to our previously developed electrochemical sensors. Both sets of electrodes achieved signal-to-noise ratios greater than 2.0 (Fig. 2, C and D); however, as quantitative sensors, FraCS had an improved ability to achieve larger signal gains per change in analyte concentration (593% versus 136%, as shown in Fig. 2C). This was consistent with our modeling predictions in Fig. 2B, and, as expected, the FraCS signals were of greater intensity, which resulted in the ability to easily and reliably differentiate large concentration profiles over multiple orders of magnitude (Fig. 2C). This observation was even more apparent when looking at a narrow range of analyte concentrations (1 to 10 nM) (Fig. 2D). For sensors challenged with solutions of 1 to 10 nM, there was a 335% increase in signal using FraCS versus 52% using traditional electrodes (Fig. 2D). As a result, FraCS showed an enhanced ability to discriminate between very small differences in analyte concentrations (even within a single order of magnitude)—a result not possible with the traditional electrodes (Fig. 2D). As further proof-of-concept validation, we estimated other quantitative metrics of FraCS [that is, limit of detection (LOD)] and experimentally confirmed that an LOD could be improved by using a FraCS-based approach (Fig. 2, C and D).


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

Sage AT, Besant JD, Mahmoudian L, Poudineh M, Bai X, Zamel R, Hsin M, Sargent EH, Cypel M, Liu M, Keshavjee S, Kelley SO - Sci Adv (2015)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4643795&req=5

Figure 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.
Mentions: In an effort to maximize the amount of [Ru(NH3)6]3+ that could access the surface of a sensor, we moved to FraCS from the sensors described in our previous work using smaller round templates (Fig. 2A). This would theoretically allow larger electrochemical currents to be generated, and the signals would then exhibit maximal concentration dependence. A mathematical model that compared the total amount of [Ru(NH3)6]3+ (current) on sensors made with round templates to FraCS was constructed by taking into account the electrode size, hybridization efficiency, and diffusional properties of [Ru(NH3)6]3+ (the model is described in “Mathematical modeling” in Materials and Methods) (Fig. 2B). By modeling the amount of ruthenium associated with analyte on the surface of FraCS, we observed that FraCS had a twofold advantage over traditional electrodes. First, the dynamic range for currents produced by FraCS could be greatly enhanced (Fig. 2B), and second, FraCS could reach a saturation current that was significantly higher than traditional electrodes (3.67 versus 0.67 nA; Fig. 2B). We then experimentally tested whether FraCS were superior for nucleic acid quantification compared to our previously developed electrochemical sensors. Both sets of electrodes achieved signal-to-noise ratios greater than 2.0 (Fig. 2, C and D); however, as quantitative sensors, FraCS had an improved ability to achieve larger signal gains per change in analyte concentration (593% versus 136%, as shown in Fig. 2C). This was consistent with our modeling predictions in Fig. 2B, and, as expected, the FraCS signals were of greater intensity, which resulted in the ability to easily and reliably differentiate large concentration profiles over multiple orders of magnitude (Fig. 2C). This observation was even more apparent when looking at a narrow range of analyte concentrations (1 to 10 nM) (Fig. 2D). For sensors challenged with solutions of 1 to 10 nM, there was a 335% increase in signal using FraCS versus 52% using traditional electrodes (Fig. 2D). As a result, FraCS showed an enhanced ability to discriminate between very small differences in analyte concentrations (even within a single order of magnitude)—a result not possible with the traditional electrodes (Fig. 2D). As further proof-of-concept validation, we estimated other quantitative metrics of FraCS [that is, limit of detection (LOD)] and experimentally confirmed that an LOD could be improved by using a FraCS-based approach (Fig. 2, C and D).

Bottom Line: Using fractal circuit sensors (FraCS), three-dimensional metal structures with large surface areas, we were able to rapidly (<20 min) and reproducibly quantify small differences in the expression of interleukin-6 (IL-6), IL-10, and ATP11B mRNA in donor lung biopsies.A proof-of-concept study using 52 human donor lungs was performed to develop a model that was used to predict, with excellent sensitivity (74%) and specificity (91%), the incidence of PGD for a donor lung.This work provides an important step toward bringing rapid diagnostic mRNA profiling to clinical application in lung transplantation.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmaceutical Sciences, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario M5S 3M2, Canada.

ABSTRACT
Biomarker profiling is being rapidly incorporated in many areas of modern medical practice to improve the precision of clinical decision-making. This potential improvement, however, has not been transferred to the practice of organ assessment and transplantation because previously developed gene-profiling techniques require an extended period of time to perform, making them unsuitable in the time-sensitive organ assessment process. We sought to develop a novel class of chip-based sensors that would enable rapid analysis of tissue levels of preimplantation mRNA markers that correlate with the development of primary graft dysfunction (PGD) in recipients after transplant. Using fractal circuit sensors (FraCS), three-dimensional metal structures with large surface areas, we were able to rapidly (<20 min) and reproducibly quantify small differences in the expression of interleukin-6 (IL-6), IL-10, and ATP11B mRNA in donor lung biopsies. A proof-of-concept study using 52 human donor lungs was performed to develop a model that was used to predict, with excellent sensitivity (74%) and specificity (91%), the incidence of PGD for a donor lung. Thus, the FraCS-based approach delivers a key predictive value test that could be applied to enhance transplant patient outcomes. This work provides an important step toward bringing rapid diagnostic mRNA profiling to clinical application in lung transplantation.

No MeSH data available.


Related in: MedlinePlus