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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

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.
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Figure 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.

Mentions: Using these sensors, we then proceeded to develop probes against the mRNAs of three previously reported donor lung assessment markers (IL-6, IL-10, and ATP11B) (9, 21) and control sequences [human glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as a positive control and Drosophila melanogaster GAPDH as a negative control]. The probes for the LTx assay were successfully designed and validated using 100 nM complementary synthetic DNA oligonucleotides (fig. S1). To test whether FraCS could accurately measure the levels of the target lung assessment mRNAs in heterogeneous samples, we isolated total RNA from donor lung tissue. For all of the respective LTx assay probes, we observed concentration-dependent signals that were highly reproducible (Fig. 3). An analysis of the ratio of two important inflammatory cytokine markers, IL-6 and IL-10, illustrates the excellent precision of the detection strategy, because the ratio of IL-6 to IL-10 remained constant at various total RNA concentrations (Fig. 3C).


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)

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

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

Figure 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.
Mentions: Using these sensors, we then proceeded to develop probes against the mRNAs of three previously reported donor lung assessment markers (IL-6, IL-10, and ATP11B) (9, 21) and control sequences [human glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as a positive control and Drosophila melanogaster GAPDH as a negative control]. The probes for the LTx assay were successfully designed and validated using 100 nM complementary synthetic DNA oligonucleotides (fig. S1). To test whether FraCS could accurately measure the levels of the target lung assessment mRNAs in heterogeneous samples, we isolated total RNA from donor lung tissue. For all of the respective LTx assay probes, we observed concentration-dependent signals that were highly reproducible (Fig. 3). An analysis of the ratio of two important inflammatory cytokine markers, IL-6 and IL-10, illustrates the excellent precision of the detection strategy, because the ratio of IL-6 to IL-10 remained constant at various total RNA concentrations (Fig. 3C).

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