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Detection of single amino acid mutation in human breast cancer by disordered plasmonic self-similar chain.

Coluccio ML, Gentile F, Das G, Nicastri A, Perri AM, Candeloro P, Perozziello G, Proietti Zaccaria R, Gongora JS, Alrasheed S, Fratalocchi A, Limongi T, Cuda G, Di Fabrizio E - Sci Adv (2015)

Bottom Line: The sensitivity demonstrated falls in the picomolar (10(-12) M) range.The success of this approach is a result of accurate design and fabrication control.The residual roughness introduced by fabrication was taken into account in optical modeling and was a further contributing factor in plasmon localization, increasing the sensitivity and selectivity of the sensors.

View Article: PubMed Central - PubMed

Affiliation: Bio-Nanotechnology and Engineering for Medicine (BIONEM), Department of Experimental and Clinical Medicine, University of Magna Graecia Viale Europa, Germaneto, Catanzaro 88100, Italy.

ABSTRACT
Control of the architecture and electromagnetic behavior of nanostructures offers the possibility of designing and fabricating sensors that, owing to their intrinsic behavior, provide solutions to new problems in various fields. We show detection of peptides in multicomponent mixtures derived from human samples for early diagnosis of breast cancer. The architecture of sensors is based on a matrix array where pixels constitute a plasmonic device showing a strong electric field enhancement localized in an area of a few square nanometers. The method allows detection of single point mutations in peptides composing the BRCA1 protein. The sensitivity demonstrated falls in the picomolar (10(-12) M) range. The success of this approach is a result of accurate design and fabrication control. The residual roughness introduced by fabrication was taken into account in optical modeling and was a further contributing factor in plasmon localization, increasing the sensitivity and selectivity of the sensors. This methodology developed for breast cancer detection can be considered a general strategy that is applicable to various pathologies and other chemical analytical cases where complex mixtures have to be resolved in their constitutive components.

No MeSH data available.


Related in: MedlinePlus

Raman spectra of pure wild-type and mutated peptides.(A and B) Raman spectra (B) show a net difference between two peptides differentiated by only the exchange of one amino acid (A; a methionine with an arginine). These spectra constitute the base set for the fitting procedure. Their net difference allows identification of mutated peptides in the mixture. (C and D) The results of PCA are also shown: a 2D map of the PC2 coefficients of two pixels (C), one pixel for each peptide, where color code is proportional to the significance of the PC2 parameter over the map; the PC2 parameter load curve (D) takes into account spectral differences between the two peptides. The combination of PC2 mapping and PC2 load curve allows identification of pixels dominated by wild-type or mutated species.
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Figure 6: Raman spectra of pure wild-type and mutated peptides.(A and B) Raman spectra (B) show a net difference between two peptides differentiated by only the exchange of one amino acid (A; a methionine with an arginine). These spectra constitute the base set for the fitting procedure. Their net difference allows identification of mutated peptides in the mixture. (C and D) The results of PCA are also shown: a 2D map of the PC2 coefficients of two pixels (C), one pixel for each peptide, where color code is proportional to the significance of the PC2 parameter over the map; the PC2 parameter load curve (D) takes into account spectral differences between the two peptides. The combination of PC2 mapping and PC2 load curve allows identification of pixels dominated by wild-type or mutated species.

Mentions: Figure 6 reports the Raman spectra of the peptide M1775 (wild type and mutated from pure samples). The noticeable difference in the spectra gives the distinction between wild-type species and mutated species in the linear combination procedure. Furthermore, principal components analysis (PCA) is performed on the Raman data set constituting 2D maps of single pixels in the low-frequency range (43). PCA is performed on the whole data set at once; consequently, the computed principal components (PCs) are exactly the same for both pixels, thus allowing a quantitative comparison of the Raman signatures recorded over them. In particular, although the first PC (not shown) is directly associated with the average signal over the two pixels, the second PC is instead related to the differences between the two Raman signals. The 2D map of PC2 coefficients (Fig. 6C) shows two different pixels: one dominated by the wild-type Raman signature (blue) and the other dominated by the mutated signature (red). Figure 6D shows the PC2 load curve, which takes into account the main differences between wild-type signals and mutated signals at once.


Detection of single amino acid mutation in human breast cancer by disordered plasmonic self-similar chain.

Coluccio ML, Gentile F, Das G, Nicastri A, Perri AM, Candeloro P, Perozziello G, Proietti Zaccaria R, Gongora JS, Alrasheed S, Fratalocchi A, Limongi T, Cuda G, Di Fabrizio E - Sci Adv (2015)

Raman spectra of pure wild-type and mutated peptides.(A and B) Raman spectra (B) show a net difference between two peptides differentiated by only the exchange of one amino acid (A; a methionine with an arginine). These spectra constitute the base set for the fitting procedure. Their net difference allows identification of mutated peptides in the mixture. (C and D) The results of PCA are also shown: a 2D map of the PC2 coefficients of two pixels (C), one pixel for each peptide, where color code is proportional to the significance of the PC2 parameter over the map; the PC2 parameter load curve (D) takes into account spectral differences between the two peptides. The combination of PC2 mapping and PC2 load curve allows identification of pixels dominated by wild-type or mutated species.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4643778&req=5

Figure 6: Raman spectra of pure wild-type and mutated peptides.(A and B) Raman spectra (B) show a net difference between two peptides differentiated by only the exchange of one amino acid (A; a methionine with an arginine). These spectra constitute the base set for the fitting procedure. Their net difference allows identification of mutated peptides in the mixture. (C and D) The results of PCA are also shown: a 2D map of the PC2 coefficients of two pixels (C), one pixel for each peptide, where color code is proportional to the significance of the PC2 parameter over the map; the PC2 parameter load curve (D) takes into account spectral differences between the two peptides. The combination of PC2 mapping and PC2 load curve allows identification of pixels dominated by wild-type or mutated species.
Mentions: Figure 6 reports the Raman spectra of the peptide M1775 (wild type and mutated from pure samples). The noticeable difference in the spectra gives the distinction between wild-type species and mutated species in the linear combination procedure. Furthermore, principal components analysis (PCA) is performed on the Raman data set constituting 2D maps of single pixels in the low-frequency range (43). PCA is performed on the whole data set at once; consequently, the computed principal components (PCs) are exactly the same for both pixels, thus allowing a quantitative comparison of the Raman signatures recorded over them. In particular, although the first PC (not shown) is directly associated with the average signal over the two pixels, the second PC is instead related to the differences between the two Raman signals. The 2D map of PC2 coefficients (Fig. 6C) shows two different pixels: one dominated by the wild-type Raman signature (blue) and the other dominated by the mutated signature (red). Figure 6D shows the PC2 load curve, which takes into account the main differences between wild-type signals and mutated signals at once.

Bottom Line: The sensitivity demonstrated falls in the picomolar (10(-12) M) range.The success of this approach is a result of accurate design and fabrication control.The residual roughness introduced by fabrication was taken into account in optical modeling and was a further contributing factor in plasmon localization, increasing the sensitivity and selectivity of the sensors.

View Article: PubMed Central - PubMed

Affiliation: Bio-Nanotechnology and Engineering for Medicine (BIONEM), Department of Experimental and Clinical Medicine, University of Magna Graecia Viale Europa, Germaneto, Catanzaro 88100, Italy.

ABSTRACT
Control of the architecture and electromagnetic behavior of nanostructures offers the possibility of designing and fabricating sensors that, owing to their intrinsic behavior, provide solutions to new problems in various fields. We show detection of peptides in multicomponent mixtures derived from human samples for early diagnosis of breast cancer. The architecture of sensors is based on a matrix array where pixels constitute a plasmonic device showing a strong electric field enhancement localized in an area of a few square nanometers. The method allows detection of single point mutations in peptides composing the BRCA1 protein. The sensitivity demonstrated falls in the picomolar (10(-12) M) range. The success of this approach is a result of accurate design and fabrication control. The residual roughness introduced by fabrication was taken into account in optical modeling and was a further contributing factor in plasmon localization, increasing the sensitivity and selectivity of the sensors. This methodology developed for breast cancer detection can be considered a general strategy that is applicable to various pathologies and other chemical analytical cases where complex mixtures have to be resolved in their constitutive components.

No MeSH data available.


Related in: MedlinePlus