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An array-based approach to determine different subtype and differentiation of non-small cell lung cancer.

Li C, Yang Y, Wei L, Wang X, Wang Z, Yin Y, Li G - Theranostics (2015)

Bottom Line: Strategically, we first select eight proteins at 0.5 nM concentration in buffer or 10 nM in human serum to verify the discriminant ability of our method, then choose adenocarcinoma and squamous-cell carcinoma that account for 90% non-small cell lung cancer, as well as their respective three tumor grades as model system to provide a realistic testing ground for clinical cancer analysis.Consequently, total differentiation between different subtype and grade of tumor tissues has been achieved with as little as 100 ng of intracellular protein, suggesting the high sensitivity and selectivity of this sensor array.Overall, this array-based approach may provide the possibility for unbiased and simplified personalized tumor classification diagnostics in the future.

View Article: PubMed Central - PubMed

Affiliation: 1. State Key Laboratory of Pharmaceutical Biotechnology, Department of Biochemistry,Nanjing University, Nanjing 210093, China.

ABSTRACT
Simple and accurate methods of discriminating subtype or differentiation of human tumor are critical for designing treatment strategies and predicting disease prognosis, and the currently used method to determine the two important factors mainly depends on histological examination by microscopy observation, which is laborious, highly trained operator required, and prone to be disruptive due to individual-to-individual judgment. Here we report a novel array-based method based on the interaction of graphene oxide (GO) and single-strand DNA modified gold nanoparticles (ssDNA-AuNPs) to distinguish between different subtypes and grades of tumors through their overall intracellular proteome signatures. Strategically, we first select eight proteins at 0.5 nM concentration in buffer or 10 nM in human serum to verify the discriminant ability of our method, then choose adenocarcinoma and squamous-cell carcinoma that account for 90% non-small cell lung cancer, as well as their respective three tumor grades as model system to provide a realistic testing ground for clinical cancer analysis. Consequently, total differentiation between different subtype and grade of tumor tissues has been achieved with as little as 100 ng of intracellular protein, suggesting the high sensitivity and selectivity of this sensor array. Overall, this array-based approach may provide the possibility for unbiased and simplified personalized tumor classification diagnostics in the future.

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Array-based sensing of eight proteins in 10 mM Tris-HCl (pH 7.4) and five proteins in human serum. (a) Absorbance response (ΔA, A - A0) patterns of the ssDNA-AuNPs-GO sensor array (NP1-NP5) against various proteins at 0.5 nM concentration: bovine serum albumin (BSA), cytochrome c (Cyto), hemoglobin (Hb), lysozyme (Lyso), horseradish peroxidase (HRP), myoglobin (Mb), transferrin (Tf) and thrombin (Th). Error bars represent standard deviations of six parallel measurements. (b) Canonical score plot for the absorbance response patterns as obtained from LDA against eight proteins at a constant concentration of 0.5 nM, with 95% confidence ellipses. (c) Absorbance response (ΔA) patterns of the ssDNA-AuNPs-GO sensor array (NP1-NP5) against five proteins (BSA, Lyso, Cyto, Hb, and Mb) spiked in human serum at 10 nM concentration. Error bars represent standard deviations of six parallel measurements. (d) Canonical score plot for the absorbance response patterns as obtained from LDA against five proteins at a fixed concentration of 10 nM, with 95% confidence ellipses. All five proteins can be well-separated and properly identified.
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Figure 4: Array-based sensing of eight proteins in 10 mM Tris-HCl (pH 7.4) and five proteins in human serum. (a) Absorbance response (ΔA, A - A0) patterns of the ssDNA-AuNPs-GO sensor array (NP1-NP5) against various proteins at 0.5 nM concentration: bovine serum albumin (BSA), cytochrome c (Cyto), hemoglobin (Hb), lysozyme (Lyso), horseradish peroxidase (HRP), myoglobin (Mb), transferrin (Tf) and thrombin (Th). Error bars represent standard deviations of six parallel measurements. (b) Canonical score plot for the absorbance response patterns as obtained from LDA against eight proteins at a constant concentration of 0.5 nM, with 95% confidence ellipses. (c) Absorbance response (ΔA) patterns of the ssDNA-AuNPs-GO sensor array (NP1-NP5) against five proteins (BSA, Lyso, Cyto, Hb, and Mb) spiked in human serum at 10 nM concentration. Error bars represent standard deviations of six parallel measurements. (d) Canonical score plot for the absorbance response patterns as obtained from LDA against five proteins at a fixed concentration of 10 nM, with 95% confidence ellipses. All five proteins can be well-separated and properly identified.

Mentions: As illustrated in Figure 3a, assay of the eight proteins (all at 500 pM) results in a variety of colorimetric responses due to their different interactions with GO and DNA-AuNPs. By contrast, the direct introduction of proteins into DNA-AuNPs without the involvement of GO cannot induce any color changes (Figure. 3b), confirming the disruption of DNA-AuNPs-GO interactions by proteins. To distinctly visualize the fingerprint-like response pattern of the NP1-NP5 array to each protein, we have also provided a heat map corresponding to the change of absorbance (Figure 3c). Consequently, absorbance enhancement has been observed for most of the elements (Figure 4a), which indicates that the assayed protein molecules may adsorb to the surface the nanomaterials, prevent the interaction between GO and the DNA shell of AuNPs and leave large numbers of AuNPs in the supernatant. The absorbance response patterns from the different protein types are found to be distinctive, reproducible, and specific. Such an outcome confirms our expectation, because each protein has its unique surface characteristics. For each protein, we test its absorbance response against the five DNA-AuNPs assemblies six times, generating a 5 × 8 × 6 matrix. Then, the raw data are subjected to LDA to generate four canonical factors (62.9, 30.3, 6.3, and 0.5% of the variation), which represent linear combinations of the absorption response matrix (four factors × eight proteins × six replicates). The first two factors are employed to generate a two-dimensional plot as presented in Figure 4b.


An array-based approach to determine different subtype and differentiation of non-small cell lung cancer.

Li C, Yang Y, Wei L, Wang X, Wang Z, Yin Y, Li G - Theranostics (2015)

Array-based sensing of eight proteins in 10 mM Tris-HCl (pH 7.4) and five proteins in human serum. (a) Absorbance response (ΔA, A - A0) patterns of the ssDNA-AuNPs-GO sensor array (NP1-NP5) against various proteins at 0.5 nM concentration: bovine serum albumin (BSA), cytochrome c (Cyto), hemoglobin (Hb), lysozyme (Lyso), horseradish peroxidase (HRP), myoglobin (Mb), transferrin (Tf) and thrombin (Th). Error bars represent standard deviations of six parallel measurements. (b) Canonical score plot for the absorbance response patterns as obtained from LDA against eight proteins at a constant concentration of 0.5 nM, with 95% confidence ellipses. (c) Absorbance response (ΔA) patterns of the ssDNA-AuNPs-GO sensor array (NP1-NP5) against five proteins (BSA, Lyso, Cyto, Hb, and Mb) spiked in human serum at 10 nM concentration. Error bars represent standard deviations of six parallel measurements. (d) Canonical score plot for the absorbance response patterns as obtained from LDA against five proteins at a fixed concentration of 10 nM, with 95% confidence ellipses. All five proteins can be well-separated and properly identified.
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Related In: Results  -  Collection

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Figure 4: Array-based sensing of eight proteins in 10 mM Tris-HCl (pH 7.4) and five proteins in human serum. (a) Absorbance response (ΔA, A - A0) patterns of the ssDNA-AuNPs-GO sensor array (NP1-NP5) against various proteins at 0.5 nM concentration: bovine serum albumin (BSA), cytochrome c (Cyto), hemoglobin (Hb), lysozyme (Lyso), horseradish peroxidase (HRP), myoglobin (Mb), transferrin (Tf) and thrombin (Th). Error bars represent standard deviations of six parallel measurements. (b) Canonical score plot for the absorbance response patterns as obtained from LDA against eight proteins at a constant concentration of 0.5 nM, with 95% confidence ellipses. (c) Absorbance response (ΔA) patterns of the ssDNA-AuNPs-GO sensor array (NP1-NP5) against five proteins (BSA, Lyso, Cyto, Hb, and Mb) spiked in human serum at 10 nM concentration. Error bars represent standard deviations of six parallel measurements. (d) Canonical score plot for the absorbance response patterns as obtained from LDA against five proteins at a fixed concentration of 10 nM, with 95% confidence ellipses. All five proteins can be well-separated and properly identified.
Mentions: As illustrated in Figure 3a, assay of the eight proteins (all at 500 pM) results in a variety of colorimetric responses due to their different interactions with GO and DNA-AuNPs. By contrast, the direct introduction of proteins into DNA-AuNPs without the involvement of GO cannot induce any color changes (Figure. 3b), confirming the disruption of DNA-AuNPs-GO interactions by proteins. To distinctly visualize the fingerprint-like response pattern of the NP1-NP5 array to each protein, we have also provided a heat map corresponding to the change of absorbance (Figure 3c). Consequently, absorbance enhancement has been observed for most of the elements (Figure 4a), which indicates that the assayed protein molecules may adsorb to the surface the nanomaterials, prevent the interaction between GO and the DNA shell of AuNPs and leave large numbers of AuNPs in the supernatant. The absorbance response patterns from the different protein types are found to be distinctive, reproducible, and specific. Such an outcome confirms our expectation, because each protein has its unique surface characteristics. For each protein, we test its absorbance response against the five DNA-AuNPs assemblies six times, generating a 5 × 8 × 6 matrix. Then, the raw data are subjected to LDA to generate four canonical factors (62.9, 30.3, 6.3, and 0.5% of the variation), which represent linear combinations of the absorption response matrix (four factors × eight proteins × six replicates). The first two factors are employed to generate a two-dimensional plot as presented in Figure 4b.

Bottom Line: Strategically, we first select eight proteins at 0.5 nM concentration in buffer or 10 nM in human serum to verify the discriminant ability of our method, then choose adenocarcinoma and squamous-cell carcinoma that account for 90% non-small cell lung cancer, as well as their respective three tumor grades as model system to provide a realistic testing ground for clinical cancer analysis.Consequently, total differentiation between different subtype and grade of tumor tissues has been achieved with as little as 100 ng of intracellular protein, suggesting the high sensitivity and selectivity of this sensor array.Overall, this array-based approach may provide the possibility for unbiased and simplified personalized tumor classification diagnostics in the future.

View Article: PubMed Central - PubMed

Affiliation: 1. State Key Laboratory of Pharmaceutical Biotechnology, Department of Biochemistry,Nanjing University, Nanjing 210093, China.

ABSTRACT
Simple and accurate methods of discriminating subtype or differentiation of human tumor are critical for designing treatment strategies and predicting disease prognosis, and the currently used method to determine the two important factors mainly depends on histological examination by microscopy observation, which is laborious, highly trained operator required, and prone to be disruptive due to individual-to-individual judgment. Here we report a novel array-based method based on the interaction of graphene oxide (GO) and single-strand DNA modified gold nanoparticles (ssDNA-AuNPs) to distinguish between different subtypes and grades of tumors through their overall intracellular proteome signatures. Strategically, we first select eight proteins at 0.5 nM concentration in buffer or 10 nM in human serum to verify the discriminant ability of our method, then choose adenocarcinoma and squamous-cell carcinoma that account for 90% non-small cell lung cancer, as well as their respective three tumor grades as model system to provide a realistic testing ground for clinical cancer analysis. Consequently, total differentiation between different subtype and grade of tumor tissues has been achieved with as little as 100 ng of intracellular protein, suggesting the high sensitivity and selectivity of this sensor array. Overall, this array-based approach may provide the possibility for unbiased and simplified personalized tumor classification diagnostics in the future.

Show MeSH
Related in: MedlinePlus