Machine Learning Color Feature Analysis of a High Throughput Nanoparticle Conjugate Sensing Assay
Plasmonic nanoparticles are finding applications within the single molecule sensing field in a "dimer" format, where interaction of the target with hairpin DNA causes a decrease in the interparticle distance, leading to a localized surface plasmon resonance shift. While this shift may be detected using spectroscopy, achieving statistical relevance requires the measurement of thousands of nanoparticle dimers and the timescales required for spectroscopic analysis are incompatible with point-of-care devices. However, using dark-field imaging of the dimer structures, simultaneous digital analysis of the plasmonic resonance shift after target interaction of thousands of dimer structures may be achieved in minutes. The main challenge of this digital analysis on the single-molecule scale was the occurrence of false signals caused by non-specifically bound clusters of nanoparticles. This effect may be reduced by digitally separating dimers from other nanoconjugate types. Variation in image intensity was observed to have a discernible impact on the color analysis of the nanoconjugate constructs and thus the accuracy of the digital separation. Color spaces wherein intensity may be uncoupled from the color information (hue, saturation, and value (HSV) and luminance, a* vector, and b* vector (LAB) were contrasted to a color space which cannot uncouple intensity (RGB) to train a classifier algorithm. Each classifier algorithm was validated to determine which color space produced the most accurate digital separation of the nanoconjugate types. The LAB-based learning classifier demonstrated the highest accuracy for digitally separating nanoparticles. Using this classifier, nanoparticle conjugates were monitored for their plasmonic color shift after interaction with a synthetic RNA target, resulting in a platform with a highly accurate yes/no response with a true positive rate of 88% and a true negative rate of 100%. The sensor response of tested single stranded RNA (ssRNA) samples was well above control responses for target concentrations in the range of 10 aM-1 pM.
| Errataetall: |
ErratumIn: Anal Chem. 2023 May 30;95(21):8393. - PMID 37184426 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:95 |
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| Enthalten in: |
Analytical chemistry - 95(2023), 16 vom: 25. Apr., Seite 6550-6558 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bennett, Danielle [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 19.10.2023 Date Revised 19.10.2023 published: Print-Electronic ErratumIn: Anal Chem. 2023 May 30;95(21):8393. - PMID 37184426 Citation Status MEDLINE |
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| doi: |
10.1021/acs.analchem.2c05292 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a ErratumIn: Anal Chem. 2023 May 30;95(21):8393. - PMID 37184426 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Plasmonic nanoparticles are finding applications within the single molecule sensing field in a "dimer" format, where interaction of the target with hairpin DNA causes a decrease in the interparticle distance, leading to a localized surface plasmon resonance shift. While this shift may be detected using spectroscopy, achieving statistical relevance requires the measurement of thousands of nanoparticle dimers and the timescales required for spectroscopic analysis are incompatible with point-of-care devices. However, using dark-field imaging of the dimer structures, simultaneous digital analysis of the plasmonic resonance shift after target interaction of thousands of dimer structures may be achieved in minutes. The main challenge of this digital analysis on the single-molecule scale was the occurrence of false signals caused by non-specifically bound clusters of nanoparticles. This effect may be reduced by digitally separating dimers from other nanoconjugate types. Variation in image intensity was observed to have a discernible impact on the color analysis of the nanoconjugate constructs and thus the accuracy of the digital separation. Color spaces wherein intensity may be uncoupled from the color information (hue, saturation, and value (HSV) and luminance, a* vector, and b* vector (LAB) were contrasted to a color space which cannot uncouple intensity (RGB) to train a classifier algorithm. Each classifier algorithm was validated to determine which color space produced the most accurate digital separation of the nanoconjugate types. The LAB-based learning classifier demonstrated the highest accuracy for digitally separating nanoparticles. Using this classifier, nanoparticle conjugates were monitored for their plasmonic color shift after interaction with a synthetic RNA target, resulting in a platform with a highly accurate yes/no response with a true positive rate of 88% and a true negative rate of 100%. The sensor response of tested single stranded RNA (ssRNA) samples was well above control responses for target concentrations in the range of 10 aM-1 pM | ||
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| 700 | 1 | |a Chen, Xueqian |e verfasserin |4 aut | |
| 700 | 1 | |a Walker, Gregory J |e verfasserin |4 aut | |
| 700 | 1 | |a Stelzer-Braid, Sacha |e verfasserin |4 aut | |
| 700 | 1 | |a Rawlinson, William D |e verfasserin |4 aut | |
| 700 | 1 | |a Hibbert, D Brynn |e verfasserin |4 aut | |
| 700 | 1 | |a Tilley, Richard D |e verfasserin |4 aut | |
| 700 | 1 | |a Gooding, J Justin |e verfasserin |4 aut | |
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