Machine-Perception Nanosensor Platform to Detect Cancer Biomarkers

Yaari, Zvi; Yang, Yoona; Apfelbaum, Elana; Settle, Alexander H.; Cullen, Quinlan; Cai, Weiping; K, Long Roche; Da, Levine; Fleisher, Martin; Ramanathan, Lakshmi; Zheng, Ming; Jagota, Anand; Da, Heller

doi:10.1101/2021.04.28.441499

Cited by 4 publications

(2 citation statements)

References 61 publications

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“…As shown herein, the directed evolution methodology can be used to tune several aspects of DNA-SWCNT sensors in order to optimize their performance for a range of applications. While applied here for the sensing of mycotoxins, the same methodology could be applied to variety of targets including small molecules 6,33 , biomarkers 32,42 and viruses 43 . Moreover, we believe that the findings of this study will have a more widespread impact on the identification of more rational design rules for DNA-SWCNT sensors.…”

Section: Discussionmentioning

confidence: 99%

Directed evolution of nanosensors for the detection of mycotoxins

Lambert

Taheri

et al. 2023

Preprint

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In this study, we develop and apply a directed evolution approach to engineer the optical sensing properties of DNA-wrapped single-walled carbon nanotubes (DNA-SWCNTs) towards mycotoxins, a class of molecules critical to detect in the food industry. We successfully demonstrate the creation of sensors for the detection of both the aflatoxin B1 (AFB1) and fumonisin B1 (FB1) mycotoxins based on the specific response of the (9,4) and (7,5) SWCNT chirality fluorescence peaks, respectively. The resulting chirality-specific responsivity was used to demonstrate the multimodal detection of both mycotoxins at different wavelengths of light in the presence of complex food medium. Moreover, we show that directed evolution can be used not only to improve the chiral-dependent selectivity of our sensors to the mycotoxins, but also the sensor sensitivity and fluorescence intensity through multiple rounds of evolution. The approach demonstrated in this study is versatile and could be generalized to other SWCNT sensors as well as other nanosensors comprising a biological element.

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Section: Discussionmentioning

confidence: 99%

Directed evolution of nanosensors for the detection of mycotoxins

Lambert

Taheri

et al. 2023

Preprint

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“…Yang et al (2019) used machine learning methods to investigate the impact of several short patterns in a DNA sequence for SWCNT chirality separation [14]. In 2021, Yaari et al developed a machine learning model for the simultaneous detection of multiple cancer biomarkers such as HE4, CA-125, and YKL-40 in biofluids by training ML with 11 known DNA structures along with 12 SWCNT chiralities [15]. Lin et al (2022) used machine learning to find optimal DNA sequences for sorting individual SWCNT chiralities [16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mycotoxin Response of DNA-Wrapped Nanotube Sensor with Machine Learning

Rabbani,

Behjati,

Lambert

et al. 2023

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DNA-wrapped single-walled carbon nanotubes (SWCNTs) have demonstrated great versatility in their use as optical sensors. SWCNTs emit a near-infrared fluorescence that is responsive to even the slightest changes in the nanotube environment, enabling sensors that can respond to single-molecule fluctuations within the vicinity of their surfaces. The fluorescence response and surface interactions of these sensors are determined by the DNA wrapping sequence. However, the lack of information on the relationship between the DNA sequence and its effect on the SWCNT fluorescence remains a bottleneck for designing sequences that are specific to analytes of interest. We have recently demonstrated the use of directed evolution to control the fluorescence response of SWCNTs through DNA design. Iterative cycles of DNA mutation, screening, and selection allowed us to evolve sequences that yield DNA-wrapped SWCNT sensors with a desired fluorescence response to mycotoxins. In this work, we apply the screening results of the DNA libraries used in this approach to train machine learning (ML) algorithms. Artificial neural network (ANN) and support vector machine (SVM) methods were used to predict the response of ssDNA-SWCNT sensors to a specific mycotoxin. The reliability of these models was further assessed through cross-validation. The ANN and SVM models with cross-validation were able to accurately classify the various DNA sequences as yielding either a high or low fluorescence response with an accuracy of 73 and 81%, respectively. The models were further used to predict the performance of alternative DNA sequences outside the initial training dataset, using the Hierarchy and k-means ++ clustering methods to evaluate the similarity and dissimilarity of each DNA sequence. Compared to the SVM model, the ANN model showed an improved ability to predict high responses for dissimilar DNA sequences. We further applied a combinatorial approach based on SVM and ANN models to design new DNA sequences for improving sensor performance. The success of this approach was validated experimentally, demonstrating the rational design of improved sensors with 95% prediction accuracy. The application of ML algorithms to directed evolution libraries of DNA thus allows one to accurately map the performances of these sensors within a particular sequence space. The computational success of this mapping provides a framework for replacing current empirical approaches with the rational design of DNA sequences for SWCNT sensing. Keywords: Nano Biosensor, DNA-SWCNT, Mycotoxin, Machine learning, Directed evolution method

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Prediction of mycotoxin response of DNA-wrapped nanotube sensor with machine learning

Rabbani,

Behjati,

Lambert

et al. 2023

Preprint

View full text Add to dashboard Cite

DNA-wrapped single-walled carbon nanotubes (DNA-SWCNTs) have demonstrated great versatility as optical sensors. SWCNTs emit a near-infrared fluorescence that is responsive to the slightest changes in their environment, enabling the creation of sensors that can respond to single-molecule fluctuations within the vicinity of their surfaces. The fluorescence response and surface interactions of these sensors are determined by the DNA wrapping sequence. However, the lack of information on the relationship between the DNA sequence and its effect on the SWCNT fluorescence remains a bottleneck for designing sensors specific to analytes of interest. The use of directed evolution was recently demonstrated in order to evolve SWCNT sensors towards mycotoxins through iterative cycles of DNA mutation, screening and selection. In the current work, we use the data acquired during the directed evolution of DNA-SWCNT sensors to train machine learning (ML) algorithms. Artificial neural network (ANN) and support vector machine (SVM) methods were used to predict the response of DNA-SWCNT sensors to the mycotoxin. The reliability of the models was assessed through cross-validation. The cross-validated ANN and SVM models were able to accurately classify the various DNA-SWCNTs as yielding either a high or low fluorescence response with an accuracy of 73 and 81%, respectively. The models were further tested on alternative similar and dissimilar DNA sequences outside of the initial training dataset. The ANN model showed a better ability to predict dissimilar DNA sequences resulting in a high sensor response in comparison with the SVM model. In addition, the possibility to combine the two SVM and ANN models with directed evolution methods was investigated. The experimental results showed that the SVM model was able to predict the response of DNA-SWCNT sensors with 95% accuracy. Finally, the Hierarchy and k-means++ clustering methods were used to examine the similarity and dissimilarity of each DNA sequence at every stage of our investigation. In this work, we show that the application of ML algorithms to directed evolution libraries of DNA allows one to accurately map the performances of DNA-SWCNT sensors within a particular DNA sequence space. Moreover, the computational success of this mapping provides a framework for replacing current empirical approaches with the rational design of DNA sequences for SWCNT sensing.

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Machine-Perception Nanosensor Platform to Detect Cancer Biomarkers

Cited by 4 publications

References 61 publications

Directed evolution of nanosensors for the detection of mycotoxins

Directed evolution of nanosensors for the detection of mycotoxins

Prediction of Mycotoxin Response of DNA-Wrapped Nanotube Sensor with Machine Learning

Prediction of mycotoxin response of DNA-wrapped nanotube sensor with machine learning

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