Fast custom wavelet analysis technique for single molecule detection and identification

Vahid Ganjalizadeh,Gopikrishnan G Meena,Aaron R Hawkins,Thomas A Wall,Matthew A Stott,Holger Schmidt

doi:10.1038/s41467-022-28703-z

Vahid Ganjalizadeh, Gopikrishnan G Meena + Show 4 more

Open Access

https://doi.org/10.1038/s41467-022-28703-z

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Abstract

Many sensors operate by detecting and identifying individual events in a time-dependent signal which is challenging if signals are weak and background noise is present. We introduce a powerful, fast, and robust signal analysis technique based on a massively parallel continuous wavelet transform (CWT) algorithm. The superiority of this approach is demonstrated with fluorescence signals from a chip-based, optofluidic single particle sensor. The technique is more accurate than simple peak-finding algorithms and several orders of magnitude faster than existing CWT methods, allowing for real-time data analysis during sensing for the first time. Performance is further increased by applying a custom wavelet to multi-peak signals as demonstrated using amplification-free detection of single bacterial DNAs. A 4x increase in detection rate, a 6x improved error rate, and the ability for extraction of experimental parameters are demonstrated. This cluster-based CWT analysis will enable high-performance, real-time sensing when signal-to-noise is hardware limited, for instance with low-cost sensors in point of care environments.

Highlights

Many sensors operate by detecting and identifying individual events in a time-dependent signal which is challenging if signals are weak and background noise is present
We demonstrate the capabilities of this Parallel Cluster Wavelet Analysis (PCWA) algorithm for both single- and multi-spot excitation signals and validate it with a demonstration of single bacterial DNA detection on an optofluidic waveguide chip
The devices are based on intersecting solid- and hollow-core antiresonant reflecting optical waveguides (ARROW) built with a foundry compatible fabrication process[17]

Summary

Introduction

Many sensors operate by detecting and identifying individual events in a time-dependent signal which is challenging if signals are weak and background noise is present. Fast, and robust signal analysis technique based on a massively parallel continuous wavelet transform (CWT) algorithm The superiority of this approach is demonstrated with fluorescence signals from a chip-based, optofluidic single particle sensor. We introduce a flexible signal processing algorithm for unsupervised detection and identification of single-particle signals It is based on highly parallel, multi-scale continuous wavelet transform (CWT) analysis and meets the challenges for point-ofcare sensors described above, speed, accuracy, and sensitivity to low signal levels. For multi-peak signals, custom-designed wavelets are introduced that enable an over 4x increase in single-molecule detection rate and 6x reduction in errors compared to previously used techniques for periodic signals This PCWA method allows for real-time extraction of additional experimental parameters such as the flow velocity of the sample liquid and its dynamic evolution. The multi-scale is suitable for further expansion by exploiting supervised machine-learning techniques toward extremely accurate multiplex detection

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