Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification

Abstract

Raman spectroscopy is a nondestructive, label-free, highly specific approach that provides the chemical information on materials. Thus, it is suitable to be used as an effective analytical tool to characterize biological samples. Here we introduce a novel method that uses artificial intelligence to analyze biological Raman spectra and identify the microbes at a single-cell level. The combination of a framework of convolutional neural network (ConvNet) and Raman spectroscopy allows the extraction of the Raman spectral features of a single microbial cell and then categorizes cells according to their spectral features. As the proof of concept, we measured Raman spectra of 14 microbial species at a single-cell level and constructed an optimal ConvNet model using the Raman data. The average accuracy of classification by ConvNet is 95.64 ± 5.46%. Meanwhile, we introduced an occlusion-based Raman spectra feature extraction to visualize the weights of Raman features for distinguishing different species.

Machine learning-assisted single-cell Raman fingerprinting for in situ and nondestructive classification of prokaryotes

“…We have applied a combination of single-cell Raman microspectroscopy and RF machine learning strategy for classification of six prokaryotic species chosen from a variety of phyla and for identification in a mixed population envisaging environmental samples. Recently, CNN, a deep learning technique, was used to classify Raman spectra of 30 clinically relevant bacteria including methicillin-resistant Staphylococcus aureus ( Ho et al., 2019 ) and of 14 microorganisms (two bacteria, five archaea, and seven fungi) ( Lu et al., 2020 ). The significance of our work compared to these studies is threefold.…”

“…First, we aimed at developing an accurate classification model applicable to a complex environmental system composed of multiple prokaryotic species. As shown in Table 1 , the prokaryotic species used in this work cover microbial-ecologically relevant species that belong to Proteobacteria , Firmicutes , Deinococcus-Thermus , Crenarchaeota , Euryarchaeota , and Thaumarchaeota , although focus in the previous studies was placed primarily on pathogenic or human-related microorganisms only from Proteobacteria and Firmicutes ( Ho et al., 2019 ; Lu et al., 2020 ). The classification accuracy exceeding 98% achieved by our approach for the mixed prokaryotic population gives hope for in situ identification of specific prokaryotic groups (e.g., archaea) at the single-cell level without the need for time-consuming, destructive analysis.…”

“…Second, our Raman measurement is as less invasive as possible to prokaryotic cells, and our preprocessing of the measured Raman spectra is also minimally demanding for non-spectroscopists. Lu and co-workers used ∼16 mW/μm 2 laser power and 60–90 s exposure time in their 785 nm-excited single-cell Raman measurements for CNN-based classification of microorganisms ( Lu et al., 2020 ). In contrast, the laser power and exposure time we used were only ∼3.8 mW/μm 2 at 632.8 nm and 30 s, respectively.…”

“…This label-free character is of great advantage when considering applications to environmental samples consisting of diverse (mostly unknown) microorganisms. However, despite these advantages over the genome sequencing and fluorescence techniques, the implementation of Raman microspectroscopy in facile yet accurate microbial classification ( Ho et al., 2019 ; Lu et al., 2020 ; Novelli-Rousseau et al., 2018 ; Uysal Ciloglu et al., 2020 ) is scant especially for environmental samples.…”

Nanostructures for Biosensing, with a Brief Overview on Cancer Detection, IoT, and the Role of Machine Learning in Smart Biosensors

“…Thrift et al demonstrated single-entity analysis by using convolutional neural network-assisted surface-enhanced Raman spectroscopy techniques for detecting ultralow (< nanomolar) concentrations of Rhodamine 800 [239]. Similarly, Lu et al reported identifying microorganisms at single cellular level using laser tweezer spectroscopy with convolutional neural networks, with a classification accuracy of ~95% [240]. Pandit et al demonstrated highly accurate detection of proteins present in low concentrations, without the use of any bioreceptor by using carbon-dot sensors assisted by a variety of machine learning algorithms [241].…”