When surface-enhanced Raman spectroscopy meets complex biofluids: A new representation strategy for reliable and comprehensive characterization

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) has gained increasing importance in molecular detection due to its high specificity and sensitivity. Complex biofluids (e.g., cell lysates and serums) typically contain large numbers of different bio-molecules with various concentrations, making it extremely challenging to be reliably and comprehensively characterized via conventional single SERS spectra due to uncontrollable electromagnetic hot spots and irregular molecular motions. The traditional approach of directly reading out the single SERS spectra or calculating the average of multiple spectra is less likely to take advantage of the full information of complex biofluid systems.

RESULTS

Herein, we propose to construct a spectral set with unordered multiple SERS spectra as a novel representation strategy to characterize full molecular information of complex biofluids. This new SERS representation not only contains details from each single spectra but captures the temporal/spatial distribution characteristics. To address the ordering-independent property of traditional chemometric methods (e.g., the Euclidean distance and the Pearson correlation coefficient), we introduce Wasserstein distance (WD) to quantitatively and comprehensively assess the quality of spectral sets on biofluids. WD performs its superiority for the quantitative assessment of the spectral sets. Additionally, WD benefits from its independence of the ordering of spectra in a spectral set, which is undesirable for traditional chemometric methods. With experiments on cell lysates and human serums, we successfully achieve the verification for the reproducibility between parallel samples, the uniformity at different positions in the same sample, the repeatability from multiple tests at one location of the same sample, and the cardinality effect of the spectral set. SERS spectral sets also manage to distinguish different classes of human serums and achieve higher accuracy than the traditional prostate-specific antigen in prostate cancer classification.

SIGNIFICANCE

The proposed SERS spectral set is a robust representation approach in accessing full information of biological samples compared to relying on a single or averaged spectra in terms of reproducibility, uniformity, repeatability, and cardinality effect. The application of WD further demonstrates the effectiveness and robustness of spectral sets in characterizing complex biofluid samples, which extends and consolidates the role of SERS.

Label-free surface-enhanced Raman spectroscopy of serum with machine-learning algorithms for gallbladder cancer diagnosis

Gallbladder cancer (GBC) is a rare but frequently fatal biliary tract malignancy that is typically only discovered when it is already advanced. In the search of an efficient diagnosis method. Therefore, in this study, we investigated a novel technique for the quick and non-invasive diagnosis of GBC based on serum surface-enhanced Raman spectroscopy (SERS). SERS spectra of serum from 41 patients with GBC and 72 normal subjects were recorded. Principal component analysis-linear discriminant analysis (PCA-LDA), and PCA-support vector machine (PCA-SVM), Linear SVM and Gaussian radial basis function-SVM (RBF-SVM) algorithms were used to establish the classification models, respectively. When the Linear SVM was used, the overall diagnostic accuracy for classifying the two groups could achieve 97.1%, and when RBF-SVM was used, the diagnostic sensitivity of GBC was 100%. The results demonstrated that SERS in combination with a machine learning algorithm is a promising candidate to be one of the diagnostic tools for GBC in the future.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Rapid Identification of Benign Gallbladder Diseases Using Serum Surface-Enhanced Raman Spectroscopy Combined with Multivariate Statistical Analysis

In this study, we looked at the viability of utilizing serum to differentiate between gallbladder (GB) stones and GB polyps using Surface-enhanced Raman spectroscopy (SERS), which has the potential to be a quick and accurate means of diagnosing benign GB diseases. Rapid and label-free SERS was used to conduct the tests on 148 serum samples, which included those from 51 patients with GB stones, 25 patients with GB polyps and 72 healthy persons. We used an Ag colloid as a Raman spectrum enhancement substrate. In addition, we employed orthogonal partial least squares discriminant analysis (OPLS-DA) and principal component linear discriminant analysis (PCA-LDA) to compare and diagnose the serum SERS spectra of GB stones and GB polyps. The diagnostic results showed that the sensitivity, specificity, and area under curve (AUC) values of the GB stones and GB polyps based on OPLS-DA algorithm reached 90.2%, 97.2%, 0.995 and 92.0%, 100%, 0.995, respectively. This study demonstrated an accurate and rapid means of combining serum SERS spectra with OPLS-DA to identify GB stones and GB polyps.

Machine Learning-Assisted Sampling of Surfance-Enhanced Raman Scattering (SERS) Substrates Improve Data Collection Efficiency

Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artificial intelligence methods, these efforts have largely focused on downstream processing (e.g., classification tasks) of previously collected data. While fully automated analysis pipelines are desirable, current progress is limited by cumbersome and manually intensive sample preparation and data collection steps. Specifically, a typical lab-scale SERS experiment requires the user to evaluate the quality and reliability of the measurement (i.e., the spectra) as the data are being collected. This need for expert user-intuition is a major bottleneck that limits applicability of SERS-based diagnostics for point-of-care clinical applications, where trained spectroscopists are likely unavailable. While application-agnostic numerical approaches (e.g., signal-to-noise thresholding) are useful, there is an urgent need to develop algorithms that leverage expert user intuition and domain knowledge to simplify and accelerate data collection steps. To address this challenge, in this work, we introduce a machine learning-assisted method at the acquisition stage. We tested six common algorithms to measure best performance in the context of spectral quality judgment. For adoption into future automation platforms, we developed an open-source python package tailored for rapid expert user annotation to train machine learning algorithms. We expect that this new approach to use machine learning to assist in data acquisition can serve as a useful building block for point-of-care SERS diagnostic platforms.

Accurate Tumor Subtype Detection with Raman Spectroscopy via Variational Autoencoder and Machine Learning

Accurate diagnosis of cancer subtypes is a great guide for the development of surgical plans and prognosis in the clinic. Raman spectroscopy, combined with the machine learning algorithm, has been demonstrated to be a powerful tool for tumor identification. However, the analysis and classification of Raman spectra for biological samples with complex compositions are still challenges. In addition, the signal-to-noise ratio of the spectra also influences the accuracy of the classification. Herein, we applied the variational autoencoder (VAE) to Raman spectra for downscaling and noise reduction simultaneously. We validated the performance of the VAE algorithm at the cellular and tissue levels. VAE successfully downscaled high-dimensional Raman spectral data to two-dimensional (2D) data for three subtypes of non-small cell lung cancer cells and two subtypes of kidney cancer tissues. Gaussian naïve bayes was applied to subtype discrimination with the 2D data after VAE encoding at both the cellular and tissue levels, significantly outperforming the discrimination results using original spectra. Therefore, the analysis of Raman spectroscopy based on VAE and machine learning has great potential for rapid diagnosis of tumor subtypes.

Highly accurate and label-free discrimination of single cancer cell using a plasmonic oxide-based nanoprobe

The detection of cancer cells at the single-cell level enables many novel functionalities such as next-generation cancer prognosis and accurate cellular analysis. While surface-enhanced Raman spectroscopy (SERS) has been widely considered as an effective tool in a low-cost and label-free manner, however, it is challenging to discriminate single cancer cells with an accuracy above 90% mainly due to the poor biocompatibility of the noble-metal-based SERS agents. Here, we report a dual-functional nanoprobe based on dopant-driven plasmonic oxides, demonstrating a maximum accuracy above 90% in distinguishing single THP-1 cell from peripheral blood mononuclear cell (PBMC) and human embryonic kidney (HEK) 293 from human macrophage cell line U937 based on their SERS patterns. Furthermore, this nanoprobe can be triggered by the bio-redox response from individual cells towards stimuli, empowering another complementary colorimetric cell detection, approximately achieving the unity discrimination accuracy at a single-cell level. Our strategy could potentially enable the future accurate and low-cost detection of cancer cells from mixed cell samples.

Label-free surface-enhanced Raman spectroscopy for diagnosis and analysis of serum samples with different types lung cancer

Screening and detection of early lung cancer is important for diagnosis and prognosis. Intervention in early stage of lung cancer can significantly improve the cure and survival of patients. Surface-enhanced Raman spectroscopy (SERS) is an increasingly popular method of diagnosing cancer. We used silver nanoparticles (AgNPs) as the Raman-enhanced substrate to increase Raman signals, which contributes to the subsequent classification of lung cancer and normal serum. SERS acquired from the serum indicated the difference in biochemical components between cancerous (n = 51) lung serum and normal (n = 18) serum. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were utilized to establish the identification model, and the various indicators of PLS-DA were all superior to those of the PLS model. Our study offers a new proposal for the universal applicability of analysis and identification with SERS of serum samples in clinical diagnosis.

Non-invasive SERS serum detection technology combined with multivariate statistical algorithm for simultaneous screening of cervical cancer and breast cancer

Surface-enhanced Raman scattering (SERS), as a rapid, reliable and non-destructive spectral detection technology, has made a series of breakthrough achievements in screening and pre-diagnosis of various cancerous tumors. In this paper, high-performance gold nanoparticles/785 porous silicon photonic crystals (Au NPs/785 PSi PhCs) active SERS substrates were specially designed for serum testing, and realized highly sensitive detection of serum from healthy people, patients with cervical cancer and breast cancer. Based on the SERS spectra of the three groups of serum, the significant differences between the healthy group and cancer group at 1030 cm^-1 and 1051 cm^-1 were analyzed, and the similar but different serum SERS spectra of cervical cancer and breast cancer patients were compared. In addition, the spectral difference detected by SERS technology combined with a multivariate statistical algorithm was used to distinguish three kinds of serum. The serum SERS spectral sensitive bands were extracted by recursive weighted partial least squares (rPLS), and the three classification diagnosis models were established by combining orthogonal partial least squares discriminant analysis (OPLS-DA), linear discriminant analysis (LDA) and principal component analysis support vector machine (PCA-SVM) for synchronous classification and discrimination of the three groups of serum. The diagnostic results showed that the overall screening accuracy of three models were 93.28%, 97.77% and 94.78%, respectively. These above results confirmed that the Au NPs/785 PSi PhCs can realize super-sensitive detection of serum, and the established diagnostic model has great potential for pre-diagnosis and simultaneous screening of cervical cancer and breast cancer.

Detection and Classification of Multi-Type Cells by Using Confocal Raman Spectroscopy

Tumor cells circulating in the peripheral blood are the prime cause of cancer metastasis and death, thus the identification and discrimination of these rare cells are crucial in the diagnostic of cancer. As a label-free detection method without invasion, Raman spectroscopy has already been indicated as a promising method for cell identification. This study uses a confocal Raman spectrometer with 532 nm laser excitation to obtain the Raman spectrum of living cells from the kidney, liver, lung, skin, and breast. Multivariate statistical methods are applied to classify the Raman spectra of these cells. The results validate that these cells can be distinguished from each other. Among the models built to predict unknown cell types, the quadratic discriminant analysis model had the highest accuracy. The demonstrated analysis model, based on the Raman spectrum of cells, is propitious and has great potential in the field of biomedical for classifying circulating tumor cells in the future.

Serum analysis method combining cellulose acetate membrane purification with surface-enhanced Raman spectroscopy for non-invasive HBV screening

A highly sensitive, non-invasive, and rapid HBV (Hepatitis B virus) screening method combining membrane protein purification with silver nanoparticle-based surface-enhanced Raman scattering (SERS) spectroscopy was developed in this study. Reproducible serum protein SERS spectra were obtained from cellulose acetate membrane-purified human serum from 94 HBV patients and 89 normal groups. Tentative assignments of serum protein SERS spectra showed that the HBV patients primarily led to specific biomedical changes of serum protein. Principal components analysis and linear discriminate analysis were introduced to analyse the obtained spectra, with the diagnostic sensitivity of 92.6% and specificity of 77.5% were achieved for differentiating HBV patients from normal groups.

Sources of variability in SERS spectra of bacteria: comprehensive analysis of interactions between selected bacteria and plasmonic nanostructures

The surface-enhanced Raman spectroscopy (SERS)-based analysis of bacteria suffers from the lack of a standard SERS detection protocol (type of substrates, excitation frequencies, and sampling methodologies) that could be employed throughout laboratories to produce repeatable and valuable spectral information. In this work, we have examined several factors influencing the spectrum and signal enhancement during SERS studies conducted on both Gram-negative and Gram-positive bacterial species: Escherichia coli and Bacillus subtilis, respectively. These factors can be grouped into those which are related to the structure and types of plasmonic systems used during SERS measurements and those that are associated with the culturing conditions, types of culture media, and method of biological sample preparation.

Intraoperative Detection and Eradication of Residual Microtumors with Gap-Enhanced Raman Tags

The inability to intraoperatively diagnose and eliminate microscopic residual tumors represents a significant challenge in cancer surgery. These residual microtumors cause lethal recurrence and metastasis. Herein, we show a crucial example of Raman imaging with gap-enhanced Raman tags (GERTs) to serve as a robust platform for intraoperative detection and eradication of residual microscopic foci, which exist in surgical margins, tumor invasion, and multifocal tumor spread. The GERTs feature gap-enhanced gold core-shell nanostructures, with Raman reporters embedding inside the interior gap junction. This nanostructure elicits highly sensitive and photostable Raman signals for microtumor detection by applying a 785 nm, low-energy laser and produces hyperthermia effects for microtumor ablation upon switching a 808 nm, high-power laser. In the orthotopic prostate metastasis tumor model, systematic delivery of GERTs enabled precise imaging and real-time ablation of macroscopic malignant lesions around the surgical bed without damaging normal tissues. Consequently, the GERTs-based surgery prevented local recurrence and delivered 100% tumor-free survival. These results suggest the efficiency of theranostic GERTs for precise detection and removal of residual miroctumors, broadening the avenues to apply Raman-based imaging for theranostic precision medicine.

SERS Sensors: Recent Developments and a Generalized Classification Scheme Based on the Signal Origin

Owing to its extreme sensitivity and easy execution, surface-enhanced Raman spectroscopy (SERS) now finds application for a wide variety of problems requiring sensitive and targeted analyte detection. This widespread application has prompted a proliferation of different SERS-based sensors, suggesting the need for a framework to classify existing methods and guide the development of new techniques. After a brief discussion of the general SERS modalities, we classify SERS-based sensors according the origin of the signal. Three major categories emerge from this analysis: surface-affinity strategy, SERS-tag strategy, and probe-mediated strategy. For each case, we describe the mechanism of action, give selected examples, and point out general misconceptions to aid the construction of new devices. We hope this review serves as a useful tutorial guide and helps readers to better classify and design practical and effective SERS-based sensors.

In Vitro and In Vivo SERS Biosensing for Disease Diagnosis

For many disease states, positive outcomes are directly linked to early diagnosis, where therapeutic intervention would be most effective. Recently, trends in disease diagnosis have focused on the development of label-free sensing techniques that are sensitive to low analyte concentrations found in the physiological environment. Surface-enhanced Raman spectroscopy (SERS) is a powerful vibrational spectroscopy that allows for label-free, highly sensitive, and selective detection of analytes through the amplification of localized electric fields on the surface of a plasmonic material when excited with monochromatic light. This results in enhancement of the Raman scattering signal, which allows for the detection of low concentration analytes, giving rise to the use of SERS as a diagnostic tool for disease. Here, we present a review of recent developments in the field of in vivo and in vitro SERS biosensing for a range of disease states including neurological disease, diabetes, cardiovascular disease, cancer, and viral disease.

Surface-Enhanced Raman Spectroscopy for Bioanalysis: Reliability and Challenges

Surface-enhanced Raman spectroscopy (SERS) inherits the rich chemical fingerprint information on Raman spectroscopy and gains sensitivity by plasmon-enhanced excitation and scattering. In particular, most Raman peaks have a narrow width suitable for multiplex analysis, and the measurements can be conveniently made under ambient and aqueous conditions. These merits make SERS a very promising technique for studying complex biological systems, and SERS has attracted increasing interest in biorelated analysis. However, there are still great challenges that need to be addressed until it can be widely accepted by the biorelated communities, answer interesting biological questions, and solve fatal clinical problems. SERS applications in bioanalysis involve the complex interactions of plasmonic nanomaterials with biological systems and their environments. The reliability becomes the key issue of bioanalytical SERS in order to extract meaningful information from SERS data. This review provides a comprehensive overview of bioanalytical SERS with the main focus on the reliability issue. We first introduce the mechanism of SERS to guide the design of reliable SERS experiments with high detection sensitivity. We then introduce the current understanding of the interaction of nanomaterials with biological systems, mainly living cells, to guide the design of functionalized SERS nanoparticles for target detection. We further introduce the current status of label-free (direct) and labeled (indirect) SERS detections, for systems from biomolecules, to pathogens, to living cells, and we discuss the potential interferences from experimental design, measurement conditions, and data analysis. In the end, we give an outlook of the key challenges in bioanalytical SERS, including reproducibility, sensitivity, and spatial and time resolution.

One-pot synthesis of a DNA-anchored SERS nanoprobe with simultaneous nanostructural tuning and Raman reporter encoding

We report a new simple one-pot synthesis method for a DNA-anchored SERS nanoprobe encoded with Raman reporters.