Raman spectroscopy: current applications in breast cancer diagnosis, challenges and future prospects

Perspectives on label-free microscopy of heterogeneous and dynamic biological systems

Significance

Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging).

Aim

This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs.

Approach

Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended.

Conclusion

Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Integrating Raman spectroscopy and RT-qPCR for enhanced diagnosis of thyroid lesions: A comparative study of biochemical and molecular markers

Thyroid cancer is the most prevalent endocrine malignancy, with increasing incidence due to advancements in diagnostic techniques. Ultrasound (US) and fine needle aspiration (FNA) cytology, widely used in clinical practice, have detection accuracies ranging from 65 % to 95 %. However, these methods may yield inconclusive or difficult-to-interpret results, emphasizing the need for complementary diagnostic techniques. This study explores the integration of Raman spectroscopy and gene expression analysis via RT-qPCR to improve the diagnosis of thyroid lesions, classified into groups: follicular thyroid carcinoma (FTC), papillary thyroid carcinoma (PTC) and goiter tissues. Healthy tissue samples were used as normalizing controls in both analysis. Raman spectroscopy analyzed 35 samples, while RT-qPCR assessed 33 samples. For comparison, the same 19 samples previously analyzed by both techniques were examined. Raman spectroscopy, a non-invasive technique, has shown effectiveness in distinguishing between benign and malignant thyroid tissues by identifying key biochemical components such as DNA, RNA, proteins, and lipids. The distinguishing of FTC from goiter using Raman spectroscopy achieved an accuracy rate of 82.3 %. Gene expression analysis via RT-qPCR focused on six genes: TG, TPO, PDGFB, SERPINA1, TFF3, and LGALS3. Specifically, SERPINA1 was overexpressed in PTC, TFF3 showed elevated levels in FTC, and LGALS3 was elevated in both PTC and FTC compared to goiter and normal tissues. These findings align with existing literature, suggesting that these genes could serve as valuable diagnostic molecular markers. The expression analysis of these genes within this subset of samples demonstrated concordance with the classification derived from PCA of Raman spectroscopy data. The integration of Raman spectroscopy and RT-qPCR offers a complementary approach to traditional histological analysis, providing enhanced sensitivity and specificity in diagnosing thyroid lesions.

Exploring breast cancer-related biochemical changes in circulating extracellular vesicles using Raman spectroscopy

Extracellular vesicles (EVs) are a subgroup of the circulating particles, released by cells in both normal and diseased states, carrying active biomolecules. They have gained significant attention as potential cancer biomarkers, particularly in breast cancer (BC). Previous research showed variations in EVs content and quantity between BC patients and healthy controls (HC). However, studying the biochemical profile of EVs remains challenging due to their low abundance and complex composition. Additionally, EVs may interact with other plasma components, like lipoproteins (LPs), forming a so called "biomolecular corona" that further complicates their analysis. Here, Raman spectroscopy (RS) is proposed as a fast tool to obtain the biochemical profile of circulating EVs in the context of BC. RS was employed to differentiate various extracellular particles (EPs) in blood, including LPs and EVs. The study also evaluated RS's capability to quantify major classes of biomolecules and compared these results with those obtained by traditional biochemical assays. Finally, compositional differences in large EVs (lEVs) and small EVs (sEVs) were assessed between HC and BC patients. RS revealed the existence of distinct biochemical signatures associated with BC, highlighting increased levels of nucleic acids and lipids in the BC group.

Surface-enhanced Raman spectroscopy for the characterization of filtrate portions of hepatitis B blood serum samples using 100 kDa ultra filtration devices

The blood serum of patients infected by the Hepatitis B virus contains high molecular weight fractions and low molecular weight fractions (LMWF) of biomarker proteins of the disease. The LMWF including the associated peptidome and metabolome, is recognized as a critical molecular population with high potential for research on disease-associated biomarkers. This fraction of biomarkers can be suppressed by HMWF, proteins such as albumin, and immunoglobulins hence difficult to be detected. The purpose of this study is to separate HMWF) and LMWF using 100 kDa centrifugal filtration devices resulting in two parts including residue (HMWF) and filtrate parts (LMWF) of blood serum followed by the analysis of the later part employing surface-enhanced Raman spectroscopy (SERS). This strategy can enhance this optical technique's capability to characterize the biochemical changes caused by the infection of HBV and the diagnosis of the disease. The silver nanoparticles (Ag-NPs) were employed as a SERS substrate to distinguish between filtrate parts of the blood serum of HBV patients and healthy individuals based on their specific SERS peaks. The SERS spectral features associated with the filtrate parts of HBV patients' blood serum are well differentiated from the healthy volunteers. Principle component analysis (PCA) was applied on the SERS spectral data sets of HBV patients and healthy individuals and found extremely beneficial for the classification of their SERS spectral groups. Moreover, partial least square regression analysis (PLSR) has shown excellent performance in the quantitative analysis of the viral load values of the HBV patients using their SERS spectral data sets.

External Validation of Raman Spectroscopy for Lyme Disease Diagnostics

Lyme disease (LD), caused by Borreliella burgdorferi, is the most common tick-borne illness in the United States, yet early-stage diagnosis remains challenging due to the limitations of current serological diagnostics. Raman spectroscopy (RS), paired with partial least squares discriminant analysis (PLS-DA), showed promise as an alternative diagnostic tool. Using RS, we analyzed 107 coded human blood samples (42 LD-positive and 65 LD-negative) obtained from the Lyme Disease Biobank. PLS-DA models showed nearly perfect internal validation performance with a sensitivity and specificity of 97.1% and 100.0%, respectively, indicating robust predictive capabilities. External validation of the developed chemometrics model with 80/20 training/validation split of all spectra gave true positive rates of 92.7% and 87.3% for serological positive and negative spectra, respectively. These findings highlight the potential of RS as a rapid and noninvasive diagnostic platform for LD, particularly when integrated with machine learning.

Raman spectroscopies for cancer research and clinical applications: a focus on cancer stem cells

Over the last 2 decades, research has increasingly focused on cancer stem cells (CSCs), considered responsible for tumor formation, resistance to therapies, and relapse. The traditional "static" CSC model used to describe tumor heterogeneity has been challenged by the evidence of CSC dynamic nature and plasticity. A comprehensive understanding of the mechanisms underlying this plasticity, and the capacity to unambiguously identify cancer markers to precisely target CSCs are crucial aspects for advancing cancer research and introducing more effective treatment strategies. In this context, Raman spectroscopy (RS) and specific Raman schemes, including CARS, SRS, SERS, have emerged as innovative tools for molecular analyses both in vitro and in vivo. In fact, these techniques have demonstrated considerable potential in the field of cancer detection, as well as in intraoperative settings, thanks to their label-free nature and minimal invasiveness. However, the RS integration in pre-clinical and clinical applications, particularly in the CSC field, remains limited. This review provides a concise overview of the historical development of RS and its advantages. Then, after introducing the CSC features and the challenges in targeting them with traditional methods, we review and discuss the current literature about the application of RS for revealing and characterizing CSCs and their inherent plasticity, including a brief paragraph about the integration of artificial intelligence with RS. By providing the possibility to better characterize the cellular diversity in their microenvironment, RS could revolutionize current diagnostic and therapeutic approaches, enabling early identification of CSCs and facilitating the development of personalized treatment strategies.

Raman spectroscopy based diagnosis of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma is currently the 12th most frequent form of cancer worldwide, characterized by a very low 5-year survival rate. Although several therapeutic approaches have been proposed to treat this form of pancreatic cancer, surgical resection is still commonly recognized as the most effective technique to slow down the disease progression and maximize the 5-year survival rate. Analogously, one critical issue is the ability of current diagnostic methodologies to distinguish between irregular growth of the tumor mass and surrounding inflammatory tissues. In this pilot study, we apply Raman spectroscopy, supported by a series of machine learning techniques, to distinguish among healthy, pancreatitis and ductal adenocarcinoma tissues, respectively, for a total of 15 cases. Raman spectroscopy is a label-free, non-destructive spectral technique exploiting Raman scattering. In turn, by applying a combination of principal component analysis and random forest classifier on the Raman spectral dataset, we achieved a maximum accuracy of up to ∼ 96%. Our findings clearly indicate that Raman spectroscopy could become a powerful spectral technique to support pathologists in improving pancreatic cancer diagnosis.

Application of Fourier transform infrared spectroscopy on Breast cancer diagnosis combined with multiple algorithms: A systematic review

INTRODUCTION

Fourier transform infrared (FT-IR) spectroscopy is an innovative diagnostic technique for improving early detection and personalized care for breast cancer patients. It allows rapid and accurate analysis of biological samples. Therefore, the purpose of this study was to assess the diagnostic accuracy of FT-IR spectroscopy for breast cancer, based on a comprehensive literature review.

METHODS

An online electronic database systematic search was conducted using PubMed/Medline, Cochrane Library, and hand databases from March 28, 2024, to April 10, 2024. We included peer-reviewed journal articles in which FT-IR spectroscopy was used to acquire data on breast cancers and manuscripts published in English. All eligible studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool.

RESULTS

Serum, breast biopsies, blood plasma, specimen, and saliva samples were included in this study. This study revealed that breast cancer diagnosis using FT-IR spectroscopy with diagnostic algorithms had a sensitivity and specificity of approximately 98 % and 100 %, respectively. Almost all studies have used more than one algorithm to analyze spectral data. This finding showed that the sensitivity of FT-IR spectroscopy reported in six included studies was greater than 90 %.

CONCLUSION

Employing multivariate analysis coupled with FT-IR spectroscopy has shown promise in differentiating between healthy and cancerous breast tissue. This review revealed that FT-IR spectroscopy will be the next gold standard for breast cancer diagnosis. However, to draw definitive conclusions, larger-scale studies, external validation, real-world clinical trials, legislative considerations, and alternative methods such as Raman spectroscopy should be considered.

Raman spectroscopy in the detection and diagnosis of lung cancer: a meta-analysis

Lung cancer is the world's biggest cause of death related to cancer, and its dismal prognosis has been greatly exacerbated by late-stage diagnosis. Even with improvements in treatment strategies, current diagnostic techniques are frequently imprecise, especially when it comes to early-stage detection. A prospective substitute is Raman spectroscopy, which provides a non-invasive, real-time, and extremely sensitive study of biological samples. The objective of this study is to assess the diagnostic efficacy of Raman spectroscopy in the identification and diagnosis of lung cancer across a range of sample types. Nine studies that focused on Raman spectroscopy as a stand-alone diagnostic tool and met strict inclusion criteria were found through a systematic review of the literature published between 2014 and 2024. Statistical methods were used to extract, pool, and show diagnostic measures. The remarkable diagnostic accuracy of Raman spectroscopy was highlighted by its pooled sensitivity and specificity which were 98.68% and 91.81%, respectively. Serum-based research showed the strongest findings, with multivariate models such as PCA-LDA supporting specificity and sensitivity values that, in several cases, reached 100%. Diagnostic accuracy was greatly improved by models such as SVM and CNN, particularly when it came to detecting minute spectral alterations associated with cancer. Raman spectroscopy shows great promise as a lung cancer diagnostic method. However, issues including spectral data standardization, sample preparation heterogeneity and the requirement for bigger, multicentre research needs to be addressed. These results will open the door for the incorporation of Raman spectroscopy into standard clinical procedures.

A high-quality fluorescence elimination dual-wavelength Raman method for biological detection and its application in cancer diagnosis

Raman-based methods offer a promising approach for in vivo biological detection. However, the fluorescence of biological samples will significantly affect Raman measurement accuracy. Moreover, due to the existence of excitation wavelength-dependent fluorescent molecules in biological tissues, especially porphyrin molecules, the fluorescence also exhibits significant wavelength dependence. To achieve high-quality Raman spectra of biological tissue, in this work we proposed a dual-wavelength Raman method. Two lasers with different wavelengths were used to excite optical signals in the same region, and the ordinary fluorescence and additional wavelength-dependent fluorescence in the biological samples could be eliminated by two-step normalization calibration; thus, the accuracy of Raman measurement was significantly enhanced. We applied this method to early cancer diagnosis and identified several molecules and structures worthy of attention in carcinogenesis for esophageal tissue, such as phenylalanine and the CC bonds of porphyrins. Normal, precancerous, and early cancer samples were successfully identified by the changes in biomolecules with associated degrees of malignancy. Thus, the imaging and diagnosis of indefinite tumors were realized, which verified the potential of the dual-wavelength Raman method in biological detection.

Convolutional neural network-assisted Raman spectroscopy for high-precision diagnosis of glioblastoma

Glioblastoma multiforme (GBM) is the most lethal intracranial tumor with a median survival of approximately 15 months. Due to its highly invasive properties, it is particularly difficult to accurately identify the tumor margins intraoperatively. The current gold standard for diagnosing GBM during surgery is pathology, but it is time-consuming. Under these circumstances, we developed a method combining Raman spectroscopy (RS) with convolutional neural networks (CNN) to distinguish GBM. Analysis of the spectra of normal brain samples (478 spectra) and GBM samples (462 spectra) from 29 in situ intracranial tumor-bearing mice showed that this method identified GBM tissue with 96.8 % accuracy. Subsequently, spectral analysis of 23 normal human brain tissues (223 spectra) versus 21 tissues from patients with pathologically diagnosed GBM (267 spectra) revealed that the accuracy of this method was 93.9 %. Most importantly, for the difference peaks in the spectra of GBM and normal brain tissue, the common difference peaks in the mouse and human spectra were at 750 cm-1, 1440 cm-1, and 1586 cm-1, which emphasized the differences in cytochrome C and lipids between GBM samples and normal brain samples in both mice and human. The preliminary results showed that CNN-assisted RS is simple to operate and can rapidly and accurately identify whether it is GBM tissue or normal brain tissue.

Intraoperative use of high-speed Raman spectroscopy during soft tissue sarcoma resection

Retroperitoneal soft tissue sarcoma (RSTS) is a rare type of cancer with limited treatment options. Achieving complete resection with negative margins is one of the most significant prognostic factors for RSTS survival. The UltraProbe is a handheld point probe Raman spectroscopy system that significantly decreases the imaging time compared to the probe systems currently used. This study aims to determine the performance of the UltraProbe in detecting STS in an in vivo environment during their resection. Thirty patients were recruited at Maisonneuve-Rosemont Hospital, Montreal, Canada. Raman spectra were acquired during STS resection using the instrument. A machine learning random forest classification algorithm was developed to predict the diagnosis associated with new Raman spectra: STS or healthy tissue. The classification of Raman spectra as well-differentiated liposarcomas or normal adipose tissue was performed with a sensitivity of 94%, specificity of 95%, and accuracy of 94%. The classification of spectra as well-differentiated and dedifferentiated liposarcomas or normal adipose tissue was performed with a sensitivity of 90%, specificity of 93%, and accuracy of 90%. The classification of spectra as non-liposarcoma STS or protein-rich non-adipose tissue was performed with a sensitivity of 87%, specificity of 81%, and accuracy of 87%.

Detection of Respiratory Disease Based on Surface-Enhanced Raman Scattering and Multivariate Analysis of Human Serum

Background/Objectives: Chronic obstructive pulmonary disease (COPD) is a significant public health concern, affecting millions of people worldwide. This study aims to use Surface-Enhanced Raman Scattering (SERS) technology to detect the presence of respiratory conditions, with a focus on COPD. Methods: The samples of human serum from 41 patients with respiratory diseases (11 patients with COPD, 20 with bronchial asthma (BA), and 10 with asthma-COPD overlap syndrome) and 103 patients with ischemic heart disease, complicated by chronic heart failure (CHF), were analyzed using SERS. A multivariate analysis of the SERS characteristics of human serum was performed using Partial Least Squares Discriminant Analysis (PLS-DA) to classify the following groups: (1) all respiratory disease patients versus the pathological referent group, which included CHF patients, and (2) patients with COPD versus those with BA. Results: We found that a combination of SERS characteristics at 638 and 1051 cm-1 could help to identify respiratory diseases. The PLS-DA model achieved a mean predictive accuracy of 0.92 for classifying respiratory diseases and the pathological referent group (0.85 sensitivity, 0.97 specificity). However, in the case of differentiating between COPD and BA, the mean predictive accuracy was only 0.61. Conclusions: Therefore, the metabolic and proteomic composition of human serum shows significant differences in respiratory disease patients compared to the pathological referent group, but the differences between patients with COPD and BA are less significant, suggesting a similarity in the serum and general pathogenetic mechanisms of these two conditions.

Comprehensive Raman Fingerprinting and Machine Learning-Based Classification of 14 Pesticides Using a 785 nm Custom Raman Instrument

Raman spectroscopy enables fast, label-free, qualitative, and quantitative observation of the physical and chemical properties of various substances. Here, we present a 785 nm custom-built Raman spectroscopy instrument designed for sensing applications in the 400-1700 cm-1 spectral range. We demonstrate the performance of the instrument by fingerprinting 14 pesticide reference samples with over twenty technical repeats per sample. We present molecular Raman fingerprints of the pesticides comprehensively and distinguish similarities and differences among them using multivariate analysis and machine learning techniques. The same pesticides were additionally investigated using a commercial 532 nm Raman instrument to see the potential variations in peak shifts and intensities. We developed a unique Raman fingerprint library for 14 reference pesticides, which is comprehensively documented in this study for the first time. The comparison shows the importance of selecting an appropriate excitation wavelength based on the target analyte. While 532 nm may be advantageous for certain compounds due to resonance enhancement, 785 nm is generally more effective for reducing fluorescence and achieving clearer Raman spectra. By employing machine learning techniques like the Random Forest Classifier, the study automates the classification of 14 different pesticides, streamlining data interpretation for non-experts. Applying such combined techniques to a wider range of agricultural chemicals, clinical biomarkers, or pollutants could provide an impetus to develop monitoring technologies in food safety, diagnostics, and cross-industry quality control applications.

Clinical study of the diagnosis of thyroid tumours using Raman spectroscopy

OBJECTIVE

The feasibility of the RS for the clinical diagnosis of thyroid tumours was explored.

METHODS

The tumour specimens from 30 benign patients and 30 malignant patients were collected. The collected specimens were subjected to RS and histopathological analysis. The Raman peak intensities of all the specimens were calculated, and the data were analysed using discriminant analysis.

RESULTS

(1) The prevalence rate of malignant tumours in females was as high as 76.7%. Central lymph node metastasis of malignant thyroid tumours accounted for 33.3% of cases, and lateral cervical lymph node metastasis accounted for only 6.7%. (2) The spectral intensity of malignant thyroid tumours was significantly greater than benign thyroid tumours at 1309 cm-1, which should be the characteristic peak of thyroid cancer. The accuracy, sensitivity, and specificity of the RS for differentiating benign from malignant thyroid tumours were 95%, 83.3% and 89.2%.

CONCLUSION

RS is feasible for the diagnosis of thyroid tumours. This study provides experimental and clinical support for the wider application of RS in the evaluation of thyroid tissue.

LEVELS OF EVIDENCE

Levels 4.

Molecular Insights into α-Synuclein Fibrillation: A Raman Spectroscopy and Machine Learning Approach

The aggregation of α-synuclein is crucial to the development of Lewy body diseases, including Parkinson's disease and dementia with Lewy bodies. The aggregation pathway of α-synuclein typically involves a defined sequence of nucleation, elongation, and secondary nucleation, exhibiting prion-like spreading. This study employed Raman spectroscopy and machine learning analysis, alongside complementary techniques, to characterize the biomolecular changes during the fibrillation of purified recombinant wild-type α-synuclein protein. Monomeric α-synuclein was produced, purified, and subjected to a 7-day fibrillation assay to generate preformed fibrils. Stages of α-synuclein fibrillation were analyzed using Raman spectroscopy, with aggregation confirmed through negative staining transmission electron microscopy, mass spectrometry, and light scattering analyses. A machine learning pipeline incorporating principal component analysis and uniform manifold approximation and projection was used to analyze the Raman spectral data and identify significant peaks, resulting in differentiation between sample groups. Notable spectral shifts in α-synuclein were found in various stages of aggregation. Early changes (D1) included increases in α-helical structures (1303, 1330 cm-1) and β-sheet formation (1045 cm-1), with reductions in COO- and CH2 bond regions (1406, 1445 cm-1). By D4, these structural shifts persist with additional β-sheet features. At D7, a decrease in β-sheet H-bonding (1625 cm-1) and tyrosine ring breathing (830 cm-1) indicates further structural stabilization, suggesting a shift from initial helical structures to stabilized β-sheets and aggregated fibrils. Additionally, alterations in peaks related to tyrosine, alanine, proline, and glutamic acid were identified, emphasizing the role of these amino acids in intramolecular interactions during the transition from α-helical to β-sheet conformational states in α-synuclein fibrillation. This approach offers insight into α-synuclein aggregation, enhancing the understanding of its role in Lewy body disease pathophysiology and potential diagnostic relevance.

Cervical cancer diagnosis model using spontaneous Raman and Coherent anti-Stokes Raman spectroscopy with artificial intelligence

Cervical cancer is the fourth most common cancer worldwide. Histopathology, which is currently considered the gold standard for cervical cancer diagnosis, can be time-consuming and subjective. Therefore, there is an urgent need for a rapid, objective, and non-destructive cervical cancer detection technique. In this study, high-wavenumber spontaneous Raman spectroscopy was used to detect cervical squamous cell carcinoma and normal tissues. The levels of lipids, fatty acids, and proteins in cervical cancerous tissues were found to be higher than those in normal tissues. Raman difference spectroscopy revealed the most significant difference at 2928 cm-1. Additionally, a Coherent anti-Stokes Raman spectroscopy (CARS) instrument was employed to enhance the wavenumber signal intensity and sensitivity. The intrinsic relationship between CARS imaging and cervical lesions was established. The CARS images indicated that the intensity of normal cervical squamous cells was zero, whereas the intensities of keratinized and non-keratinized cervical squamous cell carcinoma tissues were significantly higher. Consequently, diagnostic outcomes could be obtained by observing CARS images with the naked eye. Furthermore, the characteristic structure of keratin pearls in keratinized cervical cancer could serve as a marker for subdividing cervical cancer types. Finally, a ConvNeXt network, a machine-learning model built from CARS images, was utilized to classify different types of tissue images. The results indicated a verification accuracy of 100 %, with a loss function of 0.0927. These findings suggest that the diagnostic model established using CARS images could efficiently diagnose cervical cancer, providing novel insights into the pathological diagnosis of this disease.

Recent advances of photodiagnosis and treatment for head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) are the most common type of head and neck tumor that severely threatens human health due to its highly aggressive nature and susceptibility to distant metastasis. The diagnosis of HNSCC currently relies on biopsy and histopathological examination of suspicious lesions. However, the early mucosal changes are subtle and difficult to detect by conventional oral examination. As for treatment, surgery is still the primary treatment modality. Due to the complex anatomy and the lack of intraoperative modalities to accurately determine the incision margins, surgeons are in a dilemma between extensive tumor removal and improving the quality of patient survival. As more knowledge is gained about HNSCC, the increasing recognition of the value of optical imaging has been emphasized. Optical technology offers distinctive possibilities for early preoperative diagnosis, intraoperative real-time visualization of tumor margins, sentinel lymph node biopsies, phototherapy. Fluorescence imaging, narrow-band imaging, Raman spectroscopy, optical coherence tomography, hyperspectral imaging, and photoacoustic imaging have been reported for imaging HNSCC. This article provides a comprehensive overview of the fundamental principles and clinical applications of optical imaging in the diagnosis and treatment of HNSCC, focusing on identifying its strengths and limitations to facilitate advancements in this field.

Microwell-assembled aluminum substrates for enhanced single-cell analysis: A novel approach for cancer cell profiling by Raman spectroscopy

Single-cell analysis is critical for advancing personalized medicine, as it reveals cell population heterogeneity that influences disease outcomes. We present a microwell-assembled aluminum substrate platform that enhances single-cell Raman spectroscopy in liquid suspension by isolating individual cells and preventing stacking and movement, which significantly improves signal stability and the signal-to-noise ratio (SNR). We applied this novel platform to analyze PC-9 lung cancer cells and BEAS-2B normal bronchial epithelial cells, identifying distinct biochemical differences. Notably, cancer cells showed higher levels of adenine, cytochromes, DNA/RNA, and unsaturated lipids, along with an increased unsaturation ratio and protein content. These findings were further validated using machine learning models. An eXtreme Gradient Boosting (XGBoost) model achieved perfect classification accuracy of 100 %, underscoring the robustness of the spectral features identified by our platform. Our platform not only enhances single-cell Raman signal detection but also holds promise for biomedical applications, including early cancer detection, treatment monitoring, and drug development. The high-throughput capacity of this platform featuring over 120,000 wells, along with its compatibility with techniques such as Raman-activated cell sorting (RACS) further extends its potential for clinical diagnostics and personalized medicine.

Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy

This study aimed to identify biomolecular differences between benign gastric tissues (gastritis/intestinal metaplasia) and gastric adenocarcinoma and to evaluate the diagnostic power of Raman spectroscopy-based machine learning in gastric adenocarcinoma. Raman spectroscopy-based machine learning was applied in real-time during endoscopy in 19 patients (aged 51-85 years) with high-risk for gastric adenocarcinoma. Raman spectra were captured from suspicious lesions and adjacent normal mucosa, which were biopsied for matched histopathologic diagnosis. Spectral data were analyzed using principal component analysis (PCA) and linear discriminant analysis (LDA) with leave-one-out cross-validation (LOOCV) to develop a machine learning model for diagnosing gastric adenocarcinoma. High-quality spectra (800-3300 cm⁻¹) revealed distinct patterns: adenocarcinoma tissues had higher intensities below 3150 cm⁻¹, while benign tissues exhibited higher intensities between 3150 and 3290 cm⁻¹ (p < 0.001). The model achieved diagnostic accuracy, sensitivity, specificity, and AUC values of 0.905, 0.942, 0.787, and 0.957, respectively. Biochemical correlations suggested adenocarcinoma tissues had increased protein (e.g., phenylalanine), reduced lipids, and lower water content compared to benign tissues. This study highlights the potential of Raman spectroscopy with machine learning as a real-time diagnostic tool for gastric adenocarcinoma. Further validation could establish this technique as a non-invasive, accurate method to aid clinical decision-making during endoscopy.

Recent Plasmonic Gold- and Silver-Assisted Raman Spectra for Advanced SARS-CoV-2 Detection

COVID-19 has become one of the deadliest epidemics in the past years. In efforts to combat the deadly disease besides vaccines, drug therapies, and facemasks, significant focus has been on designing specific methods for the sensitive and accurate detection of SARS-CoV-2. Of these, surface-enhanced Raman scattering (SERS) is an attractive analytical tool for the identification of SARS-CoV-2. SERS is the phenomenon of enhancement of Raman intensity signals from molecular analytes anchored onto the surfaces of roughened plasmonic nanomaterials. This work gives an updated summary of plasmonic gold nanomaterials (AuNMs) and silver nanomaterials (AgNMs)-based SERS technologies to identify SARS-CoV-2. Due to extreme "hot spots" promoting higher electromagnetic fields on their surfaces, different shapes of AuNMs and AgNMs combined with Raman probes have been reviewed for enhancing Raman signals of probe molecules for quantifying the virus. It also reviews progress made recently in the design of certain specific Raman probe molecules capable of imparting characteristic SERS response/tags for SARS-CoV-2 detection.

Application of the OSNA Technique (One-Step Nucleic Acid Amplification Test) in Breast Cancer

Breast cancer is one of the most common cancers diagnosed in both countries with high and low levels of socio-academic development. Routine, regular screening tests being introduced in an increasing number of countries make it possible to detect breast cancer at an early stage of development, as a result of which the trend in the incidence of metastatic breast cancer has been decreasing in recent years. The latest guidelines for the treatment of this tumor do not recommend axillary dissection, which limits the need for rapid assessment of the nodes during surgery. Regardless of the progression of the disease, lymph node biopsy and their analysis is one of the most common diagnostic methods for detecting metastases. Systems using one-step amplification of nucleic acids have been present in the diagnosis of breast cancer for nearly 20 years. The one-step nucleic acid amplification (OSNA) test semi-quantitatively detects the number of cytokeratin 19 mRNA copies, a well-known tumor marker, which can be used to infer the presence of metastases in non-sentinel lymph nodes (SLN). Aim: OSNA is a widely used molecular method for SLN, intra-, or postoperative analysis. Its high accuracy has been proved over the years in clinical use. In this review, we checked current state of this technology and compared it to its competitors in the field of breast cancer diagnosis in the era of Axillary Lymph Nodes Dissection (ALND) importance decrease with intention to foresee its further potential use. Objectives: To evaluate OSNA current place in breast cancer diagnosis and treatment we compared OSNA to other lymph node assessing methods. We based our review on original articles and metanalyses published in the last decade. The research was conducted with PubMed, Science Direct, Google Scholar, and NCBI databases. The collected data allowed us to assess the accuracy of OSNA, its cost effectiveness, and its application in other cancers. Results: Regardless of the progression of the disease, a lymph node biopsy and its analysis constitutes one of the most common diagnostic methods for detecting metastases. The OSNA method is characterized by high sensitivity and specificity, and its predictive value has been confirmed by many studies over the years. While its cost effectiveness is still a matter of discussion, this method has been tested more thoroughly than other new lymph nodes assessing technologies. Conclusions: Despite the emergence of competing methods, this test is still widely used as a routine intraoperative examination of lymph nodes. Research carried out in recent years has proved its effectiveness in the diagnosis of other cancers, in the research field, and as a provider of additional data for prognosis improvement.

MXene-based SERS spectroscopic analysis of exosomes for lung cancer differential diagnosis with deep learning

Lung cancer with heterogeneity has a high mortality rate due to its late-stage detection and chemotherapy resistance. Liquid biopsy that discriminates tumor-related biomarkers in body fluids has emerged as an attractive technique for early-stage and accurate diagnosis. Exosomes, carrying membrane and cytosolic information from original tumor cells, impart themselves endogeneity and heterogeneity, which offer extensive and unique advantages in the field of liquid biopsy for cancer differential diagnosis. Herein, we demonstrate a Gramian angular summation field and MobileNet V2 (GASF-MobileNet)-assisted surface-enhanced Raman spectroscopy (SERS) technique for analyzing exosomes, aimed at precise diagnosis of lung cancer. Specifically, a composite substrate was synthesized for SERS detection of exosomes based on Ti3C2Tx Mxene and the array of gold-silver core-shell nanocubes (MGS), that combines sensitivity and signal stability. The employment of MXene facilitates the non-selective capture and enrichment of exosomes. To overcome the issue of potentially overlooking spatial features in spectral data analysis, 1-D spectra were first transformed into 2-D images through GASF. By using transformed images as the input data, a deep learning model based on the MobileNet V2 framework extracted spectral features from higher dimensions, which identified different non-small cell lung cancer (NSCLC) cell lines with an overall accuracy of 95.23%. Moreover, the area under the curve (AUC) for each category exceeded 0.95, demonstrating the great potential of integrating label-free SERS with deep learning for precise lung cancer differential diagnosis. This approach allows routine cancer management, and meanwhile, its non-specific analysis of SERS signatures is anticipated to be expanded to other cancers.

Evaluation of Salivary Diagnostics: Applications, Benefits, Challenges, and Future Prospects in Dental and Systemic Disease Detection

Saliva is a multifaceted biological fluid that plays a pivotal role in oral health and overall well-being. It is primarily produced by major salivary glands, with additional contributions from minor glands. Saliva is essential for various physiological functions, including oral lubrication, digestion, and defense against pathogens. Its intricate composition comprises proteins, electrolytes, enzymes, hormones, and microbial DNA, enabling it to act as a dynamic indicator of both local and systemic health. A literature search was conducted using PubMed, Web of Science, and Google Scholar to identify relevant studies published up to June 2024. The included studies involved human participants and provided original data or comprehensive reviews on salivary biomarkers. The findings indicate that salivary diagnostics show promise in diagnosing and monitoring systemic conditions such as diabetes and cardiovascular diseases, with salivary glucose levels correlating well with blood glucose levels. Biomarkers such as C-reactive protein (CRP) have been linked to cardiovascular risk, while saliva has been explored for cancer detection, including pancreatic and prostate cancers. Advances in techniques such as enzyme-linked immunosorbent assay (ELISA), saliva omics, and single-cell sequencing have furthered salivary diagnostics, providing insights into disease mechanisms. Additionally, quantitative mass spectrometry (qMS) and Raman spectroscopy (RS) contribute to non-invasive diagnostics for various conditions, including cancer. Collecting saliva samples from healthy individuals is crucial for early disease detection and evaluating treatment efficacy. This review underscores the growing importance of salivary tests in dental practice and their potential for diagnosing various health conditions. Further research is essential to address challenges related to variability and standardization. Dentists and healthcare professionals should consider incorporating salivary tests into clinical decision-making to enhance patient outcomes.

Regulating Electron Acceptor Unit to Construct Conjugated Polymer Probe for Raman Imaging of Tumor and Sentinel Lymph Nodes

Donor-acceptor (D-A) conjugated polymers have large Stokes shifts, high photostability, and good biocompatibility and thus are ideal for use as Raman probes in vivo. However, few D-A conjugated polymers are used as Raman probes for Raman imaging due to their weak Raman signal intensity and intrinsic fluorescence interference. Here, we developed a D-A conjugated polymer probe (CDT-TT) containing alternating cyclopentadithiophene-thienothiophene units for Raman imaging of tumor and sentinel lymph nodes (SLNs). The CDT-TT shows a strong Raman signal at 1389 cm-1 without fluorescence and lipid background signal interference under 785 nm near-infrared light. Moreover, the CDT-TT loaded nanoparticles realized the accurate imaging of tumor cells and tumor tissues. In addition, a high-resolution margin imaging of in situ SLNs was acquired. Taken together, the established method is effective for accurate Raman detection of tumors and SLNs, which may shed new light on the development of D-A polymer Raman probes for clinical imaging.

Autofluorescence imaging guided needle-type Raman spectroscopy system for breast tumor margin assessment

A trajectory-tracked, near-infrared autofluorescence imaging guided, biochemical signature-projected needle-type Raman spectroscopy (TNBN-RS) system integrated on a medical cart was developed for rapid wide-field breast tissue stratification. A wide-field (10 × 10 cm2) near-infrared autofluorescence (NIRAF) imaging subsystem was developed for gross stratification of breast tissue types based on higher NIRAF intensity associated with breast cancer, followed by projection of NIRAF-identified breast tumor margins onto the tissue of interest with a compact projector. Raman spectra were further acquired from the NIRAF projected regions for confirmed margin assessment using a needle-type Raman probe equipped with color camera-based probe trajectory tracking. The trajectory of the Raman probe and the accompanying RS biochemical signature-based margin assessment were instantly projected. A unique field of view (FOV) calibration method was proposed to calibrate the TNBN-RS FOVs, resulting in a projection accuracy of <2 mm. A graphical user interface (GUI) was developed in C# for system control, real-time processing and display of NIRAF images, Raman spectra, and projection of their results. The performance of the TNBN-RS system was validated on an ex vivo breast tissue, demonstrating its potential for rapid intraoperative breast tumor margin assessment.

Current research status of Raman spectroscopy in glioma detection

Glioma is the most common primary tumor of the nervous system. Conventional diagnostic methods for glioma often involve time-consuming or reliance on externally introduced materials. Consequently, there is an urgent need for rapid and reliable diagnostic techniques. Raman spectroscopy has emerged as a promising tool, offering rapid, accurate, and label-free analysis with high sensitivity and specificity in biomedical applications. In this review, the fundamental principles of Raman spectroscopy have been introduced, and then the progress of applying Raman spectroscopy in biomedical studies has been summarized, including the identification and typing of glioma. The challenges encountered in the clinical application of Raman spectroscopy for glioma have been discussed, and the prospects have also been envisioned.

Highly sensitive microfluidic sensor using integrated optical fiber and real-time single-cell Raman spectroscopy for diagnosis of pancreatic cancer

Pancreatic cancer is notoriously lethal due to its late diagnosis and poor patient response to treatments, posing a significant clinical challenge. This study introduced a novel approach that combines a single-cell capturing platform, tumor-targeted silver (Ag) nanoprobes, and precisely docking tapered fiber integrated with Raman spectroscopy. This approach focuses on early detection and progression monitoring of pancreatic cancer. Utilizing tumor-targeted Ag nanoparticles and tapered multimode fibers enhances Raman signals, minimizes light loss, and reduces background noise. This advanced Raman system allows for detailed molecular spectroscopic examination of individual cells, offering more practical information and enabling earlier detection and accurate staging of pancreatic cancer compared to conventional multicellular Raman spectroscopy. Transcriptomic analysis using high-throughput gene screening and transcriptomic databases confirmed the ability and accuracy of this method to identify molecular changes in normal, early, and metastatic pancreatic cancer cells. Key findings revealed that cell adhesion, migration, and the extracellular matrix are closely related to single-cell Raman spectroscopy (SCRS) results, highlighting components such as collagen, phospholipids, and carotene. Therefore, the SCRS approach provides a comprehensive view of the molecular composition, biological function, and material changes in cells, offering a novel, accurate, reliable, rapid, and efficient method for diagnosing and monitoring pancreatic cancer.

Advancing cerumen analysis: exploring innovative vibrational spectroscopy techniques with respect to their potential as new point-of-care diagnostic tools

Cerumen, commonly known as earwax, is a complex mixture composed of secretions from ceruminous glands. These secretions are heterogeneous mixtures mainly composed of lipids and proteins. Despite its prevalence, the potential diagnostic value of cerumen remains largely unexplored. Here, we present an in-depth analysis of cerumen utilizing well-known vibrational approaches such as conventional Raman spectroscopy or surface-enhanced Raman spectroscopy (SERS) together with advanced vibrational spectroscopy techniques such as coherent Raman scattering (CRS), i.e. broadband coherent anti-Stokes Raman scattering (CARS) or stimulated Raman scattering (SRS), as well as optical photothermal infrared (OPTIR) spectroscopy. Through the integration of these vibrational spectroscopic methods, lipids and proteins can be identified as the main components of cerumen; however, they contribute to the final spectral information to various extents depending on the vibrational detection scheme applied. The inherently weak Raman signal could be enhanced by linear (SERS) and non-linear (CRS) processes, resulting in efficient acquisition of fingerprint information and allowing for the detection of marker modes, which cannot be addressed by conventional Raman spectroscopy. OPTIR spectroscopy provides complementary information to Raman spectroscopy, however, without the contribution of a fluorescence background. Our findings underscore the utility of these cutting-edge techniques in unveiling the intricate molecular landscape of cerumen, paving the way for novel point-of-care diagnostic methodologies and therapeutic interventions.

Interpretable Machine Learning-Aided Optical Deciphering of Serum Exosomes for Early Detection, Staging, and Subtyping of Lung Cancer

Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, underscoring an urgent need for strategies that enable early detection and phenotypic classification. Here, we conducted a label-free surface-enhanced Raman spectroscopic (SERS) analysis of serum exosomes from 643 participants to elucidate the biochemical deregulation associated with LC progression and the unique phenotypes of different LC subtypes. Iodide-modified silver nanofilms were prepared to rapidly acquire SERS spectra with a high signal-to-noise ratio using 0.5 μL of patient exosomes. We performed interpretable and automated machine learning (ML) analysis of differential SERS features of serum exosomes to build LC diagnostic models, which achieved accuracies of 100% and 81% for stage I lung adenocarcinoma and its preneoplasia, respectively. In addition, the ML-derived exosomal SERS models effectively recognized different LC subtypes and disease stages to guide precision treatment. Our findings demonstrate that spectral fingerprinting of circulating exosomes holds promise for decoding the clinical status of LC, thus aiding in improving the clinical management of patients.

Advances in Raman spectroscopy for characterising oral cancer and oral potentially malignant disorders

Oral cancer survival rates have seen little improvement over the past few decades. This is mainly due to late detection and a lack of reliable markers to predict disease progression in oral potentially malignant disorders (OPMDs). There is a need for highly specific and sensitive screening tools to enable early detection of malignant transformation. Biochemical alterations to tissues occur as an early response to pathological processes; manifesting as modifications to molecular structure, concentration or conformation. Raman spectroscopy is a powerful analytical technique that can probe these biochemical changes and can be exploited for the generation of novel disease-specific biomarkers. Therefore, Raman spectroscopy has the potential as an adjunct tool that can assist in the early diagnosis of oral cancer and the detection of disease progression in OPMDs. This review describes the use of Raman spectroscopy for the diagnosis of oral cancer and OPMDs based on ex vivo and liquid biopsies as well as in vivo applications that show the potential of this powerful tool to progress from benchtop to chairside.

A double-branch convolutional neural network model for species identification based on multi-modal data

For species identification analysis, methods based on deep learning are becoming prevalent due to their data-driven and task-oriented nature. The most commonly used convolutional neural network (CNN) model has been well applied in Raman spectra recognition. However, when faced with similar molecules or functional groups, the features of overlapping peaks and weak peaks may not be fully extracted using the CNN model, which can potentially hinder accurate species identification. Based on these practical challenges, the fusion of multi-modal data can effectively meet the comprehensive and accurate analysis of actual samples when compared with single-modal data. In this study, we propose a double-branch CNN model by integrating Raman and image multi-modal data, named SI-DBNet. In addition, we have developed a one-dimensional convolutional neural network combining dilated convolutions and efficient channel attention mechanisms for spectral branching. The effectiveness of the model has been demonstrated using the Grad-CAM method to visualize the key regions concerned by the model. When compared to single-modal and multi-modal classification methods, our SI-DBNet model achieved superior performance with a classification accuracy of 98.8%. The proposed method provided a new reference for species identification based on multi-modal data fusion.

Raman spectroscopy combined with convolutional neural network for the sub-types classification of breast cancer and critical feature visualization

PROBLEMS

Raman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the current Raman prediction models fail to cover all the molecular sub-types of breast cancer, and lack the visualization of the model.

AIMS

Using Raman spectroscopy combined with convolutional neural network (CNN) to construct a prediction model for the existing known molecular sub-types of breast cancer, and selected critical peaks through visualization strategies, so as to achieve the purpose of mining specific biomarker information.

METHODS

Optimizing network parameters with the help of sparrow search algorithm (SSA) for the multiple parameters in the CNN to improve the prediction performance of the model. To avoid the contingency of the results, multiple sets of data were generated through Monte Carlo sampling and used to train the model, thereby improving the credibility of the results. Based on the accurate prediction of the model, the spectral regions that contributed to the classification were visualized using Gradient-weighted Class Activation Mapping (Grad-CAM), achieving the goal of visualizing characteristic peaks.

RESULTS

Compared with other algorithms, optimized CNN could obtain the highest accuracy and lowest standard error. And there was no significant difference between using full spectra and fingerprint regions (within 2 %), indicating that the fingerprint region provided the most contribution in classifying sub-types. Based on the classification results from the fingerprint region, the model performances about various sub-types were as follows: CNN (95.34 %±2.18 %)>SVM(94.90 %±1.88 %)>PLS-DA(94.52 %±2.22 %)> KNN (80.00 %±5.27 %). The critical features visualized by Grad-CAM could match well with IHC information, allowing for a more distinct differentiation of sub-types in their spatial positions.

CONCLUSION

Raman spectroscopy combined with CNN could achieve accurate and rapid identification of breast cancer molecular sub-types. Proposed visualization strategy could be proved from biochemistry information and spatial location, demonstrated that the strategy might be used for the mining of biomarkers in future.

Employing Raman Spectroscopy and Machine Learning for the Identification of Breast Cancer

BACKGROUND

Breast cancer poses a significant health risk to women worldwide, with approximately 30% being diagnosed annually in the United States. The identification of cancerous mammary tissues from non-cancerous ones during surgery is crucial for the complete removal of tumors.

RESULTS

Our study innovatively utilized machine learning techniques (Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)) alongside Raman spectroscopy to streamline and hasten the differentiation of normal and late-stage cancerous mammary tissues in mice. The classification accuracy rates achieved by these models were 94.47% for RF, 96.76% for SVM, and 97.58% for CNN, respectively. To our best knowledge, this study was the first effort in comparing the effectiveness of these three machine-learning techniques in classifying breast cancer tissues based on their Raman spectra. Moreover, we innovatively identified specific spectral peaks that contribute to the molecular characteristics of the murine cancerous and non-cancerous tissues.

CONCLUSIONS

Consequently, our integrated approach of machine learning and Raman spectroscopy presents a non-invasive, swift diagnostic tool for breast cancer, offering promising applications in intraoperative settings.

Label-free Raman spectroscopy reveals tumor microenvironmental changes induced by intermittent fasting for the prevention of breast cancer in animal model

The development of tools that can provide a holistic picture of the evolution of the tumor microenvironment in response to intermittent fasting on the prevention of breast cancer is highly desirable. Here, we show, for the first time, the use of label-free Raman spectroscopy to reveal biomolecular alterations induced by intermittent fasting in the tumor microenvironment of breast cancer using a dimethyl-benzanthracene induced rat model. To quantify biomolecular alterations in the tumor microenvironment, chemometric analysis of Raman spectra obtained from untreated and treated tumors was performed using multivariate curve resolution-alternative least squares and support vector machines. Raman measurements revealed remarkable and robust differences in lipid, protein, and glycogen content prior to morphological manifestations in a dynamically changing tumor microenvironment, consistent with the proteomic changes observed by quantitative mass spectrometry. Taken together with its non-invasive nature, this research provides prospective evidence for the clinical translation of Raman spectroscopy to identify biomolecular variations in the microenvironment induced by intermittent fasting for the prevention of breast cancer, providing new perspectives on the specific molecular effects in the tumorigenesis of breast cancer.

Nanoparticles in cancer theragnostic and drug delivery: A comprehensive review

This comprehensive review provides an in-depth analysis of how nanotechnology has revolutionized cancer theragnostic, which combines diagnostic and therapeutic methods to customize cancer treatment. The study examines the unique attributes, uses, and difficulties linked to different types of nanoparticles, including gold, iron oxide, silica, Quantum dots, Carbon nanotubes, and liposomes, in the context of cancer treatment. In addition, the paper examines the progression of nanotheranostics, emphasizing its uses in precise medication administration, photothermal therapy, and sophisticated diagnostic methods such as MRI, CT, and fluorescence imaging. Moreover, the article highlights the capacity of nanoparticles to improve the effectiveness of drugs, reduce the overall toxicity in the body, and open up new possibilities for treating cancer by releasing drugs in a controlled manner and targeting specific areas. Furthermore, it tackles concerns regarding the compatibility of nanoparticles and their potential harmful effects, emphasizing the significance of continuous study to improve nanotherapeutic methods for use in medical treatments. The review finishes by outlining potential future applications of nanotechnology in predictive oncology and customized medicine.

A Dual-Modal, Label-Free Raman Imaging Method for Rapid Virtual Staining of Large-Area Breast Cancer Tissue Sections

As one of the most common cancers, accurate, rapid, and simple histopathological diagnosis is very important for breast cancer. Raman imaging is a powerful technique for label-free analysis of tissue composition and histopathology, but it suffers from slow speed when applied to large-area tissue sections. In this study, we propose a dual-modal Raman imaging method that combines Raman mapping data with microscopy bright-field images to achieve virtual staining of breast cancer tissue sections. We validate our method on various breast tissue sections with different morphologies and biomarker expressions and compare it with the golden standard of histopathological methods. The results demonstrate that our method can effectively distinguish various types and components of tissues, and provide staining images comparable to stained tissue sections. Moreover, our method can improve imaging speed by up to 65 times compared to general spontaneous Raman imaging methods. It is simple, fast, and suitable for clinical applications.

Colorimetric detection of circulating tumor cells in breast cancer based on ladder-branch hybridization chain reaction and DFs/AuNCs nanozyme

Breast cancer is the most common malignant tumor in women, which accounts for 6.9% of all cancer-related deaths. Early diagnosis is crucial for making the best clinical decision and improving the prognosis of patients. Circulating tumor cells (CTCs) have been regarded as significant tumor biomarkers. Herein, we designed a colorimetric biosensor for breast cancer CTCs quantification based on ladder-branch hybridization chain reaction (HCR) and DNA flowers/gold nanoclusters (DFs/AuNCs) nanozyme. With the assistance of complementary DNA labeled on magnetic beads (MBs), the cleavage products of RNA-cleaving DNAzymes (RCDs) could be rapidly captured, subsequently triggering ladder-branch HCR. In addition, the DFs/AuNCs nanozyme was applied for colorimetric analysis, which further improved the sensitivity for the detection of target CTCs. Benefiting from specific RCDs, ladder-branch HCR and DFs/AuNCs, we achieved a superior detection limit of 3 cells/mL as well as a broad linear range of 10 cells/mL to 104 cells/mL. Conclusively, this colorimetric biosensor achieved sensitively and selectively detection of breast cancer CTCs without the participation of enzymes at room temperature, which might provide new insight into the early detection of breast cancer.

Label-free morpho-molecular phenotyping of living cancer cells by combined Raman spectroscopy and phase tomography

Accurate, rapid and non-invasive cancer cell phenotyping is a pressing concern across the life sciences, as standard immuno-chemical imaging and omics require extended sample manipulation. Here we combine Raman micro-spectroscopy and phase tomography to achieve label-free morpho-molecular profiling of human colon cancer cells, following the adenoma, carcinoma, and metastasis disease progression, in living and unperturbed conditions. We describe how to decode and interpret quantitative chemical and co-registered morphological cell traits from Raman fingerprint spectra and refractive index tomograms. Our multimodal imaging strategy rapidly distinguishes cancer phenotypes, limiting observations to a low number of pristine cells in culture. This synergistic dataset allows us to study independent or correlated information in spectral and tomographic maps, and how it benefits cell type inference. This method is a valuable asset in biomedical research, particularly when biological material is in short supply, and it holds the potential for non-invasive monitoring of cancer progression in living organisms.

Combined SERS-Raman screening of HER2-overexpressing or silenced breast cancer cell lines

BACKGROUND

Breast cancer (BC) is a heterogeneous neoplasm characterized by several subtypes. One of the most aggressive with high metastasis rates presents overexpression of the human epidermal growth factor receptor 2 (HER2). A quantitative evaluation of HER2 levels is essential for a correct diagnosis, selection of the most appropriate therapeutic strategy and monitoring the response to therapy.

RESULTS

In this paper, we propose the synergistic use of SERS and Raman technologies for the identification of HER2 expressing cells and its accurate assessment. To this end, we selected SKBR3 and MDA-MB-468 breast cancer cell lines, which have the highest and lowest HER2 expression, respectively, and MCF10A, a non-tumorigenic cell line from normal breast epithelium for comparison. The combined approach provides a quantitative estimate of HER2 expression and visualization of its distribution on the membrane at single cell level, clearly identifying cancer cells. Moreover, it provides a more comprehensive picture of the investigated cells disclosing a metabolic signature represented by an elevated content of proteins and aromatic amino acids. We further support these data by silencing the HER2 gene in SKBR3 cells, using the RNA interference technology, generating stable clones further analysed with the same combined methodology. Significant changes in HER2 expression are detected at single cell level before and after HER2 silencing and the HER2 status correlates with variations of fatty acids and downstream signalling molecule contents in the context of the general metabolic rewiring occurring in cancer cells. Specifically, HER2 silencing does reduce the growth ability but not the lipid metabolism that, instead, increases, suggesting that higher fatty acids biosynthesis and metabolism can occur independently of the proliferating potential tied to HER2 overexpression.

CONCLUSIONS

Our results clearly demonstrate the efficacy of the combined SERS and Raman approach to definitely pose a correct diagnosis, further supported by the data obtained by the HER2 gene silencing. Furthermore, they pave the way to a new approach to monitor the efficacy of pharmacologic treatments with the aim to tailor personalized therapies and optimize patients' outcome.

A Molecular Typing Method for Invasive Breast Cancer by Serum Raman Spectroscopy

BACKGROUND

The incidence of breast cancer ranks highest among cancers and is exceedingly heterogeneous. Immunohistochemical staining is commonly used clinically to identify the molecular subtype for subsequent treatment and prognosis.

PURPOSE

Raman spectroscopy and support vector machine (SVM) learning algorithm were utilized to identify blood samples from breast cancer patients in order to investigate a novel molecular typing approach.

METHOD

Tumor tissue coarse needle aspiration biopsy samples, and peripheral venous blood samples were gathered from 459 invasive breast cancer patients admitted to the breast department of Sichuan Cancer Hospital between June 2021 and September 2022. Immunohistochemical staining and in situ hybridization were performed on the coarse needle aspiration biopsy tissues to obtain their molecular typing pathological labels, including: 70 cases of Luminal A, 167 cases of Luminal B (HER2-positive), 57 cases of Luminal B (HER2-negative), 84 cases of HER2-positive, and 81 cases of triple-negative. Blood samples were processed to obtained Raman spectra taken for SVM classification models establishment with machine algorithms (using 80% of the sample data as the training set), and then the performance of the SVM classification models was evaluated by the independent validation set (20% of the sample data).

RESULTS

The AUC values of SVM classification models remained above 0.85, demonstrating outstanding model performance and excellent subtype discrimination of breast cancer molecular subtypes.

CONCLUSION

Raman spectroscopy of serum samples can promptly and precisely detect the molecular subtype of invasive breast cancer, which has the potential for clinical value.

Source Tracing of Kidney Injury via the Multispectral Fingerprint Identified by Machine Learning-Driven Surface-Enhanced Raman Spectroscopic Analysis

Early diagnosis of drug-induced kidney injury (DIKI) is essential for clinical treatment and intervention. However, developing a reliable method to trace kidney injury origins through retrospective studies remains a challenge. In this study, we designed ordered fried-bun-shaped Au nanocone arrays (FBS NCAs) to create microarray chips as a surface-enhanced Raman scattering (SERS) analysis platform. Subsequently, the principal component analysis (PCA)-two-layer nearest neighbor (TLNN) model was constructed to identify and analyze the SERS spectra of exosomes from renal injury induced by cisplatin and gentamycin. The established PCA-TLNN model successfully differentiated the SERS spectra of exosomes from renal injury at different stages and causes, capturing the most significant spectral features for distinguishing these variations. For the SERS spectra of exosomes from renal injury at different induction times, the accuracy of PCA-TLNN reached 97.8% (cisplatin) and 93.3% (gentamicin). For the SERS spectra of exosomes from renal injury caused by different agents, the accuracy of PCA-TLNN reached 100% (7 days) and 96.7% (14 days). This study demonstrates that the combination of label-free exosome SERS and machine learning could serve as an innovative strategy for medical diagnosis and therapeutic intervention.

Photon counting Raman spectroscopy: a benchmarking study vs surface plasmon enhancement

We demonstrate a single-photon counting Raman spectroscope and benchmark it against conventional and surface-enhanced Raman spectroscopy. For direct comparison without ambiguity, we use the same solutions of Rhodamine 6G and a common optical setup with either a spectrometer or an acousto-optic tunable filter, whereas the surface enhancement is realized with immobilized Ag nanoparticles. Our results find that the single photon counting significantly elevates the detection sensitivity by up to eight orders of magnitude, arriving at a comparable level of surface-enhanced Raman spectroscopy. Another significant advantage is with the time-resolving measurement, where we demonstrate time-gated and time-correlated single-photon counting with sub-nanosecond resolution. It offers insights into the samples' transient responses and enables the isolation of Raman scattering from fluorescence signals.

Biomedical applications, perspectives and tag design concepts in the cell - silent Raman window

Spectroscopic studies increasingly employ Raman tags exhibiting a signal in the cell - silent region of the Raman spectrum (1800-2800 cm-1), where bands arising from biological molecules are inherently absent. Raman tags bearing functional groups which contain a triple bond, such as alkyne and nitrile or a carbon-deuterium bond, have a distinct vibrational frequency in this region. Due to the lack of spectral background and cell-associated bands in the specific area, the implementation of those tags can help overcome the inherently poor signal-to-noise ratio and presence of overlapping Raman bands in measurements of biological samples. The cell - silent Raman tags allow for bioorthogonal imaging of biomolecules with improved chemical contrast and they have found application in analyte detection and monitoring, biomarker profiling and live cell imaging. This review focuses on the potential of the cell - silent Raman region, reporting on the tags employed for biomedical applications using variants of Raman spectroscopy.

Analysis of the molecular alterations in cancer cells following nanotechnology-assisted targeted radiotherapy using Raman spectroscopy

The study unveiled an innovative strategy for precise radiation targeting in cancer treatment, along with the monitoring of molecular changes induced by this therapeutic approach. In this research, we explored the impact of administering anti-HER2-AgNPs nanoconjugates either individually or in conjunction with gamma irradiation on the viability of SKBR3 breast cancer cells. The utilization of nanoconjugates resulted in an enhancement of cellular sensitivity toward radiation. The viability of the cells exhibited a decline as the dose of irradiation increased, and this decrease was further exacerbated by the passage of time following irradiation. The analysis of RS revealed distinct cellular responses in varying conditions. The observed increase in SERS intensity, resulting from the increment in dose from 0 to 2 Gy, can be attributed to the probable upregulation of HER2 expression induced by irradiation. The observed decrease in SERS intensity at doses of 4 and 6 Gy can be attributed to the likely reduction in HER2 expression. It was illustrated that the analysis of Raman spectroscopy data can aid in the identification of radiation-induced biochemical alterations in cancer cells during the application of nanoconjugates-based radiotherapy. The findings revealed that nanoconjugates have the potential to enhance cellular sensitivity to radiation along with facilitating the detection of radiation-induced biochemical alterations within cancer cells.

Convenient determination of serum HER-2 status in breast cancer patients using Raman spectroscopy

Given the significant therapeutic efficacy of anti-HER-2 treatment, the HER-2 status is a crucial piece of information that must be obtained in breast cancer patients. Currently, as per guidelines, HER-2 status is typically acquired from breast tissue of patients. However, there is growing interest in obtaining HER-2 status from serum and other samples due to the convenience and potential for dynamic monitoring. In this study, we have developed a serum Raman spectroscopy technique that allows for the rapid acquisition of HER-2 status in a convenient manner. The established HER-2 negative and positive classification model achieved an area under the curve of 0.8334. To further validate the reliability of our method, we replicated the process using immunohistochemistry and in situ hybridization. The results demonstrate that serum Raman spectroscopy, coupled with artificial intelligence algorithms, is an effective technical approach for obtaining HER-2 status.

Efficacy of tyrosine kinase inhibitors examined by a combination of Raman micro-spectroscopy and a deep wavelet scattering-based multivariate analysis framework

HER2 is a crucial therapeutic target in breast cancer, and the survival rate of breast cancer patients has increased because of this receptor's inhibition. However, tumors have shown resistance to this therapeutic strategy due to oncogenic mutations that decrease the binding of several HER2-targeted drugs, including lapatinib, and confer resistance to this drug. Neratinib can overcome this drug resistance and effectively inhibit HER2 signaling and tumor growth. In the present study, we examined the efficacy of lapatinib and neratinib using breast cancer cells by Raman microscopy combined with a deep wavelet scattering-based multivariate analysis framework. This approach discriminated between control cells and drug-treated cells with high accuracy, compared to classical principal component analysis. Both lapatinib and neratinib induced changes in the cellular biochemical composition. Furthermore, the Raman results were compared with the results of several in vitro assays. For instance, drug-treated cells exhibited (i) inhibition of ERK and AKT phosphorylation, (ii) inhibition of cellular proliferation, (iii) cell-cycle arrest, and (iv) apoptosis as indicated by western blotting, real-time cell analysis (RTCA), cell-cycle analysis, and apoptosis assays. Thus, the observed Raman spectral changes are attributed to cell-cycle arrest and apoptosis. The results also indicated that neratinib is more potent than lapatinib. Moreover, the uptake and distribution of lapatinib in cells were visualized through its label-free marker bands in the fingerprint region using Raman spectral imaging. These results show the prospects of Raman microscopy in drug evaluation and presumably in drug discovery.

Magnetic Resonance Imaging in Breast Cancer Tissue In Vitro after PDT Therapy

Photodynamic therapy (PDT) is increasingly used in modern medicine. It has found application in the treatment of breast cancer. The most common cancer among women is breast cancer. We collected cancer cells from the breast from the material received after surgery. We focused on tumors that were larger than 10 mm in size. Breast cancer tissues for this quantitative non-contrast magnetic resonance imaging (MRI) study could be seen macroscopically. The current study aimed to present findings on quantitative non-contrast MRI of breast cancer cells post-PDT through the evaluation of relaxation times. The aim of this work was to use and optimize a 1.5 T MRI system. MRI tests were performed using a clinical scanner, namely the OPTIMA MR360 manufactured by General Electric HealthCare. The work included analysis of T1 and T2 relaxation times. This analysis was performed using the MATLAB package (produced by MathWorks). The created application is based on medical MRI images saved in the DICOM3.0 standard. T1 and T2 measurements were subjected to the Shapiro-Wilk test, which showed that both samples belonged to a normal distribution, so a parametric t-test for dependent samples was used to test for between-sample variability. The study included 30 sections tested in 2 stages, with consistent technical parameters. For T1 measurements, 12 scans were performed with varying repetition times (TR) and a constant echo time (TE) of 3 ms. For T2 measurements, 12 scans were performed with a fixed repetition time of 10,000 ms and varying echo times. After treating samples with PpIX disodium salt and bubbling with pure oxygen, PDT irradiation was applied. The cell relaxation time after therapy was significantly shorter than the cell relaxation time before PDT. The cells were exposed to PpIX disodium salt as the administered pharmacological substance. The study showed that the therapy significantly affected tumor cells, which was confirmed by a significant reduction in tumor cell relaxation time on the MRI results.

Spectral insights: Navigating the frontiers of biomedical and microbiological exploration with Raman spectroscopy

Raman spectroscopy (RS), a powerful analytical technique, has gained increasing recognition and utility in the fields of biomedical and biological research. Raman spectroscopic analyses find extensive application in the field of medicine and are employed for intricate research endeavors and diagnostic purposes. Consequently, it enjoys broad utilization within the realm of biological research, facilitating the identification of cellular classifications, metabolite profiling within the cellular milieu, and the assessment of pigment constituents within microalgae. This article also explores the multifaceted role of RS in these domains, highlighting its distinct advantages, acknowledging its limitations, and proposing strategies for enhancement.

Emergence and impact of theranostic-nanoformulation of triple therapeutics for combination cancer therapy

Cancer remains a major global health threat necessitating the multipronged approaches for its prevention and management. Traditional approaches in the form of chemotherapy, surgery, and radiotherapy are often encountered with poor patient outcomes evidenced by high mortality and morbidity, compelling the need for precision medicine for cancer patients to enable personalized and targeted cancer treatment. There has been an emergence of smart multimodal theranostic nanoformulation for triple combination cancer therapy in the last few years, which dramatically enhances the overall safety of the nanoformulation for in vivo and potential clinical applications with minimal toxicity. However, it is imperative to gain insight into the limitations of this system in terms of clinical translation, cost-effectiveness, accessibility, and multidisciplinary collaboration. This review paper aims to highlight and compare the impact of the recent theranostic nanoformulations of triple therapeutics in a single nanocarrier for effective management of cancer and provide a new dimension for diagnostic and treatment simultaneously.