The optimization of sample preparation on zebrafish larvae in vibrational spectroscopy imaging

The zebrafish (Danio rerio) larvae are widely used in biomedical, pharmaceutical, and ecotoxicological studies. Their transparency and translational potential make them particularly valuable for fluorescence imaging. In addition to fluorescence imaging, microspectroscopy, which combines vibrational spectroscopy: Raman or Fourier transform infrared (FT-IR) with microscopy, allows the collection of spatially resolved, label-free information. According to available literature, it was the first application of FT-IR imaging in zebrafish larvae. This study aims to compare different fixation methods for 10-day post-fertilization (dpf) zebrafish larvae using vibrational spectroscopy imaging. Paraformaldehyde (PFA), glutaraldehyde (GA), low temperature, and embedding in gelatin and agarose were investigated. Amides, lipids, and phosphates distribution were more informative in embedded samples but with challenging handling of the sample due to stiffness at -20 °C. FT-IR and Raman mapping revealed that frozen samples had better-preserved tissue structure than chemical fixation. PFA showed uniform amide distribution, while GA treatment exhibited tissue disruptions and denser protein networks in both. Handling of embedded samples is challenging for an operator, but provides more reliable results in developmental biology or disease modeling, compared to chemical treatment.

Bacterial discrimination by Fourier transform infrared spectroscopy, MALDI-mass spectrometry and whole-genome sequencing

Aim: Proof-of-concept study, highlighting the clinical diagnostic ability of FT-IR compared with MALDI-TOF MS, combined with WGS. Materials & methods: 104 pathogenic isolates of Neisseria meningitidis, Streptococcus pneumoniae, Streptococcus pyogenes and Staphylococcus aureus were analyzed. Results: Overall prediction accuracy was 99.6% in FT-IR and 95.8% in MALDI-TOF-MS. Analysis of N. meningitidis serogroups was superior in FT-IR compared with MALDI-TOF-MS. Phylogenetic relationship of S. pyogenes was similar by FT-IR and WGS, but not S. aureus or S. pneumoniae. Clinical severity was associated with the zinc ABC transporter and DNA repair genes in S. pneumoniae and cell wall proteins (biofilm formation, antibiotic and complement permeability) in S. aureus via WGS. Conclusion: FT-IR warrants further clinical evaluation as a promising diagnostic tool.

Providing potential solutions by using FT-IR spectroscopy for biofluid analysis: Clinical impact of optical screening and diagnostic tests

BACKGROUND

Currently, the potential of FT-IR spectroscopy for rapid diagnosis of many pathologies has been demonstrated by numerous research studies including those targeting COVID-19 detection. However, the number of clinicians aware of this potential and who are willing to use spectroscopy in their clinics and hospitals is still negligible. In addition, lack of awareness creates a huge gap between clinicians and researchers involved in clinical translation of current FT-IR technology hence hindering initiatives to bring basic and applied research together for the direct benefit of patients.

METHODS

Knowledge and medical training on FT-IR on the side of clinicians should be one of the first steps to be able to integrate it into the list of complementary exams which may be requested by health professionals. Countless FT-IR applications could have a life-changing impact on patients' lives, especially screening and diagnostic tests involving biofluids such as blood, saliva and urine which are routinely non-invasively or minimally-invasively.

RESULTS

Blood may be the most difficult to obtain by the invasive method of collection, but much can be evaluated in its components, and areas such as hematology, infectiology, oncology and endocrinology can be directly benefited. Urine with a relatively simple collection method can provide pertinent information from the entire urinary system, including the actual condition of the kidneys. Saliva collection can be simpler for the patient and can provide information on diseases affecting the mouth and digestive system and can be used to diagnose diseases such as oral cancer in its early-stages. An unavoidable second step is the active involvement of industries to design robust and portable instruments for specific purposes, as the medical community requires user-friendly instruments of advanced computational algorithms. A third step resides in the legal situation involving the global use of the technique as a new diagnostic modality.

CONCLUSIONS

It is important to note that decentralized funds for variety of technologies hinders the training of clinical and medical professionals for the use of newly arising technologies and affect the engagement of these professionals with technology developers. As a result of decentralized funding, research efforts are spread out over a range of technologies which take a long time to get validated and translated to the clinic. Partnership over similar groups of technologies and efforts to test the same technologies while overcoming barriers posed to technology validation in different areas around the globe may benefit the clinical/medical, research and industry community globally.

Dimensionality reduction for deep learning in infrared microscopy: a comparative computational survey

While infrared microscopy provides molecular information at spatial resolution in a label-free manner, exploiting both spatial and molecular information for classifying the disease status of tissue samples constitutes a major challenge. One strategy to mitigate this problem is to embed high-dimensional pixel spectra in lower dimensions, aiming to preserve molecular information in a more compact manner, which reduces the amount of data and promises to make subsequent disease classification more accessible for machine learning procedures. In this study, we compare several dimensionality reduction approaches and their effect on identifying cancer in the context of a colon carcinoma study. We observe surprisingly small differences between convolutional neural networks trained on dimensionality reduced spectra compared to utilizing full spectra, indicating a clear tendency of the convolutional networks to focus on spatial rather than spectral information for classifying disease status.

The emerging applications and advancements of Raman spectroscopy in pediatric cancers

Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.

Evaluation of grade and invasiveness of bladder urothelial carcinoma using infrared imaging and machine learning

Urothelial bladder carcinoma (BC) is primarily diagnosed with a subjective examination of biopsies by histopathologists, but accurate diagnosis remains time-consuming and of low diagnostic accuracy, especially for low grade non-invasive BC. We propose a novel approach for high-throughput BC evaluation by combining infrared (IR) microscopy of bladder sections with machine learning (partial least squares-discriminant analysis) to provide an automated prediction of the presence of cancer, invasiveness and grade. Cystoscopic biopsies from 50 patients with clinical suspicion of BC were histologically examined to assign grades and stages. Adjacent tissue cross-sections were IR imaged to provide hyperspectral datasets and cluster analysis segregated IR images to extract the average spectra of epithelial and subepithelial tissues. Discriminant models, which were validated using repeated random sampling double cross-validation, showed sensitivities (AUROC) ca. 85% (0.85) for the identification of cancer in epithelium and subepithelium. The diagnosis of non-invasive and invasive cases showed sensitivity values around 80% (0.84-0.85) and 76% (0.73-0.80), respectively, while the identification of low and high grade BC showed higher sensitivity values 87-88% (0.91-0.92). Finally, models for the discrimination between cancers with different invasiveness and grades showed more modest AUROC values (0.67-0.72). This proves the high potential of IR imaging in the development of ancillary platforms to screen bladder biopsies.

Evaluating Subtle Pathological Changes in Early Myocardial Ischemia Using Spectral Histopathology

Early myocardial ischemia (EMI) is morphologically challenging, and the results from conventional histological staining may be subjective, imprecise, or even silent. The size of myocardial necrosis determines the acute and long-term mortality of EMI. The precise diagnosis of myocardial ischemia is critical for both clinical management and forensic investigation. Fourier transform infrared (FTIR) spectroscopic imaging is a highly sensitive tool for detecting protein conformations and imaging protein profiles. The aim of this study was to evaluate the application of FTIR imaging with multivariate analysis to detect biochemical changes in the protein conformation in the early phase of myocardial ischemia and to visually classify different disease states. The spectra and curve fitting results revealed that the total protein content decreased significantly in the EMI group and that the α-helix content of the secondary protein structure continuously decreased as ischemia progressed, while the β-sheet content increased. Differences in the control and EMI groups and perfused and ischemic myocardium were confirmed using principal component analysis and partial least squares discriminant analysis. Next, two support vector machine classifiers were effectively created. The accuracy, recall, and precision were 99.98, 99.96, and 100.00%, respectively, to differentiate the EMI group from the control group and 99.25, 98.95, and 99.54%, respectively, to differentiate perfused and ischemic myocardium. Ultimately, high EMI diagnostic accuracy was achieved with 100.00% recall and 100.00% precision, and ischemic myocardium diagnostic accuracy was achieved with 99.30% recall and 99.53% precision for the test set. This pilot study demonstrated that FTIR imaging is a powerful automated quantitative analysis tool to detect EMI without morphological changes and will improve diagnostic accuracy and patient prognosis.

Contributions of vibrational spectroscopy to virology: A review

Vibrational spectroscopic techniques, both infrared absorption and Raman scattering, are high precision, label free analytical techniques which have found applications in fields as diverse as analytical chemistry, pharmacology, forensics and archeometrics and, in recent times, have attracted increasing attention for biomedical applications. As analytical techniques, they have been applied to the characterisation of viruses as early as the 1970 s, and, in the context of the coronavirus disease 2019 (COVID-19) pandemic, have been explored in response to the World Health Organisation as novel methodologies to aid in the global efforts to implement and improve rapid screening of viral infection. This review considers the history of the application of vibrational spectroscopic techniques to the characterisation of the morphology and chemical compositions of viruses, their attachment to, uptake by and replication in cells, and their potential for the detection of viruses in population screening, and in infection response monitoring applications. Particular consideration is devoted to recent efforts in the detection of severe acute respiratory syndrome coronavirus 2, and monitoring COVID-19.

Raman hyperspectral imaging coupled to three-dimensional discriminant analysis: Classification of meningiomas brain tumour grades

Meningiomas remains a clinical dilemma. They are the commonest "benign" types of brain tumours and, although being typically benign, they are divided into three WHO grades categories (I, II and III) which are associated with the tumour growth rate and likelihood of recurrence. Recurrence depends on extend of surgery as well as histopathological diagnosis. There is a marked variation amongst surgeons in the follow-up arrangements for their patients even within the same unit which has a significant clinical, and financial implication. Knowing the tumour grade rapidly is an important factor to predict surgical outcomes and adequate patient treatment. Clinical follow up sometimes is haphazard and not based on clear evidence. Spectrochemical techniques are a powerful tool for cancer diagnostics. Raman hyperspectral imaging is able to generate spatially-distributed spectrochemical signatures with great sensitivity. Using this technique, 95 brain tissue samples (66 meningiomas WHO grade I, 24 meningiomas WHO grade II and 5 meningiomas that reoccurred) were analysed in order to discriminate grade I and grade II samples. Newly-developed three-dimensional discriminant analysis algorithms were used to process the hyperspectral imaging data in a 3D fashion. Three-dimensional principal component analysis quadratic discriminant analysis (3D-PCA-QDA) was able to distinguish grade I and grade II meningioma samples with 96% test accuracy (100% sensitivity and 95% specificity). This technique is here shown to be a high-throughput, reagent-free, non-destructive, and can give accurate predictive information regarding the meningioma tumour grade, hence, having enormous clinical potential with regards to being developed for intra-operative real-time assessment of disease.

Intraoperative imaging in pathology-assisted surgery

The pathological assessment of surgical specimens during surgery can reduce the incidence of positive resection margins, which otherwise can result in additional surgeries or aggressive therapeutic regimens. To improve patient outcomes, intraoperative spectroscopic, fluorescence-based, structural, optoacoustic and radiological imaging techniques are being tested on freshly excised tissue. The specific clinical setting and tumour type largely determine whether endogenous or exogenous contrast is to be detected and whether the tumour specificity of the detected biomarker, image resolution, image-acquisition times or penetration depth are to be prioritized. In this Perspective, we describe current clinical standards for intraoperative tissue analysis and discuss how intraoperative imaging is being implemented. We also discuss potential implementations of intraoperative pathology-assisted surgery for clinical decision-making.

Vibrational Biospectroscopy: An Alternative Approach to Endometrial Cancer Diagnosis and Screening

Endometrial cancer (EC) is the sixth most common cancer and the fourth leading cause of death among women worldwide. Early detection and treatment are associated with a favourable prognosis and reduction in mortality. Unlike other common cancers, however, screening strategies lack the required sensitivity, specificity and accuracy to be successfully implemented in clinical practice and current diagnostic approaches are invasive, costly and time consuming. Such limitations highlight the unmet need to develop diagnostic and screening alternatives for EC, which should be accurate, rapid, minimally invasive and cost-effective. Vibrational spectroscopic techniques, Mid-Infrared Absorption Spectroscopy and Raman, exploit the atomic vibrational absorption induced by interaction of light and a biological sample, to generate a unique spectral response: a “biochemical fingerprint”. These are non-destructive techniques and, combined with multivariate statistical analysis, have been shown over the last decade to provide discrimination between cancerous and healthy samples, demonstrating a promising role in both cancer screening and diagnosis. The aim of this review is to collate available evidence, in order to provide insight into the present status of the application of vibrational biospectroscopy in endometrial cancer diagnosis and screening, and to assess future prospects.

Clinical Spectroscopy: Lost in Translation?

This Focal Point Review paper discusses the developments of biomedical Raman and infrared spectroscopy, and the recent strive towards these technologies being regarded as reliable clinical tools. The promise of vibrational spectroscopy in the field of biomedical science, alongside the development of computational methods for spectral analysis, has driven a plethora of proof-of-concept studies which convey the potential of various spectroscopic approaches. Here we report a brief review of the literature published over the past few decades, with a focus on the current technical, clinical, and economic barriers to translation, namely the limitations of many of the early studies, and the lack of understanding of clinical pathways, health technology assessments, regulatory approval, clinical feasibility, and funding applications. The field of biomedical vibrational spectroscopy must acknowledge and overcome these hurdles in order to achieve clinical efficacy. Current prospects have been overviewed with comment on the advised future direction of spectroscopic technologies, with the aspiration that many of these innovative approaches can ultimately reach the frontier of medical diagnostics and many clinical applications.

Optimised Pre-Processing of Raman Spectra for Colorectal Cancer Detection Using High-Performance Computing

Spectral pre-processing is an essential step in data analysis for biomedical diagnostic applications of Raman spectroscopy, allowing the removal of undesirable spectral contributions that could mask biological information used for diagnosis. However, due to the specificity of pre-processing for a given sample type and the vast number of potential pre-processing combinations, optimisation of pre-processing via a manual "trial and error" format is often time intensive with no guarantee that the chosen method is optimal for the sample type. Here we present the use of high-performance computing (HPC) to trial over 2.4 million pre-processing permutations to demonstrate the optimisation on the pre-processing of human serum Raman spectra for colorectal cancer detection. The effect of varying pre-processing order, using extended multiplicative scatter correction, spectral smoothing, baseline correction, binning and normalization was considered. Permutations were assessed on their ability to detect patients with disease using a random forest (RF) algorithm trained with 102 patients (510 spectra) and independently tested with a set of 439 patients (1317 spectra) in a primary care patient cohort. Optimising via HPC enables improved performance in diagnostic abilities, with sensitivity increasing by 14.6%, specificity increasing by 6.9%, positive predictive value increasing by 3.4%, and negative predictive value increasing by 2.4% when compared to a standard pre-processing optimisation. Ultimate values of these metrics are very important for diagnostic adoption, and once diagnostics demonstrate good accuracy these types of optimisations can make a significant difference to roll-out of a test and demonstrating advantages over existing tests. We also provide tips/recommendations for pre-processing optimisation without the use of HPC. From the HPC permutations, recommendations for appropriate parameter constraints for conducting a more basic pre-processing optimisation are also detailed, thus helping model development for researchers not having access to HPC.

Breast cancer or surrounding normal tissue? A successful discrimination by FTIR or Raman microspectroscopy

Breast cancer is a type of cancer with the highest incidence worldwide in 2021, with early diagnosis and rapid treatment intervention being the reasons for the decreasing mortality rate associated with the disease.

Application of FTIR Spectroscopy for Quantitative Analysis of Blood Serum: A Preliminary Study

The aim of this study was to analyze the possibility of simultaneous determination of the concentration of components from the characteristics of FTIR spectra using the example of a model blood serum. To prepare model solutions, a set of freeze-dried control sera based on bovine blood serum was used, certified for approximately 38 parameters. Based on the values of the absorbance and areas of absorption bands in the FTIR spectra of model solutions, a regression equation was constructed by solving a nonlinear problem using the generalized reduced gradient method. By using the absorbance of the absorption bands at 1717 and 3903 cm⁻¹ and the areas of the absorption bands at 616, 3750, and 3903 cm⁻¹, it is possible to simultaneously determine the concentrations of 38 components with an error of less than 0.1%. The results obtained confirm the potential clinical use of FTIR spectroscopy as a reagent-free express method for the analysis of blood serum. However, its practical implementation requires additional research, in particular, analysis of real blood serum samples and validation of the method.

Advancing Raman spectroscopy from research to clinic: Translational potential and challenges

Raman spectroscopy has emerged as a non-invasive and versatile diagnostic technique due to its ability to provide molecule-specific information with ultrahigh sensitivity at near-physiological conditions. Despite exhibiting substantial potential, its translation from optical bench to clinical settings has been impacted by associated limitations. This perspective discusses recent clinical and biomedical applications of Raman spectroscopy and technological advancements that provide valuable insights and encouragement for resolving some of the most challenging hurdles.

Addressing Delicate and Variable Cancer Morphology in Spectral Histopathology Using Canine Visceral Hemangiosarcoma

Spectral histopathology has shown promise for the classification and diagnosis of tumors with defined morphology, but application in tumors with variable or diffuse morphologies is yet to be investigated. To address this gap, we evaluated the application of Fourier transform infrared (FTIR) imaging as an accessory diagnostic tool for canine hemangiosarcoma (HSA), a vascular endothelial cell cancer that is difficult to diagnose. To preserve the delicate vascular tumor tissue structure, and potential classification of single endothelial cells, paraffin removal was not performed, and a partial least square discrimination analysis (PLSDA) and Random Forest (RF) models to classify different tissue types at individual pixel level were established using a calibration set (24 FTIR images from 13 spleen specimens). Next, the prediction capability of the PLSDA model was tested with an independent test set (n = 11), resulting in 74% correct classification of different tissue types at an individual pixel level. Finally, the performance of the FTIR spectropathology and chemometric algorithm for diagnosis of HSA was established in a blinded set of tissue samples (n = 24), with sensitivity and specificity of 80 and 81%, respectively. Taken together, these results show that FTIR imaging without paraffin removal can be applied to tumors with diffuse morphology, and this technique is a promising tool to assist in canine splenic HSA differential diagnosis.

Multiplexed Fourier Transform Infrared and Raman Imaging

Infrared (IR) and Raman spectroscopies are being increasingly employed for the label-free analysis of biochemical samples. Both are vibrational imaging techniques, but they provide complementary information about the chemical composition of the sample, and thus the integration of Raman and IR images leads to a comprehensive understanding of the samples. Here, we summarize the steps needed for performing multiplexed infrared and Raman imaging, identifying and overcoming the two main challenges: first, the technical difficulties caused by the incompatibilities of the techniques and, second, the necessity to extract the information from the large number of vibrational variables found in both IR and Raman spectra.

An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms

Although some neurodegenerative diseases can be identified by behavioral characteristics relatively late in disease progression, we currently lack methods to predict who has developed disease before the onset of symptoms, when onset will occur, or the outcome of therapeutics. New biomarkers are needed. Here we describe spectral phenotyping, a new kind of biomarker that makes disease predictions based on chemical rather than biological endpoints in cells. Spectral phenotyping uses Fourier Transform Infrared (FTIR) spectromicroscopy to produce an absorbance signature as a rapid physiological indicator of disease state. FTIR spectromicroscopy has over the past been used in differential diagnoses of manifest disease. Here, we report that the unique FTIR chemical signature accurately predicts disease class in mouse with high probability in the absence of brain pathology. In human cells, the FTIR biomarker accurately predicts neurodegenerative disease class using fibroblasts as surrogate cells.

Raman spectral cytopathology for cancer diagnostic applications

Raman spectroscopy can provide a rapid, label-free, nondestructive measurement of the chemical fingerprint of a sample and has shown potential for cancer screening and diagnosis. Here we report a protocol for Raman microspectroscopic analysis of different exfoliative cytology samples (cervical, oral and lung), covering sample preparation, spectral acquisition, preprocessing and data analysis. The protocol takes 2 h 20 min for sample preparation, measurement and data preprocessing and up to 8 h for a complete analysis. A key feature of the protocol is that it uses the same sample preparation procedure as commonly used in diagnostic cytology laboratories (i.e., liquid-based cytology on glass slides), ensuring compatibility with clinical workflows. Our protocol also covers methods to correct for the spectral contribution of glass and sample pretreatment methods to remove contaminants (such as blood and mucus) that can obscure spectral features in the exfoliated cells and lead to variability. The protocol establishes a standardized clinical routine allowing the collection of highly reproducible data for Raman spectral cytopathology for cancer diagnostic applications for cervical and lung cancer and for monitoring suspicious lesions for oral cancer.

Detection of ovarian cancer (± neo-adjuvant chemotherapy effects) via ATR-FTIR spectroscopy: comparative analysis of blood and urine biofluids in a large patient cohort

Ovarian cancer remains the most lethal gynaecological malignancy, as its timely detection at early stages remains elusive. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy of biofluids has been previously applied in pilot studies for ovarian cancer diagnosis, with promising results. Herein, these initial findings were further investigated by application of ATR-FTIR spectroscopy in a large patient cohort. Spectra were obtained by measurements of blood plasma and serum, as well as urine, from 116 patients with ovarian cancer and 307 patients with benign gynaecological conditions. A preliminary chemometric analysis revealed significant spectral differences in ovarian cancer patients without previous chemotherapy (n = 71) and those who had received neo-adjuvant chemotherapy-NACT (n = 45), so these groups were compared separately with benign controls. Classification algorithms with blind predictive model validation demonstrated that serum was the best biofluid, achieving 76% sensitivity and 98% specificity for ovarian cancer detection, whereas urine exhibited poor performance. A drop in sensitivities for the NACT ovarian cancer group in plasma and serum indicates the potential of ATR-FTIR spectroscopy to identify chemotherapy-related spectral changes. Comparisons of regression coefficient plots for identification of biomarkers suggest that glycoproteins (such as CA125) are the main classifiers for ovarian cancer detection and responsible for smaller differences in spectra between NACT patients and benign controls. This study confirms the capacity of biofluids' ATR-FTIR spectroscopy (mainly blood serum) to diagnose ovarian cancer with high accuracy and demonstrates its potential in monitoring response to chemotherapy, which is reported for the first time. ATR-FTIR spectroscopy of blood serum achieves good segregation of ovarian cancers from benign controls, with attenuation of differences following neo-adjuvant chemotherapy.

Developing a Peak Extraction and Retention (PEER) Algorithm for Improving the Temporal Resolution of Raman Spectroscopy

In spectroscopic analysis, push-to-the-limit sensitivity is one of the important topics, particularly when facing the qualitative and quantitative analyses of the trace target. Normally, the effective recognition and extraction of weak signals are the first key steps, for which there has been considerable effort in developing various denoising algorithms for decades. Nevertheless, the lower the signal-to-noise ratio (SNR), the greater the deviation of the peak height and shape during the denoising process. Therefore, we propose a denoising algorithm along with peak extraction and retention (PEER). First, both the first and second derivatives of the Raman spectrum are used to determine Raman peaks with a high SNR whose peak information is kept away from the denoising process. Second, an optimized window smoothing algorithm is applied to the left part of the Raman spectrum, which is combined with the untreated Raman peaks to obtain the denoised Raman spectrum. The PEER algorithm is demonstrated with much better signal extraction and retention and successfully improves the temporal resolution of Raman imaging of a living cell by at least 1 order of magnitude higher than those by traditional algorithms.

Quantum Cascade Laser-Based Infrared Imaging as a Label-Free and Automated Approach to Determine Mutations in Lung Adenocarcinoma

Therapeutic decisions in lung cancer critically depend on the determination of histologic types and oncogene mutations. Therefore, tumor samples are subjected to standard histologic and immunohistochemical analyses and examined for relevant mutations using comprehensive molecular diagnostics. In this study, an alternative diagnostic approach for automatic and label-free detection of mutations in lung adenocarcinoma tissue using quantum cascade laser-based infrared imaging is presented. For this purpose, a five-step supervised classification algorithm was developed, which was not only able to detect tissue types and tumor lesions, but also the tumor type and mutation status of adenocarcinomas. Tumor detection was verified on a data set of 214 patient samples with a specificity of 97% and a sensitivity of 95%. Furthermore, histology typing was verified on samples from 203 of the 214 patients with a specificity of 97% and a sensitivity of 94% for adenocarcinoma. The most frequently occurring mutations in adenocarcinoma (KRAS, EGFR, and TP53) were differentiated by this technique. Detection of mutations was verified in 60 patient samples from the data set with a sensitivity and specificity of 95% for each mutation. This demonstrates that quantum cascade laser infrared imaging can be used to analyze morphologic differences as well as molecular changes. Therefore, this single, one-step measurement provides comprehensive diagnostics of lung cancer histology types and most frequent mutations.

Biomedical applications of vibrational spectroscopy: Oral cancer diagnostics

Vibrational spectroscopy, based on either infrared absorption or Raman scattering, has attracted increasing attention for biomedical applications. Proof of concept explorations for diagnosis of oral potentially malignant disorders and cancer are reviewed, and recent advances critically appraised. Specific examples of applications of Raman microspectroscopy for analysis of histological, cytological and saliva samples are presented for illustrative purposes, and the future prospects, ultimately for routine, chairside in vivo screening are discussed.

Detection of bacterial colonization by the spectral changes of surface-enhanced Raman reporters

In times of widespread multiple antibiotic resistance, the bacterial colonization of crucial medical surfaces should be detected as fast as possible. In this work, we present the non-destructive SERS method for the detection of bacterial colonization. SERS is an excellent tool for the monitoring of suitable substances in low concentrations. The SERS substrate was prepared by the aggregation of citrate-stabilized gold nanoparticles and the adsorption of the reporters (crystal violet, thiamine, and adenine). We have tested the substrate for the detection of clinically relevant S. aureus and P. aeruginosa bacteria. The SERS spectra before and after the substrate incubation revealed the degradation of the reporter by the growing bacteria. The growth of P. aeruginosa was detected using the substrates with preadsorbed crystal violet or adenine. The suitable reporter for the detection of S. aureus remains to be discovered. The selection of the reporters resistant to exposure but easily degraded by bacteria will open the way for the in situ monitoring of bacterial colonization, thus complementing the arsenal of methods in the battle against hospital infections.

Evaluating biochemical differences in thyroglobulin from normal and goiter tissues by infrared spectral imaging

Thyroglobulin is a glycoiodoprotein that is produced by thyroid follicular cells; it is stored in follicles in structures known as colloids. The main function of this protein is to stock the hormones triiodothyronine (T3) and thyroxine (T4) until the body requires them. This study aims to demonstrate that infrared spectral imaging with appropriate multivariate analysis can reveal biochemical changes in this glycoprotein. The results achieved herein point out biochemical differences in the colloid samples obtained from normal and goiter patients including glycosylation and changes in the secondary conformational structure. We have presented the first spectral histopathology-based method to detect biochemical differences in thyroid colloids, such as TG iodination, glycosylation, and changes in the secondary structure in normal and goiter patients. The observed changes in the colloids were mainly due to the alterations in amide I and amide II (secondary conformation of proteins) and there is a correlation with different glycosylation between normal and goiter tissues.

Quantitative analysis of human blood serum using vibrational spectroscopy

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State of the art of quantitative Vibrational Spectroscopic analysis of human blood serum is reviewed.

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Technical considerations for infrared absorption and Raman analysis are discussed.

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Quantitative analyses of Endogenous and Exogenous constituents are presented.

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The potential for clinical translation of spectroscopic serology is argued.

Analysis of bodily fluids using vibrational spectroscopy has attracted increasing attention in recent years. In particular, infrared spectroscopic screening of blood products, particularly blood serum, for disease diagnostics has been advanced considerably, attracting commercial interests. However, analyses requiring quantification of endogenous constituents or exogenous agents in blood are less well advanced. Recent advances towards this end are reviewed, focussing on infrared and Raman spectroscopic analyses of human blood serum. The importance of spectroscopic analysis in the native aqueous environment is highlighted, and the relative merits of infrared absorption versus Raman spectroscopy are considered, in this context. It is argued that Raman spectroscopic analysis is more suitable to quantitative analysis in liquid samples, and superior performance for quantification of high and low molecular weight components, is demonstrated. Applications for quantitation of viral loads, and therapeutic drug monitoring are also discussed.

Vibrational Spectroscopy for In Vitro Monitoring Stem Cell Differentiation

Stem cell technology has attracted considerable attention over recent decades due to its enormous potential in regenerative medicine and disease therapeutics. Studying the underlying mechanisms of stem cell differentiation and tissue generation is critical, and robust methodologies and different technologies are required. Towards establishing improved understanding and optimised triggering and control of differentiation processes, analytical techniques such as flow cytometry, immunohistochemistry, reverse transcription polymerase chain reaction, RNA in situ hybridisation analysis, and fluorescence-activated cell sorting have contributed much. However, progress in the field remains limited because such techniques provide only limited information, as they are only able to address specific, selected aspects of the process, and/or cannot visualise the process at the subcellular level. Additionally, many current analytical techniques involve the disruption of the investigation process (tissue sectioning, immunostaining) and cannot monitor the cellular differentiation process in situ, in real-time. Vibrational spectroscopy, as a label-free, non-invasive and non-destructive analytical technique, appears to be a promising candidate to potentially overcome many of these limitations as it can provide detailed biochemical fingerprint information for analysis of cells, tissues, and body fluids. The technique has been widely used in disease diagnosis and increasingly in stem cell technology. In this work, the efforts regarding the use of vibrational spectroscopy to identify mechanisms of stem cell differentiation at a single cell and tissue level are summarised. Both infrared absorption and Raman spectroscopic investigations are explored, and the relative merits, and future perspectives of the techniques are discussed.

Rapid analysis of disease state in liquid human serum combining infrared spectroscopy and "digital drying"

In recent years, the diagnosis of brain tumors has been investigated with attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy on dried human serum samples to eliminate spectral interferences of the water component, with promising results. This research evaluates ATR-FTIR on both liquid and air-dried samples to investigate "digital drying" as an alternative approach for the analysis of spectra obtained from liquid samples. Digital drying approaches, consisting of water subtraction and least-squares method, have demonstrated a greater random forest (RF) classification performance than the air-dried spectra approach when discriminating cancer vs control samples, reaching sensitivity values higher than 93.0% and specificity values higher than 83.0%. Moreover, quantum cascade laser infrared (QCL-IR) based spectroscopic imaging is utilized on liquid samples to assess the implications of a deep-penetration light source on disease classification. The RF classification of QCL-IR data has provided sensitivity and specificity amounting to 85.1% and 75.3% respectively.

Detection and time-tracking activation of a photosensitiser on live single colorectal cancer cells using Raman spectroscopy

Raman spectroscopy has been used to observe uptake, metabolism and response of single-cells to drugs. Photodynamic therapy is based on the use of light, a photosensitiser and oxygen to destroy tumour tissue. Here, we used single-cell Raman spectroscopy to study the uptake and intracellular degradation of a novel photosensitiser with a diphenylacetylene structure, DC473, in live single-cells from colorectal adenocarcinoma cell lines SW480, HT29 and SW620. DC473 was seen to predominantly accumulate in lipid droplets, showing higher accumulation in HT29 and SW620 cells than in SW480 cells, with a broader DC473 peak shifted to higher wavenumbers. DC473 activation and effects were tracked on live single-cells for 5 minutes. Upon exposure to UV light, the DC473 signal intensity dropped, with remaining DC473 shifting towards higher wavenumbers and widening, with a lifetime of approximately 50 seconds. Morphologically, SW480 and SW620 cells showed changes upon photodynamic therapy, whereas HT29 cells showed no changes. Morphological changes correlated with higher remaining DC473 signal after UV exposure. Our research suggests that DC473 forms aggregates within the cells that disaggregate following activation, showing the potential of Raman spectroscopy for the study of time-dependent single-cell pharmacodynamics.

Label-free, automated classification of microsatellite status in colorectal cancer by infrared imaging

Challenging histopathological diagnostics in cancer include microsatellite instability-high (MSI-H) colorectal cancer (CRC), which occurs in 15% of early-stage CRC and is caused by a deficiency in the mismatch repair system. The diagnosis of MSI-H cannot be reliably achieved by visual inspection of a hematoxylin and eosin stained thin section alone, but additionally requires subsequent molecular analysis. Time- and sample-intensive immunohistochemistry with subsequent fragment length analysis is used. The aim of the presented feasibility study is to test the ability of quantum cascade laser (QCL)-based infrared (IR) imaging as an alternative diagnostic tool for MSI-H in CRC. We analyzed samples from 100 patients with sporadic CRC UICC stage II and III. Forty samples were used to develop the random forest classifier and 60 samples to verify the results on an independent blinded dataset. Specifically, 100% sensitivity and 93% specificity were achieved based on the independent 30 MSI-H- and 30 microsatellite stable (MSS)-patient validation cohort. This showed that QCL-based IR imaging is able to distinguish between MSI-H and MSS for sporadic CRC - a question that goes beyond morphological features - based on the use of spatially resolved infrared spectra used as biomolecular fingerprints.

Liquid biopsy for cancer diagnosis using vibrational spectroscopy: systematic review

Background

Vibrational spectroscopy (VS) is a minimally invasive tool for analysing biological material to detect disease. This study aimed to review its application to human blood for cancer diagnosis.

Methods

A systematic review was undertaken using a keyword electronic database search (MEDLINE, Embase, PubMed, TRIP and Cochrane Library), with all original English‐language manuscripts examining the use of vibrational spectral analysis of human blood for cancer detection. Studies involving fewer than 75 patients in the cancer or control group, animal studies, or where the primary analyte was not blood were excluded.

Results

From 1446 results, six studies (published in 2010–2018) examining brain, bladder, oral, breast, oesophageal and hepatic cancer met the criteria for inclusion, with a total population of 2392 (1316 cancer, 1076 control; 1476 men, 916 women). For cancer detection, reported mean sensitivities in each included study ranged from 79·3 to 98 per cent, with specificities of 82·8–95 per cent and accuracies between 81·1 and 97·1 per cent. Heterogeneity in reporting strategies, methods and outcome measures made meta‐analysis inappropriate.

Conclusion

VS shows high potential for cancer diagnosis, but until there is agreement on uniform standard reporting methods and studies with adequate sample size for valid classification models have been performed, its value in clinical practice will remain uncertain.

Developing infrared spectroscopic detection for stratifying brain tumour patients: glioblastoma multiforme vs. lymphoma

Over a third of brain tumour patients visit their general practitioner more than five times prior to diagnosis in the UK, leading to 62% of patients being diagnosed as emergency presentations. Unfortunately, symptoms are non-specific to brain tumours, and the majority of these patients complain of headaches on multiple occasions before being referred to a neurologist. As there are currently no methods in place for the early detection of brain cancer, the affected patients' average life expectancy is reduced by 20 years. These statistics indicate that the current pathway is ineffective, and there is a vast need for a rapid diagnostic test. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is sensitive to the hallmarks of cancer, as it analyses the full range of macromolecular classes. The combination of serum spectroscopy and advanced data analysis has previously been shown to rapidly and objectively distinguish brain tumour severity. Recently, a novel high-throughput ATR accessory has been developed, which could be cost-effective to the National Health Service in the UK, and valuable for clinical translation. In this study, 765 blood serum samples have been collected from healthy controls and patients diagnosed with various types of brain cancer, contributing to one of the largest spectroscopic studies to date. Three robust machine learning techniques - random forest, partial least squares-discriminant analysis and support vector machine - have all provided promising results. The novel high-throughput technology has been validated by separating brain cancer and non-cancer with balanced accuracies of 90% which is comparable to the traditional fixed diamond crystal methodology. Furthermore, the differentiation of brain tumour type could be useful for neurologists, as some are difficult to distinguish through medical imaging alone. For example, the highly aggressive glioblastoma multiforme and primary cerebral lymphoma can appear similar on magnetic resonance imaging (MRI) scans, thus are often misdiagnosed. Here, we report the ability of infrared spectroscopy to distinguish between glioblastoma and lymphoma patients, at a sensitivity and specificity of 90.1% and 86.3%, respectively. A reliable serum diagnostic test could avoid the need for surgery and speed up time to definitive chemotherapy and radiotherapy.

Fourier Transform Infrared Spectroscopy as a Cancer Screening and Diagnostic Tool: A Review and Prospects

Infrared spectroscopy has long been used to characterize chemical compounds, but the applicability of this technique to the analysis of biological materials containing highly complex chemical components is arguable. However, recent advances in the development of infrared spectroscopy have significantly enhanced the capacity of this technique in analyzing various types of biological specimens. Consequently, there is an increased number of studies investigating the application of infrared spectroscopy in screening and diagnosis of various diseases. The lack of highly sensitive and specific methods for early detection of cancer has warranted the search for novel approaches. Being more simple, rapid, accurate, inexpensive, non-destructive and suitable for automation compared to existing screening, diagnosis, management and monitoring methods, Fourier transform infrared spectroscopy can potentially improve clinical decision-making and patient outcomes by detecting biochemical changes in cancer patients at the molecular level. Besides the commonly analyzed blood and tissue samples, extracellular vesicle-based method has been gaining popularity as a non-invasive approach. Therefore, infrared spectroscopic analysis of extracellular vesicles could be a useful technique in the future for biomedical applications. In this review, we discuss the potential clinical applications of Fourier transform infrared spectroscopic analysis using various types of biological materials for cancer. Additionally, the rationale and advantages of using extracellular vesicles in the spectroscopic analysis for cancer diagnostics are discussed. Furthermore, we highlight the challenges and future directions of clinical translation of the technique for cancer.

Applications of Raman spectroscopy in the development of cell therapies: state of the art and future perspectives

This comprehensive review article discusses current and future perspectives of Raman spectroscopy-based analyses of cell therapy processes and products.

Investigating pre-analytical requirements for serum and plasma based infrared spectro-diagnostic

Infrared spectroscopy is a rapid, easy-to-operate, label-free and therefore cost-effective technique. Many studies performed on biofluids (eg, serum, plasma, urine, sputum, bile and cerebrospinal fluid) have demonstrated its promising application as a clinical diagnostic tool. Given all these characteristics, infrared spectroscopy appears to be an ideal candidate to be implemented into the clinics. However, before considering its translation, a clear effort is needed to standardise protocols for biofluid spectroscopic analysis. To reach this goal, careful investigations to identify and track errors that can occur during the pre-analytical phase is a crucial step. Here, we report for the first time, results of investigations into pre-analytical factors that can affect the quality of the spectral data acquired on serum and plasma, such as the impact of long-term freezing time storage of samples as well as the month-to-month reproducibility of the spectroscopic analysis. The spectral data discrimination has revealed to be majorly impacted by a residual water content variation in serum and plasma dried samples.

Vibrational Spectroscopy Fingerprinting in Medicine: from Molecular to Clinical Practice

In the last two decades, Fourier Transform Infrared (FTIR) and Raman spectroscopies turn out to be valuable tools, capable of providing fingerprint-type information on the composition and structural conformation of specific molecular species. Vibrational spectroscopy's multiple features, namely highly sensitive to changes at the molecular level, noninvasive, nondestructive, reagent-free, and waste-free analysis, illustrate the potential in biomedical field. In light of this, the current work features recent data and major trends in spectroscopic analyses going from in vivo measurements up to ex vivo extracted and processed materials. The ability to offer insights into the structural variations underpinning pathogenesis of diseases could provide a platform for disease diagnosis and therapy effectiveness evaluation as a future standard clinical tool.

Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer

Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.

Noninvasive analysis and minimally invasive in vivo experimental challenges of the skin barrier

In this review, we aim to give a concise and selective overview of noninvasive biophysical analysis techniques for skin barrier analysis (transepidermal water loss, electrical methods, confocal Raman microspectroscopy, sebumeter, reflectance spectrophotometry, tristimulus colorimetry, diffuse reflectance spectroscopy and reflectance confocal microscopy), including advantages and limitations. Rather than giving an exhaustive description of the many techniques currently available, we show the usefulness of a representative selection of techniques in the functional and morphological evaluation of the skin barrier. Furthermore, we introduce human minimally invasive skin challenging models as a means to study the mechanisms regulating skin homoeostasis and disease and subsequently show how biophysical analysis techniques can be combined with these in vivo skin challenging models in the functional and morphological evaluation of the skin barrier in healthy human skin. We are convinced that the widespread application of biophysical analysis techniques in dermatological practice and in cosmetic sciences will prove invaluable in offering personalized and noninvasive skin treatment solutions. Furthermore, combining the human in vivo challenging models with these novel noninvasive techniques will provide valuable methodology and tools for detailed characterization of the skin barrier in health and disease.

Raman spectroscopy based differentiation of typhoid and dengue fever in infected human sera

This study investigated the potential of Raman spectroscopy, to differentiate between two different types of human pathological conditions with some symptom similarity; typhoid and dengue. Raman spectra were recorded from 20 Salmonella Typhi infected and 22 dengue virus (DENV) infected sera samples using laser wavelength 785 nm. When Raman spectrum (540 to 1830 cm^-1) of serum samples was compared it showed significant variations. Twelve characteristic Raman bands at 562, 649, 716, 780, 838, 1099, 1144, 1156, 1260, 1386, 1556 and 1746 cm^-1 were recorded specifically in case of the typhoid infected samples, whereas four Raman bands at 756, 1218, 1672 and 1686 cm^-1 were found specifically in the DENV infected samples. For statistical differentiation principal component analysis (PCA) and linear discriminant analysis (LDA) was applied which successfully separated two sets of data. These disease specific Raman signatures may be labeled as biomarkers for identification as well as differential diagnosis of typhoid and dengue infection. Hence we propose that Raman spectroscopy has the potential to not only identify but also to differentiate between different pathological conditions, having some similar symptoms like high grade fever in this case.

Spectral classification for diagnosis involving numerous pathologies in a complex clinical setting: A neuro-oncology example

Much effort is currently being placed into developing new blood tests for cancer diagnosis in the hope of moving cancer diagnosis earlier and by less invasive means than current techniques, e.g., biopsy. Current methods are expected to diagnose and begin treatment of cancer within 62 days of patient presentation, though due to high volume and pressures within the NHS in the UK any technique that can reduce time to diagnosis would allow reduction in the time to treat for patients. The use of vibrational spectroscopy, notably infrared (IR) spectroscopy, has been under investigation for many years with varying success. This technique holds promise as is would combine a generally well accepted test (a blood test) with analysis that is reagent free and cheap to run. It has been demonstrated that, when asked simple clinical questions (i.e., cancer vs. no cancer), results from spectroscopic studies are promising. However, in order to become a clinically useful tool, it is important that the test differentiates a variety of cancer types from healthy patients. This study has analysed plasma samples with attenuated total reflection Fourier-transform IR spectroscopy (ATR-FTIR), to establish if the technique is able to distinguish normal from primary or metastatic brain tumours. We have shown that when asked specific questions, i.e., high-grade glioma vs. low-grade glioma, the results show a significantly high accuracy (100%). Crucially, when combined with meningiomas and metastatic lesions, the accuracy remains high (88-100%) with only minimal overlap between the two metastatic adenocarcinoma groups. Therefore in a clinical setting, this novel technique demonstrates potential benefit when used in conjuction with existing diagnostic methods.

Virtual staining of colon cancer tissue by label-free Raman micro-spectroscopy

The great capability of the label-free classification of tissue via vibrational spectroscopy, like Raman or infrared imaging, is shown in numerous publications (review: Diem et al., J. Biophotonics, 2013, 6, 855-886). Herein, we present a new approach, virtual staining, that improves the Raman spectral histopathology (SHP) images of colorectal cancer tissue by combining the integrated Raman intensity image in the C-H stretching region (2800-3050 cm^-1) with the pseudo-colour Raman image. This allows the display of fine structures such as the filamentous composition of muscle tissue. The morphology of the virtually stained images is in agreement with the gold standard in medical diagnosis, the haematoxylin-eosin staining. The virtual staining image also represents the whole biochemical fingerprint, and several tissue components including carcinoma were identified automatically with high sensitivity and specificity. For fast tissue classifications, a similar approach was applied on coherent anti-Stokes Raman scattering (CARS) spectral data that is faster and therefore potentially more suitable for clinical applications.

Digital de-waxing on FTIR images

This paper presents a procedure that digitally neutralizes the contribution of paraffin to FTIR hyperspectral images. A brief mathematical derivation of the procedure is demonstrated and applied on one normal human colon sample to exemplify the de-waxing procedure. The proposed method includes construction of a paraffin model based on PCA, EMSC normalization and application of two techniques for spectral quality control. We discuss every step in which the researcher needs to take a subjective decision during the de-waxing procedure, and we explain how to make an adequate choice of parameters involved. Application of this procedure to 71 hyperspectral images collected from 55 human colon biopsies (20 normal, 17 ulcerative colitis, and 18 adenocarcinoma) showed that paraffin was appropriately neutralized, which made the de-waxed images adequate for analysis by pattern-recognition techniques such as k-means clustering or PCA-LDA.

Raman micro-spectroscopy monitors acquired resistance to targeted cancer therapy at the cellular level

Monitoring the drug efficacy or resistance in vitro is usually carried out by measuring the response of single few proteins. However, observation of single proteins instead of an integral cell response may lead to results that are not consistent with patient’s response to a drug. We present a Raman spectroscopic method that detects the integral cell response to drugs such as tyrosine kinase inhibitors (TKIs). Non-small cell lung cancer (NSCLC) patients with EGFR mutations develop acquired resistance to first (erlotinib)- and third (osimertinib)-generation TKIs. Large erlotinib-induced differences were detected by Raman micro-spectroscopy in NSCLC cells without T790M EGFR mutation but not in cells with this mutation. Additionally, Raman difference spectra detected the response of NSCLC cells with T790M EGFR mutation to second- (neratinib) and third-generation (osimertinib) TKIs, and the resistance of cells with T790M/C797S EGFR mutation to osimertinib. Thus, the in vitro Raman results indicated that NSCLC cells with T790M and T790M/C797S EGFR mutations are resistant to erlotinib- and osimertinib, respectively, consistent with the observed responses of patients. This study shows the potential of Raman micro-spectroscopy to monitor drug resistance and opens a new door to in vitro companion diagnostics for screening personalized therapies.