Multicomponent blood analysis by near-infrared Raman spectroscopy

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.

Comparison of Glycated Hemoglobin (HbA1c) Values Estimated by High-Performance Liquid Chromatography and Spectrophotometry: A Pilot Study

Background Invasive blood sample collection followed by high-performance liquid chromatography (HPLC) based analysis is the gold standard for estimating glycated hemoglobin level or HbA1c currently. Spectrophotometry could be an alternative that holds the potential to be translated into a portable, non-invasive device for glycated hemoglobin level estimation. This study compares HbA1c values obtained from HPLC and spectrophotometry. Methods Venous blood samples were collected from both diabetic and non-diabetic participants in a cross-sectional study. The samples were subjected to both HPLC and spectrophotometry-based estimation of HbA1c%. The results obtained were compared, and the relationship between the two estimations were assessed. Results About 15 diabetic and non-diabetic individuals participated in the study and 28 samples were included in the final analysis. The Pearson's correlation coefficient was 0.65 (95% CI, 0.37-0.82), indicating that there was a strong positive association. This was further supported by the findings from linear regression analysis with a p-value of <0.001. Conclusions The positive correlation between the HPLC and spectrophotometric values supports the hypothesis that spectrophotometry could be an alternative to conventional HPLC for the measurement of HbA1c. This needs to be further validated through larger, well-powered studies.

Compared between support vector machine (SVM) and deep belief network (DBN) for multi-classification of Raman spectroscopy for cervical diseases

In this study, a minimally invasive test method for cervical cancer in vitro was proposed by comparing Raman spectroscopy with support vector machine (SVM) model and deep belief network (DBN) model. The serum Raman spectra of cervical cancer, hysteromyoma, and healthy people were collected. After data processing, SVM classification model and DBN classification model were built respectively. The experimental results show that when the DBN network algorithm is used, the sample test set can be divided accurately and the result of cross-validation is ideal. Compared with the traditional SVM algorithm, this method firstly screened the effective feature matrix from the data, and then classified the data. With high efficiency and accuracy, based on 445 samples collected, this method improved the accuracy by 13.93%±2.47% compared with the SVM method, and provided a new direction and idea for the in vitro diagnosis of cervical diseases.

Analysis of urine Raman spectra differences from patients with diabetes mellitus and renal pathologies

Background

Chronic kidney disease (CKD) poses a major public health burden. Diabetes mellitus (DM) is one of the major causes of CKD. In patients with DM, it can be difficult to differentiate diabetic kidney disease (DKD) from other causes of glomerular damage; it should not be assumed that all DM patients with decreased eGFR and/or proteinuria have DKD. Renal biopsy is the standard for definitive diagnosis, but other less invasive methods may provide clinical benefit. As previously reported, Raman spectroscopy of CKD patient urine with statistical and chemometric modeling may provide a novel, non-invasive methodology for discriminating between renal pathologies.

Methods

Urine samples were collected from renal biopsied and non-biopsied patients presenting with CKD secondary to DM and non-diabetic kidney disease. Samples were analyzed by Raman spectroscopy, baselined with the ISREA algorithm, and subjected to chemometric modeling. Leave-one-out cross-validation was used to assess the predictive capabilities of the model.

Results

This proof-of-concept study consisted of 263 samples, including renal biopsied, non-biopsied diabetic and non-diabetic CKD patients, healthy volunteers, and the Surine™ urinalysis control. Urine samples of DKD patients and those with immune-mediated nephropathy (IMN) were distinguished from one another with 82% sensitivity, specificity, positive-predictive value (PPV), and negative-predictive value (NPV). Among urine samples from all biopsied CKD patients, renal neoplasia was identified in urine with 100% sensitivity, specificity, PPV, and NPV, and membranous nephropathy was identified with 66.7% sensitivity, 96.4% specificity, 80.0% PPV, and 93.1% NPV. Finally, DKD was identified among a population of 150 patient urine samples containing biopsy-confirmed DKD, other biopsy-confirmed glomerular pathologies, un-biopsied non-diabetic CKD patients (no DKD), healthy volunteers, and Surine™ with 36.4% sensitivity, 97.8% specificity, 57.1% PPV, and 95.1% NPV. The model was used to screen un-biopsied diabetic CKD patients and identified DKD in more than 8% of this population. IMN in diabetic patients was identified among a similarly sized and diverse population with 83.3% sensitivity, 97.7% specificity, 62.5% PPV, and 99.2% NPV. Finally, IMN in non-diabetic patients was identified with 50.0% sensitivity, 99.4% specificity, 75.0% PPV, and 98.3% NPV.

Conclusions

Raman spectroscopy of urine with chemometric analysis may be able to differentiate between DKD, IMN, and other glomerular diseases. Future work will further characterize CKD stages and glomerular pathology, while assessing and controlling for differences in factors such as comorbidities, disease severity, and other lab parameters.

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

The grading of cancer tissues is still one of the main challenges for pathologists. The development of enhanced analysis strategies hence becomes crucial to accurately identify and further deal with each individual case. Raman spectroscopy (RS) is a promising tool for the classification of tumor tissues as it allows us to obtain the biochemical maps of the tissues under analysis and to observe their evolution in terms of biomolecules, proteins, lipid structures, DNA, vitamins, and so on. However, its potential could be further improved by providing a classification system which would be able to recognize the sample tumor category by taking as input the raw Raman spectroscopy signal; this could provide more reliable responses in shorter time scales and could reduce or eliminate false-positive or -negative diagnoses. Deep Learning techniques have become ubiquitous in recent years, with models able to perform classification with high accuracy in most diverse fields of research, e.g., natural language processing, computer vision, medical imaging. However, deep models often rely on huge labeled datasets to produce reasonable accuracy, otherwise occurring in overfitting issues when the training data is insufficient. In this paper, we propose a chondrogenic tumor CLAssification through wavelet transform of RAman spectra (CLARA), which is able to classify with high accuracy Raman spectra obtained from bone tissues. CLARA recognizes and grades the tumors in the evaluated dataset with 97% accuracy by exploiting a classification pipeline consisting of the division of the original task in two binary classification steps, where the first is performed on the original RS signals while the latter is accomplished through the use of a hybrid temporal-frequency 2D transform.

Assessing the quality of stored red blood cells using handheld Spatially Offset Raman spectroscopy with multisource correlation analysis

In this work we employ Spatially Offset Raman Spectroscopy (SORS) to non-invasively identify storage-related changes in red blood cell concentrate (RCC) in-situ within standard plastic transfusion bags. To validate the measurements, we set up a parallel study comparing both bioanalytical data (obtained by blood-gas analysis, hematology analysis and spectrophotometric assays), and Raman spectrometry data from the same blood samples. We then employ Multisource Correlation Analysis (MuSCA) to correlate the different types of data in RCC. Our analysis confirmed a strong correlation of glucose, methemoglobin and oxyhemoglobin with their respective bioassay values in RCC units. Finally, by combining MuSCA with k-means clustering, we assessed changes in all Raman wavenumbers during cold storage in both RCC Raman data from the current study and parallel RCC supernatant Raman data previously acquired from the same units. Direct RCC quality monitoring during storage, would help to establish a basis for improved inventory management of blood products in blood banks and hospitals based on analytical data.

Quantifying Serum Total Lipids and Tryptophan Concentrations by Raman Spectroscopy During Standardized Obstacle Course in Horses

Raman spectroscopy is an inelastic light-scattering phenomenon that provides vibrational spectrum that contains information relative to chemical bonds and symmetry of a specific molecule, allowing the quali-quantitative simultaneous determination of several components in the biological fluids. Raman spectroscopy measurement returns a spectrum over a wavenumber range constituted by several bands representing biomarkers according to investigated biological matrices. In literature, it has been reported that at the frequencies inside the (1,300-1,360) cm^-1 total lipids, and tryptophan have been identified. On the basis of that, the aim of the present study was to compare the serum concentration of total lipids and tryptophan in horses subjected to a standardized obstacle course, in comparison with the data obtained in the (1,300-1,360) cm^-1 band. At this purpose, five clinically healthy and regularly trained Italian Saddle horses aged between 8, and 10 years old performed with the same rider an obstacle course of 350 m/min with twelve 1.30 m high jumps preceded by warm-up. Blood samples were collected by means of jugular venipuncture into a vacutainer tubes with clot activator at rest, after the exercise, and 30 minutes after the end of exercise. A high correlation was observed between the area of total lipids and tryptophan in the (1,300-1,360) cm^-1 band and their serum concentrations in all experimental conditions. Our preliminary results give a hint to study the exact correspondence between the area that identify these parameters in Raman spectrum and their serum concentration in athletic horses.

Dual-wavelength excitation combined Raman spectroscopy for detection of highly fluorescent samples

As fluorescence is the major limitation in Raman scattering, near-infrared excitation wavelength (>780nm) is preferred for fluorescence suppression in Raman spectroscopy. To reduce the risk of fluorescence interference, we developed a dual-wavelength excitation combined Raman spectroscopy (DWECRS) system at 785 and 830 nm. By a common optical path, each laser beam was focused on the same region of the sample by a single objective lens, and the dual-wavelength excitation Raman spectra were detected by a single CCD detector; in addition, 785 and 830 nm excitation Raman spectra can be directly constructed as combined Raman spectrum in the DWECRS system. The results of pure peanut oil and glycerol indicate that the combined Raman spectrum cannot only reduce fluorescence interference but also keep a high signal-to-noise ratio in the high-wavenumber region. The results of dye-ethanol solutions with different concentrations show that the handheld DWECRS system can be used as a smart method to dodge strong fluorescence. Furthermore, we developed a peak intensity ratio method with the DWECRS system to distinguish different types of edible oils. The peak intensity ratio distribution chart of edible oils showed each oil normalized center was relatively independent and nonoverlapped, which can be used as the basis of edible oil classification analysis. In the future, the DWECRS system has potential to be used as a tool for more complex applications.

COHERE - strengthening cooperation within the Canadian government on radiation research

PURPOSE

Canadian Organization on Health Effects from Radiation Exposure (COHERE) is a government initiative to better understand biological and human health risks from ionizing radiation exposures relevant to occupational and environmental settings (<100 mGy, <6 mGy/h). It is currently a partnership between two federal agencies, Health Canada (HC) and the Canadian Nuclear Safety Commission (CNSC). COHERE's vision is to contribute knowledge to reduce scientific uncertainties from low dose and dose-rate exposures. COHERE will advance our understanding by bridging the knowledge gap between human health risks and linkages to molecular- and cellular-level responses to radiation. Research focuses on identifying sensitive, early, and key molecular events of relevance to risk assessment.

CONCLUSIONS

The initiative will address questions of relevance to better apprize Canadians, including radiation workers and members of the public and Indigenous peoples, on health risks from low dose radiation exposure and inform radiation protection frameworks at a national and international level. Furthermore, it will support global efforts to conduct collaborative undertakings and better coordinate research. Here, we describe a historical overview of the research conducted, the strategic research agenda that outlines the scientific framework, stakeholders, opportunities to harmonize internationally, and how research outcomes will better inform communication of risk to Canadians.

Non-invasive monitoring of red blood cells during cold storage using handheld Raman spectroscopy

BACKGROUND

The current best practices allow for the red blood cells (RBCs) to be stored for prolonged periods in blood banks worldwide. However, due to the individual-related variability in donated blood and RBCs continual degradation within transfusion bags, the quality of stored blood varies considerably. There is currently no method for assessing the blood product quality without compromising the sterility of the unit. This study demonstrates the feasibility of monitoring storage lesion of RBCs in situ while maintaining sterility using an optical approach.

STUDY DESIGN AND METHODS

A handheld spatially offset Raman spectroscopy (RS) device was employed to non-invasively monitor hemolysis and metabolic changes in 12 red cell concentrate (RCC) units within standard sealed transfusion bags over 7 weeks of cold storage. The donated blood was analyzed in parallel by biochemical (chemical analysis, spectrophotometry, hematology analysis) and RS measurements, which were then correlated through multisource correlation analysis.

RESULTS

Raman bands of lactate (857 cm^-1 ), glucose (787 cm^-1 ), and hemolysis (1003 cm^-1 ) were found to correlate strongly with bioanalytical data over the length of storage, with correlation values 0.98 (95% confidence interval [CI]: 0.86-1.00; p = .0001), 0.95 (95% CI: 0.71-0.99; p = .0008) and 0.97 (95% CI: 0.79-1.00; p = .0004) respectively.

DISCUSSION

This study demonstrates the potential of collecting information on the clinical quality of blood units without breaching the sterility using Raman technology. This could significantly benefit quality control of RCC units, patient safety and inventory management in blood banks and hospitals.

Multivariate analysis of mean Raman spectra of erythrocytes for a fast analysis of the biochemical signature of ageing

Ageing of red blood cells (RBC) is a physiological process, fundamental to ensure a proper blood homeostasis that, in vivo, balances the production of new cells and the removal of senescent erythrocytes. A detailed characterization at the cellular level of the progression of the ageing phenomenon can reveal biological, biophysical and biochemical fingerprints for diseases related to misbalances of the cell turnover and for blood pathologies. We applied Principal Components Analysis (PCA) to mean Raman spectra of single cells at different ageing times to rapidly highlight subtle spectral differences associated with conformational and biochemical modifications. Our results demonstrate a two-step ageing process characterized by a first phase in which proteins plays a relevant role, followed by a further cellular evolution driven by alterations in the membrane lipid contribution. Moreover, we used the same approach to directly analyse relevant spectral effects associated to reduction in Haemoglobin oxygenation level and membrane fluidity induced by the ageing. The method is robust and effective, allowing to classify easily the studied cells based on their age and morphology, and consequently to evaluate the biological quality of a blood sample.

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.

Ultrasound-modulated optical glucose sensing using a 1645 nm laser

Regular and frequent blood glucose monitoring is vital in managing diabetes treatment plans and preventing severe complications. Because current invasive techniques impede patient compliance and are not infection-free, many noninvasive methods have been proposed. Among them, optical methods have drawn much attention for their rich optical contrast, but their resolution is degraded in deep tissue. Here, we present an ultrasound-modulated optical sensing (UOS) technique to noninvasively monitor glucose that uses an infrared laser (1645 nm) and a single-element focused ultrasound transducer. Focused ultrasound waves can acoustically localize diffused photons in scattering media, and thus optical contrast can be represented with much enhanced spatial resolution. To maximize the signal-to-noise ratio, we compared the modulation depths of UOS signals in both continuous and burst ultrasound transmission modes. Finally, UOS measurements of various glucose concentrations are presented and compared with those acquired in phantoms with a conventional diffuse optical sensing method. The UOS measurements in a 20 mm thick tissue-mimicking phantom show 26.6% accuracy in terms of mean absolute relative difference (MARD), which indicates the great potential of the proposed technique as a noninvasive glucose sensor.

Raman spectral discrimination in human liquid biopsies of oesophageal transformation to adenocarcinoma

The aim of this study was to determine whether Raman spectroscopy combined with chemometric analysis can be applied to interrogate biofluids (plasma, serum, saliva and urine) towards detecting oesophageal stages through to oesophageal adenocarcinoma [normal/squamous epithelium, inflammatory, Barrett's, low-grade dysplasia, high-grade dysplasia and oesophageal adenocarcinoma (OAC)]. The chemometric analysis of the spectral data was performed using principal component analysis, successive projections algorithm or genetic algorithm (GA) followed by quadratic discriminant analysis (QDA). The genetic algorithm quadratic discriminant analysis (GA-QDA) model using a few selected wavenumbers for saliva and urine samples achieved 100% classification for all classes. For plasma and serum, the GA-QDA model achieved excellent accuracy in all oesophageal stages (>90%). The main GA-QDA features responsible for sample discrimination were: 1012 cm^-1 (C─O stretching of ribose), 1336 cm^-1 (Amide III and CH₂ wagging vibrations from glycine backbone), 1450 cm^-1 (methylene deformation) and 1660 cm^-1 (Amide I). The results of this study are promising and support the concept that Raman on biofluids may become a useful and objective diagnostic tool to identify oesophageal disease stages from squamous epithelium to OAC.

Direct observation of glucose fingerprint using in vivo Raman spectroscopy

Noninvasive blood glucose monitoring has been a long-standing dream in diabetes management. The use of Raman spectroscopy, with its molecular specificity, has been investigated in this regard over the past decade. Previous studies reported on glucose sensing based on indirect evidence such as statistical correlation to the reference glucose concentration. However, these claims fail to demonstrate glucose Raman peaks, which has raised questions regarding the effectiveness of Raman spectroscopy for glucose sensing. Here, we demonstrate the first direct observation of glucose Raman peaks from in vivo skin. The signal intensities varied proportional to the reference glucose concentrations in three live swine glucose clamping experiments. Tracking spectral intensity based on linearity enabled accurate prospective prediction in within-subject and intersubject models. Our direct demonstration of glucose signal may quiet the long debate about whether glucose Raman spectra can be measured in vivo in transcutaneous glucose sensing.

Serum Raman spectroscopy as a diagnostic tool in patients with Huntington's disease

Huntington's disease is an inherited fatal progressive neurodegenerative disorder. A possible new Raman ‘spectral’ biomarker was identified that uses a tiny drop of patients' blood serum; thus can have immense diagnostic and therapeutic implications.

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder caused by an abnormal CAG expansion in exon 1 of the huntingtin (HTT) gene. Given its genetic basis it is possible to study patients both in the pre-manifest and manifest stages of the condition. While disease onset can be modelled using CAG repeat size, there are no easily accessible biomarkers that can objectively track disease progression. Here, we employed a holistic approach using spectral profiles generated using both surface-enhanced Raman spectroscopy (SERS) and Raman Spectroscopy (RS), on the serum of healthy participants and HD patients covering a wide spectrum of disease stages. We found that there was both genotype- and gender-specific segregation on using the full range in the fingerprint region with both SERS and RS. On a more detailed interrogation using specific spectral intervals, SERS revealed significant correlations with disease progression, in particular progression from pre-manifest through to advanced HD was associated with serum molecules related to protein misfolding and nucleotide catabolism. Thus, this study shows the potential of Raman spectroscopy-based techniques for stratification of patients and, of SERS, in particular, to track disease status through provision of ‘spectral’ biomarkers in HD, with clinical applications for other diseases and trials looking at disease modifying therapies.

Surface-enhanced Raman scattering-active photonic crystal fiber probe: Towards next generation liquid biopsy sensor with ultra high sensitivity

The tremendous enhancement factors that surface-enhanced Raman scattering (SERS) possesses coupled with the flexibility of photonic crystal fibers (PCFs) pave the way to a new generation of ultrasensitive biosensors. Thanks to the unique structure of PCFs, which allows direct incorporation of an analyte into the axially aligned air channels, interaction between the analyte and excitation light could be increased many folds leading to flexible, reliable and sensitive probes that can be used in preclinical or clinical biosensing. SERS-active PCF probes provide unique opportunity to develop an opto-fluidic liquid biopsy needle sensor that enables one-step integrated sample collection and testing for disease diagnosis. Specificity being a key parameter to biosensors, the PCF inside the biopsy needle could be functionalized with targeting moieties to detect specific biomarkers. In this review article, we present some of the most promising recent biosensors based on PCFs including hollow-core PCFs, suspended-core PCFs and side-channel PCFs. We provide a wide range of applications of such platform using Raman spectroscopy, label free SERS or labeled SERS detection and analyze some of the main challenges to be addressed for translating it to a clinically viable next generation sensitive biopsy needle sensing probe.

Comprehensive modeling of bloodstain aging by multivariate Raman spectral resolution with kinetics

Blood, as a cardinal biological system, is a challenging target for biochemical characterization because of sample complexity and a lack of analytical approaches. To reveal and evaluate aging process of blood compositions is an unexplored issue in forensic analysis, which is useful to elucidate the details of a crime. Here we demonstrate a spectral deconvolution model of near-infrared Raman spectra of bloodstain to comprehensively describe the aging process based on the chemical mechanism, particularly the kinetics. The bloodstain spectra monitored over several months at different temperatures are decomposed into significant spectral components by multivariate calculation. The kinetic schemes of the spectral components are explored and subsequently incorporated into the developed algorithm for the optimal spectral resolution. Consequently, the index of bloodstain aging is proposed, which can be used under different experimental conditions. This work provides a novel perspective on the chemical mechanisms in bloodstain aging and facilitates forensic applications.

Development of a flow cell based Raman spectroscopy technique to overcome photodegradation in human blood

Raman spectroscopy of blood offers significant potential for label-free diagnostics of disease. However, current techniques are limited by the use of low laser power to avoid photodegradation of blood; this translates to a low signal to noise ratio in the Raman spectra. We developed a novel flow cell based Raman spectroscopy technique that provides reproducible Raman spectra with a high signal to noise ratio and low data acquisition time while ensuring a short dwell time in the laser spot to avoid photodamage in blood lysates. We show that our novel setup is capable of detecting minute changes in blood lysate spectral features from natural aging. Moreover, we demonstrate that by rigorously controlling the experimental conditions, the aging effect due to natural oxidation does not confound the Raman spectral measurements and that blood treated with hydrogen peroxide to induce oxidative stress can be discriminated from normal blood with a high accuracy of greater than 90% demonstrating potential for use in a clinical setting.

Analysis of bodily fluids using vibrational spectroscopy: a direct comparison of Raman scattering and infrared absorption techniques for the case of glucose in blood serum

Analysis of biomarkers present in the blood stream can potentially deliver crucial information on patient health and indicate the presence of numerous pathologies.

Raman spectroscopic screening of high and low molecular weight fractions of human serum

This study explores the suitability of Raman spectroscopy as a bioanalytical tool, when coupled with ultra-filtration and multivariate analysis, to detect imbalances in both high molecular weight and low molecular weight fractions of the same samples of human patient serum, in the native liquid form.

In-line whole blood fractionation for Raman analysis of blood plasma

Raman studies of dynamically expanded cell-free plasma domains in microfluidic blood flow.

Evaluation of accuracy dependence of Raman spectroscopic models on the ratio of calibration and validation points for non-invasive glucose sensing

Optical monitoring of blood glucose levels for non-invasive diagnosis is a growing area of research. Recent efforts in this direction have been inclined towards reducing the requirement of calibration framework. Here, we are presenting a systematic investigation on the influence of variation in the ratio of calibration and validation points on the prospective predictive accuracy of spectral models. A fiber-optic probe coupled Raman system has been employed for transcutaneous measurements. Limit of agreement analysis between serum and partial least square regression predicted spectroscopic glucose values has been performed for accurate comparison. Findings are suggestive of strong predictive accuracy of spectroscopic models without requiring substantive calibration measurements. Graphical abstract.

Multicomponent analysis using a confocal Raman microscope

Measuring the concentration of multiple chemical components in a low-volume aqueous mixture by Raman spectroscopy has received significant interest in the literature. All of the contributions to date focus on the design of optical systems that facilitate the recording of spectra with high signal-to-noise ratio by collecting as many Raman scattered photons as possible. In this study, the confocal Raman microscope setup is investigated for multicomponent analysis. Partial least-squares regression is used to quantify physiologically relevant aqueous mixtures of glucose, lactic acid, and urea. The predicted error is 17.81 mg/dL for glucose, 10.6 mg/dL for lactic acid, and 7.6 mg/dL for urea, although this can be improved with increased acquisition times. A theoretical analysis of the method is proposed, which relates the numerical aperture and the magnification of the microscope objective, as well as the confocal pinhole size, to the performance of the technique.

Histological coherent Raman imaging: a prognostic review

Histopathology plays a central role in diagnosis of many diseases including solid cancers. Efforts are underway to transform this subjective art to an objective and quantitative science. Coherent Raman imaging (CRI), a label-free imaging modality with sub-cellular spatial resolution and molecule-specific contrast possesses characteristics which could support the qualitative-to-quantitative transition of histopathology. In this work we briefly survey major themes related to modernization of histopathology, review applications of CRI to histopathology and, finally, discuss potential roles for CRI in the transformation of histopathology that is already underway.

Qualitative evaluation of ferritin in serum samples by Raman spectroscopy and principal component analysis

Iron molecule is of great importance in the synthesis of hemoglobin which is essential for oxygen transport. Iron levels are quantified by accurately high sensitivity tests, such as serum ferritin (SF). However, common studies to quantify SF are long and strenuous (~ 5 h), for example enzyme-linked immunosorbent assay (ELISA). In this paper, blood serum samples were analyzed by Raman spectroscopy (RS), and a computational analysis of spectra is proposed to detect differences in SF as an alternative procedure. Serum samples were obtained from 22 patients, 9 who were clinically diagnosed with anemia and 13 controls. Patients with anemia had low levels of SF (< 30 ng/ml), and a control group had levels between 30 and 500 ng/ml. The spectra obtained were conditioned with a baseline correction and smoothing, then evaluated by principal component analysis (PCA), and a predictive model was estimated by lineal discrimination analysis (LDA). The results showed a clear differentiation of the study groups by PCA, also 99.69% sensitivity and 100% specificity by LDA. This study suggest that Raman spectroscopy is a fast (~ 5 min) and a powerful tool capable to qualitative differentiate ferritin concentrations.

Label-free Molecular Imaging and Analysis by Raman Spectroscopy

Raman scattering of a cell conveys the intrinsic information inherent to chemical structures of biomolecules. The spectroscopy of Raman scattering, or Raman spectroscopy, allows label-free and quantitative molecular sensing of a biological sample in situ without disruption. For the last five decades Raman spectroscopy has been widely utilized in biological research fields. However, it is just within the latest decade that molecular imaging and discrimination of living cells and tissues have become practically available. Here we overview recent progress in Raman spectroscopy and its application to life sciences. We discuss imaging of functional molecules in living cells and tissues; e.g., cancer cells and ischemic or infarcted hearts, together with a number of studies in the biomedical fields. We further explore comprehensive understandings of a complex spectrum by multivariate analysis for, e.g., accurate peripheral nerve detection, and characterization of the histological differences in the healing process of myocardial infarct. Although limitations still remain, e.g., weakness of the scattering intensity and practical difficulty in comprehensive molecular analysis, continuous progress in related technologies will allow wider use of Raman spectroscopy for biomedical applications.

Gaussian probe beam with high spherical aberration for glucose concentration measurement

We demonstrate that an optical probe beam with high spherical aberration used for glucose concentration measurements gives better sensitivity compared to a probe beam free of aberrations, under similar conditions. We place a singlet focusing lens at a large distance from a laser source with a Gaussian intensity profile to obtain a spherically aberrated probe beam with negligible truncation. The aberrated probe beam propagates through a transparent liquid sample. Intensity profiles of the transmitted beam are recorded by means of a homodyne profiler to perform the glucose concentration measurements accurately.

Application of a near-infrared laser tweezers Raman spectroscopy system for label-free analysis and differentiation of diabetic red blood cells

A home-made near-infrared laser tweezers Raman spectroscopy (LTRS) system was applied to detect hemoglobin variation in red blood cells (RBCs) from diabetes without exogenous labeling. Results showed significant spectral differences existed between the diabetic and normal RBCs, including the peaks dominated by protein components (e.g. 1003 cm^-1) and heme groups (e.g. 753 cm^-1) in RBCs, and accurate classification results for diabetes detection were obtained by linear discriminant analysis with 100% sensitivity (i.e. no false negatives in the study). This work indicated the great promise of LTRS as a label-free RBC analytical tool for improving the accurate detection of type II diabetes.

Coherent anti-Stokes Raman scattering under frequency comb excitation

We study the efficiency of coherent anti-Stokes Raman scattering (CARS) under frequency comb excitation. We calculate the power density of the anti-Stokes signal for two major cases: (1) molecular excitation by frequency comb and cw probe and, (2) both excitation and probing by frequency combs. In the first case, average CARS power varies as an inverse third degree of frequency combs free spectral range (FSR^-3); in the second case, it varies as FSR^-5. These results were applied to the CARS on blood glucose under frequency comb excitation. It was found that the resulting glucose CARS signal could approach nanowatt (nW) level at FSR=10  GHz.

New insights of Raman spectroscopy for oral clinical applications

Oral injuries are currently diagnosed by histopathological analysis of biopsy, which is an invasive procedure and does not give immediate results.

Raman Spectroscopy Combined with Principal Component Analysis for Screening Nasopharyngeal Cancer in Human Blood Sera

This study demonstrates the analysis of nasopharyngeal cancer (NPC) in human blood sera using Raman spectroscopy combined with the multivariate analysis technique. Blood samples of confirmed NPC patients and healthy individuals have been used in this study. The Raman spectra from all these samples were recorded using 785 nm laser for excitation. Important Raman bands at 760, 800, 815, 834, 855, 1003, 1220-1275, and 1524 cm^-1, have been observed in both normal and NPC samples. A decrease in the lipids content, phenylalanine, and β-carotene, whereas increases in amide III, tyrosine, and tryptophan have been observed in the NPC samples. The two data sets were well separated using principal component analysis (PCA) based on Raman spectral data. The spectral variations between the healthy and cancerous samples have been further highlighted by plotting loading vectors PC1 and PC2, which shows only those spectral regions where the differences are obvious.

Glucose sensing in oral mucosa simulating phantom using differential absorption based frequency domain low-coherence interferometry

The superluminescent diode based differential absorption frequency domain low-coherence interferometry (FD-DALCI) technique is proposed and demonstrated for sensing physiological concentrations of glucose (0-250 mg/dl) in oral mucosa simulating phantoms (intralipid of concentrations 0.25-0.50%) with wavelengths at 1589 and 1310 nm. The proposed technique allows simultaneous measurements of refractive index based spectral shift and estimation of physiological concentration of glucose in intralipid with scattering characteristics using the differential absorption approach. The sensitivity of the glucose concentration obtained by spectral shift measurement was ≈0.016  nm/(mg/dl), irrespective of the intralipid concentration. The resolution of the glucose level was estimated to be ≈15  mg/dl in 0.25% intralipid and ≈19  mg/dl in 0.5% intralipid using the FD-DALCI technique.

Raman Spectroscopy of Blood and Blood Components

Blood is a bodily fluid that is vital for a number of life functions in animals. To a first approximation, blood is a mildly alkaline aqueous fluid (plasma) in which a large number of free-floating red cells (erythrocytes), white cells (leucocytes), and platelets are suspended. The primary function of blood is to transport oxygen from the lungs to all the cells of the body and move carbon dioxide in the return direction after it is produced by the cells' metabolism. Blood also carries nutrients to the cells and brings waste products to the liver and kidneys. Measured levels of oxygen, nutrients, waste, and electrolytes in blood are often used for clinical assessment of human health. Raman spectroscopy is a non-destructive analytical technique that uses the inelastic scattering of light to provide information on chemical composition, and hence has a potential role in this clinical assessment process. Raman spectroscopic probing of blood components and of whole blood has been on-going for more than four decades and has proven useful in applications ranging from the understanding of hemoglobin oxygenation, to the discrimination of cancerous cells from healthy lymphocytes, and the forensic investigation of crime scenes. In this paper, we review the literature in the field, collate the published Raman spectroscopy studies of erythrocytes, leucocytes, platelets, plasma, and whole blood, and attempt to draw general conclusions on the state of the field.

Noninvasive Monitoring of Blood Glucose with Raman Spectroscopy

The successful development of a noninvasive blood glucose sensor that can operate reliably over sustained periods of time has been a much sought after but elusive goal in diabetes management. Since diabetes has no well-established cure, control of elevated glucose levels is critical for avoiding severe secondary health complications in multiple organs including the retina, kidney and vasculature. While fingerstick testing continues to be the mainstay of blood glucose detection, advances in electrochemical sensing-based minimally invasive approaches have opened the door for alternate methods that would considerably improve the quality of life for people with diabetes. In the quest for better sensing approaches, optical technologies have surfaced as attractive candidates as researchers have sought to exploit the endogenous contrast of glucose, notably its absorption, scattering, and polarization properties. Vibrational spectroscopy, especially spontaneous Raman scattering, has exhibited substantial promise due to its exquisite molecular specificity and minimal interference of water in the spectral profiles acquired from the blood-tissue matrix. Yet, it has hitherto been challenging to leverage the Raman scattering signatures of glucose for prediction in all but the most basic studies and under the least demanding conditions. In this Account, we discuss the newly developed array of methodologies that address the key challenges in measuring blood glucose accurately using Raman spectroscopy and unlock new prospects for translation to sustained noninvasive measurements in people with diabetes. Owing to the weak intensity of spontaneous Raman scattering, recent research has focused on enhancement of signals from the blood constituents by designing novel excitation-collection geometries and tissue modulation methods while our attempts have led to the incorporation of nonimaging optical elements. Additionally, invoking mass transfer modeling into chemometric algorithms has not only addressed the physiological lag between the actual blood glucose and the measured interstitial fluid glucose values but also offered a powerful tool for predictive measurements of hypoglycemia. This framework has recently been extended to provide longitudinal tracking of glucose concentration without necessitating extensive a priori concentration information. These findings are advanced by the results of recent glucose tolerance studies in human subjects, which also hint at the need for designing nonlinear calibration models that can account for subject-to-subject variations in skin heterogeneity and hematocrit levels. Together, the emerging evidence underscores the promise of a blood withdrawal-free optical platform-featuring a combination of high-throughput Raman spectroscopic instrumentation and data analysis of subtle variations in spectral expression-for diabetes screening in the clinic and, ultimately, for personalized monitoring.

Surface-enhanced Raman scattering spectra revealing the inter-cultivar differences for Chinese ornamental Flos Chrysanthemum: a new promising method for plant taxonomy

BACKGROUND

Flos Chrysanthemi, as a part of Chinese culture for a long history, is valuable for not only environmental decoration but also the medicine and food additive. Due to their voluminously various breeds and extensive distributions worldwide, it is burdensome to make recognition and classification among numerous cultivars with conventional methods which still rest on the level of morphologic observation and description. As a fingerprint spectrum for parsing molecular information, surface-enhanced Raman scattering (SERS) could be a suitable candidate technique to characterize and distinguish the inter-cultivar differences at molecular level.

RESULTS

SERS spectra were used to analyze the inter-cultivar differences among 26 cultivars of Chinese ornamental Flos Chrysanthemum. The characteristic peaks distribution patterns were abstracted from SERS spectra and varied from cultivars to cultivars. For the bands distributed in the pattern map, the similarities in general showed their commonality while in the finer scales, the deviations and especially the particular bands owned by few cultivars revealed their individualities. Since the Raman peaks could characterize specific chemical components, those diversity of patterns could indicate the inter-cultivar differences at the chemical level in fact.

CONCLUSION

In this paper, SERS technique is feasible for distinguishing the inter-cultivar differences among Flos Chrysanthemum. The Raman spectral library was built with SERS characteristic peak distribution patterns. A new method was proposed for Flos Chrysanthemum recognition and taxonomy.

Advances in Lipidomics for Cancer Biomarkers Discovery

Lipids play critical functions in cellular survival, proliferation, interaction and death, since they are involved in chemical-energy storage, cellular signaling, cell membranes, and cell-cell interactions. These cellular processes are strongly related to carcinogenesis pathways, particularly to transformation, progression, and metastasis, suggesting the bioactive lipids are mediators of a number of oncogenic processes. The current review gives a synopsis of a lipidomic approach in tumor characterization; we provide an overview on potential lipid biomarkers in the oncology field and on the principal lipidomic methodologies applied. The novel lipidomic biomarkers are reviewed in an effort to underline their role in diagnosis, in prognostic characterization and in prediction of therapeutic outcomes. A lipidomic investigation through mass spectrometry highlights new insights on molecular mechanisms underlying cancer disease. This new understanding will promote clinical applications in drug discovery and personalized therapy.

Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection

Diabetes is a serious threat to human health. Thus, research on noninvasive blood glucose detection has become crucial locally and abroad. Near-infrared transmission spectroscopy has important applications in noninvasive glucose detection. Extracting useful information and selecting appropriate modeling methods can improve the robustness and accuracy of models for predicting blood glucose concentrations. Therefore, an improved signal reconstruction and calibration modeling method is proposed in this study. On the basis of improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and correlative coefficient, the sensitive intrinsic mode functions are selected to reconstruct spectroscopy signals for developing the calibration model using the support vector regression (SVR) method. The radial basis function kernel is selected for SVR, and three parameters, namely, insensitive loss coefficient ε, penalty parameter C, and width coefficient γ, are identified beforehand for the corresponding model. Particle swarm optimization (PSO) is employed to optimize the simultaneous selection of the three parameters. Results of the comparison experiments using PSO-SVR and partial least squares show that the proposed signal reconstitution method is feasible and can eliminate noise in spectroscopy signals. The prediction accuracy of model using PSO-SVR method is also found to be better than that of other methods for near-infrared noninvasive glucose detection.

Role of Raman spectroscopy and surface enhanced Raman spectroscopy in colorectal cancer

Colorectal cancer (CRC) is the fourth most common cancer in the United Kingdom and is the second largest cause of cancer related death in the United Kingdom after lung cancer. Currently in the United Kingdom there is not a diagnostic test that has sufficient differentiation between patients with cancer and those without cancer so the current referral system relies on symptomatic presentation in a primary care setting. Raman spectroscopy and surface enhanced Raman spectroscopy (SERS) are forms of vibrational spectroscopy that offer a non-destructive method to gain molecular information about biological samples. The techniques offer a wide range of applications from in vivo or in vitro diagnostics using endoscopic probes, to the use of micro-spectrometers for analysis of biofluids. The techniques have the potential to detect molecular changes prior to any morphological changes occurring in the tissue and therefore could offer many possibilities to aid the detection of CRC. The purpose of this review is to look at the current state of diagnostic technology in the United Kingdom. The development of Raman spectroscopy and SERS in clinical applications relation for CRC will then be discussed. Finally, future areas of research of Raman/SERS as a clinical tool for the diagnosis of CRC are also discussed.

Clinical instrumentation and applications of Raman spectroscopy

Clinical diagnostic devices provide new sources of information that give insight about the state of health which can then be used to manage patient care. These tools can be as simple as an otoscope to better visualize the ear canal or as complex as a wireless capsule endoscope to monitor the gastrointestinal tract. It is with tools such as these that medical practitioners can determine when a patient is healthy and to make an appropriate diagnosis when he/she is not. The goal of diagnostic medicine then is to efficiently determine the presence and cause of disease in order to provide the most appropriate intervention. The earliest form of medical diagnostics relied on the eye - direct visual observation of the interaction of light with the sample. This technique was espoused by Hippocrates in his 5th century BCE work Epidemics, in which the pallor of a patient's skin and the coloring of the bodily fluids could be indicative of health. In the last hundred years, medical diagnosis has moved from relying on visual inspection to relying on numerous technological tools that are based on various types of interaction of the sample with different types of energy - light, ultrasound, radio waves, X-rays etc. Modern advances in science and technology have depended on enhancing technologies for the detection of these interactions for improved visualization of human health. Optical methods have been focused on providing this information in the micron to millimeter scale while ultrasound, X-ray, and radio waves have been key in aiding in the millimeter to centimeter scale. While a few optical technologies have achieved the status of medical instruments, many remain in the research and development phase despite persistent efforts by many researchers in the translation of these methods for clinical care. Of these, Raman spectroscopy has been described as a sensitive method that can provide biochemical information about tissue state while maintaining the capability of delivering this information in real-time, non-invasively, and in an automated manner. This review presents the various instrumentation considerations relevant to the clinical implementation of Raman spectroscopy and reviews a subset of interesting applications that have successfully demonstrated the efficacy of this technique for clinical diagnostics and monitoring in large (n ≥ 50) in vivo human studies.

Two efficient approaches for modeling of Raman scattering in homogeneous turbid media

The quantitative analysis of Raman spectroscopic signals in biological tissue is generally difficult. Typical samples contain a multitude of molecular species and, in addition, measurements are altered by attenuation of the Raman signal. Realistic numerical modeling of the Raman process can help to facilitate the quantitative analysis of the Raman spectra, but approaches so far are scarce and often time-consuming. In this work, we report on two different and very efficient approaches for modeling of Raman scattering in turbid media irradiated by laser light. Both approaches utilize the Monte Carlo method to simulate the Raman scattering process. We compare the efficiency of both approaches and discuss possible future extensions and experimental validation.

Quantifying creatinine and urea in human urine through Raman spectroscopy aiming at diagnosis of kidney disease

Due to their importance in the regulation of metabolites, the kidneys need continuous monitoring to check for correct functioning, mainly by urea and creatinine urinalysis. This study aimed to develop a model to estimate the concentrations of urea and creatinine in urine by means of Raman spectroscopy (RS) that could be used to diagnose kidney disease. Midstream urine samples were obtained from 54 volunteers with no kidney complaints. Samples were subjected to a standard colorimetric assay of urea and creatinine and submitted to spectroscopic analysis by means of a dispersive Raman spectrometer (830 nm, 350 mW, 30 s). The Raman spectra of urine showed peaks related mainly to urea and creatinine. Partial least squares models were developed using selected Raman bands related to urea and creatinine and the biochemical concentrations in urine measured by the colorimetric method, resulting in r = 0.90 and 0.91 for urea and creatinine, respectively, with root mean square error of cross-validation (RMSEcv) of 312 and 25.2 mg/dL, respectively. RS may become a technique for rapid urinalysis, with concentration errors suitable for population screening aimed at the prevention of renal diseases.

Development of a photon-cell interactive monte carlo simulation for non-invasive measurement of blood glucose level by Raman spectroscopy

Turbidity variation is one of the major limitations in Raman spectroscopy for quantifying blood components, such as glucose, non-invasively. To overcome this limitation, we have developed a Raman scattering simulation using a photon-cell interactive Monte Carlo (pciMC) model that tracks photon migration in both the extra- and intracellular spaces without relying on the macroscopic scattering phase function and anisotropy factor. The interaction of photons at the plasma-cell boundary of randomly oriented three-dimensionally biconcave red blood cells (RBCs) is modeled using geometric optics. The validity of the developed pciMCRaman was investigated by comparing simulation and experimental results of Raman spectroscopy of glucose level in a bovine blood sample. The scattering of the excitation laser at a wavelength of 785 nm was simulated considering the changes in the refractive index of the extracellular solution. Based on the excitation laser photon distribution within the blood, the Raman photon derived from the hemoglobin and glucose molecule at the Raman shift of 1140 cm(-1) = 862 nm was generated, and the photons reaching the detection area were counted. The simulation and experimental results showed good correlation. It is speculated that pciMCRaman can provide information about the ability and limitations of the measurement of blood glucose level.