Near-infrared spectroscopy as a novel method of ex vivo bladder cancer tissue characterisation.

OBJECTIVE: To evaluate near-infrared (NIR) spectroscopy in differentiating between benign and malignant bladder pathologies ex vivo immediately after resection, including the grade and stage of malignancy. PATIENTS AND METHODS: A total of 355 spectra were measured on 71 bladder specimens from patients undergoing transurethral resection of bladder tumour (TURBT) between April and August 2022. Scan time was 5 s, undertaken using a portable NIR spectrometer within 10 min from excision. Specimens were then sent for routine histopathological correlation. Machine learning models were applied to the spectral dataset to construct diagnostic algorithms; these were then tested for their ability to predict the histological diagnosis of each sample using its NIR spectrum. RESULTS: A two-group algorithm comparing low- vs high-grade urothelial cancer demonstrated 97% sensitivity, 99% specificity, and the area under the receiver operating characteristic curve (AUC) was 0.997. A three-group algorithm predicting stages Ta vs T1 vs T2 achieved 97% sensitivity, 92% specificity, and the AUC was 0.996. CONCLUSIONS: This first study evaluating the diagnostic potential of NIR spectroscopy in urothelial cancer shows that it can be accurately used to assess tissue in an ex vivo setting immediately after TURBT. This offers point-of-care assessment of bladder pathology, with potential to influence the extent of resection, reducing both the need for re-resection where invasive disease may be suspected, and also the potential for complications where extent of diagnostic resection can be limited. Further studies utilising fibre-optic probes offer the potential for in vivo assessment.

Synchrotron-Infrared Microspectroscopy of Live Leishmania major Infected Macrophages and Isolated Promastigotes and Amastigotes.

The prevalence of neglected tropical diseases (NTDs) is advancing at an alarming rate. The NTD leishmaniasis is now endemic in over 90 tropical and sub-tropical low socioeconomic countries. Current diagnosis for this disease involves serological assessment of infected tissue by either light microscopy, antibody tests, or culturing with in vitro or in vivo animal inoculation. Furthermore, co-infection by other pathogens can make it difficult to accurately determine Leishmania infection with light microscopy. Herein, for the first time, we demonstrate the potential of combining synchrotron Fourier-transform infrared (FTIR) microspectroscopy with powerful discrimination tools, such as partial least squares-discriminant analysis (PLS-DA), support vector machine-discriminant analysis (SVM-DA), and k-nearest neighbors (KNN), to characterize the parasitic forms of Leishmania major both isolated and within infected macrophages. For measurements performed on functional infected and uninfected macrophages in physiological solutions, the sensitivities from PLS-DA, SVM-DA, and KNN classification methods were found to be 0.923, 0.981, and 0.989, while the specificities were 0.897, 1.00, and 0.975, respectively. Cross-validated PLS-DA models on live amastigotes and promastigotes showed a sensitivity and specificity of 0.98 in the lipid region, while a specificity and sensitivity of 1.00 was achieved in the fingerprint region. The study demonstrates the potential of the FTIR technique to identify unique diagnostic bands and utilize them to generate machine learning models to predict Leishmania infection. For the first time, we examine the potential of infrared spectroscopy to study the molecular structure of parasitic forms in their native aqueous functional state, laying the groundwork for future clinical studies using more portable devices.

Characterization of freeze-dried oxidized human red blood cells for pre-transfusion testing by synchrotron FTIR microspectroscopy live-cell analysis.

Oxidative treatment of human red blood cells (RBCs) prior to freeze-drying appears to stabilize the RBCs to withstand dried storage at room temperature. To better understand the effects of oxidation and freeze-drying/rehydration on RBC lipids and proteins, single-cell measurements were performed by synchrotron-based Fourier transform infrared (FTIR) microspectroscopy 'live-cell' (unfixed) analysis. Lipid and protein spectral data of tert-butyl hydroperoxide (TBHP)-oxidized RBCs (oxRBCs), FDoxRBCs and control (untreated) RBCs were compared using principal component analysis (PCA) and band integration ratios. The oxRBCs and FDoxRBCs samples had similar spectral profiles that were clearly different to control RBCs. Spectral changes in the CH stretching region of oxRBCs and FDoxRBCs indicated the presence of increased saturated and shorter-chain lipids, consistent with lipid peroxidation and stiffening of the RBC membrane compared to control RBCs. The PCA loadings plot for the fingerprint region of control RBCs corresponding to the α-helical structure of hemoglobin, shows that oxRBCs and FDoxRBCs have conformational changes in the protein secondary structure to ß-pleated sheets and ß-turns. Finally, the freeze-drying process did not appear to compound or induce additional changes. In this context, FDoxRBCs could become a stable source of reagent RBCs for pre-transfusion blood serology testing. The synchrotron FTIR microspectroscopic live-cell protocol provides a powerful analytical tool to characterize and contrast the effects of different treatments on RBC chemical composition at the single cell level.

Towards a point-of-care multimodal spectroscopy instrument for the evaluation of human cardiac tissue.

To demonstrate that point-of-care multimodal spectroscopy using Near-Infrared (NIR) and Raman Spectroscopy (RS) can be used to diagnose human heart tissue. We generated 105 spectroscopic scans, which comprised 4 NIR and 3 RS scans per sample to generate a "multimodal spectroscopic scan" (MSS) for each heart, done across 15 patients, 5 each from the dilated cardiomyopathy (DCM), Ischaemic Heart Disease (IHD) and Normal pathologies. Each of the MSS scans was undertaken in 3 s. Data were entered into machine learning (ML) algorithms to assess accuracy of MSS in diagnosing tissue type. The median age was 50 years (IQR 49-52) for IHD, 47 (IQR 45-50) for DCM and 36 (IQR 33-52) for healthy patients (p = 0.35), 60% of which were male. MSS identified key differences in IHD, DCM and normal heart samples in regions typically associated with fibrosis and collagen (NIR wavenumbers: 1433, 1509, 1581, 1689 and 1725 nm; RS wavelengths: 1658, 1450 and 1330 cm-1). In principal component (PC) analyses, these differences explained 99.2% of the variation in 4 PCs for NIR, 81.6% in 10 PCs for Raman, and 99.0% in 26 PCs for multimodal spectroscopic signatures. Using a stack machine learning algorithm with combined NIR and Raman data, our model had a precision of 96.9%, recall of 96.6%, specificity of 98.2% and Area Under Curve (AUC) of 0.989 (Table 1). NIR and Raman modalities alone had similar levels of precision at 94.4% and 89.8% respectively (Table 1). MSS combined with ML showed accuracy of 90% for detecting dilated cardiomyopathy, 100% for ischaemic heart disease and 100% for diagnosing healthy tissue. Multimodal spectroscopic signatures, based on NIR and Raman spectroscopy, could provide cardiac tissue scans in 3-s to aid accurate diagnoses of fibrosis in IHD, DCM and normal hearts. Table 1 Machine learning performance metrics for validation data sets of (a) Near-Infrared (NIR), (b) Raman and (c and d) multimodal data using logistic regression (LR), stochastic gradient descent (SGD) and support vector machines (SVM), with combined "stack" (LR + SGD + SVM) AUC Precision Recall Specificity (a) NIR model  Logistic regression 0.980 0.944 0.933 0.967  SGD 0.550 0.281 0.400 0.700  SVM 0.840 0.806 0.800 0.900  Stack 0.933 0.794 0.800 0.900 (b) Raman model  Logistic regression 0.985 0.940 0.929 0.960  SGD 0.892 0.869 0.857 0.932  SVM 0.992 0.940 0.929 0.960  Stack 0.954 0.869 0.857 0.932 (c) MSS: multimodal (NIR + Raman) to detect DCM vs. IHD vs. normal patients  Logistic regression 0.975 0.841 0.828 0.917  SGD 0.847 0.803 0.793 0.899  SVM 0.971 0.853 0.828 0.917  Stack 0.961 0.853 0.828 0.917 (d) MSS: multimodal (NIR + Raman) to detect pathological vs. normal patients  Logistic regression 0.961 0.969 0.966 0.984  SGD 0.944 0.967 0.966 0.923  SVM 1.000 1.000 1.000 1.000  Stack 1.000 0.944 0.931 0.969 Bold values indicate values obtained from the stack algorithm and used for analyses.

Visible microspectrophotometry coupled with machine learning to discriminate the erythrocytic life cycle stages of P. falciparum malaria parasites in functional single cells.

Malaria was regarded as the most devastating infectious disease of the 21st century until the COVID-19 pandemic. Asexual blood staged parasites (ABS) play a unique role in ensuring the parasite's survival and pathogenesis. Hitherto, there have been no spectroscopic reports discriminating the life cycle stages of the ABS parasite under physiological conditions. The identification and quantification of the stages in the erythrocytic life cycle is important in monitoring the progression and recovery from the disease. In this study, we explored visible microspectrophotometry coupled to machine learning to discriminate functional ABS parasites at the single cell level. Principal Component Analysis (PCA) showed an excellent discrimination between the different stages of the ABS parasites. Support Vector Machine Analysis provided a 100% prediction for both schizonts and trophozoites, while a 92% and 98% accuracy was achieved for predicting control and ring staged infected RBCs, respectively. This work shows proof of principle for discriminating the life cycle stages of parasites in functional erythrocytes using visible microscopy and thus eliminating the drying and fixative steps that are associated with other optical-based spectroscopic techniques.

A Combined Near-Infrared and Mid-Infrared Spectroscopic Approach for the Detection and Quantification of Glycine in Human Serum.

Serum is an important candidate in proteomics analysis as it potentially carries key markers on health status and disease progression. However, several important diagnostic markers found in the circulatory proteome and the low-molecular-weight (LMW) peptidome have become analytically challenging due to the high dynamic concentration range of the constituent protein/peptide species in serum. Herein, we propose a novel approach to improve the limit of detection (LoD) of LMW amino acids by combining mid-IR (MIR) and near-IR spectroscopic data using glycine as a model LMW analyte. This is the first example of near-IR spectroscopy applied to elucidate the detection limit of LMW components in serum; moreover, it is the first study of its kind to combine mid-infrared (25-2.5 µm) and near-infrared (2500-800 nm) to detect an analyte in serum. First, we evaluated the prediction model performance individually with MIR (ATR-FTIR) and NIR spectroscopic methods using partial least squares regression (PLS-R) analysis. The LoD was found to be 0.26 mg/mL with ATR spectroscopy and 0.22 mg/mL with NIR spectroscopy. Secondly, we examined the ability of combined spectral regions to enhance the detection limit of serum-based LMW amino acids. Supervised extended wavelength PLS-R resulted in a root mean square error of prediction (RMSEP) value of 0.303 mg/mL and R2 value of 0.999 over a concentration range of 0-50 mg/mL for glycine spiked in whole serum. The LoD improved to 0.17 mg/mL from 0.26 mg/mL. Thus, the combination of NIR and mid-IR spectroscopy can improve the limit of detection for an LMW compound in a complex serum matrix.

Fourier-Transform Infra-Red Microspectroscopy Can Accurately Diagnose Colitis and Assess Severity of Inflammation.

The diagnosis and management of inflammatory bowel disease relies on histological assessment, which is costly, subjective, and lacks utility for point-of-care diagnosis. Fourier-transform infra-red spectroscopy provides rapid, non-destructive, reproducible, and automatable label-free biochemical imaging of tissue for diagnostic purposes. This study characterises colitis using spectroscopy, discriminates colitis from healthy tissue, and classifies inflammation severity. Hyperspectral images were obtained from fixed intestinal sections of a murine colitis model treated with cell therapy to improve inflammation. Multivariate analyses and classification modelling were performed using supervised and unsupervised machine-learning algorithms. Quantitative analysis of severe colitis showed increased protein, collagen, and nucleic acids, but reduced glycogen when compared with normal tissue. A partial least squares discriminant analysis model, including spectra from all intestinal layers, classified normal colon and severe colitis with a sensitivity of 91.4% and a specificity of 93.3%. Colitis severity was classified by a stacked ensemble model yielding an average area under the receiver operating characteristic curve of 0.95, 0.88, 0.79, and 0.85 for controls, mild, moderate, and severe colitis, respectively. Infra-red spectroscopy can detect unique biochemical features of intestinal inflammation and accurately classify normal and inflamed tissue and quantify the severity of inflammation. This is a promising alternative to histological assessment.

A Near-Infrared "Matchbox Size" Spectrometer to Detect and Quantify Malaria Parasitemia.

New point-of-care diagnostic approaches for malaria that are sensitive to low parasitemia, easy to use in a field setting, and affordable are urgently required to meet the World Health Organization's objective of reducing malaria cases and related life losses by 90% globally on or before 2030. In this study, an inexpensive "matchbox size" near-infrared (NIR) spectrophotometer was used for the first time to detect and quantify malaria infection in vitro from isolated dried red blood cells using a fingerpick volume of blood. This the first study to apply a miniaturized NIR device to diagnose a parasitic infection and identify marker bands indicative of malaria infection in the NIR region. An NIR device has many advantages including wavelength accuracy and repeatability, speed, resolution, and a greatly improved signal-to-noise ratio compared to existing spectroscopic options. Using multivariate data analysis, we discriminated control red blood cells from infected cells and established the limit of detection of the technique. Principal component analysis displayed a good separation between the infected and uninfected RBCs, while partial least-squares regression analysis yielded a robust parasitemia prediction with root-mean-square error of prediction values of 0.446 and 0.001% for the higher and lower parasitemia models, respectively. The R2 values of the higher and lower parasitemia models were 0.947 and 0.931, respectively. Finally, an estimated parasitemia detection limit of 0.00001% and a qunatification limit of 0.001% was achieved; to ascertain the true efficacy of the technique for point-of-care screening, clinical studies using large patient numbers are required, which is the subject of future studies.

Ultraviolet/Visible and Near-Infrared Dual Spectroscopic Method for Detection and Quantification of Low-Level Malaria Parasitemia in Whole Blood.

The scourge of malaria infection continues to strike hardest against pregnant women and children in Africa and South East Asia. For global elimination, testing methods that are ultrasensitive to low-level ring-staged parasitemia are urgently required. In this study, we used a novel approach for diagnosis of malaria infection by combining both electronic ultraviolet-visible (UV/vis) spectroscopy and near infrared (NIR) spectroscopy to detect and quantify low-level (1-0.000001%) ring-staged malaria-infected whole blood under physiological conditions uisng Multiclass classification using logistic regression, which showed that the best results were achieved using the extended wavelength range, providing an accuracy of 100% for most parasitemia classes. Likewise, partial least-squares regression (PLS-R) analysis showed a higher quantification sensitivity (R2 = 0.898) for the extended spectral region compared to UV/vis and NIR (R2 = 0.806 and 0.556, respectively). For quantifying different-stage blood parasites, the extended wavelength range was able to detect and quantify all thePlasmodium falciparum accurately compared to testing each spectral component separately. These results demonstrate the potential of a combined UV/vis-NIR spectroscopy to accurately diagnose malaria-infected patients without the need for elaborate sample preparation associated with the existing mid-IR approaches.

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.

Correction: Synchrotron macro ATR-FTIR microspectroscopy for high-resolution chemical mapping of single cells.

Correction for 'Synchrotron macro ATR-FTIR microspectroscopy for high-resolution chemical mapping of single cells' by Jitraporn Vongsvivut et al., Analyst, 2019, 144, 3226-3238, DOI: 10.1039/C8AN01543K.

Infrared nanospectroscopic mapping of a single metaphase chromosome.

The integrity of the chromatin structure is essential to every process occurring within eukaryotic nuclei. However, there are no reliable tools to decipher the molecular composition of metaphase chromosomes. Here, we have applied infrared nanospectroscopy (AFM-IR) to demonstrate molecular difference between eu- and heterochromatin and generate infrared maps of single metaphase chromosomes revealing detailed information on their molecular composition, with nanometric lateral spatial resolution. AFM-IR coupled with principal component analysis has confirmed that chromosome areas containing euchromatin and heterochromatin are distinguishable based on differences in the degree of methylation. AFM-IR distribution of eu- and heterochromatin was compared to standard fluorescent staining. We demonstrate the ability of our methodology to locate spatially the presence of anticancer drug sites in metaphase chromosomes and cellular nuclei. We show that the anticancer 'rule breaker' platinum compound [Pt[N(p-HC6F4)CH2]2py2] preferentially binds to heterochromatin, forming localized discrete foci due to condensation of DNA interacting with the drug. Given the importance of DNA methylation in the development of nearly all types of cancer, there is potential for infrared nanospectroscopy to be used to detect gene expression/suppression sites in the whole genome and to become an early screening tool for malignancy.

Infrared Based Saliva Screening Test for COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an unprecedented need for diagnostic testing that is critical in controlling the spread of COVID-19. We propose a portable infrared spectrometer with purpose-built transflection accessory for rapid point-of-care detection of COVID-19 markers in saliva. Initially, purified virion particles were characterized with Raman spectroscopy, synchrotron infrared (IR) and AFM-IR. A data set comprising 171 transflection infrared spectra from 29 subjects testing positive for SARS-CoV-2 by RT-qPCR and 28 testing negative, was modeled using Monte Carlo Double Cross Validation with 50 randomized test and model sets. The testing sensitivity was 93 % (27/29) with a specificity of 82 % (23/28) that included positive samples on the limit of detection for RT-qPCR. Herein, we demonstrate a proof-of-concept high throughput infrared COVID-19 test that is rapid, inexpensive, portable and utilizes sample self-collection thus minimizing the risk to healthcare workers and ideally suited to mass screening.

Quantification and Identification of Microproteinuria Using Ultrafiltration and ATR-FTIR Spectroscopy.

The presence of low amounts of specific proteins in urine can be an indicator of diagnosis and prognosis of several diseases including renal failure and cancer. Hence, there is an urgent need for Point-of-care (PoC) methods, which can quantify microproteinuria levels (30-300 ppm) and identify the major proteins associated with the microproteinuria. In this study, we coupled ultracentrifugation with attenuated total reflectance-Fourier transform infrared (ATR-FTIR) to identify and quantify proteins in urine at low parts per million levels. The process involves the preconcentration of proteins from 500 µL of urine using an ultrafiltration device. After several washings, the isolated proteins are dried onto the ATR crystal forming a thin film. Imaging studies showed that the absorbance of the protein bands was linear with the amount of mass deposited on the crystal. The methodology was first evaluated with artificial urine spiked with 30-300 ppm of albumin. The calibration showed acceptable linearity (R2 = 0.97) and a limit of detection of 6.7 ppm. Linear relationships were also observed from urine of healthy subjects spiked with microproteinuria concentrations of albumin, immunoglobulin, and hemoglobin, giving a prediction error of the spiked concentration of 23 ppm. When multiple proteins were spiked into the real urine, multivariate analysis was able to decompose the data set into the different proteins, but the multicomponent evaluation was challenging for proteins at low levels. Although the introduction of a preprocessing step reduces the PoC capability of the method, it largely increases its performance, showing great potential as a tool for the diagnosis and prognosis of several illnesses affecting urine proteic composition.

Monitoring the Progression of Liver Fluke-Induced Cholangiocarcinoma in a Hamster Model Using Synchrotron FTIR Microspectroscopy and Focal Plane Array Infrared Imaging.

Cholangiocarcinoma (CCA) is a bile duct cancer that originates in the bile duct epithelium. Northeastern Thailand has the highest incidence of CCA, and there is a direct correlation with liver fluke (Opisthorchis viverrini) infection. The high mortality rate of CCA is a consequence of delayed diagnosis. Fourier transform infrared (FTIR) spectroscopy is a powerful technique that detects the absorbance of molecular vibrations and is perfectly suited for the interrogation of biological samples. In this study, we applied synchrotron radiation-FTIR (SR-FTIR) microspectroscopy and focal plane array (FPA-FTIR) microspectroscopy to characterize periductal fibrosis and bile duct cells progressing to CCA induced by inoculating O. viverrini metacercariae into hamsters. SR-FTIR and FPA-FTIR measurements were performed in liver sections harvested from 1-, 2-, 3-, and 6-month post-infected hamsters compared to uninfected liver tissues. Principal component analysis (PCA) of the tissue samples showed a clear discrimination among uninfected and early-stage (1 and 2 months) and cancerous-stage (3 and 6 months) tissues. The discrimination is based on intensity changes in the phosphodiester band (1081 cm-1), amino acid residue (∼1396 cm-1), and C═O stretching carboxylic esters (1745 cm-1). Infected tissues also show definitive bands at ∼1280, 1234, and 1201 cm-1 characteristic of the collagen triplet and indicative of fibrosis. Hierarchical cluster analysis (HCA) was performed on the FPA data and showed a classification into specific cell types. Hepatocyte, fibrotic lesion, and bile duct (cancer) were classified and HCA mapping showed similar cellular distribution pattern compared to Sirius red staining. This study was also extended to less invasive sample analysis using attenuated total reflectance-FTIR (ATR-FTIR) spectroscopy. Sera from O. viverrini-infected and uninfected hamsters were analyzed using multivariate analysis, including principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA). PCA was able to classify spectra of normal, early-stage CCA, and CCA, while the PLS-DA gave 100% accuracy for the validation. The model was established from 17 samples (11 normal, 6 cancer) in the calibration set and 9 samples in the validation set (4 normal, 2 cancer, 3 precancerous). These results indicate that FTIR-based technology is a potential tool to detect the progression of CCA, especially in the early stages of the disease.

Rapid Approach for Detection of Antibiotic Resistance in Bacteria Using Vibrational Spectroscopy.

Here, we applied vibrational spectroscopy to investigate the drug response following incubation of S. aureus with oxacillin. The main focus of this work was to identify the chemical changes caused by oxacillin over time and to determine the feasibility of the spectroscopic approach to detect antimicrobial resistance. The oxacillin-induced changes in the chemical composition of susceptible bacteria, preceding (and leading to) the inhibition of growth, included an increase in the relative content of nucleic acids, alteration in the α-helical/ß-sheet protein ratio, structural changes in carbohydrates (observed via changes in the band at 1035 cm-1), and significant thickening of the cell wall. These observations enabled a dose-dependent discrimination between susceptible bacteria incubated with and without oxacillin after 120 min. In methicillin resistant strains, no spectral differences were observed between cells, regardless of drug exposure. These results pave the way for a new, rapid spectroscopic approach to detect drug resistance in pathogens, based on their early positive/negative drug response.

Vibrational Spectroscopic Based Approach for Diagnosing Babesia bovis Infection.

Babesia bovis parasites present a serious and significant health concern for the beef and dairy industries in many parts of the world. Difficulties associated with the current diagnostic techniques include the following: they are prone to human error (microscopy) or expensive and time-consuming (polymerase chain reaction) to perform. Little is known about the biochemical changes in blood that are associated with Babesia infections. The discovery of new biomarkers will lead to improved diagnostic outcomes for the cattle industry. Vibrational spectroscopic technologies can record a chemical snapshot of the entire organism and the surrounding cell thereby providing a phenotype of the organism and the host infected cell. Here, we demonstrate the applicability of vibrational spectroscopic imaging techniques including Atomic Force Microscopy Infrared (AFM-IR) and confocal Raman microscopy to discover new biomarkers for B. bovis infections. Furthermore, we applied Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) to detect B. bovis in red blood cells (RBCs). Based on changes in the IR spectral bands, with ATR-FTIR in combination with Partial Least Squares-Discriminant Analysis we were able to discriminate infected samples from controls with a sensitivity and specificity of 92.0% and 91.7%, respectively, in less than 2 min, excluding sample extraction and preparation. The proposed method utilized a lysis approach to remove hemoglobin from the suspension of infected and uninfected cells, which significantly increased the sensitivity and specificity compared to measurements performed on intact infected red blood cells (intact infected RBC, 77.3% and 79.2%). This work represents a holistic spectroscopic study from the level of the single infected RBC using AFM-IR and confocal Raman to the detection of the parasite in a cell population using ATR-FTIR for a babesiosis diagnostic.

Multimodal detection and analysis of a new type of advanced Heinz body-like aggregate (AHBA) and cytoskeleton deformation in human RBCs.

A new type of aggregate, formed in human red blood cells (RBCs) in response to glutaraldehyde treatment, was discovered and analyzed with the classical and advanced biomolecular imaging techniques. Advanced Heinz body-like aggregates (AHBA) formed in a single human RBC are characterized by a higher level of hemoglobin (Hb) degradation compared to typical Heinz bodies, which consist of hemichromes. The complete destruction of the porphyrin structure of Hb and the aggregation of the degraded proteins in the presence of Fe3+ ions are observed. The presence of such aggregated, highly degraded proteins inside RBCs, without cell membrane destruction, has been never reported before. For the first time the spatial differentiation of two kinds of protein mixtures inside a single RBC, with different phenylalanine (Phe) conformations, is visualized. The non-resonant Raman spectra of altered RBCs with AHBA are characterized by the presence of a strong band located at 1037 cm-1, which confirms that glutaraldehyde interacts strongly with Phe. The shape-shifting of RBCs from a biconcave disk to a spherical structure and sinking of AHBA to the bottom of the cell are observed. Results reveal that the presence of AHBA should be considered when fixing RBCs and indicate the analytical potential of Raman spectroscopy, atomic force microscopy and scanning near-field optical microscopy in AHBA detection and analysis.

Parasites under the Spotlight: Applications of Vibrational Spectroscopy to Malaria Research.

New technologies to diagnose malaria at high sensitivity and specificity are urgently needed in the developing world where the disease continues to pose a huge burden on society. Infrared and Raman spectroscopy-based diagnostic methods have a number of advantages compared with other diagnostic tests currently on the market. These include high sensitivity and specificity for detecting low levels of parasitemia along with ease of use and portability. Here, we review the application of vibrational spectroscopic techniques for monitoring and detecting malaria infection. We discuss the role of vibrational (infrared and Raman) spectroscopy in understanding the processes of parasite biology and its application to the study of interactions with antimalarial drugs. The distinct molecular phenotype that characterizes malaria infection and the high sensitivity enabling detection of low parasite densities provides a genuine opportunity for vibrational spectroscopy to become a front-line tool in the elimination of this deadly disease and provide molecular insights into the chemistry of this unique organism.

Vibrational Spectroscopy as a Sensitive Probe for the Chemistry of Intra-Phase Bacterial Growth.

Bacterial growth in batch cultures occurs in four phases (lag, exponential/log, stationary and death phase) that differ distinctly in number of different bacteria, biochemistry and physiology. Knowledge regarding the growth phase and its kinetics is essential for bacterial research, especially in taxonomic identification and monitoring drug interactions. However, the conventional methods by which to assess microbial growth are based only on cell counting or optical density, without any insight into the biochemistry of cells or processes. Both Raman and Fourier transform infrared (FTIR) spectroscopy have shown potential to determine the chemical changes occurring between different bacterial growth phases. Here, we extend the application of spectroscopy and for the first time combine both Raman and FTIR microscopy in a multimodal approach to detect changes in the chemical compositions of bacteria within the same phase (intra-phase). We found a number of spectral markers associated with nucleic acids (IR: 964, 1082, 1215 cm-1; RS: 785, 1483 cm-1), carbohydrates (IR: 1035 cm-1; RS: 1047 cm-1) and proteins (1394 cm-1, amide II) reflecting not only inter-, but also intra-phase changes in bacterial chemistry. Principal component analysis performed simultaneously on FTIR and Raman spectra enabled a clear-cut, time-dependent discrimination between intra-lag phase bacteria probed every 30 min. This demonstrates the unique capability of multimodal vibrational spectroscopy to probe the chemistry of bacterial growth even at the intra-phase level, which is particularly important for the lag phase, where low bacterial numbers limit conventional analytical approaches.