Metabolic profiling of murine radiation-induced lung injury with Raman spectroscopy and comparative machine learning.

Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thoracic radiotherapy. As such, it is important to characterize metabolic associations with the early and late stages of RILI, namely pneumonitis and pulmonary fibrosis. Recently, Raman spectroscopy has shown utility for the differentiation of pneumonitic and fibrotic tissue states in a mouse model; however, the specific metabolite-disease associations remain relatively unexplored from a Raman perspective. This work harnesses Raman spectroscopy and supervised machine learning to investigate metabolic associations with radiation pneumonitis and pulmonary fibrosis in a mouse model. To this end, Raman spectra were collected from lung tissues of irradiated/non-irradiated C3H/HeJ and C57BL/6J mice and labelled as normal, pneumonitis, or fibrosis, based on histological assessment. Spectra were decomposed into metabolic scores via group and basis restricted non-negative matrix factorization, classified with random forest (GBR-NMF-RF), and metabolites predictive of RILI were identified. To provide comparative context, spectra were decomposed and classified via principal component analysis with random forest (PCA-RF), and full spectra were classified with a convolutional neural network (CNN), as well as logistic regression (LR). Through leave-one-mouse-out cross-validation, we observed that GBR-NMF-RF was comparable to other methods by measure of accuracy and log-loss (p > 0.10 by Mann-Whitney U test), and no methodology was dominant across all classification tasks by measure of area under the receiver operating characteristic curve. Moreover, GBR-NMF-RF results were directly interpretable and identified collagen and specific collagen precursors as top fibrosis predictors, while metabolites with immune and inflammatory functions, such as serine and histidine, were top pneumonitis predictors. Further support for GBR-NMF-RF and the identified metabolite associations with RILI was found as CNN interpretation heatmaps revealed spectral regions consistent with these metabolites.

Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network.

Reprogramming of cellular metabolism is a driving factor of tumour progression and radiation therapy resistance. Identifying biochemical signatures associated with tumour radioresistance may assist with the development of targeted treatment strategies to improve clinical outcomes. Raman spectroscopy (RS) can monitor post-irradiation biomolecular changes and signatures of radiation response in tumour cells in a label-free manner. Convolutional Neural Networks (CNN) perform feature extraction directly from data in an end-to-end learning manner, with high classification performance. Furthermore, recently developed CNN explainability techniques help visualize the critical discriminative features captured by the model. In this work, a CNN is developed to characterize tumour response to radiotherapy based on its degree of radioresistance. The model was trained to classify Raman spectra of three human tumour cell lines as radiosensitive (LNCaP) or radioresistant (MCF7, H460) over a range of treatment doses and data collection time points. Additionally, a method based on Gradient-Weighted Class Activation Mapping (Grad-CAM) was used to determine response-specific salient Raman peaks influencing the CNN predictions. The CNN effectively classified the cell spectra, with accuracy, sensitivity, specificity, and F1 score exceeding 99.8%. Grad-CAM heatmaps of H460 and MCF7 cell spectra (radioresistant) exhibited high contributions from Raman bands tentatively assigned to glycogen, amino acids, and nucleic acids. Conversely, heatmaps of LNCaP cells (radiosensitive) revealed activations at lipid and phospholipid bands. Finally, Grad-CAM variable importance scores were derived for glycogen, asparagine, and phosphatidylcholine, and we show that their trends over cell line, dose, and acquisition time agreed with previously established models. Thus, the CNN can accurately detect biomolecular differences in the Raman spectra of tumour cells of varying radiosensitivity without requiring manual feature extraction. Finally, Grad-CAM may help identify metabolic signatures associated with the observed categories, offering the potential for automated clinical tumour radiation response characterization.

A Direct Immunoassay Based on Surface-Enhanced Spectroscopy Using AuNP/PS-b-P2VP Nanocomposites.

A biosensor was developed for directly detecting human immunoglobulin G (IgG) and adenosine triphosphate (ATP) based on stable and reproducible gold nanoparticles/polystyrene-b-poly(2-vinylpyridine) (AuNP/PS-b-P2VP) nanocomposites. The substrates were functionalized with carboxylic acid groups for the covalent binding of anti-IgG and anti-ATP and the detection of IgG and ATP (1 to 150 µg/mL). SEM images of the nanocomposite show 17 ± 2 nm AuNP clusters adsorbed over a continuous porous PS-b-P2VP thin film. UV-VIS and SERS were used to characterize each step of the substrate functionalization and the specific interaction between anti-IgG and the targeted IgG analyte. The UV-VIS results show a redshift of the LSPR band as the AuNP surface was functionalized and SERS measurements showed consistent changes in the spectral features. Principal component analysis (PCA) was used to discriminate between samples before and after the affinity tests. Moreover, the designed biosensor proved to be sensitive to different concentrations of IgG with a limit-of-detection (LOD) down to 1 µg/mL. Moreover, the selectivity to IgG was confirmed using standard solutions of IgM as a control. Finally, ATP direct immunoassay (LOD = 1 µg/mL) has demonstrated that this nanocomposite platform can be used to detect different types of biomolecules after proper functionalization.

Reconstruction of Raman Spectra of Biochemical Mixtures Using Group and Basis Restricted Non-Negative Matrix Factorization.

Raman spectroscopy is a useful tool for obtaining biochemical information from biological samples. However, interpretation of Raman spectroscopy data in order to draw meaningful conclusions related to the biochemical make up of cells and tissues is often difficult and could be misleading if care is not taken in the deconstruction of the spectral data. Our group has previously demonstrated the implementation of a group- and basis-restricted non-negative matrix factorization (GBR-NMF) framework as an alternative to more widely used dimensionality reduction techniques such as principal component analysis (PCA) for the deconstruction of Raman spectroscopy data as related to radiation response monitoring in both cellular and tissue data. While this method provides better biological interpretability of the Raman spectroscopy data, there are some important factors which must be considered in order to provide the most robust GBR-NMF model. We here evaluate and compare the accuracy of a GBR-NMF model in the reconstruction of three mixture solutions of known concentrations. The factors assessed include the effect of solid versus solutions bases spectra, the number of unconstrained components used in the model, the tolerance of different signal to noise thresholds, and how different groups of biochemicals compare to each other. The robustness of the model was assessed by how well the relative concentration of each individual biochemical in the solution mixture is reflected in the GBR-NMF scores obtained. We also evaluated how well the model can reconstruct original data, both with and without the inclusion of an unconstrained component. Overall, we found that solid bases spectra were generally comparable to solution bases spectra in the GBR-NMF model for all groups of biochemicals. The model was found to be relatively tolerant of high levels of noise in the mixture solutions using solid bases spectra. Additionally, the inclusion of an unconstrained component did not have a significant effect on the deconstruction, on the condition that all biochemicals in the mixture were included as bases chemicals in the model. We also report that some groups of biochemicals achieve a more accurate deconstruction using GBR-NMF than others, likely due to similarity in the individual bases spectra.

Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study.

This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.

Raman spectroscopy and supervised learning as a potential tool to identify high-dose-rate-brachytherapy induced biochemical profiles of prostate cancer.

High-dose-rate-brachytherapy (HDR-BT) is an increasingly attractive alternative to external beam radiation-therapy for patients with intermediate risk prostate cancer. Despite this, no bio-marker based method currently exists to monitor treatment response, and the changes which take place at the biochemical level in hypo-fractionated HDR-BT remain poorly understood. The aim of this pilot study is to assess the capability of Raman spectroscopy (RS) combined with principal component analysis (PCA) and random-forest classification (RF) to identify radiation response profiles after a single dose of 13.5 Gy in a cohort of nine patients. We here demonstrate, as a proof-of-concept, how RS-PCA-RF could be utilised as an effective tool in radiation response monitoring, specifically assessing the importance of low variance PCs in complex sample sets. As RS provides information on the biochemical composition of tissue samples, this technique could provide insight into the changes which take place on the biochemical level, as result of HDR-BT treatment.

Nanotechnology Driven Cancer Chemoradiation: Exploiting the Full Potential of Radiotherapy with a Unique Combination of Gold Nanoparticles and Bleomycin.

One of the major issues in current radiotherapy (RT) is the associated normal tissue toxicity. Enhancement of the RT effect with novel radiosensitizers can address this need. In this study, gold nanoparticles (GNPs) and bleomycin (BLM) were used as a unique combination of radiosensitizers. GNPs offer a two-fold promise as a delivery vehicle for BLM and as a radiosensitizing agent. In this study, GNPs were functionalized and complexed with BLM using a gold-thiol bond (denoted GNP-BLM). Our results show that there was a 40% and 10% decrease in cell growth with GNP-BLM vs. free BLM for the MIA PaCa-2 and PC-3 cell lines, respectively. Testing the GNP-BLM platform with RT showed an 84% and 13% reduction in cell growth in MIA PaCa-2 cells treated with GNP-BLM and GNPs, respectively. Similar results were seen with PC-3 cells. The efficacy of this approach was verified by mapping DNA double-strand breaks (DSBs) as well. Therefore, this proposed incorporation of nanomedicine with RT is promising in achieving a significantly higher therapeutic ratio which is necessary to make a paradigm change to the current clinical approach.

Exploring Diffusion and Cellular Uptake: Charged Gold Nanoparticles in an in Vitro Breast Cancer Model.

Gold nanoparticles have emerged as a prominent tool in nanomedicine, particularly for applications in cancer diagnostic and treatment. One of the challenges for the successful implementation of gold nanoparticles in cancer therapy is their delivery to the specific cancer area within the tumor microenvironment. The presence of cancer enables a poorly organized vascularization system, increasing the pressure with the microenvironment, limiting the uptake of particles. The physicochemical properties of the gold nanoparticles (size, shape, and surface charge) also have a significant effect on diffusion to the tumor site and cellular uptake. In this work, we analyzed the transport of 10 nm gold nanoparticles with different surface charges (neutral, negative, and positive) through a hydrogel composite. Three-dimensional in vitro models composed of breast cancer cells loaded in the hydrogel composite were used for the qualitative and quantitative evaluation of cellular uptake of the gold nanoparticles. Surprisingly, an inverse correlation between the diffusion coefficients of the nanoparticles and cellular uptake was demonstrated. Positively charged gold nanoparticles displayed high cellular uptake, although their diffusion coefficient indicated slow transport through the hydrogel matrix. Neutral particles, on the other hand, displayed fast diffusion but the lowest cellular uptake. The results obtained indicate that nanoparticle diffusion and cellular uptake should be studied together in realistic in vitro models for a true evaluation of transport in tumor microenvironments.

Raman spectroscopy detects metabolic signatures of radiation response and hypoxic fluctuations in non-small cell lung cancer.

BACKGROUND: Radiation therapy is a standard form of treating non-small cell lung cancer, however, local recurrence is a major issue with this type of treatment. A better understanding of the metabolic response to radiation therapy may provide insight into improved approaches for local tumour control. Cyclic hypoxia is a well-established determinant that influences radiation response, though its impact on other metabolic pathways that control radiosensitivity remains unclear. METHODS: We used an established Raman spectroscopic (RS) technique in combination with immunofluorescence staining to measure radiation-induced metabolic responses in human non-small cell lung cancer (NSCLC) tumour xenografts. Tumours were established in NOD.CB17-Prkdcscid/J mice, and were exposed to radiation doses of 15 Gy or left untreated. Tumours were harvested at 2 h, 1, 3 and 10 days post irradiation. RESULTS: We report that xenografted NSCLC tumours demonstrate rapid and stable metabolic changes, following exposure to 15 Gy radiation doses, which can be measured by RS and are dictated by the extent of local tissue oxygenation. In particular, fluctuations in tissue glycogen content were observed as early as 2 h and as late as 10 days post irradiation. Metabolically, this signature was correlated to the extent of tumour regression. Immunofluorescence staining for γ-H2AX, pimonidazole and carbonic anhydrase IX (CAIX) correlated with RS-identified metabolic changes in hypoxia and reoxygenation following radiation exposure. CONCLUSION: Our results indicate that RS can identify sequential changes in hypoxia and tumour reoxygenation in NSCLC, that play crucial roles in radiosensitivity.

Collagen Type I-Gelatin Methacryloyl Composites: Mimicking the Tumor Microenvironment.

Therapeutic drugs can penetrate tissues by diffusion and advection. In a healthy tissue, the interstitial fluid is composed of an influx of nutrients and oxygen from blood vessels. In the case of cancerous tissue, the interstitial fluid is poorly drained because of the lack of lymphatic vasculature, resulting in an increase in interstitial pressure. Furthermore, cancer cells invade healthy tissue by pressing and pushing the surrounding environment, creating an increase in pressure inside the tumor area. This results in a large differential pressure between the tumor and the healthy tissue, leading to an increase in extracellular matrix (ECM) stiffness. Because of high interstitial pressure in addition to matrix stiffening, penetration and distribution of systemic therapies are limited to diffusion, decreasing the efficacy of cancer treatment. This work reports on the development of a microfluidic system that mimics in vitro healthy and cancerous microenvironments using collagen I and gelatin methacryloyl (GelMA) composite hydrogels. The microfluidic device developed here contains a simplistic design with a central chamber and two lateral channels. In the central chamber, hydrogel composites were used to mimic the ECM, whereas lateral channels simulated capillary vessels. The transport of fluorescein sodium salt and fluorescently labeled gold nanoparticles from capillary-mimicking channels through the ECM-mimicking hydrogel was explored by tracking fluorescence. By tuning the hydrogel composition and concentration, the impact of the tumor microenvironment properties on the transport of those species was evaluated. In addition, breast cancer MCF-7 cells were embedded in the hydrogel composites, displaying the formation of 3D clusters with high viability and, consequently, the development of an in vitro tumor model.

Breast cancer subtype specific biochemical responses to radiation.

External beam radiotherapy is a common form of treatment for breast cancer. Among patients and across different breast cancer subtypes, the response to radiation is heterogeneous. Radiation-induced biochemical changes were examined by Raman spectroscopy using cell lines that represent a spectrum of human breast cancer. Principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) revealed unique Raman spectral features in the HER2 and Ki67 subtype. The changes in Raman spectral profiles to different doses of radiation (0-50 Gy) included variations in the levels of proteins, lipids, nucleic acids and glycogen. Importantly, the differences in radiation-induced changes on the normal breast epithelial cell line MCF10A could be discriminated within and across the various breast tumor cell lines. These results demonstrate a novel approach to uncover differences between breast cancer cell subtypes and surrounding normal tissues by their biochemical variations in response to radiation.

Ex Vivo Detection of Circulating Tumor Cells from Whole Blood by Direct Nanoparticle Visualization.

The detection of circulating tumor cells (CTCs) from blood samples can predict prognosis, response to systemic chemotherapy, and metastatic spread of carcinoma. Therefore, approaches for CTC identification is an important aspect of current cancer research. Here, a method for the direct visualization of nanoparticle-coated CTCs under dark field illumination is presented. A metastatic breast cancer cell line (4T1) was transduced with a non-native target protein (Thy1.1). Positive 4T1-Thy1.1 cells incubated with antibody-coated metallic nanoshells appeared overly bright at low magnification, allowing a quick screening of samples and easy visual detection of even single isolated CTCs. The use of a nontransduced cell line as control creates the ideal scenario to evaluate nonspecific binding. A murine metastatic tumor model with the 4T1-Thy1.1 cell line was also implemented. Blood was drawn from mice over the course of one month, and CTCs were successfully detected in all positive subjects. This work validates the use of metallic nanoshells as labels for direct visualization of CTCs while providing guidelines to a systematic development of nanotechnology-based detection systems for CTCs.

Raman Spectroscopic Signatures Reveal Distinct Biochemical and Temporal Changes in Irradiated Human Breast Adenocarcinoma Xenografts.

Radiation therapy plays a crucial role in the management of breast cancer. However, current standards of care have yet to accommodate patient-specific radiation sensitivity. Raman spectroscopy is promising for applications in radiobiological studies and as a technique for personalized radiation oncology, since it can detect spectral changes in irradiated tissues. In this study, we used established Raman spectroscopic approaches to investigate the biochemical nature and temporal evolution of spectral changes in human breast adenocarcinoma xenografts after in vivo irradiation. Spectral alterations related to cell cycle variations with radiation dose were identified for tumors treated using a range of single-fraction ionizing radiation doses. Additional dose-dependent spectral changes linked to specific proteins, nucleic acids and lipids were also identified in irradiated tumors with a clear temporal evolution of the expression of these signatures. This study reveals distinct shifts in Raman spectra after in vivo irradiation of human breast adenocarcinoma xenografts, emphasizing the significance of Raman spectroscopy for assessing tumor response during radiation therapy.

SPR platform based on image acquisition for HER2 antigen detection.

HER2 antigen is a marker used for breast cancer diagnosis and prevention. Its determination has great importance since breast cancer is one of the most insidious types of cancer in women. HER2 antigen assessment in human serum is traditionally achieved by enzyme-linked immunosorbent assay (ELISA method), but it has some disadvantages, such as suppressing the thermodynamic-kinetic studies regarding the antibody-antigen interaction, and the use of labeled molecules that can promote false positive responses. Biosensors based on surface plasmon resonance (SPR) are sensitive optical techniques widely applied on bioassays. The plasmonic devices do not operate with labeled molecules, overcoming conventional immunoassay limitations, and enabling a direct detection of target analytes. In this way, a new SPR biosensor to assess HER2 antigen has been proposed, using nanohole arrays on a gold thin film by signal transduction of transmitted light measurements from array image acquisitions. These metallic nanostructures may couple the light directly on surface plasmons using a simple collinear arrangement. The proposed device reached an average sensitivity for refractive index (RI) variation on a metal surface of 4146 intensity units/RIU (RIU = RI units). The device feasibility on biomolecular assessment was evaluated. For this, 3 ng ml-1 known HER2 antigen concentration was efficiently flowed (using a microfluidic system) and detected from aqueous solutions. This outcome shows that the device may be a powerful apparatus for bioassays, particularly toward breast cancer diagnosis and prognosis.

Raman spectroscopy identifies radiation response in human non-small cell lung cancer xenografts.

External beam radiation therapy is a standard form of treatment for numerous cancers. Despite this, there are no approved methods to account for patient specific radiation sensitivity. In this report, Raman spectroscopy (RS) was used to identify radiation-induced biochemical changes in human non-small cell lung cancer xenografts. Chemometric analysis revealed unique radiation-related Raman signatures that were specific to nucleic acid, lipid, protein and carbohydrate spectral features. Among these changes was a dramatic shift in the accumulation of glycogen spectral bands for doses of 5 or 15 Gy when compared to unirradiated tumours. When spatial mapping was applied in this analysis there was considerable variability as we found substantial intra- and inter-tumour heterogeneity in the distribution of glycogen and other RS spectral features. Collectively, these data provide unique insight into the biochemical response of tumours, irradiated in vivo, and demonstrate the utility of RS for detecting distinct radiobiological responses in human tumour xenografts.

Radiation-Induced Glycogen Accumulation Detected by Single Cell Raman Spectroscopy Is Associated with Radioresistance that Can Be Reversed by Metformin.

Altered cellular metabolism is a hallmark of tumor cells and contributes to a host of properties associated with resistance to radiotherapy. Detection of radiation-induced biochemical changes can reveal unique metabolic pathways affecting radiosensitivity that may serve as attractive therapeutic targets. Using clinically relevant doses of radiation, we performed label-free single cell Raman spectroscopy on a series of human cancer cell lines and detected radiation-induced accumulation of intracellular glycogen. The increase in glycogen post-irradiation was highest in lung (H460) and breast (MCF7) tumor cells compared to prostate (LNCaP) tumor cells. In response to radiation, the appearance of this glycogen signature correlated with radiation resistance. Moreover, the buildup of glycogen was linked to the phosphorylation of GSK-3ß, a canonical modulator of cell survival following radiation exposure and a key regulator of glycogen metabolism. When MCF7 cells were irradiated in the presence of the anti-diabetic drug metformin, there was a significant decrease in the amount of radiation-induced glycogen. The suppression of glycogen by metformin following radiation was associated with increased radiosensitivity. In contrast to MCF7 cells, metformin had minimal effects on both the level of glycogen in H460 cells following radiation and radiosensitivity. Our data demonstrate a novel approach of spectral monitoring by Raman spectroscopy to assess changes in the levels of intracellular glycogen as a potential marker and resistance mechanism to radiation therapy.

A Raman spectroscopic study of cell response to clinical doses of ionizing radiation.

The drive toward personalized radiation therapy (RT) has created significant interest in determining patient-specific tumor and normal tissue responses to radiation. Raman spectroscopy (RS) is a non-invasive and label-free technique that can detect radiation response through assessment of radiation-induced biochemical changes in tumor cells. In the current study, single-cell RS identified specific radiation-induced responses in four human epithelial tumor cell lines: lung (H460), breast (MCF-7, MDA-MB-231), and prostate (LNCaP), following exposure to clinical doses of radiation (2-10 Gy). At low radiation doses (2 Gy), H460 and MCF-7 cell lines showed an increase in glycogen-related spectral features, and the LNCaP cell line showed a membrane phospholipid-related radiation response. In these cell lines, only spectral information from populations receiving 10 Gy or less was required to identify radiation-related features using principal component analysis (PCA). In contrast, the MDA-MB-231 cell line showed a significant increase in protein relative to nucleic acid and lipid spectral features at doses of 6 Gy or higher, and high-dose information (30, 50 Gy) was required for PCA to identify this biological response. The biochemical nature of the radiation-related changes occurring in cells exposed to clinical doses was found to segregate by status of p53 and radiation sensitivity. Furthermore, the utility of RS to identify a biological response in human tumor cells exposed to therapeutic doses of radiation was found to be governed by the extent of the biochemical changes induced by a radiation response and is therefore cell line specific. The results of this study demonstrate the utility and effectiveness of single-cell RS to identify and measure biological responses in tumor cells exposed to standard radiotherapy doses.

Leukemic marker detection using a spectro-polarimetric surface plasmon resonance platform.

In this paper, we present a proof of concept screening for monoclonal immunoglobulin as a leukemia tumor marker using a surface plasmon resonance (SPR) bio-sensing platform. This screening method is based on measurements of immunoglobulin levels in human serum and the determination of the relative concentrations of kappa and lambda light chains. The kappa/lambda ratio is used to determine the presence of monoclonal immunoglobulin. Tests have been performed using standard solutions of immunoglobulins and serum samples from patients with known leukemic diagnoses. This platform has a resolution of 5×10(-7) refractive index unit (RIU) per channel, which is up to 10 times better than other SPR imaging systems for multi-sensing applications. The results obtained with this technique are in agreement with those acquired using conventional methods for immunoglobulin detection, indicating that our polarimetric SPR platform should be suitable for a cheap and efficient tool for early leukemia biomarker screening and monitoring applications.

Quantification of ovarian cancer markers with integrated microfluidic concentration gradient and imaging nanohole surface plasmon resonance.

Nanohole array-based biosensors integrated with a microfluidic concentration gradient generator were used for imaging detection and quantification of ovarian cancer markers. Calibration curves based on controlled concentrations of the analyte were created using a microfluidic stepped diffusive mixing scheme. Quantification of samples with unknown concentration of analyte was achieved by image-intensity comparison with the calibration curves. The biosensors were first used to detect the immobilization of ovarian cancer marker antibodies, and subsequently applied for the quantification of the ovarian cancer marker r-PAX8 (with a limit of detection of about 5 nM and a dynamic range from 0.25 to 9.0 µg.mL(-1)). The proposed biosensor demonstrated the ability of self-generating calibration curves on-chip in an integrated microfluidic platform, representing a further step towards the development of comprehensive lab-on-chip biomedical diagnostics based on nanohole array technology.

Variability in Raman spectra of single human tumor cells cultured in vitro: correlation with cell cycle and culture confluency.

In this work we investigate the capability of Raman microscopy (RM) to detect inherent sources of biochemically based spectral variability between single cells of a human tumor cell line (DU145) cultured in vitro. Principal component analysis (PCA) is used to identify differences in single-cell Raman spectra. These spectral differences correlate with (1) cell cycle progression and (2) changing confluency of a cell culture during the first 3 to 4 days after sub-culturing. Cell cycle regulatory drugs are used to synchronize the cell cycle progression of cell cultures, and flow cytometry is used to determine the cell cycle distribution of cell cultures at the time of Raman analysis. Spectral variability arising from cell cycle progression is (1) expressed as varying intensities of protein and nucleic acid features relative to lipid features, (2) well correlated with known biochemical changes in cells as they progress through the cell cycle, and (3) shown to be the most significant source of inherent spectral variability between cells. Furthermore, the specific biomolecules responsible for the observed spectral variability due to both cell cycle progression and changes in cell culture confluency can be identified in the first and second components of principal component analysis (PCA). Our characterization of the inherent sources of variability in Raman spectra of single human cells will be useful for understanding subtle spectral differences in RM studies of single cells.