Quantum Cascade Lasers in Biomedical Infrared Imaging

Linearly Polarized Infrared Spectroscopy for the Analysis of Biological Materials

The analysis of biological samples with polarized infrared spectroscopy (p-IR) has long been a widely practiced method for the determination of sample orientation and structural properties. In contrast to earlier works, which employed this method to investigate the fundamental chemistry of biological systems, recent interests are moving toward "real-world" applications for the evaluation and diagnosis of pathological states. This focal point review provides an up-to-date synopsis of the knowledge of biological materials garnered through linearly p-IR on biomolecules, cells, and tissues. An overview of the theory with special consideration to biological samples is provided. Different modalities which can be employed along with their capabilities and limitations are outlined. Furthermore, an in-depth discussion of factors regarding sample preparation, sample properties, and instrumentation, which can affect p-IR analysis is provided. Additionally, attention is drawn to the potential impacts of analysis of biological samples with inherently polarized light sources, such as synchrotron light and quantum cascade lasers. The vast applications of p-IR for the determination of the structure and orientation of biological samples are given. In conclusion, with considerations to emerging instrumentation, findings by other techniques, and the shift of focus toward clinical applications, we speculate on the future directions of this methodology.

Widely-Tunable Quantum Cascade-Based Sources for the Development of Optical Gas Sensors

Spectroscopic techniques based on Distributed FeedBack (DFB) Quantum Cascade Lasers (QCL) provide good results for gas detection in the mid-infrared region in terms of sensibility and selectivity. The main limitation is the QCL relatively low tuning range (~10 cm^-1) that prevents from monitoring complex species with broad absorption spectra in the infrared region or performing multi-gas sensing. To obtain a wider tuning range, the first solution presented in this paper consists of the use of a DFB QCL array. Tuning ranges from 1335 to 1387 cm^-1 and from 2190 to 2220 cm^-1 have been demonstrated. A more common technique that will be presented in a second part is to implement a Fabry-Perot QCL chip in an external-cavity (EC) system so that the laser could be tuned on its whole gain curve. The use of an EC system also allows to perform Intra-Cavity Laser Absorption Spectroscopy, where the gas sample is placed within the laser resonator. Moreover, a technique only using the QCL compliance voltage technique can be used to retrieve the spectrum of the gas inside the cavity, thus no detector outside the cavity is needed. Finally, a specific scheme using an EC coherent QCL array can be developed. All these widely-tunable Quantum Cascade-based sources can be used to demonstrate the development of optical gas sensors.

Infrared spectral microscopy as a tool to monitor lung fibrosis development in a model system

Tissue fibrosis is a progressive and destructive disease process that can occur in many different organs including the liver, kidney, skin, and lungs. Fibrosis is typically initiated by inflammation as a result of chronic insults such as infection, chemicals and autoimmune diseases. Current approaches to examine organ fibrosis are limited to radiological and histological analyses. Infrared spectroscopic imaging offers a potential alternative approach to gain insight into biochemical changes associated with fibrosis progression. In this study, we demonstrate that IR imaging of a mouse model of pulmonary fibrosis can identify biochemical changes observed with fibrosis progression and the beginning of resolution using K-means analysis, spectral ratios and multivariate data analysis. This study demonstrates that IR imaging may be a useful approach to understand the biochemical events associated with fibrosis initiation, progression and resolution for both the clinical setting and for assessing novel anti-fibrotic drugs in a model system.

Volume Fabrication of Quantum Cascade Lasers on 200 mm-CMOS pilot line

The manufacturing cost of quantum cascade lasers is still a major bottleneck for the adoption of this technology for chemical sensing. The integration of Mid-Infrared sources on Si substrate based on CMOS technology paves the way for high-volume low-cost fabrication. Furthermore, the use of Si-based fabrication platform opens the way to the co-integration of QCL Mid-InfraRed sources with SiGe-based waveguides, enabling realization of optical sensors fully integrated on planar substrate. We report here the fabrication and the characterization of DFB-QCL sources using top metal grating approach working at 7.4 µm fully implemented on our 200 mm CMOS pilot line. These QCL featured threshold current density of 2.5 kA/cm² and a linewidth of 0.16 cm⁻¹ with a high fabrication yield. This approach paves the way toward a Mid-InfraRed spectrometer at the silicon chip level.

Hyperspectral Tissue Image Segmentation Using Semi-Supervised NMF and Hierarchical Clustering

Hyperspectral imaging (HSI) of tissue samples in the mid-infrared (mid-IR) range provides spectro-chemical and tissue structure information at sub-cellular spatial resolution. Disease states can be directly assessed by analyzing the mid-IR spectra of different cell types (e.g., epithelial cells) and sub-cellular components (e.g., nuclei), provided that we can accurately classify the pixels belonging to these components. The challenge is to extract information from hundreds of noisy mid-IR bands at each pixel, where each band is not very informative in itself, making annotations of unstained tissue HSI images particularly tricky. Because the tissue structure is not necessarily identical between the two sections, only a few regions in unstained HSI image can be annotated with high confidence, even when serial (or adjacent) hematoxylin and eosin stained section is used as a visual guide. In order to completely use both labeled and unlabeled pixels in training images, we have developed an HSI pixel classification method that uses semi-supervised learning for both spectral dimension reduction and hierarchical pixel clustering. Compared to the supervised classifiers, the proposed method was able to account for the vast differences in the spectra of sub-cellular components of the same cell type and to achieve an F1 score of 71.18% on twofold cross-validation across 20 tissue images. To generate further interest in this promising modality, we have released our source code and also showed that disease classification is straightforward after HSI image segmentation.

Combining random forest and 2D correlation analysis to identify serum spectral signatures for neuro-oncology

Fourier transform infrared (FTIR) spectroscopy has long been established as an analytical technique for the measurement of vibrational modes of molecular systems. More recently, FTIR has been used for the analysis of biofluids with the aim of becoming a tool to aid diagnosis. For the clinician, this represents a convenient, fast, non-subjective option for the study of biofluids and the diagnosis of disease states. The patient also benefits from this method, as the procedure for the collection of serum is much less invasive and stressful than traditional biopsy. This is especially true of patients in whom brain cancer is suspected. A brain biopsy is very unpleasant for the patient, potentially dangerous and can occasionally be inconclusive. We therefore present a method for the diagnosis of brain cancer from serum samples using FTIR and machine learning techniques. The scope of the study involved 433 patients from whom were collected 9 spectra each in the range 600-4000 cm(-1). To begin the development of the novel method, various pre-processing steps were investigated and ranked in terms of final accuracy of the diagnosis. Random forest machine learning was utilised as a classifier to separate patients into cancer or non-cancer categories based upon the intensities of wavenumbers present in their spectra. Generalised 2D correlational analysis was then employed to further augment the machine learning, and also to establish spectral features important for the distinction between cancer and non-cancer serum samples. Using these methods, sensitivities of up to 92.8% and specificities of up to 91.5% were possible. Furthermore, ratiometrics were also investigated in order to establish any correlations present in the dataset. We show a rapid, computationally light, accurate, statistically robust methodology for the identification of spectral features present in differing disease states. With current advances in IR technology, such as the development of rapid discrete frequency collection, this approach is of importance to enable future clinical translation and enables IR to achieve its potential.

Image-Based Tracking of Anticancer Drug-Loaded Nanoengineered Polyelectrolyte Capsules in Cellular Environments Using a Fast Benchtop Mid-Infrared (MIR) Microscope

Drug delivery monitoring and tracking in the human body are two of the biggest challenges in targeted therapy to be addressed by nanomedicine. The ability of imaging drugs and micro-/nanoengineered drug carriers and of visualizing their interactions at the cellular interface in a label-free manner is crucial in providing the ability of tracking their cellular pathways and will help understand their biological impact, allowing thus to improve the therapeutic efficacy. We present a fast, label-free technique to achieve high-resolution imaging at the mid-infrared (MIR) spectrum that provides chemical information. Using our custom-made benchtop infrared microscope using a high-repetition-rate pulsed laser (80 MHz, 40 ps), we were able to acquire images with subwavelength resolution (0.8 × λ) at very high speeds. As a proof-of-concept, we embarked on the investigation of nanoengineered polyelectrolyte capsules (NPCs) containing the anticancer drug, docetaxel. These NPCs were synthesized using a layer-by-layer approach built upon a calcium carbonate (CaCO₃) core, which was then removed away with ethylenediaminetetraacetic acid. The obtained MIR images show that NPCs are attached to the cell membrane, which is a good step toward an efficient drug delivery. This has been confirmed by both three-dimensional confocal fluorescence and stimulated emission depletion microscopy. Coupled with additional instrumentation and data processing advancements, this setup is capable of video-rate imaging speeds and will be significantly complementing current super-resolution microscopy techniques while providing an unperturbed view into living cells.

Characterization of human breast cancer tissues by infrared imaging

Fourier Transform InfraRed (FTIR) spectroscopy coupled to microscopy (IR imaging) has shown unique advantages in detecting morphological and molecular pathologic alterations in biological tissues. The aim of this study was to evaluate the potential of IR imaging as a diagnostic tool to identify characteristics of breast epithelial cells and the stroma. In this study a total of 19 breast tissue samples were obtained from 13 patients. For 6 of the patients, we also obtained Non-Adjacent Non-Tumor tissue samples. Infrared images were recorded on the main cell/tissue types identified in all breast tissue samples. Unsupervised Principal Component Analyses and supervised Partial Least Square Discriminant Analyses (PLS-DA) were used to discriminate spectra. Leave-one-out cross-validation was used to evaluate the performance of PLS-DA models. Our results show that IR imaging coupled with PLS-DA can efficiently identify the main cell types present in FFPE breast tissue sections, i.e. epithelial cells, lymphocytes, connective tissue, vascular tissue and erythrocytes. A second PLS-DA model could distinguish normal and tumor breast epithelial cells in the breast tissue sections. A patient-specific model reached particularly high sensitivity, specificity and MCC rates. Finally, we showed that the stroma located close or at distance from the tumor exhibits distinct spectral characteristics. In conclusion FTIR imaging combined with computational algorithms could be an accurate, rapid and objective tool to identify/quantify breast epithelial cells and differentiate tumor from normal breast tissue as well as normal from tumor-associated stroma, paving the way to the establishment of a potential complementary tool to ensure safe tumor margins.