Rapid identification of goblet cells in unstained colon thin sections by means of quantum cascade laser-based infrared microspectroscopy

Seeding multivariate algorithms for spectral analysis, a data augmentation approach to enhance analytical performance

Seeding spectral datasets by augmenting the data matrix with either the full spectrum or selected spectral features in order to bias multivariate analysis towards the solution of interest is explored. It is demonstrated that such seeding can have a profound effect on the endpoint of the analysis. Using Raman spectroscopic data of human lung adenocarcinoma cells (A549) in vitro, systematic perturbations to the spectra are introduced to simulate dose dependent exposure to a drug (cisplatin), and/or cellular response, representing reduced viability. Taking Principal Components Analysis (PCA) as the first example, seeding with the known spectral profiles of the drug exposure is demonstrated to greatly increase the ability of the algorithm to differentiate two distinct data subsets, representing control and exposed. The improved differentiation is quantified by further Linear Discriminant Analysis of the PCA data. Other examples of where seeding may be applied include, simulated datasets consisting of simultaneous changes in the spectral markers of exposure dose and cellular response, which are used for Multivariate Curve Resolution - Alternating Least Squares analysis (MCR-ALS). In the example presented, adding pure components to the dataset improves the ability of the algorithm to both model the systematic variation of concentration dependent data and extract the component spectra more accurately than the unseeded dataset. The seeded approach thus provides improved performance for differential analysis of datasets, as well as spectral unmixing analyses, to monitor, for example, the kinetic evolution of a reaction mixture, or metabolic pathway.

Deep MALDI-MS spatial omics guided by quantum cascade laser mid-infrared imaging microscopy

In spatial'omics, highly confident molecular identifications are indispensable for the investigation of complex biology and for spatial biomarker discovery. However, current mass spectrometry imaging (MSI)-based spatial 'omics must compromise between data acquisition speed and biochemical profiling depth. Here, we introduce fast, label-free quantum cascade laser mid-infrared imaging microscopy (QCL-MIR imaging) to guide MSI to high-interest tissue regions as small as kidney glomeruli, cultured multicellular spheroid cores or single motor neurons. Focusing on smaller tissue areas enables extensive spatial lipid identifications by on-tissue tandem-MS employing imaging parallel reaction monitoring-Parallel Accumulation-Serial Fragmentation (iprm-PASEF). QCL-MIR imaging-guided MSI allowed for unequivocal on-tissue elucidation of 157 sulfatides selectively accumulating in kidneys of arylsulfatase A-deficient mice used as ground truth concept and provided chemical rationales for improvements to ion mobility prediction algorithms. Using this workflow, we characterized sclerotic spinal cord lesions in mice with experimental autoimmune encephalomyelitis (EAE), a model of multiple sclerosis, and identified upregulation of inflammation-related ceramide-1-phosphate and ceramide phosphatidylethanolamine as markers of white matter lipid remodeling. Taken together, widely applicable and fast QCL-MIR imaging-based guidance of MSI ensures that more time is available for exploration and validation of new biology by default on-tissue tandem-MS analysis.

Full fingerprint hyperspectral imaging of prostate cancer tissue microarrays within clinical timeframes using quantum cascade laser microscopy

One of the major limitations for clinical applications of infrared spectroscopic imaging modalities is the acquisition time required to obtain reasonable images of tissues with high spatial resolution and good signal-to-noise ratio (SNR). The time to acquire a reasonable signal to noise spectroscopic scan of a standard microscope slide region of tissue can take many hours. As a trade-off, systems can allow for discrete wavenumber acquisitions, sacrificing potentially vital chemical bands in order to reach specific acquisition targets. Recent instrumentation developments now allow for the full fingerprint imaging of entire microscope slides in under 30 minutes, enabling rapid, high quality spectroscopic imaging of tissues within clinical timeframes without sacrificing frequency bands. Here we compare the data from a novel QCL microscope to an FTIR microscope covering multiple aspects of spectroscopic imaging of a large, clinically relevant, prostate cancer tissue cohort (N = 1281). Comparisons of hyperspectral data acquisition quality in both achieved signal to noise and image contrast alongside the capacity for unsupervised and supervised modelling of tissue constituents are reported. We conclude that it is now possible to collect full fingerprint spectra and derive clinically relevant data in a timeframe suitable for translation into the pathology laboratory without the need to resort to discrete frequency imaging with subsequent loss of information.

Label-free Aβ plaque detection in Alzheimer's disease brain tissue using infrared microscopy and neural networks

We present a novel method for the label-free detection of amyloid-beta (Aβ) plaques, the key hallmark of Alzheimer's disease, in human brain tissue sections. Conventionally, immunohistochemistry (IHC) is employed for the characterization of Aβ plaques, hindering subsequent analysis. Here, a semi-supervised convolutional neural network (CNN) is trained to detect Aβ plaques in quantum cascade laser infrared (QCL-IR) microscopy images. Laser microdissection (LMD) is then used to precisely extract plaques from snap-frozen, unstained tissue sections. Mass spectrometry-based proteomics reveals a loss of soluble proteins in IHC stained samples. Our method prevents this loss and provides a novel tool that expands the scope of molecular analysis methods to chemically native plaques. Insight into soluble plaque components will complement our understanding of plaques and their role in Alzheimer's disease.

High-speed scanless entire bandwidth mid-infrared chemical imaging

Mid-infrared spectroscopy probes molecular vibrations to identify chemical species and functional groups. Therefore, mid-infrared hyperspectral imaging is one of the most powerful and promising candidates for chemical imaging using optical methods. Yet high-speed and entire bandwidth mid-infrared hyperspectral imaging has not been realized. Here we report a mid-infrared hyperspectral chemical imaging technique that uses chirped pulse upconversion of sub-cycle pulses at the image plane. This technique offers a lateral resolution of 15 µm, and the field of view is adjustable between 800 µm × 600 µm to 12 mm × 9 mm. The hyperspectral imaging produces a 640 × 480 pixel image in 8 s, which covers a spectral range of 640-3015 cm^-1, comprising 1069 wavelength points and offering a wavenumber resolution of 2.6-3.7 cm^-1. For discrete frequency mid-infrared imaging, the measurement speed reaches a frame rate of 5 kHz, the repetition rate of the laser. As a demonstration, we effectively identified and mapped different components in a microfluidic device, plant cell, and mouse embryo section. The great capacity and latent force of this technique in chemical imaging promise to be applied to many fields such as chemical analysis, biology, and medicine.

Digital Histopathology by Infrared Spectroscopic Imaging

Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical makeup and heterogeneity of biological tissues. Combining this novel contrast mechanism in microscopy with the use of artificial intelligence can transform the practice of histopathology, which currently relies largely on human examination of morphologic patterns within stained tissue. First, this review summarizes IR imaging instrumentation especially suited to histopathology, analyses of its performance, and major trends. Second, an overview of data processing methods and application of machine learning is given, with an emphasis on the emerging use of deep learning. Third, a discussion on workflows in pathology is provided, with four categories proposed based on the complexity of methods and the analytical performance needed. Last, a set of guidelines, termed experimental and analytical specifications for spectroscopic imaging in histopathology, are proposed to help standardize the diversity of approaches in this emerging area.

Random forest microplastic classification using spectral subsamples of FT-IR hyperspectral images

In this work, a random decision forest model is built for fast identification of Fourier-transform infrared spectra of the eleven most common types of microplastics in the environment. The random decision forest input data is reduced to a combination of highly discriminative single wavenumbers selected using a machine learning classifier. This dimension reduction allows input from systems with individual wavenumber measurements, and decreases prediction time. The training and testing spectra are extracted from Fourier-transform infrared hyperspectral images of pure-type microplastic samples, automatizing the process with reference spectra and a fast background correction and identification algorithm. Random decision forest classification results are validated using procedurally generated ground truth. The classification accuracy achieved on said ground truths are not expected to carry over to environmental samples as those usually contain a broader variety of materials.

Advances in Digital Pathology: From Artificial Intelligence to Label-Free Imaging

BACKGROUND

Digital pathology, in its primary meaning, describes the utilization of computer screens to view scanned histology slides. Digitized tissue sections can be easily shared for a second opinion. In addition, it allows tissue image analysis using specialized software to identify and measure events previously observed by a human observer. These tissue-based readouts were highly reproducible and precise. Digital pathology has developed over the years through new technologies. Currently, the most discussed development is the application of artificial intelligence to automatically analyze tissue images. However, even new label-free imaging technologies are being developed to allow imaging of tissues by means of their molecular composition.

SUMMARY

This review provides a summary of the current state-of-the-art and future digital pathologies. Developments in the last few years have been presented and discussed. In particular, the review provides an outlook on interesting new technologies (e.g., infrared imaging), which would allow for deeper understanding and analysis of tissue thin sections beyond conventional histopathology.

KEY MESSAGES

In digital pathology, mathematical methods are used to analyze images and draw conclusions about diseases and their progression. New innovative methods and techniques (e.g., label-free infrared imaging) will bring significant changes in the field in the coming years.

Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets

The use of infrared spectroscopy to augment decision-making in histopathology is a promising direction for the diagnosis of many disease types. Hyperspectral images of healthy and diseased tissue, generated by infrared spectroscopy, are used to build chemometric models that can provide objective metrics of disease state. It is important to build robust and stable models to provide confidence to the end user. The data used to develop such models can have a variety of characteristics which can pose problems to many model-building approaches. Here we have compared the performance of two machine learning algorithms - AdaBoost and Random Forests - on a variety of non-uniform data sets. Using samples of breast cancer tissue, we devised a range of training data capable of describing the problem space. Models were constructed from these training sets and their characteristics compared. In terms of separating infrared spectra of cancerous epithelium tissue from normal-associated tissue on the tissue microarray, both AdaBoost and Random Forests algorithms were shown to give excellent classification performance (over 95% accuracy) in this study. AdaBoost models were more robust when datasets with large imbalance were provided. The outcomes of this work are a measure of classification accuracy as a function of training data available, and a clear recommendation for choice of machine learning approach.

Rapid brain structure and tumour margin detection on whole frozen tissue sections by fast multiphotometric mid-infrared scanning

Frozen section analysis is a frequently used method for examination of tissue samples, especially for tumour detection. In the majority of cases, the aim is to identify characteristic tissue morphologies or tumour margins. Depending on the type of tissue, a high number of misdiagnoses are associated with this process. In this work, a fast spectroscopic measurement device and workflow was developed that significantly improves the speed of whole frozen tissue section analyses and provides sufficient information to visualize tissue structures and tumour margins, dependent on their lipid and protein molecular vibrations. That optical and non-destructive method is based on selected wavenumbers in the mid-infrared (MIR) range. We present a measuring system that substantially outperforms a commercially available Fourier Transform Infrared (FT-IR) Imaging system, since it enables acquisition of reduced spectral information at a scan field of 1 cm² in 3 s, with a spatial resolution of 20 µm. This allows fast visualization of segmented structure areas with little computational effort. For the first time, this multiphotometric MIR system is applied to biomedical tissue sections. We are referencing our novel MIR scanner on cryopreserved murine sagittal and coronal brain sections, especially focusing on the hippocampus, and show its usability for rapid identification of primary hepatocellular carcinoma (HCC) in mouse liver.

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

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

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

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

Integrating Microfabrication into Biological Investigations: the Benefits of Interdisciplinarity

The advent of micro and nanotechnologies, such as microfabrication, have impacted scientific research and contributed to meaningful real-world applications, to a degree seen during historic technological revolutions. Some key areas benefitting from the invention and advancement of microfabrication platforms are those of biological and biomedical sciences. Modern therapeutic approaches, involving point-of-care, precision or personalized medicine, are transitioning from the experimental phase to becoming the standard of care. At the same time, biological research benefits from the contribution of microfluidics at every level from single cell to tissue engineering and organoids studies. The aim of this commentary is to describe, through proven examples, the interdisciplinary process used to develop novel biological technologies and to emphasize the role of technical knowledge in empowering researchers who are specialized in a niche area to look beyond and innovate.

Biomedical applications of mid-infrared quantum cascade lasers - a review

Mid-infrared spectroscopy has been applied to research in biology and medicine for more than 20 years and conceivable applications have been identified. More recently, these applications have been shown to benefit from the use of quantum cascade lasers due to their specific properties, namely high spectral power density, small beam parameter product, narrow emission spectrum and, if needed, tuning capabilities. This review provides an overview of the achievements and illustrates some applications which benefit from the key characteristics of quantum cascade laser-based mid-infrared spectroscopy using examples such as breath analysis, the investigation of serum, non-invasive glucose monitoring in bulk tissue and the combination of spectroscopy and microscopy of tissue thin sections for rapid histopathology.

Use of Hyperspectral/Multispectral Imaging in Gastroenterology. Shedding Some⁻Different⁻Light into the Dark

Hyperspectral/Multispectral imaging (HSI/MSI) technologies are able to sample from tens to hundreds of spectral channels within the electromagnetic spectrum, exceeding the capabilities of human vision. These spectral techniques are based on the principle that every material has a different response (reflection and absorption) to different wavelengths. Thereby, this technology facilitates the discrimination between different materials. HSI has demonstrated good discrimination capabilities for materials in fields, for instance, remote sensing, pollution monitoring, field surveillance, food quality, agriculture, astronomy, geological mapping, and currently, also in medicine. HSI technology allows tissue observation beyond the limitations of the human eye. Moreover, many researchers are using HSI as a new diagnosis tool to analyze optical properties of tissue. Recently, HSI has shown good performance in identifying human diseases in a non-invasive manner. In this paper, we show the potential use of these technologies in the medical domain, with emphasis in the current advances in gastroenterology. The main aim of this review is to provide an overview of contemporary concepts regarding HSI technology together with state-of-art systems and applications in gastroenterology. Finally, we discuss the current limitations and upcoming trends of HSI in gastroenterology.

Denoising influence on discrete frequency classification results for quantum cascade laser based infrared microscopy

Currently, there is great interest in bringing the application of IR spectroscopy into the clinic. This however will require a significant reduction in measurement time as Fourier Transform Infrared (FT-IR) imaging takes hours to days to scan a clinically relevant specimen. A potential remedy for this issue is the use of Quantum Cascade Laser Infrared (QCL IR) microscopy performed in Discrete Frequency (DF) mode for maximum speed gain. This gain could be furthermore improved by applying a proper denoising algorithm that takes into account the specific data structure. We have recently compared spectral and spatial denoising techniques in the context of Fourier Transform IR (FT-IR) imaging and showed that the optimal methods depend heavily on the exact data structure. In general multivariate denoising methods such as Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) are the most effective for a dataset containing multiple bands. Histologic classification of QCL IR images of pancreatic tissue using Random Forest was therefore performed to investigate which denoising schemes are the most optimal for such experimental data structure. This work is the first to show the effects of denoising on classification accuracy of QCL data and is likely to be transferable to other QCL microscopes and other modalities using DF imaging, e.g. AFM-IR or CARS/SRS imaging.

High definition infrared chemical imaging of colorectal tissue using a Spero QCL microscope

Mid-infrared microscopy has become a key technique in the field of biomedical science and spectroscopy. This label-free, non-destructive technique permits the visualisation of a wide range of intrinsic biochemical markers in tissues, cells and biofluids by detection of the vibrational modes of the constituent molecules. Together, infrared microscopy and chemometrics is a widely accepted method that can distinguish healthy and diseased states with high accuracy. However, despite the exponential growth of the field and its research world-wide, several barriers currently exist for its full translation into the clinical sphere, namely sample throughput and data management. The advent and incorporation of quantum cascade lasers (QCLs) into infrared microscopes could help propel the field over these remaining hurdles. Such systems offer several advantages over their FT-IR counterparts, a simpler instrument architecture, improved photon flux, use of room temperature camera systems, and the flexibility of a tunable illumination source. In this current study we explore the use of a QCL infrared microscope to produce high definition, high throughput chemical images useful for the screening of biopsied colorectal tissue.

A protocol for rapid, label-free histochemical imaging of fibrotic liver

Mid-infrared microscopy is a non-destructive, quantitative and label-free spectroscopic imaging technique that, as a result of recent instrument advancements, is now at the point of enabling high-throughput automated biochemical screening of whole histology samples. Currently the mid-infrared field is undergoing a paradigm shift that has not been seen since the introduction of scanning Fourier Transform interferometric spectrometers. The latest mid-infrared microscopes are powered by tunable quantum cascade laser (QCL) sources which offer a number of advantages including measurement protocol flexibility, ease-of-use and a greatly enhanced data acquisition speed at high spectral and spatial resolution. In this study we use a wide-field QCL infrared microscope to develop and validate a fast four-frequency protocol for imaging fibrosis in unstained liver tissue. We compare our results to the gold standard Masson's trichrome histochemical staining protocol.

Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology

Cancer alters both the morphological and the biochemical properties of multiple cell types in a tissue. Generally, the morphology of epithelial cells is practical for routine disease diagnoses. Here, infrared spectroscopic imaging biochemically characterizes breast cancer, both epithelial cells and the tumor-associated microenvironment. Unfortunately, conventional spectral analyses are slow. Hence, we designed and built a laser confocal microscope that demonstrates a high signal-to-noise ratio for confident diagnoses. The instrument cuts down imaging time from days to minutes, making the technology feasible for research and clinical translation. Finally, automated human breast cancer biopsy imaging is reported in ∼1 hour, paving the way for routine research into the total tumor (epithelial plus microenvironment) properties and rapid, label-free diagnoses.

Histopathology based on spatial patterns of epithelial cells is the gold standard for clinical diagnoses and research in carcinomas; although known to be important, the tissue microenvironment is not readily used due to complex and subjective interpretation with existing tools. Here, we demonstrate accurate subtyping from molecular properties of epithelial cells using emerging high-definition Fourier transform infrared (HD FT-IR) spectroscopic imaging combined with machine learning algorithms. In addition to detecting four epithelial subtypes, we simultaneously delineate three stromal subtypes that characterize breast tumors. While FT-IR imaging data enable fully digital pathology with rich information content, the long spectral scanning times required for signal averaging and processing make the technology impractical for routine research or clinical use. Hence, we developed a confocal design in which refractive IR optics are designed to provide high-definition, rapid spatial scanning and discrete spectral tuning using a quantum cascade laser (QCL) source. This instrument provides simultaneously high resolving power (2-μm pixel size) and high signal-to-noise ratio (SNR) (>1,300), providing a speed increase of ∼50-fold for obtaining classified results compared with present imaging spectrometers. We demonstrate spectral fidelity and interinstrument operability of our developed instrument by accurate analysis of a 100-case breast tissue set that was analyzed in a day, considerably speeding research. Clinical breast biopsies typical of a patients’ caseload are analyzed in ∼1 hour. This study paves the way for comprehensive tumor-microenvironment analyses in feasible time periods, presenting a critical step in practical label-free molecular histopathology.

Quantum Cascade Laser-Based Infrared Microscopy for Label-Free and Automated Cancer Classification in Tissue Sections

A feasibility study using a quantum cascade laser-based infrared microscope for the rapid and label-free classification of colorectal cancer tissues is presented. Infrared imaging is a reliable, robust, automated, and operator-independent tissue classification method that has been used for differential classification of tissue thin sections identifying tumorous regions. However, long acquisition time by the so far used FT-IR-based microscopes hampered the clinical translation of this technique. Here, the used quantum cascade laser-based microscope provides now infrared images for precise tissue classification within few minutes. We analyzed 110 patients with UICC-Stage II and III colorectal cancer, showing 96% sensitivity and 100% specificity of this label-free method as compared to histopathology, the gold standard in routine clinical diagnostics. The main hurdle for the clinical translation of IR-Imaging is overcome now by the short acquisition time for high quality diagnostic images, which is in the same time range as frozen sections by pathologists.

Advancements in quantum cascade laser-based infrared microscopy of aqueous media

The large mid-infrared absorption coefficient of water frequently hampers the rapid, label-free infrared microscopy of biological objects in their natural aqueous environment. However, the high spectral power density of quantum cascade lasers is shifting this limitation such that mid-infrared absorbance images can be acquired in situ within signal-to-noise ratios of up to 100. Even at sample thicknesses well above 50 μm, signal-to-noise ratios above 10 are readily achieved. The quantum cascade laser-based microspectroscopy of aqueous media is exemplified by imaging an aqueous yeast solution and quantifying glucose consumption, ethanol generation as well as the production of carbon dioxide gas during fermentation.

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.

Optical properties of porcine dermis in the mid-infrared absorption band of glucose

Mid-infrared absorption and scattering properties of porcine dermis are quantified using quantum cascade laser-based goniometry.

Quantum cascade lasers (QCLs) in biomedical spectroscopy

This review focuses on the recent applications of QCLs in mid-IR spectroscopy of clinically relevant samples.

Development of a practical spatial-spectral analysis protocol for breast histopathology using Fourier transform infrared spectroscopic imaging

Breast cancer screening provides sensitive tumor identification, but low specificity implies that a vast majority of biopsies are not ultimately diagnosed as cancer. Automated techniques to evaluate biopsies can prevent errors, reduce pathologist workload and provide objective analysis. Fourier transform infrared (FT-IR) spectroscopic imaging provides both molecular signatures and spatial information that may be applicable for pathology. Here, we utilize both the spectral and spatial information to develop a combined classifier that provides rapid tissue assessment. First, we evaluated the potential of IR imaging to provide a diagnosis using spectral data alone. While highly accurate histologic [epithelium, stroma] recognition could be achieved, the same was not possible for disease [cancer, no-cancer] due to the diversity of spectral signals. Hence, we employed spatial data, developing and evaluating increasingly complex models, to detect cancers. Sub-mm tumors could be very confidently predicted as indicated by the quantitative measurement of accuracy via receiver operating characteristic (ROC) curve analyses. The developed protocol was validated with a small set and statistical performance used to develop a model that predicts study design for a large scale, definitive validation. The results of evaluation on different instruments, at higher noise levels, under a coarser spectral resolution and two sampling modes [transmission and transflection], indicate that the protocol is highly accurate under a variety of conditions. The study paves the way to validating IR imaging for rapid breast tumor detection, its statistical validation and potential directions for optimization of the speed and sampling for clinical deployment.

IR-Live: fabrication of a low-cost plastic microfluidic device for infrared spectromicroscopy of living cells

Water is a strong mid-infrared absorber, which has hindered the full exploitation of label-free and non-invasive infrared (IR) spectromicroscopy techniques for the study of living biological samples. To overcome this barrier, many researchers have built sophisticated fluidic chambers or microfluidic chips wherein the depth of the liquid medium in the sample compartment is limited to 10 μm or less. Here we report an innovative and simple way to fabricate plastic devices with infrared transparent view-ports enabling infrared spectromicroscopy of living biological samples; therefore the device is named "IR-Live". Advantages of this approach include lower production costs, a minimal need to access a micro-fabrication facility, and unlimited mass or waste exchange for the living samples surrounding the view-port area. We demonstrate that the low-cost IR-Live in combination with microfluidic perfusion techniques enables long term (>60 h) cell culture, which broadens the capability of IR spectromicroscopy for studying living biological samples. To illustrate this, we first applied the device to study protein and lipid polarity in migrating REF52 fibroblasts by collecting 2-dimensional spectral chemical maps at a micrometer spatial resolution. Then, we demonstrated the suitability of our approach to study dynamic cellular events by collecting a time series of spectral maps of U937 monocytes during the early stage of cell attachment to a bio-compatible surface.

Real-time mid-infrared imaging of living microorganisms

The speed and efficiency of quantum cascade laser-based mid-infrared microspectroscopy are demonstrated using two different model organisms as examples. For the slowly moving Amoeba proteus, a quantum cascade laser is tuned over the wavelength range of 7.6 µm to 8.6 µm (wavenumbers 1320 cm(-1) and 1160 cm(-1) , respectively). The recording of a hyperspectral image takes 11.3 s whereby an average signal-to-noise ratio of 29 is achieved. The limits of time resolution are tested by imaging the fast moving Caenorhabditis elegans at a discrete wavenumber of 1265 cm(-1) . Mid-infrared imaging is performed with the 640 × 480 pixel video graphics array (VGA) standard and at a full-frame time resolution of 0.02 s (i.e. well above the most common frame rate standards). An average signal-to-noise ratio of 16 is obtained. To the best of our knowledge, these findings constitute the first mid-infrared imaging of living organisms at VGA standard and video frame rate.