Discrete frequency infrared microspectroscopy and imaging with a tunable quantum cascade laser

Inspiring a convergent engineering approach to measure and model the tissue microenvironment

Understanding the molecular and physical complexity of the tissue microenvironment (TiME) in the context of its spatiotemporal organization has remained an enduring challenge. Recent advances in engineering and data science are now promising the ability to study the structure, functions, and dynamics of the TiME in unprecedented detail; however, many advances still occur in silos that rarely integrate information to study the TiME in its full detail. This review provides an integrative overview of the engineering principles underlying chemical, optical, electrical, mechanical, and computational science to probe, sense, model, and fabricate the TiME. In individual sections, we first summarize the underlying principles, capabilities, and scope of emerging technologies, the breakthrough discoveries enabled by each technology and recent, promising innovations. We provide perspectives on the potential of these advances in answering critical questions about the TiME and its role in various disease and developmental processes. Finally, we present an integrative view that appreciates the major scientific and educational aspects in the study of the TiME.

Fast cancer imaging in pancreatic biopsies using infrared imaging

Pancreatic cancer, particularly Pancreatic ductal adenocarcinoma, remains a highly lethal form of cancer with limited early diagnosis and treatment options. Infrared (IR) spectroscopy, combined with machine learning, has demonstrated great potential in detecting various cancers. This study explores the translation of a diagnostic model from Fourier Transform Infrared to Quantum Cascade Laser (QCL) microscopy for pancreatic cancer classification. Furthermore, QCL microscopy offers faster measurements with selected frequencies, improving clinical feasibility. Thus, the goals of the study include establishing a QCL-based model for pancreatic cancer classification and creating a fast surgical margin detection model using reduced spectral information. The research involves preprocessing QCL data, training Random Forest (RF) classifiers, and optimizing the selection of spectral features for the models. Results demonstrate successful translation of the diagnostic model to QCL microscopy, achieving high predictive power (AUC = 98%) in detecting cancerous tissues. Moreover, a model for rapid surgical margin recognition, based on only a few spectral frequencies, is developed with promising differentiation between benign and cancerous regions. The findings highlight the potential of QCL microscopy for efficient pancreatic cancer diagnosis and surgical margin detection within clinical timeframes of minutes per surgical resection tissue.

Rapid hyperspectral photothermal mid-infrared spectroscopic imaging from sparse data for gynecologic cancer tissue subtyping

Ovarian cancer detection has traditionally relied on a multi-step process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology. However, this technology is slow. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude. Our method significantly accelerates data collection by capturing a combination of highresolution and interleaved, lower-resolution infrared band images and applying computational techniques for data interpolation. We effectively minimize data collection requirements by leveraging sparse data acquisition and employing curvelet-based reconstruction algorithms. This approach enhances imaging speed without compromising image quality and ensures robust tissue segmentation. This method resolves the longstanding trade-off between imaging resolution and data collection speed, enabling the reconstruction of high-quality, high-resolution images from undersampled datasets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squared error (MSE), structural similarity index (SSIM), and tissue subtype classification accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by Receiver Operating Characteristic (ROC) curves. Our statistically robust analysis, based on data from 100 ovarian cancer patient samples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gynecological tissue types with segmentation accuracy exceeding 95%. Our work demonstrates the feasibility of integrating rapid MIR hyperspectral photothermal imaging with machine learning in enhancing ovarian cancer tissue characterization, paving the way for quantitative, label-free, automated histopathology. It represents a significant leap forward from traditional histopathological methods, offering profound implications for cancer diagnostics and treatment decision-making.

Label-free biomedical optical imaging

Label-free optical imaging employs natural and nondestructive approaches for the visualisation of biomedical samples for both biological assays and clinical diagnosis. Currently, this field revolves around multiple broad technology-oriented communities, each with a specific focus on a particular modality despite the existence of shared challenges and applications. As a result, biologists or clinical researchers who require label-free imaging are often not aware of the most appropriate modality to use. This manuscript presents a comprehensive review of and comparison among different label-free imaging modalities and discusses common challenges and applications. We expect this review to facilitate collaborative interactions between imaging communities, push the field forward and foster technological advancements, biophysical discoveries, as well as clinical detection, diagnosis, and monitoring of disease.

Infrared spectroscopic laser scanning confocal microscopy for whole-slide chemical imaging

Chemical imaging, especially mid-infrared spectroscopic microscopy, enables label-free biomedical analyses while achieving expansive molecular sensitivity. However, its slow speed and poor image quality impede widespread adoption. We present a microscope that provides high-throughput recording, low noise, and high spatial resolution where the bottom-up design of its optical train facilitates dual-axis galvo laser scanning of a diffraction-limited focal point over large areas using custom, compound, infinity-corrected refractive objectives. We demonstrate whole-slide, speckle-free imaging in ~3 min per discrete wavelength at 10× magnification (2 μm/pixel) and high-resolution capability with its 20× counterpart (1 μm/pixel), both offering spatial quality at theoretical limits while maintaining high signal-to-noise ratios (>100:1). The data quality enables applications of modern machine learning and capabilities not previously feasible - 3D reconstructions using serial sections, comprehensive assessments of whole model organisms, and histological assessments of disease in time comparable to clinical workflows. Distinct from conventional approaches that focus on morphological investigations or immunostaining techniques, this development makes label-free imaging of minimally processed tissue practical.

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.

Mid-Infrared Photothermal Microscopy: Principle, Instrumentation, and Applications

Midinfrared photothermal (MIP) microscopy, also called optical photothermal infrared (O-PTIR) microscopy, is an emerging tool for bond-selective chemical imaging of living biological and material samples. In MIP microscopy, a visible probe beam detects the photothermal-based contrast induced by a vibrational absorption. With submicron spatial resolution, high spectral fidelity, and reduced water absorption background, MIP microscopy has overcome the limitations in infrared chemical imaging methods. In this review, we summarize the basic principle of MIP microscopy, the different origins of MIP contrasts, and recent technology development that pushed the resolution, speed, and sensitivity of MIP imaging to a new stage. We further emphasize its broad applications in life science and material characterization, and provide a perspective of future technical advances.

Imaging approaches for monitoring three-dimensional cell and tissue culture systems

The past decade has seen an increasing demand for more complex, reproducible and physiologically relevant tissue cultures that can mimic the structural and biological features of living tissues. Monitoring the viability, development and responses of such tissues in real-time are challenging due to the complexities of cell culture physical characteristics and the environments in which these cultures need to be maintained in. Significant developments in optics, such as optical manipulation, improved detection and data analysis, have made optical imaging a preferred choice for many three-dimensional (3D) cell culture monitoring applications. The aim of this review is to discuss the challenges associated with imaging and monitoring 3D tissues and cell culture, and highlight topical label-free imaging tools that enable bioengineers and biophysicists to non-invasively characterise engineered living tissues.

Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning

Tumor grade assessment is critical to the treatment of cancers. A pathologist typically evaluates grade by examining morphologic organization in tissue using hematoxylin and eosin (H&E) stained tissue sections. Fourier transform infrared spectroscopic (FT-IR) imaging provides an alternate view of tissue in which spatially specific molecular information from unstained tissue can be utilized. Here, we examine the potential of IR imaging for grading colon cancer in biopsy samples. We used a 148-patient cohort to develop a deep learning classifier to estimate the tumor grade using IR absorption. We demonstrate that FT-IR imaging can be a viable tool to determine colorectal cancer grades, which we validated on an independent cohort of surgical resections. This work demonstrates that harnessing molecular information from FT-IR imaging and coupling it with morphometry is a potential path to develop clinically relevant grade prediction models.

Statistical Considerations and Tools to Improve Histopathologic Protocols with Spectroscopic Imaging

Advances in infrared (IR) spectroscopic imaging instrumentation and data science now present unique opportunities for large validation studies of the concept of histopathology using spectral data. In this study, we examine the discrimination potential of IR metrics for different histologic classes to estimate the sample size needed for designing validation studies to achieve a given statistical power and statistical significance. Next, we present an automated annotation transfer tool that can allow large-scale training/validation, overcoming the limitations of sparse ground truth data with current manual approaches by providing a tool to transfer pathologist annotations from stained images to IR images across diagnostic categories. Finally, the results of a combination of supervised and unsupervised analysis provide a scheme to identify diagnostic groups/patterns and isolating pure chemical pixels for each class to better train complex histopathological models. Together, these methods provide essential tools to take advantage of the emerging capabilities to record and utilize large spectroscopic imaging datasets.

On the Limit of Detection in Infrared Spectroscopic Imaging

Infrared (IR) spectroscopic imaging instruments' performance can be characterized and optimized by an analysis of their limit of detection (LOD). Here we report a systematic analysis of the LOD for Fourier transform IR (FT-IR) and discrete frequency IR (DFIR) imaging spectrometers. In addition to traditional measurements of sample and blank data, we propose a decision theory perspective to pose the determination of LOD as a binary classification problem under different assumptions of noise uniformity and correlation. We also examine three spectral analysis approaches, namely, absorbance at a single frequency, average of absorbance over selected frequencies and total spectral distance - to suit instruments that acquire discrete or contiguous spectral bandwidths. The analysis is validated by refining the fabrication of a bovine serum albumin protein microarray to provide eight uniform spots from ∼2.8 nL of solution for each concentration over a wide range (0.05-10 mg/mL). Using scanning parameters that are typical for each instrument, we estimate a LOD of 0.16 mg/mL and 0.12 mg/mL for widefield and line scanning FT-IR imaging systems, respectively, using the spectral distance approach, and 0.22 mg/mL and 0.15 mg/mL using an optimal set of discrete frequencies. As expected, averaging and the use of post-processing techniques such as minimum noise fraction transformation results in LODs as low as ∼0.075 mg/mL that correspond to a spotted protein mass of ∼112 fg/pixel. We emphasize that these measurements were conducted at typical imaging parameters for each instrument and can be improved using the usual trading rules of IR spectroscopy. This systematic analysis and methodology for determining the LOD can allow for quantitative measures of confidence in imaging an analyte's concentration and a basis for further improving IR imaging technology.

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.

Mid-infrared multispectral confocal microscope using off-axis parabolic mirrors to study epiretinal membranes

Mid-infrared (mid-IR) multispectral microscopy, especially operating at the wavelength of 5-11 µm, is an effective tool for detecting, identifying, and quantifying the structure and composition of biological tissues. Compared with that based on the optical lens, the mid-infrared microscope composed of off-axis parabolic (OAP) mirrors is low cost, simple, and suitable for longer range of wavelength without chromatic aberrations, while keeping the optical transmission efficiency. Here we report a compact and versatile mid-infrared multispectral confocal microscope based on off-axis parabolic mirrors. We also perform numerical calculations based on the vectorial diffraction theory on OAP mirrors and analyze the typical aberrations and misalignment of the OAP-based optical system. Finally, we perform multispectral imaging of the epiretinal membrane of the human eyes with the spectrum selected according to its resonance absorption peak. The system is designed to perform multispectral or even hyperspectral imaging to identify and predict potential disease.

Design Considerations for Discrete Frequency Infrared Microscopy Systems

Discrete frequency infrared chemical imaging is transforming the practice of microspectroscopy by enabling a diversity of instrumentation and new measurement capabilities. While a variety of hardware implementations have been realized, design considerations that are unique to infrared (IR) microscopes have not yet been compiled in literature. Here, we describe the evolution of IR microscopes, provide rationales for design choices, and catalog some major considerations for each of the optical components in an imaging system. We analyze design choices that use these components to optimize performance, under their particular constraints, while providing illustrative examples. We then summarize a framework to assess the factors that determine an instrument's performance mathematically. Finally, we provide a validation approach by enumerating performance metrics that can be used to evaluate the capabilities of imaging systems or suitability for specific intended applications. Together, the presented concepts and examples should aid in understanding available instrument configurations, while guiding innovations in design of the next generation of IR chemical imaging spectrometers.

Application of 2D IR Bioimaging: Hyperspectral Images of Formalin-Fixed Pancreatic Tissues and Observation of Slow Protein Degradation

We used two-dimensional IR bioimaging to study the structural heterogeneity of formalin-fixed mouse pancreas. Images were generated from the hyperspectral data sets by plotting quantities associated with the amide I vibrational mode, which is created by the backbone carbonyl stretch. Images that measure the fundamental vibrational frequencies, cross peaks, and anharmonic shifts are presented. Histograms are generated for each quantity, providing averaged values and distributions around the mean that serve as metrics for protein structures. Images were generated from tissue that had been stored in a formalin fixation for 3, 8, and 48 weeks. Over this period, all three metrics show that that the β-sheet content of the samples increased, consistent with protein aggregation. Our results indicate that formalin fixation does not entirely arrest the degradation of a protein structure in pancreas tissue.

Bond-selective imaging by optically sensing the mid-infrared photothermal effect

Mid-infrared (IR) spectroscopic imaging using inherent vibrational contrast has been broadly used as a powerful analytical tool for sample identification and characterization. However, the low spatial resolution and large water absorption associated with the long IR wavelengths hinder its applications to study subcellular features in living systems. Recently developed mid-infrared photothermal (MIP) microscopy overcomes these limitations by probing the IR absorption-induced photothermal effect using a visible light. MIP microscopy yields submicrometer spatial resolution with high spectral fidelity and reduced water background. In this review, we categorize different photothermal contrast mechanisms and discuss instrumentations for scanning and widefield MIP microscope configurations. We highlight a broad range of applications from life science to materials. We further provide future perspective and potential venues in MIP microscopy field.

Analysis of Fixed and Live Single Cells Using Optical Photothermal Infrared with Concomitant Raman Spectroscopy

This paper reports the first use of a novel completely optically based photothermal method (O-PTIR) for obtaining infrared spectra of both fixed and living cells using a quantum cascade laser (QCL) and optical parametric oscillator (OPO) laser as excitation sources, thus enabling all biologically relevant vibrations to be analyzed at submicron spatial resolution. In addition, infrared data acquisition is combined with concomitant Raman spectra from exactly the same excitation location, meaning the full vibrational profile of the cell can be obtained. The pancreatic cancer cell line MIA PaCa-2 and the breast cancer cell line MDA-MB-231 are used as model cells to demonstrate the capabilities of the new instrumentation. These combined modalities can be used to analyze subcellular structures in both fixed and, more importantly, live cells under aqueous conditions. We show that the protein secondary structure and lipid-rich bodies can be identified on the submicron scale.

Concurrent Vibrational Circular Dichroism Measurements with Infrared Spectroscopic Imaging

Vibrational circular dichroism (VCD) spectroscopy has emerged as a powerful platform to quantify chirality, a vital biological property that performs a pivotal role in the metabolism of life organisms. With a photoelastic modulator (PEM) integrated into an infrared spectrometer, the differential response of a sample to the direction of circularly polarized light can be used to infer conformation handedness. However, these optical components inherently exhibit chromatic behavior and are typically optimized at discrete spectral frequencies. Advancements of discrete frequency infrared (DFIR) spectroscopic microscopes in spectral image quality and data throughput are promising for use toward analytical VCD measurements. Utilizing the PEM advantages incorporated into a custom-built QCL microscope, we demonstrate a point scanning VCD instrument capable of acquiring spectra rapidly across all fingerprint region wavelengths in transmission configuration. Moreover, for the first time, we also demonstrate the VCD imaging performance of our instrument for site-specific chirality mapping of biological tissue samples. This study offers some insight into future possibilities of examining small, localized changes in tissue that have major implications for systemic diseases and their progression, while also laying the groundwork for additional modeling and validation in advancing the capability of VCD spectroscopy and imaging.

Hyperspectral infrared microscopy with visible light

Hyperspectral microscopy is an imaging technique that provides spectroscopic information with high spatial resolution. When applied in the relevant wavelength region, such as in the infrared (IR), it can reveal a rich spectral fingerprint across different regions of a sample. Challenges associated with low efficiency and high cost of IR light sources and detector arrays have limited its broad adoption. Here, we introduce a new approach to IR hyperspectral microscopy, where the IR spectral map is obtained with off-the-shelf components built for visible light. The method is based on the nonlinear interference of correlated photons generated via parametric down-conversion. In this proof-of-concept we demonstrate the chemical mapping of a patterned sample, where different areas have distinctive IR spectroscopic fingerprints. The method provides a wide field of view, fast readout, and negligible heat delivered to the sample, which opens prospects for its further development for applications in material and biological studies.

Resolution Enhancement in Wide-Field IR Imaging and Time-Domain Spectroscopy Using Dielectric Microspheres

Wide-field imaging through dielectric microspheres has emerged in recent years as a simple and effective approach for generating super-resolution images at visible wavelengths. We present, to our knowledge, the first demonstration that dielectric microspheres can be used in a wide-field infrared (IR) microscope to enhance the far field resolution. We have observed a substantial improvement in resolution and magnification when images are collected through polystyrene microspheres. In addition, we demonstrate that spectroscopic imaging with a pulse-shaper based femtosecond mid-IR laser system is possible through the dielectric microspheres, which is a promising first step toward applying this technique to ultrafast IR imaging methods such as pump-probe and 2DIR microscopy.

All-digital histopathology by infrared-optical hybrid microscopy

Optical microscopy for biomedical samples requires expertise in staining to visualize structure and composition. Midinfrared (mid-IR) spectroscopic imaging offers label-free molecular recording and virtual staining by probing fundamental vibrational modes of molecular components. This quantitative signal can be combined with machine learning to enable microscopy in diverse fields from cancer diagnoses to forensics. However, absorption of IR light by common optical imaging components makes mid-IR light incompatible with modern optical microscopy and almost all biomedical research and clinical workflows. Here we conceptualize an IR-optical hybrid (IR-OH) approach that sensitively measures molecular composition based on an optical microscope with wide-field interferometric detection of absorption-induced sample expansion. We demonstrate that IR-OH exceeds state-of-the-art IR microscopy in coverage (10-fold), spatial resolution (fourfold), and spectral consistency (by mitigating the effects of scattering). The combined impact of these advances allows full slide infrared absorption images of unstained breast tissue sections on a visible microscope platform. We further show that automated histopathologic segmentation and generation of computationally stained (stainless) images is possible, resolving morphological features in both color and spatial detail comparable to current pathology protocols but without stains or human interpretation. IR-OH is compatible with clinical and research pathology practice and could make for a cost-effective alternative to conventional stain-based protocols for stainless, all-digital pathology.

High-resolution, high-contrast mid-infrared imaging of fresh biological samples with ultraviolet-localized photoacoustic microscopy

Mid-infrared (MIR) microscopy provides rich chemical and structural information about biological samples, without staining. Conventionally, the long MIR wavelength severely limits the lateral resolution owing to optical diffraction; moreover, the strong MIR absorption of water ubiquitous in fresh biological samples results in high background and low contrast. To overcome these limitations, we propose a method that employs photoacoustic detection highly localized with a pulsed ultraviolet (UV) laser on the basis of the Grüneisen relaxation effect. For cultured cells, our method achieves water-background suppressed MIR imaging of lipids and proteins at UV resolution, at least an order of magnitude finer than the MIR diffraction limits. Label-free histology using this method is also demonstrated in thick brain slices. Our approach provides convenient high-resolution and high-contrast MIR imaging, which can benefit diagnosis of fresh biological samples.

A comparison of mid-infrared spectral regions on accuracy of tissue classification

Infrared (IR) spectroscopic imaging, utilizing both the molecular and structural disease signatures, enables extensive profiling of tumors and their microenvironments. Here, we examine the relative merits of using either the fingerprint or the high frequency regions of the IR spectrum for tissue histopathology. We selected a complex model as a test case, evaluating both stromal and epithelial segmentation for various breast pathologies. IR spectral classification in each of these spectral windows is quantitatively assessed by estimating area under the curve (AUC) of the receiver operating characteristic curve (ROC) for pixel level accuracy and images for diagnostic ability. We found only small differences, though some that may be sufficiently important in diagnostic tasks to be clinically significant, between the two regions with the fingerprint region-based classifiers consistently emerging as more accurate. The work provides added evidence and comparison with fingerprint region, complex models, and previously untested tissue type (breast) - that the use of restricted spectral regions can provide high accuracy. Our study indicates that the fingerprint region is ideal for epithelial and stromal models to obtain high pixel level accuracies. Glass slides provide a limited spectral feature set but provides accurate information at the patient level.

Whole-Organism Analysis by Vibrational Spectroscopy

Vibrational spectroscopy has contributed to the understanding of biological materials for many years. As the technology has advanced, the technique has been brought to bear on the analysis of whole organisms. Here, we discuss advanced and recently developed infrared and Raman spectroscopic instrumentation to whole-organism analysis. We highlight many of the recent contributions made in this relatively new area of spectroscopy, particularly addressing organisms associated with disease with emphasis on diagnosis and treatment. The application of vibrational spectroscopic techniques to entire organisms is still in its infancy, but new developments in imaging and chemometric processing will likely expand in the field in the near future.

Multicolor Discrete Frequency Infrared Spectroscopic Imaging

Advancement of discrete frequency infrared (DFIR) spectroscopic microscopes in image quality and data throughput are critical to their use for analytical measurements. Here, we report the development and characterization of a point scanning instrument with minimal aberrations and capable of diffraction-limited performance across all fingerprint region wavelengths over arbitrarily large samples. The performance of this system is compared to commercial state of the art Fourier transform infrared (FT-IR) imaging systems. We show that for large samples or smaller set of discrete frequencies, point scanning far exceeds (∼10-100 fold) comparable data acquired with FT-IR instruments. Further we show improvements in image quality using refractive lenses that show significantly improved contrast across the spatial frequency bandwidth. Finally, we introduce the ability to image two tunable frequencies simultaneously using a single detector by means of demodulation to further speed up data acquisition and reduce the impact of scattering. Together, the advancements provide significantly better spectral quality and spatial fidelity than current state of the art imaging systems while promising to make spectral scanning even faster.

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.

Watt-scale super-octave mid-infrared intrapulse difference frequency generation

The development of high-power, broadband sources of coherent mid-infrared radiation is currently the subject of intense research that is driven by a substantial number of existing and continuously emerging applications in medical diagnostics, spectroscopy, microscopy, and fundamental science. One of the major, long-standing challenges in improving the performance of these applications has been the construction of compact, broadband mid-infrared radiation sources, which unify the properties of high brightness and spatial and temporal coherence. Due to the lack of such radiation sources, several emerging applications can be addressed only with infrared (IR)-beamlines in large-scale synchrotron facilities, which are limited regarding user access and only partially fulfill these properties. Here, we present a table-top, broadband, coherent mid-infrared light source that provides brightness at an unprecedented level that supersedes that of synchrotrons in the wavelength range between 3.7 and 18 µm by several orders of magnitude. This result is enabled by a high-power, few-cycle Tm-doped fiber laser system, which is employed as a pump at 1.9 µm wavelength for intrapulse difference frequency generation (IPDFG). IPDFG intrinsically ensures the formation of carrier-envelope-phase stable pulses, which provide ideal prerequisites for state-of-the-art spectroscopy and microscopy.

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.

High-resolution infrared imaging of biological samples with third-order sum-frequency generation microscopy

We studied the use of vibrationally resonant, third-order sum-frequency generation (TSFG) for imaging of biological samples. We found that laser-scanning TSFG provides vibrationally sensitive imaging capabilities of lipid droplets and structures in sectioned tissue samples. Although the contrast is based on the infrared-activity of molecular modes, TSFG images exhibit a high lateral resolution of 0.5 µm or better. We observed that the imaging properties of TSFG resemble the imaging properties of coherent anti-Stokes Raman scattering (CARS) microscopy, offering a nonlinear infrared alternative to coherent Raman methods. TSFG microscopy holds promise as a high-resolution imaging technique in the fingerprint region where coherent Raman techniques often provide insufficient sensitivity.

Upconversion raster scanning microscope for long-wavelength infrared imaging of breast cancer microcalcifications

Long-wavelength identification of microcalcifications in breast cancer tissue is demonstrated using a novel upconversion raster scanning microscope. The system consists of quantum cascade lasers (QCL) for illumination and an upconversion system for efficient, high-speed detection using a silicon detector. Absorbance spectra and images of regions of ductal carcinoma in situ (DCIS) from the breast have been acquired using both upconversion and Fourier-transform infrared (FTIR) systems. The spectral images are compared and good agreement is found between the upconversion and the FTIR systems.

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.

On the role of interference in laser-based mid-infrared widefield microspectroscopy

A laser's high degree of coherence leads to interferences, which-in the absence of precautions-can cause severe image distortions such as fringes and speckles and which thereby strongly hamper a meaningful interpretation of hyperspectral images in laser-based widefield microspectroscopy. While images and spectra of homogenous samples may already suffer from interferences, any structured object such as a tissue thin section will add to these distortions due to wavelength- and, in particular, sample-dependent phase shifts (structure sizes, absorption coefficients, refractive indices). This effect is devastating for the universal applicability of laser-based microspectroscopy especially in the mid-infrared (MIR), where cell sizes are of the same dimension as the wavelength of the illumination source. Here, we show that the impact of interferences is strongly mitigated by reducing the time-averaged spatiotemporal coherence properties of the illumination using a moving plus a stationary scatterer. In this case, the illumination path provides a pseudothermal radiation source and spatially resolved spectra can be obtained at the quality of the reference method, that is, Fourier-transform infrared microspectroscopy, without compromising spectral or spatial resolution.

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.

Selecting optimal features from Fourier transform infrared spectroscopy for discrete-frequency imaging

Tissue histology utilizing chemical and immunohistochemical labels plays an important role in biomedicine and disease diagnosis. Recent research suggests that mid-infrared (IR) spectroscopic imaging may augment histology by providing quantitative molecular information. One of the major barriers to this approach is long acquisition time using Fourier-transform infrared (FTIR) spectroscopy. Recent advances in discrete frequency sources, particularly quantum cascade lasers (QCLs), may mitigate this problem by allowing selective sampling of the absorption spectrum. However, DFIR imaging only provides a significant advantage when the number of spectral samples is minimized, requiring a priori knowledge of important spectral features. In this paper, we demonstrate the use of a GPU-based genetic algorithm (GA) using linear discriminant analysis (LDA) for DFIR feature selection. Our proposed method relies on pre-acquired broadband FTIR images for feature selection. Based on user-selected criteria for classification accuracy, our algorithm provides a minimal set of features that can be used with DFIR in a time-frame more practical for clinical diagnosis.

Mitigating fringing in discrete frequency infrared imaging using time-delayed integration

Infrared (IR) spectroscopic microscopes provide the potential for label-free quantitative molecular imaging of biological samples, which can be used to aid in histology, forensics, and pharmaceutical analysis. Most IR imaging systems use broadband illumination combined with a spectrometer to separate the signal into spectral components. This technique is currently too slow for many biomedical applications such as clinical diagnosis, primarily due to the availability of bright mid-infrared sources and sensitive MCT detectors. There has been a recent push to increase throughput using coherent light sources, such as synchrotron radiation and quantum cascade lasers. While these sources provide a significant increase in intensity, the coherence introduces fringing artifacts in the final image. We demonstrate that applying time-delayed integration in one dimension can dramatically reduce fringing artifacts with minimal alterations to the standard infrared imaging pipeline. The proposed technique also offers the potential for less expensive focal plane array detectors, since linear arrays can be more readily incorporated into the proposed framework.

Rapid visualization of macromolecular orientation by discrete frequency mid-infrared spectroscopic imaging

Infrared (IR) spectroscopic imaging has been used to measure the composition and orientation of polymeric systems for decades. IR microscopy can provide detailed views of microscopic regions, allowing the observation of both morphology and molecular properties of a sample, but involves a trade-off between the spatial extent and details of molecular content. Here we describe an approximately two orders of magnitude faster approach to measure the spherulitic structure and molecular orientation in large semi-crystalline polymer samples compared to extant Fourier transform infrared (FT-IR) spectroscopic imaging. This discrete frequency approach utilizes individual narrowband emission lines of a quantum cascade laser (QCL) source to spectrally image large areas rapidly. The inherent polarization of the laser beam is employed to measure orientation, enabling calculation of Hermans in-plane orientation function along with molecular chain angles distribution.

Mapping Solvation Environments in Porous Metal-Organic Frameworks with Infrared Chemical Imaging

We report here the first mesoscale characterization of solvent environments in the metal-organic framework (MOF) Cu₃(BTC)₂ using infrared imaging. Two characteristic populations of the MOF structures corresponding to the carboxylate binding to the Cu(II) (metal) ions were observed, which reflect a regular solvated MOF structure with axial solvents in the binuclear copper paddlewheel and an unsolvated defect mode that lacks axial solvent coordination. Infrared imaging also shows strong correlation between solvent localization and the spatial distribution of the solvated population within the MOF. This is a vital result as any remnant solvent molecules adsorbed inside of MOFs can render them less effective. We propose fast IR imaging as a potential characterization technique that can measure adsorbate and defect distributions in MOFs.

Bond-Selective Imaging of Cells by Mid-Infrared Photothermal Microscopy in High Wavenumber Region

Using a visible beam to probe the thermal effect induced by infrared absorption, mid-infrared photothermal (MIP) microscopy allows bond-selective chemical imaging at submicron spatial resolution. Current MIP microscopes cannot reach the high wavenumber region due to the limited tunability of the existing quantum cascade laser source. We extend the spectral range of MIP microscopy by difference frequency generation (DFG) from two chirped femtosecond pulses. Flexible wavelength tuning in both C-D and C-H regions was achieved with mid-infrared power up to 22.1 mW and spectral width of 29.3 cm^-1. Distribution of fatty acid in live human lung cancer cells was revealed by MIP imaging of the C-D bond at 2192 cm^-1.

Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology

Pathology is essential for research in disease and development, as well as for clinical decision making. For more than 100 years, pathology practice has involved analyzing images of stained, thin tissue sections by a trained human using an optical microscope. Technological advances are now driving major changes in this paradigm toward digital pathology (DP). The digital transformation of pathology goes beyond recording, archiving, and retrieving images, providing new computational tools to inform better decision making for precision medicine. First, we discuss some emerging innovations in both computational image analytics and imaging instrumentation in DP. Second, we discuss molecular contrast in pathology. Molecular DP has traditionally been an extension of pathology with molecularly specific dyes. Label-free, spectroscopic images are rapidly emerging as another important information source, and we describe the benefits and potential of this evolution. Third, we describe multimodal DP, which is enabled by computational algorithms and combines the best characteristics of structural and molecular pathology. Finally, we provide examples of application areas in telepathology, education, and precision medicine. We conclude by discussing challenges and emerging opportunities in this area.

Spectroscopic imaging of biomaterials and biological systems with FTIR microscopy or with quantum cascade lasers

Spectroscopic imaging of biomaterials and biological systems has received increased interest within the last decade because of its potential to aid in the detection of disease using biomaterials/biopsy samples and to probe the states of live cells in a label-free manner. The factors behind this increased attention include the availability of improved infrared microscopes and systems that do not require the use of a synchrotron as a light source, as well as the decreasing costs of these systems. This article highlights the current technical challenges and future directions of mid-infrared spectroscopic imaging within this field. Specifically, these are improvements in spatial resolution and spectral quality through the use of novel added lenses and computational algorithms, as well as quantum cascade laser imaging systems, which offer advantages over traditional Fourier transform infrared systems with respect to the speed of acquisition and field of view. Overcoming these challenges will push forward spectroscopic imaging as a viable tool for disease diagnostics and medical research.

Graphical abstract

Quantum Cascade Laser Spectral Histopathology: Breast Cancer Diagnostics Using High Throughput Chemical Imaging

Fourier transform infrared (FT-IR) microscopy coupled with machine learning approaches has been demonstrated to be a powerful technique for identifying abnormalities in human tissue. The ability to objectively identify the prediseased state and diagnose cancer with high levels of accuracy has the potential to revolutionize current histopathological practice. Despite recent technological advances in FT-IR microscopy, sample throughput and speed of acquisition are key barriers to clinical translation. Wide-field quantum cascade laser (QCL) infrared imaging systems with large focal plane array detectors utilizing discrete frequency imaging have demonstrated that large tissue microarrays (TMA) can be imaged in a matter of minutes. However, this ground breaking technology is still in its infancy, and its applicability for routine disease diagnosis is, as yet, unproven. In light of this, we report on a large study utilizing a breast cancer TMA comprised of 207 different patients. We show that by using QCL imaging with continuous spectra acquired between 912 and 1800 cm^-1, we can accurately differentiate between 4 different histological classes. We demonstrate that we can discriminate between malignant and nonmalignant stroma spectra with high sensitivity (93.56%) and specificity (85.64%) for an independent test set. Finally, we classify each core in the TMA and achieve high diagnostic accuracy on a patient basis with 100% sensitivity and 86.67% specificity. The absence of false negatives reported here opens up the possibility of utilizing high throughput chemical imaging for cancer screening, thereby reducing pathologist workload and improving patient care.

Quantitative IR microscopy and spectromics open the way to 3D digital pathology

Currently, only mass-spectrometry (MS) microscopy brings a quantitative analysis of chemical contents of tissue samples in 3D. Here, the reconstruction of a 3D quantitative chemical images of a biological tissue by FTIR spectro-microscopy is reported. An automated curve-fitting method is developed to extract all intense absorption bands constituting IR spectra. This innovation benefits from three critical features: (1) the correction of raw IR spectra to make them quantitatively comparable; (2) the automated and iterative data treatment allowing to transfer the IR-absorption spectrum into a IR-band spectrum; (3) the reconstruction of an 3D IR-band matrix (x, y, z for voxel position and a 4^th dimension with all IR-band parameters). Spectromics, which is a new method for exploiting spectral data for tissue metadata reconstruction, is proposed to further translate the related chemical information in 3D, as biochemical and anatomical tissue parameters. An example is given with oxidative stress distribution and the reconstruction of blood vessels in tissues. The requirements of IR microscopy instrumentation to propose 3D digital histology as a clinical routine technology is briefly discussed.