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

Multimodal analytical tools for the molecular and elemental characterisation of lesions in brain tissue of multiple sclerosis patients

Multiple sclerosis (MS) is a prevalent immune-mediated inflammatory disease of the central nervous system inducing a widespread degradation of myelin and resulting in neurological deficits. Recent advances in molecular and atomic imaging provide the means to probe the microenvironment in affected brain tissues at an unprecedented level of detail and may provide new insights. This study showcases state-of-the-art spectroscopic and mass spectrometric techniques to compare distributions of molecular and atomic entities in MS lesions and surrounding brain tissues. MS brains underwent post-mortem magnetic resonance imaging (MRI) to locate and subsequently dissect MS lesions and surrounding white matter. Digests of lesions and unaffected white matter were analysed via ICP-MS/MS revealing significant differences in concentrations of Li, Mg, P, K, Mn, V, Rb, Ag, Gd and Bi. Micro x-ray fluorescence spectroscopy (μXRF) and laser ablation - inductively coupled plasma - time of flight - mass spectrometry (LA-ICP-ToF-MS) were used as micro-analytical imaging techniques to study distributions of both endogenous and xenobiotic elements. The essential trace elements Fe, Cu and Zn were subsequently calibrated using in-house manufactured gelatine standards. Lipid distributions were studied using IR-micro spectroscopy and matrix assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI). MALDI-MSI was complemented with high-resolution tandem mass spectrometry and trapped ion mobility spectroscopy for the annotation of specified phospho- and sphingolipids, revealing specific lipid species decreased in MS lesions compared to surrounding white matter. This explorative study demonstrated that modern molecular and atomic mapping techniques provide high-resolution imaging for relevant bio-indicative entities which may complement our current understanding of the underlying pathophysiological processes.

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.

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.

Dimensionality reduction for deep learning in infrared microscopy: a comparative computational survey

While infrared microscopy provides molecular information at spatial resolution in a label-free manner, exploiting both spatial and molecular information for classifying the disease status of tissue samples constitutes a major challenge. One strategy to mitigate this problem is to embed high-dimensional pixel spectra in lower dimensions, aiming to preserve molecular information in a more compact manner, which reduces the amount of data and promises to make subsequent disease classification more accessible for machine learning procedures. In this study, we compare several dimensionality reduction approaches and their effect on identifying cancer in the context of a colon carcinoma study. We observe surprisingly small differences between convolutional neural networks trained on dimensionality reduced spectra compared to utilizing full spectra, indicating a clear tendency of the convolutional networks to focus on spatial rather than spectral information for classifying disease status.

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.

Pseudoheterodyne interferometry for multicolor near-field imaging

We report the development and characterization of a detection technique for scattering-type scanning near-field optical microscopy (s-SNOM) that enables near-field amplitude and phase imaging at two or more wavelengths simultaneously. To this end, we introduce multispectral pseudoheterodyne (PSH) interferometry, where infrared lasers are combined to form a beam with a discrete spectrum of laser lines and a time-multiplexing scheme is employed to allow for the use of a single infrared detector. We first describe and validate the implementation of multispectral PSH into a commercial s-SNOM instrument. We then demonstrate its application for the real-time correction of the negative phase contrast (NPC), which provides reliable imaging of weak IR absorption at the nanoscale. We anticipate that multispectral PSH could improve data throughput, reduce effects of sample and interferometer drift, and help to establish multicolor s-SNOM imaging as a regular imaging modality, which could be particularly interesting as new infrared light sources become available.

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.

Spatial Omics Imaging of Fresh-Frozen Tissue and Routine FFPE Histopathology of a Single Cancer Needle Core Biopsy: A Freezing Device and Multimodal Workflow

The complex molecular alterations that underlie cancer pathophysiology are studied in depth with omics methods using bulk tissue extracts. For spatially resolved tissue diagnostics using needle biopsy cores, however, histopathological analysis using stained FFPE tissue and the immunohistochemistry (IHC) of a few marker proteins is currently the main clinical focus. Today, spatial omics imaging using MSI or IRI is an emerging diagnostic technology for the identification and classification of various cancer types. However, to conserve tissue-specific metabolomic states, fast, reliable, and precise methods for the preparation of fresh-frozen (FF) tissue sections are crucial. Such methods are often incompatible with clinical practice, since spatial metabolomics and the routine histopathology of needle biopsies currently require two biopsies for FF and FFPE sampling, respectively. Therefore, we developed a device and corresponding laboratory and computational workflows for the multimodal spatial omics analysis of fresh-frozen, longitudinally sectioned needle biopsies to accompany standard FFPE histopathology of the same biopsy core. As a proof-of-concept, we analyzed surgical human liver cancer specimens using IRI and MSI with precise co-registration and, following FFPE processing, by sequential clinical pathology analysis of the same biopsy core. This workflow allowed for a spatial comparison between different spectral profiles and alterations in tissue histology, as well as a direct comparison for histological diagnosis without the need for an extra biopsy.

Fast and label-free automated detection of microsatellite status in early colon cancer using artificial intelligence integrated infrared imaging

PURPOSE

Microsatellite instability (MSI) due to mismatch repair (MMR) defects accounts for 15-20% of colon cancers (CC). MSI testing is currently standard of care in CC with immunohistochemistry of the four MMR proteins representing the gold standard. Instead, label-free quantum cascade laser (QCL) based infrared (IR) imaging combined with artificial intelligence (AI) may classify MSI/microsatellite stability (MSS) in unstained tissue sections user-independently and tissue preserving.

METHODS

Paraffin-embedded unstained tissue sections of early CC from patients participating in the multicentre AIO ColoPredict Plus (CPP) 2.0 registry were analysed after dividing into three groups (training, test, and validation). IR images of tissue sections using QCL-IR microscopes were classified by AI (convolutional neural networks [CNN]) using a two-step approach. The first CNN (modified U-Net) detected areas of cancer while the second CNN (VGG-Net) classified MSI/MSS. End-points were area under receiver operating characteristic (AUROC) and area under precision recall curve (AUPRC).

RESULTS

The cancer detection in the first step was based on 629 patients (train n = 273, test n = 138, and validation n = 218). Resulting classification AUROC was 1.0 for the validation dataset. The second step classifying MSI/MSS was performed on 547 patients (train n = 331, test n = 69, and validation n = 147) reaching AUROC and AUPRC of 0.9 and 0.74, respectively, for the validation cohort.

CONCLUSION

Our novel label-free digital pathology approach accurately and rapidly classifies MSI vs. MSS. The tissue sections analysed were not processed leaving the sample unmodified for subsequent analyses. Our approach demonstrates an AI-based decision support tool potentially driving improved patient stratification and precision oncology in the future.

Spatial distribution of microplastics in the tropical Indian Ocean based on laser direct infrared imaging and microwave-assisted matrix digestion

Suspended particulate matter was collected from subsurface (6 m) water along an E-W transect through the tropical Indian Ocean using a specialized inert (plastic free) fractionated filtration system. The samples were subjected to a new microwave-assisted "one-pot" matrix removal (efficiency: 94.3% ± 0.3% (1 SD, n = 3)) and microplastic extraction protocol (recovery: 95% ± 4%). The protocol enables a contamination-minimized digestion and requires only four filtration steps. In comparison, classical sample processing approaches involve up to eight filtration steps until the final analysis. Microplastics were identified and physically characterized by means of a novel quantum cascade laser-based imaging routine. LDIR imaging facilitates the analysis of up to 1000 particles/fibers (<300 μm) within approximately 1-2 h. In comparison to FTIR and Raman imaging, it can help to circumvent uncertainties, e. g. from subsampling strategies due to long analysis and post-processing times of large datasets. Over 97% of all particles were correctly identified by the automated routine - without spectral reassignments. Moreover, 100% agreement was obtained between ATR-FTIR and LDIR-based analysis regarding particles and fibers >300 μm. The mean microplastic concentration of the analyzed samples was 50 ± 30 particles/fibers m^-3 (1 SD, n = 21). Number concentrations ranged from 8 to 132 particles/fibers m^-3 (20-300 μm). The most abundant polymer clusters were acrylates/polyurethane/varnish (49%), polyethylene terephthalate (26%), polypropylene (8%), polyethylene (4%) and ethylene-vinyl acetate (4%). 96% of the microplastic particles had a diameter <100 μm. Though inter-study comparison is difficult, the investigated area exhibits a high contamination with particulate plastics compared to other open ocean regions. A distinct spatial trend was observed with an increasing share of the size class 20-50 μm from east to west.

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.

Clinical Spectroscopy: Lost in Translation?

This Focal Point Review paper discusses the developments of biomedical Raman and infrared spectroscopy, and the recent strive towards these technologies being regarded as reliable clinical tools. The promise of vibrational spectroscopy in the field of biomedical science, alongside the development of computational methods for spectral analysis, has driven a plethora of proof-of-concept studies which convey the potential of various spectroscopic approaches. Here we report a brief review of the literature published over the past few decades, with a focus on the current technical, clinical, and economic barriers to translation, namely the limitations of many of the early studies, and the lack of understanding of clinical pathways, health technology assessments, regulatory approval, clinical feasibility, and funding applications. The field of biomedical vibrational spectroscopy must acknowledge and overcome these hurdles in order to achieve clinical efficacy. Current prospects have been overviewed with comment on the advised future direction of spectroscopic technologies, with the aspiration that many of these innovative approaches can ultimately reach the frontier of medical diagnostics and many clinical applications.

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.

Quantum Mechanics and Surgical Pathology: A Brief Introduction

Quantum mechanics (QM) and surgical pathology might seem totally unrelated fields of science. Because QM or particle physics explains the very basic structure and function of nature, there are growing interconnections between the fundamentals and applications of QM and biologic sciences. QM is not only applied to the structure of atoms but also probes the structure of biologic molecules, explains their mutational changes and has provided an insight into the basic mechanisms of many different biologic systems. Many of the current applications in biologic sciences, medicine, and surgical pathology rely on the principles of QM. Because surgical pathology uses quantum phenomena such as light and studies disease's alterations that are ultimately governed by quantum changes at nanoscale levels, QM will have potential future implications for the progress of surgical pathology. These might include quantum-enhanced refinements in light, ancillary tools, and interpretation assistance computerized systems. The future of applying the concepts, discoveries, and tools of QM in surgical pathology might create something analogous to quantum biology; that is, quantum pathology or "QuPath."

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.

Infrared nanoscopy and tomography of intracellular structures

Although techniques such as fluorescence-based super-resolution imaging or confocal microscopy simultaneously gather both morphological and chemical data, these techniques often rely on the use of localized and chemically specific markers. To eliminate this flaw, we have developed a method of examining cellular cross sections using the imaging power of scattering-type scanning near-field optical microscopy and Fourier-transform infrared spectroscopy at a spatial resolution far beyond the diffraction limit. Herewith, nanoscale surface and volumetric chemical imaging is performed using the intrinsic contrast generated by the characteristic absorption of mid-infrared radiation by the covalent bonds. We employ infrared nanoscopy to study the subcellular structures of eukaryotic (Chlamydomonas reinhardtii) and prokaryotic (Escherichia coli) species, revealing chemically distinct regions within each cell such as the microtubular structure of the flagellum. Serial 100 nm-thick cellular cross-sections were compiled into a tomogram yielding a three-dimensional infrared image of subcellular structure distribution at 20 nm resolution. The presented methodology is able to image biological samples complementing current fluorescence nanoscopy but at less interference due to the low energy of infrared radiation and the absence of labeling.

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.

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.

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.

Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning

High-content imaging and single-cell genomics are two of the most prominent high-throughput technologies for studying cellular properties and functions at scale. Recent studies have demonstrated that information in large imaging datasets can be used to estimate gene mutations and to predict the cell-cycle state and the cellular decision making directly from cellular morphology. Thus, high-throughput imaging methodologies, such as imaging flow cytometry can potentially aim beyond simple sorting of cell-populations. We introduce IFC-seq, a machine learning methodology for predicting the expression profile of every cell in an imaging flow cytometry experiment. Since it is to-date unfeasible to observe single-cell gene expression and morphology in flow, we integrate uncoupled imaging data with an independent transcriptomics dataset by leveraging common surface markers. We demonstrate that IFC-seq successfully models gene expression of a moderate number of key gene-markers for two independent imaging flow cytometry datasets: (i) human blood mononuclear cells and (ii) mouse myeloid progenitor cells. In the case of mouse myeloid progenitor cells IFC-seq can predict gene expression directly from brightfield images in a label-free manner, using a convolutional neural network. The proposed method promises to add gene expression information to existing and new imaging flow cytometry datasets, at no additional cost.

Rapid Identification and Quantification of Microplastics in the Environment by Quantum Cascade Laser-Based Hyperspectral Infrared Chemical Imaging

The monitoring of the emerging contaminant, microplastics, in the environment, in water supply, and for food safety is of major interest to science, consumers, and governments. While the chemical analysis of these particles is considered mandatory, a rapid and reliable method for the determination of particle sizes, shapes, and numbers is missing, as existing methods are not fitting into current laboratory measurement routines. In this study, we present an approach for circumventing these issues through the application of quantum cascade laser-based microscopy combined with an automated data analysis. This method allows the measurement of an area of 144 mm² in 36 min, with a pixel resolution of 4.2 μm, which is an appropriate timeframe and spatial resolution for routine measurements. The performance was compared to the existing state-of-the-art Fourier transform infrared microscopy analyses. Further, the application of the method on various environmental samples was investigated to examine its capacity to provide number and variety of present particles. The described analytical procedure overcomes the last restrictions for schedulable and rapid microplastic monitoring, resulting in a highly detailed data set for particle numbers, particle shapes, and polymer types.

Infrared Scattering-Type Scanning Near-Field Optical Microscopy of Biomembranes in Water

Infrared (IR) absorption spectroscopy detects the state and chemical composition of biomolecules solely by their inherent vibrational fingerprints. Major disadvantages like the lack of spatial resolution and sensitivity have lately been overcome by the use of pointed probes as local sensors enabling the detection of quantities as few as hundreds of proteins with nanometer precision. However, the strong absorption of infrared radiation by liquid water still prevents simple access to the measured quantity: the light scattered at the probing atomic force microscope tip. Here we report on the local IR response of biological membranes immersed in aqueous bulk solution. We make use of a silicon solid immersion lens as the substrate and focusing optics to achieve detection efficiencies sufficient to yield IR near-field maps of purple membranes. Finally, we suggest a means to improve the imaging quality by tracing the tip by a laser-scanning approach.

Translation of an esophagus histopathological FT-IR imaging model to a fast quantum cascade laser modality

The technical progress in fast quantum cascade laser (QCL) microscopy offers a platform where chemical imaging becomes feasible for clinical diagnostics. QCL systems allow the integration of previously developed FT-IR-based pathology recognition models in a faster workflow. The translation of such models requires a systematic approach, focusing only on the spectral frequencies that carry crucial information for discrimination of pathologic features. In this study, we optimize an FT-IR-based histopathological method for esophageal cancer detection to work with a QCL system. We explore whether the classifier's performance is affected by paraffin presence from tissue blocks compared to removing it chemically. Working with paraffin-embedded samples reduces preprocessing time in the lab and allows samples to be archived after analysis. Moreover, we test, whether the creation of a QCL model requires a preestablished FTIR model or can be optimized using solely QCL measurements.

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.

Label-Free Infrared Spectral Histology of Skin Tissue Part II: Impact of a Lumican-Derived Peptide on Melanoma Growth

Melanoma is the most aggressive type of cutaneous malignancies. In addition to its role as a regulator of extracellular matrix (ECM) integrity, lumican, a small leucine-rich proteoglycan, also exhibits anti-tumor properties in melanoma. This work focuses on the use of infrared spectral imaging (IRSI) and histopathology (IRSH) to study the effect of lumican-derived peptide (L9Mc) on B16F1 melanoma primary tumor growth. Female C57BL/6 mice were injected with B16F1 cells treated with L9Mc (n = 10) or its scrambled peptide (n = 8), and without peptide (control, n = 9). The melanoma primary tumors were subjected to histological and IR imaging analysis. In addition, immunohistochemical staining was performed using anti-Ki-67 and anti-cleaved caspase-3 antibodies. The IR images were analyzed by common K-means clustering to obtain high-contrast IRSH that allowed identifying different ECM tissue regions from the epidermis to the tumor area, which correlated well with H&E staining. Furthermore, IRSH showed good correlation with immunostaining data obtained with anti-Ki-67 and anti-cleaved caspase-3 antibodies, whereby the L9Mc peptide inhibited cell proliferation and increased strongly apoptosis of B16F1 cells in this mouse model of melanoma primary tumors.

Surface cleaning and sample carrier for complementary high-resolution imaging techniques

Nowadays, high-resolution imaging techniques are extensively applied in a complementary way to gain insights into complex phenomena. For a truly complementary analytical approach, a common sample carrier is required that is suitable for the different preparation methods necessary for each analytical technique. This sample carrier should be capable of accommodating diverse analytes and maintaining their pristine composition and arrangement during deposition and preparation. In this work, a new type of sample carrier consisting of a silicon wafer with a hydrophilic polymer coating was developed. The robustness of the polymer coating toward solvents was strengthened by cross-linking and stoving. Furthermore, a new method of UV-ozone cleaning was developed that enhances the adhesion of the polymer coating to the wafer and ensures reproducible surface-properties of the resulting sample carrier. The hydrophilicity of the sample carrier was recovered applying the new method of UV-ozone cleaning, while avoiding UV-induced damages to the polymer. Noncontact 3D optical profilometry and contact angle measurements were used to monitor the hydrophilicity of the coating. The hydrophilicity of the polymer coating ensures its spongelike behavior so that upon the deposition of an analyte suspension, the solvent and solutes are separated from the analyte by absorption into the polymer. This feature is essential to limit the coffee-ring effect and preserve the native identity of an analyte upon deposition. The suitability of the sample carrier for various sample types was tested using nanoparticles from suspension, bacterial cells, and tissue sections. To assess the homogeneity of the analyte distribution and preservation of sample integrity, optical and scanning electron microscopy, helium ion microscopy, laser ablation inductively coupled plasma mass spectrometry, and time-of-flight secondary ion mass spectrometry were used. This demonstrates the broad applicability of the newly developed sample carrier and its value for complementary imaging.

Spatially multiplexed RNA in situ hybridization to reveal tumor heterogeneity

Multiplexed RNA in situ hybridization for the analysis of gene expression patterns plays an important role in investigating development and disease. Here, we present a method for multiplexed RNA-ISH to detect spatial tumor heterogeneity in tissue sections. We made use of a microfluidic chip to deliver ISH-probes locally to regions of a few hundred micrometers over time periods of tens of minutes. This spatial multiplexing method can be combined with ISH-approaches based on signal amplification, with bright field detection and with the commonly used format of formalin-fixed paraffin-embedded tissue sections. By using this method, we analyzed the expression of HER2 with internal positive and negative controls (ActB, dapB) as well as predictive biomarker panels (ER, PgR, HER2) in a spatially multiplexed manner on single mammary carcinoma sections. We further demonstrated the applicability of the technique for subtype differentiation in breast cancer. Local analysis of HER2 revealed medium to high spatial heterogeneity of gene expression (Cohen effect size r = 0.4) in equivocally tested tumor tissues. Thereby, we exemplify the importance of using such a complementary approach for the analysis of spatial heterogeneity, in particular for equivocally tested tumor samples. As the method is compatible with a range of ISH approaches and tissue samples, it has the potential to find broad applicability in the context of molecular analysis of human diseases.

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.

Applications of hyperspectral imaging in the detection and diagnosis of solid tumors

Hyperspectral imaging (HSI) is an emerging new technology in solid tumor diagnosis and detection. It incorporates traditional imaging and spectroscopy together to obtain both spatial and spectral information from tissues simultaneously in a non-invasive manner. This imaging modality is based on the principle that different tissues inherit different spectral reflectance responses that present as unique spectral fingerprints. HSI captures those composition-specific fingerprints to identify cancerous and normal tissues. It becomes a promising tool for performing tumor diagnosis and detection from the label-free histopathological examination to real-time intraoperative assistance. This review introduces the basic principles of HSI and summarizes its methodology and recent advances in solid tumor detection. In particular, the advantages of HSI applied to solid tumors are highlighted to show its potential for clinical use.

Automated Osteosclerosis Grading of Clinical Biopsies Using Infrared Spectroscopic Imaging

Osteosclerosis and myefibrosis are complications of myeloproliferative neoplasms. These disorders result in excess growth of trabecular bone and collagen fibers that replace hematopoietic cells, resulting in abnormal bone marrow function. Treatments using imatinib and JAK2 pathway inhibitors can be effective on osteosclerosis and fibrosis; therefore, accurate grading is critical for tracking treatment effectiveness. Current grading standards use a four-class system based on analysis of biopsies stained with three histological stains: hematoxylin and eosin (H&E), Masson's trichrome, and reticulin. However, conventional grading can be subjective and imprecise, impacting the effectiveness of treatment. In this Article, we demonstrate that mid-infrared spectroscopic imaging may serve as a quantitative diagnostic tool for quantitatively tracking disease progression and response to treatment. The proposed approach is label-free and provides automated quantitative analysis of osteosclerosis and collagen fibrosis.

A review of applications of principles of quantum physics in oncology: do quantum physics principles have any role in oncology research and applications?

Background:

Research in the applications of the principles of quantum physics in oncology has progressed significantly over the past decades; and several research groups with professionals from diverse scientific background, including electrical engineers, mathematicians, biologists, atomic physicists, computer programmers, and biochemists, are working collaboratively in an unprecedented and pioneering economic, organisational and human effort searching for a wider and more effective, potentially definitive, understanding of the cancers. It is hypothesised that the principles of quantum physics could open new and broader understanding of the cancers and the development of new effective, targeted, accurate, personalised and possibly definitive cancer treatment.

Materials and methods:

This paper reports on a review of recent studies in the field of the applications of the principles of quantum physics in biology, chemistry, biochemistry and quantum physics in cancer research, including quantum physics principles and cancer, quantum modelling techniques, quantum dots and its applications in oncology, quantum cascade laser histopathology and quantum computing applications.

Conclusions:

The applications of the principles of quantum physics in oncology, chemistry and biology are providing new perspectives and greater insights into a long-studied disease, which could result in a greater understanding of the cancers and the potential for personalised and definitive treatment methods.

Infrared Spectroscopy with a Fiber-Coupled Quantum Cascade Laser for Attenuated Total Reflection Measurements Towards Biomedical Applications

The development of rapid and accurate biomedical laser spectroscopy systems in the mid-infrared has been enabled by the commercial availability of external-cavity quantum cascade lasers (EC-QCLs). EC-QCLs are a preferable alternative to benchtop instruments such as Fourier transform infrared spectrometers for sensor development as they are small and have high spectral power density. They also allow for the investigation of multiple analytes due to their broad tuneability and through the use of multivariate analysis. This article presents an in vitro investigation with two fiber-coupled measurement setups based on attenuated total reflection spectroscopy and direct transmission spectroscopy for sensing. A pulsed EC-QCL (1200–900 cm−1) was used for measurements of glucose and albumin in aqueous solutions, with lactate and urea as interferents. This analyte composition was chosen as an example of a complex aqueous solution with relevance for biomedical sensors. Glucose concentrations were determined in both setup types with root-mean-square error of cross-validation (RMSECV) of less than 20 mg/dL using partial least-squares (PLS) regression. These results demonstrate accurate analyte measurements, and are promising for further development of fiber-coupled, miniaturised in vivo sensors based on mid-infrared spectroscopy.

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.

Deep representation learning for domain adaptable classification of infrared spectral imaging data

Motivation

Applying infrared microscopy in the context of tissue diagnostics heavily relies on computationally preprocessing the infrared pixel spectra that constitute an infrared microscopic image. Existing approaches involve physical models, which are non-linear in nature and lead to classifiers that do not generalize well, e.g. across different types of tissue preparation. Furthermore, existing preprocessing approaches involve iterative procedures that are computationally demanding, so that computation time required for preprocessing does not keep pace with recent progress in infrared microscopes which can capture whole-slide images within minutes.

Results

We investigate the application of stacked contractive autoencoders as an unsupervised approach to preprocess infrared microscopic pixel spectra, followed by supervised fine-tuning to obtain neural networks that can reliably resolve tissue structure. To validate the robustness of the resulting classifier, we demonstrate that a network trained on embedded tissue can be transferred to classify fresh frozen tissue. The features obtained from unsupervised pretraining thus generalize across the large spectral differences between embedded and fresh frozen tissue, where under previous approaches separate classifiers had to be trained from scratch.

Availability and implementation

Our implementation can be downloaded from https://github.com/arnrau/SCAE_IR_Spectral_Imaging.

Supplementary information

Supplementary data are available at Bioinformatics online.

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.

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.

Diffraction limited mid-infrared reflectance microspectroscopy with a supercontinuum laser

Chemical mapping was demonstrated with a mid-infrared (MIR) microspectroscopy setup based on a supercontinuum source (SC) emitting in the spectral range from 1.55 to 4.5 µm and a MEMS-based Fabry-Pérot filter spectrometer. Diffraction limited spatial resolution in reflection geometry was achieved. A multilayer film consisting of different polymers and mixtures thereof was measured and results were compared to those gained with a conventional FTIR microscope equipped with a thermal MIR source. Results show that compared to thermal sources, the application of the SC source results in higher signal-to-noise ratios together with better spatial resolution and faster scanning. Furthermore, diffraction limited imaging of red blood cells was demonstrated for the first time in the MIR spectral region in reflection mode. The distinctive characteristics of the MIR spectral region in conjunction with the high brightness, spatial coherence and broadband nature of supercontinuum radiation show the potential for improving infrared microscopy significantly.

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.