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

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.

A mid-infrared laser microscope for the time-resolved study of light-induced protein conformational changes

We have developed a confocal laser microscope operating in the mid-infrared range for the study of light-sensitive proteins, such as rhodopsins. The microscope features a co-aligned infrared and visible illumination path for the selective excitation and probing of proteins located in the IR focus only. An external-cavity tunable quantum cascade laser provides a wavelength tuning range (5.80-6.35 µm or 1570-1724 cm-1) suitable for studying protein conformational changes as a function of time delay after visible light excitation with a pulsed LED. Using cryogen-free detectors, the relative changes in the infrared absorption of rhodopsin thin films around 10-4 have been observed with a time resolution down to 30 ms. The measured full-width at half maximum of the Airy disk at λ = 6.08 µm in transmission mode with a confocal arrangement of apertures is 6.6 µm or 1.1λ. Dark-adapted sample replacement at the beginning of each photocycle is then enabled by exchanging the illuminated thin-film location with the microscope mapping stage synchronized to data acquisition and LED excitation and by averaging hundreds of time traces acquired in different nearby locations within a homogeneous film area. We demonstrate that this instrument provides crucial advantages for time-resolved IR studies of rhodopsin thin films with a slow photocycle. Time-resolved studies of inhomogeneous samples may also be possible with the presented instrument.

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.

In search of the correlation between nanomechanical and biomolecular properties of prostate cancer cells with different metastatic potential

Nanomechanical properties of living cells, as measured with atomic force microscopy (AFM), are increasingly recognized as criteria that differentiate normal and pathologically altered cells. Locally measured cell elastic properties, described by the parameter known as Young's modulus, are currently proposed as a new diagnostic parameter that can be used at the early stage of cancer detection. In this study, local mechanical properties of normal human prostate (RWPE-1) cells and a range of malignant (22Rv1) and metastatic prostate cells (LNCaP, Du145 and PC3) were investigated. It was found that non-malignant prostate cells are stiffer than cancer cells while the metastatic cells are much softer than malignant cells from the primary tumor site. Next, the biochemical properties of the cells were measured using confocal Raman (RS) and Fourier-transform infrared (FT-IR) spectroscopies to reveal these cells' biochemical composition as malignant transformation proceeds. Nanomechanical and biochemical profiles of five different prostate cell lines were subsequently analyzed using partial least squares regression (PLSR) in order to identify which spectral features of the RS and FT-IR spectra correlate with the cell's elastic properties. The PLSR-based model could predict Young's modulus values based on both RS and FT-IR spectral information. These outcomes show not only that AFM, RS and FT-IR techniques can be used for discrimination between normal and cancer cells, but also that a linear correlation between mechanical response and biomolecular composition of the cells that undergo malignant transformation can be found. This knowledge broadens our understanding of how prostate cancer cells evolve thorough the multistep process of tumor pathogenesis.