Phase and Index of Refraction Imaging by Hyperspectral Reflectance Confocal Microscopy.

A hyperspectral reflectance confocal microscope (HSCM) was realized by CNR-ISC (Consiglio Nazionale delle Ricerche-Istituto dei Sistemi Complessi) a few years ago. The instrument and data have been already presented and discussed. The main activity of this HSCM has been within biology, and reflectance data have shown good matching between spectral signatures and the nature or evolution on many types of cells. Such a relationship has been demonstrated mainly with statistical tools like Principal Component Analysis (PCA), or similar concepts, which represent a very common approach for hyperspectral imaging. However, the point is that reflectance data contains much more useful information and, moreover, there is an obvious interest to go from reflectance, bound to the single experiment, to reflectivity, or other physical quantities, related to the sample alone. To accomplish this aim, we can follow well-established analyses and methods used in reflectance spectroscopy. Therefore, we show methods of calculations for index of refraction n, extinction coefficient k and local thicknesses of frequency starting from phase images by fast Kramers-Kronig (KK) algorithms and the Abeles matrix formalism. Details, limitations and problems of the presented calculations as well as alternative procedures are given for an example of HSCM images of red blood cells (RBC).

Living matter observations with a novel hyperspectral supercontinuum confocal microscope for VIS to near-IR reflectance spectroscopy.

A broad range hyper-spectroscopic microscope fed by a supercontinuum laser source and equipped with an almost achromatic optical layout is illustrated with detailed explanations of the design, implementation and data. The real novelty of this instrument, a confocal spectroscopic microscope capable of recording high resolution reflectance data in the VIS-IR spectral range from about 500 nm to 2.5 µm wavelengths, is the possibility of acquiring spectral data at every physical point as defined by lateral coordinates, X and Y, as well as at a depth coordinate, Z, as obtained by the confocal optical sectioning advantage. With this apparatus we collect each single scanning point as a whole spectrum by combining two linear spectral detector arrays, one CCD for the visible range, and one InGaAs infrared array, simultaneously available at the sensor output channel of the home made instrument. This microscope has been developed for biomedical analysis of human skin and other similar applications. Results are shown illustrating the technical performances of the instrument and the capability in extracting information about the composition and the structure of different parts or compartments in biological samples as well as in solid statematter. A complete spectroscopic fingerprinting of samples at microscopic level is shown possible by using statistical analysis on raw data or analytical reflectance models based on Abelés matrix transfer methods.

The Future of Hyperspectral Imaging.

The Special Issue on hyperspectral imaging (HSI), entitled "The Future of Hyperspectral Imaging", has published 12 papers. Nine papers are related to specific current research and three more are review contributions: In both cases, the request is to propose those methods or instruments so as to show the future trends of HSI. Some contributions also update specific methodological or mathematical tools. In particular, the review papers address deep learning methods for HSI analysis, while HSI data compression is reviewed by using liquid crystals spectral multiplexing as well as DMD-based Raman spectroscopy. Specific topics explored by using data obtained by HSI include alert on the sprouting of potato tubers, the investigation on the stability of painting samples, the prediction of healing diabetic foot ulcers, and age determination of blood-stained fingerprints. Papers showing advances on more general topics include video approach for HSI dynamic scenes, localization of plant diseases, new methods for the lossless compression of HSI data, the fusing of multiple multiband images, and mixed modes of laser HSI imaging for sorting and quality controls.

Classification of M1/M2-polarized human macrophages by label-free hyperspectral reflectance confocal microscopy and multivariate analysis.

The possibility of detecting and classifying living cells in a label-free and non-invasive manner holds significant theranostic potential. In this work, Hyperspectral Imaging (HSI) has been successfully applied to the analysis of macrophagic polarization, given its central role in several pathological settings, including the regulation of tumour microenvironment. Human monocyte derived macrophages have been investigated using hyperspectral reflectance confocal microscopy, and hyperspectral datasets have been analysed in terms of M1 vs. M2 polarization by Principal Components Analysis (PCA). Following PCA, Linear Discriminant Analysis has been implemented for semi-automatic classification of macrophagic polarization from HSI data. Our results confirm the possibility to perform single-cell-level in vitro classification of M1 vs. M2 macrophages in a non-invasive and label-free manner with a high accuracy (above 98% for cells deriving from the same donor), supporting the idea of applying the technique to the study of complex interacting cellular systems, such in the case of tumour-immunity in vitro models.

Label-free and non-invasive discrimination of HaCaT and melanoma cells in a co-culture model by hyperspectral confocal reflectance microscopy.

A novel hyperspectral confocal microscopy method to separate different cell populations in a co-culture model is presented here. The described methodological and instrumental approach allows discrimination of different cell types using a non-invasive, label free method with good accuracy with a single cell resolution. In particular, melanoma cells are discriminated from HaCaT cells by hyperspectral confocal imaging, principal component analysis and optical frequencies signing, as confirmed by fluorescence labelling cross check. The identification seems to be quite robust to be insensitive to the cellular shape within the studied samples, enabling to separate cells according to their cytotype down to a single cell sensitivity. Set of hyperspectral images of melanoma-keratinocytes co-culture model (left), score plot of principal component analysis and spectral analysis of principal components coefficients (center), label-free spectral identification of cell populations (right).