Reconstruction of the absorption spectrum of an object spot from the colour values of the corresponding pixel(s) in its digital image: the challenge of algal colours

Advanced Microscopy Techniques for Molecular Biophysics

Though microscopy is most often intended as a technique for providing qualitative assessment of cellular and subcellular properties, when coupled with other instruments such as wavelength selectors, lasers, photoelectric devices and computers, it can perform a wide variety of quantitative measurements, which are demanding in establishing relationships between the properties and structures of biological material in all their spatial and temporal complexities. These combinations of instruments are a powerful approach to improve non-destructive investigations of cellular and subcellular properties (both physical and chemical) at a macromolecular scale resolution. Since many subcellular compartments in living cells are characterized by structurally organized molecules, this review deals with three advanced microscopy techniques well-suited for these kind of investigations, i.e., microspectrophotometry (MSP), super-resolution localization microscopy (SRLM) and holotomographic microscopy (HTM). These techniques can achieve an insight view into the role intracellular molecular organizations such as photoreceptive and photosynthetic structures and lipid bodies play in many cellular processes as well as their biophysical properties. Microspectrophotometry uses a set-up based on the combination of a wide-field microscope and a polychromator, which allows the measurement of spectroscopic features such as absorption spectra. Super resolution localization microscopy combines dedicated optics and sophisticated software algorithms to overcome the diffraction limit of light and allow the visualization of subcellular structures and dynamics in greater detail with respect to conventional optical microscopy. Holotomographic microscopy combines holography and tomography techniques into a single microscopy set-up, and allows 3D reconstruction by means of the phase separation of biomolecule condensates. This review is organized in sections, which for each technique describe some general aspects, a peculiar theoretical aspect, a specific experimental configuration and examples of applications (fish and algae photoreceptors, single labeled proteins and endocellular aggregates of lipids).

Water monitoring by means of digital microscopy identification and classification of microalgae

Marine and freshwater microalgae belong to taxonomically and morphologically diverse groups of organisms spanning many phyla with thousands of species. These organisms play an important role as indicators of water ecosystem conditions since they react quickly and predictably to a broad range of environmental stressors, thus providing early signals of dangerous changes. Traditionally, microscopic analysis has been used to identify and enumerate different types of organisms present within a given environment at a given point in time. However, this approach is both time-consuming and labor intensive, as it relies on manual processing and classification of planktonic organisms present within collected water samples. Furthermore, it requires highly skilled specialists trained to recognize and distinguish one taxa from another on the basis of often subtle morphological differences. Given these restrictions, a considerable amount of effort has been recently funneled into automating different steps of both the sampling and classification processes, making it possible to generate previously unprecedented volumes of plankton image data and obtain an essential database to analyze the composition of plankton assemblages. In this review we report state-of-the-art methods used for automated plankton classification by means of digital microscopy. The computer-microscope system hardware and the image processing techniques used for recognition and classification of planktonic organisms (segmentation, shape feature extraction, pigment signature determination and neural network grouping) will be described. An introduction and overview of the topic, its current state and indications of future directions the field is expected to take will be provided, organizing the review for both experts and researchers new to the field.