automated microscopy
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In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.
- MeSH
- algoritmy MeSH
- buňky klasifikace cytologie MeSH
- holografie metody MeSH
- lidé MeSH
- mikroskopie metody MeSH
- rozpoznávání automatizované MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
This article presents a method that allows for reliable automated image acquisition of specimens with high information content in light microscopy with emphasis on fluorescence microscopy applications. Automated microscopy typically relies on autofocusing used for the analysis of information content behaviour along the z-axis within each field of view. However, in the case of a field of view containing more objects that do not lie precisely in one z-plane, traditional autofocusing methods fail due to their principle of operation. We avoid this issue by reducing the original problem to a set of simple and performable tasks: we divide the field of view into a small number of tiles and process each of them individually. The obtained results enable discovering z-planes with rich information content that remain hidden during global analysis of the whole field of view. Our approach therefore outperforms other acquisition methods including the manual one. A large part of the contribution is oriented towards practical application.
Tightly controlled spatiotemporal specificity of gene expression is intrinsic to developmental and adaptation responses of living systems throughout the kingdoms. Forward genetic screens employing well-characterized reporter lines can be used to identify as yet unknown genetic factors driving specific aspects of individual regulatory pathways. However, such screens are demanding with respect to data acquisition and analysis from thousands of mutant lines. Here, we describe a method that allows screening of a mutagenized GUS reporter line in Arabidopsis using an automated microscopy imaging system as a tool for rapid and efficient identification of mutants with modified expression profile for a gene of interest.
We present a new algorithm to analyse information content in images acquired using automated fluorescence microscopy. The algorithm belongs to the group of autofocusing methods, but differs from its predecessors in that it can handle thick specimens and operate also in confocal mode. It measures the information content in images using a 'content function', which is essentially the same concept as a focus function. Unlike previously presented algorithms, this algorithm tries to find all significant axial positions in cases where the content function applied to real data is not unimodal, which is often the case. This requirement precludes using algorithms that rely on unimodality. Moreover, choosing a content function requires careful consideration, because some functions suppress local maxima. First, we test 19 content functions and evaluate their ability to show local maxima clearly. The results show that only six content functions succeed. To save time, the acquisition procedure needs to vary the step size adaptively, because a wide range of possible axial positions has to be passed so as not to miss a local maximum. The algorithm therefore has to assess the steepness of the content function online so that it can decide to use a bigger or smaller step size to acquire the next image. Therefore, the algorithm needs to know about typical behaviour of content functions. We show that for normalized variance, one of the most promising content functions, this knowledge can be obtained after normalizing with respect to the theoretical maximum of this function, and using hierarchical clustering. The resulting algorithm is more reliable and efficient than a simple procedure with constant steps.
Konfokální mikroskopie rohovky (Corneal Confocal Microscopy; CCM) je neinvazivní metoda morfologického vyšetření rohovky umožňující m.j. vizualizaci korneálních nervových vláken, která jsou tenká, málo myelinizovaná či nemyelizovaná. CCM je tedy diagnostická metoda neuropatie tenkých vláken, resp. obecně periferních neuropatií. Cílem práce bylo zavedení vyšetření CCM do klinické neurologické praxe v České republice, nastavení vhodných normativních dat a stanovení reprodukovatelnosti vyšetření. Soubor a metodika: CCM byla vyšetřena v souborech 71 zdravých dobrovolníků a 54 pacientů s diabetickou polyneuropatií (Diabetic Polyneuropathy; DPN). Ze zjištěných dat byly stanoveny normy pro tři oddělené věkové kategorie. Nálezy byly vyhodnoceny automatickou i manuální analýzou (a to nezávisle dvěma hodnotiteli) ke stanovení spolehlivosti vyšetření. Výsledky: Vyšetření CCM bylo časově a metodicky nenáročné a bylo naprostou většinou pacientů dobře tolerováno. Stanovená věkově stratifikovaná normativní data vykazují velmi dobrou použitelnost ve sledovaných souborech pacientů. U pacientů s DPN byly prokázány signifikantní změny všech sledovaných CCM parametrů oproti zdravým kontrolám. Při hodnocení CCM snímků manuální analýzou byla prokázána velmi dobrá shoda dvou hodnotitelů. Při hodnocení automatickým softwarem však byly hodnoty všech sledovaných CCM parametrů signifikantně nižší. Závěr: Prezentovaná studie v souhrnu prokázala jednoduchost, bezpečnost a dobrou spolehlivost vyšetření rohovkové inervace pomocí konfokální mikroskopie rohovky na poměrně rozsáhlém souboru zdravých kontrol a skupině pacientů s DPN a poukázala rovněž na rozdílnost automatického a manuálního hodnocení.
Corneal confocal microscopy (CCM) is a novel noninvasive method enabling morphological evaluation of corneal structures including nerve fibers. These fibers are almost exclusively of A-delta and C type, i.e. small unmyelinated and poorly myelinated. CCM is thus used as a diagnostic tool for peripheral neuropathies and in particular small fiber neuropathy. The aim of this study was to introduce this method into clinical practice in the Czech Republic, to set-up appropriate normative data and to verify reproducibility of the method. Material and methods: A group of 71 healthy controls was examined using the CCM. The data were used to set normal values in three distinct age-related groups and compare these with CCM findings in a group of 54 patients with diabetic polyneuropathy (DPN). Fully-automated as well as expert manual analysis (by two evaluators) were used for quantification of nerve fiber densities, length and branches to verify reliability of the results. Results: CCM evaluation was easy, well-tolerated and time-efficient in the majority of patients/controls. Age-related normal values showed very good applicability in evaluated groups of healthy individuals and DPN patients. Compared to healthy controls, DPN patients showed highly significant changes of all the evaluated CCM parameters. Results by the two evaluators of the expert manual analysis showed very good reliability, while results from the automated analysis showed significantly lower values on the majority of the CCM parameters. Conclusion: The present study proved on a rather large cohort of healthy controls and a smaller sample of DPN patients that CCM is a easy to use, safe and reliable approach to evaluating corneal innervation. The data also highlighted the differences between automated analysis expert manual CCM analysis.
Photosynthesis research employs several biophysical methods, including the detection of fluorescence. Even though fluorescence is a key method to detect photosynthetic efficiency, it has not been applied/adapted to single-cell confocal microscopy measurements to examine photosynthetic microorganisms. Experiments with photosynthetic cells may require automation to perform a large number of measurements with different parameters, especially concerning light conditions. However, commercial microscopes support custom protocols (through Time Controller offered by Olympus or Experiment Designer offered by Zeiss) that are often unable to provide special set-ups and connection to external devices (e.g., for irradiation). Our new system combining an Arduino microcontroller with the Cell⊕Finder software was developed for controlling Olympus FV1000 and FV1200 confocal microscopes and the attached hardware modules. Our software/hardware solution offers (1) a text file-based macro language to control the imaging functions of the microscope; (2) programmable control of several external hardware devices (light sources, thermal controllers, actuators) during imaging via the Arduino microcontroller; (3) the Cell⊕Finder software with ergonomic user environment, a fast selection method for the biologically important cells and precise positioning feature that reduces unwanted bleaching of the cells by the scanning laser. Cell⊕Finder can be downloaded from http://www.alga.cz/cellfinder. The system was applied to study changes in fluorescence intensity in Synechocystis sp. PCC6803 cells under long-term illumination. Thus, we were able to describe the kinetics of phycobilisome decoupling. Microscopy data showed that phycobilisome decoupling appears slowly after long-term (>1 h) exposure to high light.
Biocompatibility testing of new materials is often performed in vitro by measuring the growth rate of mammalian cancer cells in time-lapse images acquired by phase contrast microscopes. The growth rate is measured by tracking cell coverage, which requires an accurate automatic segmentation method. However, cancer cells have irregular shapes that change over time, the mottled background pattern is partially visible through the cells and the images contain artifacts such as halos. We developed a novel algorithm for cell segmentation that copes with the mentioned challenges. It is based on temporal differences of consecutive images and a combination of thresholding, blurring, and morphological operations. We tested the algorithm on images of four cell types acquired by two different microscopes, evaluated the precision of segmentation against manual segmentation performed by a human operator, and finally provided comparison with other freely available methods. We propose a new, fully automated method for measuring the cell growth rate based on fitting a coverage curve with the Verhulst population model. The algorithm is fast and shows accuracy comparable with manual segmentation. Most notably it can correctly separate live from dead cells.
