Academic literature on the topic 'High-Content automated microscopy'

Fluorescence microscopy is one of the most powerful tools to investigate complex cellular processes such as cell division, cell motility, or intracellular trafficking. The availability of RNA interference (RNAi) technology and automated microscopy has opened the possibility to perform cellular imaging in functional genomics and other large-scale applications. Although imaging often dramatically increases the content of a screening assay, it poses new challenges to achieve accurate quantitative annotation and therefore needs to be carefully adjusted to the specific needs of individual screening applications. In this review, we discuss principles of assay design, large-scale RNAi, microscope automation, and computational data analysis. We highlight strategies for imaging-based RNAi screening adapted to different library and assay designs.

Genome-wide, cell-based screens using high-content screening (HCS) techniques and automated fluorescence microscopy generate thousands of high-content images that contain an enormous wealth of cell biological information. Such screens are key to the analysis of basic cell biological principles, such as control of cell cycle and cell morphology. However, these screens will ultimately only shed light on human disease mechanisms and potential cures if the analysis can keep up with the generation of data. A fundamental step toward automated analysis of high-content screening is to construct a robust platform for automatic cellular phenotype identification. The authors present a framework, consisting of microscopic image segmentation and analysis components, for automatic recognition of cellular phenotypes in the context of the Rho family of small GTPases. To implicate genes involved in Rac signaling, RNA interference (RNAi) was used to perturb gene functions, and the corresponding cellular phenotypes were analyzed for changes. The data used in the experiments are high-content, 3-channel, fluorescence microscopy images of Drosophila Kc167 cultured cells stained with markers that allow visualization of DNA, polymerized actin filaments, and the constitutively activated Rho protein RacV12. The performance of this approach was tested using a cellular database that contained more than 1000 samples of 3 predefined cellular phenotypes, and the generalization error was estimated using a cross-validation technique. Moreover, the authors applied this approach to analyze the whole high-content fluorescence images of Drosophila cells for further HCS-based gene function analysis. ( Journal of Biomolecular Screening 2008:29-39)

Each bioactive compound induces phenotypic changes in target cells that can be made visible by labelling selected molecules of the cells with fluorescent dyes and/or directly observed under the high-throughput microscope. A comparison of the cellular phenotype induced by a compound of interest with known cellular targets allows predicting its mode of action. Over the past 15 years, high-throughput microscopy has been one of the fastest growing fields in cell biology. When combined with automated multiparametric image and data analysis, it is referred to as high-content screening (HCS). Whilst HCS has been successfully applied to the bioactivity characterization of natural products, recent studies used automated microscopy and software to increase speed and to reduce subjective interpretation. In 2017, Institute of Natural Products Chemistry (INPC-VAST) has been equipped with a HCS platform (Olympus Scan^R) that designed for fully automated image acquisition and analysis of biological samples to visually inspect the cellular morphology induced by hit compounds as well as to discriminate from false positives. Accordingly, this short review covers the concepts of HCS and its application in screening of biologically active natural products whose molecular targets could be identified through such approaches.

In this article, the authors describe the image analysis software DetecTiff©, which allows fully automated object recognition and quantification from digital images. The core module of the LabView©-based routine is an algorithm for structure recognition that employs intensity thresholding and size-dependent particle filtering from microscopic images in an iterative manner. Detected structures are converted into templates, which are used for quantitative image analysis. DetecTiff © enables processing of multiple detection channels and provides functions for template organization and fast interpretation of acquired data. The authors demonstrate the applicability of DetecTiff© for automated analysis of cellular uptake of fluorescencelabeled low-density lipoproteins as well as diverse other image data sets from a variety of biomedical applications. Moreover, the performance of DetecTiff© is compared with preexisting image analysis tools. The results show that DetecTiff© can be applied with high consistency for automated quantitative analysis of image data (e.g., from large-scale functional RNAi screening projects). ( Journal of Biomolecular Screening 2009:944-955)

Automated microscopes have enabled the unprecedented collection of images at a rate that precludes visual inspection. Automated image analysis is required to identify interesting samples and extract quantitative information for high-content screening (HCS). However, researchers are impeded by the lack of metrics and software tools to identify image-based aberrations that pollute data, limiting experiment quality. The authors have developed and validated approaches to identify those image acquisition artifacts that prevent optimal extraction of knowledge from high-content microscopy experiments. They have implemented these as a versatile, open-source toolbox of algorithms and metrics readily usable by biologists to improve data quality in a wide variety of biological experiments.

Advances in automated imaging microscopy allow fast acquisitions of multidimensional biological samples. Those microscopes open new possibilities for analyzing subcellular structures and spatial cellular arrangements. In this article, the authors describe a 3D image analysis framework adapted to medium-throughput screening. Upon adaptive and regularized segmentation, followed by precise 3D reconstruction, they achieve automatic quantification of numerous relevant 3D descriptors related to the shape, texture, and fluorescence intensity of multiple stained subcellular structures. A global analysis of the 3D reconstructed scene shows additional possibilities to quantify the relative position of organelles. Implementing this methodology, the authors analyzed the subcellular reorganization of the nucleus, the Golgi apparatus, and the centrioles occurring during the cell cycle. In addition, they quantified the effect of a genetic mutation associated with the early onset primary dystonia on the redistribution of torsinA from the bulk endoplasmic reticulum to the perinuclear space of the nuclear envelope. They show that their method enables the classification of various translocation levels of torsinA and opens the possibility for compound-based screening campaigns restoring the normal torsinA phenotype.

Analysis of skeletal muscle cross sections is an important experimental technique in muscle biology. Many aspects of immunohistochemistry and fluorescence microscopy can now be automated, but most image quantification techniques still require extensive human input, slowing progress and introducing the possibility of user bias. MyoVision is a new software package that was developed to overcome these limitations. The software improves upon previously reported automatic techniques and analyzes images without requiring significant human input and correction. When compared with data derived by manual quantification, MyoVision achieves an accuracy of ≥94% for basic measurements such as fiber number, fiber type distribution, fiber cross-sectional area, and myonuclear number. Scientists can download the software free from www.MyoVision.org and use it to automate the analysis of their own experimental data. This will improve the efficiency and consistency of the analysis of muscle cross sections and help to reduce the burden of routine image quantification in muscle biology. NEW & NOTEWORTHY Scientists currently analyze images of immunofluorescently labeled skeletal muscle using time-consuming techniques that require sustained human supervision. As well as being inefficient, these techniques can increase variability in studies that quantify morphological adaptations of skeletal muscle at the cellular level. MyoVision is new software that overcomes these limitations by performing high-content analysis of muscle cross sections with minimal manual input. It is open source and freely available.

In many departments of cytology, cytogenetics, hematology, and pathology, research projects using high-resolution computerized microscopy are now being mounted for computation of morphometric measurements on various structural components, as well as for determination of cellular DNA content. The majority of these measurements are made in a partially automated, computer-assisted mode, wherein there is strong interaction between the user and the computerized microscope. At the same time, full automation has been accomplished for both sample preparation and sample examination for clinical determination of the white blood cell differential count. At the time of writing, approximately 1,000 robot differential counting microscopes are in the field, analyzing images of human white blood cells, red blood cells, and platelets at the overall rate of about 100,000 slides per day. This mammoth through-put represents a major accomplishment in the application of machine vision to automated microscopy for hematology. In other areas of automated high-resolution microscopy, such as cytology and cytogenetics, no commercial instruments are available (although a few metaphase-finding machines are available and other new machines have been announced during the past year). This is a disappointing product, considering the nearly half century of research effort in these areas. This paper provides examples of the state of the art in automation of cell analysis for blood smears, cervical smears, and chromosome preparations. Also treated are new developments in multi-resolution automated microscopy, where images are now being generated and analyzed by a single machine over a range of 64:1 magnification and from 10,000 X 20,000 to 500 X 500 in total picture elements (pixels). Examples of images of human lymph node and liver tissue are presented. Semi-automated systems are not treated, although there is mention of recent research in the automation of tissue analysis.

Les maladies infectieuses ont de tout temps constitué une menace pour l'humanité. La preuve en a encore été faite récemment par la pandémie de COVID-19 (COronaVIrus Disease 2019). Cependant, cette dernière a également mis en avant le potentiel des nanotechnologies pour le développement de thérapies innovantes, grâce aux vaccins contenant des nanoparticules (NPs) pour la protection et la vectorisation d'ARN messager. Ce travail explore le potentiel de NPs de PLGA (acide poly(lactique-co-glycolique)) pour le traitement de deux maladies pulmonaires : la tuberculose, un mal vieux de plusieurs millénaires ainsi que la maladie infectieuse la plus meurtrière actuellement, et le COVID-19, la deuxième pandémie de ce siècle.Pour commencer, un travail de recherche bibliographique s'intéresse à la physiopathologie et au traitement de Mycobacterium tuberculosis (Mtb), mais surtout à l'évolution des NPs depuis trente ans pour l'optimisation de la thérapie antituberculeuse. Cette revue, publiée dans Pharmaceutics en 2023, met en exergue les NPs et antibiotiques les plus étudiés pour y parvenir, et donne des pistes sur l'avenir des traitements personnalisés.Pour l'étude des NPs de PLGA préparées, une technique de caractérisation, la NTA (nanoparticle tracking analysis), est détournée de son usage originel pour l'exploration des interactions cellules-NPs. En effet, les NPs sont incubées avec des cultures cellulaires avant que les surnageants ne soient analysés par NTA, permettant ainsi de quantifier leur internalisation au cours du temps. Un tel usage, détaillé dans un article paru dans l'International Journal of Pharmaceutics en 2021, n'avait jamais été décrit dans la littérature auparavant.Le potentiel des NPs pour le ciblage de Mtb est ensuite exploré. In vitro, il s'avère que les NPs sont préférentiellement internalisées par les cellules infectées par rapport aux cellules non-infectées. En outre, il existe une corrélation positive entre le nombre de bactéries intracellulaires et le nombre de NPs capturées. In vivo, chez la souris, une seule injection de NPs en intranasal permet de cibler l'organe d'intérêt (les poumons), le type cellulaire d'intérêt (les macrophages alvéolaires, siège de l'infection par Mtb), ainsi que les cellules infectées par rapport aux cellules non-infectées, les premières capturant trois fois plus de NPs en moyenne que les secondes. Ces résultats ont fait l'objet d'un article actuellement en cours de révision.Enfin, une étude est menée pour encapsuler et solubiliser une molécule active au sein des NPs pour le traitement du COVID-19. Un travail d'optimisation permet d'obtenir un taux d'encapsulation de 98,3%, une charge de 24,9%, et une concentration dans l'eau de 5 mg/mL pour cette molécule hydrophobe. Son mécanisme de libération est également étudié. Chez la souris et chez le hamster, il apparaît que quelques injections en intranasal seulement permettent de réduire la charge virale pulmonaire de 1,4 log10/mL, avec une toxicité très limitée. Par ailleurs, il est démontré chez la souris que la molécule encapsulée empêche l'inflammation pulmonaire habituellement associée au COVID-19. Cette étude, qui sera prochainement soumise pour publication, pose les bases d'une thérapie post-infection pour les sujets les plus vulnérables face au virus. D'autres résultats non-inclus dans l'article, par ailleurs, s'intéressent à différentes formulations de NPs pour influer sur la libération de la molécule et prolonger son activité antivirale et anti-inflammatoire in vivo. L'ensemble de cette étude a fait l'objet d'un dépôt de brevet en 2023.En conclusion, ce travail démontre le potentiel des NPs de PLGA pour le traitement de deux des maladies infectieuses pulmonaires les plus meurtrières actuellement, et offre des perspectives pour des études futures
Infectious diseases have always been a threat to mankind, as reminded by the recent COVID-19 (COronaVIrus Disease 2019) pandemic. However, the latter has also highlighted the potential of nanotechnologies for the development of innovative therapies, thanks to vaccines containing nanoparticles (NPs) for messenger RNA protection and vectorization. This work explores the potential of PLGA (poly(lactic-co-glycolic acid)) NPs for the treatment of two lung diseases: tuberculosis (TB), a millennia-old ailment as well as the deadliest infectious disease worldwide, and COVID-19, the second pandemic of this century.To begin with, we take interest in the physiopathology and treatment of Mycobacterium tuberculosis (Mtb), but most of all, in the evolution of NPs over the last thirty years for the optimization of TB therapy. This literature review, published in Pharmaceutics in 2023, highlights the most studied NPs and antibiotics to this end, and offers perspectives for the future of advanced and tailored treatments.For the study of the prepared PLGA NPs, a characterization technique, NTA (nanoparticle tracking analysis), is diverted from its original use to explore cell-NP interactions. NPs are incubated with cell cultures before the supernatants are analyzed by NTA, thus enabling to quantify NP internalization over time. Such a use, detailed in an article published in the International Journal of Pharmaceutics in 2021, had never been described in the literature before.The NP potential for the targeting of Mtb is then explored. In vitro, it appears that NPs are preferentially internalized by infected cells as compared to non-infected ones. Furthermore, there is a positive correlation between the number of intracellular bacteria and the number of captured NPs. In vivo, in a mouse model, a single intranasal NP injection allows for the targeting of the organ of interest (the lungs), the cell type of interest (alveolar macrophages, the site of Mtb infection), and infected cells rather than non-infected ones, the former capturing three times more NPs on average than the latter. These results are the subject of an article currently being reviewed.Finally, a study takes interest in the encapsulation and solubilization of an active molecule for the treatment of COVID-19. Optimization studies resulted in drug encapsulation of 98.3%, drug loading of 24.9%, and a concentration in water of 5 mg/mL for this hydrophobic molecule. Its release mechanism was also unraveled. In a mouse and in a hamster model, it appears that a few intranasal injections reduce the lung viral load by 1.4 log10/mL, with very limited toxicity. In a mouse model, the encapsulated molecule is shown to prevent lung inflammation usually associated with COVID-19. This study, which will be submitted for publication shortly, lays the foundations for a post-infection therapy for the most vulnerable patients. Other results, non-included in the article, explore different NP formulations to influence and prolong drug release in vivo. A patent has been filed for this study in 2023.In conclusion, this work demonstrates the potential of PLGA NPs for the treatment of two of the deadliest infectious lung diseases currently, and offers prospects for future studies

Flow cytometry is a sensitive and quantitative platform for the measurement of particle fluorescence. In flow cytometry, the particles in a sample flow in single file through a focused laser beam at rates of hundreds to thousands of particles per second. During the time each particle is in the laser beam, on the order of ten microseconds, one or more fluorescent dyes associated with that particle are excited. The fluorescence emitted from each particle is collected through a microscope objective, spectrally filtered, and detected with photomultiplier tubes. Flow cytometry is uniquely capable of the precise and quantitative molecular analysis of genomic sequence information, interactions between purified biomolecules and cellular function. Combined with automated sample handling for increased sample throughput, these features make flow cytometry a versatile platform with applications at many stages of drug discovery. Traditionally, the particles studied are cells, especially blood cells; flow cytometry is used extensively in immunology. This volume shows how flow cytometry is integrated into modern biotechnology, dealing with issues of throughput, content, sensitivity, and high throughput informatics with applications in genomics, proteomics and protein-protein interactions, drug discovery, vaccine development, plant and reproductive biology, pharmacology and toxicology, cell-cell interactions and protein engineering.

Abstract Once upon a time most cell biologists could synchronize mammalian cells in different stages of the cell cycle, albeit with different degrees of success. However, few molecular probes were then available. Now, with the great resurgence of interest in cell-cycle control and the beginnings of molecular understanding, many workers who have recently entered the field require tightly synchronized cells for their experiments. This chapter presents a selection of protocols which should provide synchronized cells in each cycle phase, and in most instances, cells from different parts of each phase. From the methods commonly used we have selected those which provide excellent synchrony with good reversibility, balanced growth, and high viability. We have also concentrated on methods which (except for centrifugal elutriation) can be carried out without expensive equipment, apart from the standard CO incubators, bench centrifuges, inverted microscopes, Coulter counters, and -70 °C freezers. A 37 °C hot room is a useful resource for synchronization, and a good workshop is needed to build equipment for high-pressure nitrous oxide arrest. A Cytospin cytocentrifuge (Shandon Scientific) for the rapid production of cell monolayers for microscopic inspection is strongly recommended. Details of methods involving high technology-separation of viable cells on the basis of DNA content by fluorescence activated cell sorting, or automated mechanical synchronization systems-can be found in refs 1 and 2, respectively.

We present a robust, low-cost neural network-based optical autofocus system that can operate over a range of ±100µm with submicron precision, enabling automated high-content super-resolved imaging with a 1.3 NA objective lens

Abstract The task of automating the detection of lithology in drill cuttings is an essential aspect of reservoir engineering. As a wellbore is drilled, the rotating bit breaks down the rocks at the bottom of the hole, and these fragments are then transported to the surface through the drilling mud. These fragments are separated from the mud by a shaker upon reaching the surface, enabling the mud to be recycled. The leftover rock fragments, known as drill cuttings, can provide a wealth of information about the geology of the wellbore, drilling speed, and the oil and gas content in the rocks. Once the cuttings are cleaned of the drilling mud, their various properties can be examined using a range of techniques. The importance of automating this analysis process lies in its ability to rapidly evaluate the drilling procedure and predict possible emergencies. Moreover, an accurate analysis of the geological properties of the drill cuttings can provide real-time data similar to well logging. In the domain of oil and gas exploration and drilling, lithology identification can be achieved using a variety of data samples. These include well log data (such as acoustic logs, resistivity logs, gamma-ray logs, and spectral gamma-ray logs) [1,2], laboratory data (such as core samples, X-ray diffraction (XRD), X-ray fluorescence (XRF), and Near-infrared (NIR) spectroscopy, scanning electron microscopy (SEM) [3, 4]), and data collected during drilling (like white light or UV images, images captured under fluorescent light, or real-time drilling parameters such as weight on bit, torque, rate of penetration, mud properties, etc.), but such data could have some bias, a "depth shift", which makes inaccurate correspondence between images from recorded depth, and depth of drilling data ([5]). Acquiring well log or laboratory data tends to be very costly, whereas data collected during drilling is relatively inexpensive. Drill cuttings contain a lot of information as they cover a wider stratigraphic range compared to cores. Analyzing drill cuttings nearly in real-time is a cost-effective approach to characterizing reservoirs, which includes evaluations of mineralogy, petrophysical properties, and mechanical properties. Drill cuttings are especially advantageous due to the cost-effectiveness of obtaining them and the comprehensive depth of the stratigraphic section they represent. Consequently, automating on-site lithology detection based on data collected during drilling is highly desirable. There are several methods to prepare and analyze drill cutting samples, such as using whole cuttings (unprocessed), creating thin sections (where rock samples are ground down to a specific thickness, usually around 30 microns), or making polished sections (which involves preparing a flat, smooth surface on a rock sample). However, the latter two methods necessitate laboratory equipment, making them impractical for field use. In our study, we explore the feasibility of detecting lithology solely through images taken under white light, which represents the most economical data acquisition method during drilling. In our research, we propose a method to determine the lithological composition of drill cuttings by utilizing digital photographs. This method is based on high-resolution images of cuttings taken from specific depths and historical lithological data from previously drilled and examined wells. We created a deep learning model, more specifically a convolutional model, that can predict the probability of a sample being classified into a particular rock category. This model was trained using wells with known lithological data, which were crucial for testing and verifying the model's effectiveness. Looking ahead, we anticipate that this machine learning model will be able to predict the lithological composition of a sample from cleaned cuttings images with a certain level of probability.

Keeping core parts of machines in proper condition is essential to improving productivity and quality of products. Metallic wear of knitting needles for circular knitting machines should be controlled within specific conditions. Currently, inspections of them are visually performed by skilled examiners, and automated inspection systems, which can measure 3-D shapes, are demanded. Because the needles have mirror glossed, complexly shaped surfaces, conventional lighting method, such as dome lights and diffuse on-axis lights, cannot irradiate the light evenly throughout the object and causes brightness unevenness, and that leads to 3-D measurement errors in image processors. To increase accuracy, we propose a new 3-D measurement system which equips omnidirectional EL (electroluminescence) lightings and DEHF (Dynamic Enhancement of High Frequency) method. Here, the system applies the shape from focus method, which moves the optical system vertically with respect to the fixed object and obtains a sequence of images that correspond to different levels of object focus. In the formation process of shallow depth of field microscopic images, a defocused imaging system plays the role of a low-pass filter. For this reason, the regions with high frequency components can be regarded as a focused area. The high frequency components are finally regarded as the contour of the object by the method. It recovers the 3-D shape of the object by estimation of height of the contours each image and arranging in the original order of the sequence of the height of the contours. The followings are novelties of our proposed system. Firstly, omnidirectional EL lightings irradiate an object with uniform lights from all directions. They are composed of the following lights: coaxial through objective lens, object lens perimeter, side and bottom lights, and each of which can adjust brightness; therefore they can reduce unevenness of brightness on the object. We adopted inorganic EL sheet as the lighting device. EL sheet is capable of plane emission and prevent the occurrence of the unevenness of the irradiated light by the point source of light. Secondly, algorithm for shape from focus can be improved by our DEHF method. Even if the above lightings are applied, there still remains low frequency non-uniformity of brightness. DEFH method removes the low frequency by subtracting mean filtered image from original one, and remaining high frequency content can be emphasized. We built a microscope based prototype system and conducted experiments. Through them, the validity of our proposed method was confirmed.

Fly ash is a by-product of coal combustion, made up of particles that are collected through various methods. This by-product has been used successfully as a partial Portland cement replacement in concrete, but the performance predictions of fly ash in concrete have been difficult to predict, especially at high fly ash replacement rates. This study focuses on comparing the performance of concrete with a variety of fly ash mixtures as well as the particle distribution and chemical makeup of fly ash. The slump, unit weight, compressive strength, and isothermal calorimetry tests were used to measure the performance of concrete at 0%, 20%, and 40% fly ash replacement levels. The particle distribution of fly ash was measured with an automated scanning electron microscope. Additionally, the major and minor oxides from the chemical makeup of fly ash were measured for each mixture and inputted into a table. The particle distribution and chemical makeup of fly ash were compared to the performance of slump, unit weight, compressive strength, isothermal calorimetry, and surface electrical resistivity.