Dataset of chicken-embryo blood cells exposed to quercetin, methyl methanesulfonate, or cadmium chloride

Toxicological analysis of the effects of natural compounds is frequently mandated to assess their safety. In addition to more simple in vitro cellular systems, more complex biological systems can be used to evaluate toxicity. This dataset is comprised of bright-field microscopy images of chicken-embryo blood cells, a complex biological model that recapitulates several features found in human organisms, including circulation in blood stream and biodistribution to different organs. In the presented collection of blood smear images, cells were exposed to the flavonoid quercetin, and the two mutagens methyl methanesulfonate (MMS) and cadmium chloride (CdCl2). In ovo models offer a unique opportunity to investigate the effects of various substances, pathogens, or cancer treatments on developing embryos, providing valuable insights into potential risks and therapeutic strategies. In toxicology, in ovo models allow for early detection of harmful compounds and their impact on embryonic development, aiding in the assessment of environmental hazards. In immunology, these models offer a controlled system to explore the developing immune responses and the interaction between pathogens and host defenses. Additionally, in ovo models are instrumental in oncology research as they enable the study of tumor development and response to therapies in a dynamic, rapidly developing environment. Thus, these versatile models play a crucial role in advancing our understanding of complex biological processes and guiding the development of safer therapeutics and interventions. The data presented here can aid in understanding the potential toxic effects of these substances on hematopoiesis and the overall health of the developing organism. Moreover, the large dataset of blood smear images can serve as a resource for training machine learning algorithms to automatically detect and classify blood cells, provided that specific optimized conditions such as image magnification and background light are maintained for comparison. This can lead to the development of automated tools for blood cell analysis, which can be useful in research. Moreover, the data is amenable to the use as teaching and learning resource for histology and developmental biology.

Artificial Intelligence in Point-of-Care Testing

With the projected increase in the global population, current healthcare delivery models will face severe challenges. Rural and remote areas, whether in developed or developing countries, are characterized by the same challenges: the unavailability of hospitals, lack of trained and skilled staff performing tests, and poor compliance with quality assurance protocols. Point-of-care testing using artificial intelligence (AI) is poised to be able to address these challenges. In this review, we highlight some key areas of application of AI in point-of-care testing, including lateral flow immunoassays, bright-field microscopy, and hematology, demonstrating this rapidly expanding field of laboratory medicine.

Apoptosis Detection by Quantification of Cell Debris in Bright-Field Microscopy Images

We describe a simple protocol for quantification of apoptosis by counting subcellular debris particles in bright-field microscopy images of cell culture media samples. The only necessary equipment is a bright-field microscope (fitted with a digital camera) and a computer for image processing and analysis. The method gives comparable results to established fluorescence markers in several different applications and is, in principle, compatible with any culture format. Given its simplicity, accessibility, and inexpensive nature, the method can complement or provide an alternative to other methods for apoptosis detection.

DeepHCS++: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening

In this paper, we propose a novel microscopy image translation method for transforming a bright-field microscopy image into three different fluorescence images to observe the apoptosis, nuclei, and cytoplasm of cells, which visualize dead cells, nuclei of cells, and cytoplasm of cells, respectively. These biomarkers are commonly used in high-content drug screening to analyze drug response. The main contribution of the proposed work is the automatic generation of three fluorescence images from a conventional bright-field image; this can greatly reduce the time-consuming and laborious tissue preparation process and improve throughput of the screening process. Our proposed method uses only a single bright-field image and the corresponding fluorescence images as a set of image pairs for training an end-to-end deep convolutional neural network. By leveraging deep convolutional neural networks with a set of image pairs of bright-field and corresponding fluorescence images, our proposed method can produce synthetic fluorescence images comparable to real fluorescence microscopy images with high accuracy. Our proposed model uses multi-task learning with adversarial losses to generate more accurate and realistic microscopy images. We assess the efficacy of the proposed method using real bright-field and fluorescence microscopy image datasets from patient-driven samples of a glioblastoma, and validate the method's accuracy with various quality metrics including cell number correlation (CNC), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), cell viability correlation (CVC), error maps, and R² correlation.

Light Microscopy and Raman Imaging of Carotenoids in Plant Cells In Situ and in Released Carotene Crystals

Light microscopy with a bright field mode offers an easy and fast examination of plant specimen for carotenoid presence in its cells. Using basic techniques such as hand sectioned or squashed preparations, carotenoid-rich chromoplasts can be identified without applying any staining procedure and their localization within the cell, their shape and number can be assessed. More detailed information can be obtained by using Raman spectroscopy which is suitable for the analysis of carotenoids due to their unique Raman spectra and allows semiquantification of their contents. Raman imaging (mapping) can be additionally used to show the distribution of carotenoids within the sample. Raman spectra can be taken from extracted carotenoids but can be also obtained directly from plant tissues or cells as Raman measurements are nondestructive for the sample. Here we describe preparations of intact tissue samples, monolayer cell samples, isolated protoplasts as well as carotene crystals released from chromoplasts that are suitable for subsequent observations using light microscopy and for analysis using Raman spectroscopy.

Out-of-focus brain image detection in serial tissue sections

BACKGROUND

A large part of image processing workflow in brain imaging is quality control which is typically done visually. One of the most time consuming steps of the quality control process is classifying an image as in-focus or out-of-focus (OOF).

NEW METHOD

In this paper we introduce an automated way of identifying OOF brain images from serial tissue sections in large datasets (>1.5 PB). The method utilizes steerable filters (STF) to derive a focus value (FV) for each image. The FV combined with an outlier detection that applies a dynamic threshold allows for the focus classification of the images.

RESULTS

The method was tested by comparing the results of our algorithm with a visual inspection of the same images. The results support that the method works extremely well by successfully identifying OOF images within serial tissue sections with a minimal number of false positives.

COMPARISON WITH EXISTING METHODS

Our algorithm was also compared to other methods and metrics and successfully tested in different stacks of images consisting solely of simulated OOF images in order to demonstrate the applicability of the method to other large datasets.

CONCLUSIONS

We have presented a practical method to distinguish OOF images from large datasets that include serial tissue sections that can be included in an automated pre-processing image analysis pipeline.

Single-Molecule Magnetic Tweezer Analysis of Topoisomerases

Magnetic tweezers (MT) provide a powerful single-molecule approach to study the mechanism of topoisomerases, giving the experimenter the ability to change and read out DNA topology in real time. By using diverse DNA substrates, one can study different aspects of topoisomerase function and arrive at a better mechanistic understanding of these fascinating enzymes. Here we describe methods for the creation of three different DNA substrates used in MT experiments with topoisomerases: double-stranded DNA (dsDNA) tethers, "braided" (intertwined or catenated) DNA tether pairs, and dsDNA tethers with single-stranded DNA (ssDNA) regions. Additionally, we discuss how to build flow cells for bright-field MT microscopy, as well as how to noncovalently attach anti-digoxigenin to the coverslip surface for tethering digoxigenin-labeled DNAs. Finally, we describe procedures for the identification of a suitable DNA substrate for MT study and data collection.

Super-resolved 3-D imaging of live cells' organelles from bright-field photon transmission micrographs

Current biological and medical research is aimed at obtaining a detailed spatiotemporal map of a live cell's interior to describe and predict cell's physiological state. We present here an algorithm for complete 3-D modelling of cellular structures from a z-stack of images obtained using label-free wide-field bright-field light-transmitted microscopy. The method visualizes 3-D objects with a volume equivalent to the area of a camera pixel multiplied by the z-height. The computation is based on finding pixels of unchanged intensities between two consecutive images of an object spread function. These pixels represent strongly light-diffracting, light-absorbing, or light-emitting objects. To accomplish this, variables derived from Rényi entropy are used to suppress camera noise. Using this algorithm, the detection limit of objects is only limited by the technical specifications of the microscope setup-we achieve the detection of objects of the size of one camera pixel. This method allows us to obtain 3-D reconstructions of cells from bright-field microscopy images that are comparable in quality to those from electron microscopy images.