Light microscopy applications in systems biology: opportunities and challenges

An Image Processing Algorithm for Facile and Reproducible Quantification of Vomocytosis

Vomocytosis is a process that occurs when internalized fungal pathogens escape from phagocytes without compromising the viability of the pathogen and the host cell. Manual quantification of time-lapse microscopy videos is currently used as the standard to study pathogen behavior and vomocytosis incidence. However, human-driven quantification of vomocytosis (and the closely related phenomenon, exocytosis) is incredibly burdensome, especially when a large volume of cells and interactions needs to be analyzed. In this study, we designed a MATLAB algorithm that measures the extent of colocalization between the phagocyte and fungal cell (Cryptococcus neoformans; CN) and rapidly reports the occurrence of vomocytosis in a high throughput manner. Our code processes multichannel, time-lapse microscopy videos of cocultured CN and immune cells that have each been fluorescently stained with unique dyes and provides quantitative readouts of the spatiotemporally dynamic process that is vomocytosis. This study also explored metrics, such as the rate of change of pathogen colocalization with the host cell, that could potentially be used to predict vomocytosis occurrence based on the quantitative data collected. Ultimately, the algorithm quantifies vomocytosis events and reduces the time for video analysis from over 1 h to just 10 min, a reduction in labor of 83%, while simultaneously minimizing human error. This tool significantly minimizes the vomocytosis analysis pipeline, accelerates our ability to elucidate unstudied aspects of this phenomenon, and expedites our ability to characterize CN strains for the study of their epidemiology and virulence.

Preparing Arabidopsis thaliana root protoplasts for cryo electron tomography

The use of protoplasts in plant biology has become a convenient tool for the application of transient gene expression. This model system has allowed the study of plant responses to biotic and abiotic stresses, protein location and trafficking, cell wall dynamics, and single-cell transcriptomics, among others. Although well-established protocols for isolating protoplasts from different plant tissues are available, they have never been used for studying plant cells using cryo electron microscopy (cryo-EM) and cryo electron tomography (cryo-ET). Here we describe a workflow to prepare root protoplasts from Arabidopsis thaliana plants for cryo-ET. The process includes protoplast isolation and vitrification on EM grids, and cryo-focused ion beam milling (cryo-FIB), with the aim of tilt series acquisition. The whole workflow, from growing the plants to the acquisition of the tilt series, may take a few months. Our protocol provides a novel application to use plant protoplasts as a tool for cryo-ET.

Pre-Implantation Bovine Embryo Evaluation-From Optics to Omics and Beyond

Approximately 80% of the ~1.5 million bovine embryos transferred in 2021 were in vitro produced. However, only ~27% of the transferred IVP embryos will result in live births. The ~73% pregnancy failures are partly due to transferring poor-quality embryos, a result of erroneous stereomicroscopy-based morphological evaluation, the current method of choice for pre-transfer embryo evaluation. Numerous microscopic (e.g., differential interference contrast, electron, fluorescent, time-lapse, and artificial-intelligence-based microscopy) and non-microscopic (e.g., genomics, transcriptomics, epigenomics, proteomics, metabolomics, and nuclear magnetic resonance) methodologies have been tested to find an embryo evaluation technique that is superior to morphologic evaluation. Many of these research tools can accurately determine embryo quality/viability; however, most are invasive, expensive, laborious, technically sophisticated, and/or time-consuming, making them futile in the context of in-field embryo evaluation. However accurate they may be, using complex methods, such as RNA sequencing, SNP chips, mass spectrometry, and multiphoton microscopy, at thousands of embryo production/collection facilities is impractical. Therefore, future research is warranted to innovate field-friendly, simple benchtop tests using findings already available, particularly from omics-based research methodologies. Time-lapse monitoring and artificial-intelligence-based automated image analysis also have the potential for accurate embryo evaluation; however, further research is warranted to innovate economically feasible options for in-field applications.

DIRECT, a low-cost system for high-speed, low-noise imaging of fluorescent bio-samples

A targeted imaging system has been developed for applications requiring recording from stationary samples at high spatiotemporal resolutions. It works by illuminating regions of interest in rapid sequence, and recording the signal from the whole field of view onto a single photodetector. It can be implemented at low cost on an existing microscope without compromising existing functionality. The system is characterized in terms of speed, spatial resolution, and tissue penetration depth, before being used to record individual action potentials from ASAP-3 expressing neurons in an ex vivo mouse brain slice preparation.

A Review on Data Fusion of Multidimensional Medical and Biomedical Data

Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods.

An affordable, handheld multimodal microscopic system with onboard cell morphology and counting features on a mobile device

A simple yet effective, handheld and flexible bright-field and fluorescence microscopic platform on a smartphone with varying optical magnifications is reported for morphological analysis and onboard cell counting features.

Interdisciplinary approach to biomedical research: a panacea to efficient research output during the global pandemic

Biomedical research is rapidly growing due to inventions and developments in science and technology. Several interdisciplinary fields should be combined to find the remedy of diseases including pandemics. To accomplish this, interdisciplinary research is a prerequisite. Using improved techniques in microscopy and genetic engineering, the systemic perspective of the human body and related diseases can be found. Recent genetic-based inheritance studies of diseases, understanding various omics, stem cell systems, and gene editing tools including CRISPR relevant to biomedical research require multidisciplinary approach. Improvements in the field of bioinformatics and efficient use of model organisms in clinical testing including drug assessment are important disciplines common in different researches. The merging of different closely related areas of medical research will produce suitable changes in diagnosis and treatment. In the present scenario of increased global pandemic hits like COVID-19, an understanding on the interdisciplinary approach is needed for controlling the spread and finding vaccines.

Activatable MRI probes for the specific detection of bacteria

Activatable fluorescent probes have been successfully used as molecular tools for biomedical research in the last decades. Fluorescent probes allow the detection of molecular events, providing an extraordinary platform for protein and cellular research. Nevertheless, most of the fluorescent probes reported are susceptible to interferences from endogenous fluorescence (background signal) and limited tissue penetration is expected. These drawbacks prevent the use of fluorescent tracers in the clinical setting. To overcome the limitation of fluorescent probes, we and others have developed activatable magnetic resonance probes. Herein, we report for the first time, an oligonucleotide-based probe with the capability to detect bacteria using magnetic resonance imaging (MRI). The activatable MRI probe consists of a specific oligonucleotide that targets micrococcal nuclease (MN), a nuclease derived from Staphylococcus aureus. The oligonucleotide is flanked by a superparamagnetic iron oxide nanoparticle (SPION) at one end, and by a dendron functionalized with several gadolinium complexes as enhancers, at the other end. Therefore, only upon recognition of the MRI probe by the specific bacteria is the probe activated and the MRI signal can be detected. This approach may be widely applied to detect bacterial infections or other human conditions with the potential to be translated into the clinic as an activatable contrast agent.

Design of a 3D printed smartphone microscopic system with enhanced imaging ability for biomedical applications

Portable, low-cost smartphone platform microscopic systems have emerged as a potential tool for imaging of various micron and submicron scale particles in recent years (Ozcan; Pirnstill and Coté; Breslauer et al.; Zhu et al.). In most of the reported works, it involves either the use of sophisticated optical set-ups along with a high-end computational tool for postprocessing of the captured images, or it requires a high-end configured smartphone to obtain enhanced imaging of the sample. Present work reports the working of a low-cost, field-portable 520× optical microscope using a smartphone. The proposed smartphone microscopic system has been designed by attaching a 3D printed compact optical set-up to the rear camera of a regular smartphone. By using cloud-based services, an image processing algorithm has been developed which can be accessed anytime through a mobile broadband network. Using this facility, the quality of the captured images can be further enhanced, thus obviating the need for dedicated computational tools for postprocessing of the images. With the designed microscopic system, an optical resolution ∼2 µm has been obtained. Upon postprocessing, the resolution of the captured images can be improved further. It is envisioned that with properly designed optical set-up in 3D printer and by developing an image processing application in the cloud, it is possible to obtain a low-cost, user-friendly, field-portable optical microscope on a regular smartphone that performs at par with that of a laboratory-grade microscope. LAY DESCRIPTION: With the ever-improving features both in hardware and software part, smartphone becomes ubiquitous in the modern civilised society with approximately 8.1 billion cell phone users across the world, and ∼40% of them can be considered as smartphones. This technology is undoubtedly the leading technology of the 21st century. Very recently, various researchers across the globe have utilised different sensing components embedded in the smartphone to convert it into a field-portable low-cost and user-friendly tool which can be used for different sensing and imaging purposes. By using simple optical components such as lens, pinhole, diffuser etc. and the camera of the smartphone, various groups have converted the phone into a microscopic imaging system. Again, by removing the camera lenses of the phone, holography images of microscopic particles by directly casting its shadows on the CMOS sensor on the phone has been demonstrated. The holographic images have subsequently been processed using the dedicated computational tool, and the original photos of the samples can be obtained. All the reported smartphone-based microscopic systems either suffer from relatively low field-of-view (FOV), resolution or it needs a high computational platform. Present work, demonstrate an alternative approach by which a reasonably good resolution (<2 µm) along with high optical magnification (520×) and a large FOV (150 µm) has been obtained on a regular smartphone. For postprocessing of the captured images an image processing algorithm has been developed in the cloud and the same can be accessed by the smartphone application, obviating the need of dedicated computational tool and a high-end configured smartphone for the proposed microscope. For the development of the proposed microscopic system, a simple optical set-up has been fabricated in a 3D printer. The set-up houses all the required optical components and the sample specimen with the 3D-printed XY stage, and it can be attached easily to the rear camera of the smartphone. Using the proposed microscopic system, enhanced imaging of USAF target and red blood cells have been successfully demonstrated. With the readily available optical components and a regular smartphone, the net cost involvement is significantly low (less than $250, including the smartphone). We envisioned that the designed system could be utilised for point-of-care diagnosis in resource-poor settings where access to the laboratory facilities is very limited.

Automated cell boundary and 3D nuclear segmentation of cells in suspension

To characterize cell types, cellular functions and intracellular processes, an understanding of the differences between individual cells is required. Although microscopy approaches have made tremendous progress in imaging cells in different contexts, the analysis of these imaging data sets is a long-standing, unsolved problem. The few robust cell segmentation approaches that exist often rely on multiple cellular markers and complex time-consuming image analysis. Recently developed deep learning approaches can address some of these challenges, but they require tremendous amounts of data and well-curated reference data sets for algorithm training. We propose an alternative experimental and computational approach, called CellDissect, in which we first optimize specimen preparation and data acquisition prior to image processing to generate high quality images that are easier to analyze computationally. By focusing on fixed suspension and dissociated adherent cells, CellDissect relies only on widefield images to identify cell boundaries and nuclear staining to automatically segment cells in two dimensions and nuclei in three dimensions. This segmentation can be performed on a desktop computer or a computing cluster for higher throughput. We compare and evaluate the accuracy of different nuclear segmentation approaches against manual expert cell segmentation for different cell lines acquired with different imaging modalities.

Unsupervised Two-Path Neural Network for Cell Event Detection and Classification Using Spatiotemporal Patterns

Automatic event detection in cell videos is essential for monitoring cell populations in biomedicine. Deep learning methods have advantages over traditional approaches for cell event detection due to their ability to capture more discriminative features of cellular processes. Supervised deep learning methods, however, are inherently limited due to the scarcity of annotated data. Unsupervised deep learning methods have shown promise in general (non-cell) videos because they can learn the visual appearance and motion of regularly occurring events. Cell videos, however, can have rapid, irregular changes in cell appearance and motion, such as during cell division and death, which are often the events of most interest. We propose a novel unsupervised two-path input neural network architecture to capture these irregular events with three key elements: 1) a visual encoding path to capture regular spatiotemporal patterns of observed objects with convolutional long short-term memory units; 2) an event detection path to extract information related to irregular events with max-pooling layers; and 3) integration of the hidden states of the two paths to provide a comprehensive representation of the video that is used to simultaneously locate and classify cell events. We evaluated our network in detecting cell division in densely packed stem cells in phase-contrast microscopy videos. Our unsupervised method achieved higher or comparable accuracy to standard and state-of-the-art supervised methods.

The influence of spatial and temporal resolutions on the analysis of cell-cell interaction: a systematic study for time-lapse microscopy applications

Cell-cell interactions are an observable manifestation of underlying complex biological processes occurring in response to diversified biochemical stimuli. Recent experiments with microfluidic devices and live cell imaging show that it is possible to characterize cell kinematics via computerized algorithms and unravel the effects of targeted therapies. We study the influence of spatial and temporal resolutions of time-lapse videos on motility and interaction descriptors with computational models that mimic the interaction dynamics among cells. We show that the experimental set-up of time-lapse microscopy has a direct impact on the cell tracking algorithm and on the derived numerical descriptors. We also show that, when comparing kinematic descriptors in two diverse experimental conditions, too low resolutions may alter the descriptors’ discriminative power, and so the statistical significance of the difference between the two compared distributions. The conclusions derived from the computational models were experimentally confirmed by a series of video-microscopy acquisitions of co-cultures of unlabelled human cancer and immune cells embedded in 3D collagen gels within microfluidic devices. We argue that the experimental protocol of acquisition should be adapted to the specific kind of analysis involved and to the chosen descriptors in order to derive reliable conclusions and avoid biasing the interpretation of results.

Benchmarking Classification Models for Cell Viability on Novel Cancer Image Datasets

Background:

Dye-exclusion based cell viability analysis has been broadly used in cell biology including anticancer drug discovery studies. Viability analysis refers to the whole decision making process for the distinction of dead cells from live ones. Basically, cell culture samples are dyed with a special stain called trypan blue, so that the dead cells are selectively colored to darkish. This distinction provides critical information that may be used to expose influences of the studied drug on considering cell culture including cancer. Examiner’s experience and tiredness substantially affect the consistency throughout the manual observation of cell viability. The unsteady results of cell viability may end up with biased experimental results accordingly. Therefore, a machine learning based automated decision-making procedure is inevitably needed to improve consistency of the cell viability analysis.

Objective:

In this study, we investigate various combinations of classifiers and feature extractors (i.e. classification models) to maximize the performance of computer vision-based viability analysis.

Method:

The classification models are tested on novel hemocytometer image datasets which contain two types of cancer cell images, namely, caucasian promyelocytic leukemia (HL60), and chronic myelogenous leukemia (K562).

Results:

From the experimental results, k-Nearest Neighbor (KNN) and Random Forest (RF) by combining Local Phase Quantization (LPQ) achieve the lowest misclassification rates that are 0.031 and 0.082, respectively.

Conclusion:

The experimental results show that KNN and RF with LPQ can be powerful alternatives to the conventional manual cell viability analysis. Also, the collected datasets are released from the “biochem.atilim.edu.tr/datasets/” web address publically to academic studies.

Strategies and Solutions to Maintain and Retain Data from High Content Imaging, Analysis, and Screening Assays

Data analysis and management in high content screening (HCS) has progressed significantly in the past 10 years. The analysis of the large volume of data generated in HCS experiments represents a significant challenge and is currently a bottleneck in many screening projects. In most screening laboratories, HCS has become a standard technology applied routinely to various applications from target identification to hit identification to lead optimization. An HCS data management and analysis infrastructure shared by several research groups can allow efficient use of existing IT resources and ensures company-wide standards for data quality and result generation. This chapter outlines typical HCS workflows and presents IT infrastructure requirements for multi-well plate-based HCS.

A Dynamic Survival Detection and Analysis System for Mosquito Larvae Viability in Drug Assays

Bioinformatics studies have emerged in the domain of larval behavior analysis in recent years. A dynamic survival detection and analysis system for automatically monitoring a large amount of mosquito larvae in bioassays with multiwell plates by acquiring and processing videos is proposed in this article. In our system, equipment is designed for acquiring the video of the mosquito larvae in several multiwell plates simultaneously by a camera, and a video analysis module is developed for detecting the survival states of larvae in each well in real time. Also, a novel model and a new image registration algorithm are proposed to accurately obtain the survival state by analyzing the larval motion activities and the weights of larvae in each well. In our experiments, several spinosad bioassays against 2-instar Aedes aegypti with 96-well plates are used to evaluate the proposed system, and the accuracy of the larval survival state in our system is more than 85%. Moreover, this investigation has indicated that the developed system not only can be used in the mosquito larval bioassays but also can be suitable to detect and analyze the behaviors of large amount of other larvae.

Enteric neurons from Parkinson's disease patients display ex vivo aberrations in mitochondrial structure

Based on autopsy material mitochondrial dysfunction has been proposed being part of the pathophysiological cascade of Parkinson's disease (PD). However, in living patients, evidence for such dysfunction is scarce. As the disease presumably starts at the enteric level, we studied ganglionic and mitochondrial morphometrics of enteric neurons. We compared 65 ganglia from 11 PD patients without intestinal symptoms and 41 ganglia from 4 age-matched control subjects. We found that colon ganglia from PD patients had smaller volume, contained significantly more mitochondria per ganglion volume, and displayed a higher total mitochondrial mass relative to controls. This suggests involvement of mitochondrial dysfunction in PD at the enteric level. Moreover, in PD patients the mean mitochondrial volume declined in parallel with motor performance. Ganglionic shrinking was evident in the right but not in the left colon. In contrast, mitochondrial changes prevailed in the left colon suggesting that a compensatory increase in mitochondrial mass might counterbalance mitochondrial dysfunction in the left colon but not in the right colon. Reduction in ganglia volume and combined mitochondrial morphometrics had both predictive power to discriminate between PD patients and control subjects, suggesting that both parameters could be used for early discrimination between PD patients and healthy individuals.

Applications in image-based profiling of perturbations

A dramatic shift has occurred in how biologists use microscopy images. Whether experiments are small-scale or high-throughput, automatically quantifying biological properties in images is now widespread. We see yet another revolution under way: a transition towards using automated image analysis to not only identify phenotypes a biologist specifically seeks to measure ('screening') but also as an unbiased and sensitive tool to capture a wide variety of subtle features of cell (or organism) state ('profiling'). Mapping similarities among samples using image-based (morphological) profiling has tremendous potential to transform drug discovery, functional genomics, and basic biological research. Applications include target identification, lead hopping, library enrichment, functionally annotating genes/alleles, and identifying small molecule modulators of gene activity and disease-specific phenotypes.

Automated characterization and parameter-free classification of cell tracks based on local migration behavior

Cell migration is the driving force behind the dynamics of many diverse biological processes. Even though microscopy experiments are routinely performed today by which populations of cells are visualized in space and time, valuable information contained in image data is often disregarded because statistical analyses are performed at the level of cell populations rather than at the single-cell level. Image-based systems biology is a modern approach that aims at quantitatively analyzing and modeling biological processes by developing novel strategies and tools for the interpretation of image data. In this study, we take first steps towards a fully automated characterization and parameter-free classification of cell track data that can be generally applied to tracked objects as obtained from image data. The requirements to achieve this aim include: (i) combination of different measures for single cell tracks, such as the confinement ratio and the asphericity of the track volume, and (ii) computation of these measures in a staggered fashion to retrieve local information from all possible combinations of track segments. We demonstrate for a population of synthetic cell tracks as well as for in vitro neutrophil tracks obtained from microscopy experiment that the information contained in the track data is fully exploited in this way and does not require any prior knowledge, which keeps the analysis unbiased and general. The identification of cells that show the same type of migration behavior within the population of all cells is achieved via agglomerative hierarchical clustering of cell tracks in the parameter space of the staggered measures. The recognition of characteristic patterns is highly desired to advance our knowledge about the dynamics of biological processes.

The hallmarks of Parkinson's disease

Since the discovery of dopamine as a neurotransmitter in the 1950s, Parkinson's disease (PD) research has generated a rich and complex body of knowledge, revealing PD to be an age-related multifactorial disease, influenced by both genetic and environmental factors. The tremendous complexity of the disease is increased by a nonlinear progression of the pathogenesis between molecular, cellular and organic systems. In this minireview, we explore the complexity of PD and propose a systems-based approach, organizing the available information around cellular disease hallmarks. We encourage our peers to adopt this cell-based view with the aim of improving communication in interdisciplinary research endeavors targeting the molecular events, modulatory cell-to-cell signaling pathways and emerging clinical phenotypes related to PD.