An infrastructure for high-throughput microscopy: instrumentation, informatics, and integration

Application and progress of highcontent imaging in molecular biology

Humans have adopted many different methods to explore matter imaging, among which high content imaging (HCI) could conduct automated imaging analysis of cells while maintaining its structural and functional integrity. Meanwhile, as one of the most important research tools for diagnosing human diseases, HCI is widely used in the frontier of medical research, and its future application has attracted researchers' great interests. Here, the meaning of HCI was briefly explained, the history of optical imaging and the birth of HCI were described, and the experimental methods of HCI were described. Furthermore, the directions of the application of HCI were highlighted in five aspects: protein localization changes, gene identification, chemical and genetic analysis, microbiology, and drug discovery. Most importantly, some challenges and future directions of HCI were discussed, and the application and optimization of HCI were expected to be further explored.

MAPS: machine-assisted phenotype scoring enables rapid functional assessment of genetic variants by high-content microscopy

BACKGROUND

Genetic testing is widely used in evaluating a patient's predisposition to hereditary diseases. In the case of cancer, when a functionally impactful mutation (i.e. genetic variant) is identified in a disease-relevant gene, the patient is at elevated risk of developing a lesion in their lifetime. Unfortunately, as the rate and coverage of genetic testing has accelerated, our ability to assess the functional status of new variants has fallen behind. Therefore, there is an urgent need for more practical, streamlined and cost-effective methods for classifying variants.

RESULTS

To directly address this issue, we designed a new approach that uses alterations in protein subcellular localization as a key indicator of loss of function. Thus, new variants can be rapidly functionalized using high-content microscopy (HCM). To facilitate the analysis of the large amounts of imaging data, we developed a new software toolkit, named MAPS for machine-assisted phenotype scoring, that utilizes deep learning to extract and classify cell-level features. MAPS helps users leverage cloud-based deep learning services that are easy to train and deploy to fit their specific experimental conditions. Model training is code-free and can be done with limited training images. Thus, MAPS allows cell biologists to easily incorporate deep learning into their image analysis pipeline. We demonstrated an effective variant functionalization workflow that integrates HCM and MAPS to assess missense variants of PTEN, a tumor suppressor that is frequently mutated in hereditary and somatic cancers.

CONCLUSIONS

This paper presents a new way to rapidly assess variant function using cloud deep learning. Since most tumor suppressors have well-defined subcellular localizations, our approach could be widely applied to functionalize variants of uncertain significance and help improve the utility of genetic testing.

Functionality of Food Components and Emerging Technologies

This review article introduces nutrition and functional food ingredients, explaining the widely cited terms of bioactivity, bioaccessibility, and bioavailability. The factors affecting these critical properties of food components are analyzed together with their interaction and preservation during processing. Ultimately, the effect of emerging (non-thermal) technologies on different food components (proteins, carbohydrates, lipids, minerals, vitamins, polyphenols, glucosinolates, polyphenols, aroma compounds, and enzymes) is discussed in spite of preserving their functional properties. Non-thermal technologies can maintain the bioavailability of food components, improve their functional and technological properties, and increase the recovery yields from agricultural products. However, the optimization of operational parameters is vital to avoid degradation of macromolecules and the oxidation of labile compounds.

Workflow and metrics for image quality control in large-scale high-content screens

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.

Visualizing and quantifying the differential cleavages of the eukaryotic translation initiation factors eIF4GI and eIF4GII in the enterovirus-infected cell

Enterovirus (EV) infection has been shown to cause a marked shutoff of host protein synthesis, an event mainly achieved through the cleavages of eukaryotic translation initiation factors eIF4GI and eIF4GII that are mediated by viral 2A protease (2A(pro)). Using fluorescence resonance energy transfer (FRET), we developed genetically encoded and FRET-based biosensors to visualize and quantify the specific proteolytic process in intact cells. This was accomplished by stable expression of a fusion substrate construct composed of the green fluorescent protein 2 (GFP(2)) and red fluorescent protein 2 (DsRed2), with a cleavage motif on eIF4GI or eIF4GII connected in between. The FRET biosensor showed a real-time and quantifiable impairment of FRET upon EV infection. Levels of the reduced FRET closely correlated with the cleavage kinetics of the endogenous eIF4Gs isoforms. The FRET impairments were solely attributed to 2A(pro) catalytic activity, irrespective of other viral-encoded protease, the activated caspases or general inhibition of protein synthesis in the EV-infected cells. The FRET biosensors appeared to be a universal platform for several related EVs. The spatiotemporal and quantitative imaging enabled by FRET can shed light on the protease-substrate behaviors in their normal milieu, permitting investigation into the molecular mechanism underlying virus-induced host translation inhibition.

High-content analysis in neuroscience

High-content analysis (HCA) combines automated microscopy and automated image analysis to quantify complex cellular anatomy and biochemistry objectively, accurately and quickly. High-content assays that are applicable to neuroscience include those that can quantify various aspects of dendritic trees, protein aggregation, transcription factor translocation, neurotransmitter receptor internalization, neuron and synapse number, cell migration, proliferation and apoptosis. The data that are generated by HCA are rich and multiplexed. HCA thus provides a powerful high-throughput tool for neuroscientists.

High throughput microscopy: from raw images to discoveries

Technological advances in automated microscopy now allow rapid acquisition of many images without human intervention, images that can be used for large-scale screens. The main challenge in such screens is the conversion of the raw images into interpretable information and hence discoveries. This post-acquisition component of image-based screens requires computational steps to identify cells, choose the cells of interest, assess their phenotype, and identify statistically significant 'hits'. Designing such an analysis pipeline requires careful consideration of the necessary hardware and software components, image analysis, statistical analysis and data presentation tools. Given the increasing availability of such hardware and software, these types of experiments have come within the reach of individual labs, heralding many interesting new ways of acquiring biological knowledge.

The potential of high-content high-throughput microscopy in drug discovery

Fluorescence microscopy is a powerful method to study protein function in its natural habitat, the living cell. With the availability of the green fluorescent protein and its spectral variants, almost any gene of interest can be fluorescently labelled in living cells opening the possibility to study protein localization, dynamics and interactions. The emergence of automated cellular systems allows rapid visualization of large groups of cells and phenotypic analysis in a quantitative manner. Here, we discuss recent advances in high-content high-throughput microscopy and its potential application to several steps of the drug discovery process.

Prospecting for Live Cell BioImaging Probes With Cheminformatic Assisted Image Arrays (CAIA)

High-throughput microscopic screening instruments can generate huge collections of images of live cells incubated with combinatorial libraries of fluorescent molecules. Organizing and visualizing these images to discern biologically important patterns that link back to chemical structure is a challenge. We present an analysis and visualization methodology - Cheminformatic Assisted Image Array (CAIA) - that greatly facilitates data mining efforts. For illustration, we considered a collection of microscopic images acquired from cells incubated with each member of a combinatorial library of styryl molecules being screened for candidate bioimaging probes. By sorting CAIAs based on quantitative image features, the relative contribution of each combinatorial building block on probe intracellular distribution could be visually discerned. The results revealed trends hidden in the dataset: most interestingly, the building blocks of the styryl molecules appeared to behave as chemical address tags, additively and independently encoding spatial patterns of intracellular fluorescence. Translated into practice, CAIA facilitated discovery of several outstanding styryl molecules for live cell nuclear imaging applications.