Introduction to the quantitative analysis of two-dimensional fluorescence microscopy images for cell-based screening

Artificial intelligence for high content imaging in drug discovery

Artificial intelligence (AI) and high-content imaging (HCI) are contributing to advancements in drug discovery, propelled by the recent progress in deep neural networks. This review highlights AI's role in analysis of HCI data from fixed and live-cell imaging, enabling novel label-free and multi-channel fluorescent screening methods, and improving compound profiling. HCI experiments are rapid and cost-effective, facilitating large data set accumulation for AI model training. However, the success of AI in drug discovery also depends on high-quality data, reproducible experiments, and robust validation to ensure model performance. Despite challenges like the need for annotated compounds and managing vast image data, AI's potential in phenotypic screening and drug profiling is significant. Future improvements in AI, including increased interpretability and integration of multiple modalities, are expected to solidify AI and HCI's role in drug discovery.

Robust and unbiased estimation of the background distribution for automated quantitative imaging

Background estimation is the first step in quantitative analysis of images. It has an impact on all subsequent analyses, in particular for segmentation and calculation of ratiometric quantities. Most methods recover only a single value such as the median or yield a biased estimation in non-trivial cases. We introduce, to our knowledge, the first method to recover an unbiased estimation of background distribution. It leverages the lack of local spatial correlation in background pixels to robustly select a subset that accurately represents the background. The resulting background distribution can be used to test for foreground membership of individual pixels or estimate confidence intervals in derived quantities.

Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis

Automated microscopy and computational image analysis has transformed cell biology, providing quantitative, spatially resolved information on cells and their constituent molecules from the sub-micron to the whole-organ scale. Here we explore the application of spatial statistics to the cellular relationships within tissue microscopy data and discuss how spatial statistics offers cytometry a powerful yet underused mathematical tool set for which the required data are readily captured using standard protocols and microscopy equipment. We also highlight the often-overlooked need to carefully consider the structural heterogeneity of tissues in terms of the applicability of different statistical measures and their accuracy and demonstrate how spatial analyses offer a great deal more than just basic quantification of biological variance. Ultimately, we highlight how statistical modeling can help reveal the hierarchical spatial processes that connect the properties of individual cells to the establishment of biological function.

Quantifying biofilm propagation on chemically modified surfaces

Conditions affecting biofilm formation differ among bacterial species and this presents a challenge to studying biofilms in the lab. This work leverages functionalized silanes to control surface chemistry in the study of early biofilm propagation, quantified with a semi-automated image processing algorithm. These methods support the study of Pantoea sp. YR343, a gram-negative bacterium isolated from the poplar rhizosphere. We found that Pantoea sp. YR343 does not readily attach to hydrophilic surfaces but will form biofilms with a “honeycomb” morphology on hydrophobic surfaces. Our image processing algorithm described here quantified the evolution of the honeycomb morphology over time, and found the propagation to display a logarithmic behavior. This methodology was repeated with a flagella-deficient fliR mutant of Pantoea sp. YR343 which resulted in reduced surface attachment. Quantifiable differences between Pantoea WT and ΔfliR biofilm morphologies were captured by the image processing algorithm, further demonstrating the insight gained from these methods.

Single Image-Based Vignetting Correction for Improving the Consistency of Neural Activity Analysis in 2-Photon Functional Microscopy

High-resolution functional 2-photon microscopy of neural activity is a cornerstone technique in current neuroscience, enabling, for instance, the image-based analysis of relations of the organization of local neuron populations and their temporal neural activity patterns. Interpreting local image intensity as a direct quantitative measure of neural activity presumes, however, a consistent within- and across-image relationship between the image intensity and neural activity, which may be subject to interference by illumination artifacts. In particular, the so-called vignetting artifact—the decrease of image intensity toward the edges of an image—is, at the moment, widely neglected in the context of functional microscopy analyses of neural activity, but potentially introduces a substantial center-periphery bias of derived functional measures. In the present report, we propose a straightforward protocol for single image-based vignetting correction. Using immediate-early gene-based 2-photon microscopic neural image data of the mouse brain, we show the necessity of correcting both image brightness and contrast to improve within- and across-image intensity consistency and demonstrate the plausibility of the resulting functional data.

Automated Image Analysis of FRET Signals for Subcellular cAMP Quantification

A variety of FRET probes have been developed to examine cAMP localization and dynamics in single cells. These probes offer a readily accessible approach to measure localized cAMP signals. However, given the low signal-to-noise ratio of most FRET probes and the dynamic nature of the intracellular environment, there have been marked limitations in the ability to use FRET probes to study localized signaling events within the same cell. Here, we outline a methodology to dissect kinetics of cAMP-mediated FRET signals in single cells using automated image analysis approaches. We additionally extend these approaches to the analysis of subcellular regions. These approaches offer a unique opportunity to assess localized cAMP kinetics in an unbiased, quantitative fashion.

Practical machine learning for disease diagnosis

Deep learning neural networks are a powerful tool in the analytical toolbox of modern microscopy, but they come with an exacting requirement for accurately annotated, ground truth cell images. Otesteanu et al. (2021) elegantly streamline this process, implementing network training by using patient-level rather than cell-level disease classification.

Role of 3D Volumetric and Perfusion Imaging for Detecting Early Changes in Pancreatic Adenocarcinoma

Purpose

There is a major shortage of reliable early detection methods for pancreatic cancer in high-risk groups. The focus of this preliminary study was to use Time Intensity-Density Curve (TIDC) and Marley Equation analyses, in conjunction with 3D volumetric and perfusion imaging to demonstrate their potential as imaging biomarkers to assist in the early detection of Pancreatic Ductal Adenocarcinoma (PDAC).

Experimental Designs

A quantitative retrospective and prospective study was done by analyzing multi-phase Computed Tomography (CT) images of 28 patients undergoing treatment at different stages of pancreatic adenocarcinoma using advanced 3D imaging software to identify the perfusion and radio density of tumors.

Results

TIDC and the Marley Equation proved useful in quantifying tumor aggressiveness. Perfusion delays in the venous phase can be linked to Vascular Endothelial Growth Factor (VEGF)-related activity which represents the active part of the tumor. 3D volume analysis of the multiphase CT scan of the patient showed clear changes in arterial and venous perfusion indicating the aggressive state of the tumor.

Conclusion

TIDC and 3D volumetric analysis can play a significant role in defining the response of the tumor to treatment and identifying early-stage aggressiveness.

A Study of Gene Expression, Structure, and Contractility of iPSC-Derived Cardiac Myocytes from a Family with Heart Disease due to LMNA Mutation

Genetic mutations to the Lamin A/C gene (LMNA) can cause heart disease, but the mechanisms making cardiac tissues uniquely vulnerable to the mutations remain largely unknown. Further, patients with LMNA mutations have highly variable presentation of heart disease progression and type. In vitro patient-specific experiments could provide a powerful platform for studying this phenomenon, but the use of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) introduces heterogeneity in maturity and function thus complicating the interpretation of the results of any single experiment. We hypothesized that integrating single cell RNA sequencing (scRNA-seq) with analysis of the tissue architecture and contractile function would elucidate some of the probable mechanisms. To test this, we investigated five iPSC-CM lines, three controls and two patients with a (c.357-2A>G) mutation. The patient iPSC-CM tissues had significantly weaker stress generation potential than control iPSC-CM tissues demonstrating the viability of our in vitro approach. Through scRNA-seq, differentially expressed genes between control and patient lines were identified. Some of these genes, linked to quantitative structural and functional changes, were cardiac specific, explaining the targeted nature of the disease progression seen in patients. The results of this work demonstrate the utility of combining in vitro tools in exploring heart disease mechanics.

Correction of uneven illumination in color microscopic image based on fully convolutional network

The correction of uneven illumination in microscopic image is a basic task in medical imaging. Most of the existing methods are designed for monochrome images. An effective fully convolutional network (FCN) is proposed to directly process color microscopic image in this paper. The proposed method estimates the distribution of illumination information in input image, and then carry out the correction of the corresponding uneven illumination through a feature encoder module, a feature decoder module, and a detail supplement module. In this process, overlapping residual blocks are designed to better transfer the illumination information, and in particular a well-designed weighted loss function ensures that the network can not only correct the illumination but also preserve image details. The proposed method is compared with some related methods on real pathological cell images qualitatively and quantitatively. Experimental results show that our method achieves the excellent performance. The proposed method is also applied to the preprocessing of whole slide imaging (WSI) tiles, which greatly improves the effect of image mosaicking.

An enhanced fluorescence detection of a nitroaromatic compound using bacteria embedded in porous poly lactic-co-glycolic acid microbeads

The detection of explosive nitroaromatic compounds has caused worldwide concern for human safety. In this study, we introduce a fluorescent biosensor based on porous biocompatible microspheres loaded with a bioreporter for the detection of nitroaromatic compounds. Poly(lactic-co-glycolic acid) microbeads were designed as biosensors embedded with the bacterial bioreporters. The genetically engineered bacterial bioreporter can express a green fluorescent protein in response to nitroaromatic compounds (e.g., trinitrotoluene and dinitrotoluene). The modified surface structure in microbeads provides a large surface area, as well as easy penetration, and increases the number of attached bioreporters for enhanced fluorescent signals of biosensors. Moreover, the addition of the M13 bacteriophage in open porous microbeads significantly amplified the fluorescence signal for detection by the π-π interaction between peptides in the M13 bacteriophage and nitroaromatic compounds. The modification of the surface morphology, as well as the genetically engineered M13 phage, significantly amplifies the fluorescence signal, which makes the detection of explosives easier, and has great potential for the stand-off remote sensing of TNT buried in the field.

Comprehensive in vitro antidiabetic screening of Aspalathus linearis using a target-directed screening platform and cellomics

The antidiabetic potential of Aspalathus linearis has been investigated for over a decade, however, its characterisation remains incomplete with results scattered across numerous journals making the information difficult to compare and integrate. To explore whether any potential antidiabetic mechanisms for A. linearis have been neglected and to compare the suitability of extracts of green and "fermented" A. linearis as potential antidiabetic treatment strategies, this study utilised a comprehensive in vitro antidiabetic target-directed screening platform in combination with high content screening and analysis/cellomics. The antidiabetic screening platform consisted of 20 different screening assays that incorporated 5 well-characterised antidiabetic targets i.e. the intestine, liver, skeletal muscle, adipose tissue/obesity and pancreatic β-cells. Both the green and fermented extracts of A. linearis demonstrated very broad antidiabetic mechanisms as they revealed several promising activities that could be useful in combatting insulin resistance, inflammation, oxidative stress, protein glycation and pancreatic β-cell dysfunction and death - with a strong tendency to attenuate postprandial hyperglycaemia and the subsequent metabolic dysfunction which arises as a result of poor glycaemic control. The green extract was more successful at combatting oxidative stress in INS-1 pancreatic β-cells and enhancing intracellular calcium levels in the absence of glucose. Conversely, the fermented extract demonstrated a greater ability to inhibit α-glucosidase activity as well as palmitic acid-induced free fatty acid accumulation in C3A hepatocytes and differentiated L6 myotubes, however, further studies are required to clarify the potentially toxic and pro-inflammatory nature of the fermented extract.

High-Content Imaging to Phenotype Human Primary and iPSC-Derived Cells

Increasingly powerful microscopy, liquid handling, and computational techniques have enabled cell imaging in high throughput. Microscopy images are quantified using high-content analysis platforms linking object features to cell behavior. This can be attempted on physiologically relevant cell models, including stem cells and primary cells, in complex environments, and conceivably in the presence of perturbations. Recently, substantial focus has been devoted to cell profiling for cell therapy, assays for drug discovery or biomarker identification for clinical decision-making protocols, bringing this wealth of information into translational applications. In this chapter, we focus on two protocols enabling to (1) benchmark human cells, in particular human endothelial cells as a case study and (2) extract cells from blood for follow-up experiments including image-based drug testing. We also present concepts of high-content imaging and discuss the benefits and challenges, with the aim of enabling readers to tailor existing pipelines and bring such approaches closer to translational research and the clinic.

Evaluating the Effects of Disinfectants on Bacterial Biofilms Using a Microfluidics Flow Cell and Time-Lapse Fluorescence Microscopy

A commercially available microfluidics flow cell was utilized together with widefield fluorescence microscopy to evaluate the effects of disinfectants on bacterial strains. The flow cell’s inner surface supports the formation of biofilms of numerous bacterial species. The modular setup of the flow cell accessories allows connection to syringes, pumps and collection vials, facilitating aseptic experiments in a controlled fluidics environment which can be documented with precisely timed microscopy imaging. The flow cell is inoculated with a suspension of bacteria in a nutrient medium and incubated for several days allowing bacterial cells to form a biofilm. Shortly before performing an assay, the biofilm is labelled with a dual-fluorescent DNA probe which distinguishes unharmed and damaged bacteria. Then a disinfectant sample (or control) is gently injected and time-lapse imaging is used for quantifying the course of bacterial biomass response. We use a simplified widefield microscopy method that allows intensive recording and quantification of time series of two-dimensional frames for tracking the course of disinfectant action on a variety of microbial strains. This procedure has potential for the rapid evaluation of novel products.

A Deep Learning Pipeline for Nucleus Segmentation

Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to evaluate the feasibility of training nuclear segmentation models on small, custom annotated image datasets that have been augmented, we have designed a computational pipeline to systematically compare different nuclear segmentation model architectures and model training strategies. Using this approach, we demonstrate that transfer learning and tuning of training parameters, such as the composition, size, and preprocessing of the training image dataset, can lead to robust nuclear segmentation models, which match, and often exceed, the performance of existing, off-the-shelf deep learning models pretrained on large image datasets. We envision a practical scenario where deep learning nuclear segmentation models trained in this way can be shared across a laboratory, facility, or institution, and continuously improved by training them on progressively larger and varied image datasets. Our work provides computational tools and a practical framework for deep learning-based biological image segmentation using small annotated image datasets. Published [2020]. This article is a U.S. Government work and is in the public domain in the USA.

Interactive machine learning for fast and robust cell profiling

Automated profiling of cell morphology is a powerful tool for inferring cell function. However, this technique retains a high barrier to entry. In particular, configuring image processing parameters for optimal cell profiling is susceptible to cognitive biases and dependent on user experience. Here, we use interactive machine learning to identify the optimum cell profiling configuration that maximises quality of the cell profiling outcome. The process is guided by the user, from whom a rating of the quality of a cell profiling configuration is obtained. We use Bayesian optimisation, an established machine learning algorithm, to learn from this information and automatically recommend the next configuration to examine with the aim of maximising the quality of the processing or analysis. Compared to existing interactive machine learning tools that require domain expertise for per-class or per-pixel annotations, we rely on users’ explicit assessment of output quality of the cell profiling task at hand. We validated our interactive approach against the standard human trial-and-error scheme to optimise an object segmentation task using the standard software CellProfiler. Our toolkit enabled rapid optimisation of an object segmentation pipeline, increasing the quality of object segmentation over a pipeline optimised through trial-and-error. Users also attested to the ease of use and reduced cognitive load enabled by our machine learning strategy over the standard approach. We envision that our interactive machine learning approach can enhance the quality and efficiency of pipeline optimisation to democratise image-based cell profiling.

Reprint of "Multi-modal image cytometry approach - From dynamic to whole organ imaging"

Optical imaging is a valuable tool to visualise biological processes in the context of the tissue. Each imaging modality provides the biologist with different types of information - cell dynamics and migration over time can be tracked with time-lapse imaging (e.g. intra-vital imaging); an overview of whole tissues can be acquired using optical clearing in conjunction with light sheet microscopy; finer details such as cellular morphology and fine nerve tortuosity can be imaged at higher resolution using the confocal microscope. Multi-modal imaging combined with image cytometry - a form of quantitative analysis of image datasets - provides an objective basis for comparing between sample groups. Here, we provide an overview of technical aspects to look out for in an image cytometry workflow, and discuss issues related to sample preparation, image post-processing and analysis for intra-vital and whole organ imaging.

FairSubset: A tool to choose representative subsets of data for use with replicates or groups of different sample sizes

High-impact journals are promoting transparency of data. Modern scientific methods can be automated and produce disparate samples sizes. In many cases, it is desirable to retain identical or pre-defined sample sizes between replicates or groups. However, choosing which subset of originally acquired data that best matches the entirety of the data set without introducing bias is not trivial. Here, we released a free online tool, FairSubset, and its constituent Shiny App R code to subset data in an unbiased fashion. Subsets were set at the same N across samples and retained representative average and standard deviation information. The method can be used for quantitation of entire fields of view or other replicates without biasing the data pool toward large N samples. We showed examples of the tool’s use with fluorescence data and DNA-damage related Comet tail quantitation. This FairSubset tool and the method to retain distribution information at the single-datum level may be considered for standardized use in fair publishing practices.

High-content screen in human pluripotent cells identifies miRNA-regulated pathways controlling pluripotency and differentiation

Background

By post-transcriptionally regulating multiple target transcripts, microRNAs (miRNAs or miR) play important biological functions. H1 embryonic stem cells (hESCs) and NTera-2 embryonal carcinoma cells (ECCs) are two of the most widely used human pluripotent model cell lines, sharing several characteristics, including the expression of miRNAs associated to the pluripotent state or with differentiation. However, how each of these miRNAs functionally impacts the biological properties of these cells has not been systematically evaluated.

Methods

We investigated the effects of 31 miRNAs on NTera-2 and H1 hESCs, by transfecting miRNA mimics. Following 3–4 days of culture, cells were stained for the pluripotency marker OCT4 and the G2 cell-cycle marker Cyclin B1, and nuclei and cytoplasm were co-stained with Hoechst and Cell Mask Blue, respectively. By using automated quantitative fluorescence microscopy (i.e., high-content screening (HCS)), we obtained several morphological and marker intensity measurements, in both cell compartments, allowing the generation of a multiparametric miR-induced phenotypic profile describing changes related to proliferation, cell cycle, pluripotency, and differentiation.

Results

Despite the overall similarities between both cell types, some miRNAs elicited cell-specific effects, while some related miRNAs induced contrasting effects in the same cell. By identifying transcripts predicted to be commonly targeted by miRNAs inducing similar effects (profiles grouped by hierarchical clustering), we were able to uncover potentially modulated signaling pathways and biological processes, likely mediating the effects of the microRNAs on the distinct groups identified. Specifically, we show that miR-363 contributes to pluripotency maintenance, at least in part, by targeting NOTCH1 and PSEN1 and inhibiting Notch-induced differentiation, a mechanism that could be implicated in naïve and primed pluripotent states.

Conclusions

We present the first multiparametric high-content microRNA functional screening in human pluripotent cells. Integration of this type of data with similar data obtained from siRNA screenings (using the same HCS assay) could provide a large-scale functional approach to identify and validate microRNA-mediated regulatory mechanisms controlling pluripotency and differentiation.

Electronic supplementary material

The online version of this article (10.1186/s13287-019-1318-6) contains supplementary material, which is available to authorized users.

Long-term fluorescence hyperspectral imaging of on-chip treated co-culture tumour spheroids to follow clonal evolution

Multicellular tumour spheroids are an ideal in vitro tumour model to study clonal heterogeneity and drug resistance in cancer research because different cell types can be mixed at will. However, measuring the individual response of each cell population over time is challenging: current methods are either destructive, such as flow cytometry, or cannot image throughout a spheroid, such as confocal microscopy. Our group previously developed a wide-field fluorescence hyperspectral imaging system to study spheroids formed and cultured in microfluidic chips. In the present study, two subclones of a single parental ovarian cancer cell line transfected to express different fluorophores were produced and co-culture spheroids were formed on-chip using ratios forming highly asymmetric subpopulations. We performed a 3D proliferation assay on each cell population forming the spheroids that matched the 2D growth behaviour. Response assays to PARP inhibitors and platinum-based drugs were also performed to follow the clonal evolution of mixed populations. Our experiments show that hyperspectral imaging can detect spheroid response before observing a decrease in spheroid diameter. Hyperspectral imaging and microfluidic-based spheroid assays provide a versatile solution to study clonal heterogeneity, able to measure response in subpopulations presenting as little as 10% of the initial spheroid.

Establishing cell painting in a smaller chemical biology lab - A report from the frontier

In this paper we will outline the efforts we have made recently to establish the profiling platform known as cell painting in our laboratory. This platform, which is based on fluorescence microscopy, allows rapid and cheap access to bioactivity fingerprints for small molecules and thereby can contribute with important information in many experimental situations that is faced in laboratories involved in molecular probe design, mode-of-action studies or that perform focused phenotypic screens. We have tried to achieve the following two objectives: (1) provide a detailed description of the hurdles that we had to overcome during establishment and describe our final protocol; (2) provide a more pedagogical description of the different methods used to analyse and represent data from this experiment. Finally, we provide an example of how the method can be used to clarify mechanistic dichotomies.

Algorithmic Mapping and Characterization of the Drug-Induced Phenotypic-Response Space of Parasites Causing Schistosomiasis

Neglected tropical diseases, especially those caused by helminths, constitute some of the most common infections of the world's poorest people. Amongst these, schistosomiasis (bilharzia or 'snail fever'), caused by blood flukes of the genus Schistosoma, ranks second only to malaria in terms of human impact: two hundred million people are infected and close to 800 million are at risk of infection. Drug screening against helminths poses unique challenges: the parasite cannot be cloned and is difficult to target using gene knockouts or RNAi. Consequently, both lead identification and validation involve phenotypic screening, where parasites are exposed to compounds whose effects are determined through the analysis of the ensuing phenotypic responses. The efficacy of leads thus identified derives from one or more or even unknown molecular mechanisms of action. The two most immediate and significant challenges that confront the state-of-the-art in this area are: the development of automated and quantitative phenotypic screening techniques and the mapping and quantitative characterization of the totality of phenotypic responses of the parasite. In this paper, we investigate and propose solutions for the latter problem in terms of the following: (1) mathematical formulation and algorithms that allow rigorous representation of the phenotypic response space of the parasite, (2) application of graph-theoretic and network analysis techniques for quantitative modeling and characterization of the phenotypic space, and (3) application of the aforementioned methodology to analyze the phenotypic space of S. mansoni - one of the etiological agents of schistosomiasis, induced by compounds that target its polo-like kinase 1 (PLK 1) gene - a recently validated drug target. In our approach, first, bio-image analysis algorithms are used to quantify the phenotypic responses of different drugs. Next, these responses are linearly mapped into a low- dimensional space using Principle Component Analysis (PCA). The phenotype space is modeled using neighborhood graphs which are used to represent the similarity amongst the phenotypes. These graphs are characterized and explored using network analysis algorithms. We present a number of results related to both the nature of the phenotypic space of the S. mansoni parasite as well as algorithmic issues encountered in constructing and analyzing the phenotypic-response space. In particular, the phenotype distribution of the parasite was found to have a distinct shape and topology. We have also quantitatively characterized the phenotypic space by varying critical model parameters. Finally, these maps of the phenotype space allows visualization and reasoning about complex relationships between putative drugs and their system-wide effects and can serve as a highly efficient paradigm for assimilating and unifying information from phenotypic screens both during lead identification and lead optimization.

Human-Derived Neurons and Neural Progenitor Cells in High Content Imaging Applications

Due to advances in the fields of stem cell biology and cellular engineering, a variety of commercially available human-derived neurons and neural progenitor cells (NPCs) are now available for use in research applications, including small molecule efficacy or toxicity screening. The use of human-derived neural cells is anticipated to address some of the uncertainties associated with the use of nonhuman culture models or transformed cell lines derived from human tissues. Many of the human-derived neurons and NPCs currently available from commercial sources recapitulate critical process of nervous system development including NPC proliferation, neurite outgrowth, synaptogenesis, and calcium signaling, each of which can be evaluated using high content image analysis (HCA). Human-derived neurons and NPCs are also amenable to culture in multiwell plate formats and thus may be adapted for use in HCA-based screening applications. This article reviews various types of HCA-based assays that have been used in conjunction with human-derived neurons and NPC cultures. This article also highlights instances where lower throughput analysis of neurodevelopmental processes has been performed and which demonstrate a potential for adaptation to higher-throughout imaging methods. Finally, a generic protocol for evaluating neurite outgrowth in human-derived neurons using a combination of immunocytochemistry and HCA is presented. The information provided in this article is intended to serve as a resource for cell model and assay selection for those interested in evaluating neurodevelopmental processes in human-derived cells.

Age of heart disease presentation and dysmorphic nuclei in patients with LMNA mutations

Nuclear shape defects are a distinguishing characteristic in laminopathies, cancers, and other pathologies. Correlating these defects to the symptoms, mechanisms, and progression of disease requires unbiased, quantitative, and high-throughput means of quantifying nuclear morphology. To accomplish this, we developed a method of automatically segmenting fluorescently stained nuclei in 2D microscopy images and then classifying them as normal or dysmorphic based on three geometric features of the nucleus using a package of Matlab codes. As a test case, cultured skin-fibroblast nuclei of individuals possessing LMNA splice-site mutation (c.357-2A>G), LMNA nonsense mutation (c.736 C>T, pQ246X) in exon 4, LMNA missense mutation (c.1003C>T, pR335W) in exon 6, Hutchinson-Gilford Progeria Syndrome, and no LMNA mutations were analyzed. For each cell type, the percentage of dysmorphic nuclei, and other morphological features such as average nuclear area and average eccentricity were obtained. Compared to blind observers, our procedure implemented in Matlab codes possessed similar accuracy to manual counting of dysmorphic nuclei while being significantly more consistent. The automatic quantification of nuclear defects revealed a correlation between in vitro results and age of patients for initial symptom onset. Our results demonstrate the method’s utility in experimental studies of diseases affecting nuclear shape through automated, unbiased, and accurate identification of dysmorphic nuclei.

Automated tracking and quantification of angiogenic vessel formation in 3D microfluidic devices

Angiogenesis, the growth of new blood vessels from pre-existing vessels, is a critical step in cancer invasion. Better understanding of the angiogenic mechanisms is required to develop effective antiangiogenic therapies for cancer treatment. We culture angiogenic vessels in 3D microfluidic devices under different Sphingosin-1-phosphate (S1P) conditions and develop an automated vessel formation tracking system (AVFTS) to track the angiogenic vessel formation and extract quantitative vessel information from the experimental time-lapse phase contrast images. The proposed AVFTS first preprocesses the experimental images, then applies a distance transform and an augmented fast marching method in skeletonization, and finally implements the Hungarian method in branch tracking. When applying the AVFTS to our experimental data, we achieve 97.3% precision and 93.9% recall by comparing with the ground truth obtained from manual tracking by visual inspection. This system enables biologists to quantitatively compare the influence of different growth factors. Specifically, we conclude that the positive S1P gradient increases cell migration and vessel elongation, leading to a higher probability for branching to occur. The AVFTS is also applicable to distinguish tip and stalk cells by considering the relative cell locations in a branch. Moreover, we generate a novel type of cell lineage plot, which not only provides cell migration and proliferation histories but also demonstrates cell phenotypic changes and branch information.

A high-content image analysis approach for quantitative measurements of chemosensitivity in patient-derived tumor microtissues

Organotypic, three-dimensional (3D) cancer models have enabled investigations of complex microtissues in increasingly realistic conditions. However, a drawback of these advanced models remains the poor biological relevance of cancer cell lines, while higher clinical significance would be obtainable with patient-derived cell cultures. Here, we describe the generation and data analysis of 3D microtissue models from patient-derived xenografts (PDX) of non-small cell lung carcinoma (NSCLC). Standard of care anti-cancer drugs were applied and the altered multicellular morphologies were captured by confocal microscopy, followed by automated image analyses to quantitatively measure phenotypic features for high-content chemosensitivity tests. The obtained image data were thresholded using a local entropy filter after which the image foreground was split into local regions, for a supervised classification into tumor or fibroblast cell types. Robust statistical methods were applied to evaluate treatment effects on growth and morphology. Both novel and existing computational approaches were compared at each step, while prioritizing high experimental throughput. Docetaxel was found to be the most effective drug that blocked both tumor growth and invasion. These effects were also validated in PDX tumors in vivo. Our research opens new avenues for high-content drug screening based on patient-derived cell cultures, and for personalized chemosensitivity testing.

High-Throughput Imaging for the Discovery of Cellular Mechanisms of Disease

High-throughput imaging (HTI) is a powerful tool in the discovery of cellular disease mechanisms. While traditional approaches to identify disease pathways often rely on knowledge of the causative genetic defect, HTI-based screens offer an unbiased discovery approach based on any morphological or functional defects of disease cells or tissues. In this review, we provide an overview of the use of HTI for the study of human disease mechanisms. We discuss key technical aspects of HTI and highlight representative examples of its practical applications for the discovery of molecular mechanisms of disease, focusing on infectious diseases and host-pathogen interactions, cancer, and rare genetic diseases. We also present some of the current challenges and possible solutions offered by novel cell culture systems and genome engineering approaches.

Image Quality Ranking Method for Microscopy

Automated analysis of microscope images is necessitated by the increased need for high-resolution follow up of events in time. Manually finding the right images to be analyzed, or eliminated from data analysis are common day-to-day problems in microscopy research today, and the constantly growing size of image datasets does not help the matter. We propose a simple method and a software tool for sorting images within a dataset, according to their relative quality. We demonstrate the applicability of our method in finding good quality images in a STED microscope sample preparation optimization image dataset. The results are validated by comparisons to subjective opinion scores, as well as five state-of-the-art blind image quality assessment methods. We also show how our method can be applied to eliminate useless out-of-focus images in a High-Content-Screening experiment. We further evaluate the ability of our image quality ranking method to detect out-of-focus images, by extensive simulations, and by comparing its performance against previously published, well-established microscopy autofocus metrics.

A Practical Approach to Quantitative Processing and Analysis of Small Biological Structures by Fluorescent Imaging

Standards in quantitative fluorescent imaging are vaguely recognized and receive insufficient discussion. A common best practice is to acquire images at Nyquist rate, where highest signal frequency is assumed to be the highest obtainable resolution of the imaging system. However, this particular standard is set to insure that all obtainable information is being collected. The objective of the current study was to demonstrate that for quantification purposes, these correctly set acquisition rates can be redundant; instead, linear size of the objects of interest can be used to calculate sufficient information density in the image. We describe optimized image acquisition parameters and unbiased methods for processing and quantification of medium-size cellular structures. Sections of rabbit aortas were immunohistochemically stained to identify and quantify sympathetic varicosities, >2 μm in diameter. Images were processed to reduce background noise and segment objects using free, open-access software. Calculations of the optimal sampling rate for the experiment were based on the size of the objects of interest. The effect of differing sampling rates and processing techniques on object quantification was demonstrated. Oversampling led to a substantial increase in file size, whereas undersampling hindered reliable quantification. Quantification of raw and incorrectly processed images generated false structures, misrepresenting the underlying data. The current study emphasizes the importance of defining image-acquisition parameters based on the structure(s) of interest. The proposed postacquisition processing steps effectively removed background and noise, allowed for reliable quantification, and eliminated user bias. This customizable, reliable method for background subtraction and structure quantification provides a reproducible tool for researchers across biologic disciplines.

Morphology and Phenotype of Peripheral Erythrocytes of Fish: A Rapid Screening of Images by Using Software

Many information of biological study as stained cells analysis under microscope cannot be obtained rich information like detail morphology, shape, size, proper intensity etc. but image analysis software can easily be detected all these parameters within short duration. The cells types can be yeast cells to mammalian cells. An attempt has been made to detect cellular abnormalities from an image of metronidazole (MTZ) treated compared to control images of peripheral erythrocytes of fish by using non-commercial, open-source, CellProfiler (CP) image analysis software (Ver. 2.1.0). The comparative results were obtained after analysis the software. In conclusion, this image based screening of Giemsa stained fish erythrocytes can be a suitable tool in biological research for primary toxicity prediction at DNA level alongwith cellular phenotypes. Moreover, still suggestions are needed in relation to accuracy of present analysis for Giemsa stained fish erythrocytes because previous works have been carried out images of cells with fluorescence dye.

Automated image analysis of FRET signals for subcellular cAMP quantification

A variety of FRET probes have been developed to examine cAMP localization and dynamics in single cells. These probes offer a readily accessible approach to measure localized cAMP signals. However, given the low signal-to-noise ratio of most FRET probes and the dynamic nature of the intracellular environment, there have been marked limitations in the ability to use FRET probes to study localized signaling events within the same cell. Here, we outline a methodology to dissect kinetics of cAMP-mediated FRET signals in single cells using automated image analysis approaches. We additionally extend these approaches to the analysis of subcellular regions. These approaches offer an unique opportunity to assess localized cAMP kinetics in an unbiased, quantitative fashion.

MATtrack: A MATLAB-Based Quantitative Image Analysis Platform for Investigating Real-Time Photo-Converted Fluorescent Signals in Live Cells

We introduce here MATtrack, an open source MATLAB-based computational platform developed to process multi-Tiff files produced by a photo-conversion time lapse protocol for live cell fluorescent microscopy. MATtrack automatically performs a series of steps required for image processing, including extraction and import of numerical values from Multi-Tiff files, red/green image classification using gating parameters, noise filtering, background extraction, contrast stretching and temporal smoothing. MATtrack also integrates a series of algorithms for quantitative image analysis enabling the construction of mean and standard deviation images, clustering and classification of subcellular regions and injection point approximation. In addition, MATtrack features a simple user interface, which enables monitoring of Fluorescent Signal Intensity in multiple Regions of Interest, over time. The latter encapsulates a region growing method to automatically delineate the contours of Regions of Interest selected by the user, and performs background and regional Average Fluorescence Tracking, and automatic plotting. Finally, MATtrack computes convenient visualization and exploration tools including a migration map, which provides an overview of the protein intracellular trajectories and accumulation areas. In conclusion, MATtrack is an open source MATLAB-based software package tailored to facilitate the analysis and visualization of large data files derived from real-time live cell fluorescent microscopy using photoconvertible proteins. It is flexible, user friendly, compatible with Windows, Mac, and Linux, and a wide range of data acquisition software. MATtrack is freely available for download at eleceng.dit.ie/courtney/MATtrack.zip.

New tools for comparing microscopy images: quantitative analysis of cell types in Bacillus subtilis

Fluorescence microscopy is a method commonly used to examine individual differences between bacterial cells, yet many studies still lack a quantitative analysis of fluorescence microscopy data. Here we introduce some simple tools that microbiologists can use to analyze and compare their microscopy images. We show how image data can be converted to distribution data. These data can be subjected to a cluster analysis that makes it possible to objectively compare microscopy images. The distribution data can further be analyzed using distribution fitting. We illustrate our methods by scrutinizing two independently acquired data sets, each containing microscopy images of a doubly labeled Bacillus subtilis strain. For the first data set, we examined the expression of srfA and tapA, two genes which are expressed in surfactin-producing and matrix-producing cells, respectively. For the second data set, we examined the expression of eps and tapA; these genes are expressed in matrix-producing cells. We show that srfA is expressed by all cells in the population, a finding which contrasts with a previously reported bimodal distribution of srfA expression. In addition, we show that eps and tapA do not always have the same expression profiles, despite being expressed in the same cell type: both operons are expressed in cell chains, while single cells mainly express eps. These findings exemplify that the quantification and comparison of microscopy data can yield insights that otherwise would go unnoticed.

Automated image-based assay for evaluation of HIV neutralization and cell-to-cell fusion inhibition

Background

Standardized techniques to detect HIV-neutralizing antibody responses are of great importance in the search for an HIV vaccine.

Methods

Here, we present a high-throughput, high-content automated plaque reduction (APR) assay based on automated microscopy and image analysis that allows evaluation of neutralization and inhibition of cell-cell fusion within the same assay. Neutralization of virus particles is measured as a reduction in the number of fluorescent plaques, and inhibition of cell-cell fusion as a reduction in plaque area.

Results

We found neutralization strength to be a significant factor in the ability of virus to form syncytia. Further, we introduce the inhibitory concentration of plaque area reduction (ICpar) as an additional measure of antiviral activity, i.e. fusion inhibition.

Conclusions

We present an automated image based high-throughput, high-content HIV plaque reduction assay. This allows, for the first time, simultaneous evaluation of neutralization and inhibition of cell-cell fusion within the same assay, by quantifying the reduction in number of plaques and mean plaque area, respectively. Inhibition of cell-to-cell fusion requires higher quantities of inhibitory reagent than inhibition of virus neutralization.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2334-14-472) contains supplementary material, which is available to authorized users.

An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm

Characterizing cytoarchitecture is crucial for understanding brain functions and neural diseases. In neuroanatomy, it is an important task to accurately extract cell populations' centroids and contours. Recent advances have permitted imaging at single cell resolution for an entire mouse brain using the Nissl staining method. However, it is difficult to precisely segment numerous cells, especially those cells touching each other. As presented herein, we have developed an automated three-dimensional detection and segmentation method applied to the Nissl staining data, with the following two key steps: 1) concave points clustering to determine the seed points of touching cells; and 2) random walker segmentation to obtain cell contours. Also, we have evaluated the performance of our proposed method with several mouse brain datasets, which were captured with the micro-optical sectioning tomography imaging system, and the datasets include closely touching cells. Comparing with traditional detection and segmentation methods, our approach shows promising detection accuracy and high robustness.

Dynamics of BMP signaling in limb bud mesenchyme and polydactyly

Mutations in the Bone Morphogenetic Protein (BMP) pathway are associated with a range of defects in skeletal formation. Genetic analysis of BMP signaling requirements is complicated by the presence of three partially redundant BMPs that are required for multiple stages of limb development. We generated an inducible allele of a BMP inhibitor, Gremlin, which reduces BMP signaling. We show that BMPs act in a dose and time dependent manner in which early reduction of BMPs result in digit loss, while inhibiting overall BMP signaling between E10.5 and E11.5 allows polydactylous digit formation. During this period, inhibiting BMPs extends the duration of FGF signaling. Sox9 is initially expressed in normal digit ray domains but at reduced levels that correlate with the reduction in BMP signaling. The persistence of elevated FGF signaling likely promotes cell proliferation and survival, inhibiting the activation of Sox9 and secondarily, inhibiting the differentiation of Sox9-expressing chondrocytes. Our results provide new insights into the timing and clarify the mechanisms underlying BMP signaling during digit morphogenesis.

Unsupervised cell identification on multidimensional X-ray fluorescence datasets

A novel approach to locate, identify and refine positions and whole areas of cell structures based on elemental contents measured by X-ray fluorescence microscopy is introduced. It is shown that, by initializing with only a handful of prototypical cell regions, this approach can obtain consistent identification of whole cells, even when cells are overlapping, without training by explicit annotation. It is robust both to different measurements on the same sample and to different initializations. This effort provides a versatile framework to identify targeted cellular structures from datasets too complex for manual analysis, like most X-ray fluorescence microscopy data. Possible future extensions are also discussed.

Glutamate-based magnetic resonance spectroscopy in neuroleptic malignant syndrome

Glutamate neurotoxicity is implicated in a number of neurological diseases, including Neuroleptic Malignant syndrome. Therefore, functional magnetic resonance imaging can help in diagnosis and monitoring such conditions. However, reports of this application are scarce in the literature. In this manuscript, glutamate based imaging of the basal ganglia showed increased levels of the neurotransmitter bilaterally. In addition, a radon transform of the functional image was performed to look for any asymmetry in cerebral activation. Although no asymmetry was detected in this case, this novel analysis can be applied in physiological and pathological scenarios to visualize contribution of different brain structures.

Interleukin-6 secretion by astrocytes is dynamically regulated by PI3K-mTOR-calcium signaling

After contusion spinal cord injury (SCI), astrocytes become reactive and form a glial scar. While this reduces spreading of the damage by containing the area of injury, it inhibits regeneration. One strategy to improve the recovery after SCI is therefore to reduce the inhibitory effect of the scar, once the acute phase of the injury has passed. The pleiotropic cytokine interleukin-6 (IL-6) is secreted immediately after injury and regulates scar formation; however, little is known about the role of IL-6 in the sub-acute phases of SCI. Interestingly, IL-6 also promotes axon regeneration, and therefore its induction in reactive astrocytes may improve regeneration after SCI. We found that IL-6 is expressed by astrocytes and neurons one week post-injury and then declines. Using primary cultures of rat astrocytes we delineated the molecular mechanisms that regulate IL-6 expression and secretion. IL-6 expression requires activation of p38 and depends on NF-κB transcriptional activity. Activation of these pathways in astrocytes occurs when the PI3K-mTOR-AKT pathway is inhibited. Furthermore, we found that an increase in cytosolic calcium concentration was necessary for IL-6 secretion. To induce IL-6 secretion in astrocytes, we used torin2 and rapamycin to block the PI3K-mTOR pathway and increase cytosolic calcium, respectively. Treating injured animals with torin2 and rapamycin for two weeks, starting two weeks after injury when the scar has been formed, lead to a modest effect on mechanical hypersensitivity, limited to the period of treatment. These data, taken together, suggest that treatment with torin2 and rapamycin induces IL-6 secretion by astrocytes and may contribute to the reduction of mechanical hypersensitivity after SCI.

Understanding health and disease with multidimensional single-cell methods

Current efforts in the biomedical sciences and related interdisciplinary fields are focused on gaining a molecular understanding of health and disease, which is a problem of daunting complexity that spans many orders of magnitude in characteristic length scales, from small molecules that regulate cell function to cell ensembles that form tissues and organs working together as an organism. In order to uncover the molecular nature of the emergent properties of a cell, it is essential to measure multiple-cell components simultaneously in the same cell. In turn, cell heterogeneity requires multiple-cells to be measured in order to understand health and disease in the organism. This review summarizes current efforts towards a data-driven framework that leverages single-cell technologies to build robust signatures of healthy and diseased phenotypes. While some approaches focus on multicolor flow cytometry data and other methods are designed to analyze high-content image-based screens, we emphasize the so-called Supercell/SVM paradigm (recently developed by the authors of this review and collaborators) as a unified framework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond their specific contributions to basic and translational biomedical research, these efforts illustrate, from a larger perspective, the powerful synergy that might be achieved from bringing together methods and ideas from statistical physics, data mining, and mathematics to solve the most pressing problems currently facing the life sciences.

Automated annotation of gene expression image sequences via non-parametric factor analysis and conditional random fields

Motivation: Computational approaches for the annotation of phenotypes from image data have shown promising results across many applications, and provide rich and valuable information for studying gene function and interactions. While data are often available both at high spatial resolution and across multiple time points, phenotypes are frequently annotated independently, for individual time points only. In particular, for the analysis of developmental gene expression patterns, it is biologically sensible when images across multiple time points are jointly accounted for, such that spatial and temporal dependencies are captured simultaneously.

Methods: We describe a discriminative undirected graphical model to label gene-expression time-series image data, with an efficient training and decoding method based on the junction tree algorithm. The approach is based on an effective feature selection technique, consisting of a non-parametric sparse Bayesian factor analysis model. The result is a flexible framework, which can handle large-scale data with noisy incomplete samples, i.e. it can tolerate data missing from individual time points.

Results: Using the annotation of gene expression patterns across stages of Drosophila embryonic development as an example, we demonstrate that our method achieves superior accuracy, gained by jointly annotating phenotype sequences, when compared with previous models that annotate each stage in isolation. The experimental results on missing data indicate that our joint learning method successfully annotates genes for which no expression data are available for one or more stages.

Contact: uwe.ohler@duke.edu

Multiplex cytological profiling assay to measure diverse cellular states

Computational methods for image-based profiling are under active development, but their success hinges on assays that can capture a wide range of phenotypes. We have developed a multiplex cytological profiling assay that “paints the cell” with as many fluorescent markers as possible without compromising our ability to extract rich, quantitative profiles in high throughput. The assay detects seven major cellular components. In a pilot screen of bioactive compounds, the assay detected a range of cellular phenotypes and it clustered compounds with similar  annotated protein targets or chemical structure based on cytological profiles. The results demonstrate that the assay captures subtle patterns in the combination of morphological labels, thereby detecting the effects of chemical compounds even though their targets are not stained directly. This image-based assay provides an unbiased approach to characterize compound- and disease-associated cell states to support future probe discovery.

High content analysis of differentiation and cell death in human adipocytes

Understanding adipocyte biology and its homeostasis is in the focus of current obesity research. We aimed to introduce a high-content analysis procedure for directly visualizing and quantifying adipogenesis and adipoapoptosis by laser scanning cytometry (LSC) in a large population of cell. Slide-based image cytometry and image processing algorithms were used and optimized for high-throughput analysis of differentiating cells and apoptotic processes in cell culture at high confluence. Both preadipocytes and adipocytes were simultaneously scrutinized for lipid accumulation, texture properties, nuclear condensation, and DNA fragmentation. Adipocyte commitment was found after incubation in adipogenic medium for 3 days identified by lipid droplet formation and increased light absorption, while terminal differentiation of adipocytes occurred throughout day 9-14 with characteristic nuclear shrinkage, eccentric nuclei localization, chromatin condensation, and massive lipid deposition. Preadipocytes were shown to be more prone to tumor necrosis factor alpha (TNFα)-induced apoptosis compared to mature adipocytes. Importantly, spontaneous DNA fragmentation was observed at early stage when adipocyte commitment occurs. This DNA damage was independent from either spontaneous or induced apoptosis and probably was part of the differentiation program. © 2013 International Society for Advancement of Cytometry.

Crimean-Congo hemorrhagic fever virus utilizes a clathrin- and early endosome-dependent entry pathway

The early events in Crimean-Congo hemorrhagic fever virus (CCHFV) have not been completely characterized. Earlier work indicated that CCHFV likely enters cells by clathrin-mediated endocytosis (CME). Here we provide confirmatory evidence for CME entry by showing that CCHFV infection is inhibited in cells treated with Pitstop 2, a drug that specifically and reversibly interferes with the dynamics of clathrin-coated pits. Additionally, we show that CCHFV infection is inhibited by siRNA depletion of the clathrin pit associated protein AP-2. Following CME entry, we show that CCHFV has a pH-dependent entry step, with virus inactivation occurring at pH 6.0 and below. To more precisely define the endosomal trafficking of CCHFV, we show for the first time that overexpression of the dominant negative forms of Rab5 protein but not Rab7 protein inhibits CCHFV infection. These results indicate that CCHFV likely enters cells through the early endosomal compartment.

A versatile, bar-coded nuclear marker/reporter for live cell fluorescent and multiplexed high content imaging

The screening of large numbers of compounds or siRNAs is a mainstay of both academic and pharmaceutical research. Most screens test those interventions against a single biochemical or cellular output whereas recording multiple complementary outputs may be more biologically relevant. High throughput, multi-channel fluorescence microscopy permits multiple outputs to be quantified in specific cellular subcompartments. However, the number of distinct fluorescent outputs available remains limited. Here, we describe a cellular bar-code technology in which multiple cell-based assays are combined in one well after which each assay is distinguished by fluorescence microscopy. The technology uses the unique fluorescent properties of assay-specific markers comprised of distinct combinations of different 'red' fluorescent proteins sandwiched around a nuclear localization signal. The bar-code markers are excited by a common wavelength of light but distinguished ratiometrically by their differing relative fluorescence in two emission channels. Targeting the bar-code to cell nuclei enables individual cells expressing distinguishable markers to be readily separated by standard image analysis programs. We validated the method by showing that the unique responses of different cell-based assays to specific drugs are retained when three assays are co-plated and separated by the bar-code. Based upon those studies, we discuss a roadmap in which even more assays may be combined in a well. The ability to analyze multiple assays simultaneously will enable screens that better identify, characterize and distinguish hits according to multiple biologically or clinically relevant criteria. These capabilities also enable the re-creation of complex mixtures of cell types that is emerging as a central area of interest in many fields.

OXPHOS mutations and neurodegeneration

Mitochondrial oxidative phosphorylation (OXPHOS) sustains organelle function and plays a central role in cellular energy metabolism. The OXPHOS system consists of 5 multisubunit complexes (CI-CV) that are built up of 92 different structural proteins encoded by the nuclear (nDNA) and mitochondrial DNA (mtDNA). Biogenesis of a functional OXPHOS system further requires the assistance of nDNA-encoded OXPHOS assembly factors, of which 35 are currently identified. In humans, mutations in both structural and assembly genes and in genes involved in mtDNA maintenance, replication, transcription, and translation induce 'primary' OXPHOS disorders that are associated with neurodegenerative diseases including Leigh syndrome (LS), which is probably the most classical OXPHOS disease during early childhood. Here, we present the current insights regarding function, biogenesis, regulation, and supramolecular architecture of the OXPHOS system, as well as its genetic origin. Next, we provide an inventory of OXPHOS structural and assembly genes which, when mutated, induce human neurodegenerative disorders. Finally, we discuss the consequences of mutations in OXPHOS structural and assembly genes at the single cell level and how this information has advanced our understanding of the role of OXPHOS dysfunction in neurodegeneration.