Automated interpretation of subcellular patterns from immunofluorescence microscopy

Automatic recognition of protein subcellular location patterns in single cells from immunofluorescence images based on deep learning

With the improvement of single-cell measurement techniques, there is a growing awareness that individual differences exist among cells, and protein expression distribution can vary across cells in the same tissue or cell line. Pinpointing the protein subcellular locations in single cells is crucial for mapping functional specificity of proteins and studying related diseases. Currently, research about single-cell protein location is still in its infancy, and most studies and databases do not annotate proteins at the cell level. For example, in the human protein atlas database, an immunofluorescence image stained for a particular protein shows multiple cells, but the subcellular location annotation is for the whole image, ignoring intercellular difference. In this study, we used large-scale immunofluorescence images and image-level subcellular locations to develop a deep-learning-based pipeline that could accurately recognize protein localizations in single cells. The pipeline consisted of two deep learning models, i.e. an image-based model and a cell-based model. The former used a multi-instance learning framework to comprehensively model protein distribution in multiple cells in each image, and could give both image-level and cell-level predictions. The latter firstly used clustering and heuristics algorithms to assign pseudo-labels of subcellular locations to the segmented cell images, and then used the pseudo-labels to train a classification model. Finally, the image-based model was fused with the cell-based model at the decision level to obtain the final ensemble model for single-cell prediction. Our experimental results showed that the ensemble model could achieve higher accuracy and robustness on independent test sets than state-of-the-art methods.

Performance analysis of automated evaluation of antinuclear antibody indirect immunofluorescent tests in a routine setting

Purpose

Indirect immunofluorescence (IIF) on the human epithelial cell-line HEp-2 (or derivatives) serves as the gold standard in antinuclear antibody (ANA) screening. IIF, and its evaluation, is a labor-intensive method, making ANA testing a major challenge for present clinical laboratories. Nowadays, several automated ANA pattern recognition systems are on the market. In the current study, the EUROPattern Suite is evaluated for its use in daily practice in a routine setting.

Methods

A total of 1033 consecutive routine samples was used to screen for ANA. Results (positive/negative ANA screening, pattern identification and titer) were compared between software-generated results (EUROPattern) and visual interpretation (observer) of automatically acquired digital images.

Results

Considering the visual interpretation as reference, a relative sensitivity of 99.3% and a relative specificity of 88.9% were obtained for negative and positive discrimination by the software (EPa). A good agreement between visual and software-based interpretation was observed with respect to pattern recognition (mean kappa: for 7 patterns: 0.7). Interestingly, EPa software distinguished more patterns per positive sample than the observer (on average 1.5 and 1.2, respectively). Finally, a concordance of 99.3% was observed within the range of 1 titer step difference between EPa and observer.

Conclusions

The ANA IIF results reported by the EPa software are in very good agreement with the results reported by the observer with respect to being negative/positive, pattern recognition and titer, making automated ANA IIF evaluation an objective and time-efficient tool for routine testing.

Electronic supplementary material

The online version of this article (10.1007/s13317-018-0108-y) contains supplementary material, which is available to authorized users.

Automated Processing and Evaluation of Anti-Nuclear Antibody Indirect Immunofluorescence Testing

Indirect immunofluorescence (IIF) is considered by the American College of Rheumatology (ACR) and the international consensus on ANA patterns (ICAP) the gold standard for the screening of anti-nuclear antibodies (ANA). As conventional IIF is labor intensive, time-consuming, subjective, and poorly standardized, there have been ongoing efforts to improve the standardization of reagents and to develop automated platforms for assay incubation, microscopy, and evaluation. In this study, the workflow and performance characteristics of a fully automated ANA IIF system (Sprinter XL, EUROPattern Suite, IFA 40: HEp-20-10 cells) were compared to a manual approach using visual microscopy with a filter device for single-well titration and to technologist reading. The Sprinter/EUROPattern system enabled the processing of large daily workload cohorts in less than 8 h and the reduction of labor hands-on time by more than 4 h. Regarding the discrimination of positive from negative samples, the overall agreement of the EUROPattern software with technologist reading was higher (95.6%) than when compared to the current method (89.4%). Moreover, the software was consistent with technologist reading in 80.6–97.5% of patterns and 71.0–93.8% of titers. In conclusion, the Sprinter/EUROPattern system provides substantial labor savings and good concordance with technologist ANA IIF microscopy, thus increasing standardization, laboratory efficiency, and removing subjectivity.

Efficient computational model for classification of protein localization images using Extended Threshold Adjacency Statistics and Support Vector Machines

BACKGROUND AND OBJECTIVE

Discriminative and informative feature extraction is the core requirement for accurate and efficient classification of protein subcellular localization images so that drug development could be more effective. The objective of this paper is to propose a novel modification in the Threshold Adjacency Statistics technique and enhance its discriminative power.

METHODS

In this work, we utilized Threshold Adjacency Statistics from a novel perspective to enhance its discrimination power and efficiency. In this connection, we utilized seven threshold ranges to produce seven distinct feature spaces, which are then used to train seven SVMs. The final prediction is obtained through the majority voting scheme. The proposed ETAS-SubLoc system is tested on two benchmark datasets using 5-fold cross-validation technique.

RESULTS

We observed that our proposed novel utilization of TAS technique has improved the discriminative power of the classifier. The ETAS-SubLoc system has achieved 99.2% accuracy, 99.3% sensitivity and 99.1% specificity for Endogenous dataset outperforming the classical Threshold Adjacency Statistics technique. Similarly, 91.8% accuracy, 96.3% sensitivity and 91.6% specificity values are achieved for Transfected dataset.

CONCLUSIONS

Simulation results validated the effectiveness of ETAS-SubLoc that provides superior prediction performance compared to the existing technique. The proposed methodology aims at providing support to pharmaceutical industry as well as research community towards better drug designing and innovation in the fields of bioinformatics and computational biology. The implementation code for replicating the experiments presented in this paper is available at: https://drive.google.com/file/d/0B7IyGPObWbSqRTRMcXI2bG5CZWs/view?usp=sharing.

Mining textural knowledge in biological images: Applications, methods and trends

Texture analysis is a major task in many areas of computer vision and pattern recognition, including biological imaging. Indeed, visual textures can be exploited to distinguish specific tissues or cells in a biological sample, to highlight chemical reactions between molecules, as well as to detect subcellular patterns that can be evidence of certain pathologies. This makes automated texture analysis fundamental in many applications of biomedicine, such as the accurate detection and grading of multiple types of cancer, the differential diagnosis of autoimmune diseases, or the study of physiological processes. Due to their specific characteristics and challenges, the design of texture analysis systems for biological images has attracted ever-growing attention in the last few years. In this paper, we perform a critical review of this important topic. First, we provide a general definition of texture analysis and discuss its role in the context of bioimaging, with examples of applications from the recent literature. Then, we review the main approaches to automated texture analysis, with special attention to the methods of feature extraction and encoding that can be successfully applied to microscopy images of cells or tissues. Our aim is to provide an overview of the state of the art, as well as a glimpse into the latest and future trends of research in this area.

New automated indirect immunofluorescent antinuclear antibody testing compares well with established manual immunofluorescent screening and titration for antinuclear antibody on HEp-2 cells

The IIF using the HEp-2 cell substrate should be still considered the "gold standard" techniques for determination of antinuclear antibody (ANA). Standardization and automation can be considered to be still in progress. Aim of this study was to evaluate the performance of the commercially automated indirect immunofluorescent antinuclear HEp-2 antibody assay. The study was designed to compare two commercially available HEp-2 ANA by indirect immunofluorescent antibody assays using a sensitivity panel (120 clinically determined patients) and a specificity panel consisting of 78 clinically confirmed negative patients. We compared the NOVA View^® system [INOVA Diagnostics San Diego, USA] with the new HELIOS Processor from AESKU Systems/AESKU.Diagnostics (Wendelsheim, Germany) to assess their capability for screening, pattern recognition and titration of the samples. These automated methods were directly compared to manual reading of the same processed slides on respective microscopes and also compared with the known clinical information. The results of the two automated methods were in very good agreement with recognizing negative and positive samples. The HELIOS system detected 188 samples correctly as negative or positive versus 187 detected by the NOVA View^® system. The diagnostic sensitivity of the systems was 95.8 versus 96.7 % for HELIOS and NOVA View^®, respectively. The systems exhibited a diagnostic specificity of 93.5 % for the HELIOS system and 91.0 % for the NOVA View^®. Both systems are suitable for fast and reliable detection of positivity/negativity due to their high sensitivity and will lead to a further increase of standardization in autoimmunity.

EUROPattern Suite technology for computer-aided immunofluorescence microscopy in autoantibody diagnostics

Antinuclear autoantibodies (ANA) are highly informative biomarkers in autoimmune diagnostics. The increasing demand for effective test systems, however, has led to the development of a confusingly large variety of different platforms. One of them, the indirect immunofluorescence (IIF), is regarded as the common gold standard for ANA screening, as described in a position statement by the American College of Rheumatology in 2009. Technological solutions have been developed aimed at standardization and automation of IIF to overcome methodological limitations and subjective bias in IIF interpretation. In this review, we present the EUROPattern Suite, a system for computer-aided immunofluorescence microscopy (CAIFM) including automated acquisition of digital images and evaluation of IIF results. The system was originally designed for ANA diagnostics on human epithelial cells, but its applications have been extended with the latest system update version 1.5 to the analysis of antineutrophil cytoplasmic antibodies (ANCA) and anti-dsDNA antibodies.

Current state of diagnostic technologies in the autoimmunology laboratory

The methods for detecting and measuring autoantibodies have evolved markedly in recent years, encompassing three generations of analytical technologies. Many different immunoassay methods have been developed and used for research and laboratory practice purposes, from the early conventional (or monoplex) analytical methods able to detect single autoantibodies to the more recent multiplex platforms that can quantify tens of molecules. Although it has been in use for over 50 years, indirect immunofluorescence remains the standard method for research on many types of autoantibodies, due to its characteristics of diagnostic sensitivity and also to recent technological innovations which permit it a greater level of automation and standardization. The recent multiplex immunometric methods, with varying levels of automation, present characteristics of higher diagnostic accuracy, but are not yet widely diffused in autoimmunology laboratories due to the limited number of autoantibodies that are detectable, and due to the high cost of reagents and systems. Technological advancement in autoimmunology continues to evolve rapidly, and in the coming years new proteomic techniques will be able to radically change the approach to diagnostics and possibly also clinical treatment of autoimmune diseases. The scope of this review is to update the state of the art of technologies and methods for the measurement of autoantibodies, with special reference to innovations in indirect immunofluorescence and in multiple proteomic methods.

Machine learning in cell biology - teaching computers to recognize phenotypes

Recent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline.

Automated antinuclear immunofluorescence antibody screening: a comparative study of six computer-aided diagnostic systems

BACKGROUND

Indirect immunofluorescence (IIF) plays an important role in immunological assays for detecting and measuring autoantibodies. However, the method is burdened by some unfavorable features: the need for expert morphologists, the subjectivity of interpretation, and a low degree of standardization and automation. Following the recent statement by the American College of Rheumatology that the IIF technique should be considered as the standard screening method for the detection of anti-nuclear antibodies (ANA), the biomedical industry has developed technological solutions which might significantly improve automation of the procedure, not only in the preparation of substrates and slides, but also in microscope reading.

METHODS

We collected 104 ANA-positive sera from patients with a confirmed clinical diagnosis of autoimmune disease and 40 ANA-negative sera from healthy blood donors. One aliquot of each serum, without information about pattern and titer, was sent to six laboratories of our group, where the sera were tested with the IIF manual method provided by each of the six manufacturers of automatic systems. Assignment of result (pos/neg), of pattern and titer was made by consensus at a meeting attended by all members of the research team. Result was assigned if consensus for pos/neg was reached by at least four of six certifiers, while for the pattern and for the titer, the value observed with higher frequency (mode) was adopted. Seventeen ANA-positive sera and six ANA-negative sera were excluded. Therefore, the study with the following automatic instrumentation was conducted on 92 ANA-positive sera and on 34 ANA-negative sera: Aklides, EUROPattern, G-Sight (I-Sight-IFA), Helios, Image Navigator, and Nova View. Analytical imprecision was measured in five aliquots of the same serum, randomly added to the sample series.

RESULTS

Overall sensitivity of the six automated systems was 96.7% and overall specificity was 89.2%. Most false negatives were recorded for cytoplasmic patterns, whereas among nuclear patterns those with a low level of fluorescence (i.e., multiple nuclear dots, midbody, nuclear rim) were sometimes missed. The intensity values of the light signal of various instruments showed a good correlation with the titer obtained by manual reading (Spearman's rho between 0.672 and 0.839; P<0.0001 for all the systems). Imprecision ranged from 1.99% to 25.2% and, for all the systems, it was lower than that obtained by the manual IIF test (39.1%). The accuracy of pattern recognition, which is for now restricted to the most typical patterns (homogeneous, speckled, nucleolar, centromere, multiple nuclear dots and cytoplasmic) was limited, ranging from 52% to 79%.

CONCLUSIONS

This study, which is the first to compare the diagnostic accuracy of six systems for automated ANA-IIF reading on the same series of sera, showed that all systems are able to perform very well the task for which they were created. Indeed, cumulative automatic discrimination between positive and negative samples had 95% accuracy. All the manufacturers are actively continuing the development of new and more sophisticated software for a better definition in automatic recognition of patterns and light signal conversion in end-point titer. In the future, this may avert the need for serum dilution for titration, which will be a great advantage in economic terms and time-saving.

New platform technology for comprehensive serological diagnostics of autoimmune diseases

Antibody assessment is an essential part in the serological diagnosis of autoimmune diseases. However, different diagnostic strategies have been proposed for the work up of sera in particular from patients with systemic autoimmune rheumatic disease (SARD). In general, screening for SARD-associated antibodies by indirect immunofluorescence (IIF) is followed by confirmatory testing covering different assay techniques. Due to lacking automation, standardization, modern data management, and human bias in IIF screening, this two-stage approach has recently been challenged by multiplex techniques particularly in laboratories with high workload. However, detection of antinuclear antibodies by IIF is still recommended to be the gold standard method for antibody screening in sera from patients with suspected SARD. To address the limitations of IIF and to meet the demand for cost-efficient autoantibody screening, automated IIF methods employing novel pattern recognition algorithms for image analysis have been introduced recently. In this respect, the AKLIDES technology has been the first commercially available platform for automated interpretation of cell-based IIF testing and provides multiplexing by addressable microbead immunoassays for confirmatory testing. This paper gives an overview of recently published studies demonstrating the advantages of this new technology for SARD serology.

Automation in indirect immunofluorescence testing: a new step in the evolution of the autoimmunology laboratory

Indirect immunofluorescence (IIF) plays an important role in immunological and immunometric assays for detecting and measuring autoantibodies. This technology was the first multiplex method used to detect cardinal autoantibodies for the diagnosis of autoimmune diseases. Over the last 20 years, research has enabled the progressive identification of cell and tissue autoantigens which are the target of autoantibodies originally detected by IIF. Accordingly, newer immunometric methods, capable of measuring concentrations of specific autoantibodies directed against these autoantigens, allowed for a gradual replacement of the IIF method in the autoimmunology laboratory. Currently, IIF remains the method of choice only in selected fields of autoimmune diagnostics. Following the recent statement by the American College of Rheumatology that the IIF technique should be considered as the standard screening method for the detection of ANA, the biomedical industry has developed technological solutions which significantly improve automation of the procedure, not only in the preparation of substrates and slides, but also in microscope reading. This review summarizes the general and specific features of new available commercial systems (Aklides, Medipan; Nova View, Inova; Zenit G Sight, A. Menarini Diagnostics; Europattern, Euroimmun; Helios, Aesku.Diagnostics; Image Navigator, Immuno Concepts; Cytospot, Autoimmun Diagnostika) for automation of the IIF method. The expected advantages of automated IIF are the reduction in frequency of false negative and false positive results, the reduction of intra- and inter-laboratory variability, the improvement of correlation of staining patterns with corresponding autoantibody reactivities, and higher throughput in the laboratory workflow.

Chemical address tags of fluorescent bioimaging probes

Chemical address tags can be defined as specific structural features shared by a set of bioimaging probes having a predictable influence on cell-associated visual signals obtained from these probes. Here, using a large image dataset acquired with a high content screening instrument, machine vision and cheminformatics analysis have been applied to reveal chemical address tags. With a combinatorial library of fluorescent molecules, fluorescence signal intensity, spectral, and spatial features characterizing each one of the probes' visual signals were extracted from images acquired with the three different excitation and emission channels of the imaging instrument. With multivariate regression, the additive contribution from each one of the different building blocks of the bioimaging probes toward each measured, cell-associated image-based feature was calculated. In this manner, variations in the chemical features of the molecules were associated with the resulting staining patterns, facilitating quantitative, objective analysis of chemical address tags. Hierarchical clustering and paired image-cheminformatics analysis revealed key structure-property relationships amongst many building blocks of the fluorescent molecules. The results point to different chemical modifications of the bioimaging probes that can exert similar (or different) effects on the probes' visual signals. Inspection of the clustered structures suggests intramolecular charge migration or partial charge distribution as potential mechanistic determinants of chemical address tag behavior.

Image analysis to quantify histological and immunofluorescent staining of ex vivo skin and skin cell cultures

Image processing steps and analysis techniques were developed for the quantification of photomicrographs obtained from light and fluorescence microscopy. The substrates examined were either skin cell cultures, such as normal human keratinocytes (NHK) or fibroblasts, or ex vivo skin sections. Examples of the analyses are provided for the comparison of skincare active ingredient treated samples vs. placebo to demonstrate the utility of the methods to quantify and provide numerical data for a procedure that is typically qualitative in nature and based on observations by a histologist. Quantifiable experiments that are discussed include: Fontana Masson staining for melanin expression; Nile red staining to detect cellular lipid droplets; nuclei staining with diamidino-phenylindole (DAPI); and immunofluorescent staining of protein expression with a primary antibody directed against the protein (antigen) and a secondary antibody tagged with a fluorescent dye (Alexa Fluor 488) against the primary antibody.

Automated evaluation of autoantibodies on human epithelial-2 cells as an approach to standardize cell-based immunofluorescence tests

Introduction

Analysis of autoantibodies (AAB) by indirect immunofluorescence (IIF) is a basic tool for the serological diagnosis of systemic rheumatic disorders. Automation of autoantibody IIF reading including pattern recognition may improve intra- and inter-laboratory variability and meet the demand for cost-effective assessment of large numbers of samples. Comparing automated and visual interpretation, the usefulness for routine laboratory diagnostics was investigated.

Methods

Autoantibody detection by IIF on human epithelial-2 (HEp-2) cells was conducted in a total of 1222 consecutive sera of patients with suspected systemic rheumatic diseases from a university routine laboratory (n = 924) and a private referral laboratory (n = 298). IIF results from routine diagnostics were compared with a novel automated interpretation system.

Results

Both diagnostic procedures showed a very good agreement in detecting AAB (kappa = 0.828) and differentiating respective immunofluorescence patterns. Only 98 (8.0%) of 1222 sera demonstrated discrepant results in the differentiation of positive from negative samples. The contingency coefficients of chi-square statistics were 0.646 for the university laboratory cohort with an agreement of 93.0% and 0.695 for the private laboratory cohort with an agreement of 90.6%, P < 0.0001, respectively. Comparing immunofluorescence patterns, 111 (15.3%) sera yielded differing results.

Conclusions

Automated assessment of AAB by IIF on HEp-2 cells using an automated interpretation system is a reliable and robust method for positive/negative differentiation. Employing novel mathematical algorithms, automated interpretation provides reproducible detection of specific immunofluorescence patterns on HEp-2 cells. Automated interpretation can reduce drawbacks of IIF for AAB detection in routine diagnostics providing more reliable data for clinicians.

Machine vision-assisted analysis of structure-localization relationships in a combinatorial library of prospective bioimaging probes

With a combinatorial library of bioimaging probes, it is now possible to use machine vision to analyze the contribution of different building blocks of the molecules to their cell-associated visual signals. For this purpose, cell-permeant, fluorescent styryl molecules were synthesized by condensation of 168 aldehyde with 8 pyridinium/quinolinium building blocks. Images of cells incubated with fluorescent molecules were acquired with a high content screening instrument. Chemical and image feature analysis revealed how variation in one or the other building block of the styryl molecules led to variations in the molecules' visual signals. Across each pair of probes in the library, chemical similarity was significantly associated with spectral and total signal intensity similarity. However, chemical similarity was much less associated with similarity in subcellular probe fluorescence patterns. Quantitative analysis and visual inspection of pairs of images acquired from pairs of styryl isomers confirm that many closely-related probes exhibit different subcellular localization patterns. Therefore, idiosyncratic interactions between styryl molecules and specific cellular components greatly contribute to the subcellular distribution of the styryl probes' fluorescence signal. These results demonstrate how machine vision and cheminformatics can be combined to analyze the targeting properties of bioimaging probes, using large image data sets acquired with automated screening systems.

Challenges of automated screening and differentiation of non-organ specific autoantibodies on HEp-2 cells

Analysis of autoantibodies (AAB) by indirect immunofluorescence (IIF) remains the hallmark of diagnosing autoimmune diseases despite the introduction of multiplex techniques. Non-organ specific AAB are screened in routine diagnostics by IIF on HEp-2 cells. However, IIF results vary due to objective (e.g., cell fixation) and subjective factors (e.g., expert knowledge). Therefore, inter- and intralaboratory variance is relatively high. Standardisation of AAB testing by IIF remains a critical issue in and between routine laboratories and may be improved by automated interpretation systems. An overview of existing interpretation techniques will be given taking into account own data of the first fully automated reading system AKLIDES. The novel system provides fully automated reading of IIF images and software algorithms for the mathematical description of IIF AAB patterns. It can be used for screening and preclassification of non-organ specific AAB in routine diagnostics regarding systemic autoimmune and autoimmune liver diseases. Furthermore, this system paves the way for economic data processing of cell-based IIF assays and can contribute to the reduction of interlaboratory variance of AAB testing. More sophisticated pattern recognition algorithms and novel calibration systems will improve standardised quantifications of IIF image interpretation.

An incremental approach to automated protein localisation

Background

The subcellular localisation of proteins in intact living cells is an important means for gaining information about protein functions. Even dynamic processes can be captured, which can barely be predicted based on amino acid sequences. Besides increasing our knowledge about intracellular processes, this information facilitates the development of innovative therapies and new diagnostic methods. In order to perform such a localisation, the proteins under analysis are usually fused with a fluorescent protein. So, they can be observed by means of a fluorescence microscope and analysed. In recent years, several automated methods have been proposed for performing such analyses. Here, two different types of approaches can be distinguished: techniques which enable the recognition of a fixed set of protein locations and methods that identify new ones. To our knowledge, a combination of both approaches – i.e. a technique, which enables supervised learning using a known set of protein locations and is able to identify and incorporate new protein locations afterwards – has not been presented yet. Furthermore, associated problems, e.g. the recognition of cells to be analysed, have usually been neglected.

Results

We introduce a novel approach to automated protein localisation in living cells. In contrast to well-known techniques, the protein localisation technique presented in this article aims at combining the two types of approaches described above: After an automatic identification of unknown protein locations, a potential user is enabled to incorporate them into the pre-trained system. An incremental neural network allows the classification of a fixed set of protein location as well as the detection, clustering and incorporation of additional patterns that occur during an experiment. Here, the proposed technique achieves promising results with respect to both tasks. In addition, the protein localisation procedure has been adapted to an existing cell recognition approach. Therefore, it is especially well-suited for high-throughput investigations where user interactions have to be avoided.

Conclusion

We have shown that several aspects required for developing an automatic protein localisation technique – namely the recognition of cells, the classification of protein distribution patterns into a set of learnt protein locations, and the detection and learning of new locations – can be combined successfully. So, the proposed method constitutes a crucial step to render image-based protein localisation techniques amenable to large-scale experiments.

Comparison of quantitative methods for cell-shape analysis

Morphology is an important large-scale manifestation of the global organizational and physiological state of cells, and is commonly used as a qualitative or quantitative measure of the outcome of various assays. Here we evaluate several different basic representations of cell shape - binary masks, distance maps and polygonal outlines - and different subsequent encodings of those representations - Fourier and Zernike decompositions, and the principal and independent components analyses - to determine which are best at capturing biologically important shape variation. We find that principal components analysis of two-dimensional shapes represented as outlines provide measures of morphology which are quantitative, biologically meaningful, human interpretable and work well across a range of cell types and parameter settings.

High-throughput screening of cellular features using high-resolution light-microscopy; Application for profiling drug effects on cell adhesion

High-resolution light-microscopy and high-throughput screening are two essential methodologies for characterizing cellular phenotypes. Optimally combining these methodologies in cell-based screening to test detailed molecular and cellular responses to multiple perturbations constitutes a major challenge. Here we describe the development and application of a screening microscope platform that automatically acquires and interprets sub-micron resolution images at fast rates. The analysis pipeline is based on the quantification of multiple subcellular features and statistical comparisons of their distributions in treated vs. control cells. Using this platform, we screened 2200 natural extracts for their effects on the fine structure and organization of focal adhesions. This screen identified 15 effective extracts whose fractionation and characterization were further analyzed using the same microscope system. The significance of combining resolution, throughput and multi-parametric analyses for biomedical research and drug discovery is discussed.

A non-invasive method for in situ quantification of subpopulation behaviour in mixed cell culture

Ongoing advances in quantitative molecular- and cellular-biology highlight the need for correspondingly quantitative methods in tissue-biology, in which the presence and activity of specific cell-subpopulations can be assessed in situ. However, many experimental techniques disturb the natural tissue balance, making it difficult to draw realistic conclusions concerning in situ cell behaviour. In this study, we present a widely applicable and minimally invasive method which combines fluorescence cell labelling, retrospective image analysis and mathematical data processing to detect the presence and activity of cell subpopulations, using adhesion patterns in STRO-1 immunoselected human mesenchymal populations and the homogeneous osteoblast-like MG63 continuous cell line as an illustration. Adhesion is considered on tissue culture plastic and fibronectin surfaces, using cell area as a readily obtainable and individual cell specific measure of spreading. The underlying statistical distributions of cell areas are investigated and mappings between distributions are examined using a combination of graphical and non-parametric statistical methods. We show that activity can be quantified in subpopulations as small as 1% by cell number, and outline behaviour of significant subpopulations in both STRO-1+/- fractions. This method has considerable potential to understand in situ cell behaviour and thus has wide applicability, for example in developmental biology and tissue engineering.

High-throughput fluorescence microscopy for systems biology

In this post-genomic era, we need to define gene function on a genome-wide scale for model organisms and humans. The fundamental unit of biological processes is the cell. Among the most powerful tools to assay such processes in the physiological context of intact living cells are fluorescence microscopy and related imaging techniques. To enable these techniques to be applied to functional genomics experiments, fluorescence microscopy is making the transition to a quantitative and high-throughput technology.

Objective clustering of proteins based on subcellular location patterns

The goal of proteomics is the complete characterization of all proteins. Efforts to characterize subcellular location have been limited to assigning proteins to general categories of organelles. We have previously designed numerical features to describe location patterns in microscope images and developed automated classifiers that distinguish major subcellular patterns with high accuracy (including patterns not distinguishable by visual examination). The results suggest the feasibility of automatically determining which proteins share a single location pattern in a given cell type. We describe an automated method that selects the best feature set to describe images for a given collection of proteins and constructs an effective partitioning of the proteins by location. An example for a limited protein set is presented. As additional data become available, this approach can produce for the first time an objective systematics for protein location and provide an important starting point for discovering sequence motifs that determine localization.

The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging

The Open Microscopy Environment (OME) defines a data model and software implementation to serve as an informatics framework for imaging in biological microscopy experiments.

The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. OME is designed to support high-content cell-based screening as well as traditional image analysis applications. The OME Data Model, expressed in Extensible Markup Language (XML) and realized in a traditional database, is both extensible and self-describing, allowing it to meet emerging imaging and analysis needs.