Objective evaluation of differences in protein subcellular distribution

Quantification of lipid droplets and associated proteins in cellular models of obesity via high-content/high-throughput microscopy and automated image analysis

Intracellular lipid droplets are associated with a myriad of afflictions including obesity, fatty liver disease, coronary artery disease, and infectious diseases (eg, HCV and tuberculosis). To develop high-content analysis (HCA) techniques to analyze lipid droplets and associated proteins, primary human preadipocytes were plated in 96-well dishes in the presence of rosiglitazone (rosi), a PPAR-(c) agonist that promotes adipogenesis. The cells were then labeled for nuclei, lipid droplets, and proteins such as perilipin, protein kinase C (PKC), and hormone-sensitive lipase (HSL). The cells were imaged via automated digital microscopy and algorithms were developed to quantify lipid droplet (Lipid Droplet algorithm) and protein expression and colocalization (Colocalization algorithm). The algorithms, which were incorporated into Vala Science Inc's CyteSeer((R)) image cytometry program, quantified the rosi-induced increases in lipid droplet number, size, and intensity, and the expression of perilipin with exceptional consistency (Z' values of 0.54-0.71). Regarding colocalization with lipid droplets, Pearson's correlation coefficients of 0.38 (highly colocalized), 0.16 (moderate), and -0.0010 (random) were found for perilipin, PKC, and HSL, respectively. For hepatocytes (AML12, HuH-7, and primary cells), the algorithms also quantified the stimulatory and inhibitory effect of oleic acid and triacsin C on lipid droplets (Z's > 0.50) and ADFP expression/colocalization. Oleic acid-induced lipid droplets in HeLa cells and macrophages (THP-1) were also well quantified. The results suggest that HCA techniques can be utilized to quantify lipid droplets and associated proteins in many cell models relevant to a variety of diseases.

Exploration of chromatic aberration for multiplanar imaging: proof of concept with implications for fast, efficient autofocus

Image-based autofocus determines focus directly from the specimen (as opposed to reflective surface positioning with an offset), but sequential acquisition of a stack of images to measure resolution/sharpness and find best focus is slower than reflective positioning. Simultaneous imaging of multiple focal planes, which is also useful for 3D imaging of live cells, is faster but requires complicated optics. With color CCD cameras and white light sources commonly available, we asked if axial chromatic aberration can be utilized to acquire multiple focal planes simultaneously, and if it can be controlled through a range sufficient for practical use. For proof of concept, we theoretically and experimentally explored the focal differences between three narrow wavelength bands on a 3-chip color CCD camera with and without glass inserts of various thicknesses and dispersions. Ray tracing yielded changes in foci of 0.65-0.9 microm upon insertion of 12.5-mm thick glass samples for green (G, 522 nm) vs. blue (B, 462 nm) and green vs. red (G-R, 604 nm). On a microscope: (1) With no glass inserts, the differences in foci were 2.15 microm (G-B) and 0.43 microm (G-R); (2) With glass inserts, the maximum change in foci for G vs. B was 0.44 microm and for G vs. R was 0.26 microm; and (3) An 11.3 mm thick N-BK7 glass insert shifted the foci 0.9 microm (R), 0.6 microm (G), and 0.35 microm (B), such that the B and R foci were farther apart (2.1 microm vs. 1.7 microm) and the R and G foci were closer together (0.25 microm vs. 0.45 microm). The slopes of the differences in foci were dependent on thickness, index of refraction, and dispersion. The measured differences in foci are comparable to the axial steps of 0.1-0.24 microm commonly used for autofocus, and focal plane separation can be altered by inserting optical elements of various dispersions and thicknesses. By enabling acquisition of multiple, axially offset images simultaneously, chromatic aberration, normally an imaging pariah, creates a possible mechanism for efficient multiplanar imaging of multiple spectral bands from white light illumination.

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.

Detection of protein-protein interactions through vesicle targeting

The detection of protein-protein interactions through two-hybrid assays has revolutionized our understanding of biology. The remarkable impact of two-hybrid assay platforms derives from their speed, simplicity, and broad applicability. Yet for many organisms, the need to express test proteins in Saccharomyces cerevisiae or Escherichia coli presents a substantial barrier because variations in codon specificity or bias may result in aberrant protein expression. In particular, nonstandard genetic codes are characteristic of several eukaryotic pathogens, for which there are currently no genetically based systems for detection of protein-protein interactions. We have developed a protein-protein interaction assay that is carried out in native host cells by using GFP as the only foreign protein moiety, thus circumventing these problems. We show that interaction can be detected between two protein pairs in both the model yeast S. cerevisiae and the fungal pathogen Candida albicans. We use computational analysis of microscopic images to provide a quantitative and automated assessment of confidence.

Statistical and visual differentiation of subcellular imaging

BACKGROUND

Automated microscopy technologies have led to a rapid growth in imaging data on a scale comparable to that of the genomic revolution. High throughput screens are now being performed to determine the localisation of all of proteins in a proteome. Closer to the bench, large image sets of proteins in treated and untreated cells are being captured on a daily basis to determine function and interactions. Hence there is a need for new methodologies and protocols to test for difference in subcellular imaging both to remove bias and enable throughput. Here we introduce a novel method of statistical testing, and supporting software, to give a rigorous test for difference in imaging. We also outline the key questions and steps in establishing an analysis pipeline.

RESULTS

The methodology is tested on a high throughput set of images of 10 subcellular localisations, and it is shown that the localisations may be distinguished to a statistically significant degree with as few as 12 images of each. Further, subtle changes in a protein's distribution between nocodazole treated and control experiments are shown to be detectable. The effect of outlier images is also examined and it is shown that while the significance of the test may be reduced by outliers this may be compensated for by utilising more images. Finally, the test is compared to previous work and shown to be more sensitive in detecting difference. The methodology has been implemented within the iCluster system for visualising and clustering bio-image sets.

CONCLUSION

The aim here is to establish a methodology and protocol for testing for difference in subcellular imaging, and to provide tools to do so. While iCluster is applicable to moderate (<1000) size image sets, the statistical test is simple to implement and will readily be adapted to high throughput pipelines to provide more sensitive discrimination of difference.

Computational Systems Bioinformatics and Bioimaging for Pathway Analysis and Drug Screening

The premise of today's drug development is that the mechanism of a disease is highly dependent upon underlying signaling and cellular pathways. Such pathways are often composed of complexes of physically interacting genes, proteins, or biochemical activities coordinated by metabolic intermediates, ions, and other small solutes and are investigated with molecular biology approaches in genomics, proteomics, and metabonomics. Nevertheless, the recent declines in the pharmaceutical industry's revenues indicate such approaches alone may not be adequate in creating successful new drugs. Our observation is that combining methods of genomics, proteomics, and metabonomics with techniques of bioimaging will systematically provide powerful means to decode or better understand molecular interactions and pathways that lead to disease and potentially generate new insights and indications for drug targets. The former methods provide the profiles of genes, proteins, and metabolites, whereas the latter techniques generate objective, quantitative phenotypes correlating to the molecular profiles and interactions. In this paper, we describe pathway reconstruction and target validation based on the proposed systems biologic approach and show selected application examples for pathway analysis and drug screening.

Visualizing and clustering high throughput sub-cellular localization imaging

Background

The expansion of automatic imaging technologies has created a need to be able to efficiently compare and review large sets of image data. To enable comparisons of image data between samples we need to define the normal variation within distinct images of the same sample. Even with tightly controlled experimental conditions, protein expression can vary widely between cells, and because of the difficulty in viewing and comparing large image sets this might not be observed. Here we introduce a novel methodology, iCluster, for visualizing, clustering and comparing large sub-cellular localization image sets. For each member of an image set, iCluster generates statistics that have been found to be useful in distinguishing sub-cellular localization. The statistics are mapped into two or three dimensions such as to preserve distances between the statistics vectors. The complete image set is then visualized in two or three dimensions using the coordinates so determined. The result is images that are statistically similar are spatially close in the visualization allowing for easy comparison of images that are similar and distinguishment of dissimilar images into distinct clusters.

Results

The methodology was tested on a set of 502 previously published images containing 10 known sub-cellular localizations. The clustering of images of like type was evaluated both by examining the classes of nearest neighbors to each image and by visual inspection. In three dimensions, 3-neighbor classification accuracy was 83.2%. Visually, each class clustered well with the majority of classes localizing to distinct regions of the space. In two dimensions, 3-neighbor classification accuracy was 68.9%, though visually clustering into classes could be readily discerned. Computational expense was found to be relatively low, and sets of up to 1400 images visualized and interacted with in real time.

Conclusion

The feasibility of automated spatial layout to allow comparison and discrimination of high throughput sub-cellular imaging has been demonstrated. There are many potential applications such as image database curation, semi-automated interactive classification, outlier detection and reference image comparison. By allowing the observation of the full range of imaging data available using modern microscopes these methods will provide an invaluable tool for cell biologists.

Robust classification of subcellular location patterns in high resolution 3D fluorescence microscope images

Knowledge of a protein's subcellular location is essential to a complete understanding of its functions. Automated interpretation methods for protein location patterns are needed for proteomics projects, and we have previously described systems for classifying the major subcellular patterns in cultured mammalian cells. We describe here the calculation of improved 3D Haralick texture features, which yielded a near-perfect classification accuracy when combined with 3D morphological and edge features. In particular, a set of 7 features achieved 98% overall accuracy for classifying 10 major subcellular location patterns in HeLa cells.

Fast automated cell phenotype image classification

Background

The genomic revolution has led to rapid growth in sequencing of genes and proteins, and attention is now turning to the function of the encoded proteins. In this respect, microscope imaging of a protein's sub-cellular localisation is proving invaluable, and recent advances in automated fluorescent microscopy allow protein localisations to be imaged in high throughput. Hence there is a need for large scale automated computational techniques to efficiently quantify, distinguish and classify sub-cellular images. While image statistics have proved highly successful in distinguishing localisation, commonly used measures suffer from being relatively slow to compute, and often require cells to be individually selected from experimental images, thus limiting both throughput and the range of potential applications. Here we introduce threshold adjacency statistics, the essence which is to threshold the image and to count the number of above threshold pixels with a given number of above threshold pixels adjacent. These novel measures are shown to distinguish and classify images of distinct sub-cellular localization with high speed and accuracy without image cropping.

Results

Threshold adjacency statistics are applied to classification of protein sub-cellular localization images. They are tested on two image sets (available for download), one for which fluorescently tagged proteins are endogenously expressed in 10 sub-cellular locations, and another for which proteins are transfected into 11 locations. For each image set, a support vector machine was trained and tested. Classification accuracies of 94.4% and 86.6% are obtained on the endogenous and transfected sets, respectively. Threshold adjacency statistics are found to provide comparable or higher accuracy than other commonly used statistics while being an order of magnitude faster to calculate. Further, threshold adjacency statistics in combination with Haralick measures give accuracies of 98.2% and 93.2% on the endogenous and transfected sets, respectively.

Conclusion

Threshold adjacency statistics have the potential to greatly extend the scale and range of applications of image statistics in computational image analysis. They remove the need for cropping of individual cells from images, and are an order of magnitude faster to calculate than other commonly used statistics while providing comparable or better classification accuracy, both essential requirements for application to large-scale approaches.

Modelling data across labs, genomes, space and time

Logical models and physical specifications provide the foundation for storage, management and analysis of complex sets of data, and describe the relationships between measured data elements and metadata - the contextual descriptors that define the primary data. Here, we use imaging applications to illustrate the purpose of the various implementations of data specifications and the requirement for open, standardized, data formats to facilitate the sharing of critical digital data and metadata.

Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics

Proteomics, the large scale identification and characterization of many or all proteins expressed in a given cell type, has become a major area of biological research. In addition to information on protein sequence, structure and expression levels, knowledge of a protein's subcellular location is essential to a complete understanding of its functions. Currently, subcellular location patterns are routinely determined by visual inspection of fluorescence microscope images. We review here research aimed at creating systems for automated, systematic determination of location. These employ numerical feature extraction from images, feature reduction to identify the most useful features, and various supervised learning (classification) and unsupervised learning (clustering) methods. These methods have been shown to perform significantly better than human interpretation of the same images. When coupled with technologies for tagging large numbers of proteins and high-throughput microscope systems, the computational methods reviewed here enable the new subfield of location proteomics. This subfield will make critical contributions in two related areas. First, it will provide structured, high-resolution information on location to enable Systems Biology efforts to simulate cell behavior from the gene level on up. Second, it will provide tools for Cytomics projects aimed at characterizing the behaviors of all cell types before, during, and after the onset of various diseases.

Steady State Analysis and Experimental Validation of a Model for Hepatic High-Density Lipoprotein Transport

Transport of high-density lipoprotein (HDL) in the hepatocyte plays a fundamental role in reverse cholesterol transport and regulation of plasma HDL levels. On the basis of a recently developed kinetic model, the steady state distribution of HDL was analyzed. Fractional fluorescence of labeled HDL in the basolateral membrane, sorting endosomes (SE), the subapical compartment/ apical recycling compartment, the biliary canaliculus and in late endosomes and lysosomes (LE/LYS) including expected standard deviation is predicted. Improved parameter estimation was obtained by including kinetic data of apical endocytosis of fluorescent markers for LE/LYS, asialoorosomucoid and Rhodamine-dextran, in the regression. Predicted values using the refined kinetic parameters are in good agreement with experimental values of compartmental steady state fluorescence of Alexa488-HDL in polarized hepatic HepG2 cells. From calculated steady state fluxes, it is suggested that export of HDL from basolateral SE is the key step for determining the transport of HDL through the hepatocyte. The analysis provides testable predictions for high-throughput fluorescence microscopy screening experiments on potential inhibitors of hepatic HDL processing. By quantitative fluorescence imaging and model analysis, it is shown that the phosphoinositide kinase inhibitor wortmannin prevents apical transport of fluorescent HDL from basolateral SE. The results support that endosomes of polarized hepatic cells have different sorting functions and that apical endocytosis is an integrative trafficking step in hepatocytes.

A live cell, image-based approach to understanding the enzymology and pharmacology of 2-bromopalmitate and palmitoylation

The addition of a lipid moiety to a protein increases its hydrophobicity and subsequently its attraction to lipophilic environments like membranes. Indeed most lipid-modified proteins are localized to membranes where they associate with multiprotein signaling complexes. Acylation and prenylation are the two common categories of lipidation. The enzymology and pharmacology of prenylation are well understood but relatively very little is known about palmitoylation, the most common form of acylation. One distinguishing characteristic of palmitoylation is that it is a dynamic modification. To understand more about how palmitoylation is regulated, we fused palmitoylation substrates to fluorescent proteins and reported their subcellular distribution and trafficking. We used automated high-throughput fluorescence microscopy and a specialized computer algorithm to image and measure the fraction of palmitoylation reporter on the plasma membrane versus the cytoplasm. Using this system we determined the residence half-life of palmitate on the dipalmitoyl substrate peptide from GAP43 as well as the EC(50) for 2-bromopalmitate, a common inhibitor of palmitoylation.

Automated Interpretation of Protein Subcellular Location Patterns

Proteomics is a major current focus of biomedical research, and location proteomics is the important branch of proteomics that systematically studies the subcellular distributions for all proteins expressed in a given cell type. Fluorescence microscopy of labeled proteins is currently the main methodology to obtain location information. Traditionally, microscope images are analyzed by visual inspection, which suffers from inefficiency and inconsistency. Automated and objective interpretation approaches are therefore needed for location proteomics. In this article, we briefly review recent advances in automated imaging interpretation tools, including supervised classification (which assigns location pattern labels to previously unseen images), unsupervised clustering (which groups proteins based on the similarity among their subcellular distributions), and additional statistical tools that can aid cell and molecular biologists who use microscopy in their work.

Location proteomics: a systems approach to subcellular location

Systems Biology requires comprehensive systematic data on all aspects and levels of biological organization and function. In addition to information on the sequence, structure, activities and binding interactions of all biological macromolecules, the creation of accurate predictive models of cell behaviour will require detailed information on the distribution of those molecules within cells and the ways in which those distributions change over the cell cycle and in response to mutations or external stimuli. Current information on subcellular location in protein databases is limited to unstructured text descriptions or sets of terms assigned by human curators. These entries do not permit basic operations that are common to other biological databases, such as measurement of the degree of similarity between the distributions of two proteins, and they are not able to fully capture the complexity of protein patterns that can be observed. The field of location proteomics seeks to provide automated, objective high-resolution descriptions of protein location patterns within cells. Methods have been developed to group proteins into statistically indistinguishable location patterns using automated analysis of fluorescence microscope images. The resulting clusters, or location families, are analogous to clusters found for other domains, such as protein sequence families. Preliminary work suggests the feasibility of expressing each unique pattern as a generative model that can be incorporated into comprehensive models of cell behaviour.

Assay and subcellular localization of pyrroline-5-carboxylate dehydrogenase in rat liver

Delta1-pyrroline-5-carboxylate dehydrogenase (P5CDh) catalyzes the conversion of Delta1-pyrroline-5-carboxylate to glutamate in a reaction requiring NADP+ as a cofactor. Delta1-pyrroline-5-carboxylate is formed in liver from proline by proline oxidase (EC number not assigned) or from ornithine via ornithine aminotransferase. A spectrophotometric assay for P5CDh was shown to be valid if rotenone was included in the assay to prevent reoxidation of NADH. Using this new assay, liver was fractionated using differential centrifugation and the distribution of P5CDh was compared to that of appropriate marker enzymes. P5CDh is enriched only in the mitochondrial fractions, as are the mitochondrial enzymes, succinate cytochrome c reductase, proline oxidase, glutaminase, and ornithine aminotransferase. Thus, it can be concluded that P5CDh occurs only in mitochondria, not in both mitochondria and cytoplasm, as had previously been reported.

Nuclear structure in cancer cells

Nuclear architecture - the spatial arrangement of chromosomes and other nuclear components - provides a framework for organizing and regulating the diverse functional processes within the nucleus. There are characteristic differences in the nuclear architectures of cancer cells, compared with normal cells, and some anticancer treatments restore normal nuclear structure and function. Advances in understanding nuclear structure have revealed insights into the process of malignant transformation and provide a basis for the development of new diagnostic tools and therapeutics.

From quantitative microscopy to automated image understanding

Quantitative microscopy has been extensively used in biomedical research and has provided significant insights into structure and dynamics at the cell and tissue level. The entire procedure of quantitative microscopy is comprised of specimen preparation, light absorption/reflection/emission from the specimen, microscope optical processing, optical/electrical conversion by a camera or detector, and computational processing of digitized images. Although many of the latest digital signal processing techniques have been successfully applied to compress, restore, and register digital microscope images, automated approaches for recognition and understanding of complex subcellular patterns in light microscope images have been far less widely used. We describe a systematic approach for interpreting protein subcellular distributions using various sets of subcellular location features (SLF), in combination with supervised classification and unsupervised clustering methods. These methods can handle complex patterns in digital microscope images, and the features can be applied for other purposes such as objectively choosing a representative image from a collection and performing statistical comparisons of image sets.

Automated interpretation of subcellular patterns from immunofluorescence microscopy

Immunofluorescence microscopy is widely used to analyze the subcellular locations of proteins, but current approaches rely on visual interpretation of the resulting patterns. To facilitate more rapid, objective, and sensitive analysis, computer programs have been developed that can identify and compare protein subcellular locations from fluorescence microscope images. The basis of these programs is a set of features that numerically describe the characteristics of protein images. Supervised machine learning methods can be used to learn from the features of training images and make predictions of protein location for images not used for training. Using image databases covering all major organelles in HeLa cells, these programs can achieve over 92% accuracy for two-dimensional (2D) images and over 95% for three-dimensional images. Importantly, the programs can discriminate proteins that could not be distinguished by visual examination. In addition, the features can also be used to rigorously compare two sets of images (e.g., images of a protein in the presence and absence of a drug) and to automatically select the most typical image from a set. The programs described provide an important set of tools for those using fluorescence microscopy to study protein location.

Automated Interpretation of Protein Subcellular Location Patterns: Implications for Early Cancer Detection and Assessment

Fluorescence microscopy is a powerful tool for analyzing the subcellular distributions of proteins, but that power has not been fully utilized because most analysis of those distributions has been done by visual examination. This limitation can be overcome using automated pattern recognition methods widely used in other fields. This article summarizes work demonstrating that automated systems can recognize the patterns of major organelles in both two- and three-dimensional images of cultured cells, and that these systems can distinguish similar patterns better than visual examination. The basis for these systems are sets of Subcellular Location Features that capture the essence of subcellular patterns without being sensitive to the extensive variation that occurs in the size, shape, and orientation of cells in microscope images. These features can also be used to make sensitive, statistical comparisons of the distribution of a protein between two conditions, such as in the presence and absence of a drug. The possible use of automated pattern analysis methods for improving detection of abnormal cells in cancerous or precancerous tissues is also discussed.

Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools

Cellular behavior is complex. Successfully understanding systems at ever-increasing complexity is fundamental to advances in modern science and unraveling the functional details of cellular behavior is no exception. We present a collection of prospectives to provide a glimpse of the techniques that will aid in collecting, managing and utilizing information on complex cellular processes via molecular imaging tools. These include: 1) visualizing intracellular protein activity with fluorescent markers, 2) high throughput (and automated) imaging of multilabeled cells in statistically significant numbers, and 3) machine intelligence to analyze subcellular image localization and pattern. Although not addressed here, the importance of combining cell-image-based information with detailed molecular structure and ligand-receptor binding models cannot be overlooked. Advanced molecular imaging techniques have the potential to impact cellular diagnostics for cancer screening, clinical correlations of tissue molecular patterns for cancer biology, and cellular molecular interactions for accelerating drug discovery. The goal of finally understanding all cellular components and behaviors will be achieved by advances in both instrumentation engineering (software and hardware) and molecular biochemistry.