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

Multiplex Immunofluorescence and the Digital Image Analysis Workflow for Evaluation of the Tumor Immune Environment in Translational Research

A robust understanding of the tumor immune environment has important implications for cancer diagnosis, prognosis, research, and immunotherapy. Traditionally, immunohistochemistry (IHC) has been regarded as the standard method for detecting proteins in situ, but this technique allows for the evaluation of only one cell marker per tissue sample at a time. However, multiplexed imaging technologies enable the multiparametric analysis of a tissue section at the same time. Also, through the curation of specific antibody panels, these technologies enable researchers to study the cell subpopulations within a single immunological cell group. Thus, multiplexed imaging gives investigators the opportunity to better understand tumor cells, immune cells, and the interactions between them. In the multiplexed imaging technology workflow, once the protocol for a tumor immune micro environment study has been defined, histological slides are digitized to produce high-resolution images in which regions of interest are selected for the interrogation of simultaneously expressed immunomarkers (including those co-expressed by the same cell) by using an image analysis software and algorithm. Most currently available image analysis software packages use similar machine learning approaches in which tissue segmentation first defines the different components that make up the regions of interest and cell segmentation, then defines the different parameters, such as the nucleus and cytoplasm, that the software must utilize to segment single cells. Image analysis tools have driven dramatic evolution in the field of digital pathology over the past several decades and provided the data necessary for translational research and the discovery of new therapeutic targets. The next step in the growth of digital pathology is optimization and standardization of the different tasks in cancer research, including image analysis algorithm creation, to increase the amount of data generated and their accuracy in a short time as described herein. The aim of this review is to describe this process, including an image analysis algorithm creation for multiplex immunofluorescence analysis, as an essential part of the optimization and standardization of the different processes in cancer research, to increase the amount of data generated and their accuracy in a short time.

Image-based profiling for drug discovery: due for a machine-learning upgrade?

Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug's activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.

Classification of cancer cells using computational analysis of dynamic morphology

BACKGROUND AND OBJECTIVE

Detection of metastatic tumor cells is important for early diagnosis and staging of cancer. However, such cells are exceedingly difficult to detect from blood or biopsy samples at the disease onset. It is reported that cancer cells, and especially metastatic tumor cells, show very distinctive morphological behavior compared to their healthy counterparts on aptamer functionalized substrates. The ability to quickly analyze the data and quantify the cell morphology for an instant real-time feedback can certainly contribute to early cancer diagnosis. A supervised machine learning approach is presented for identification and classification of cancer cell gestures for early diagnosis.

METHODS

We quantified the morphologically distinct behavior of metastatic cells and their healthy counterparts captured on aptamer-functionalized glass substrates from time-lapse optical micrographs. As a proof of concept, the morphologies of human glioblastoma (hGBM) and astrocyte cells were used. The cells were captured and imaged with an optical microscope. Multiple feature vectors were extracted to quantify and differentiate the complex physical gestures of cancerous and non-cancerous cells. Three different classifier models, Support Vector Machine (SVM), Random Forest Tree (RFT), and Naïve Bayes Classifier (NBC) were trained with the known dataset using machine learning algorithms. The performances of the classifiers were compared for accuracy, precision, and recall measurements using five-fold cross-validation technique.

RESULTS

All the classifier models detected the cancer cells with an average accuracy of at least 82%. The NBC performed the best among the three classifiers in terms of Precision (0.91), Recall (0.9), and F₁-score (0.89) for the existing dataset.

CONCLUSIONS

This paper presents a standalone system built on machine learning techniques for cancer screening based on cell gestures. The system offers rapid, efficient, and novel identification of hGBM brain tumor cells and can be extended to define single cell analysis metrics for many other types of tumor cells.

Image-Based Profiling Can Discriminate the Effects of Inhibitors on Signaling Pathways under Differential Ligand Stimulation

A major advantage of image-based phenotypic profiling of compounds is that numerous image features can be sampled and quantitatively evaluated in an unbiased way. However, since this assay is a discovery-oriented screening, it is difficult to determine the optimal experimental setup in advance. In this study, we examined whether variable cellular stimulation affects the efficacy of the image-based profiling of compounds. Seven different epidermal growth factor receptor (EGFR) ligands were used, and the expression of EGFR signaling molecules was monitored at various time points. Significant quantitative differences in image features were detected among the differentially treated samples. Next, 14 different compounds that affect EGFR signaling were profiled. Nearly half of the compounds were classified into distinct clusters, irrespective of differential ligand stimulation. The results suggest that image-based phenotypic profiling is quite robust in its ability to predict compound interaction with its target. Although this method will have to be validated in other experimental systems, the robustness of image-based compound profiling demonstrated in this work provides a valid basis for further study and its extended application.

Mining biomedical images towards valuable information retrieval in biomedical and life sciences

Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries.

Recent Advances on Luminescent Enhancement-Based Porous Silicon Biosensors

Luminescence-based detection paradigms have key advantages over other optical platforms such as absorbance, reflectance or interferometric based detection. However, autofluorescence, low quantum yield and lack of photostability of the fluorophore or emitting molecule are still performance-limiting factors. Recent research has shown the need for enhanced luminescence-based detection to overcome these drawbacks while at the same time improving the sensitivity, selectivity and reducing the detection limits of optical sensors and biosensors. Nanostructures have been reported to significantly improve the spectral properties of the emitting molecules. These structures offer unique electrical, optic and magnetic properties which may be used to tailor the surrounding electrical field of the emitter. Here, the main principles behind luminescence and luminescence enhancement-based detections are reviewed, with an emphasis on europium complexes as the emitting molecule. An overview of the optical porous silicon microcavity (pSiMC) as a biosensing platform and recent proof-of-concept examples on enhanced luminescence-based detection using pSiMCs are provided and discussed.

New concepts for building vocabulary for cell image ontologies

Background

There are significant challenges associated with the building of ontologies for cell biology experiments including the large numbers of terms and their synonyms. These challenges make it difficult to simultaneously query data from multiple experiments or ontologies. If vocabulary terms were consistently used and reused across and within ontologies, queries would be possible through shared terms. One approach to achieving this is to strictly control the terms used in ontologies in the form of a pre-defined schema, but this approach limits the individual researcher's ability to create new terms when needed to describe new experiments.

Results

Here, we propose the use of a limited number of highly reusable common root terms, and rules for an experimentalist to locally expand terms by adding more specific terms under more general root terms to form specific new vocabulary hierarchies that can be used to build ontologies. We illustrate the application of the method to build vocabularies and a prototype database for cell images that uses a visual data-tree of terms to facilitate sophisticated queries based on a experimental parameters. We demonstrate how the terminology might be extended by adding new vocabulary terms into the hierarchy of terms in an evolving process. In this approach, image data and metadata are handled separately, so we also describe a robust file-naming scheme to unambiguously identify image and other files associated with each metadata value. The prototype database http://sbd.nist.gov/ consists of more than 2000 images of cells and benchmark materials, and 163 metadata terms that describe experimental details, including many details about cell culture and handling. Image files of interest can be retrieved, and their data can be compared, by choosing one or more relevant metadata values as search terms. Metadata values for any dataset can be compared with corresponding values of another dataset through logical operations.

Conclusions

Organizing metadata for cell imaging experiments under a framework of rules that include highly reused root terms will facilitate the addition of new terms into a vocabulary hierarchy and encourage the reuse of terms. These vocabulary hierarchies can be converted into XML schema or RDF graphs for displaying and querying, but this is not necessary for using it to annotate cell images. Vocabulary data trees from multiple experiments or laboratories can be aligned at the root terms to facilitate query development. This approach of developing vocabularies is compatible with the major advances in database technology and could be used for building the Semantic Web.

Data ontology and an information system realization for web-based management of image measurements

Image acquisition, processing, and quantification of objects (morphometry) require the integration of data inputs and outputs originating from heterogeneous sources. Management of the data exchange along this workflow in a systematic manner poses several challenges, notably the description of the heterogeneous meta-data and the interoperability between the software used. The use of integrated software solutions for morphometry and management of imaging data in combination with ontologies can reduce meta-data loss and greatly facilitate subsequent data analysis. This paper presents an integrated information system, called LabIS. The system has the objectives to automate (i) the process of storage, annotation, and querying of image measurements and (ii) to provide means for data sharing with third party applications consuming measurement data using open standard communication protocols. LabIS implements 3-tier architecture with a relational database back-end and an application logic middle tier realizing web-based user interface for reporting and annotation and a web-service communication layer. The image processing and morphometry functionality is backed by interoperability with ImageJ, a public domain image processing software, via integrated clients. Instrumental for the latter feat was the construction of a data ontology representing the common measurement data model. LabIS supports user profiling and can store arbitrary types of measurements, regions of interest, calibrations, and ImageJ settings. Interpretation of the stored measurements is facilitated by atlas mapping and ontology-based markup. The system can be used as an experimental workflow management tool allowing for description and reporting of the performed experiments. LabIS can be also used as a measurements repository that can be transparently accessed by computational environments, such as Matlab. Finally, the system can be used as a data sharing tool.

Microfluidics-integrated time-lapse imaging for analysis of cellular dynamics

An understanding of the mechanisms regulating cellular responses has recently been augmented by innovations enabling the observation of phenotypes at high spatio-temporal resolution. Technologies such as microfluidics have sought to expand the throughput of these methods, although assimilation with advanced imaging strategies has been limited. Here, we describe the pairing of high resolution time-lapse imaging with microfluidic multiplexing for the analysis of cellular dynamics, utilizing a design selected for facile fabrication and operation, and integration with microscopy instrumentation. This modular, medium-throughput platform enables the long-term imaging of living cells at high numerical aperture (via oil immersion) by using a conserved 96-well, approximately 6 x 5 mm(2) imaging area with a variable input/output channel design chosen for the number of cell types and microenvironments under investigation. In the validation of this system, we examined fundamental features of cell cycle progression, including mitotic kinetics and spindle orientation dynamics, through the high-resolution parallel analysis of model cell lines subjected to anti-mitotic agents. We additionally explored the self-renewal kinetics of mouse embryonic stem cells, and demonstrate the ability to dynamically assess and manipulate stem cell proliferation, detect rare cell events, and measure extended time-scale correlations. We achieved an experimental throughput of >900 cells/experiment, each observed at >40x magnification for up to 120 h. Overall, these studies illustrate the capacity to probe cellular functions and yield dynamic information in time and space through the integration of a simple, modular, microfluidics-based imaging platform.

Histopathological image analysis: a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.

Histopathological image analysis: a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.

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.

A FRET-based assay for characterization of alternative splicing events using peptide nucleic acid fluorescence in situ hybridization

We describe a quantitative method for detecting RNA alternative splicing variants that combines in situ hybridization of fluorescently labeled peptide nucleic acid (PNA) probes with confocal microscopy Förster resonance energy transfer (FRET). The use of PNA probes complementary to sequences flanking a given splice junction allows to specifically quantify, within the cell, the RNA isoform generating such splice junction by FRET measure. As a proof of concept we analyzed two alternative splicing events originating from lymphocyte antigen 6 (LY6) complex, locus G5B (LY6G5B) pre-mRNA. These are characterized by the removal of the first intron (Fully Spliced Isoform, FSI) or by retention of such intron (Intron-Retained Isoform, IRI). The use of PNA probe pairs labeled with donor (Cy3) and acceptor (Cy5) fluorophores, suitable to FRET, flanking FSI and IRI specific splice junctions specifically detected both mRNA isoforms in HeLa cells. We have observed that the method works efficiently with probes 5–11 nt apart. The data supports that this FRET-based PNA fluorescence in situ hybridization (FP–FISH) method offers a conceptually new approach for characterizing at the subcellular level not only splice variant isoform structure, location and dynamics but also potentially a wide variety of close range RNA–RNA interactions.

Toward a confocal subcellular atlas of the human proteome

Information on protein localization on the subcellular level is important to map and characterize the proteome and to better understand cellular functions of proteins. Here we report on a pilot study of 466 proteins in three human cell lines aimed to allow large scale confocal microscopy analysis using protein-specific antibodies. Approximately 3000 high resolution images were generated, and more than 80% of the analyzed proteins could be classified in one or multiple subcellular compartment(s). The localizations of the proteins showed, in many cases, good agreement with the Gene Ontology localization prediction model. This is the first large scale antibody-based study to localize proteins into subcellular compartments using antibodies and confocal microscopy. The results suggest that this approach might be a valuable tool in conjunction with predictive models for protein localization.

Use of automated imaging and analysis technology for the detection of aneuploidy in human sperm

OBJECTIVE

To determine the precision and accuracy of an automated cell counting system when applied to counting aneuploidies in sperm samples.

DESIGN

Prospective pilot study.

SETTING

Andrology clinic and research laboratory in a university teaching hospital.

PATIENT(S)

Ten anonymous sperm donors of known fertility and two patients seeking infertility treatment.

INTERVENTION(S)

Semen samples were processed for detection of aneuploidies for chromosomes 13, 18, 21, X, and Y with use of fluorescent in situ hybridization. The detection of chromosome aneuploidy was performed both by manual counting and by the use of an automated cell counting system with manual review of aneuploid sperm.

MAIN OUTCOME MEASURE(S)

Semen samples were judged for the percent aneuploidy for chromosomes 13, 18, 21, X, and Y when counted manually or with the use of the automated cell counting system and review by a technician.

RESULT(S)

The sperm aneuploidy rates determined by the automated cell counting system and careful review were comparable with those obtained by manual counting by a trained technician.

CONCLUSION(S)

These preliminary data demonstrate that automated cell counting devices may be useful in increasing productivity in aneuploidy detection in sperm and may become an alternative to the labor-intensive manual counting by technicians.

Random subwindows and extremely randomized trees for image classification in cell biology

Background

With the improvements in biosensors and high-throughput image acquisition technologies, life science laboratories are able to perform an increasing number of experiments that involve the generation of a large amount of images at different imaging modalities/scales. It stresses the need for computer vision methods that automate image classification tasks.

Results

We illustrate the potential of our image classification method in cell biology by evaluating it on four datasets of images related to protein distributions or subcellular localizations, and red-blood cell shapes. Accuracy results are quite good without any specific pre-processing neither domain knowledge incorporation. The method is implemented in Java and available upon request for evaluation and research purpose.

Conclusion

Our method is directly applicable to any image classification problems. We foresee the use of this automatic approach as a baseline method and first try on various biological image classification problems.

Image-based multivariate profiling of drug responses from single cells

Quantitative analytical approaches for discovering new compound mechanisms are required for summarizing high-throughput, image-based drug screening data. Here we present a multivariate method for classifying untreated and treated human cancer cells based on approximately 300 single-cell phenotypic measurements. This classification provides a score, measuring the magnitude of the drug effect, and a vector, indicating the simultaneous phenotypic changes induced by the drug. These two quantities were used to characterize compound activities and identify dose-dependent multiphasic responses. A systematic survey of profiles extracted from a 100-compound compendium of image data revealed that only 10-15% of the original features were required to detect a compound effect. We report the most informative image features for each compound and fluorescence marker set using a method that will be useful for determining minimal collections of readouts for drug screens. Our approach provides human-interpretable profiles and automatic determination of on- and off-target effects.

Large-scale automated analysis of location patterns in randomly tagged 3T3 cells

Location proteomics is concerned with the systematic analysis of the subcellular location of proteins. In order to perform high-resolution, high-throughput analysis of all protein location patterns, automated methods are needed. Here we describe the use of such methods on a large collection of images obtained by automated microscopy to perform high-throughput analysis of endogenous proteins randomly-tagged with a fluorescent protein in NIH 3T3 cells. Cluster analysis was performed to identify the statistically significant location patterns in these images. This allowed us to assign a location pattern to each tagged protein without specifying what patterns are possible. To choose the best feature set for this clustering, we have used a novel method that determines which features do not artificially discriminate between control wells on different plates and uses Stepwise Discriminant Analysis (SDA) to determine which features do discriminate as much as possible among the randomly-tagged wells. Combining this feature set with consensus clustering methods resulted in 35 clusters among the first 188 clones we obtained. This approach represents a powerful automated solution to the problem of identifying subcellular locations on a proteome-wide basis for many different cell types.

CellProfiler: image analysis software for identifying and quantifying cell phenotypes

CellProfiler, the first free, open-source system for flexible and high-throughput cell image analysis is described.

Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).

Matrix Metalloproteinase 26 Proteolysis of the NH2-Terminal Domain of the Estrogen Receptor β Correlates with the Survival of Breast Cancer Patients

Estrogens have many cellular functions, including their interactions with estrogen receptors alpha and beta (ERalpha and ERbeta). Earlier, we determined that the estrogen-ER complex stimulates the transcriptional activity of the matrix metalloproteinase 26 (MMP-26) gene promoter. We then determined that ERbeta is susceptible to MMP-26 proteolysis whereas ERalpha is resistant to the protease. MMP-26 targets the NH(2)-terminal region of ERbeta coding for the divergent NH(2)-terminal A/B domain that is responsible for the ligand-independent transactivation function. As a result, MMP-26 proteolysis generates the COOH-terminal fragments of ERbeta. Immunohistochemical analysis of tissue microarrays derived from 121 cancer patients corroborated these data and revealed an inverse correlation between the ERalpha-dependent expression of MMP-26 and the levels of the intact ERbeta in breast carcinomas. MMP-26 is not expressed in normal mammary epithelium. The levels of MMP-26 are strongly up-regulated in ductal carcinoma in situ (DCIS). In the course of further disease progression through stages I to III, the expression of MMP-26 decreases. In contrast to many tumor-promoting MMPs, the expression of MMP-26 in DCIS correlated with a longer patient survival. Our data suggest the existence of an MMP-26-mediated intracellular pathway that targets ERbeta and that MMP-26, a novel and valuable cancer marker, contributes favorably to the survival of the ERalpha/beta-positive cohort of breast cancer patients.

Internet-based robotic laser scissors and tweezers microscopy

We have engineered a robotic laser ablation and tweezers microscope that can be operated via the internet using most internet accessible devices, including laptops, desktop computers, and personal data assistants (PDAs). The system affords individual investigators the ability to conduct micromanipulation experiments (cell surgery or trapping) from remote locations (i.e., between the US and Australia). This system greatly expands the availability of complex and expensive research technologies via investigator-networking over the internet. It serves as a model for other "internet-friendly" technologies leading to large scale networking and data-sharing between investigators, groups, and institutions on a global scale. The system offers three unique features: (1) the freedom to operate the system from any internet-capable computer, (2) the ability to image, ablate, and/or trap cells and their organelles by "remote-control," and (3) the security and convenience of controlling the system in the laboratory on the user's own personal computer and not on the host machine. Four "proof of principle" experiments were conducted: (1) precise control of microscope movement and live cell visualization, (2) subcellular microsurgery on the microtubule organizing center of live cells viewed under phase contrast and fluorescence microscopy, (3) precise targeting of multiple sites within single red blood cells, and (4) optical trapping of 10 microm diameter polystyrene microspheres.

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.

Adenoviral Sensors for High‐Content Cellular Analysis

To maximize the potential of high-content cellular analysis for investigating complex cellular signaling pathways and processes, we have generated a library of adenoviral encoded cellular sensors based on protein translocation and reporter gene activation that enable a diverse set of assays to be applied to lead compound profiling in drug discovery and development. Adenoviral vector transduction is an efficient and technically simple system for expression of cellular sensors in diverse cell types, including primary cells. Adenoviral vector-mediated transient expression of cellular sensors, either as fluorescent protein fusions or live cell gene reporters, allows rapid assay development for profiling the activities of candidate drugs across multiple cellular systems selected for biological and physiological relevance to the target disease state.

Methods for the functional genomic analysis of ubiquitin ligases

Ubiquitin ligases (E3s) are critical components of the ubiquitin-proteasome system as they are the major determinants of specificity in ubiquitin conjugation. The number of predicted E3s in the mammalian genome is exceeding 400 and is represented by two major subfamilies: HECT domain-containing E3s and RING finger-type E3s. Given the size of this protein family and lack of knowledge on the functions of most of these 400 proteins, their functional annotation should benefit from modern genomic tools. This article presents a methodology consisting of the use of a cDNA expression library to identify suppressors of polyglutamine (polyQ)-mediated protein aggregate formation in cells, as an example of a genomic approach to assign functions to E3s. In this screen, we identified novel RING finger-type E3s exhibiting suppressor activity among >50% of all the potential E3s in the mouse and human genomes. This method could be adapted easily to identify E3s that function in other processes and signaling pathways.

Systems Cell Biology Knowledge Created from High Content Screening

High content screening (HCS), the large-scale automated analysis of the temporal and spatial changes in cells and cell constituents in arrays of cells, has the potential to create enormous systems cell biology knowledge bases. HCS is being employed along with the continuum of the early drug discovery process, including lead optimization where new knowledge is being used to facilitate the decision-making process. We demonstrate methodology to build new systems cell biology knowledge using a multiplexed HCS assay, designed with the aid of knowledge-mining tools, to measure the phenotypic response of a panel of human tumor cell types to a panel of natural product-derived microtubule-targeted anticancer agents and their synthetic analogs. We show how this new systems cell biology knowledge can be used to design a lead compound optimization strategy for at least two members of the panel, (-)-laulimalide and (+)-discodermolide, that exploits cell killing activity while minimally perturbing the regulation of the cell cycle and the stability of microtubules. Furthermore, this methodology can also be applied to basic biomedical research on cells.

Microscopy and image analysis

This unit provides an overview of light microscopy, including objectives, light sources, filters, film, and color photography for fluorescence microscopy and fluorescence in situ hybridization (FISH). Computerized image-analysis systems currently used in clinical cytogenetics are also discussed.

High-content screening and profiling of drug activity in an automated centrosome-duplication assay

Maintenance of centrosome number is essential for cell-cycle progression and genomic stability, but investigation of this regulation has been limited by assay difficulty. We present a fully automated image-based centrosome-duplication assay that is accurate and robust enough for both careful cell-biology studies and high-throughput screening, and employ this assay in a series of chemical-genetic studies. We observe that a simple cytometric profiling strategy, which is based on organelle size, groups compounds with similar mechanisms of action; this suggests a simple strategy for excluding compounds that undesirably target such activities as protein synthesis and microtubule dynamics. Screening a library of compounds of known activity, we found unexpected effects on centrosome duplication by a number of drugs, most notably isoform-specific protein kinase C inhibitors and retinoic acid receptor agonists. From a 16 320-member library of uncharacterized small molecules, we identified five potent centrosome-duplication inhibitors that do not target microtubule dynamics or protein synthesis. The analysis methodology reported here is directly relevant to studies of centrosome regulation in a variety of systems and is adaptable to a wide range of other biological problems.

Identification of novel mammalian growth regulatory factors by genome-scale quantitative image analysis

Functional profiling technologies using arrayed collections of genome-scale siRNA and cDNA arrayed libraries enable the comprehensive global analysis of gene function. However, the current repertoire of high-throughput detection methodologies has limited the scope of cellular phenotypes that can be studied. In this report, we describe the systematic identification of mammalian growth-regulatory factors achieved through the integration of automated microscopy, pattern recognition analysis, and cell-based functional genomics. The effects of 7364 human and mouse proteins, encoded by individually arrayed cDNAs, upon proliferation and viability in U2OS osteosarcoma cells were evaluated in a live-cell, kinetic assay using quantitative image analysis. Overexpression of more than 86 cDNAs (1.15%) conferred dramatic increases in the proliferation, as determined cell enumeration. These included several known growth regulators, as well as previously uncharacterized ones (LRRK1, Ankrd25). In addition, novel functional roles for two genes (5033414D02Rik, 2810429O05Rik), now termed Gatp1 and Gatp2, respectively, were identified. Further analysis demonstrated that these encoded proteins promoted cellular proliferation and transformation in primary cells. Conversely, cells depleted for Gatp1 underwent apoptosis upon serum reduction, suggesting that Gatp1 is essential for cell survival under growth-factor-restricted conditions. Taken together, our findings offer new insight into the regulation of cellular growth and proliferation, and demonstrate the value and feasibility of assessing cellular phenotypes through genome-level computational image analysis.

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.

Analyzing and mining image databases

Image mining is the application of computer-based techniques that extract and exploit information from large image sets to support human users in generating knowledge from these sources. This review focuses on biomedical applications, in particular automated imaging at the cellular level. An image database is an interactive software application that combines data management, image analysis and visual data mining. The main characteristic of such a system is a layer that represents objects within an image, and that represents a large spectrum of quantitative and semantic object features. The image analysis needs to be adapted to each particular experiment, so 'end-user programming' will be desirable to make the technology more widely applicable.

An unbiased cell morphology-based screen for new, biologically active small molecules

We have implemented an unbiased cell morphology-based screen to identify small-molecule modulators of cellular processes using the Cytometrix (TM) automated imaging and analysis system. This assay format provides unbiased analysis of morphological effects induced by small molecules by capturing phenotypic readouts of most known classes of pharmacological agents and has the potential to read out pathways for which little is known. Four human-cancer cell lines and one noncancerous primary cell type were treated with 107 small molecules comprising four different protein kinase-inhibitor scaffolds. Cellular phenotypes induced by each compound were quantified by multivariate statistical analysis of the morphology, staining intensity, and spatial attributes of the cellular nuclei, microtubules, and Golgi compartments. Principal component analysis was used to identify inhibitors of cellular components not targeted by known protein kinase inhibitors. Here we focus on a hydroxyl-substituted analog (hydroxy-PP) of the known Src-family kinase inhibitor PP2 because it induced cell-specific morphological features distinct from all known kinase inhibitors in the collection. We used affinity purification to identify a target of hydroxy-PP, carbonyl reductase 1 (CBR1), a short-chain dehydrogenase-reductase. We solved the X-ray crystal structure of the CBR1/hydroxy-PP complex to 1.24 A resolution. Structure-based design of more potent and selective CBR1 inhibitors provided probes for analyzing the biological function of CBR1 in A549 cells. These studies revealed a previously unknown function for CBR1 in serum-withdrawal-induced apoptosis. Further studies indicate CBR1 inhibitors may enhance the effectiveness of anticancer anthracyclines. Morphology-based screening of diverse cancer cell types has provided a method for discovering potent new small-molecule probes for cell biological studies and anticancer drug candidates.

Microfabricated platform for studying stem cell fates

Platforms that allow parallel, quantitative analysis of single cells will be integral to realizing the potential of postgenomic biology. In stem cell biology, the study of clonal stem cells in multiwell formats is currently both inefficient and time-consuming. Thus, to investigate low-frequency events of interest, large sample sizes must be interrogated. We report a simple, versatile, and efficient micropatterned arraying system conducive to the culture and dynamic monitoring of stem cell proliferation. This platform enables: 1) parallel, automated, long-term ( approximately days to weeks), live-cell microscopy of single cells in culture; 2) tracking of individual cell fates over time (proliferation, apoptosis); and 3) correlation of differentiated progeny with founder clones. To achieve these goals, we used microfabrication techniques to create an array of approximately 10,000 microwells on a glass coverslip. The dimensions of the wells are tunable, ranging from 20 to >500 microm in diameter and 10-500 microm in height. The microarray can be coated with adhesive proteins and is integrated into a culture chamber that permits rapid (approximately min), addressable monitoring of each well using a standard programmable microscope stage. All cells share the same media (including paracrine survival signals), as opposed to cells in multiwell formats. The incorporation of a coverslip as a substrate also renders the platform compatible with conventional, high-magnification light and fluorescent microscopy. We validated this approach by analyzing the proliferation dynamics of a heterogeneous adult rat neural stem cell population. Using this platform, one can further interrogate the response of distinct stem cell subpopulations to microenvironmental cues (mitogens, cell-cell interactions, and cell-extracellular matrix interactions) that govern their behavior. In the future, the platform may also be adapted for the study of other cell types by tailoring the surface coatings, microwell dimensions, and culture environment, thereby enabling parallel investigation of many distinct cellular responses.

Regulation of actin-based cell migration by cAMP/PKA

A wide variety of soluble signaling substances utilize the cyclic AMP-dependent protein kinase (PKA) pathway to regulate cellular behaviors including intermediary metabolism, ion channel conductivity, and transcription. A growing literature suggests that integrin-mediated cell adhesion may also utilize PKA to modulate adhesion-associated events such as actin cytoskeletal dynamics and migration. PKA is dynamically regulated by integrin-mediated cell adhesion to extracellular matrix (ECM). Furthermore, while some hallmarks of cell migration and cytoskeletal organization require PKA activity (e.g. activation of Rac and Cdc42; actin filament assembly), others are inhibited by it (e.g. activation of Rho and PAK; interaction of VASP with the c-Abl tyrosine kinase). Also, cell migration and invasion can be impeded by either inhibition or hyper-activation of PKA. Finally, a number of A-kinase anchoring proteins (AKAPs) serve to associate PKA with various components of the actin cytoskeleton, thereby enhancing and/or specifying cAMP/PKA signaling in those regions. This review discusses the growing literature that supports the hypothesis that PKA plays a central role in cytoskeletal regulation and cell migration.

Evidence-based medicine, medical decision analysis, and pathology

Recent advances in molecular pathology and other technologies such as proteomics present pathologists with the challenge of integrating the new information generated with high-throughput methods with current diagnostic models based mostly on histopathology and clinicopathologic correlations. Parallel developments in the field of medical informatics and bioinformatics provide the technical and mathematical methods to approach these problems in a rational manner. However, it remains unclear whether pathologists or other medical specialists will become primarily responsible for the development and maintenance of these multivariate and multidisciplinary diagnostic and prognostic models that are hoped to provide more accurate, individualized patient-based information. Evidence-based medicine (EBM) and medical decision analysis (MDA) are relatively new disciplines that use quantitative methods to assess the value of information, differentiate fact from myth, and integrate so-called best evidence into multivariate models for the assessment of prognosis, response to therapy, selection of laboratory tests, and other complex problems that influence individual patient care. We review from an epistemological viewpoint the current approach to information in pathology and describe some of the concepts developed by the practitioners of EBM and MDA.

High-throughput gene silencing using cell arrays

A recently established transfected cell array (TCA) technology has opened new experimental dimensions in the field of functional genomics. Cell arrays allow for transfection of several thousands different DNA molecules in microarray format. The effects of overexpression of hundreds of proteins on cellular physiology can be observed in a single experiment. The TCA technique has also found its application in RNA interference (RNAi) research. Small interfering RNAs (siRNA) as well as plasmid expressing short hairpin RNAs can be transferred into the cells through the process of reverse transfection. The silencing of numerous genes in spatially separated manner can be thus monitored. This review will provide an overview on current concepts concerning combination of cell array and RNAi for high-throughput loss-of-function studies.

Single-cell microbiology: tools, technologies, and applications

The field of microbiology has traditionally been concerned with and focused on studies at the population level. Information on how cells respond to their environment, interact with each other, or undergo complex processes such as cellular differentiation or gene expression has been obtained mostly by inference from population-level data. Individual microorganisms, even those in supposedly "clonal" populations, may differ widely from each other in terms of their genetic composition, physiology, biochemistry, or behavior. This genetic and phenotypic heterogeneity has important practical consequences for a number of human interests, including antibiotic or biocide resistance, the productivity and stability of industrial fermentations, the efficacy of food preservatives, and the potential of pathogens to cause disease. New appreciation of the importance of cellular heterogeneity, coupled with recent advances in technology, has driven the development of new tools and techniques for the study of individual microbial cells. Because observations made at the single-cell level are not subject to the "averaging" effects characteristic of bulk-phase, population-level methods, they offer the unique capacity to observe discrete microbiological phenomena unavailable using traditional approaches. As a result, scientists have been able to characterize microorganisms, their activities, and their interactions at unprecedented levels of detail.

Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

BACKGROUND

Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein subcellular location patterns in both 2D and 3D fluorescence microscope images. Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D fluorescence microscope images.

RESULTS

We report here a thorough comparison of the performance on this problem of eight different state-of-the-art classification methods, including neural networks, support vector machines with linear, polynomial, radial basis, and exponential radial basis kernel functions, and ensemble methods such as AdaBoost, Bagging, and Mixtures-of-Experts. Ten-fold cross validation was used to evaluate each classifier with various parameters on different Subcellular Location Feature sets representing both 2D and 3D fluorescence microscope images, including new feature sets incorporating features derived from Gabor and Daubechies wavelet transforms. After optimal parameters were chosen for each of the eight classifiers, optimal majority-voting ensemble classifiers were formed for each feature set. Comparison of results for each image for all eight classifiers permits estimation of the lower bound classification error rate for each subcellular pattern, which we interpret to reflect the fraction of cells whose patterns are distorted by mitosis, cell death or acquisition errors. Overall, we obtained statistically significant improvements in classification accuracy over the best previously published results, with the overall error rate being reduced by one-third to one-half and with the average accuracy for single 2D images being higher than 90% for the first time. In particular, the classification accuracy for the easily confused endomembrane compartments (endoplasmic reticulum, Golgi, endosomes, lysosomes) was improved by 5-15%. We achieved further improvements when classification was conducted on image sets rather than on individual cell images.

CONCLUSIONS

The availability of accurate, fast, automated classification systems for protein location patterns in conjunction with high throughput fluorescence microscope imaging techniques enables a new subfield of proteomics, location proteomics. The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction.