In vivo functional proteomics: mammalian genome annotation using CD-tagging

An Endogenously Tagged Fluorescent Fusion Protein Library in Mouse Embryonic Stem Cells

Embryonic stem cells (ESCs), with their dual capacity to self-renew and differentiate, are commonly used to study differentiation, epigenetic regulation, lineage choices, and more. Using non-directed retroviral integration of a YFP/Cherry exon into mouse ESCs, we generated a library of over 200 endogenously tagged fluorescent fusion proteins and present several proof-of-concept applications of this library. We show the utility of this library to track proteins in living cells; screen for pluripotency-related factors; identify heterogeneously expressing proteins; measure the dynamics of endogenously labeled proteins; track proteins recruited to sites of DNA damage; pull down tagged fluorescent fusion proteins using anti-Cherry antibodies; and test for interaction partners. Thus, this library can be used in a variety of different directions, either exploiting the fluorescent tag for imaging-based techniques or utilizing the fluorescent fusion protein for biochemical pull-down assays, including immunoprecipitation, co-immunoprecipitation, chromatin immunoprecipitation, and more.

A novel method for quantitative measurements of gene expression in single living cells

Gene expression is at the heart of virtually any biological process, and its deregulation is at the source of numerous pathological conditions. While impressive progress has been made in genome-wide measurements of mRNA and protein expression levels, it is still challenging to obtain highly quantitative measurements in single living cells. Here we describe a novel approach based on internal tagging of endogenous proteins with a reporter allowing luminescence and fluorescence time-lapse microscopy. Using luminescence microscopy, fluctuations of protein expression levels can be monitored in single living cells with high sensitivity and temporal resolution over extended time periods. The integrated protein decay reporter allows measuring protein degradation rates in the absence of protein synthesis inhibitors, and in combination with absolute protein levels allows determining absolute amounts of proteins synthesized over the cell cycle. Finally, the internal tag can be excised by inducible expression of Cre recombinase, which enables to estimate endogenous mRNA half-lives. Our method thus opens new avenues in quantitative analysis of gene expression in single living cells.

Discovery of Small-Molecule Nonfluorescent Inhibitors of Fluorogen-Fluorogen Activating Protein Binding Pair

A new class of biosensors, fluorogen activating proteins (FAPs), has been successfully used to track receptor trafficking in live cells. Unlike the traditional fluorescent proteins (FPs), FAPs do not fluoresce unless bound to their specific small-molecule fluorogens, and thus FAP-based assays are highly sensitive. Application of the FAP-based assay for protein trafficking in high-throughput flow cytometry resulted in the discovery of a new class of compounds that interferes with the binding between fluorogens and FAP, thus blocking the fluorescence signal. These compounds are high-affinity, nonfluorescent analogs of fluorogens with little or no toxicity to the tested cells and no apparent interference with the normal function of FAP-tagged receptors. The most potent compound among these, N,4-dimethyl-N-(2-oxo-2-(4-(pyridin-2-yl)piperazin-1-yl)ethyl)benzenesulfonamide (ML342), has been investigated in detail. X-ray crystallographic analysis revealed that ML342 competes with the fluorogen, sulfonated thiazole orange coupled to diethylene glycol diamine (TO1-2p), for the same binding site on a FAP, AM2.2. Kinetic analysis shows that the FAP-fluorogen interaction is more complex than a homogeneous one-site binding process, with multiple conformational states of the fluorogen and/or the FAP, and possible dimerization of the FAP moiety involved in the process.

Real-time detection of protein trafficking with high-throughput flow cytometry (HTFC) and fluorogen-activating protein (FAP) base biosensor

We combined fluorogen-activating protein (FAP) technology with high-throughput flow cytometry to detect real-time protein trafficking to and from the plasma membrane in living cells. The hybrid platform allows drug discovery for trafficking receptors, such as G protein-coupled receptors, receptor tyrosine kinases, and ion channels, which were previously not suitable for high-throughput screening by flow cytometry. The system has been validated using the β2-adrenergic receptor (β2AR) system and extended to other GPCRs. When a chemical library containing ∼ 1200 off-patent drugs was screened against cells expressing FAP-tagged β2AR, all known β2AR active ligands in the library were successfully identified, together with a few compounds that were later confirmed to regulate receptor internalization in a nontraditional manner. The unexpected discovery of new ligands by this approach indicates the potential of using this protocol for GPCR de-orphanization. In addition, screens of multiplexed targets promise improved efficiency with minor protocol modification.

Determining the subcellular location of new proteins from microscope images using local features

MOTIVATION

Evaluation of previous systems for automated determination of subcellular location from microscope images has been done using datasets in which each location class consisted of multiple images of the same representative protein. Here, we frame a more challenging and useful problem where previously unseen proteins are to be classified.

RESULTS

Using CD-tagging, we generated two new image datasets for evaluation of this problem, which contain several different proteins for each location class. Evaluation of previous methods on these new datasets showed that it is much harder to train a classifier that generalizes across different proteins than one that simply recognizes a protein it was trained on. We therefore developed and evaluated additional approaches, incorporating novel modifications of local features techniques. These extended the notion of local features to exploit both the protein image and any reference markers that were imaged in parallel. With these, we obtained a large accuracy improvement in our new datasets over existing methods. Additionally, these features help achieve classification improvements for other previously studied datasets.

AVAILABILITY

The datasets are available for download at http://murphylab.web.cmu.edu/data/. The software was written in Python and C++ and is available under an open-source license at http://murphylab.web.cmu.edu/software/. The code is split into a library, which can be easily reused for other data and a small driver script for reproducing all results presented here. A step-by-step tutorial on applying the methods to new datasets is also available at that address.

CONTACT

murphy@cmu.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High-throughput flow cytometry compatible biosensor based on fluorogen activating protein technology

Monitoring the trafficking of multiple proteins simultaneously in live cells is of great interest because many receptor proteins are found to function together with others in the same cell. However, existing fluorescent labeling techniques have restricted the mechanistic study of functional receptor pairs. We have expanded a hybrid system combining fluorogen-activating protein (FAP) technology and high-throughput flow cytometry to a new type of biosensor that is robust, sensitive, and versatile. This provides the opportunity to study multiple trafficking proteins in the same cell. Human beta2 adrenergic receptor (β2AR) fused with FAP AM2.2 and murine C-C chemokines receptor type 5 fused with FAP MG13 was chosen for our model system. The function of the receptor and the binding between MG13 and fluorogen MG-2p have been characterized by flow cytometry and confocal microscopy assays. The binding of fluorogen and the FAP pair is highly specific, while both FAP-tagged fusion proteins function similarly to their wild-type counterparts. The system has successfully served as a counter screen assay to eliminate false positive compounds identified in a screen against NIH Molecular Libraries Small Molecule Repository targeting regulators of the human β2AR.

Shaping cells and organs in Drosophila by opposing roles of fat body-secreted Collagen IV and perlecan

Basement membranes (BMs) are resilient polymer structures that surround organs in all animals. Tissues, however, undergo extensive morphological changes during development. It is not known whether the assembly of BM components plays an active morphogenetic role. To study in vivo the biogenesis and assembly of Collagen IV, the main constituent of BMs, we used a GFP-based RNAi method (iGFPi) designed to knock down any GFP-trapped protein in Drosophila. We found with this method that Collagen IV is synthesized by the fat body, secreted to the hemolymph (insect blood), and continuously incorporated into the BMs of the larva. We also show that incorporation of Collagen IV determines organ shape, first by mechanically constricting cells and second through recruitment of Perlecan, which counters constriction by Collagen IV. Our results uncover incorporation of Collagen IV and Perlecan into BMs as a major determinant of organ shape and animal form.

Model building and intelligent acquisition with application to protein subcellular location classification

MOTIVATION

We present a framework and algorithms to intelligently acquire movies of protein subcellular location patterns by learning their models as they are being acquired, and simultaneously determining how many cells to acquire as well as how many frames to acquire per cell. This is motivated by the desire to minimize acquisition time and photobleaching, given the need to build such models for all proteins, in all cell types, under all conditions. Our key innovation is to build models during acquisition rather than as a post-processing step, thus allowing us to intelligently and automatically adapt the acquisition process given the model acquired.

RESULTS

We validate our framework on protein subcellular location classification, and show that the combination of model building and intelligent acquisition results in time and storage savings without loss of classification accuracy, or alternatively, higher classification accuracy for the same total acquisition time.

AVAILABILITY AND IMPLEMENTATION

The data and software used for this study will be made available upon publication at http://murphylab.web.cmu.edu/software and http://www.andrew.cmu.edu/user/jelenak/Software.

CONTACT

jelenak@cmu.edu.

AUTOMATED ESTIMATION OF MICROTUBULE MODEL PARAMETERS FROM 3-D LIVE CELL MICROSCOPY IMAGES

While basic principles of microtubule organization are well understood, much remains to be learned about the extent and significance of variation in that organization among cell types and conditions. Large numbers of images of microtubule distributions for many cell types can be readily obtained by high throughput fluorescence microscopy but direct estimation of the parameters underlying the organization is problematic because it is difficult to resolve individual microtubules present at the microtubule-organizing center or at regions of high crossover. Previously, we developed an indirect, generative model-based approach that can estimate such spatial distribution parameters as the number and mean length of microtubules. In order to validate this approach, we have applied it to 3D images of NIH 3T3 cells expressing fluorescently-tagged tubulin in the presence and absence of the microtubule depolymerizing drug nocodazole. We describe here the first application of our inverse modeling approach to live cell images and demonstrate that it yields estimates consistent with expectations.

Development of benzothiazole 'click-on' fluorogenic dyes

'Click-on' fluorogenic reaction: a non-fluorescent benzothiazole with an electron-deficient alkyne group at 2-position reacts with azide containing molecules could form fluorescent adducts.

Generation of double-labeled reporter cell lines for studying co-dynamics of endogenous proteins in individual human cells

Understanding the dynamic relationship between components of a system or pathway at the individual cell level is a current challenge. To address this, we developed an approach that allows simultaneous tracking of several endogenous proteins of choice within individual living human cells. The approach is based on fluorescent tagging of proteins at their native locus by directed gene targeting. A fluorescent tag-encoding DNA is introduced as a new exon into the intronic region of the gene of interest, resulting in expression of a full-length fluorescently tagged protein. We used this approach to establish human cell lines simultaneously expressing two components of a major antioxidant defense system, thioredoxin 1 (Trx) and thioredoxin reductase 1 (TrxR1), labeled with CFP and YFP, respectively. We find that the distributions of both proteins between nuclear and cytoplasmic compartments were highly variable between cells. However, the two proteins did not vary independently of each other: protein levels of Trx and TrxR1 in both the whole cell and the nucleus were substantially correlated. We further find that in response to a stress-inducing drug (CPT), both Trx and TrxR1 accumulated in the nuclei in a manner that was highly temporally correlated. This accumulation considerably reduced cell-to-cell variability in nuclear content of both proteins, suggesting a uniform response of the thioredoxin system to stress. These results indicate that Trx and TrxR1 act in concert in response to stress in regard to both time course and variability. Thus, our approach provides an efficient tool for studying dynamic relationship between components of systems of interest at a single-cell level.

Protein subcellular localization profiling of breast cancer cells by dissociable antibody microarray staining

We have developed dissociable antibody microarray (DAMA) staining technology that provides a new approach to the global analysis of protein subcellular localization (SCL) in fixed cells. We have developed and optimized this technology for protein SCL profiling, generated ChipView, a program for management and analysis of molecular image database, and utilized the technique to identify proteins with unique SCL in breast cancer cell lines. We compared the SCL profiles of 325 proteins among nine different breast cell lines, and have identified one protein, Cyclin B1, with distinctively different SCLs between normal and cancer cell lines. With classic individual immunostaining, Cyclin B1 was confirmed to localize to the cytoplasm of seven breast cancer cell lines and in both cytoplasm and nuclei of two normal breast cell lines, and to have higher expression levels in the cancer cell lines tested.

Automated analysis of protein subcellular location in time series images

MOTIVATION

Image analysis, machine learning and statistical modeling have become well established for the automatic recognition and comparison of the subcellular locations of proteins in microscope images. By using a comprehensive set of features describing static images, major subcellular patterns can be distinguished with near perfect accuracy. We now extend this work to time series images, which contain both spatial and temporal information. The goal is to use temporal features to improve recognition of protein patterns that are not fully distinguishable by their static features alone.

RESULTS

We have adopted and designed five sets of features for capturing temporal behavior in 2D time series images, based on object tracking, temporal texture, normal flow, Fourier transforms and autoregression. Classification accuracy on an image collection for 12 fluorescently tagged proteins was increased when temporal features were used in addition to static features. Temporal texture, normal flow and Fourier transform features were most effective at increasing classification accuracy. We therefore extended these three feature sets to 3D time series images, but observed no significant improvement over results for 2D images. The methods for 2D and 3D temporal pattern analysis do not require segmentation of images into single cell regions, and are suitable for automated high-throughput microscopy applications.

AVAILABILITY

Images, source code and results will be available upon publication at http://murphylab.web.cmu.edu/software

CONTACT

murphy@cmu.edu.

A generative model of microtubule distributions, and indirect estimation of its parameters from fluorescence microscopy images

The microtubule network plays critical roles in many cellular processes, and quantitative models of how its organization varies across cell types and conditions are required for understanding those roles and as input to cell simulations. High-throughput image acquisition technologies are potentially valuable for this purpose, but do not provide sufficient resolution for current analysis methods that rely on tracing of individual microtubules. We describe a parametric conditional model of microtubule distribution that can generate a microtubule network in intact cells using a persistent random walk approach. The model parameters are physically meaningful as they directly describe the spatial distribution of microtubules and include the number of microtubules as well as the mean of the length distribution. We also present an indirect method for estimating the parameters of the model from three-dimensional fluorescence microscope images of cells that relies on comparing acquired images with simulated images generated from the model. Our results show that our method can reasonably recover parameters for a given query image, and we present the distributions of parameters estimated by our method for a collection of HeLa cell images. (c) 2010 International Society for Advancement of Cytometry.

Dynamic Proteomics: a database for dynamics and localizations of endogenous fluorescently-tagged proteins in living human cells

Recent advances allow tracking the levels and locations of a thousand proteins in individual living human cells over time using a library of annotated reporter cell clones (LARC). This library was created by Cohen et al. to study the proteome dynamics of a human lung carcinoma cell-line treated with an anti-cancer drug. Here, we report the Dynamic Proteomics database for the proteins studied by Cohen et al. Each cell-line clone in LARC has a protein tagged with yellow fluorescent protein, expressed from its endogenous chromosomal location, under its natural regulation. The Dynamic Proteomics interface facilitates searches for genes of interest, downloads of protein fluorescent movies and alignments of dynamics following drug addition. Each protein in the database is displayed with its annotation, cDNA sequence, fluorescent images and movies obtained by the time-lapse microscopy. The protein dynamics in the database represents a quantitative trace of the protein fluorescence levels in nucleus and cytoplasm produced by image analysis of movies over time. Furthermore, a sequence analysis provides a search and comparison of up to 50 input DNA sequences with all cDNAs in the library. The raw movies may be useful as a benchmark for developing image analysis tools for individual-cell dynamic-proteomics. The database is available at http://www.dynamicproteomics.net/.

Cell-type-specific isolation of ribosome-associated mRNA from complex tissues

Gene profiling techniques allow the assay of transcripts from organs, tissues, and cells with an unprecedented level of coverage. However, most of these approaches are still limited by the fact that organs and tissues are composed of multiple cell types that are each unique in their patterns of gene expression. To identify the transcriptome from a single cell type in a complex tissue, investigators have relied upon physical methods to separate cell types or in situ hybridization and immunohistochemistry. Here, we describe a strategy to rapidly and efficiently isolate ribosome-associated mRNA transcripts from any cell type in vivo. We have created a mouse line, called RiboTag, which carries an Rpl22 allele with a floxed wild-type C-terminal exon followed by an identical C-terminal exon that has three copies of the hemagglutinin (HA) epitope inserted before the stop codon. When the RiboTag mouse is crossed to a cell-type-specific Cre recombinase-expressing mouse, Cre recombinase activates the expression of epitope-tagged ribosomal protein RPL22(HA), which is incorporated into actively translating polyribosomes. Immunoprecipitation of polysomes with a monoclonal antibody against HA yields ribosome-associated mRNA transcripts from specific cell types. We demonstrate the application of this technique in brain using neuron-specific Cre recombinase-expressing mice and in testis using a Sertoli cell Cre recombinase-expressing mouse.

Protein dynamics in individual human cells: experiment and theory

A current challenge in biology is to understand the dynamics of protein circuits in living human cells. Can one define and test equations for the dynamics and variability of a protein over time? Here, we address this experimentally and theoretically, by means of accurate time-resolved measurements of endogenously tagged proteins in individual human cells. As a model system, we choose three stable proteins displaying cell-cycle–dependant dynamics. We find that protein accumulation with time per cell is quadratic for proteins with long mRNA life times and approximately linear for a protein with short mRNA lifetime. Both behaviors correspond to a classical model of transcription and translation. A stochastic model, in which genes slowly switch between ON and OFF states, captures measured cell–cell variability. The data suggests, in accordance with the model, that switching to the gene ON state is exponentially distributed and that the cell–cell distribution of protein levels can be approximated by a Gamma distribution throughout the cell cycle. These results suggest that relatively simple models may describe protein dynamics in individual human cells.

Systematic definition of protein constituents along the major polarization axis reveals an adaptive reuse of the polarization machinery in pheromone-treated budding yeast

Polarizing cells extensively restructure cellular components in a spatially and temporally coupled manner along the major axis of cellular extension. Budding yeast are a useful model of polarized growth, helping to define many molecular components of this conserved process. Besides budding, yeast cells also differentiate upon treatment with pheromone from the opposite mating type, forming a mating projection (the ‘shmoo’) by directional restructuring of the cytoskeleton, localized vesicular transport and overall reorganization of the cytosol. To characterize the proteomic localization changes accompanying polarized growth, we developed and implemented a novel cell microarray-based imaging assay for measuring the spatial redistribution of a large fraction of the yeast proteome, and applied this assay to identify proteins localized along the mating projection following pheromone treatment. We further trained a machine learning algorithm to refine the cell imaging screen, identifying additional shmoo-localized proteins. In all, we identified 74 proteins that specifically localize to the mating projection, including previously uncharacterized proteins (Ycr043c, Ydr348c, Yer071c, Ymr295c, and Yor304c-a) and known polarization complexes such as the exocyst. Functional analysis of these proteins, coupled with quantitative analysis of individual organelle movements during shmoo formation, suggests a model in which the basic machinery for cell polarization is generally conserved between processes forming the bud and the shmoo, with a distinct subset of proteins used only for shmoo formation. The net effect is a defined ordering of major organelles along the polarization axis, with specific proteins implicated at the proximal growth tip.

Upon sensing mating pheromone, budding yeast cells form a mating projection (the ‘shmoo’) that serves as a model for polarized cell growth, involving cytoskeletal/cytosolic restructuring and directed vesicular transport. We developed a cell microarray-based imaging assay for measuring localization of the yeast proteome during polarized growth. We find major organelles ordered along the polarization axis, localize 74 proteins to the growth tip, and observe adaptive reuse of general polarization machinery.

Location proteomics: systematic determination of protein subcellular location

Proteomics seeks the systematic and comprehensive understanding of all aspects of proteins, and location proteomics is the relatively new subfield of proteomics concerned with the location of proteins within cells. This review provides a guide to the widening selection of methods for studying location proteomics and integrating the results into systems biology. Automated and objective methods for determining protein subcellular location have been described based on extracting numerical features from fluorescence microscope images and applying machine learning approaches to them. Systems to recognize all major protein subcellular location patterns in both two-dimensional and three-dimensional HeLa cell images with high accuracy (over 95% and 98%, respectively) have been built. The feasibility of objectively grouping proteins into subcellular location families, and in the process of discovering new subcellular patterns, has been demonstrated using cluster analysis of images from a library of randomly tagged protein clones. Generative models can be built to effectively capture and communicate the patterns in these families. While automated methods for high-resolution determination of subcellular location are now available, the task of applying these methods to all expressed proteins in many different cell types under many conditions represents a very significant challenge.

Dynamic proteomics of individual cancer cells in response to a drug

Why do seemingly identical cells respond differently to a drug? To address this, we studied the dynamics and variability of the protein response of human cancer cells to a chemotherapy drug, camptothecin. We present a dynamic-proteomics approach that measures the levels and locations of nearly 1000 different endogenously tagged proteins in individual living cells at high temporal resolution. All cells show rapid translocation of proteins specific to the drug mechanism, including the drug target (topoisomerase-1), and slower, wide-ranging temporal waves of protein degradation and accumulation. However, the cells differ in the behavior of a subset of proteins. We identify proteins whose dynamics differ widely between cells, in a way that corresponds to the outcomes-cell death or survival. This opens the way to understanding molecular responses to drugs in individual cells.

Coordinate suppression of Sdpr and Fhl1 expression in tumors of the breast, kidney, and prostate

The Src tyrosine kinase associates with the focal adhesion adaptor protein Cas (Crk-associated substrate) to suppress the expression of potential tumor suppressor genes. For example, Src utilizes Cas to suppress the expression of the LIM-only protein Fhl1 (four and a half LIM domains 1), in order to promote non-anchored tumor-cell growth and migration. Here, we report that the promoter region of the Fhl1 gene was methylated more in Src-transformed cells than non-transformed cells. In addition, global expression analysis indicates that Fhl1 induced expression of serum deprivation response factor (Sdpr) in Src-transformed cells. Moreover, Fhl1 and Sdpr was expressed in approximately 87% and 40% of samples obtained from non-transformed breast, 100% of samples obtained from non-transformed kidney, and over 60% of samples obtained from non-transformed prostate. In contrast, Fhl1 and Sdpr was detected in approximately 40% and 7% of matched samples from mammary carcinoma, less than 11% of matched samples from kidney carcinoma, and in less than 22% of matched samples from prostate carcinoma. These data indicate that Fhl1 and Sdpr expression was significantly reduced in tumors of the breast (P < 0.02 and P < 0.001), kidney (P < 0.01), and prostate (P < 0.05). In addition, although Src can activate mitogen-activated protein kinase (MAPK) to promote tumor-cell growth, our data indicate that Src did not rely on MAPK activity to suppress the expression of Fhl1 and Sdpr in transformed cells. Thus, Src induced methylation of the promoter region of the Fhl1 gene; Src suppressed Fhl1 and Sdpr expression independent of mitogen-activated protein kinase (MAPK) activity; Fhl1 induced the expression of Sdpr in Src-transformed cells; and Fhl1 and Sdpr expression was suppressed in tumors of the breast, kidney, and prostate.

Automated Proteome-Wide Determination of Subcellular Location Using High Throughput Microscopy

A major source of information for identifying subcellular location on a proteome-wide basis will be imaging of tagged proteins in living cells using fluorescence microscopy. We have previously developed automated systems to interpret images from such experiments and demonstrated that they can perform as well or better than visual inspection. Recent work demonstrates that these methods can be applied to large collections of images from sources as diverse as yeast expressing GFP-tagged proteins and human tissues imaged by immunocytochemistry. A distinct but related task is learning what location patterns exist. We have demonstrated clustering of mouse proteins into subcellular location families that share a statistically indistinguishable pattern. To communicate each pattern, we have developed approaches to learning generative models of subcellular patterns. Integration of high-throughput microscopy and automated model building with cell modeling systems will permit accurate, well-structured information on subcellular location to be incorporated into systems biology efforts.

Automated, systematic determination of protein subcellular location using fluorescence microscopy

Proteomics is the comprehensive study of all aspects of protein behavior. The subfield of 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, automation is needed both for acquisition and analysis. Automated methods for analyzing subcellular location patterns in fluorescence microscope images have been developed and shown to work well for static 2D and 3D images of single cells. This chapter reviews this work and describes current efforts to extend these approaches, including classification of temporal patterns and building of generative models to represent location patterns.

Generation of a fluorescently labeled endogenous protein library in living human cells

We present a protocol to tag proteins expressed from their endogenous chromosomal locations in individual mammalian cells using central dogma tagging. The protocol can be used to build libraries of cell clones, each expressing one endogenous protein tagged with a fluorophore such as the yellow fluorescent protein. Each round of library generation produces 100-200 cell clones and takes about 1 month. The protocol integrates procedures for high-throughput single-cell cloning using flow cytometry, high-throughput cDNA generation and 3' rapid amplification of cDNA ends, semi-automatic protein localization screening using fluorescent microscopy and freezing cells in 96-well format.

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.

Dynamic proteomics in mammalian cells: capabilities and challenges

A long term goal for molecular biologists is to visualize and quantify the levels and localizations of all proteins at the single cell level under endogenous regulation throughout time. Recent advances in protein tagging, microscopy, and image analysis have brought this goal much closer. But how to integrate these techniques to arrive at proteome scale results? Here I review one approach, incorporating random endogenous gene tagging, high-throughput incubated time-lapse microscopy, and automated image analysis, that can provide information on, for example, the accumulation rates of proteins throughout the cell cycle and the variability of protein level expression. Dynamic proteomics has the potential to shed light on many long standing questions and could contribute to challenging undertakings such as following signal transduction in a mammalian cell from input to output.

Variability and memory of protein levels in human cells

Protein expression is a stochastic process that leads to phenotypic variation among cells. The cell-cell distribution of protein levels in microorganisms has been well characterized but little is known about such variability in human cells. Here, we studied the variability of protein levels in human cells, as well as the temporal dynamics of this variability, and addressed whether cells with higher than average protein levels eventually have lower than average levels, and if so, over what timescale does this mixing occur. We measured fluctuations over time in the levels of 20 endogenous proteins in living human cells, tagged by the gene for yellow fluorescent protein at their chromosomal loci. We found variability with a standard deviation that ranged, for different proteins, from about 15% to 30% of the mean. Mixing between high and low levels occurred for all proteins, but the mixing time was longer than two cell generations (more than 40 h) for many proteins. We also tagged pairs of proteins with two colours, and found that the levels of proteins in the same biological pathway were far more correlated than those of proteins in different pathways. The persistent memory for protein levels that we found might underlie individuality in cell behaviour and could set a timescale needed for signals to affect fully every member of a cell population.

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.

Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins

We examined cell cycle-dependent changes in the proteome of human cells by systematically measuring protein dynamics in individual living cells. We used time-lapse microscopy to measure the dynamics of a random subset of 20 nuclear proteins, each tagged with yellow fluorescent protein (YFP) at its endogenous chromosomal location. We synchronized the cells in silico by aligning protein dynamics in each cell between consecutive divisions. We observed widespread (40%) cell-cycle dependence of nuclear protein levels and detected previously unknown cell cycle-dependent localization changes. This approach to dynamic proteomics can aid in discovery and accurate quantification of the extensive regulation of protein concentration and localization in individual living cells.

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.

Improved tagging strategy for protein identification in mammalian cells

Background

The tagging strategy enables full-length endogenous proteins in mammalian cells to be expressed as green fluorescent fusion proteins from their authentic promoters.

Results

We describe improved genetic tools to facilitate protein tagging in mammalian cells based on a mobile genetic element that harbors an artificial exon encoding a protein tag. Insertion of the artificial exon within introns of cellular genes results in expression of hybrid proteins consisting of the tag sequence fused in-frame to sequences of a cellular protein. We have used lentiviral vectors to stably introduce enhanced green fluorescent protein (EGFP) tags into expressed genes in target cells. The data obtained indicate that this strategy leads to bona fide tripartite fusion proteins and that the EGFP tag did not affect the subcellular localization of such proteins.

Conclusion

The tools presented here have the potential for protein discovery, and subsequent investigation of their subcellular distribution and role(s) under defined physiological conditions, as well as for protein purification and protein-protein interaction studies.

Object type recognition for automated analysis of protein subcellular location

The new field of location proteomics seeks to provide a comprehensive, objective characterization of the subcellular locations of all proteins expressed in a given cell type. Previous work has demonstrated that automated classifiers can recognize the patterns of all major subcellular organelles and structures in fluorescence microscope images with high accuracy. However, since some proteins may be present in more than one organelle, this paper addresses a more difficult task: recognizing a pattern that is a mixture of two or more fundamental patterns. The approach utilizes an object-based image model, in which each image of a location pattern is represented by a set of objects of distinct, learned types. Using a two-stage approach in which object types are learned and then cell-level features are calculated based on the object types, the basic location patterns were well recognized. Given the object types, a multinomial mixture model was built to recognize mixture patterns. Under appropriate conditions, synthetic mixture patterns can be decomposed with over 80% accuracy, which, for the first time, shows that the problem of computationally decomposing subcellular patterns into fundamental organelle patterns can be solved.

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.

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.

Developments in modulating protein function for effective target validation

The targets of more than 95% of clinically approved drugs are proteins. Thus, the plethora of targets derived from genomics and proteomics efforts must be validated at the protein level. However, most of the preferred target validation technologies are gene- or transcript-based. Protein-based or proteinetic approaches, which are more relevant to determine target druggability, are now emerging.:

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.

Protein localization in proteomics

A global analysis of the localization of 4156 yeast proteins has just been accomplished. Smaller scale analyses have been performed in a variety of organisms. These studies typically use green fluorescent protein as a tag for proteins in living cells. Improvements in the yellow and sapphire color variants will increase their utility. Reengineering of the red fluorescent protein has produced faster maturing tetrameric and monomeric variants not prone to aggregation. Techniques for high-throughput tagging of proteins include integration by homologous recombination, integration using mobile elements or recombinational cloning to produce plasmids expressing fusion proteins. Alternatives to localizing tagged proteins are to use antibodies or aptamers to detect the untagged protein.

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.