Automated subcellular location determination and high-throughput microscopy

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.

Maturation of the mammalian secretome

A recent use of quantitative proteomics to determine the constituents of the endoplasmic reticulum and Golgi complex is discussed in the light of other available methodologies for cataloging the proteins associated with the mammalian secretory pathway.

Methodology development for predicting subcellular localization and other attributes of proteins

Facing the explosion of newly generated protein sequences in the postgenomic age, we are challenged to develop computational methods for the fast and accurate identification of their subcellular localization and other attributes. This review summarizes recent methodology developments, with a focus on artificial neural networks, the statistical learning and support vector machine, the fuzzy logic-based algorithm and the evidence-theory-based algorithm, as well as the ensemble classifier approach. Meanwhile, an outline of the use of different descriptors for protein samples is given. In addition, a series of web servers established recently based on various ensemble classifiers are also briefly introduced.

Illuminating the metastatic process

Until recently most studies of metastasis only measured the end point of the process--macroscopic metastases. Although these studies have provided much useful information, the details of the metastatic process remain somewhat mysterious owing to difficulties in studying cell behaviour with high spatial and temporal resolution in vivo. The use of luminescent and fluorescent proteins and developments in optical imaging technology have enabled the direct observation of cancer cells spreading from their site of origin and arriving at secondary sites. This Review will describe recent advances in our understanding of the different steps of metastasis gained from cellular resolution imaging, and how these techniques can be used in preclinical drug evaluation.

Expression profiles of the genes associated with metabolism and transport of amino acids and their derivatives in rat liver regeneration

Amino acids (AA) are components of protein and precursors of many important biological molecules. To address effects of the genes associated with metabolism and transport of AA and their derivatives during rat liver regeneration (LR), we firstly obtained the above genes by collecting databases data and retrieving related thesis, and then analyzed their expression profiles during LR using Rat Genome 230 2.0 array. The LR-associated genes were identified by comparing the gene expression difference between partial hepatectomy (PH) and sham-operation (SO) rat livers. It was approved that 134 genes associated with metabolism of AA and their derivatives and 26 genes involved in transport of them were LR-associated. The initially and totally expressing number of these genes occurring in initial phase of LR (0.5-4 h after PH), G0/G1 (4-6 h after PH), cell proliferation (6-66 h after PH), cell differentiation and structure-function reconstruction of liver tissue (72-168 h after PH) were respectively 76, 17, 79, 5 and 162, 89, 564, 195, illustrating that these LR-associated genes were initially expressed mainly in initial stage, and functioned in different phases. Frequencies of up-regulation and down-regulation of them being separately 564 and 357 demonstrated that genes up-regulated outnumbered those down-regulated. Categorization of their expression patterns into 22 types implied the diversity of cell physiological and biochemical activities. According to expression changes and patterns of the above-mentioned genes in LR, it was presumed that histidine biosynthesis in the metaphase and anaphase, valine metabolism in the anaphase, and metabolism of glutamate, glutamine, asparate, asparagine, methionine, alanine, leucine and aromatic amino acid almost were enhanced in the whole LR; as for amino acid derivatives, transport of neutral amino acids, urea, gamma-aminobutyric acid, betaine and taurine, metabolism of dopamine, heme, S-adenosylmethionine, thyroxine, and biosynthesis of hydroxyproline, nitric oxide, orinithine, polyamine, carnitine, selenocysteine were augmented during the entire liver restoration. Above results showed that metabolism and transport of AA and their derivates were necessary in liver regeneration.

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.

Topo-Proteomic in situ Analysis of Psoriatic Plaque under Efalizumab Treatment

In a pilot study 6 psoriasis patients were treated over 12 weeks with efalizumab targeting the CD11a subunit of LFA-1. The treatment was well tolerated. Five of these patients proved to be responders with an average decrease in psoriasis area and severity index (PASI) from 21.3 +/- 5.4 (day 0) to 3.9 +/- 0.6 (week 12). The nonresponder was subsequently successfully treated with cyclosporin. Skin biopsies were taken before and after efalizumab treatment and subjected to Multi-Epitope Ligand Cartography (MELC) robot microscopy. A MELC library of 46 antibodies including FITC-labeled efalizumab was chosen focusing upon inflammatory epitopes. Quantification of marker expression was performed using a special adaptation to the needs of skin tissue in terms of pixel events normalized to a standardized horizontal skin width of 100 mum. The before-versus-after comparison for the responders revealed at the 'single epitope level' of MELC analysis a significant decrease (p < 0.05) in epidermal thickness (represented by pan-cytokeratin, CD71, CD138), of the expression of common leukocyte antigen (CD45), T-cell markers (CD2, CD4, CD8, CD45R0), CD11a, efalizumab binding site (EfaBS), and CD58. At the 'EfaBS-centered, double colocation level' a corresponding decrease was observed for CD2, CD3, CD4, CD8, CD11a, CD13, CD26, CD44, CD45, CD45R0, CD54, CD62L, HLA-DR, and TIA-1. MELC analysis at the 'multicombinatorial level' revealed predominant combinatorial molecular phenotype (CMP) motifs, which showed an efalizumab treatment-dependent significant decrease. These CMP motifs were defined as toponomic combinations of lead markers for (i) leukocytes in general (CD45), (ii) T cells (CD2, CD3, CD4, CD45R0, CD45RA), (iii) macrophages (CD68), (iv) cell activation (CD13, CD26, HLA-DR), and (v) cell adhesion (CD11a, EfaBS). Thirty-five of the most relevant 50 CMP motifs were directly related to the T-cell type. A descriptive statistical analysis of the nonresponder before treatment showed a below-responder range degree of expression for CD4, CD8, CD44 (H-CAM), CD56, CD62L, HLA-DQ, and also for these epitopes in colocation with EfaBS. In the nonresponder and before treatment we observed an above-responder range degree of expression for CD54 (ICAM-1) as LFA-1 ligand. In conclusion, the topo-proteomic data provide new diversified insights into the pleiotropic cellular dynamics in psoriatic skin lesions under effective efalizumab treatment. Moreover, the data may be relevant to the future development of possible strategies for individual prediction of efalizumab treatment response or nonresponse.

A multiresolution approach to automated classification of protein subcellular location images

Background

Fluorescence microscopy is widely used to determine the subcellular location of proteins. Efforts to determine location on a proteome-wide basis create a need for automated methods to analyze the resulting images. Over the past ten years, the feasibility of using machine learning methods to recognize all major subcellular location patterns has been convincingly demonstrated, using diverse feature sets and classifiers. On a well-studied data set of 2D HeLa single-cell images, the best performance to date, 91.5%, was obtained by including a set of multiresolution features. This demonstrates the value of multiresolution approaches to this important problem.

Results

We report here a novel approach for the classification of subcellular location patterns by classifying in multiresolution subspaces. Our system is able to work with any feature set and any classifier. It consists of multiresolution (MR) decomposition, followed by feature computation and classification in each MR subspace, yielding local decisions that are then combined into a global decision. With 26 texture features alone and a neural network classifier, we obtained an increase in accuracy on the 2D HeLa data set to 95.3%.

Conclusion

We demonstrate that the space-frequency localized information in the multiresolution subspaces adds significantly to the discriminative power of the system. Moreover, we show that a vastly reduced set of features is sufficient, consisting of our novel modified Haralick texture features. Our proposed system is general, allowing for any combinations of sets of features and any combination of classifiers.

Prospecting for Live Cell BioImaging Probes With Cheminformatic Assisted Image Arrays (CAIA)

High-throughput microscopic screening instruments can generate huge collections of images of live cells incubated with combinatorial libraries of fluorescent molecules. Organizing and visualizing these images to discern biologically important patterns that link back to chemical structure is a challenge. We present an analysis and visualization methodology - Cheminformatic Assisted Image Array (CAIA) - that greatly facilitates data mining efforts. For illustration, we considered a collection of microscopic images acquired from cells incubated with each member of a combinatorial library of styryl molecules being screened for candidate bioimaging probes. By sorting CAIAs based on quantitative image features, the relative contribution of each combinatorial building block on probe intracellular distribution could be visually discerned. The results revealed trends hidden in the dataset: most interestingly, the building blocks of the styryl molecules appeared to behave as chemical address tags, additively and independently encoding spatial patterns of intracellular fluorescence. Translated into practice, CAIA facilitated discovery of several outstanding styryl molecules for live cell nuclear imaging applications.