Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.Peer reviewe

Knowledge-based gene expression classification via matrix factorization

Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks. Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.Siemens AG, MunichDFG (Graduate College 638)DAAD (PPP Luso - Alem˜a and PPP Hispano - Alemanas

Identification of meat spoilage gene biomarkers in Pseudomonas putida using gene profiling

While current food science research mainly focuses on microbial changes in food products that lead to foodborne illnesses, meat spoilage remains as an unsolved problem for the meat industry. This can result in important economic losses, food waste and loss of consumer confidence in the meat market. Gram-negative bacteria involved in meat spoilage are aerobes or facultative anaerobes. These represent the group with the greatest meat spoilage potential, where Pseudomonas tend to dominate the microbial consortium under refrigeration and aerobic conditions. Identifying stress response genes under different environmental conditions can help researchers gain an understanding of how Pseudomonas adapts to current packaging and storage conditions. We examined the gene expression profile of Pseudomonas putida KT2440, which plays an important role in the spoilage of meat products. Gene expression profiles were evaluated to select the most differentially expressed genes at different temperatures (30 °C and 10 °C) and decreasing glucose concentrations, in order to identify key genes actively involved with the spoilage process. A total of 739 and 1269 were found to be differentially expressed at 30 °C and 10 °C respectively; of which 430 and 568 genes were overexpressed, and 309 and 701 genes were repressed at 30 °C and 10 °C respectively

Disruption of 3D MCF-12A breast cell cultures by estrogens - An in vitro model for ER-mediated changes indicative of hormonal carcinogenesis

Copyright @ 2012 The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and 85 reproduction in any medium, provided the original author and source are credited. The article was made available through the Brunel University Open Access Publishing Fund.Introduction: Estrogens regulate the proliferation of normal and neoplastic breast epithelium. Although the intracellular mechanisms of estrogens in the breast are largely understood, little is known about how they induce changes in the structure of the mammary epithelium, which are characteristic of breast cancer. In vitro three dimensional (3D) cultures of immortalised breast epithelial cells recapitulate features of the breast epithelium in vivo, including formation of growth arrested acini with hollow lumen and basement membrane. This model can also reproduce features of malignant transformation and breast cancer, such as increased cellular proliferation and filling of the lumen. However, a system where a connection between estrogen receptor (ER) activation and disruption of acini formation can be studied to elucidate the role of estrogens is still missing. Methods/Principal Findings: We describe an in vitro 3D model for breast glandular structure development, using breast epithelial MCF-12A cells cultured in a reconstituted basement membrane matrix. These cells are estrogen receptor (ER)α, ERβ and G-protein coupled estrogen receptor 1 (GPER) competent, allowing the investigation of the effects of estrogens on mammary gland formation and disruption. Under normal conditions, MCF-12A cells formed organised acini, with deposition of basement membrane and hollow lumen. However, treatment with 17β-estradiol, and the exogenous estrogens bisphenol A and propylparaben resulted in deformed acini and filling of the acinar lumen. When these chemicals were combined with ER and GPER inhibitors (ICI 182,780 and G-15, respectively), the deformed acini recovered normal features, such as a spheroid shape, proliferative arrest and luminal clearing, suggesting a role for the ER and GPER in the estrogenic disruption of acinar formation. Conclusion: This new model offers the opportunity to better understand the role of the ER and GPER in the morphogenesis of breast glandular structure as well as the events implicated in breast cancer initiation and progression.This study was partly funded by a School of Pharmacy Studentship

The role of microRNAs in thyroid carcinomas

Thyroid cancers (TCs) are the most common malignancies of endocrine organs. They originate from cells of different origin within the thyroid gland, which is located at the base of the neck. Several forms of TCs have been classified and great variability is observed in molecular, cellular and clinical features. The most common forms have favorable prognosis but a number of very aggressive TCs, which are characterized by a less differentiated cellular phenotype, have no effective treatment at the moment. While TC causes are not completely understood, many genetic factors involved in their onset have been discovered. In particular, activating mutations of BRAF, RET or RAS genes are known to be specifically associated with TC initiation, progression and outcome. The involvement of microRNAs in thyroid neoplasms has recently changed the paradigm for biomarker discovery in TC, suggesting that these small non-coding RNAs could be used to develop, refine or strengthen strategies for diagnosis and management of TCs. In this review, the importance of microRNA profiling in TC is explored suggesting that these molecules can be included in procedures that can perform better than any known clinical index in the identification of adverse outcomes