Antti Niemistö

Insulin-like growth factor binding protein 2 promotes ovarian cancer cell invasion

BackgroundInsulin-like growth factor binding protein 2 (IGFBP2) is overexpressed in ovarian malignant tissues and in the serum and cystic fluid of ovarian cancer patients, suggesting an important role of IGFBP2 in the biology of ovarian cancer. The purpose of this study was to assess the role of increased IGFBP2 in ovarian cancer cells.ResultsUsing western blotting and tissue microarray analyses, we showed that IGFBP2 was frequently overexpressed in ovarian carcinomas compared with normal ovarian tissues. Furthermore, IGFBP2 was significantly overexpressed in invasive serous ovarian carcinomas compared with borderline serous ovarian tumors. To test whether increased IGFBP2 contributes to the highly invasive nature of ovarian cancer cells, we generated IGFBP2-overexpressing cells from an SKOV3 ovarian cancer cell line, which has a very low level of endogenous IGFBP2. A Matrigel invasion assay showed that these IGFBP2-overexpressing cells were more invasive than the control cells. We then designed small interference RNA (siRNA) molecules that attenuated IGFBP2 expression in PA-1 ovarian cancer cells, which have a high level of endogenous IGFBP2. The Matrigel invasion assay showed that the attenuation of IGFBP2 expression indeed decreased the invasiveness of PA-1 cells.ConclusionsWe therefore showed that IGFBP2 enhances the invasion capacity of ovarian cancer cells. Blockage of IGFBP2 may thus constitute a viable strategy for targeted cancer therapy.

Systems analysis identifies an essential role for SHANK-associated RH domain-interacting protein (SHARPIN) in macrophage Toll-like receptor 2 (TLR2) responses.

Precise control of the innate immune response is essential to ensure host defense against infection while avoiding inflammatory disease. Systems-level analyses of Toll-like receptor (TLR)-stimulated macrophages suggested that SHANK-associated RH domain-interacting protein (SHARPIN) might play a role in the TLR pathway. This hypothesis was supported by the observation that macrophages derived from chronic proliferative dermatitis mutation (cpdm) mice, which harbor a spontaneous null mutation in the Sharpin gene, exhibited impaired IL-12 production in response to TLR activation. Systems biology approaches were used to define the SHARPIN-regulated networks. Promoter analysis identified NF-κB and AP-1 as candidate transcription factors downstream of SHARPIN, and network analysis suggested selective attenuation of these pathways. We found that the effects of SHARPIN deficiency on the TLR2-induced transcriptome were strikingly correlated with the effects of the recently described hypomorphic L153P/panr2 point mutation in Ikbkg [NF-κB Essential Modulator (NEMO)], suggesting that SHARPIN and NEMO interact. We confirmed this interaction by co-immunoprecipitation analysis and furthermore found it to be abrogated by panr2. NEMO-dependent signaling was affected by SHARPIN deficiency in a manner similar to the panr2 mutation, including impaired p105 and ERK phosphorylation and p65 nuclear localization. Interestingly, SHARPIN deficiency had no effect on IκBα degradation and on p38 and JNK phosphorylation. Taken together, these results demonstrate that SHARPIN is an essential adaptor downstream of the branch point defined by the panr2 mutation in NEMO.

Simulating fluorescent microscope images of cell populations

High-throughput cell measurement techniques producing images of cell populations have raised a need for accurate automated image analysis methods. Validating the analysis methods used for automated cytometry is an issue yet to be solved. Manual validation, being an exhaustively laborious task, enables comparison but does not provide solution for large scale analysis. By creating a parametric model for cell shape, and simulating images of cell populations including errors and aberrations caused by the measurement system, validation of different image analysis methods is enabled. As a result, studies with large populations, where the number of cells and many other key parameters are user-tunable, can be carried out by using simulated cell population images. The cell image simulator, as well as validation case studies for segmentation and image restoration are presented

Dissecting genealogy and cell cycle as sources of cell-to-cell variability in MAPK signaling using high-throughput lineage tracking

Cells, even those having identical genotype, exhibit variability in their response to external stimuli. This variability arises from differences in the abundance, localization, and state of cellular components. Such nongenetic differences are likely heritable between successive generations and can also be influenced by processes such as cell cycle, age, or interplay between different pathways. To address the contribution of nongenetic heritability and cell cycle in cell-to-cell variability we developed a high-throughput and fully automated microfluidic platform that allows for concurrent measurement of gene expression, cell-cycle periods, age, and lineage information under a large number of temporally changing medium conditions and using multiple strains. We apply this technology to examine the role of nongenetic inheritance in cell heterogeneity of yeast pheromone signaling. Our data demonstrate that the capacity to respond to pheromone is passed across generations and that the strength of the response correlations between related cells is affected by perturbations in the signaling pathway. We observe that a ste50Δ mutant strain exhibits highly heterogeneous response to pheromone originating from a unique asymmetry between mother and daughter response. On the other hand, fus3Δ cells were found to exhibit an unusually high correlation between mother and daughter cells that arose from a combination of extended cell-cycle periods of fus3Δ mothers, and decreased cell-cycle modulation of the pheromone pathway. Our results contribute to the understanding of the origins of cell heterogeneity and demonstrate the importance of automated platforms that generate single-cell data on several parameters.

A novel method for splitting clumps of convex objects incorporating image intensity and using rectangular window-based concavity point-pair search

A novel nonparametric concavity point analysis-based method for splitting clumps of convex objects in binary images is presented. The method is based on finding concavity point-pairs by using a variable-size rectangular window. The concavity point-pairs can be either connected with a straight split line or with a line that follows a path of minimum or maximum intensity on an accompanying grayscale image. Using straight lines can result in non-smooth contours. Therefore, post-processing steps that remove acute angles between split lines are proposed. Results obtained with images that have clumps of biological cells show that the method gives accurate results.

Genome-Wide Analysis of Effectors of Peroxisome Biogenesis

Peroxisomes are intracellular organelles that house a number of diverse metabolic processes, notably those required for β-oxidation of fatty acids. Peroxisomes biogenesis can be induced by the presence of peroxisome proliferators, including fatty acids, which activate complex cellular programs that underlie the induction process. Here, we used multi-parameter quantitative phenotype analyses of an arrayed mutant collection of yeast cells induced to proliferate peroxisomes, to establish a comprehensive inventory of genes required for peroxisome induction and function. The assays employed include growth in the presence of fatty acids, and confocal imaging and flow cytometry through the induction process. In addition to the classical phenotypes associated with loss of peroxisomal functions, these studies identified 169 genes required for robust signaling, transcription, normal peroxisomal development and morphologies, and transmission of peroxisomes to daughter cells. These gene products are localized throughout the cell, and many have indirect connections to peroxisome function. By integration with extant data sets, we present a total of 211 genes linked to peroxisome biogenesis and highlight the complex networks through which information flows during peroxisome biogenesis and function.

Image-based feedback control for real-time sorting of microspheres in a microfluidic device.

We describe a control system to automatically distribute antibody-functionalized beads to addressable assay chambers within a PDMS microfluidic device. The system used real-time image acquisition and processing to manage the valve states required to sort beads with unit precision. The image processing component of the control system correctly counted the number of beads in 99.81% of images (2689 of 2694), with only four instances of an incorrect number of beads being sorted to an assay chamber, and one instance of inaccurately counted beads being improperly delivered to waste. Post-experimental refinement of the counting script resulted in one counting error in 2694 images of beads (99.96% accuracy). We analyzed a range of operational variables (flow pressure, bead concentration, etc.) using a statistical model to characterize those that yielded optimal sorting speed and efficiency. The integrated device was able to capture, count, and deliver beads at a rate of approximately four per minute so that bead arrays could be assembled in 32 individually addressable assay chambers for eight analytical measurements in duplicate (512 beads total) within 2.5 hours. This functionality demonstrates the successful integration of a robust control system with precision bead handling that is the enabling technology for future development of a highly multiplexed bead-based analytical device.

Quantification of vesicles in differentiating human SH-SY5Y neuroblastoma cells by automated image analysis

A new automated image analysis method for quantification of fluorescent dots is presented. This method facilitates counting the number of fluorescent puncta in specific locations of individual cells and also enables estimation of the number of cells by detecting the labeled nuclei. The method is here used for counting the AM1-43 labeled fluorescent puncta in human SH-SY5Y neuroblastoma cells induced to differentiate with all-trans retinoic acid (RA), and further stimulated with high potassium (K+) containing solution. The automated quantification results correlate well with the results obtained manually through visual inspection. The manual method has the disadvantage of being slow, labor-intensive, and subjective, and the results may not be reproducible even in the intra-observer case. The automated method, however, has the advantage of allowing fast quantification with explicitly defined methods, with no user intervention. This ensures objectivity of the quantification. In addition to the number of fluorescent dots, further development of the method allows its use for quantification of several other parameters, such as intensity, size, and shape of the puncta, that are difficult to quantify manually.

Extraction of the Number of Peroxisomes in Yeast Cells by Automated Image Analysis

An automated image analysis method for extracting the number of peroxisomes in yeast cells is presented. Two images of the cell population are required for the method: a bright field microscope image from which the yeast cells are detected and the respective fluorescent image from which the number of peroxisomes in each cell is found. The segmentation of the cells is based on clustering the local mean-variance space. The watershed transformation is thereafter employed to separate cells that are clustered together. The peroxisomes are detected by thresholding the fluorescent image. The method is tested with several images of a budding yeast Saccharomyces cerevisiae population, and the results are compared with manually obtained results

Computational methods for estimation of cell cycle phase distributions of yeast cells

Two computational methods for estimating the cell cycle phase distribution of a budding yeast (Saccharomyces cerevisiae) cell population are presented. The first one is a nonparametric method that is based on the analysis of DNA content in the individual cells of the population. The DNA content is measured with a fluorescence-activated cell sorter (FACS). The second method is based on budding index analysis. An automated image analysis method is presented for the task of detecting the cells and buds. The proposed methods can be used to obtain quantitative information on the cell cycle phase distribution of a budding yeast S. cerevisiae population. They therefore provide a solid basis for obtaining the complementary information needed in deconvolution of gene expression data. As a case study, both methods are tested with data that were obtained in a time series experiment with S. cerevisiae. The details of the time series experiment as well as the image and FACS data obtained in the experiment can be found in the online additional material at http://www.cs.tut.fi/sgn/csb/yeastdistrib/.