Dataset Information

Local cell metrics: a novel method for analysis of cell-cell interactions.

ABSTRACT:

Background

The regulation of many cell functions is inherently linked to cell-cell contact interactions. However, effects of contact interactions among adherent cells can be difficult to detect with global summary statistics due to the localized nature and noise inherent to cell-cell interactions. The lack of informatics approaches specific for detecting cell-cell interactions is a limitation in the analysis of large sets of cell image data, including traditional and combinatorial or high-throughput studies. Here we introduce a novel histogram-based data analysis strategy, termed local cell metrics (LCMs), which addresses this shortcoming.

Results

The new LCM method is demonstrated via a study of contact inhibition of proliferation of MC3T3-E1 osteoblasts. We describe how LCMs can be used to quantify the local environment of cells and how LCMs are decomposed mathematically into metrics specific to each cell type in a culture, e.g., differently-labelled cells in fluorescence imaging. Using this approach, a quantitative, probabilistic description of the contact inhibition effects in MC3T3-E1 cultures has been achieved. We also show how LCMs are related to the naïve Bayes model. Namely, LCMs are Bayes class-conditional probability functions, suggesting their use for data mining and classification.

Conclusion

LCMs are successful in robust detection of cell contact inhibition in situations where conventional global statistics fail to do so. The noise due to the random features of cell behavior was suppressed significantly as a result of the focus on local distances, providing sensitive detection of cell-cell contact effects. The methodology can be extended to any quantifiable feature that can be obtained from imaging of cell cultures or tissue samples, including optical, fluorescent, and confocal microscopy. This approach may prove useful in interpreting culture and histological data in fields where cell-cell interactions play a critical role in determining cell fate, e.g., cancer, developmental biology, and tissue regeneration.

SUBMITTER: Su J

PROVIDER: S-EPMC2944256 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Local cell metrics: a novel method for analysis of cell-cell interactions.

Su Jing J Zapata Pedro J PJ Chen Chien-Chiang CC Meredith J Carson JC

BMC bioinformatics 20091023

<h4>Background</h4>The regulation of many cell functions is inherently linked to cell-cell contact interactions. However, effects of contact interactions among adherent cells can be difficult to detect with global summary statistics due to the localized nature and noise inherent to cell-cell interactions. The lack of informatics approaches specific for detecting cell-cell interactions is a limitation in the analysis of large sets of cell image data, including traditional and combinatorial or hig ...[more]

PMID: 19852804

Similar Datasets

Project description:Biotechnology-derived therapeutics manufacturing is highly regulated to assure product quality, safety and efficacy. Process conditions are closely monitored as they can influence product characteristics. Culture of mammalian cells at different scales is a vital part of biomanufacturing. Currently, scale up of bioreactors is largely done based on engineering parameters such as oxygen transfer and mixing characteristics. There is a lack of genomic translational studies in mammalian cell culture scale-up that can help delineate measurable cellular attributes for improved process understanding. These could be used for quantifying physiological response of cells due to changes in bioreactor environment. In this study, we identified 18 process-sensitive sentinel genes using microarray statistical analysis, which we further verified by qRT-PCR. These were differentially expressed transcripts between a typical 5 liter bench-scale bubble-aerated and impeller-agitated bioreactor and novel 35 mL high-throughput minibioreactors. Using expression changes and biochemical-pathway guidance of these sentinel genes, we were able to tune engineering parameters such that the improved scale-down resulted in both process parameter and sentinel gene profile convergence between the two systems, further solidifying the functionality evidence of these sentinel genes. This study serves as a starting point for developing qRT-PCR assays as comparability metrics that could be performed near-at-line during the cell culture process itself leading to enhanced process control. Additional broad applications of sentinel genes could be to validate different manufacturing facilities for the same product, and allow better process definition by fingerprinting the manufacturing process for more rapid biogenerics and vaccine approvals. We ran consecutive bioreactor (5L and HTCB) runs, each with an independent vial thaw, to achieve multiple biological replicates per time-point. Bioreactors were sampled approximately every 12 hours for RNA extraction. For the 5L bioreactors, microarray samples were run for day 1 (n=2), day 2 (n=2), day 3 (n=3), and day 3.5 (n=3). Here 2 or 3 of the three biological replicates run for each time-point were included in the analysis, based on >70% genes found. For the High-Throughput Controlled Minibioreactors (HTCB), microarray samples were run for day 1 (n=3), day 2 (n=3), day 3 (n=3), day 4 (n=2), and day 4.5 (n=2). Here 2 to 4 of the 4 biological replicate runs were included in the analysis, based on >70% genes found. We define early exponential as day 1, peak exponential as day 2 and day 3 and late stationary as day 3.5.

Project description:Nodal spreading influence is the capability of a node to activate the rest of the network when it is the seed of spreading. Combining nodal properties (centrality metrics) derived from local and global topological information respectively has been shown to better predict nodal influence than using a single metric. In this work, we investigate to what extent local and global topological information around a node contributes to the prediction of nodal influence and whether relatively local information is sufficient for the prediction. We show that by leveraging the iterative process used to derive a classical nodal centrality such as eigenvector centrality, we can define an iterative metric set that progressively incorporates more global information around the node. We propose to predict nodal influence using an iterative metric set that consists of an iterative metric from order 1 to K produced in an iterative process, encoding gradually more global information as K increases. Three iterative metrics are considered, which converge to three classical node centrality metrics, respectively. In various real-world networks and synthetic networks with community structures, we find that the prediction quality of each iterative based model converges to its optimal when the metric of relatively low orders ( K∼4 ) are included and increases only marginally when further increasing K. This fast convergence of prediction quality with K is further explained by analyzing the correlation between the iterative metric and nodal influence, the convergence rate of each iterative process and network properties. The prediction quality of the best performing iterative metric set with K=4 is comparable with the benchmark method that combines seven centrality metrics: their prediction quality ratio is within the range [91%,106%] across all three quality measures and networks. In two spatially embedded networks with an extremely large diameter, however, iterative metric of higher orders, thus a large K, is needed to achieve comparable prediction quality with the benchmark.

Project description:Software is a complex entity, and its development needs careful planning and a high amount of time and cost. To assess quality of program, software measures are very helpful. Amongst the existing measures, coupling is an important design measure, which computes the degree of interdependence among the entities of a software system. Higher coupling leads to cognitive complexity and thus a higher probability occurrence of faults. Well in time prediction of fault-prone modules assists in saving time and cost of testing. This paper aims to capture important aspects of coupling and then assess the effectiveness of these aspects in determining fault-prone entities in the software system. We propose two coupling metrics, i.e., Vovel-in and Vovel-out, that capture the level of coupling and the volume of information flow. We empirically evaluate the effectiveness of the Vovel metrics in determining the fault-prone classes using five projects, i.e., Eclipse JDT, Equinox framework, Apache Lucene, Mylyn, and Eclipse PDE UI. Model building is done using univariate logistic regression and later Spearman correlation coefficient is computed with the existing coupling metrics to assess the coverage of unique information. Finally, the least correlated metrics are used for building multivariate logistic regression with and without the use of Vovel metrics, to assess the effectiveness of Vovel metrics. The results show the proposed metrics significantly improve the predicting of fault prone classes. Moreover, the proposed metrics cover a significant amount of unique information which is not covered by the existing well-known coupling metrics, i.e., CBO, RFC, Fan-in, and Fan-out. This paper, empirically evaluates the impact of coupling metrics, and more specifically the importance of level and volume of coupling in software fault prediction. The results advocate the prudent addition of proposed metrics due to their unique information coverage and significant predictive ability.

Dataset Information

Local cell metrics: a novel method for analysis of cell-cell interactions.

Background

Results

Conclusion

Publications

Local cell metrics: a novel method for analysis of cell-cell interactions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets