Bayesian hierarchical models for the prediction of the driver flow and passenger waiting times in a stochastic carpooling service.

Carpooling is an integral component in smart carbon-neutral cities, in particular to facilitate home-work commuting. We study an innovative carpooling service which offers stochastic passenger-driver matching. Stochastic matching is when a passenger makes a carpooling request, and then waits for the first driver from a population of drivers who are already en route. Crucially a designated driver is not assigned as in a traditional carpooling service. For this new form of stochastic carpooling, we propose a two-stage Bayesian hierarchical model to predict the driver flow and the passenger waiting times. The first stage focuses on prediction of the aggregated daily driver flows, and the second stage processes these daily driver flow into hourly predictions of the passenger waiting times. We demonstrate, for an operational carpooling service, that the predictions from our Bayesian hierarchical model outperform the predictions from a frequentist model and a Bayesian non-hierarchical model. The inferences from our proposed model provide insights for the service operator in their evidence-based decision making.

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.

In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template--used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.

A novel organelle map framework for high-content cell morphology analysis in high throughput.

A screening procedure was developed that takes advantage of the cellular normalization by micropatterning and a novel quantitative organelle mapping approach that allows unbiased and automated cell morphology comparison using black-box statistical testing. Micropatterns of extracellular matrix proteins force cells to adopt a reproducible shape and distribution of intracellular compartments avoiding strong cell-to-cell variation that is a major limitation of classical culture conditions. To detect changes in cell morphology induced by compound treatment, fluorescently labeled intracellular structures from several tens of micropatterned cells were transformed into probabilistic density maps. Then, the similarity or difference between two given density maps was quantified using statistical testing that evaluates differences directly from the data without additional analysis or any subjective decision. The versatility of this organelle mapping approach for different magnifications and its performance for different cell shapes has been assessed. Density-based analysis detected changes in cell morphology due to compound treatment in a small-scale proof-of-principle screen demonstrating its compatibility with high-throughput screening. This novel tool for high-content and high-throughput cellular phenotyping can potentially be used for a wide range of applications from drug screening to careful characterization of cellular processes.

Studying intracellular trafficking pathways with probabilistic density maps.

The compartmentalization of cellular functions in complex membranous organelles is a key feature of eukaryotic cells. To cope with the enormous complexity of trafficking pathways that connect these compartments, new approaches need to be considered and introduced into the field of cell biology. We exploit the advantages of the "micropatterning technique," which is to bring cells to adopt a highly reproducible shape, and probabilistic density mapping, which quantifies spatial organization of trafficking compartments, to study regulatory mechanisms of intracellular trafficking. Here, we provide a protocol to analyze and quantify alterations in trafficking compartments upon cellular manipulation. We demonstrate how this approach can be employed to study the regulation of Rab6-labeled transport carriers by the cytoskeleton.

Closed-form density-based framework for automatic detection of cellular morphology changes.

A primary method for studying cellular function is to examine cell morphology after a given manipulation. Fluorescent markers attached to proteins/intracellular structures of interest in conjunction with 3D fluorescent microscopy are frequently exploited for functional analysis. Despite the central role of morphology comparisons in cell biological approaches, few statistical tools are available that allow biological scientists without a high level of statistical training to quantify the similarity or difference of fluorescent images containing multifactorial information. We transform intracellular structures into kernels and develop a multivariate two-sample test that is nonparametric and asymptotically normal to directly and quantitatively compare cellular morphologies. The asymptotic normality bypasses the computationally intensive calculations used by the usual resampling techniques to compute the P-value. Because all parameters required for the statistical test are estimated directly from the data, it does not require any subjective decisions. Thus, we provide a black-box method for unbiased, automated comparison of cell morphology. We validate the performance of our test statistic for finite synthetic samples and experimental data. Employing our test for the comparison of the morphology of intracellular multivesicular bodies, we detect changes in their distribution after disruption of the cellular microtubule cytoskeleton with high statistical significance in fixed samples and live cell analysis. These results demonstrate that density-based comparison of multivariate image information is a powerful tool for automated detection of cell morphology changes. Moreover, the underlying mathematics of our test statistic is a general technique, which can be applied in situations where two data samples are compared.

Probabilistic density maps to study global endomembrane organization.

We developed a computational imaging approach that describes the three-dimensional spatial organization of endomembranes from micromanipulation-normalized mammalian cells with probabilistic density maps. Applied to several well-known marker proteins, this approach revealed the average steady-state organization of early endosomes, multivesicular bodies or lysosomes, endoplasmic reticulum exit sites, the Golgi apparatus and Golgi-derived transport carriers in crossbow-shaped cells. The steady-state organization of each tested endomembranous population was well-defined, unique and in some cases depended on the cellular adhesion geometry. Density maps of all endomembrane populations became stable when pooling several tens of cells only and were reproducible in independent experiments, allowing construction of a standardized cell model. We detected subtle changes in steady-state organization induced by disruption of the cellular cytoskeleton, with statistical significance observed for just 20 cells. Thus, combining micropatterning with construction of endomembrane density maps allows the systematic study of intracellular trafficking determinants.

Ezrin tunes T-cell activation by controlling Dlg1 and microtubule positioning at the immunological synapse.

T-cell receptor (TCR) signalling is triggered and tuned at immunological synapses by the generation of signalling complexes that associate into dynamic microclusters. Microcluster movement is necessary to tune TCR signalling, but the molecular mechanism involved remains poorly known. We show here that the membrane-microfilament linker ezrin has an important function in microcluster dynamics and in TCR signalling through its ability to set the microtubule network organization at the immunological synapse. Importantly, ezrin and microtubules are important to down-regulate signalling events leading to Erk1/2 activation. In addition, ezrin is required for appropriate NF-AT activation through p38 MAP kinase. Our data strongly support the notion that ezrin regulates immune synapse architecture and T-cell activation through its interaction with the scaffold protein Dlg1. These results uncover a crucial function for ezrin, Dlg1 and microtubules in the organization of the immune synapse and TCR signal down-regulation. Moreover, they underscore the importance of ezrin and Dlg1 in the regulation of NF-AT activation through p38.

Chromosome arm length and nuclear constraints determine the dynamic relationship of yeast subtelomeres.

Physical interactions between distinct chromosomal genomic loci are important for genomic functions including recombination and gene expression, but the mechanisms by which these interactions occur remain obscure. Using telomeric association as a model system, we analyzed here the in vivo organization of chromosome ends of haploid yeast cells during interphase. We separately labeled most of the 32 subtelomeres and analyzed their positions both in nuclear space and relative to three representative reference subtelomeres by high-throughput 3D microscopy and image processing. We show that subtelomeres are positioned nonrandomly at the nuclear periphery, depending on the genomic size of their chromosome arm, centromere attachment to the microtubule organizing center (spindle pole body, SPB), and the volume of the nucleolus. The distance of subtelomeres to the SPB increases consistently with chromosome arm length up to approximately 300 kb; for larger arms the influence of chromosome arm length is weaker, but the effect of the nucleolar volume is stronger. Distances between pairs of subtelomeres also exhibit arm-length dependence and suggest, together with dynamic tracking experiments, that potential associations between subtelomeres are unexpectedly infrequent and transient. Our results suggest that interactions between subtelomeres are nonspecific and instead governed by physical constraints, including chromosome structure, attachment to the SPB, and nuclear crowding.

Highest density difference region estimation with application to flow cytometric data.

Motivated by the needs of scientists using flow cytometry, we study the problem of estimating the region where two multivariate samples differ in density. We call this problem highest density difference region estimation and recognise it as a two-sample analogue of highest density region or excess set estimation. Flow cytometry samples are typically in the order of 10,000 and 100,000 and with dimension ranging from about 3 to 20. The industry standard for the problem being studied is called Frequency Difference Gating, due to Roederer and Hardy (2001). After couching the problem in a formal statistical framework we devise an alternative estimator that draws upon recent statistical developments such as patient rule induction methods. Improved performance is illustrated in simulations. While motivated by flow cytometry, the methodology is suitable for general multivariate random samples where density difference regions are of interest.

High-resolution statistical mapping reveals gene territories in live yeast.

The nonrandom positioning of genes inside eukaryotic cell nuclei is implicated in central nuclear functions. However, the spatial organization of the genome remains largely uncharted, owing to limited resolution of optical microscopy, paucity of nuclear landmarks and moderate cell sampling. We developed a computational imaging approach that creates high-resolution probabilistic maps of subnuclear domains occupied by individual loci in budding yeast through automated analysis of thousands of living cells. After validation, we applied the technique to genes involved in galactose metabolism and ribosome biogenesis. We found that genomic loci are confined to 'gene territories' much smaller than the nucleus, which can be remodeled during transcriptional activation, and that the nucleolus is an important landmark for gene positioning. The technique can be used to visualize and quantify territory positions relative to each other and to nuclear landmarks, and should advance studies of nuclear architecture and function.