Comparative autoregressive moving average analysis of kinetochore microtubule dynamics in yeast

Multistep Track Segmentation and Motion Classification for Transient Mobility Analysis

Molecular interactions are often transient and might change within the window of observation, leading to changes in molecule movement. Therefore, accurate motion analysis often requires transient motion classification. Here we present an accurate and computationally efficient transient mobility analysis framework, termed "divide-and-conquer moment scaling spectrum" (DC-MSS). DC-MSS works in a multistep fashion: 1) it utilizes a local movement descriptor throughout a track to divide it into initial segments of putatively different motion classes; 2) it classifies these segments via moment scaling spectrum (MSS) analysis of molecule displacements; and 3) it uses the MSS analysis results to refine the track segmentation. This strategy uncouples the initial identification of motion switches from motion classification, allowing DC-MSS to circumvent the sensitivity-accuracy tradeoff of classic rolling window approaches for transient motion analysis, while at the same time harnessing the classification power of MSS analysis. Testing of DC-MSS demonstrates that it detects switches among free diffusion, confined diffusion, directed diffusion, and immobility with great sensitivity. To illustrate the utility of DC-MSS, we have applied it to single-particle tracks of the transmembrane protein CD44 on the surface of macrophages, revealing actin cortex-dependent transient mobility changes.

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light's diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we've termed the interpretation problem.

Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments

Understanding the basis for intracellular motion is critical as the field moves toward a deeper understanding of the relation between Brownian forces, molecular crowding, and anisotropic (or isotropic) energetic forcing. Effective forces and other parameters used to summarize molecular motion change over time in live cells due to latent state changes, e.g., changes induced by dynamic micro-environments, photobleaching, and other heterogeneity inherent in biological processes. This study discusses limitations in currently popular analysis methods (e.g., mean square displacement-based analyses) and how new techniques can be used to systematically analyze Single Particle Tracking (SPT) data experiencing abrupt state changes in time or space. The approach is to track GFP tagged chromatids in metaphase in live yeast cells and quantitatively probe the effective forces resulting from dynamic interactions that reflect the sum of a number of physical phenomena. State changes can be induced by various sources including: microtubule dynamics exerting force through the centromere, thermal polymer fluctuations, and DNA-based molecular machines including polymerases and protein exchange complexes such as chaperones and chromatin remodeling complexes. Simulations aiming to show the relevance of the approach to more general SPT data analyses are also studied. Refined force estimates are obtained by adopting and modifying a nonparametric Bayesian modeling technique, the Hierarchical Dirichlet Process Switching Linear Dynamical System (HDP-SLDS), for SPT applications. The HDP-SLDS method shows promise in systematically identifying dynamical regime changes induced by unobserved state changes when the number of underlying states is unknown in advance (a common problem in SPT applications). We expand on the relevance of the HDP-SLDS approach, review the relevant background of Hierarchical Dirichlet Processes, show how to map discrete time HDP-SLDS models to classic SPT models, and discuss limitations of the approach. In addition, we demonstrate new computational techniques for tuning hyperparameters and for checking the statistical consistency of model assumptions directly against individual experimental trajectories; the techniques circumvent the need for "ground-truth" and/or subjective information.

S. cerevisiae chromosomes biorient via gradual resolution of syntely between S phase and anaphase

Following DNA replication, eukaryotic cells must biorient all sister chromatids prior to cohesion cleavage at anaphase. In animal cells, sister chromatids gradually biorient during prometaphase, but current models of mitosis in S. cerevisiae assume that biorientation is established shortly after S phase. This assumption is based on the observation of a bilobed distribution of yeast kinetochores early in mitosis and suggests fundamental differences between yeast mitosis and mitosis in animal cells. By applying super-resolution imaging methods, we show that yeast and animal cells share the key property of gradual and stochastic chromosome biorientation. The characteristic bilobed distribution of yeast kinetochores, hitherto considered synonymous for biorientation, arises from kinetochores in mixed attachment states to microtubules, the length of which discriminates bioriented from syntelic attachments. Our results offer a revised view of mitotic progression in S. cerevisiae that augments the relevance of mechanistic information obtained in this powerful genetic system for mammalian mitosis.

Quantitative image analysis identifies pVHL as a key regulator of microtubule dynamic instability

The product of the von Hippel-Lindau tumor suppressor gene stabilizes microtubules by inhibiting GTPase activity.

Von Hippel-Lindau (VHL) tumor suppressor gene mutations predispose carriers to kidney cancer. The protein pVHL has been shown to interact with microtubules (MTs), which is critical to cilia maintenance and mitotic spindle orientation. However, the function for pVHL in the regulation of MT dynamics is unknown. We tracked MT growth via the plus end marker EB3 (end-binding protein 3)-GFP and inferred additional parameters of MT dynamics indirectly by spatiotemporal grouping of growth tracks from live cell imaging. Our data establish pVHL as a near-optimal MT-stabilizing protein: it attenuates tubulin turnover, both during MT growth and shrinkage, inhibits catastrophe, and enhances rescue frequencies. These functions are mediated, in part, by inhibition of tubulin guanosine triphosphatase activity in vitro and at MT plus ends and along the MT lattice in vivo. Mutants connected to the VHL cancer syndrome are differentially compromised in these activities. Thus, single cell–level analysis of pVHL MT regulatory function allows new predictions for genotype to phenotype associations that deviate from the coarser clinically defined mutant classifications.

Computational processing and analysis of dynamic fluorescence image data

With the many modes of live cell fluorescence imaging made possible by the rapid advances of fluorescent protein technology, researchers begin to face a new challenge: How to transform the vast amounts of unstructured image data into quantitative information for the discovery of new cell behaviors and the rigorous testing of mechanistic hypotheses? Although manual and semiautomatic computer-assisted image analysis are still used extensively, the demand for more reproducible and complete image measurements of complex cellular dynamics increases the need for fully automatic computational image processing approaches for both mechanistic studies and screening applications in cell biology. This chapter provides an overview of the issues that arise with the use of computational algorithms in live cell imaging studies, with particular emphasis on the close coordination of sample preparation, image acquisition, and computational image analysis. It also aims to introduce the terminology and central concepts of computer vision to facilitate the communication between cell biologists and computer scientists in collaborative imaging projects.

Structures and functions of yeast kinetochore complexes

The kinetochore is a key cell division organelle that enables high-fidelity transmission of genetic information by coupling chromosomes to spindle microtubules during mitosis and meiosis. Despite its cytological description more than a century ago, remarkably little information is available on kinetochore function at a molecular level. Recently, important advances elucidating the overall organization of kinetochores, as well as information about the structures and molecular mechanisms of kinetochore function, have been achieved through a detailed analysis of the kinetochores of the budding yeast Saccharomyces cerevisiae. Here we review the current understanding of kinetochore function in budding yeast and draw comparisons to recent findings in other organisms.

Phenotypic clustering of yeast mutants based on kinetochore microtubule dynamics

MOTIVATION

Kinetochores are multiprotein complexes which mediate chromosome attachment to microtubules (MTs) of the mitotic spindle. They regulate MT dynamics during chromosome segregation. Our goal is to identify groups of kinetochore proteins with similar effects on MT dynamics, revealing pathways through which kinetochore proteins transform chemical and mechanical input signals into cues of MT regulation.

RESULTS

We have developed a hierarchical, agglomerative clustering algorithm that groups Saccharomyces cerevisiae strains based on MT-mediated chromosome dynamics measured by high-resolution live cell microscopy. Clustering is based on parameters of autoregressive moving average (ARMA) models of the probed dynamics. We have found that the regulation of wildtype MT dynamics varies with cell cycle and temperature, but not with the chromosome an MT is attached to. By clustering the dynamics of mutants, we discovered that the three genes IPL1, DAM1 and KIP3 co-regulate MT dynamics. Our study establishes the clustering of chromosome and MT dynamics by ARMA descriptors as a sensitive framework for the systematic identification of kinetochore protein subcomplexes and pathways for the regulation of MT dynamics.

AVAILABILITY

The clustering code, written in Matlab, can be downloaded from http://lccb.scripps.edu. ('download' hyperlink at bottom of website).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Linking data to models: data regression

Mathematical models are an essential tool in systems biology, linking the behaviour of a system to the interactions between its components. Parameters in empirical mathematical models must be determined using experimental data, a process called regression. Because experimental data are noisy and incomplete, diagnostics that test the structural identifiability and validity of models and the significance and determinability of their parameters are needed to ensure that the proposed models are supported by the available data.