Automation of random conical tilt and orthogonal tilt data collection using feature-based correlation

Visualization by electron microscopy has provided many insights into the composition, quaternary structure, and mechanism of macromolecular assemblies. By preserving samples in stain or vitreous ice it is possible to image them as discrete particles, and from these images generate three-dimensional structures. This ‘single-particle’ approach suffers from two major shortcomings; it requires an initial model to reconstitute 2D data into a 3D volume, and it often fails when faced with conformational variability. Random conical tilt (RCT) and orthogonal tilt (OTR) are methods developed to overcome these problems, but the data collection required, particularly for vitreous ice specimens, is difficult and tedious. In this paper, we present an automated approach to RCT/OTR data collection that removes the burden of manual collection and offers higher quality and throughput than is otherwise possible. We show example datasets collected under stain and cryo conditions and provide statistics related to the efficiency and robustness of the process. Furthermore, we describe the new algorithms that make this method possible, which include new calibrations, improved targeting and feature-based tracking.     

The Kohonen self-organizing map: a tool for the clustering and alignment of single particles imaged using random conical tilt

An important step in determining the three-dimensional structure of single macromolecules is to bring common features in the images into register through alignment and classification. Here, we took advantage of the striking computational properties of the Kohonen self-organizing map (SOM) to align and classify images of channels obtained by random conical geometry into more homogeneous subsets. First, we used simulations with artificially created images to deduce simple geometrical rules governing the mapping of bounded (differing in size and shape) and unbounded (differing in in-plane orientation) variations in the output plane. Second, we measured the effect of noise on the accuracy of the algorithm to separate homogeneous subsets. Finally, we applied the rules ascertained in the previous steps to separate freeze-fracture images of the cytoplasmic and external domains of the small (approximately 118 kDa) aquaporin-0 water channel. Comparison with the results obtained from a similar input set using alignment-through-classification showed that both methods converged to stable classes exhibiting the same overall shapes (tetragonal and octagonal) for the cytoplasmic and external views of the channel. Processing with the SOM, however, was simplified by the utilization of the geometric rules governing the mapping of bounded and unbounded variations as well as the lack of subjectivity in selecting the reference images during alignment.     

A penalized likelihood approach for an illness-death model with interval-censored data: application to age-specific incidence of dementia

We consider the problem of estimating the intensity functions for a continuous time 'illness-death' model with intermittently observed data. In such a case, it may happen that a subject becomes diseased between two visits and dies without being observed. Consequently, there is an uncertainty about the precise number of transitions. Estimating the intensity of transition from health to illness by survival analysis (treating death as censoring) is biased downwards. Furthermore, the dates of transitions between states are not known exactly. We propose to estimate the intensity functions by maximizing a penalized likelihood. The method yields smooth estimates without parametric assumptions. This is illustrated using data from a large cohort study on cerebral ageing. The age-specific incidence of dementia is estimated using an illness-death approach and a survival approach.