Spaghetti Tracer: A Framework for Tracing Semiregular Filamentous Densities in 3D Tomograms

Abstract

Within cells, cytoskeletal filaments are often arranged into loosely aligned bundles. These fibrous bundles are dense enough to exhibit a certain regularity and mean direction, however, their packing is not sufficient to impose a symmetry between—or specific shape on—individual filaments. This intermediate regularity is computationally difficult to handle because individual filaments have a certain directional freedom, however, the filament densities are not well segmented from each other (especially in the presence of noise, such as in cryo-electron tomography). In this paper, we develop a dynamic programming-based framework, Spaghetti Tracer, to characterizing the structural arrangement of filaments in the challenging 3D maps of subcellular components. Assuming that the tomogram can be rotated such that the filaments are oriented in a mean direction, the proposed framework first identifies local seed points for candidate filament segments, which are then grown from the seeds using a dynamic programming algorithm. We validate various algorithmic variations of our framework on simulated tomograms that closely mimic the noise and appearance of experimental maps. As we know the ground truth in the simulated tomograms, the statistical analysis consisting of precision, recall, and F1 scores allows us to optimize the performance of this new approach. We find that a bipyramidal accumulation scheme for path density is superior to straight-line accumulation. In addition, the multiplication of forward and backward path densities provides for an efficient filter that lifts the filament density above the noise level. Resulting from our tests is a robust method that can be expected to perform well (F1 scores 0.86–0.95) under experimental noise conditions.

An image processing pipeline for electron cryo-tomography in RELION-5

“…Gaining structural insights from cryo-ET data is complex and remains an active area of research, with ongoing developments in CTF estimation 39 , tilt series alignment 8 , tomogram denoising 21,23,24,45,52 , automated localisation of structures of interest 30,[53][54][55][56][57][58][59][60][61][62] and approaches to deal with structural heterogeneity among extracted particles [63][64][65][66] , amongst many others. As a result, cryo-ET image processing pipelines, including the one described here, will need to be continuously updated in the coming years.…”

Untangling Irregular Actin Cytoskeleton Architectures in Tomograms of the Cell with Struwwel Tracer

“…Actin filaments are thin and flexible. They assemble to form diverse organizational patterns, from hexagonal closely packed bundles in the shaft of hair cell stereocilia [6], to directionally biased semi-ordered strands in the stereocilium taper region [4,7], to irregular, randomly oriented actin networks in filopodia [8]. The three types of actin organization can be likened to the appearance of a person's hair in various stages of tidiness (brushed, windblown, unkempt), and each type requires specialized algorithms for their computational interpretation.…”

“…The three types of actin organization can be likened to the appearance of a person's hair in various stages of tidiness (brushed, windblown, unkempt), and each type requires specialized algorithms for their computational interpretation. Our recent algorithm development was mainly focused on ordered or semi-ordered patterns in stereocilia, where filaments form strands with a mean direction that can be exploited in directional denoising or deconvolution [4,6,7]. In many cells, however, such as in the ancestral Archaea [2], the filaments are randomly oriented and form highly irregular networks, which defy any directionally biased denoising approaches.…”

“…Among the cryo-ET-related actin tracing methods, some are only applicable for the tracing of well-ordered filaments [4,6,7]. Among these, one noteworthy tool is our Spaghetti Tracer approach [7].…”

Editorial: Special Issue “Protein Modeling and Simulation: Selected Articles from the Computational Structural Bioinformatics Workshop 2021”

“…To characterize the structural arrangement of cytoskeletal filaments in 3D maps of subcellular components, Sazzed et al presented a framework named Spaghetti Tracer [ 6 ], which uses a dynamic programming (DP)-based algorithm. Spaghetti Tracer incorporates the spatial domain technique that operates directly on the voxels of the 3D tomogram.…”