Biology subjects

Elbert, D. L.

Publications and source records attributed to Elbert, D. L..

3 recordsLinked to original sources

bioRxiv2025

A heat method to interpolate z-stacked cell images that preserves interfaces without explicit identification of object boundaries

A multitude of methods have been developed to morph one image into another. In scientific and medical imaging, it is common to have lower resolution in one imaging direction. To improve the quality of surface meshes generated from these images, morphing is used to produce intermediate images to achieve equal resolution in all directions. The most common method is based on interpolating signed distance functions that describe the shapes of objects in the images. Most methods rely on identification and recording of object boundaries. The situation becomes more complex in the case of interpolating images containing multiple objects of interest wherein the morphing method must not introduce gaps or overlaps of objects in the intermediate images. The goal of the present work was to develop an interpolation method for biological cells in transmission electron microscopy images meeting the constraints listed above. A heat diffusion method was developed that produces steady state isotherms in a 3D model of the intervening space between two images. The isotherms are then used to produce paths from pixels that are unique to a biological cell in one frame towards the overlap region. Instead of identifying boundaries, only membership in the overlap region is tested. For each pixel that terminates paths, the path lengths of all members are normalized to the maximum path length. The path lengths are then used to produce intermediate frames. The method succeeds in avoiding the introduction of gaps or overlap between neighboring biological cells without the need for boundary identification.

bioengineering↗

bioRxiv2025

A new framework for analyzing mass transport in cortical brain tissue at <10 nm resolution

Transmission electron microscopy of brain tissue yields high resolution maps of the spatial organization of cells in the central nervous system. Automated segmentation identifies distinct biological cells and the resulting segmented images are easily converted into surface meshes. The surface meshes are structural models of cell surfaces, providing a framework for modeling of mass transport within the interstitial fluid of brain tissue that surrounds the cells. Our goal is to model the production and clearance of proteins implicated in the development of Alzheimers Disease. This work introduces a new custom computational framework to allow massive parallelization of the solution of the mathematical equations of mass transfer. The diffusion equation was solved directly on the surface meshes of multiple biological cells in parallel, with exchange of mass across co-localized faces at the end of each time step. Mass transfer across the interfaces of multiple analysis volumes was incorporated using a semi-implicit approach. To demonstrate the capabilities of the framework, unsteady mass transfer along cell surfaces was modeled in an array of eight 4 x 4 x 4 m analysis volumes containing 2175 biological cells at 8 nm resolution, consisting of 313 million face elements. Areas of enhanced and hindered diffusive transport were identified, suggesting structural motifs that may contribute to the development of insoluble plaques. The tortuosity and fractional anisotropy were consistent with Diffusion Tensor Imaging (DTI) measurements in cortical tissue. This new framework allows modeling of diffusive mass transport while preserving anatomical details at <10 nm resolution. Author summaryA framework was developed to model mass transport around thousands of biological cells in parallel. This may be useful to identify structural features in cortical tissue that are more prone to the development of amyloid plaques.

bioengineering↗

bioRxiv2025

Methods for simulations with thousands of interacting objects to model mass transport in the brain

Clearance of toxic species that may cause neurodegenerative diseases relies on convection and diffusion of mass around cells in the central nervous system. In this manuscript, methods that allow the nanoscale modeling of mass transport around cells in the brain at a resolution of 8 nm are described. The cells are modeled as surface meshes and a parallelization scheme is used to solve the diffusion equation directly on the surface mesh of each biological cell independently. This is followed by mass exchange between biological cells that are in direct contact. The analysis volume size is fixed at 4 m x 4 m x 4 m but arrays of analysis volumes of arbitrary size may be analyzed, with fluxes across analysis volume boundaries updated at each time step by a semi-implicit formulation. Setup of the discretized equations is described, along with the face matching that allows mass transfer between cells. Parallelization is via a manager/worker framework. One-sided RMA communication in MPI is used to coordinate the efforts of multiple workers, with common information handled by the manager. This specialized framework is suitable for analyzing diffusional transport around thousands of interacting objects simultaneously. The framework is implemented with custom code developed in Julia, which is used here as a rapid prototyping language. Using model geometries, the accuracy of the discretization methods are demonstrated, with limitations and next steps described. Authors summaryThe methods that enable modeling diffusional mass transport around thousands of objects in parallel are described. The accuracy of the method with model objects is demonstrated.

bioengineering↗