Biology subjects

Heebner, J.

Publications and source records attributed to Heebner, J..

8 recordsLinked to original sources

bioRxiv2026

Shaping cryogenic 3D volume imaging by pFIB ion species and milling voltage

Cryogenic plasma focused ion beam-scanning electron microscopy (cryo-pFIB-SEM) enables nanoscale volume imaging of vitrified cells and tissues, but the fidelity of each newly exposed block-face depends on how ions interact with compositionally heterogeneous biological material. Using xenon, argon, oxygen, and nitrogen plasma beams across a 30-2 kV voltage range, we examined how ion species and accelerating voltage influence block-face quality and ultrastructural fidelity. Curtaining imposed a species-dependent lower limit on accelerating voltage, with xenon and argon supporting uniform milling to 10 kV and oxygen and nitrogen to 15 kV. Monte Carlo simulations predicted substantially reduced ion penetration and atomic displacement under these species-specific low-kV conditions. Experimentally, lowering milling voltage suppressed charging and topographic artifacts, with the largest effects observed at heterogeneous interfaces. Oxygen and nitrogen produced greater membrane sharpness and organelle contrast than the noble gases, and these differences influenced machine-learning-based recognition of fine organelle features in 3D datasets. Together, these findings show that cryo-pFIB-SEM image quality cannot be optimized by accelerating voltage or ion species independently. Instead, milling performance reflects a tradeoff among surface fidelity, charging, contrast, and curtaining that depends on local specimen composition. Species-specific low-kV milling therefore provides a practical framework for matching cryo-pFIB-SEM acquisition conditions to the structural and biophysical properties of the biological target.

cell biology↗

bioRxiv2026

Spatiotemporal biogenesis of thylakoid membranes in the green alga Chlamydomonas reinhardtii

Thylakoid membranes are essential for oxygenic photosynthesis, yet the mechanisms underlying their spatial and temporal biogenesis remain poorly understood. Here, using a light-induced membrane regeneration system in Chlamydomonas reinhardtii, we generate a time-resolved map of thylakoid formation and photosynthetic complex assembly at unprecedented spatial and temporal resolution by integrating super-resolution fluorescence microscopy, cryo-electron tomography, proteomics, and spectroscopy. We show that thylakoid biogenesis is globally distributed and multipolar, with new membrane formation occurring at multiple sites across the chloroplast, including regions adjacent to the inner envelope membranes in both basal and lobe regions. We identify F-ATP synthase storage membranes and map the redistribution of translating ribosomes from T-zone enrichment to a chloroplast-wide distribution at early stages of thylakoid formation, consistent with decentralized synthesis of photosynthetic complexes. We further delineate the hierarchical assembly of photosystem supercomplexes, starting with formation of reaction centers. Together, these findings refine the spatial organization of photosynthetic membrane biogenesis and establish a unified spatiotemporal framework linking thylakoid formation, translational activation, and functional maturation, providing a mechanistic basis for plastid engineering to enhance photosynthetic efficiency.

Cell Biology↗

bioRxiv2026

TomoSegNet: Augmented membrane segmentation for cryo-electron tomography by simulating the cellular context

Membrane segmentation is an essential task in the workflow for processing cryo-electron tomography data. Recently, machine learning algorithms have successfully been adopted to perform membrane segmentation. However, the performance of these approaches is limited by the training dataset, as models are trained from manual annotations, thereby hindering the models generalization and preventing the recovery of membranes that have vanished due to distortions. Here, we address these limitations by generating training data with a simulator. To provide a representative and realistic dataset, we have extended the current state-of-the-art in simulators for cryo-electron tomography by incorporating a biophysical model for membranes. We demonstrate that our machine learning model, trained solely from synthetic data, and thanks to the physical knowledge learned from the simulator, outperforms the current state-of-the-art for membrane segmentation in a diverse set of experimental data. This performance is particularly noteworthy in terms of recovering membranes lost due to imaging distortions.

bioinformatics↗

bioRxiv2025

In situ architecture of the endosymbiont Wolbachia pipientis

Hidden within host cells, the endosymbiont Wolbachia pipientis is the most prevalent bacterial infection in the animal kingdom. Scientific breakthroughs over the past century yielded fundamental mechanisms by which Wolbachia controls arthropod reproduction to shape dynamic ecological and evolutionary trajectories. However, the structure and spatial organization of symbiont machineries that underpin intracellular colonization and orchestrate maternal inheritance remain unknown. Here, we used cryo-electron tomography to directly image the nanoscale architecture of bacterial tools deployed for host manipulation and germline transmission. We discovered that Wolbachia assembles multiple structures at the host-endosymbiont interface including a filamentous ladder-like framework hypothesized to serve as a specialized motility mechanism that enables bacterial translocation to specific host cell compartments during embryogenesis and somatic tissue dissemination. In addition, we present the first in situ structure of the Rickettsiales vir homolog type IV secretion system (rvh T4SS). We provide evidence that the rvh T4SS nanomachine exhibits architectural similarities to the pED208-encoded T4SS apparatus including the biogenesis of rigid conjugative pili extending hundreds of nanometers beyond the bacterial cell surface. Coupled with integrative structural modeling, we demonstrate that in contrast to canonical T4SS architectures, the -proteobacterial T4SS outer membrane complex assembles a periplasmic baseplate structure predicted to comprise VirB9 oligomers complexed with cognate VirB10 subunits that form extended antennae projections surrounding the translocation channel pore. Collectively, these studies provide an unprecedented view into Wolbachia structural cell biology and unveil the molecular blueprints for architectural paradigms that reinforce ancient host-microbe symbioses.

microbiology↗

bioRxiv2025

Training Generalized Segmentation Networks with Real and Synthetic Cryo-ET data.

Deep learning excels at segmenting objects within noisy cryo-electron tomograms, but the approach is typically bottlenecked by access to ground truth training data. To address this issue we have developed CryoTomoSim (CTS), an open-source software package that builds coarse-grained models of macromolecular complexes embedded in vitreous ice and then simulates transmitted electron tilt series for tomographic reconstruction. Using CTS outputs, we demonstrate the effects of key microscope parameters (dose, defocus, and pixel size) on deep learning-based segmentation, and show that including both molecular crowding and diversity within synthetic datasets is key to training cellular segmentation networks from purely synthetic inputs. While very effective as initial models, the accuracy of these networks is currently limited, and real cellular data is necessary to train the most accurate and generalizable U-Nets. Using a co-training approach, we first segment over 100 tomograms from neuronal growth cones to quantify their cytoskeletal distributions and then we build a generalized cellular cryo-ET segmentation network called NeuralSeg that can segment a subset of cellular features in tomograms from all domains of life.

cell biology↗

bioRxiv2024

Towards community-driven visual proteomics with large-scale cryo-electron tomography of Chlamydomonas reinhardtii

In situ cryo-electron tomography (cryo-ET) has emerged as the method of choice to investigate structures of biomolecules in their native context. However, challenges remain in the efficient production of large-scale cryo-ET datasets, as well as the community sharing of this information-rich data. Here, we applied a cryogenic plasma-based focused ion beam (cryo-PFIB) instrument for high-throughput milling of the green alga Chlamydomonas reinhardtii, a useful model organism for in situ visualization of numerous fundamental cellular processes. Combining cryo-PFIB sample preparation with recent advances in cryo-ET data acquisition and processing, we generated a dataset of 1829 reconstructed and annotated tomograms, which we provide as a community resource to drive method development and inspire biological discovery. To assay the quality of this dataset, we performed subtomogram averaging (STA) of both soluble and membrane-bound complexes ranging in size from >3 MDa to [~]200 kDa, including 80S ribosomes, Rubisco, nucleosomes, microtubules, clathrin, photosystem II, and mitochondrial ATP synthase. The majority of these density maps reached sub-nanometer resolution, demonstrating the potential of this C. reinhardtii dataset, as well as the promise of modern cryo-ET workflows and open data sharing towards visual proteomics.

cell biology↗

bioRxiv2023

Rapid Synthesis of Cryo-ET Data for Training Deep Learning Models

Deep learning excels at cryo-tomographic image restoration and segmentation tasks but is hindered by a lack of training data. Here we introduce cryo-TomoSim (CTS), a MATLAB-based software package that builds coarse-grained models of macromolecular complexes embedded in vitreous ice and then simulates transmitted electron tilt series for tomographic reconstruction. We then demonstrate the effectiveness of these simulated datasets in training different deep learning models for use on real cryotomographic reconstructions. Computer-generated ground truth datasets provide the means for training models with voxel-level precision, allowing for unprecedented denoising and precise molecular segmentation of datasets. By modeling phenomena such as a three-dimensional contrast transfer function, probabilistic detection events, and radiation-induced damage, the simulated cryo-electron tomograms can cover a large range of imaging content and conditions to optimize training sets. When paired with small amounts of training data from real tomograms, networks become incredibly accurate at segmenting in situ macromolecular assemblies across a wide range of biological contexts. SummaryBy pairing rapidly synthesized Cryo-ET data with computed ground truths, deep learning models can be trained to accurately restore and segment real tomograms of biological structures both in vitro and in situ.

cell biology↗

bioRxiv2021

Cofilin Regulates Filopodial Structure and Flexibility in Neuronal Growth Cones

Cofilin is best known for its ability to sever actin filaments, and facilitate cytoskeletal recycling inside of cells. At higher concentrations, in vitro, cofilin stabilizes a more flexible, hyper-twisted state of actin known as "cofilactin", but a structural role for cofilactin, in situ, has not been observed. Combining cryo-electron tomography and live-cell imaging in neuronal growth cones, we show that filopodial actin bundles can switch between a fascin-linked and a cofilin-decorated state, composed of hyper-twisted cofilactin filaments. These cofilactin bundles contribute to the flexibility of filopodial actin networks, thus regulating growth cone searching dynamics. Our results provide mechanistic insight into the processes underlying proper brain development, as well as fundamentals of cytoskeletal mechanics inside confined cellular spaces.

cell biology↗