Biology subjects

Simpson, J. C.

Publications and source records attributed to Simpson, J. C..

3 recordsLinked to original sources

bioRxiv2025

SXTractor: A Self-Supervised Feature Extractor of Soft X-Ray Images That Enables Few-Shot Tomogram Segmentation

Soft X-ray tomography (SXT) is a powerful, non-invasive bio-imaging technique that enables visualization of cellular structures in near-native states. Despite its potential, the development of dedicated image analysis tools -- particularly deep-learning-based models -- has been limited, largely due to the limited accessibility of soft X-ray microscopes and the scarcity of labeled SXT data. To address this deficit, in this work, we present SXTractor, a self-supervised SXT feature extractor based on the DINO framework. SXTractor can be fine-tuned with minimal labeled data and effectively adapted to various downstream tasks. We demonstrate its utility on few-shot tomogram segmentation, where it significantly outperforms the model when trained from scratch. Furthermore, it achieves few-shot segmentation performance comparable to that of the Segment Anything Model (SAM), despite SAM being a segmentation-specific model pretrained on millions of labeled images with a significantly larger model size. Most importantly, SXTractor enables a diverse range of downstream applications of deep learning to SXT, thus offering a practical and scalable solution for SXT image analysis in data-constrained settings.

bioengineering↗

bioRxiv2025

Robust Deep Denoising of Soft X-Ray Tomography Data for Biological Research: Targeting Tilt Series Versus Reconstructed Tomograms

1.Soft X-ray microscopy (SXM) is a powerful tool for nanoscale 3D imaging of hydrated, intact cells. However, its application is limited by pixel-wise-correlated noise inherent in the imaging process. While the deep-learning-based framework Noise2Inverse has been proposed for tomography denoising, the lack of robust validation for newly revealed features raises concerns regarding their practical biological utility. We argue that denoising the raw tilt series, instead of on the reconstructed tomogram, offers a significant advantage by leveraging the tomographic reconstruction process to mitigate local prediction errors through averaging. Consequently, features present in the tomogram reconstructed from a denoised tilt series exhibit higher credibility, as falsely predicted details on certain tilt series slices are likely to be averaged out. Nevertheless, denoising the tilt series remains challenging due to the spatially correlated noise that occurs in bioimaging. Existing self-supervised methods relying on a single noisy image struggle with such noise, while the Noise2Noise framework necessitates paired noisy datasets for training. This study addresses these challenges by investigating practical imaging workflows in SXM and related bioimaging modalities to identify existing imaging resources as training data for tilt series denoising. We compare the denoising performance when applied to tilt series versus reconstructed tomograms and evaluate the efficacy of chosen methods on real biological specimens to assess their applicability within practical SXM research. Our findings establish a robust and efficient denoising strategy for SXM by directly addressing the complexities of noise in the raw tilt series. HighlightsO_LITargeting tilt series for denoising inherently validates the correctness of the denoised tomogram via the reconstruction process. C_LIO_LIExisting multi-frame imaging schemes provide necessary resources for training Noise2Noise framework for tilt series denoising. C_LIO_LINoise2Noise applied to tilt series reveals finer and more reliable details than direct denoising on reconstructed tomograms (Noise2Inverse). C_LIO_LIDenoising tilt series minimizes inference time compared to Noise2Inverse in soft X-ray tomography. C_LI

bioengineering↗

bioRxiv2024

Demonstrating Soft X-Ray Tomography in the lab for correlative cryogenic biological imaging using X-rays and light microscopy

Soft X-ray tomography (SXT) enables native-contrast three-dimensional (3D) imaging of fully hydrated, cryogenically preserved biological samples, revealing ultrastructural details without the need for staining, embedding, or sectioning. Traditionally available only at synchrotron facilities, recent advances in laser-driven plasma sources have led to the development of compact soft X-ray microscopes, such as the SXT-100. The SXT-100 achieves imaging resolutions down to 54 nm full-pitch, with tomograms acquired in 30 minutes to two hours. Integrated with an epifluorescence microscope, the SXT-100 facilitates correlative workflows by bridging fluorescence and electron microscopy while preserving the structural integrity of vitrified samples. We demonstrate the capabilities of the SXT-100 through various use cases, including imaging Euglena gracilis, Saccharomyces cerevisiae yeast cells, and nanoparticles in mammalian cells. The relatively short tomogram acquisition times, the virtually non-destructive nature of soft X-ray tomography, and its quantitative imaging capabilities underscore its potential as a powerful tool for advanced biological imaging. Future developments promise enhanced throughput and deeper integration with emerging correlative imaging modalities, and a wider variety of sample types including tissue.

biophysics↗