ReviewArchives of biochemistry and biophysics2015
Imaging and characterizing cells using tomography.
Review in Archives of biochemistry and biophysics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Classifying biophysical subpopulations of insulin secretory granules using quantitative whole-cell structure analysis.Structure (London, England : 1993) · 2026Article
- Automated segmentation of soft X-ray tomography: Native cellular structure with submicron resolution at high-throughput for whole-cell quantitative imaging in yeast.Molecular biology of the cell · 2025Article
- Classifying Biophysical Subpopulations of Insulin Secretory Granules using Quantitative Whole Cell Structure Analysis.bioRxiv : the preprint server for biology · 2025Article
- Loss of ZNF148 enhances insulin secretion in human pancreatic β cells.JCI insight · 2023Article
- Evaluation of imaging setups for quantitative phase contrast nanoCT of mineralized biomaterials.Journal of synchrotron radiation · 2022Article
- Soft X-ray tomography to map and quantify organelle interactions at the mesoscale.Structure (London, England : 1993) · 2022Article
- ClusterAlign: A fiducial tracking and tilt series alignment tool for thick sample tomography.Biological imaging · 2022Article
- Single-Cell Chemistry of Photoactivatable Platinum Anticancer Complexes.Journal of the American Chemical Society · 2021Article
- X-ray-Based Techniques to Study the Nano-Bio Interface.ACS nano · 2021Article
- Assaying three-dimensional cellular architecture using X-ray tomographic and correlated imaging approaches.The Journal of biological chemistry · 2020Review
- Strong intracellular signal inactivation produces sharper and more robust signaling from cell membrane to nucleus.PLoS computational biology · 2020Article
- Soft X-ray tomography: virtual sculptures from cell cultures.Current opinion in structural biology · 2019Review
- Imaging cell morphology and physiology using X-rays.Biochemical Society transactions · 2019Review
- Studying the Spatial Organization of ESCRTs in Cytokinetic Abscission Using the High-Resolution Imaging Techniques SIM and Cryo-SXT.Methods in molecular biology (Clifton, N.J.) · 2019Article
- A comparison of absorption and phase contrast for X-ray imaging of biological cells.Journal of synchrotron radiation · 2018Article
- Comparison of 3D cellular imaging techniques based on scanned electron probes: Serial block face SEM vs. Axial bright-field STEM tomography.Journal of structural biology · 2018Article
- Chromatin organization regulates viral egress dynamics.Scientific reports · 2017Article
- Mesoscale imaging with cryo-light and X-rays: Larger than molecular machines, smaller than a cell.Biology of the cell · 2017Review
- Towards correlative super-resolution fluorescence and electron cryo-microscopy.Biology of the cell · 2016Review
- Resolving new ultrastructural features of cytokinetic abscission with soft-X-ray cryo-tomography.Scientific reports · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
We can learn much about cell function by imaging and quantifying sub-cellular structures, especially if this is done non-destructively without altering said structures. Soft X-ray tomography (SXT) is a high-resolution imaging technique for visualizing cells and their interior structure in 3D. A tomogram of the cell, reconstructed from a series of 2D projection images, can be easily segmented and analyzed. SXT has a very high specimen throughput compared to other high-resolution structure imaging modalities; for example, tomographic data for reconstructing an entire eukaryotic cell is acquired in a matter of minutes. SXT visualizes cells without the need for chemical fixation, dehydration, or staining of the specimen. As a result, the SXT reconstructions are close representations of cells in their native state. SXT is applicable to most cell types. The deep penetration of soft X-rays allows cells, even mammalian cells, to be imaged without being sectioned. Image contrast in SXT is generated by the differential attenuation soft X-ray illumination as it passes through the specimen. Accordingly, each voxel in the tomographic reconstruction has a measured linear absorption coefficient (LAC) value. LAC values are quantitative and give rise to each sub-cellular component having a characteristic LAC profile, allowing organelles to be identified and segmented from the milieu of other cell contents. In this chapter, we describe the fundamentals of SXT imaging and how this technique can answer real world questions in the study of the nucleus. We also describe the development of correlative methods for the localization of specific molecules in a SXT reconstruction. The combination of fluorescence and SXT data acquired from the same specimen produces composite 3D images, rich with detailed information on the inner workings of cells.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.