Article Synopsis
- A new deconvolution algorithm, called sparse structured illumination microscopy (Sparse-SIM), significantly enhances the spatial resolution of live-cell super-resolution (SR) microscopes by leveraging biological structure knowledge.
- This method can achieve approximately 60-nanometer resolution at up to 564 frames per second, enabling the observation of complex cellular structures like fusion pores and nuclear pores in real time.
- Additionally, sparse deconvolution enhances the three-dimensional resolution in spinning-disc confocal-based SIM, making it effective for multi-color, live-cell imaging at around 90-nanometer resolution even under low signal conditions.
Video Abstracts
Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
A main determinant of the spatial resolution of live-cell super-resolution (SR) microscopes is the maximum photon flux that can be collected. To further increase the effective resolution for a given photon flux, we take advantage of a priori knowledge about the sparsity and continuity of biological structures to develop a deconvolution algorithm that increases the resolution of SR microscopes nearly twofold. Our method, sparse structured illumination microscopy (Sparse-SIM), achieves ~60-nm resolution at a frame rate of up to 564 Hz, allowing it to resolve intricate structures, including small vesicular fusion pores, ring-shaped nuclear pores formed by nucleoporins and relative movements of inner and outer mitochondrial membranes in live cells. Sparse deconvolution can also be used to increase the three-dimensional resolution of spinning-disc confocal-based SIM, even at low signal-to-noise ratios, which allows four-color, three-dimensional live-cell SR imaging at ~90-nm resolution. Overall, sparse deconvolution will be useful to increase the spatiotemporal resolution of live-cell fluorescence microscopy.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1038/s41587-021-01092-2 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Sparse deconvolution of cell type medleys in spatial transcriptomics.
PLoS Comput Biol
June 2025
Faculty of Natural Sciences and Engineering, Kadir Has University, Istanbul, Turkiye.
Mapping cell distributions across spatial locations with whole-genome coverage is essential for understanding cellular responses and signaling However, current deconvolution models aim to estimate the proportions of distinct cell types in each spatial transcriptomics spot by integrating reference single-cell data. These models often assume strong overlap between the reference and spatial datasets, neglecting biology-grounded constraints such as sparsity and cell-type variations, as well as technical sparsity. As a result, these methods rely on over-permissive algorithms that ignore given constraints leading to inaccurate predictions, particularly in heterogeneous or unmatched datasets.
View Article and Find Full Text PDFFull-Waveform Inversion With Low-frequency Extrapolation Based on Sparse Deconvolution for Ultrasound Computed Tomography.
Ultrasound Med Biol
August 2025
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China.
Objective: In ultrasound computed tomography (USCT), full-waveform inversion (FWI) is a promising algorithm for high-resolution sound-speed reconstruction. When implementing FWI in practical imaging systems, insufficient high-quality, low-frequency information often leads to cycle skipping, which subsequently degrades convergence and accuracy. To address this problem, this paper proposes a modified FWI algorithm.
View Article and Find Full Text PDFUltrasonic Time-of-Flight Diffraction Imaging Enhancement for Pipeline Girth Weld Testing via Time-Domain Sparse Deconvolution and Frequency-Domain Synthetic Aperture Focusing.
Sensors (Basel)
March 2025
Hangzhou Shenhao Technology Co., Ltd., Hangzhou 311113, China.
Ultrasonic TOFD imaging, as an important non-destructive testing method, has a wide range of applications in pipeline girth weld inspection and testing. Due to the limited bandwidth of ultrasonic transducers, near-surface defects in the weld are masked and cannot be recognized, resulting in poor longitudinal resolution. Affected by the inherent diffraction effect of scattered acoustic waves, defect images have noticeable trailing, resulting in poor transverse resolution of TOFD imaging and making quantitative defect detection difficult.
View Article and Find Full Text PDFQuantification of Protein Secondary Structures from Discrete Frequency Infrared Images Using Machine Learning.
Appl Spectrosc
March 2025
Department of Chemistry and Biochemistry, University of Alabama, Tuscaloosa, Alabama 354127, USA.
Discrete frequency infrared (IR) imaging is an exciting experimental technique that has shown promise in various applications in biomedical science. This technique often involves acquiring IR absorptive images at specific frequencies of interest that enable pathologically relevant chemical contrast. However, certain applications, such as tracking the spatial variations in protein secondary structure of tissue specimens, necessary for the characterization of neurodegenerative diseases, require deeper analysis of spectral data.
View Article and Find Full Text PDFMultifocal structured illumination microscopy (MSIM) provides a twofold resolution enhancement over the optical diffraction limit at depths of up to 50 μm in samples. This is achieved through sparse multifocal excitation patterns and digital image post-processing, making MSIM a highly advantageous technique for the three-dimensional super-resolution (SR) imaging of thick specimens. However, the spatial resolution of MSIM is inherently constrained by its underlying imaging principles.
View Article and Find Full Text PDF