Moments reconstruction and local dynamic range compression of high order Superresolution Optical Fluctuation Imaging

Yi, Xiyu; Son, Sungho; Ando, Ryoko; Miyawaki, Atsushi; Weiss, Shimon

doi:10.1101/500819

Cited by 7 publications

(23 citation statements)

References 41 publications

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“…Figure 4 shows the deconvolution results on the relevant dataset used in our prior publication [11], we can see that the deconvolved results shows much more detailed structures as compared to the image without deconvolution. More detailed demonstrations are available in the corresponding Jupyter Notebook (E3).…”

Section: Shrinking Kernel Deconvolution -Deconvsk (E3)mentioning

confidence: 95%

“…Figure (2) provides the data-flow diagram that demonstrate the connections (arrows) between different processing steps (green squares) and different types of data (purple ovals). Three collections of SOFI analysis routines are implemented in the PysofiData class, including the "Shared Processes" that contains the traditional SOFI analysis steps [1], the "SOFI 2.0" collection that contains the routines for SOFI 2.0 processing [11] and the "MOCA" collection that enables multi-order cumulant analysis (MOCA) [10]. In the "Shared Processes" block, the processing steps including bleaching correction (BC), Fourier interpolation (FI), and moment and cumulant calculations.…”

Section: Pysofi Overviewmentioning

confidence: 99%

“…Brief descriptions of some notebooks (E1 to E8) are provided in the relevant section (fourth column). The theory behind E9 to E13 are not included in this manuscript but the relevant concepts are discussed in [22] and [11]. The notebooks are the PySOFI implementations of the relevant methods to support the utilization of them.…”

Section: Moment and Cumulant Reconstructions (E1)mentioning

confidence: 99%

“…The finterp.py module provides Fourier interpolation on a TIFF stack, which is a necessary step for fSOFI alike analysis [9]. filtering.py, deconvsk.py, and ldrc.py constitute a collection of modules relevant to SOFI 2.0 [11] analysis. Specifically, the filtering.py module is for pixel-wise noise filtering along the time axis, the deconvsk.py module is for shrinking kernel deconvolution (DeconvSK) [10], and the ldrc.py is for local dynamic range compression (ldrc) of images with a large dynamic range of pixel values [10].…”

Section: Pysofi Overviewmentioning

confidence: 99%

“…It has attracted a growing community of active practitioners and developers over a decade. The advancements utilizing this technology include innovations in blinking dyes and fluorescent proteins, sample preparation [2,3,4,5,6], illumination schemes, experiment designs, data processing methods [7,8,9,10,11,12,13,14,15], and integration with other methods [16,17,18,19,20,21]. With ℎ order cumulant analysis, the theoretical resolution improvement of SOFI is 1 √ .…”

Section: Introductionmentioning

confidence: 99%

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PySOFI: an open source Python package for SOFI

Miao

Weiss

2021

Preprint

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Super-resolution optical fluctuation imaging (SOFI) is a highly democratizable technique that provides optical super-resolution (SR) without requirement of sophisticated imaging instruments. An open source package for SOFI algorithm is needed to support not only the utilization of SOFI, but also the community adoption and participation for further development of SOFI. In this work, we developed PySOFI, an open source python package for SOFI analysis that offers the flexibility to inspect, test, modify, improve and extend the algorithm. We provide a complete documentation for the package and a collection of Jupyter Notebooks to demonstrate the usage of the package. We discuss the architecture of PySOFI, illustrate how to use each functional module, and demonstrate how to extend the PySOFI package with additional modules. We expect PySOFI to facilitate efficient adoption, testing, modification, dissemination and prototyping of new SOFI-relevant algorithms.

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Section: Shrinking Kernel Deconvolution -Deconvsk (E3)mentioning

confidence: 95%

Section: Pysofi Overviewmentioning

confidence: 99%

Section: Moment and Cumulant Reconstructions (E1)mentioning

confidence: 99%

Section: Pysofi Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PySOFI: an open source Python package for SOFI

Miao

Weiss

2021

Preprint

Self Cite

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Single particle tracking with compressive sensing using progressive refinement method on sparse recovery (spt-PRIS)

Shrestha

McDonald

et al. 2022

Preprint

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Single particle tracking (SPT) is an indispensable tool for scientific studies. However, SPT for datasets with a high density of particles is still challenging, especially for the study of particle interactions where the point spread functions (PSFs) are overlapping. In this study, we present spt-PRIS, a new SPT solution where we apply compressive sensing to SPT by integrating the progressive refinement method on sparse recovery (PRIS) into the framework of the state-of-the-art SPT algorithm (uTrack). We systematically characterized and validated spt-PRIS performance using simulations, applied it to the experimental data of membrane-bound KRAS4b proteins in either 2-lipid or 8-lipid membrane supported lipid bilayers (SLB), and compared the results to the conventional method (uTrack). Our results show that spt-PRIS is effective for SPT when the data contains overlapping PSFs and provides unprecedented information about KRAS4b subpopulations. spt-PRIS is helpful for a broad range of scientific studies where precise and fast high-density localization is beneficial. spt-PRIS is also flexible for extensions for multi-species, multi-multi-channel, and multi-dimensional SPT methods with the generalization of PRIS reconstruction schemes.

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Enhancing detectable fluorescence fluctuation for high-throughput and four-dimensional live-cell super-resolution imaging

Wei

Zhao

Han

et al. 2022

Preprint

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Super-resolution (SR) imaging with high-throughput is invaluable to fast and high-precision profiling in a wide range of biomedical applications. However, prevalent SR methods require sophisticated acquisition devices and specific imaging control, and may cost a fairly long time on a single field-of-view. These essentially increase the construction difficulty, including challenges in imaging throughput, system establishment, and automation. Using the natural photophysics of fluorescence, fluctuation-based microscopy techniques can routinely break the diffraction limit with no need for additional optical components, but its long acquisition time still poses a challenge for high-throughput imaging or visualizing transient organelle dynamics. Here, we propose an SR method based on the Auto-Correlation with two-step Deconvolution (SACD) that reduces the number of frames required by maximizing the detectable fluorescence fluctuation behavior in each measurement, with further removal of tunable parameters by a Fourier ring correlation analysis. It only needs 20 frames for twofold lateral and axial resolution improvements, while the SR optical fluctuation imaging (SOFI) needs more than 1000 frames. By capturing raw images for ~10 minutes, we record an SR image with ~128 nm resolution that contains 2.4 gigapixels covering an area of ~2.0 mm * 1.4 mm, including more than 2,000 cells. Beyond that, by applying continuity and sparsity joint constraint, the Sparse deconvolution-assisted SACD enables 4D live-cell SR imaging of events such as mitochondrial fission and fusion. Overall, as an open-sourced module, we anticipate SACD can offer direct access to SR, which may facilitate the biology studies of cells and organisms with high-throughput and low-cost.

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Moments reconstruction and local dynamic range compression of high order Superresolution Optical Fluctuation Imaging

Cited by 7 publications

References 41 publications

PySOFI: an open source Python package for SOFI

PySOFI: an open source Python package for SOFI

Single particle tracking with compressive sensing using progressive refinement method on sparse recovery (spt-PRIS)

Enhancing detectable fluorescence fluctuation for high-throughput and four-dimensional live-cell super-resolution imaging

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