Evidence map›Paper›PMID 38854017›Full record

ArticlebioRxiv : the preprint server for biology2024

LiveLattice: Real-time visualization of tilted light-sheet microscopy data using a memory-efficient transformation algorithm.

Zichen Wang, Hiroyuki Hakozaki, Gillian McMahon, Marta Medina-Carbonero, Johannes Schöneberg

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Zichen WangDepartment of Pharmacology, University of California, San Diego, San Diego, CA, 92093.ORCID 0000-0003-4759-3053
Hiroyuki HakozakiDepartment of Pharmacology, University of California, San Diego, San Diego, CA, 92093.ORCID 0009-0008-2503-0480
Gillian McMahonDepartment of Pharmacology, University of California, San Diego, San Diego, CA, 92093.ORCID 0009-0003-5690-5511
Marta Medina-CarboneroDepartment of Pharmacology, University of California, San Diego, San Diego, CA, 92093.ORCID 0000-0002-8151-1982
Johannes SchönebergDepartment of Pharmacology, University of California, San Diego, San Diego, CA, 92093.ORCID 0000-0001-7083-1828

Funding

Training in Multi-Scale Analysis of Biological Structure and FunctionT32EB009380 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Andrew D. McCulloch, Padmini Rangamani · 2009 to 2026
$4.8M
Decode Mitochondrial Morphology Dynamics to Predict Cell Fate DecisionsDP2GM150022 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SCHOENEBERG, JOHANNES · 2022 to 2025
$2.4M
Modeling and Analysis of the Spatio-Temporal Dynamics of the Mitochondrial NetworkR01GM148765 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Johannes Schoeneberg · 2023 to 2026
$1.2M
NIBIB NIH HHS T32 EB009380NIGMS NIH HHS DP2 GM150022NIGMS NIH HHS R01 GM148765
6 · The paper itself

Abstract

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), rely on a tilting of the sample plane with respect to the light sheet of 30-45 degrees to ease sample preparation. Data from such tilted-sample-plane LSFMs require subsequent deskewing and rotation for proper visualization and analysis. Such transformations currently demand substantial memory allocation. This poses computational challenges, especially with large datasets. The consequence is long processing times compared to data acquisition times, which currently limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a novel GPU-accelerated memory-efficient algorithm that integrates deskewing and rotation into a single transformation, significantly reducing memory requirements and reducing the preprocessing run time by at least 10-fold for large image stacks. Benchmarked against conventional methods and existing software, our approach demonstrates linear scalability. Processing large 3D stacks of up to 15 GB is now possible within one minute using a single GPU with 24 GB of memory. Applied to 4D LLSM datasets of human hepatocytes, human lung organoid tissue, and human brain organoid tissue, our method outperforms alternatives, providing rapid, accurate preprocessing within seconds. Importantly, such processing speeds now allow visualization of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data processing, visualization, and analysis on standard workstations, thereby revolutionizing biological imaging applications for LLSM, SPIM and similar light microscopes.

Indexed as

algorithm developmentfluorescence microscopylattice light-sheet microscopylight-sheet microscopymicroscopy data processingmicroscopy visualization

Identifiers

PMID38854017
PMCPMC11160600

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.