Evidence map›Paper›PMID 39360400›Full record

ArticleJournal of microscopy2025

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

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

Abstract read
In one paragraph

Article in Journal of microscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Polarization-engineered aberration-resilient light sheet microscopy.bioRxiv : the preprint server for biology · 2026
    Article
  4. Article
  5. Article
  6. Article
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, California, USA.ORCID 0000-0003-4759-3053
Hiroyuki HakozakiDepartment of Pharmacology, University of California, San Diego, California, USA.ORCID 0009-0008-2503-0480
Gillian McMahonDepartment of Pharmacology, University of California, San Diego, California, USA.ORCID 0009-0003-5690-5511
Marta Medina-CarboneroDepartment of Pharmacology, University of California, San Diego, California, USA.ORCID 0000-0002-8151-1982
Johannes SchönebergDepartment of Pharmacology, University of California, San Diego, California, USA.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
American Heart Association 24PRE1196243Hartwell FoundationNIBIB NIH HHS T32 EB009380NIGMS NIH HHS DP2 GM150022NIH GM 1DP2GM150022-01NIH NIBIB T32EB009380W. M. Keck Foundation
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), move the sample substrate at an oblique angle relative to the detection objective's optical axis. Data from such tilted-sample-scan LSFMs require subsequent deskewing and rotation for proper visualisation and analysis. Such data preprocessing operations currently demand substantial memory allocation and pose significant computational challenges for large 4D dataset. The consequence is prolonged data preprocessing time compared to data acquisition time, which 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 memory-efficient transformation algorithm for deskewing and rotating the raw dataset, significantly reducing memory usage and the run time by more than 10-fold for large image stacks. Benchmarked against the conventional method and existing software, our approach demonstrates linear runtime compared to the cubic and quadratic runtime of the other approaches. Preprocessing a raw 3D volume of 2 GB (512 × 1536 × 600 pixels) can be accomplished in 3 s using a GPU with 24 GB of memory on a single workstation. Applied to 4D LLSM datasets of human hepatocytes, lung organoid tissue and brain organoid tissue, our method provided rapid and accurate preprocessing within seconds. Importantly, such preprocessing speeds now allow visualisation 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 preprocessing, visualisation, and analysis on standard workstations, thereby revolutionising biological imaging applications for LLSM and similar microscopes.

Indexed as

AlgorithmsImage Processing, Computer-AssistedImaging, Three-DimensionalAnimalsHumansMicroscopy, Fluorescencealgorithm developmentfluorescence microscopylattice light‐sheet microscopylight‐sheet microscopymicroscopy data processingmicroscopy visualisation

Identifiers

PMID39360400
PMCPMC11733850

What OpenQuestion holds

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Registered trials

None linked

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.