Evidence map›Paper›PMID 42859450›Full record

ArticleBio-protocol2026

Tapenade: Spatial Quantification of Mechanical and Genetic Fields in Dense 3D Organoids From Cell to Tissue Scale.

Alice Gros, Jules Vanaret, Sham Tlili, Léo Guignard

Abstract read
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Article in Bio-protocol, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Alice GrosAix Marseille Univ, CNRS IBDM (UMR 7288), Turing Centre for Living Systems, Marseille, France.
Jules VanaretAix Marseille Univ, CNRS IBDM (UMR 7288), Turing Centre for Living Systems, Marseille, France.
Sham TliliAix Marseille Univ, CNRS IBDM (UMR 7288), Turing Centre for Living Systems, Marseille, France.
Léo GuignardAix Marseille Univ, CNRS IBDM (UMR 7288), Turing Centre for Living Systems, Marseille, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Whole-mount 3D imaging of multilayered biological tissues enables quantitative analysis of cell states and organization in their spatial context. However, extracting unbiased and meaningful quantitative information from dense, multilayered samples remains challenging due to imaging artifacts, increased density, and limited signal-to-noise ratio. Open source bioimage analysis workflows tailored to this type of analysis are scarce, and analysis bottlenecks like image curation or cell segmentation are seldom available without coding expertise. Here, we present a step-by-step computational protocol for the analysis of dense 3D organoid datasets using the Tapenade (Thorough Analysis PipEliNe for Advanced DEep imaging) workflow. Starting from multichannel image stacks, the protocol guides users through software installation, registration and fusion of multi-view datasets, preprocessing, and nuclei segmentation. It further details the generation of quantitative outputs, including morphometric measurements, deformation fields, and spatial correlation analyses. The workflow can be executed through open-source Python scripts or user-friendly Napari interfaces, allowing interactive parameter tuning and 3D visualization at each stage. This pipeline provides an accessible and modular framework for nonspecialist users to perform reproducible, multiscale quantitative analysis of 3D organoid images, while retaining flexibility for advanced users to customize individual steps. Key features • Multiscale quantitative analysis of dense 3D tissues: developed on thick whole-mount immunostained stem cell aggregates, Tapenade enables quantitative analysis from the cellular to the tissue scale. • End-to-end pipeline: Tapenade provides graphical interfaces, notebooks, and step-by-step tutorials to streamline preprocessing and quantitative measurements, including morphometrics, deformation fields, and spatial correlations. • Integrated visualization and modular analysis: Napari plugins support interactive parameter tuning and real-time exploration of large volumetric datasets. • Scalability: Tapenade is compatible with standard bioimaging formats (e.g., TIFF, OME-TIFF, CZI, Zarr) and scalable to large 3D datasets through lazy loading and batch-processing strategies.

Indexed as

3D image analysisMultiscaleNuclei segmentationOpen-sourceOrganoidsQuantitative analysisSpatial correlation

Identifiers

PMID42859450
PMCPMC13652711

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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.