Plugin to perform IMS to microscopy registration using laser ablation marks.

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    napari plugin to perform MALDI IMS - microscopy registration using laser ablation marks as described in Anal. Chem. 2018, 90, 21, 12395–12403. This plugin is a work-in-progress but is mostly functional.

    N.B. This tool is NOT a general purpose registration framework to find transforms between IMS (MALDI or otherwise) and microscopy. It is built to align MALDI IMS pixels to their corresponding laser ablation marks as captured by microscopy AFTER the IMS experiment. This approach has the advantage of providing direct evidence of registration performance as IMS pixels are aligned to their explicit spatial origin in microscopy space, improving overall accuracy and confidence of microscopy-driven IMS data analysis.


    You can install napari-imsmicrolink via pip:

    pip install napari-imsmicrolink

    Typical experiment workflow

    1. Acquire pre-IMS microscopy (autofluorescence, brightfield) - optional

    2. Perform normal IMS sample preparation.

    3. Acquire post-IMS microscopy (autofluorescence, brightfield) with matrix still on sample that reveals laser ablation marks.

    4. Gather IMS data that contains XY integer coordinates for the IMS experiment (.imzML, Bruker spotlist (.txt, .csv), Bruker peaks.sqlite (FTICR), Bruker .tsf (TIMS qTOF only))

    5. Run napari-imsmicrolink with data 3 and 4

    6. Once registered, use wsireg to align other microscopy modalities to IMS-registered post-IMS microscopy

    This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.


    Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.


    Distributed under the terms of the MIT license, "napari-imsmicrolink" is free and open source software


    If you encounter any problems, please file an issue along with a detailed description.


    • 0.1.8

    Release date:

    • 18 November 2022

    First released:

    • 14 December 2021


    • MIT

    Supported data:

    • Information not submitted

    Plugin type:

    GitHub activity:

    • Stars: 5
    • Forks: 0
    • Issues + PRs: 5

    Python versions supported:

    Operating system:


    • numpy
    • tifffile
    • dask
    • zarr (>=2.10.3)
    • qtpy
    • aicsimageio[bioformats]
    • bioformats-jar
    • SimpleITK
    • pandas
    • h5py
    • opencv-python
    • czifile
    • imagecodecs

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