PyImageJ: A library for integrating ImageJ and Python

Rueden, Curtis; Hiner, Mark; Evans, Edward L.; Pinkert, Michael A.; Lucas, Alice M.; Carpenter, Anne E.; Cimini, Beth A.; Eliceiri, Kevin W.

doi:10.1038/s41592-022-01655-4

Cited by 24 publications

(10 citation statements)

References 10 publications

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“…The puncta detection and segmentation workflow were first prototyped with the ImageJ Ops image processing framework ( 76 ) and PyImageJ ( 77 ), a Python library that wraps ImageJ’s Java code base and enables access to ImageJ from the Python environment. The workflow was later ported to the Konstanz Information Miner (KNIME) ( 78 ) to take advantage of KNIME’s intuitive workflow graphical user interface (GUI).…”

Section: Methodsmentioning

confidence: 99%

Live Cell Imaging Reveals HBV Capsid Translocation from the Nucleus To the Cytoplasm Enabled by Cell Division

Romero

Unchwaniwala

Evans

et al. 2023

mBio

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Section: Methodsmentioning

confidence: 99%

Live Cell Imaging Reveals HBV Capsid Translocation from the Nucleus To the Cytoplasm Enabled by Cell Division

Romero

Unchwaniwala

Evans

et al. 2023

mBio

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“…Rueden et al created PyImageJ as a bridge between Python and ImageJ. 53 IDR Stream uses the BaSiC method for illumination correction of each well. 30 We use the Python implementation of the BaSiC method, named BaSiCPy.…”

Section: Applying Illumination Correction With Idr_streammentioning

confidence: 99%

Toward generalizable phenotype prediction from single-cell morphology representations

Tomkinson,

Kern,

Mattson

et al. 2024

Preprint

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Functional cell processes (e.g., molecular signaling, response to environmental stimuli, mitosis, etc.) impact cell phenotypes, which scientists can easily and robustly measure with cell morphology. However, linking these morphology measurements with phenotypes remains challenging because biologically interpretable phenotypes require manually annotated labels. Automatic phenotype annotation from cell morphology would link biological processes with their phenotypic outcomes and deepen understanding of cell function. We propose that nuclear morphology can be a predictive marker for cell phenotypes that is generalizable across cell types. Nucleus morphology is commonly and easily accessible with microscopy, but annotating specific phenotypic information requires labels. Therefore, we reanalyzed a pre-labeled, publicly-available nucleus microscopy dataset from the MitoCheck consortium to predict single-cell phenotypes. We extracted single-cell morphology features using CellProfiler and DeepProfiler, which provide fast, robust, and generalizable data processing pipelines. We trained multinomial, multi-class elastic net logistic regression models to classify nuclei into one of 15 phenotypes such as "Anaphase", "Apoptosis", and "Binuclear". In a held-out test set, we observed an overall F1 score of 0.84, where individual phenotype scores ranged from 0.64 (indicating moderate performance) to 0.99 (indicating high performance). Notably, phenotypes such as "Elongated", "Metaphase", and "Apoptosis" showed high performance. While CellProfiler and DeepProfiler morphology features were generally equally effective, combining feature spaces yielded the best results for 9 of the 15 phenotypes. However, leave-one-image-out (LOIO) cross-validation analysis showed a significant performance decline, indicating our model could not reliably predict phenotype in new single images. Poor performance, which we show was unrelated to factors like illumination correction or model selection, limits generalizability to new datasets and highlights the challenges of morphology to phenotype annotation. Nevertheless, we modified and applied our approach to the JUMP Cell Painting pilot data. Our modified approach improved dataset alignment and highlighted many perturbations that are known to be associated with specific phenotypes. We propose several strategies that could pave the way for more generalizable methods in single-cell phenotype prediction, which is a step toward morphology representation ontologies that would aid in cross-dataset interpretability.

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“…S2). The detections were processed by the Python wrapper 'PyImageJ' 35 of ImageJ and TrackMate: the tip probabilities were processed by the TrackMate's MaskDetector and then tracked using the 'Sparse LAP Tracker' (allowed frame gap: 1, maximal gap closing distance: 3 frames, maximum linking distance: 3 frames). Tracks with a duration of 45 seconds or less were excluded from the analysis.…”

Section: Confocal Fluorescence and Reflection Microscopy Imagingmentioning

confidence: 99%

Structure and dynamics of human cardiac fibroblast nanotubes

Schmid-Herbstritt,

Stief,

Greiner

et al. 2023

Preprint

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Efficient and dynamic interactions between cardiac fibroblasts and their environment are essential for the maintenance of tissue homeostasis in healthy hearts and play an important role during pathological remodelling. Here, we investigate a relatively obscure mechanism through which human atrial fibroblasts communicate with each other, with other cells, and with the extracellular matrix (ECM) – nanotubes (NT). We investigated NT structure and dynamics in primary right atrial fibroblasts isolated from patients in sinus rhythm (SR) and atrial fibrillation (AF), in an immortalised human atrial fibroblasts cell line, and in intact human tissue, using a wide range of imaging approaches (including confocal microscopy, label-free reflection microscopy, rotating coherent scattering microscopy, and cryo-electron tomography). We show that fibroblasts maintain continuous NT activityin vitro, with numerous protrusions constantly probing the surrounding environment. NT structure and activity change during AF and following pharmacological (transforming growth factor-β, latrunculin B) and environmental (hypoxia) interventions. We also show that cardiac fibroblast NT mediate intercellular organelle exchange and dynamically interact with ECM. Finally, we present evidence for the presence of fibroblast-borne NT in human atrial tissue. Our results advance our understanding of how cardiac fibroblasts interact with their environment. NT are versatile structures capable of both sensory and actuating functions, and offer a dynamic and rapid communication conduit that facilitates cell–cell and cell–extracellular matrix interactions.

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PyImageJ: A library for integrating ImageJ and Python

Cited by 24 publications

References 10 publications

Live Cell Imaging Reveals HBV Capsid Translocation from the Nucleus To the Cytoplasm Enabled by Cell Division

Live Cell Imaging Reveals HBV Capsid Translocation from the Nucleus To the Cytoplasm Enabled by Cell Division

Toward generalizable phenotype prediction from single-cell morphology representations

Structure and dynamics of human cardiac fibroblast nanotubes

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