Cytokit: A single-cell analysis toolkit for high dimensional fluorescent microscopy imaging
Eric CzechiD, Bülent Arman AksoyiD, Pınar AksoyiD, Jeff HammerbacheriD
Abstract
BackgroundMultiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind.\n\nResultsCytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence. Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. The efficacy of these operations is demonstrated through several imaging experiments that pair Cytokit results with those from an independent but comparable assay. A further validation also demonstrates that previously published results can be reproduced from a publicly available multiplexed image dataset.\n\nConclusionCytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. This project is best suited to bioinformaticians or other technical users that wish to analyze such data in a batch-oriented, high-throughput setting. All source code, documentation, and data generated for this article are available under the Apache License 2.0 at https://github.com/hammerlab/cytokit.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 61
- FWCI
- 4.71
- Field percentile
- top 4%
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data
Lasse Meyer, Nils Eling, Bernd Bodenmiller·bioRxiv·May 24, 2023
- A framework for multiplex imaging optimization and reproducible analysis
J. R. Eng, E. Bucher, … J. W. Gray·bioRxiv·Nov 29, 2021
- CYCLOPS: an open end-to-end platform for cyclic multiplex imaging and single-cell phenotyping
Sarwah Al-Khalidi, Nikki R. Paul, … Edward W. Roberts·bioRxiv·Jun 14, 2026
- Spatialproteomics - an interoperable toolbox for analyzing highly multiplexed fluorescence image data
Matthias Meyer-Bender, Harald S. Vöhringer, … Wolfgang K. Huber·Nature MethodsPeer-reviewed·Jul 24, 2026
- cytomapper: an R/Bioconductor package for visualisation of highly multiplexed imaging data
Nils Eling, Nicolas Damond, … Bernd Bodenmiller·BioinformaticsPeer-reviewed·Dec 10, 2020
- An end-to-end workflow for multiplexed image processing and analysis
Jonas Windhager, Vito RT Zanotelli, … Nils Eling·Nature ProtocolsPeer-reviewed·Oct 10, 2023