Published January 22, 2026 | Version 0.1.0
Other Open

Imaging and Flow Cytometry data for "Efficient profiling of total RNA in single cells with STORM-seq"

Authors/Creators

  • 1. ROR icon Van Andel Institute

Contributors

  • 1. ROR icon Van Andel Institute

Description

Summary

The following release contains the raw CyCIF imaging data (zarr format) and flow cytometry (.fcs) files supporting the manuscript entitled "Efficient profiling of total RNA in single cells with STORM-seq". Related code is also found at https://github.com/biobenkj/stormseq_protocols.

Utilization of Zarr files with QuPath classifiers

Import Zarr arrays into QuPath (recommend 6.0 or greater) and begin analysis as follows:

Using Existing QuPath Classifiers

An existing QuPath project with images and detections (not cell objects) is required to use the object classifiers. The objects additionally need to have the same measurements with the same channel names. Alternatively, edit channel names in classifier json to match channel names in project.


Required measurements
 
- CD31: Mean
- CD31: Median
- CD31: Min
- CD31: Max
- CD31: Std. Dev.
- CD45: Mean
- CD45: Median
- CD45: Min
- CD45: Max
- CD45: Std. Dev.
- PanCK: Mean
- PanCK: Median
- PanCK: Min
- PanCK: Max
- PanCK: Std. Dev.

How to use


Copy the json files of the object classifiers (or whole folder) into the classifier folder of the QuPath project. The classifier can now be used through the GUI in `Classify > Object Classifier > Load Object Classifier` or through scripting using the command `runObjectClassifier("name of classifier")`.

Full script to create detections and run classifier can be found in `CellPose.groovy`.

The CellPose python environment and QuPath extension are required for using the full script. Links to resources below:

[QuPath 6.0](https://qupath.github.io/)
[CellPose](https://github.com/MouseLand/cellpose)
[CellPose QuPath Extension](https://github.com/BIOP/qupath-extension-cellpose)

Disclaimer 
The object classifiers were specifically trained on the CellDIVE dataset included in the Johnson, B.K. and Majewski, M.F. et.al. publication. This model may not directly transfer to data collected from different microscopes, different staining protocols, or different imaging parameters for other samples stained for CD31, CD45, and/or PanCK. We recommend training your own object classifier for your own imaging data using the same approach described in the methods section. 

Van Andel Institute Optical Imaging Core - RRID:SCR_021968

 

Flow cytometry data (.fcs files and gating schemes)

The raw .fcs files are included for both the STORM-seq sorts (donors 1 and 2) as well as the matched bulk analysis supporting the CyCIF results (patient 2824, 2829, and 2830). Gating schemes are provided for each sample as either a pdf or png screenshot to reconstruct gating strategy in FlowJo or related software capable of processing .fcs files.

Van Andel Institute Flow Cytometry - RRID:SCR_022685

Files

cycif_storm_processing.zip

Files (104.4 GB)

Name Size Download all
md5:9f86544eaa0047c0ed6366cc9d5afc89
41.7 kB Preview Download
md5:58a50a30972b4e0af62989fe2caa6401
104.3 GB Download
md5:76f24a657a3fa18079bd6e32393bcda0
113.0 MB Preview Download

Additional details

Related works

Is supplement to
Preprint: 10.1101/2022.03.14.484332 (DOI)

Funding

National Institutes of Health
R37CA230748
Ovarian Cancer Research Alliance
MIG-891749
Gray Foundation

Software

Repository URL
https://github.com/biobenkj/stormseq_protocols/blob/main/analysis/stormseq_manuscript/cycif
Programming language
Groovy , Markdown , JSON
Development Status
Active