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

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