DOI: 10.5061/dryad.brv15dvj1
title: Demo dataset for: SPACEc, a streamlined, interactive Python workflow for multiplexed image processing and analysis

description: Multiplexed imaging technologies provide insights into complex tissue
 architectures. However, challenges arise due to software fragmentation
 with cumbersome data handoffs, inefficiencies in processing large images
 (8 to 40 gigabytes per image), and limited spatial analysis capabilities.
 To efficiently analyze multiplexed imaging data, we developed SPACEc, a
 scalable end-to-end Python solution, that handles image extraction, cell
 segmentation, and data preprocessing and incorporates
 machine-learning-enabled, multi-scaled, spatial analysis, operated through
 a user-friendly and interactive interface. The demonstration dataset was
 derived from a previous analysis and contains TMA cores from a human
 tonsil and tonsillitis sample that were acquired with the Akoya
 PhenocyclerFusion platform. The dataset can be used to test the workflow
 and establish it on a user’s system or to familiarize oneself with the
 pipeline.
description: Tissue samples: Tonsil cores were extracted
 from a larger multi-tumor tissue microarray (TMA), which included a total
 of 66 unique tissues (51 malignant and semi-malignant tissues, as well as
 15 non-malignant tissues). Representative tissue regions were annotated on
 corresponding hematoxylin and eosin (H&E)-stained sections by a
 board-certified surgical pathologist (S.Z.). Annotations were used to
 generate the 66 cores each with cores of 1mm diameter. FFPE tissue blocks
 were retrieved from the tissue archives of the Institute of Pathology,
 University Medical Center Mainz, Germany, and the Department of
 Dermatology, University Medical Center Mainz, Germany. The multi-tumor-TMA
 block was sectioned at 3µm thickness onto SuperFrost Plus microscopy
 slides before being processed for CODEX multiplex imaging as previously
 described. CODEX multiplexed imaging and
 processing To run the CODEX machine, the slide was
 taken from the storage buffer and placed in PBS for 10 minutes to
 equilibrate. After drying the PBS with a tissue, a flow cell was sealed
 onto the tissue slide. The assembled slide and flow cell were then placed
 in a PhenoCycler Buffer made from 10X PhenoCycler Buffer \&
 Additive for at least 10 minutes before starting the experiment. A 96-well
 reporter plate was prepared with each reporter corresponding to the
 correct barcoded antibody for each cycle, with up to 3 reporters per cycle
 per well. The fluorescence reporters were mixed with 1X PhenoCycler
 Buffer, Additive, nuclear-staining reagent, and assay reagent according to
 the manufacturer's instructions. With the reporter plate and
 assembled slide and flow cell placed into the CODEX machine, the automated
 multiplexed imaging experiment was initiated. Each imaging cycle included
 steps for reporter binding, imaging of three fluorescent channels, and
 reporter stripping to prepare for the next cycle and set of markers. This
 was repeated until all markers were imaged. After the experiment, a
 .qptiff image file containing individual antibody channels and the DAPI
 channel was obtained. Image stitching, drift compensation, deconvolution,
 and cycle concatenation are performed within the Akoya PhenoCycler
 software. The raw imaging data output (tiff, 377.442nm per pixel for 20x
 CODEX) is first examined with QuPath software (https://qupath.github.io/)
 for inspection of staining quality. Any markers that produce unexpected
 patterns or low signal-to-noise ratios should be excluded from the ensuing
 analysis. The qptiff files must be converted into tiff files for input
 into SPACEc. Data preprocessing includes image stitching, drift
 compensation, deconvolution, and cycle concatenation performed using the
 Akoya Phenocycler software. The raw imaging data (qptiff, 377.442 nm/pixel
 for 20x CODEX) files from the Akoya PhenoCycler technology were first
 examined with QuPath software (https://qupath.github.io/) to inspect
 staining qualities. Markers with untenable patterns or low signal-to-noise
 ratios were excluded from further analysis. A custom CODEX analysis
 pipeline was used to process all acquired CODEX data (scripts available
 upon request). The qptiff files were converted into tiff files for tissue
 detection (watershed algorithm) and cell segmentation.
description: # SPACEc multiplexed imaging analysis pipeline: Example dataset ##
 Descriptions ### tonsil\_TMA.tif Example tif file as input for the tissue
 extraction and segmentation. The image shows two TMA cores from a human
 tonsil (right) and tonsillitis sample (left). The image was acquired on
 the Akoya PhenoCycler Fusion platform. The tissue was stained with a 58
 oligo-tagged antibody panel and DAPI. ### channelnames.txt Text file that
 contains the channel names for the tif file. This file is used as input
 for image segmentation along with the image. ###
 adata\_nn\_demo\_tonsil.h5ad An Anndata object is saved as h5ad file. The
 data were generated with SPACEc. Therefore the example image was segmented
 using mesmer and intensities normalized using Z-score normalization. Data
 were filtered as outlined in our example workflow (see GitHub). The object
 holds the normalized intensity data for each cell in the X slot and all
 metadata including the cell centroid coordinates in the adata.obs slot.
 The Anndata object contains previously annotated data as a reference for
 cell-type annotation using SVM-based annotation. ## Description of the
 data and file structure The folder structure is shown below ❖    Data    
     ➢   raw               ■      tonsil_tma.tiff               ■     
 channelNames.txt        ➢   processed               ■     
 adata_nn_demo_tonsil.h5ad The most common approach to visualize TIFF is
 through the use of FIJI (ImageJ). However, QuPath and Python also offer
 functionality for opening large, stacked TIFF files. The h5ad file format
 can be opened in Python using the Scanpy package, which provides
 user-friendly functions to interact with single-cell data in the Anndata
 format. Details can be found
 at [https://github.com/yuqiyuqitan/SPACEc.](https://github.com/yuqiyuqitan/SPACEc)  For opening .h5ad file in python ``` # open a terminal pip install scanpy python # within the python import scanpy as sc adata = sc.read(adata_nn_demo_tonsil.h5ad) ``` ## Code/Software The Anndata object was created using SPACEc 0.0.8. Further information on how to use the example dataset and demonstration notebooks can be found here: https://spacec.readthedocs.io/en/stable/readme.html