DOI: 10.5061/dryad.k0p2ngfcc
title: CODEX multiplexed imaging of immunotherapy in human and mouse melanomas

description: Our research used CODEX (Co-Detection by Indexing) multiplexed imaging to
 gain insights into melanoma tumors in both murine models and human
 samples. CODEX imaging involves an iterative process of annealing and
 stripping fluorophore-labeled oligonucleotide barcodes, complementing the
 barcodes attached to over 40 antibodies used for tissue staining.
 Subsequently, images underwent standard CODEX image processing (tile
 stitching, drift compensation, cycle concatenation, background
 subtraction, deconvolution, and determination of best focal plane), single
 cell segmentation, and column marker z-normalization by tissue. Our
 datasets comprise individual cells as rows, each characterized by 40+
 antibody fluorescence values quantified from various markers evaluated for
 each study. These markers correspond to the antibodies targeting specific
 proteins within the tissue, quantified at the single-cell level. The
 values represent per-cell/area-averaged fluorescent intensities,
 z-normalized along each column. Each cell is mapped with its cell type and
 cellular neighborhood, defined by x and y coordinates representing pixel
 locations in the original image. Refer to the table in the "Usage
 Notes" section below for further details. The CODEX multiplexed
 imaging data is organized into three distinct files, each representing key
 aspects of our research and the studies detailed in our manuscript. We
 then used this data investigate the major cellular organization of the
 tumor sections we imaged, with downstream spatial statistics and analyses
 like cellular neighborhood analysis and cell-cell interaction analysis.
 These data could be used to understand the cellular interactions,
 composition, and structure of anti-tumor melanoma responses induced by
 antigen-specific immunotherapy either with adoptive T cell transfer for
 checkpoint blockade immunotherapy. These datasets offer valuable insights
 for researchers interested in anti-tumor microenvironments, immune
 responses, and therapeutic interventions such as T cell therapies. 1.
 Time-course of tumor microenvironment following antigen-specific T cell
 therapy in mice We investigate the dynamic interplay between immune
 responses, antigen-specific T cell interactions, and tumor progression in
 a murine melanoma model. We activated PMEL CD8+ T cells with cognate
 antigen gp100 and IL-2 for 10 days ex vivo and transferred into mice with
 established B16-F10 tumors. Tumors were harvested and imaged with CODEX
 imaging at 0-, 1-, 3-, 5-, and 12-days post-treatment (n=3-7 per time
 point). Our 42-plex CODEX antibody panel characterizes immune cell types,
 T cell phenotypes, stromal cell types, and tumor cell phenotypes,
 resulting in a rich dataset of 1,052,125 cells across 42 marker channels.
 2. Tumor microenvironment following antigen-specific T cell therapies with
 different phenotypes in mice We delve deeper into the modulation of the
 tumor microenvironment by manipulating T cell phenotypes. By comparing
 activated T cells stimulated with and without 2-hydroxycitrate (2HC), a
 metabolic inhibitor of acetyl CoA production, we explore the impact of
 phenotype on tumor progression. Our datasets from mice treated with 2HC T
 cells or T cells provide insights into the role of T cell phenotype
 manipulation in the tumor microenvironment (n=4-7 per group). 3. Tumor
 microenvironment before and after checkpoint blockade in human melanoma
 patients of both responders and non-responders Our research extends to
 human melanoma patients with advanced, metastatic, stage IV tumors. We
 examine 12 FFPE tumor samples from six patients, each with samples taken
 before and after checkpoint inhibitor therapy. Our CODEX multiplexed
 imaging, using a panel of 58 antibodies, reveals changes in immune,
 stromal, and tumor compartments. We segmented 5,019,159 individual cells
 from the 12 CODEX images, facilitating unsupervised clustering to identify
 39 major cell types based on their expression profiles. Our accompanying
 donor metadata table links donor IDs to essential clinical information,
 including treatment response, demographics, and sample details.
description: For a detailed description of each of
 the steps of protocols and processes to obtain this data see the detailed
 materials and methods in the associated manuscript. Briefly, murine
 melanomas were extracted from sacrificed mice at indicated timepoint post
 therapy and frozen in OCT. These were assembled into an array of 4-10
 tissues, cut into 7 um slices, and stained with a panel of 40+ CODEX
 DNA-oligonucleotide barcoded antibodies. Tissues were imaged with a
 Keyence microscope at 20x objective and then processed using image
 stitching, drift compensation, deconvolution, and cycle concatenation. For
 human melanoma samples, we took available tumor tissue and cut with a 3µm
 section thickness and mounted onto Superfrost PLUS slides for further
 staining with a panel of 58 CODEX DNA-oligonucleotide barcoded antibodies
 imaged with the Phenocycler Fusion machine and processed using
 manufacturer’s v1.6.0 (e.g., background-subtracted, stitched, and shading
 correction). Processed data
 were then segmented using CellVisionSegmenter, a neural network
 R-CNN-based single-cell segmentation algorithm. Cell type analysis was
 completed on B004, 5, and 6 by z normalization of protein markers used for
 clustering and then overclustered using leiden-based clustering. The cell
 type labels were verified by looking back at the original image. With set
 cell type labels we performed neighborhood analysis by clustering windows
 of the 10 nearest neighbors around a given cell and were named based on
 cell type enrichment and location in the tissue. Broad categories for cell
 types, neighborhoods, and communities were expert annotated based on
 epithelial, immune, or other stromal compartments.
description: # CODEX multiplexed imaging of immunotherapy in human and mouse melanomas
 Our research used CODEX
 (Co-Detection by Indexing) multiplexed imaging to gain insights into
 melanoma tumors in both murine models and human samples. CODEX imaging
 involves an iterative process of annealing and stripping
 fluorophore-labeled oligonucleotide barcodes, complementing the barcodes
 attached to over 40 antibodies used for tissue staining. Subsequently,
 images underwent standard CODEX image processing (tile stitching, drift
 compensation, cycle concatenation, background subtraction, deconvolution,
 and determination of best focal plane), single cell segmentation, and
 column marker z-normalization by tissue. ## Description of the data and
 file structure Our datasets comprise individual cells as rows, each
 characterized by 40+ antibody fluorescence values quantified from various
 markers evaluated for each study. These markers correspond to the
 antibodies targeting specific proteins within the tissue, quantified at
 the single-cell level. The values represent per-cell/area-averaged
 fluorescent intensities, z-normalized along each column. Each of these
 antibodies are described in detail in the Supplementary Table 2 of the
 published manuscript. Each cell is mapped with its cell type and cellular
 neighborhood, defined by x and y coordinates representing pixel locations
 in the original image. We then used this data investigate the major
 cellular organization of the tumor sections we imaged, with downstream
 spatial statistics and analyses like cellular neighborhood analysis and
 cell-cell interaction analysis. These data could be used to understand the
 cellular interactions, composition, and structure of anti-tumor melanoma
 responses induced by antigen-specific immunotherapy either with adoptive T
 cell transfer for checkpoint blockade immunotherapy. These datasets offer
 valuable insights for researchers interested in anti-tumor
 microenvironments, immune responses, and therapeutic interventions such as
 T cell therapies. Specifics about each dataset and specific columns is
 found below: **1. Time-course of tumor microenvironment following
 antigen-specific T cell therapy in mice** **Filename:
 23_07_04_day013512_neighborhoods.csv** Tumors were harvested and imaged
 with CODEX imaging at 0-, 1-, 3-, 5-, and 12-days post-T cell therapy
 (n=3-7 per time point). Our CODEX antibody panel characterizes immune cell
 types, T cell phenotypes, stromal cell types, and tumor cell phenotypes,
 resulting in a rich dataset of 1,052,125 cells across marker channels.
 Antibody panels were not consistent across all experiments, hence why some
 markers have Nan values within the columns after concatenation of the
 data. Which antibodies were used for which experiment is also described in
 detail in the Supplementary Table 2 of the manuscript. We opted to include
 these extra markers (even though there is missing data and these were not
 used in clustering, for potential use in the future). All other
 descriptive columns are described below: | x | Tissue x position in each
 region imaged | | --------------------- |
 ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | | y | Tissue y position in each region imaged | | array | Experiment group for each set of samples | | date\_array | Tissue array from which each region was imaged | | Xcorr | Corrected x position in each array imaged | | Ycorr | Corrected y position in each array imaged | | replicate | Which replicate of the treated mice the cells came from | | unique\_region | Label for unique region from both day\_replicate and region (can be used for x, y coordinates) | | region\_num | Number region from initial imaging data | | day harvested | Day that the tumors were taken from mice | | Cell common | Cell type labels used for the paper analysis | | neigh\_name\_all | Neighborhood labels used for the paper analysis (done on unified dataset) | | prior\_index | Original cell index from previous experiments | | original cell type | Cell type labels that were created on a per experiment basis that were later merged into 'Cell common' for comparison between experiments | | original neighborhood | Neighborhood labels that were created on a per experiment basis that were later merged into 'Neighborhood common' for comparison between a subset of experiments | | Neighborhood common | Neighborhood labels from subset of the data taken together | **2. Tumor microenvironment following antigen-specific T cell therapies with different phenotypes in mice** **Filename: Day3_Markers_Dryad.csv** We compared activated T cells stimulated with and without 2-hydroxycitrate (2HC), a metabolic inhibitor of acetyl CoA production as a therapy for established melanomas in mice. Our datasets from mice treated with 2HC T cells or T cells provide insights into the role of T cell phenotype manipulation in the tumor microenvironment (n=4-7 per group). Our CODEX antibody panel characterizes immune cell types, T cell phenotypes, stromal cell types, and tumor cell phenotypes, resulting in a rich dataset of 520,385 cells across marker channels. Antibody panels were not consistent across all experiments, hence why some markers have Nan values within the columns after concatenation of the data. Which antibodies were used for which experiment is also described in detail in the Supplementary Table 2 of the manuscript. We opted to include these extra markers (even though there is missing data and these were not used in clustering, for potential use in the future). All other descriptive columns are described below: | x | Tissue x position in each region imaged | | --------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | | y | Tissue y position in each region imaged | | date\_array | Tissue array from which each region was imaged | | x\_array | Corrected x position in each array imaged | | y\_array | Corrected y position in each array imaged | | treatment | What treatment condition the mice received | | replicate | Which replicate of the treated mice the cells came from | | unique\_region | Label for unique region from both day\_replicate and region (can be used for x, y coordinates) | | region\_num | Number region from initial imaging data | | day harvested | Day that the tumors were taken from mice | | experiment | Which experiment the samples were from | | tissue | What type of tissue was imaged | | Cell common | Cell type labels used for the paper analysis | | Cell cat | Major cell categories | | Major cat | Immune vs. non-immune cell category label for cells | | prior\_index | Original cell index from previous experiments | | original cell type | Cell type labels that were created on a per experiment basis that were later merged into 'Cell common' for comparison between experiments | | original neighborhood | Neighborhood labels that were created on a per experiment basis that were later merged into 'Neighborhood common' for comparison between a subset of experiments | | Neighborhood common | Neighborhood labels used for the paper analysis | **3. Tumor microenvironment before and after checkpoint blockade in human melanoma patients of both responders and non-responders** **Filename: 23_09_11_Melanoma_Marker_Cell_Neighborhoods.csv** **Metadata Filename: metadata.csv** We examined 12 FFPE tumor samples from six patients with advanced, metastatic, stage IV tumors, each with samples taken before and after checkpoint inhibitor therapy. Our CODEX multiplexed imaging, using a panel of 58 antibodies, reveals changes in immune, stromal, and tumor compartments. We segmented 5,019,159 individual cells from the 12 CODEX images, facilitating unsupervised clustering to identify 39 major cell types based on their expression profiles. Our accompanying donor metadata table links donor IDs to essential clinical information, including treatment response, demographics, and sample details. Antibody panels were not consistent across all experiments, hence why some markers have Nan values within the columns after concatenation of the data. We opted to include these extra markers (even though there is missing data and these were not used in clustering, for potential use in the future). All other descriptive columns are described below: | x | Tissue x position in each region imaged | | ------------------- | ---------------------------------------------------------------------------------------- | | y | Tissue y position in each region imaged | | cellid | Original segmentation label for the cell | | donor | Unique donor label that connects with the metadata csv also attached | | Overall\_Cell\_Type | Lowest resolution labels of cell types | | Cell\_Type\_Common | 2nd lowest resolution labels of cell types | | Cell\_Type\_Sub | 3rd lowest resolution labels of cell types | | Cell\_Type | 4th lowest resolution labels of cell types and original cell type labels from clustering | | filename | Unique label for individual CODEX Phenocylcer runs | | region | Number region from initial imaging data | | Neighborhood | Neighborhood labels used for the paper analysis | ## Sharing/Access information N/a ## Code/Software N/a