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Dryad

CODEX multiplexed imaging of immunotherapy in human and mouse melanomas

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Dec 21, 2023 version files 6.27 GB

Abstract

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.