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. 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.