Processed CODEX multiplexed imaging data of cellular microenvironment around T cell stimulating hydrogels
Data files
Jan 26, 2024 version files 206.07 MB
-
20231127_Gels_CellTypeAnnotated.csv
206.07 MB
-
README.md
3.41 KB
Abstract
Our research used CODEX (Co-Detection by Indexing) multiplexed imaging to gain insights into the cellular microenvironment surrounding T cell stimulating hydrogels. These hydrogels were engineered with signals that could locally expand antigen-specific T cells for use in tumor immunotherapy. 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, defined by x and y coordinates representing pixel locations in the original image.
We then used this data to investigate how different proportions of the cell types change over time in response to the stimulating hydrogel injection with antigen-specific T cells. These data could be used to understand the cellular interactions, composition, and structure of T cell stimulating biomaterials for antigen-specific immunotherapy and with adoptive T cell transfer. These datasets offer valuable insights for researchers interested in engineering T cell stimulating microenvironments, immune responses, and therapeutic interventions such as T cell therapies.
We investigate the dynamic interplay between immune responses, antigen-specific T cell interactions, and hydrogel environment in a murine melanoma model. We injected antigen-specific T cells with microparticle T cell stimulating hydrogels into mice subcutaneously. Injection sites were take out at different time points day=0 (just after injection), day=3, and day=9 (n=3-6 per time point). Our 51-plex CODEX antibody panel characterizes immune cell types, T cell phenotypes, and stromal cell types, resulting in a rich dataset of 241,685 cells across 51 marker channels.
https://doi.org/10.5061/dryad.pc866t1wz
Our research used CODEX (Co-Detection by Indexing) multiplexed imaging to gain insights into the microenvironment in and surrounding bioengineered hydrogels meant to stimulate antigen-specific T cell responses in situ. 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 of the manuscript. Each cell is mapped with its cell type and 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 hydrogel microenvironments we imaged, with downstream analysis of composition from various subgroups of cells. These data could be used to understand the cellular interactions, composition, and structure of responses induced by antigen-specific T cell stimulating hydrogels or biomaterials with adoptive T cell transfer. These datasets offer valuable insights for researchers interested in immunotherapies with both biomaterials and T cell therapies.
Filename: 20231127_Gels_CellTypeAnnotated.csv\
Hydrogel injection sites were harvested and imaged with CODEX imaging at 0-, 3-, and 9-days post-T cell therapy (n=3-6 per time point). Our CODEX antibody panel characterizes immune cell types, T cell phenotypes, and stromal cell types, resulting in a rich dataset of 241,685 cells across marker channels. 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 |
| region_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 |
| cell_type | Cell type labels used for the paper analysis |
| region | Number region from initial imaging data |
| day | Day that the injection sites were taken from mice |
| cell_id | Original cell index from segmentation |
Sharing/Access information
N/A
Code/Software
N/A
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 injection sites were extracted from sacrificed mice at indicated timepoint post therapy and frozen in OCT. These were assembled into an array of 4-7 tissues, cut into 7 um slices, and stained with a panel of 40+ CODEX DNA-oligonucleotide barcoded antibodies. Tissues were imaged with a Phenocycler Fusion imaging system from Akoya Biosciences 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 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.