CD45+ cells from human bladder cancer specimens
Data files
Oct 20, 2023 version files 128.40 MB
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B3_all_genes.csv
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B3_cell_metadata.csv
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B3_DGE.mtx
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B4_all_genes.csv
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B4_cell_metadata.csv
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B4_DGE.mtx
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B5_all_genes.csv
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B5_cell_metadata.csv
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B5_DGE.mtx
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README.md
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T3_all_genes.csv
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T3_cell_metadata.csv
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T3_DGE.mtx
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T4_all_genes.csv
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T4_cell_metadata.csv
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T4_DGE.mtx
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T5_all_genes.csv
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T5_cell_metadata.csv
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T5_DGE.mtx
Abstract
Background
NK cells are important innate defenders against tumours and have unique abilities to recognize and eliminate cancer cells. Responses to targeted antibody therapeutics are typically limited in bladder tumours, and the functional and immunosuppressive phenotypes of NK cells in this disease are largely unknown.
Methods
Single cell RNA sequencing (scRNAseq) and high-dimensional flow cytometry were used to investigate the phenotype of intratumoural NK cells compared to circulating in patients with bladder cancer.
Findings
NK cells residing within bladder tumours had reduced expression of FcγRIIIa/CD16, the critical receptor for NK-cell-mediated ADCC, on both a transcriptional and protein level. Transcriptional signatures of TGF-β-signalling, a pleiotropic cytokine with known immunosuppressive effects on NK cells, were upregulated in tumour NK cells compared to the blood. In concert, a high TGF-β signature expression also correlated with worse survival and CD16 downregulation. We directly validated this TGF-β mediated CD16 downregulation on NK cells in vitro and it was accompanied by a transition to ILC1-like NK cells. We also uncovered a high proportion of tumour infiltrating-Treg cells, and in vivo studies show that NK cells delivered in the presence of accompanying immune cells have a greater reduction of CD16 compared to NK cells delivered alone.
Interpretation
Thus, this study highlights how TGF-β rich bladder cancers could inhibit NK cell ADCC by downregulating CD16, whereby Treg cells could be a major limiter of NK cell effector functions in these tumours.
README
CD45+ cells from human bladder cancer specimens
Joshua Wong
This README file was generated on 19/10/2023
- Collaboration with the Surgical Oncology Unit at the Princess Alexandra Hospital for Bladder Cancer project. in revision in eBioMedicine [TGF-B signalling limits effector function capacity of NK cell anti-tumour immunity in human bladder tumours] EBIOM-D-23-03549.
- Date of data collection 2022-2023
- Geographic location of data collection: Brisbane, Australia
- Author Information A. Principal Investigator Contact Information Name: A/Prof. Fernando Guimaraes Institution: The University of Queensland, AUS Email: f.guimaraes@uq.edu.au
- Associate or Co-investigator Contact Information Name: Joshua Wong Institution: The University of Queensland, AUS Email: joshua.wong@uq.net.au
DATA & FILE OVERVIEW
- File List:
A) B3_DGE.mtx
B) B3_all_genes.csv
C) B3_cell_metadata.csv
D) B4_DGE.mtx
E) B4_all_genes.csv
F) B4_cell_metadata.csv
G) B5_DGE.mtx
H) B5_all_genes.csv
I) B5_cell_metadata.csv
J) T3_DGE.mtx
K) T3_all_genes.csv
L) T3_cell_metadata.csv
M) T4_DGE.mtx
N) T4_all_genes.csv
O) T4_cell_metadata.csv
P) T5_DGE.mtx
Q) T5_all_genes.csv
R) T5_cell_metadata.csv
G) scRNAseq_analysis_FINAL.R
DGE.mtx
is a sparse matrix with cell-gene counts. Each row corresponds to a cell and eachcolumn corresponds to a gene (see "
genes.csv" file for names/gene-id of each column).
all_genes.csv
contains the gene name, gene id, and genome for each column in DGE.mtx.This file is the same as the file in the refrence genome dir.
cell_metadata.csv
file contains information about each cell including the cell barcode,species, sample, well in each round of barcoding, and number of transcript/genes detected.
- Relationship between files, if important:
B3, B4, B5 matrix files are CD45+ samples from patient blood and must be integrated together
T3, T4, T5 matrix files are CD45+ samples from patient tumour and must be integrated together
- Additional related data collected that was not included in the current data package: None
- Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA
METHOD
Single-cell RNA-seq data is pre-processed using the ParseBiosciences-Pipeline. Data normalization, unsupervised cell clustering, and differential expression analysis were carried out by the Seurat R package.
How to use this Script
R version 4.1.0
Seurat Version 4.3.0
1. Reading in Parse matrix and gene count files into R
- use the function readMM read in mtx.file
example
B3_mat <- readMM(paste0(B3_DGE_folder, "B3_DGE.mtx"))
- Use read.delim to read in CSV files.
Examples
B3_cell_meta <- read.delim(paste0(B3_DGE_folder, "B3_cell_metadata.csv"),
stringsAsFactor = FALSE, sep = ",")
B3_genes <- read.delim(paste0(B3_DGE_folder, "B3_all_genes.csv"),
stringsAsFactor = FALSE, sep = ",")
- For blood or tumour samples subset:
location <- "blood"
B3_cell_meta["location"] <- location
head(B3_cell_meta)
Please refer to following tutorial for assistance https://support.parsebiosciences.com/hc/en-us/articles/360053078092-Seurat-Tutorial-65k-PBMCs
2. Seurat_setup.R
Cells with low quality metrics such as high mitochondrial gene content (> 5%) and low number of genes detected (<200) were removed. Cells with transcripts from both hg38 were removed as doublets. RNA counts were log normalized using the standard Seurat workflow (14). To visualize cells based on an unsupervised transcriptomic analysis, we first ran PCA using 2,000 variable genes. The integrated sample counts were scaled, and variable features used for principal-component analysis (PCA). The top 50 principal components from this analysis were then used as an input for dimensionality reduction by Uniform Manifold Approximation and Projection (UMAP). Shared-nearest-neighbour based clustering using the top 50 principal components was used to generate clusters with a resolution = 1.4. After clustering cells, we further filtered out the NK cell cluster using the ‘subset’ function. The top highly variable genes were again selected and PCA was used to find principal components. The top 15 PCs were visualized using UMAP. The signature genes identifying each cluster were found using FindAllMarkers.
This was done following standard seurat workflow https://satijalab.org/seurat/articles/pbmc3k_tutorial.html
3. Single sample GSEA
Single sample gene set enrichment analysis (ssGSEA) was performed using the escape package (v1.8.0). Hallmark gene sets were retrieved from the molecular signature database (MSigDB).
This was done following standard workflow: https://bioconductor.org/packages/devel/bioc/vignettes/escape/inst/doc/vignette.html
Methods
All experiments using human samples were conducted according to approvals by the Metro South Human Research Ethics Committee (clearances #HREC/2019/QMS/55385 and HREC/2022/QMS/89430), which were ratified by the UQ Human Research Ethics Committee. Bladder tumor samples were freshly acquired from the Surgical Oncology Unit at the Princess Alexandra Hospital. After an optimized digestion protocol, single cell suspensions were sorted by flow cytometry of CD45+ cells from blood or tumor samples, they were centrifuged at 200 x g for 10 minutes. Supernatant was discarded and pellets were resuspended in 750 μL of Cell Prefixation Buffer (Parse Biosciences, #PBSSB1001). Samples were then fixed using the Parse Biosciences Cell Fixation (v1) kit (Parse Biosciences, #PBSSB1001), as per the manufacturer’s instructions and stored at -80° until the start of barcoding and library prep with the Evercode Whole Transcriptome Mini (v1) kit.