DOI: 10.5061/dryad.vt4b8gtz9
title: CD45+ cells from human bladder cancer specimens

description: 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.
description: 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<sup>+</sup> 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.
description: ### CD45+ cells from human bladder cancer specimens Joshua Wong This
 README file was generated on 19/10/2023 1. 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. 2. Date of data collection 2022-2023 3.
 Geographic location of data collection: Brisbane, Australia 4. Author
 Information A. Principal Investigator Contact Information Name: A/Prof.
 Fernando Guimaraes Institution: The University of Queensland, AUS Email:
 f.guimaraes@uq.edu.au 5. Associate or Co-investigator Contact Information
 Name: Joshua Wong Institution: The University of Queensland, AUS Email:
 joshua.wong@uq.net.au ### DATA & FILE OVERVIEW 1. 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. 2. 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 3.
 Additional related data collected that was not included in the current
 data package: None 4. 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 1. use the function readMM read in mtx.file example B3\_mat
 &lt;- readMM(paste0(B3\_DGE\_folder, "B3\_DGE.mtx")) 2. Use
 read.delim to read in CSV files. Examples B3\_cell\_meta &lt;-
 read.delim(paste0(B3\_DGE\_folder, "B3\_cell\_metadata.csv"),
 stringsAsFactor = FALSE, sep = ",") B3\_genes &lt;-
 read.delim(paste0(B3\_DGE\_folder, "B3\_all\_genes.csv"),
 stringsAsFactor = FALSE, sep = ",") 3. For blood or tumour
 samples subset: location &lt;- "blood"
 B3\_cell\_meta["location"] &lt;- 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 (&gt; 5%) and low number of genes detected (&lt;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