Deconvolution of the tumor-educated platelet transcriptome reveals activated platelet and inflammatory cell transcript signatures [CITE-seq]

Tumor-educated platelets (TEPs) are a potential method of liquid biopsy for the diagnosis and monitoring of cancer. However, the mechanism underlying tumor education of platelets is not known, and transcripts associated with TEPs are often not tumor-associated transcripts. We demonstrate that direct tumor transfer of transcripts to circulating platelets is an unlikely source of the TEP signal. We use CDSeq, a latent Dirichlet allocation algorithm, to deconvolute the TEP signal in blood samples from patients with glioblastoma. We demonstrate that a significant proportion of transcripts in the platelet transcriptome are derived from non-platelet cells, and the use of this algorithm allows the removal of contaminant transcripts. Furthermore, we used the results of this algorithm to demonstrate that TEPs represent a subset of more activated platelets, which also contain transcripts normally associated with non-platelet inflammatory cells, suggesting that these inflammatory cells, possibly in the tumor microenvironment, transfer transcripts to platelets that are then found in circulation. Our analysis suggests a useful and efficient method of processing TEP transcriptomic data to enable the isolation of a unique TEP signal associated with specific tumors. GS 8-11 glioblastoma sphere cells (1 × 105 per mouse) were implanted into the brains of 6- to 8-week-old Foxn1nu mice, one week after guide screw placement, using a stereotactic apparatus under anesthesia. A minimum of five mice were included in each group. Animals in the treatment group that showed signs of distress or were moribund were euthanized and blood was collected using an 18-gauge needle by cardiac puncture. Five weeks after tumor implantation, approximately 1 ml of blood was collected from each mouse in a BD Vacutainer yellow-top tube containing acid citrate dextrose to prevent platelet activation. Platelet-rich plasma (PRP) was collected by centrifuging the tubes at 100 × g for 15 min. The centrifuge was set to accelerations of 5 and 2 to avoid platelet activation. PRP was separated carefully from the other components of the blood and centrifuged at 1000 × g for 10 min after the addition of 2 µL of 1 mM PGE2 to 2 ml of PRP. RNA was isolated from the resulting platelet pellets using the mirVana™ miRNA Isolation Kit (Invitrogen, USA, Cat #AM1560), followed by standard RNA-seq library preparation and paired-end sequencing using NextSeq 2000 (Illumina). FASTQ files were processed using Trimmomatic version 0.36, STAR (using the hg38 genome as reference), and HTSeq. The DSVD lung adenocarcinoma mouse model was intranasally induced with Ad-CMV-Cre (5 × 107 plaque-forming units) as previously described. Approximately 1 ml of blood was collected from each mouse by cardiac puncture using an 18 g needle, three weeks after tumor implantation. The samples were stained with a panel of 13 barcoded CITE-seq-compatible antibodies. Briefly, a maximum of 1 million cells per sample were resuspended in 100 µL of cell staining buffer (2% BSA / 0.01% Tween, PBS) with 10 µL of Fc receptor block (TrueStain FcX, BioLegend, USA) for 10 min. This was followed by a 30-minute staining with the antibodies at 4°C. A concentration of 1 µg / 100 µl was used for all the antibody markers used in this study. The cells were then washed three times with 1 ml of staining buffer, followed by centrifugation (350 × g for 5 min at 4°C) (50). Stained cells were then used for single-cell reverse transcription using 10X Genomics and libraries were prepared as previously described (32). Briefly, cDNA amplification was performed in the presence of an antibody oligo-specific primer to increase the yield of antibody-derived tags (ADTs). The amplified cDNA was separated by SPRI size selection into cDNA fractions containing mRNA-derived cDNA (>300bp) and ADT-derived cDNAs (<180bp). Sequencing libraries were generated from the mRNA and ADT cDNA fractions, which were quantified, pooled, and sequenced on an Illumina NovaSeq platform (Illumina). CITE-seq data were processed using Seurat v.4 pipeline (51). Platelets were selected from the single-cell dataset by visual inspection of the UMAP plot of the total data set, selecting the cell cluster that contained high levels of platelet markers including Ppbp and Pf4, then removing cells with >2% ribosomal protein transcripts, >2% hemoglobin transcripts, >10% mitochondrial mRNA transcripts, or increased levels of Malat1. Platelet datasets from normal mice and mice with implanted tumors were integrated using the Seurat pipeline. The integrated data were then processed using the standard Seurat pipeline, with markers of Seurat clusters found using the FindConservedMarkers function.