DOI: 10.5061/dryad.rjdfn2zkg
title: Microenvironment characteristics and molecular classification in pheochromocytoma patients

description: Pheochromocytomas (PCCs) are rare neuroendocrine tumors that originate
 from chromaffin cells in the adrenal gland. However, the cellular
 molecular characteristics and immune microenvironment of PCCs are
 incompletely understood. Here, we performed single-cell RNA sequencing
 (scRNA-seq) on 16 tissues from 4 sporadic unclassified PCC patients and 1
 hereditary PCC patient with Von Hippel-Lindau (VHL) syndrome. We found
 that intra-tumoral heterogeneity was less extensive than the
 inter-individual heterogeneity of PCCs. Further, the unclassified PCC
 patients were divided into two types, metabolism-type (marked by NDUFA4L2
 and COX4I2) and kinase-type (marked by RET and PNMT), validated by
 immunohistochemical staining. Trajectory analysis of tumor evolution
 revealed that metabolism-type PCC cells display phenotype of consistently
 active metabolism and increased metastasis potential, while kinase-type
 PCC cells showed decreased epinephrine synthesis and neuron-like
 phenotypes. Cell-cell communication analysis showed activation of the
 annexin pathway and a strong inflammation reaction in metabolism-type PCCs
 and activation of FGF signaling in the kinase-type PCC. Although
 multispectral immunofluorescence staining showed a lack of CD8+ T
 cell infiltration in both metabolism-type and kinase-type PCCs, only the
 kinase-type PCC exhibited downregulation
 of HLA-Ⅰ molecules that possibly regulated
 by RET, suggesting the potential of combined therapy with kinase
 inhibitors and immunotherapy for kinase-type PCCs; in contrast, the
 application of immunotherapy to metabolism-type PCCs (with antigen
 presentation ability) is likely unsuitable. Our study presents a
 single-cell transcriptomics-based molecular classification and
 microenvironment characterization of PCCs, providing clues for potential
 therapeutic strategies to treat PCCs.
description: The collected tumor and adjacent specimens from PCC patients were
 digested into single cell suspensions, and 3'-scRNA-seq (Chromium
 Single Cell 3′ v3 Libraries) analysis was performed on each
 sample.
description: # Microenvironment characteristics and molecular classification in
 pheochromocytoma patients
 [https://doi.org/10.5061/dryad.rjdfn2zkg](https://doi.org/10.5061/dryad.rjdfn2zkg) The collected tumor and adjacent specimens from pheochromocytoma (PCC) patients were digested into single cell suspensions, and 3'-scRNA-seq (Chromium Single Cell 3′ v3 Libraries) analysis was performed on each sample. ## Description of the data and file structure Five PCC patients were included. We collected 1 resected tumor specimen from P1 (P1_T1), 3 specimens from distinct intra-tumoral sites from P2 (P2_T1, P2_T2, and P2_T3) or P5 (P5_T1, P5_T2, and P5_T3), and 2 specimens from distinct intra-tumoral sites from P3 (P3_T1 and P3_T2), or P4 (P4_T1 and P4_T2). To enable comparisons, one normal adrenal medullary tissue adjacent to the tumor was collected from each patient (P1_A, P2_A, P3_A, P4_A, and P5_A). The fresh tumor specimens were stored in the tissue preservation solution (JSENB) and washed with Hanks Balanced Salt Solution (HBSS, HyClone) for 3 times and minced into 1-2 mm pieces. Then the tissue pieces were digested with 2 ml tissue dissociation solution at 37℃ for 15 min in 15 ml centrifuge tube with sustained agitation. After digestion, using 40 mm sterile strainers to filter the samples and centrifuging the samples at 1,000 rpm for 5 min. Then the supernatant was discarded, and the sediment was resuspended in 1 ml phosphate buffered saline (PBS, Solarbio). To remove the red blood cells, 2 ml red blood cell lysis buﬀer (BD) was added at 25°C for 10 min. The solution was then centrifuged at 500 g for 5 min and suspended in PBS. The cells were stained with trypan blue (Sigma) and microscopically evaluated. Utilizing the 10x Genomics Chromium Single Cell 3′ v3 Library Kit and Chromium instrument, approximately 17,500 cells were partitioned into nanoliter droplets to achieve single-cell resolution for a maximum of 10,000 individual cells per sample. The resulting cDNA was tagged with a common 16 nt cell barcode and 10 nt Unique Molecular Identifier during the RT reaction. Full-length cDNA from poly-A mRNA transcripts was enzymatically fragmented and size selected to optimize the cDNA amplicon size (approximately 400 bp) for library construction (10x Genomics). The concentration of the 10x single-cell library was accurately determined through qPCR (Kapa Biosystems) to produce cluster counts appropriate for the HiSeq4000 or NovaSeq6000 platform (Illumina). In all, 26 × 98 bp (3′ v3 libraries) sequence data were generated targeting between 25 and 50K read pairs/cell, which provided digital gene expression profiles for each individual cell. For single-cell RNA-seq analysis, we used CellRanger (10x Genomics, v.6.1.2) to pre-process the single-cell RNA-seq data after obtaining the paired-end raw reads. Cell barcodes and unique molecular identifiers (UMIs) of the library were extracted from read 1. Then, the reads were split according to their cell (barcode) IDs, and the UMI sequences from read 2 were simultaneously recorded for each cell. Quality control on these raw readings was subsequently performed to eliminate adapter contamination, duplicates, and low-quality bases. After filtering barcodes and low-quality readings that were not related to cells, we mapped the cleaned readings to the human genome (GRCh38) and retained the uniquely mapped readings for UMIs counts. Next, we estimated the accurate molecular counts and generated a UMI count matrix for each cell by counting UMIs for each sample. Finally, we generated a gene-barcode matrix that showed the barcoded cells and gene expression counts. The dataset includes files, each of which is for creating a Seurat object in R. ## Sharing/Access information Human transcriptome reference used for our analysis is available at 10x Genomics website ([https://cf.10xgenomics.com/supp/cell-exp/refdata-cellranger-GRCh38-3.0.0.tar.gz);](https://cf.10xgenomics.com/supp/cell-exp/refdata-cellranger-GRCh38-3.0.0.tar.gz\);) the code used for scRNA-seq analysis is available at 10x Genomics website ([https://www.10xgenomics.com/support/software/cell-ranger/latest/analysis/running-pipelines/cr-gex-count](https://www.10xgenomics.com/support/software/cell-ranger/latest/analysis/running-pipelines/cr-gex-count)) and at GitHub website ([https://github.com/satijalab/seurat/releases/tag/v4.0.2).](https://github.com/satijalab/seurat/releases/tag/v4.0.2\).) ## Code/Software The single-cell RNA sequencing data was processed using cellranger (v.6.1.2) and analyzed with the R package Seurat (v.4.0.2).