{"database":"biostudies-arrayexpress","file_versions":[],"scores":null,"additional":{"submitter":["Jasper Panten"],"organism":["Mus musculus"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/E-MTAB-12940"],"description":["Most cancers including pancreatic ductal adenocarcinoma (PDAC) are infiltrated by PNS neurons participating in their complex tumor microenvironment. However, their cell bodies and nuclei are located in the para- and pre-vertebral PNS ganglia located far from the tumor mass itself. Thus, molecular information on healthy organ- vs. cancer-infiltrating neurons is currently lacking in any sequencing dataset of healthy or tumor tissue. To specifically identify and molecularly characterize the identity and transcriptomes of PDAC-infiltrating neurons at single cell resolution, we developed “Trace-n-seq”. This method is based on retrograd tracing of axons from target tissues to their respective ganglia, followed by individual FACS-isolation and transcriptomic analysis. We characterized >2000 sympathetic and sensory neurons that infiltrate PDAC, healthy pancreas, or other abdominal organs."],"repository":["biostudies-arrayexpress"],"sample_protocol":["Library Construction - Whole transcriptome amplification was achieved by addition of KAPA HiFi HotStart ReadyMix (Kapa Biosystems) and IS PCR primer (ISPCR) to the reverse transcription product and amplification on a thermal cycler using the following protocol: 98°C for 3 minutes, followed by 16 cycles of 98°C for 20 s, 67°C for 15 s, 72°C for 6 minutes, followed by a final 5-minute extension at 72°C. cDNA was exemplarily assessed using a High-Sensitivity DNA chip (Agilent Bioanalyzer), confirming the expected size distribution of $1000-2000 bp and to dilute to 0.3ng/ul. Tagmentation reactions were carried out with the Nextera XT DNA Sample Preparation Kit (Illumina) using 300 pg of cDNA per single cell as input, with modified manufacturer’s instructions as described. Libraries were indexed, pooled and purified twice with AMPure XP SPRI beads at a volume ratio of 0.9x, size distribution assessed using a High Sensitivity DNA chip (Agilent Bioanalyzer) and Qubit High-Sensitivity DNA kit (Invitrogen).","Nucleic Acid Extraction - Single-cell libraries were generated according to the SMART-seq2.5 protocol. Briefly, RNA from single-cell lysates in oligo-dT primer (SMART-seq2 30 Oligo-dT Primer), dNTPs (NEB), and RNase inhibitor (Fisher Scientific) were annealed at 72°C for 3 minutes on a thermal cycler. Reverse transcription was carried out in a master mix of Maxima RNaseH-minus RT enzyme and buffer (Fisher Scientific), PEG 50%, H2O, RNase inhibitor, and a 50 template switch oligonucleotide (SMART-seq2 50 TSO) using the following protocol: 42°C for 90 minutes, followed by inactivation at 70°C for 15 minutes.","Sequencing - Libraries were sequenced using NextSeq500/550 High Output v2 kits (75 cycles, Illumina) using single end sequencing.","Sample Collection - Adult mice with retrogradely-labeled neurons were sacrificed cervical dislocation. T5-T13 DRGs and CG were quickly removed without nerves attached. Ganglia were immediately digested with a pre-heated (37°C) digestion mixture as described by Linnarsson et al. In brief, 2.7ml of digestion solution contain 400ul TrypLE Express (Life Technologies), 2000ul Papain (Worthington; 25U/ml in aCSF), 100ul DNase I (Worthington; 1mM in aCSF) and 200ul Collagenase/Dispase (Roche; 20mg/ml in CS). Vybrant dye (Vybrant Ruby XY) and NeuO dye (Stem Cell Technology) was added to the digestion mix. Vybrant Dye incorporates into nucleated cells to stain for live neurons while NeuO is a dye used for in vitro enrichment of neurons. We implemented the dye to further enrich for the neuronal cell population. Ganglia were digested on a heating block at 37°C and shaking for 1.5 h. every 30 min the cell suspension was further mechanically disrupted by pipetting up and down starting with a 1 ml pipette going down to a 200 ul pipette. As soon as all ganglion were dissociated the cell suspensions were filtered using a 40mm cell strainer (FALCON) and collected in a 15ml plastic tube. The digestion solution was diluted with 10 ml RPMI medium containing 5% BSA and 1% FCS and centrifuged at 100 g for 4min at 4°C. The supernatant was removed and the pellet resuspended in 200ul (CG) and 500ul (DRG) RPMI medium containing 5% BSA and 1% FCS. The tissue of interest innervating neurons were facs-sorted pre-gated on nucleated (Vybrant Ruby+) and NeuO+ cells and finally selected for fast blue signal. The cells were sorted in 384 well plates containing 1.2 ul of lysis buffer (0.2% Triton, RNse inhibitor, 10uM polyTPrimer, 10mM dNTPs). Plate was briefly centrifuged and snap frozen and stored on -80°C until further processing."],"figure_sub":["Organization","MINSEQE Score","Assays and Data","Processed Data","MAGE-TAB Files"],"data_protocol":["Data Transformation - Low-level analysis of scRNA-Seq data was performed using largely functions from the scran (v1.20.1) and scater (v1.20.1) R packages [53]). First, cells with less than 50.000 mapped reads and 4000 detected genes and cells with more than 20% of reads mapping to mitochondria were removed. Next, counts were normalised using the computeSumFactors function and log-transformed. The resulting gene expression matrix was used for dimensionality reduction by principal component analysis (prcomp, stats), tSNE (Rtsne, Rtsne, v0.15) and UMAP (umap, umap, v0.2.7.0). To identify clusters, we used graph-based community detection using the Louvain algorithm implemented by the functions buildSNNGraph and cluster_louvain of the package igraph (v1.2.10). Cell types were annotated using a label-transfer approach from a previously annotated reference dataset [22].To this end, we identified highly variable genes using the findTopHVGs function from the scran package in the query dataset, and only used these genes for further annotation. We then computed pairwise pearson-correlation coefficients between each query and each reference cell type and annotated each query cell as the cell type with the highest correlation coefficient. We also annotated the dataset using the SingleR function in the SingleR package (v1.6.1 [54]) and found high concordance between the two approaches. We lifted both main and subcelltypes as well as neurotransmitter status from the [22] dataset, and furthermore annotated the DRG neurons with a separate reference dataset [23] with higher resolution. To visualize the integration, we used an MNN-based correction algorithm [55] to integrate the reference and query datasets, subset to the intersection of the 1000 most highly variable genes each and computed a joint UMAP."],"omics_type":["Metabolomics","Unknown","Transcriptomics","Genomics","Proteomics"],"instrument_platform":["NextSeq 550"],"study_type":["RNA-seq of coding RNA from single cells"],"species":["Mus musculus"],"pubmed_authors":["Jasper Panten"],"additional_accession":[]},"is_claimable":false,"name":"Single-cell RNA-Sequencing of neurons labeled by retrograde FastBlue tracing (SmartSeq)","description":"Most cancers including pancreatic ductal adenocarcinoma (PDAC) are infiltrated by PNS neurons participating in their complex tumor microenvironment. However, their cell bodies and nuclei are located in the para- and pre-vertebral PNS ganglia located far from the tumor mass itself. Thus, molecular information on healthy organ- vs. cancer-infiltrating neurons is currently lacking in any sequencing dataset of healthy or tumor tissue. To specifically identify and molecularly characterize the identity and transcriptomes of PDAC-infiltrating neurons at single cell resolution, we developed “Trace-n-seq”. This method is based on retrograd tracing of axons from target tissues to their respective ganglia, followed by individual FACS-isolation and transcriptomic analysis. We characterized >2000 sympathetic and sensory neurons that infiltrate PDAC, healthy pancreas, or other abdominal organs.","dates":{"release":"2025-05-01T00:00:00Z","modification":"2025-08-08T12:03:05.558Z","creation":"2025-05-01T14:59:41.481Z"},"accession":"E-MTAB-12940","cross_references":{"ENA":["ERP172083"],"EFO":["EFO_0002944","EFO_0004170","EFO_0005684","EFO_0005518","EFO_0003816","EFO_0004184"]}}