Authors : Lucie Gomes, inspired by DECAFE ( GeNeHetX )
Last Update : 08 July 2025
Contact : lucie.gomes@inserm.fr

Sequencing: Total RNA was extracted using the exoRNeasy kit (Qiagen), ensuring high-quality RNA suitable for downstream analysis. Library preparation was performed using the SMARTer Stranded Total RNA-Seq Kit, and sequencing was conducted on an Illumina NovaSeq X platform, providing high-depth, strand-specific transcriptomic data.

RNA-seq Processing Pipeline: Raw sequencing reads were initially assessed for quality using FastQC, identifying potential issues such as adapter contamination, low-quality bases, or sequence duplication. High-quality reads were then aligned to the Ensembl GRCh38 (release 107) reference genome using the STAR aligner, enabling accurate mapping across exon-exon junctions. Gene-level quantification was performed using featureCounts, based on Ensembl gene annotations, to generate raw count matrices for downstream differential expression and enrichment analyses.

Analysis: While this application focuses on the visualization of normalized expression counts across experimental conditions, the data can serve as a starting point for further downstream analyses. These may include differential gene expression (DGE) analysis to identify genes showing statistically significant expression changes between conditions. Subsequent analyses such as Gene Set Enrichment Analysis (GSEA) or pathway enrichment can also be performed using tools and databases like Gene Ontology (GO), KEGG, or Reactome to provide functional interpretation of the results and uncover biological processes or molecular pathways of interest.

Steps:
 - Enter password to unlock panels
 - Select your cell-line (All, Endothelial cells, PDAC cells or CAF cells)
 - Choose your gene of interest
 - Customize the plot by selecting conditions (main_condition, color and facet)
 - Choose the download format and export the plot

Goal
This study aims to investigate the molecular interactions between the ROBO1 receptor, expressed by pancreatic ductal adenocarcinoma (PDAC) cells (PACO2), and its ligands, SLIT1 and SLIT2, which are expressed by cancer-associated fibroblasts (CAFs) and endothelial cells (Ec). Through a combination of culture alone and co-culture systems, the experimental design explores both autonomous and intercellular effects. Functional perturbations were introduced by knocking out ROBO1 in PACO2 cells and SLIT genes in CAFs and Ec, with the goal of deciphering the contribution of the ROBO/SLIT signaling axis to the tumor microenvironment and cancer progression.

Normalised Count matrix : RNA-Seq count matrix with Sample_ID as column names and genes as row names. This matrix is created with the sequencing output and normalized with VST approach. It is mapped onto a reference genome to identify which genes are present in the sample, then the number found for each gene is counted.

Annotation file: A tsv file containing all experimental annotation helpfull for data analysis. The following modalities are available in the visualisation panel:
 - sequencing_ID : ID sample from sequencing
 - specimenType : Sample cell type (CAF, PACO2 or EC)
 - specimenTreatment : Treatment of the sample
 - co-culture_cellLine : Cell type of the co-culture
 - co-culture_treatment : Treament of the co-culture
 - specimenTreatmentTime : Culture Time (24h or 72h)
 - sampleReplicate : Number of the replicate (R1 or R2)
 - knockoutStatus : Indicate if specimenTreatment or co-culture_treatment have a knockout condition (KO or noKO)
 - co_culture_cond : Merge of co-culture_cellLine+co-culture_treatment columns