Single-cell RNA sequencing is a powerful approach for characterising cellular heterogeneity and elucidating transcriptional programs that drive tumour plasticity, therapeutic resistance, and disease progression. In this study, we present a single-cell transcriptomic dataset comprising 41 patient-derived cell cultures (PDCs) established from pancreatic ductal adenocarcinoma (PDAC). The dataset was generated using Evercode™ technology, which is based on Split Pool Ligation-based Transcriptome sequencing (SPLiT-seq). This scalable method enables high-throughput profiling across multiple samples without droplet-based microfluidics, allowing efficient capture of inter-sample heterogeneity. This functionally annotated collection of experimentally derived models reveals transcriptional heterogeneity both within and across PDCs, enabling in-depth exploration of PDAC cell diversity, to support the development of computational tools for single-cell analysis, and to guide functional studies on tumour cell subpopulations. This resource constitutes a valuable reference for computational and experimental studies aiming to decipher PDAC heterogeneity and identify therapeutic vulnerabilities.
Chocoloff et al. (Mon,) studied this question.