ABSTRACT
Prognostically relevant RNA expression states exist in pancreatic ductal adenocarcinoma (PDAC), but our understanding of their drivers, stability, and relationship to therapeutic response is limited. To examine these attributes systematically, we profiled metastatic biopsies and matched organoid models at single-cell resolution. In vivo, we identify a new intermediate PDAC transcriptional cell state and uncover distinct site- and state-specific tumor microenvironments (TMEs). Benchmarking against this reference map, we reveal strong culture-specific biases in cancer cell transcriptional state representation in organoids driven by loss of TME signals. Adding back in vivo-relevant factors, we restore expression state heterogeneity and show plasticity in culture models. Further, we prove that non-genetic modulation of cell state can strongly influence drug responses, uncovering state-specific vulnerabilities. This work provides a broadly applicable framework for aligning cell states across in vivo and ex vivo settings, identifying drivers of transcriptional plasticity, and manipulating cell state to target associated vulnerabilities.
BRIEF SUMMARY OF SAMPLE PREPARATION AND SINGLE CELL TRANSCRIPTOMIC METHODS
We established a pipeline to generate matched single-cell RNA-sequencing (scRNA-seq) profiles and organoid models using core needle biopsies from patients with metastatic PDAC (n=23). Tissue samples were minced into small portions using a scalpel and then digested at 37°C for 15 minutes using digest medium that consisted of human complete organoid medium, 1 mg/mL collagenase XI, 10 ug/mL DNase, and 10 uM Y27632. After tissue dissociation, cells were washed, treated with ACK lysing buffer to lyse red blood cells, washed again, and counted using a hemocytometer with 0.4% Trypan blue added at 1:1 dilution for viability assessment. We allocated between 10,000 and 15,000 viable cells per array (Seq-Well S^3 platform), and where possible we prepared two arrays per sample. Most samples were processed and loaded onto Seq-Well arrays within 2-3 hours of biopsy acquisition. Library preparation was performed using Nextera XT DNA tagmentation and N700 and N500 indices specific to a given sample. Tagmented and amplified sequences were purified with a 0.6X SPRI, and cDNA was quantified (Qubit dsDNA High sensitivity assay kit, Thermo Fisher) and the base pair distribution measured (High sensitivity D5000 screen tape, Agilent). cDNA was loaded onto either an Illumina Nextseq (75 Cycle NextSeq 500/550 v2.5 kit) or Novaseq (100 Cycle NovaSeq 6000 S2 kit) at 2.4 pM. Regardless of platform, the paired end read structure was 21 bases (cell barcode and UMI) by 50 bases (transcriptomic information) with an 8 base pair (bp) custom read one primer. The demultiplex and alignment protocol was followed as previously described. While Novaseq data were directly output as FASTQs, Nextseq BCL files were converted to FASTQs using bcl2fastq2. The resultant Nextseq and Novaseq FASTQs were demultiplexed by sample based on Nextera N700 and N500 indices. Reads were then aligned to the hg19 transcriptome using the cumulus/dropseq_tools pipeline on Terra maintained by the Broad Institute using standard settings. Each biopsy sample’s digital gene expression (DGE) matrix (cells x genes) was trimmed to exclude low quality cells (<400 genes detected; <1,000 UMIs; >50% mitochondrial reads) before being merged (preserving all unique genes) to create the larger biopsy dataset. The merged dataset was further trimmed to remove cells with >8,000 genes which represent outliers and likely doublet cells. We also removed genes that were not detected in at least 50 cells. The same metrics were applied to the organoid single-cell cohort.
Correspondence to: Peter S. Winter (pswinter@mit.edu), Andrew J. Aguirre (andrew_aguirre@dfci.harvard.edu), and Alex K. Shalek (shalek@mit.edu)
