(Elhossiny et al, 2026, Cancer Discovery) Paper Code

Introduction

This is documentation for the code used for analysis in (Elhossiny et al. 2026). Here, we leverage single-cell RNAseq and spatial transcriptomics data to investigate the epithelial and stromal co-evolution in pancreatic cancer.

Data

We used a cohort of scRNAseq samples composed of healthy (n = 24), Adjacent normal to tumor (n = 3) and PDAC (n = 18) samples and 10x Visium samples composed of healthy (n = 11), Adjacent normal to tumor (n = 2) and PDAC (n = 7). The samples are integrated from (Elhossiny et al. 2026), (Carpenter et al. 2024), (Carpenter et al. 2023), (Steele et al. 2020) studies.

Downloading raw and processed data

• Raw data for spatial samples and full resolution H&E are availablehere
  
• Raw data for the new scRNAseq samples can be found here
• Raw data for the previous studies can be found in these GEO repositories: GSE229413, GSE155698, phs003436.v1.p1
• Raw data for organoids samples can be found here
• Processed data objects here

Analysis

The analysis workflow and findings are explained in detail in (Elhossiny et al. 2026).

scRNAseq Analysis

• Alignment using CellRanger as detailed here CellRanger Alignmnet
• Ambient RNA correction using cellbender (Fleming et al. 2023) as detailed here scRNAseq Ambient RNA Correction
• Quality control, processing and integration as detailed here scRNAseq Data Processing and Integration
• Copy number variation inference was done using Numbat (Gao et al. 2023) as described here CNV Inference using Numbat
• Fibroblasts and Macrophages subpopulation analysis were done as decribed here scRNAseq Fibroblast Subpopulation Analysis and Macrophages Subpopulation Analysis
• Gene set scoring on TCGA-PAAD dataset was done as described here TCGA-PAAD Scoring

Spatial Transcriptomics Analysis

Visium

• Alignment using SpaceRanger was done is described here
• Data normalization and seurat object generation is described here Spatial Transcriptomics Data Processing
• Cell type deconvolution was done using RCTD (Cable et al. 2022) is described here Spatial Transcriptomics Cell Type Deconvolution
• Integration and Spatially-informed clustering using BayesSpace (Zhao et al. 2021) is described here Spatial Transcriptomics Clustering (BayesSpace)
• Ligand-Receptor interaction analysis was done using LIANA+ (Dimitrov et al. 2024) is described here LIANA+ Analysis
• Neighborhood analysis is described here Spatial Transcriptomics Neighborhood Analysis
• Pseudobulk analysis of epithelial compartment is described here Spatial Transcriptomics Epithelial Domains Analysis
• Pseudobulk analysis of stromal compartment is described here Spatial Transcriptomics Stromal Domains Analysis

Xenium

• Segmentation was done using Proseg (Jones et al. 2025) is described here Xenium Resegmentation
• Data processing and integration is described here Xenium Data Analysis
• Spatial regression was done using semla (Larsson et al. 2023) is described here Xenium Spatial Regression
• Visualization of data is done as described here Xenium Polygons Visualization

CosMx

• Projection of Fibroblasts signature on external CosMx dataset is described here CosMx Data Analysis

Interactive Visualization

You can explore the data interactively on https://pascadimagliano-lab.github.io/PancAtlas/

Contact us

If you have any questions please feel free to contact the authors, Ahmed M. Elhossiny (hossiny@umich.edu) and Marina Pasca di Magliano (marinapa@umich.edu)

References

Cable, Dylan M, Evan Murray, Luli S Zou, et al. 2022. “Robust Decomposition of Cell Type Mixtures in Spatial Transcriptomics.” Nature Biotechnology 40 (4): 517–26.

Carpenter, Eileen S, Ahmed M Elhossiny, Padma Kadiyala, et al. 2023. “Analysis of Donor Pancreata Defines the Transcriptomic Signature and Microenvironment of Early Neoplastic Lesions.” Cancer Discovery 13 (6): 1324–45.

Carpenter, Eileen S, Padma Kadiyala, Ahmed M Elhossiny, et al. 2024. “KRT17High/CXCL8+ Tumor Cells Display Both Classical and Basal Features and Regulate Myeloid Infiltration in the Pancreatic Cancer Microenvironment.” Clinical Cancer Research 30 (11): 2497–513.

Dimitrov, Daniel, Philipp Sven Lars Schäfer, Elias Farr, et al. 2024. “LIANA+ Provides an All-in-One Framework for Cell–Cell Communication Inference.” Nature Cell Biology 26 (9): 1613–22.

Elhossiny, Ahmed M, Padma Kadiyala, Jude Ogechukwu Okoye, et al. 2026. “Asynchronous Evolution of Epithelium and Stroma Differentiates Precursor Lesions from Pancreatic Cancer.” Cancer Discovery, ahead of print. https://doi.org/10.1158/2159-8290.CD-25-2001.

Fleming, Stephen J, Mark D Chaffin, Alessandro Arduini, et al. 2023. “Unsupervised Removal of Systematic Background Noise from Droplet-Based Single-Cell Experiments Using CellBender.” Nature Methods 20 (9): 1323–35.

Gao, Teng, Ruslan Soldatov, Hirak Sarkar, et al. 2023. “Haplotype-Aware Analysis of Somatic Copy Number Variations from Single-Cell Transcriptomes.” Nature Biotechnology 41 (3): 417–26.

Jones, Daniel C, Anna E Elz, Azadeh Hadadianpour, Heeju Ryu, David R Glass, and Evan W Newell. 2025. “Cell Simulation as Cell Segmentation.” Nature Methods, 1–12.

Larsson, Ludvig, Lovisa Franzén, Patrik L Ståhl, and Joakim Lundeberg. 2023. “Semla: A Versatile Toolkit for Spatially Resolved Transcriptomics Analysis and Visualization.” Bioinformatics 39 (10): btad626.

Steele, Nina G, Eileen S Carpenter, Samantha B Kemp, et al. 2020. “Multimodal Mapping of the Tumor and Peripheral Blood Immune Landscape in Human Pancreatic Cancer.” Nature Cancer 1 (11): 1097–112.

Zhao, Edward, Matthew R Stone, Xing Ren, et al. 2021. “Spatial Transcriptomics at Subspot Resolution with BayesSpace.” Nature Biotechnology 39 (11): 1375–84.