PanIN & PDAC Spatial Transcriptomics Paper Code

Introduction

This is documentation for the code used for analysis in Preprint Link. 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 = 5), Adjacent normal to tumor (n = 2) and PDAC (n = 7). The samples are integrated from (Steele et al. 2020), (Carpenter et al. 2023), (Carpenter et al. 2024) and Preprint Link studies.

Downloading raw and processed data

• Raw fastq data can be find in these dbGAP repositories:
• Raw count matrices can be found in these GEO repositories:
• Processed data objects and full resolution H&E are available on Zenodo:

Data acquisition can also be done as described in scRNAseq Acquisition and Spatial Transcriptomics Acquisition

Analysis

The analysis workflow and findings are explained in detail in Preprint Link

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

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, Aleksandrina Goeva, Evan Z Macosko, Fei Chen, and Rafael A Irizarry. 2022. “Robust Decomposition of Cell Type Mixtures in Spatial Transcriptomics.” Nature Biotechnology 40 (4): 517–26.

Carpenter, Eileen S, Ahmed M Elhossiny, Padma Kadiyala, Jay Li, Jake McGue, Brian D Griffith, Yaqing Zhang, 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, Samantha B Kemp, Jay Li, Nina G Steele, Rémy Nicolle, 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): 2497–2513.

Dimitrov, Daniel, Philipp Sven Lars Schäfer, Elias Farr, Pablo Rodriguez-Mier, Sebastian Lobentanzer, Pau Badia-i-Mompel, Aurelien Dugourd, Jovan Tanevski, Ricardo Omar Ramirez Flores, and Julio Saez-Rodriguez. 2024. “LIANA+ Provides an All-in-One Framework for Cell–Cell Communication Inference.” Nature Cell Biology 26 (9): 1613–22.

Fleming, Stephen J, Mark D Chaffin, Alessandro Arduini, Amer-Denis Akkad, Eric Banks, John C Marioni, Anthony A Philippakis, Patrick T Ellinor, and Mehrtash Babadi. 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, Adam Kurkiewicz, Evan Biederstedt, Po-Ru Loh, and Peter V Kharchenko. 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, Veerin R Sirihorachai, Stephanie The, Lawrence Delrosario, Jenny Lazarus, et al. 2020. “Multimodal Mapping of the Tumor and Peripheral Blood Immune Landscape in Human Pancreatic Cancer.” Nature Cancer 1 (11): 1097–1112.

Zhao, Edward, Matthew R Stone, Xing Ren, Jamie Guenthoer, Kimberly S Smythe, Thomas Pulliam, Stephen R Williams, et al. 2021. “Spatial Transcriptomics at Subspot Resolution with BayesSpace.” Nature Biotechnology 39 (11): 1375–84.