DOI: 10.5061/dryad.7m0cfxq4s
title: Processed data objects for snPATHO-seq paper

description: Formalin-fixed paraffin-embedded (FFPE) samples are valuable but
 underutilized in single-cell omics research due to their low DNA and RNA
 quality. In this study, leveraging a recent advance in single-cell genomic
 technology, we introduce snPATHO-seq, a versatile method to derive
 high-quality single-nucleus transcriptomic data from FFPE samples. We
 benchmarked the performance of the snPATHO-seq workflow against existing
 10x 3’ and Flex assays designed for frozen or fresh samples and
 highlighted the consistency in snRNA-seq data produced by all workflows.
 The snPATHO-seq workflow also demonstrated high robustness when tested
 across a wide range of normal and diseased FFPE tissue samples. When
 combined with FFPE spatial transcriptomic technologies such as FFPE
 Visium, the snPATHO-seq provides a multi-modal sampling approach for FFPE
 samples, allowing more comprehensive transcriptomic characterization.
description: Frozen PBMC samples were thawed and processed directly for 10x
 3' and Flex chemistry. For frozen breast cancer
 tissue samples, samples were dissociated into single-nucleus suspension
 before being processed for 10x 3' and Flex chemistry for gene
 expression analysis. For FFPE tissue samples, nuclei
 were extracted using either the snPATHO-seq protocol or the 10x scFFPE
 protocol followed by 10x Flex chemistry processing for gene expression
 detection. FFPE Visium data was generated using the 10x
 FFPE Visium CytAssist workflow according to the manufacturer's
 recommendation.
description: # Processed data objects for snPATHO-seq paper This repository contains
 the processed snRNA-seq (as Seurat V4 objects) and Visium (as STutility
 objects) objects used to generate the visualization in the snPATHO-seq
 manuscript
 (preprint: [https://www.biorxiv.org/content/10.1101/2023.12.07.570700v1](https://www.biorxiv.org/content/10.1101/2023.12.07.570700v1)). ## Overview of data processing For snRNA-seq data, cellranger outputs were first filtered using the CellBender package v0.2.0 to remove ambient background and identify cells/nuclei before being processed into a Seurat object (Seurat v4.3.0.9002). Low-quality cells/nuclei were defined as cells/nuclei with less than 200 UMIs, over 8000 UMIs, or over 10% mitochondrial gene products. Additional doublets were identified using the DoubletFinder package (v2.0.3) with default parameters. For samples processed using different snRNA-seq methods, the data was integrated using the Seurat CCA method with default parameters and annotated manually to derive a common cell type representation that can be applied to all datasets. For Visium data, spaceranger outputs were formatted into STutility (v1.1.1, [https://github.com/jbergenstrahle/STUtility](https://github.com/jbergenstrahle/STUtility)) objects for processing and visualization. A detailed description of the methods used for data generation and processing can also be found in the manuscript (paper in preparation, preprint: [https://www.biorxiv.org/content/10.1101/2023.12.07.570700v1](https://www.biorxiv.org/content/10.1101/2023.12.07.570700v1)). ## Description of the data and file structure ### File: Dryad metadata.xlsx **Description:** Summary of all snRNA-seq and Visium spatial transcriptomics datasets generated in this study. ### File: PBMC\_integrated\_annotation\_modifed\_by\_subclustering.rds **Description:** Seurat object used for Figure 1 and Supplementary Figure 2 (manuscript in preparation). Data was collected from two technical replicates using samples from the same donor. The 10x 3' and Flex methods were used for data collection. **Key metadata columns:** * sample_id: dataset IDs (per snRNA-seq method, per technical replicate). * new_annotation: Cell type annotations. ### Files: \[sample\_id]\_integrated\_seuarat\_object.rds **Files following this naming structure:** * 4399_integrated_seuarat_object.rds * 4411_integrated_seuarat_object.rds * 4066_integrated_seuarat_object.rds **Description:** Seurat object used for Figures 2 and 3 and Supplementary Figures 4, 6, 7, 8, and 9 (manuscript in preparation). The 10x 3', Flex, and snPATHO-seq methods were used for data collection. **Key metadata columns:** * sample_id: dataset IDs (per sample, per processing method). * processing_method: snRNA-seq methods used to collect the data. * major_annotation: Cell type annotations. ### File: \[sample\_id]\_Visium.rds **Files following this naming structure:** * 4399FFPE_Visium.rds * 4411FFPE_Visium.rds * 4066FFPE_Visium.rds **Description:** STutility object used for Figure 3 and Supplementary Figure 7 (manuscript in preparation). The 10x Visium FFPE CytAssist workflow was used for data collection. ### Files: \[sample\_id]\_annotated.rds **Files following this naming structure:** * melanoma_7167A_annotated.rds * lungcancer_20011329LC_annotated.rds * Ovary_230303_annotated.rds * liver_8754A_annotated.rds * Lung_20011329NL_annotated.rds * Kidney_1305272B_annotated.rds * Kidney_FHIL_annotated.rds * Endometrium_220952_annotated.rds * Glioblastoma_1773A_annotated.rds * EndocervicalAdenocarcinoma_6707_annotated.rds * ColonCRC_1328A_annotated.rds * Colon_213641_annotated.rds **Description:** Seurat object used for Figure 4 and Supplementary Figure 9 (manuscript in preparation). The 10x scFFPE and snPATHO-seq methods were used for data collection. **Key metadata columns:** * sample_id: Sample ID as documented in the metadata file. * dataset_id: dataset IDs (per sample, per processing method). * processing_method: snRNA-seq methods used to collect the data. * initial_annotation: Cell type annotations. ## Code/software For the Seurat package, please see [https://satijalab.org/seurat/](https://satijalab.org/seurat/) for more details. Seurat v4.3.0.9002 was used to generate the snRNA-seq data objects. For the STutility package, please see [https://github.com/jbergenstrahle/STUtility](https://github.com/jbergenstrahle/STUtility) for more details. STutility v1.1.1 was used to generate the Visium data objects. For custom scripts used to process the data and generate the visualization in the manuscript, please see [https://github.com/TaopengWang/snPATHO-seq_public](https://github.com/TaopengWang/snPATHO-seq_public) for more details.## Access information Data was derived from the following sources: * Gene Expression Omnibus (GEO) accession code: GSE268426 (snRNA-seq data) and GSE268427 (Visium data). ## Sharing/Access information To access the source data, including the fastq/bam files and the processed cellranger/spaceranger files, please access the Gene Expression Omnibus (GEO) under the accession codes GSE268426 (snRNA-seq) and GSE268427 (Visium). ## Code/Software Scripts used to process the data and generate the illustrations included in the manuscript can be accessed through: [https://github.com/TaopengWang/snPATHO-seq_public](https://github.com/TaopengWang/snPATHO-seq_public)