Spatiotemporal analyses of the pan-cancer single-cell landscape reveal widespread profibrotic ecotypes associated with tumor immunity

Cite us!

Ya Han, Lele Zhang, Dongqing Sun, Guangxu Cao, Yuting Wang, Jiali Yue, Junjie Hu, Fang Li, Taiwen Li, Peng Zhang, Qiu Wu*, Chenfei Wang*. Spatiotemporal analyses of the pan-cancer single-cell landscape reveal widespread profibrotic ecotypes regulating tumor immunity. [DOI] [PubMed]

Note:

Code related to the analyses in the TabulaTIME can be found on GitHub at https://github.com/yahan9416/TabulaTiME

Contact:

Ya Han: hanyaya@tongji.edu.cn
Qiu Wu: qiu_wu@tongji.edu.cn
Chenfei Wang: 08chenfeiwang@tongji.edu.cn

TabulaTIME | Chenfei Wang Lab 2024

沪ICP备2024096254-1

Lineages

Annotation

Subsampling

Individual Gene Expression Exploration

Gene1:

Gene2:

Top Differential Expressed Genes Across Each Subcell Types

Download: Differential expression genes of all subcell types

Instruction: The Functional Heterogeneity module allows users to analyze and visualize the enriched pathways and signature score in diverse subcell types. To elucidate the functional characteristics of the subtypes, we leveraged gene sets with HALLMARKs and Kyoto of Genes and Genomes (KEGG) pathways from the Molecular Database (MSigDB v6.1). Enrichment analysis was carried out on cell type upregulated gene sets using the hypergeometric test, implemented via the clusterProfiler package. Pathways with an adjusted q-value less than 0.01 were deemed significantly enriched. Additionally, functional-associated signature gene lists obtained from previously published studies were used to describe the functional diversity of cell types. The AddModuleScore function in Seurat was applied to calculate the score for individual metacells.

Top Pathways Enrichments For Each Subcell Type

Download: Top enriched pathways in subcell types based on the top 100 differential expressed genes

Instruction: The Intratumor Heterogeneity module allows users to select lineages and subcell types and visualize the signature scores associated with intratumor heterogeneity (ITH). Each tumor harbors a spectrum of cellular states contributing to ITH, driven by genetics, epigenetics, and microenvironmental influences. To characterize ITH, we employed non-negative matrix factorization (NMF) to delineate expression heterogeneity within each cell lineage for each sample. Following quality control, the NMF outputs for each cell lineage were clustered into meta-programs (MPs) based on Jaccard similarity using hierarchical clustering. MPs suspected of representing low-quality data, such as those derived from single studies or specific cancer types, were subsequently filtered out. Finally, we identified the top 50 genes based on their occurrence frequency within each MP and annotated their functional relevance.

Lineage-Specific Intratumor Heterogeneity Exploration

Cell Type-Specific Intratumor Heterogeneity Exploration

Instruction: The Patient Outcome module allows users to explore the clinical relevance of specific subcell types. For each cell type, we identified the top 100 upregulated genes and computed cell type signature scores using gene set variation analysis (GSVA). To mitigate the influence of subcell type similarities within each lineage on survival analysis, GSVA scores were lineage-corrected and normalized to sum to 1 per patient. Univariate Cox Regression was employed to investigate the association between cell type signature scores and survival time across different cancer types. Furthermore, to visually depict the impact of individual cell types on survival time, Kaplan–Meier survival curves were used to compare high and low groups based on median cell type signature scores.

Individual Subcell Type Clinical Relevance Exploration

Lineage:

Cancer type:

Lineage:

Cancer type:

Exploring Clinical Relevance of Tumor Microenvironment Subtypes in Specific Cancer Types

Cancer type1:

Cancer type2:

Delineating Distinct Tumor Microenvironment Subtypes Across Patients

Download: Patient stratification information

Integrated expression of all metacells across 36 cancer types

Meta information of metacells, including the clinical, cancer type, and cell type annotation informations

Differential expression genes of all subcell types

Enriched pathways of all subcell types by the top 100 differential expressed genes

Functional signatures collected from published studies

Intratumor heterogeneity signatures of each cell lineages derived from NMF analysis

Patient stratification for solid tumors within TCGA cohort

SELINA : reference-based annotation method