Dataset Information

Integrated single-cell and spatial transcriptomic profiling reveals higher intratumour heterogeneity and epithelial-fibroblast interactions in recurrent bladder cancer.

ABSTRACT:

Background

Recurrent bladder cancer is the most common type of urinary tract malignancy; nevertheless, the mechanistic basis for its recurrence is uncertain. Innovative technologies such as single-cell transcriptomics and spatial transcriptomics (ST) offer new avenues for studying recurrent tumour progression at the single-cell level while preserving spatial data.

Method

This study integrated single-cell RNA (scRNA) sequencing and ST profiling to examine the tumour microenvironment (TME) of six bladder cancer tissues (three from primary tumours and three from recurrent tumours).

Findings

scRNA data-based ST deconvolution analysis revealed a much higher tumour heterogeneity along with TME in recurrent tumours than in primary tumours. High-resolution ST analysis further identified that while the overall natural killer/T cell and malignant cell count or the ratio of total cells was similar or even lower in the recurrent tumours, a higher interaction between epithelial and immune cells was detected. Moreover, the analysis of spatial communication reveals a marked increase in activity between cancer-associated fibroblasts (CAFs) and malignant cells, as well as other immune cells in recurrent tumours.

Interpretation

We observed an enhanced interplay between CAFs and malignant cells in bladder recurrent tumours. These findings were first observed at the spatial level.

SUBMITTER: Shi ZD

PROVIDER: S-EPMC10366350 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrated single-cell and spatial transcriptomic profiling reveals higher intratumour heterogeneity and epithelial-fibroblast interactions in recurrent bladder cancer.

Shi Zhen-Duo ZD Sun Zhuo Z Zhu Zuo-Bin ZB Liu Xing X Chen Jun-Zhi JZ Hao Lin L Zhu Jie-Fei JF Pang Kun K Wu Di D Dong Yang Y Liu Yu-Fei YF Chen Wei-Hua WH Liang Qing Q Zhuo Shi-Chao SC Han Cong-Hui CH

Clinical and translational medicine 20230701 7

<h4>Background</h4>Recurrent bladder cancer is the most common type of urinary tract malignancy; nevertheless, the mechanistic basis for its recurrence is uncertain. Innovative technologies such as single-cell transcriptomics and spatial transcriptomics (ST) offer new avenues for studying recurrent tumour progression at the single-cell level while preserving spatial data.<h4>Method</h4>This study integrated single-cell RNA (scRNA) sequencing and ST profiling to examine the tumour microenvironmen ...[more]

PMID: 37488671

Similar Datasets

Project description:Psoriasis, which is one of the most common skin diseases, involves an array of complex immune constituents including T cells, dendritic cells and monocytes. Particularly, the cytokine IL17A, primarily generated by TH17 cells, assumes a crucial function in the etiology of psoriasis. In this study, a comprehensive investigation utilizing bulk RNA analysis, single-cell RNA sequencing, and spatial transcriptomics was employed to elucidate the underlying mechanisms of psoriasis. Our study revealed that there is an overlap between the genes that are differentially expressed in psoriasis patients receiving three anti-IL17A monoclonal antibody drugs and the genes that are differentially expressed in lesion versus non-lesion samples in these patients. Further analysis using single-cell and spatial data from psoriasis samples confirmed the expression of hub genes that had low expressions in psoriasis tissue but were up-regulated after anti-IL17A treatments. These genes were found to be associated with the treatment effects of brodalumab and methotrexate, but not adalimumab, etanercept, and ustekinumab. Additionally, these genes were predominantly expressed in fibroblasts. In our study, fibroblasts were categorized into five clusters. Notably, hub genes exhibited predominant expression in cluster 3 fibroblasts, which were primarily engaged in the regulation of the extracellular matrix and were predominantly located in the reticular dermis. Subsequent analysis unveiled that cluster 3 fibroblasts also established communication with epithelial cells and monocytes via the ANGPTL-SDC4 ligand-receptor configuration, and their regulation was governed by the transcription factor TWIST1. Conversely, cluster 4 fibroblasts, responsible for vascular endothelial regulation, were predominantly distributed in the papillary dermis. Cluster 4 predominantly engaged in interactions with endothelial cells via MDK signals and was governed by the distinctive transcription factor, ERG. By means of an integrated analysis encompassing bulk transcriptomics, single-cell RNA sequencing, and spatial transcriptomics, we have discerned genes and clusters of fibroblasts that potentially contribute to the pathogenesis of psoriasis.

Project description:Background & aimsIntratumour heterogeneity (ITH) fosters the vulnerability of RNA expression-based biomarkers derived from a single biopsy to tumour sampling bias, and is regarded as an unaddressed confounding factor for patient precision stratification using molecular biomarkers. This study aimed to identify an ITH-free predictive biomarker in hepatocellular carcinoma (HCC).MethodsWe interrogated the confounding effect of ITH on performance of molecular biomarkers and quantified transcriptomic heterogeneity utilising three multiregional HCC transcriptome datasets involving 142 tumoural regions from 30 patients. A de novo strategy based on the heterogeneity metrics was devised to develop a surveillant biomarker (a utility gadget using RNA; AUGUR) using three datasets involving 715 liver samples from 509 patients with HCC. The performance of AUGUR was assessed in seven cross-platform HCC cohorts that encompassed 1,206 patients.ResultsAn average discordance rate of 39.9% at the level of individual patients was observed applying 13 published prognostic signatures to classify tumour regions. We partitioned genes into four heterogeneity quadrants, from which we developed and validated a reproducible robust ITH-free expression signature AUGUR that showed significant positive associations with adverse features of HCC. High AUGUR risk increased the risk of disease progression and mortality independent of established clinicopathological indices, which maintained concordance across seven cohorts. Moreover, AUGUR compared favourably to the discriminative ability, prognostic accuracy, and patient risk concordant rates of 13 published signatures. Finally, a well-calibrated predictive nomogram integrating AUGUR and tumour-node-metastasis (TNM) stage was established, which generated a numerical probability of mortality.ConclusionsWe constructed and validated an ITH-free AUGUR and nomogram that overcame sampling bias and provided reliable prognostic information for patients with HCC.Impact and implicationsIntratumour heterogeneity (ITH) is prevalent in hepatocellular carcinoma (HCC), and is regarded as an unaddressed confounding factor for biomarker design and application. We examined the confounding effect of transcriptomic ITH in patient risk classification, and found existing molecular biomarkers of HCC were vulnerable to tumour sampling bias. We then developed an ITH-free expression biomarker (a utility gadget using RNA; AUGUR) that overcame clinical sampling bias and maintained prognostic reproducibility and generalisability across multiple HCC patient cohorts from different commercial platforms. Furthermore, we established and validated a well-calibrated nomogram based on AUGUR and tumour-node-metastasis (TNM) stage that provided an individualised prognostic information for patients with HCC.

Dataset Information

Integrated single-cell and spatial transcriptomic profiling reveals higher intratumour heterogeneity and epithelial-fibroblast interactions in recurrent bladder cancer.

Background

Method

Findings

Interpretation

Publications

Integrated single-cell and spatial transcriptomic profiling reveals higher intratumour heterogeneity and epithelial-fibroblast interactions in recurrent bladder cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets