WeChat Mini Program
Old Version Features

Single-cell Multiomics Profiling Reveals Heterogeneous Transcriptional Programs and Microenvironment in DSRCTs

CELL REPORTS MEDICINE(2024)

ATIP-Avenir INSERM and ERC StG Group

Cited 1|Views37
Abstract
Desmoplastic small round cell tumor (DSRCT) is a rare, aggressive sarcoma driven by the EWSR1::WT1 chimeric transcription factor. Despite this unique oncogenic driver, DSRCT displays a polyphenotypic differentiation of unknown causality. Using single -cell multi-omics on 12 samples from five patients, we find that DSRCT tumor cells cluster into consistent subpopulations with partially overlapping lineage- and metabolism -related transcriptional programs. In vitro modeling shows that high EWSR1::WT1 DNA -binding activity associates with most lineage -related states, in contrast to glycolytic and profibrotic states. Single -cell chromatin accessibility analysis suggests that EWSR1::WT1 binding site variability may drive distinct lineage -related transcriptional programs, supporting some level of cell -intrinsic plasticity. Spatial transcriptomics reveals that glycolytic and profibrotic states specifically localize within hypoxic niches at the periphery of tumor cell islets, suggesting an additional role of tumor cell -extrinsic microenvironmental cues. We finally identify a single -cell transcriptomics-derived epithelial signature associated with improved patient survival, highlighting the clinical relevance of our findings.
More
Translated text
Key words
desmoplastic small round cell tumor,sarcoma,EWSR1::WT1,transcription factor,molecular and cellular heterogeneity,plasticity,single-cell RNA-sequencing,spatial transcriptomics,microenvironment,cancer-associated fibroblasts
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined