Unveiling Spatial Heterogeneity in Medulloblastoma: a Multi-Omics Analysis of Cellular State and Geographical Organization.
Neuro-oncology（2025）
Beijing Genomics Institute-Shenzhen | Beijing Neurosurgical Institute | Texas Children's Cancer and Hematology Center | School of Public Health | Department of Computer Science | China National Gene Bank | Department of Neurosurgery | Beijing Chao-Yang Hospital
Abstract
BACKGROUND:Despite numerous studies on medulloblastoma (MB) cell heterogeneity, the spatial characteristics of cellular states remain unclear. METHODS:We analyze single-nucleus and spatial transcriptomes and chromatin accessibility from human MB spanning four subgroups, to identify malignant cell populations and describe the spatial evolutionary trajectories. The spatial CNVs patterns and niches were analyzed to investigate the cellular interactions. RESULTS:Three main malignant cell populations were identified, including progenitor-like, cycling and differentiated populations. Gene signatures of cell populations strongly correlate to clinical outcomes. These tumor cell populations are geographically organized as stem-like and mature regions, highlighting their spatially heterogeneous nature. Progenitor-like and cycling cells are mainly concentrated in stem-like regions, whereas various differentiated populations are primarily distributed in mature regions. By analyzing chromosomal alterations, we find that stem-like region typically harbors a single pattern of CNVs, reflecting high originality and uniformity, which is in stark contrast to mature region exhibiting multiple patterns with a broader range of biological functions. Projecting cellular state program onto spatial sections fully illustrates the evolution from stem-like region to various functional zones in mature region, which is correlated to microenvironmental components along the paths to maintain stemness or promote differentiation. Conclusions. This multi-omics database comprehensively facilitates the understanding of MB spatial evolutionary organization.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
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