Hydrogel-Based Tumor Tissue Microarchitecture Reshapes Dendritic Cell Metabolic Profile and Functions.
Advanced healthcare materials(2025)
Abstract
The extracellular matrix (ECM) plays a pivotal role in immunomodulation, providing structural and biochemical cues that shape immune cell function. In pathological conditions like cancer and chronic inflammation, dysregulated remodeling often results in altered ECM composition and architecture, with fibrillar alignment being a hallmark linked to disease progression. Here, how ECM alignment influences dendritic cell (DC) behavior using 3D biomimetic collagen matrices with controlled fibril anisotropy is investigated. This results show that immature DCs in aligned matrices exhibited increased expression of CD86 and HLA-DR with elevated secretion of CXCL8 and CCL2 chemokines, which may enhance immune cell recruitment. However, transcriptomic and metabolomic analysis revealed significant downregulation of oxidative phosphorylation and an insufficient compensatory shift toward glycolysis, resulting in reduced ATP production. This metabolic constraint correlated with impaired/reduced DC migratory speed and distance. In contrast, mature DCs displayed minimal sensitivity to ECM alignment, maintaining uniform differentiation and functional profiles across matrix conditions. T-cell coculture experiments revealed that ECM alignment dampens T-cell activation and proliferation, likely through direct modulation of T-cell behavior. These findings highlight the stage-specific effects of ECM alignment on DC function, highlighting its role in DC immunomodulation, with implications for therapeutic development in cancer and other pathological contexts.
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
Summary is being generated by the instructions you defined