Optimizing the Functionalization of Super-Paramagnetic Nanobeads for Droplet-Based Protein Secretion Profiling of Immune Cells
Sensors and Actuators B: Chemical（2023）SCI 1区
BIOASTER
Abstract
The extremely dynamic functions of an active immune response are mainly determined by the presence of various soluble factors. The secreted factors organize the individual immune cells into functional tissues regulating the immune response. Dynamical single-cell studies are therefore fundamental to investigate the heterogeneity of immune cells' behaviors over time. The available tools lack either of single cell resolution or time resolution; new tools are required to access both single cell and time resolution in order to better understand these dynamical single cell mechanisms. The powerful resolution and sensitivity improvement provided by droplet-based microfluidics deciphering the dynamic processes at the single-cell level could help to describe the fundamental mechanisms underlying immunity, develop new strategies for vaccination and cancer immunotherapy, or diagnose inflammatory diseases. The use of functionalized paramagnetic nanoparticles is a key feature of our droplet-based immunoassay. This work describes the optimization and comparison of various functionalization methods for the integration of ready-to-use super-paramagnetic nanobeads into our droplet-based assay. Among the three functionalization tested methods, the most promising one is based on boronic acid chemistry, in terms of both compatibility with the droplet microfluidic system and detection sensitivity. It was successfully applied to the characterization of TNF alpha secretion from human single monocytes.
MoreTranslated text
Key words
Nanoparticle functionalization,Microsystem,Droplet microfluidics,Single-cell,Protein secretion,Time-lapse fluorescence imaging,Cell encapsulation,Cellular dynamics
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