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通过亲和阵列捕获对脂质和 N-聚糖进行单细胞多组学基质辅助激光解吸/电离质谱成像分析

Single-Cell Multiomic MALDI-MSI Analysis of Lipids and N-Glycans through Affinity Array Capture.

作者信息

Dressman James W, Bayram Muhammed F, Angel Peggi M, Drake Richard R, Mehta Anand S

机构信息

Medical University of South Carolina, Department of Pharmacology and Immunology, Basic Science Building Room 310, 173 Ashley Avenue, Charleston, South Carolina 29425, United States.

出版信息

Anal Chem. 2025 Jun 24;97(24):12493-12502. doi: 10.1021/acs.analchem.4c06233. Epub 2025 Jun 11.

Abstract

This study presents a novel approach utilizing matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) for rapid single-cell analysis, enabling multiomic data acquisition from the same cell. Previously, single-cell approaches by MALDI-MSI have focused on increasing resolution to differentiate the analyte signal from adjacent cells in complex tissue or cultured cells. Laser ablation can occur on two separate cells, mixing single-cell data and requiring statistical computation to discern one cell's spectra from the other. Here, we describe a targeted single-cell approach that enables single-cell capture and sampling, assisted by micro-contact printing. We developed an AI-based application, SoloCell, to facilitate the automated selection of captured single cells within printed arrays. This method successfully captures thousands of single cells in a grid format, allowing for rapid lipid and N-glycan profiling from the same cell at a rate of 6 cells per second. This innovative technology combines the power of single-cell capture, AI automation, and imaging mass spectrometry to provide researchers with an unprecedented tool for unraveling the complexity of cellular populations.

摘要

本研究提出了一种利用基质辅助激光解吸/电离质谱成像(MALDI-MSI)进行快速单细胞分析的新方法,能够从同一细胞中获取多组学数据。此前,MALDI-MSI的单细胞方法主要集中在提高分辨率,以区分复杂组织或培养细胞中相邻细胞的分析物信号。激光烧蚀可能发生在两个不同的细胞上,混合单细胞数据并需要进行统计计算以区分一个细胞与另一个细胞的光谱。在这里,我们描述了一种靶向单细胞方法,该方法在微接触印刷的辅助下实现单细胞捕获和采样。我们开发了一个基于人工智能的应用程序SoloCell,以促进在印刷阵列中自动选择捕获的单细胞。该方法成功地以网格格式捕获了数千个单细胞,能够以每秒6个细胞的速度从同一细胞中快速进行脂质和N-聚糖分析。这项创新技术结合了单细胞捕获、人工智能自动化和成像质谱的力量,为研究人员提供了一个前所未有的工具,用于揭示细胞群体的复杂性。

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