在生物学研究中，高空间分辨率的质谱成像（MSI）技术能够帮助我们清晰地看到组织和细胞内分子的分布情况。不过，要想获得更高的空间分辨率，就需要把像素尺寸变得更小，同时增加像素数量。这虽然能让图像更清晰，但也带来了新的问题——数据采集时间会变得很长，分析效率大大降低。传统的深度学习方法在MSI中有一定潜力，但它通常需要大量的训练数据集或者成对的图像，可这些数据往往很难获取。为了解决这些问题，科学家们提出了基于参考的质谱成像超分辨率（RSR - MSI）方法。这个方法巧妙地利用光学显微镜图像作为参考帧，从其中提取丰富的纹理信息。然后把这些信息和原始质谱图像的离子强度数据结合起来，构建出一个针对特定图像的超分辨率网络。使用这种方法时，只需要一张低分辨率的质谱图像和一张参考光学图像，就能成功重建出生物组织和单细胞的高分辨率质谱图像，图像中包含丰富的化学和纹理细节。而且，它还能让常规的逐像素扫描时间大幅缩短，大约缩短一个数量级。更棒的是，不需要对现有的质谱仪器进行任何定制修改，就能实现高空间分辨率成像。这项研究为单细胞MSI在亚细胞分辨率下的图像超分辨率方法应用提供了验证，为细胞生物学研究中高空间分辨率、高通量的MSI技术发展奠定了基础。

期刊

Analytical Chemistry

标题

RSR-MSI: Reference-Based Super-Resolution for Mass Spectrometry Imaging of Tissues and Single Cells

作者

Yifan Fang, Cipeng Wu, Chentao Zhang, Yizhu Xu, Zhibin Yin, Zhouyi Xu, Wei Hang

摘要

High-spatial-resolution mass spectrometry imaging (MSI) visualizes molecular distributions in tissues and cells. However, achieving higher spatial resolution typically necessitates smaller pixel dimensions and an increased number of pixels, leading to longer data acquisition times and diminished analytical throughput. Although deep learning approaches have demonstrated significant potential in MSI, they typically require large training data sets or paired images, which are often unavailable. Herein, we propose the reference-based super-resolution for mass spectrometry imaging (RSR-MSI) method, with optical microscopy images as reference frames to extract abundant texture information. By integrating this with ion intensity data from the original MS images, we develop an image-specific super-resolution network. Employing solely a single low-resolution MS image coupled with a reference optical image, we successfully reconstruct high-resolution MS images for biological tissues and single cells, producing results with rich chemical and textural details. This approach significantly decreases the routine pixel-by-pixel scanning time by an order of magnitude while achieving high spatial resolution using existing mass spectrometry instruments without any customized modifications. Overall, our work introduces and validates the application of image super-resolution methods within the realm of single-cell MSI at subcellular resolution, paving the way for the development of high-spatial-resolution and high-throughput MSI for cellular biology research.

原文链接

https://pubs.acs.org/doi/10.1021/acs.analchem.5c05933

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