质谱成像
代谢物
质谱法
转录组
计算生物学
化学
基因
生物
色谱法
基因表达
生物化学
作者
Trevor M. Godfrey,Yasmin Shanneik,Wanqiu Zhang,Thao Tran,Nico Verbeeck,Nathan Heath Patterson,Faith E. Jackobs,Chandandeep Nagi,Maheshwari Ramineni,Lívia S. Eberlin
标识
DOI:10.1002/anie.202502028
摘要
Innovations in spatial omics technologies applied to human tissues have led to breakthrough discoveries in various diseases, including cancer. Two of these approaches ‐ spatial transcriptomics and spatial metabolomics ‐ have blossomed independently, fueled by technologies such as spatial transcriptomics (ST) and mass spectrometry imaging (MSI). While powerful, these technologies only offer insights into the spatial distributions of restricted classes of molecules and have not yet been integrated to provide more holistic insights into biological questions. These techniques can be performed on adjacent serial sections from the same sample, but section‐to‐section variability can convolute data integration. We present a novel method combining desorption electrospray ionization mass spectrometry imaging (DESI‐MSI) spatial metabolomics and Visium spatial transcriptomics on the same tissue sections. We show that RNA quality is maintained after performing DESI‐MSI on a tissue and that ST data is unperturbed following DESI‐MSI. We demonstrate this workflow on human breast and lung cancer tissues and identify novel correlations between metabolites and RNA transcripts in cancer specific regions.
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