Kruse Angela R S, Lardenoije Roy, Migas Lukasz G, Scott Claire F, Marshall Cody, Malek Morad C, Eskaros Adel, Pham Thai, Aamodt Kristie, Colley Madeline, Ventura-Antunes Lissa, Farrow Melissa A, Van de Plas Raf, Goncalves Joana, Schrag Matthew, Powers Alvin C, Spraggins Jeffrey M
Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA.
Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA.
bioRxiv. 2025 Sep 17:2025.09.12.675904. doi: 10.1101/2025.09.12.675904.
Spatial 'omics technologies are a powerful tool for mapping the relationship between cellular organization and molecular distributions in healthy and diseased tissue microenvironments. Here, we describe a novel multimodal pipeline that represents experimental and computational advances for spatiomolecular analysis of tissue samples across molecular classes. This adaptable method integrates matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) lipidomics, spatial transcriptomics (ST), multiplexed immunofluorescence microscopy (MxIF), and histopathological staining to uncover spatiomolecular profiles associated with unique cellular niches and pathological features. We demonstrate the power of this approach using two different complex human disease systems: Alzheimer's disease in human brain tissue and type 2 diabetes mellitus in the human pancreas. By identifying molecular markers associated with disease pathology in the pancreas and brain, we shed light on biologically significant pathways that are impacted in these two spatially complex diseases and highlight the powerful potential of accurate, high-resolution multimodal integration approaches.
空间组学技术是一种强大的工具，可用于绘制健康和患病组织微环境中细胞组织与分子分布之间的关系。在此，我们描述了一种新颖的多模态流程，该流程代表了跨分子类别对组织样本进行空间分子分析的实验和计算进展。这种适应性方法整合了基质辅助激光解吸/电离（MALDI）成像质谱（IMS）脂质组学、空间转录组学（ST）、多重免疫荧光显微镜（MxIF）和组织病理学染色，以揭示与独特细胞生态位和病理特征相关的空间分子图谱。我们使用两种不同的复杂人类疾病系统展示了这种方法的强大功能：人类脑组织中的阿尔茨海默病和人类胰腺中的2型糖尿病。通过识别与胰腺和大脑疾病病理学相关的分子标记，我们揭示了这两种空间复杂疾病中受到影响的生物学重要途径，并突出了准确、高分辨率多模态整合方法的强大潜力。
