automated slide scanner microscope for bright field and fluorescence microscopy

高分辨率显微镜允许在健康和疾病中执行单细胞表型。我们的主要兴趣是更好地表征人类动脉粥样硬化斑块。结合激光显微解剖显微镜和超灵敏质谱法，一种称为深度视觉蛋白质组学（DVP）的创新方法首次实现了单细胞水平的蛋白质组学分析。该方法除了允许单细胞异质性的功能表征外，还可以实现组织的空间蛋白质组学表征，并且在DNA和RNA水平上可能优于经典的单细胞方法。DVP使用高分辨率成像，人工智能(AI) -即生物图像分析软件(BIAS) -基于图像分析方法进行单细胞表型和分离，并采用新的超灵敏蛋白质组学工作流程。具体来说，我们的目标是结合强大的想象技术和公正的蛋白质组学，深入表征和研究斑块浸润白细胞和平滑肌细胞（SMCs）中体细胞突变的生物学相关性。我们假设体细胞突变可能在突变白细胞入侵后介导动脉粥样硬化斑块的前动脉粥样硬化作用。非常相似的是，我们推测，与突变的白细胞相比，体细胞突变影响的SMCs可能支持克隆扩增、SMCs重编程、影响SMC表型转变并最终影响斑块稳定性。DVP将蛋白质丰富度与复杂的细胞和亚细胞表型结合在一起，同时保持空间背景。DVP产生的数据将帮助我们发现新的蛋白质特征，这将首次为人类动脉粥样硬化斑块中具有完整空间信息的表型水平上的蛋白质组变异提供分子见解。

High-resolution microscopy allows to perform single-cell phenotyping in health and disease. Our key interest is to better characterize the human atherosclerotic plaque. In combination with laser microdissection microscopy and ultra-sensitive mass spectrometry, proteomic profiling at the single cell level could be achieved for the first time in an innovative approach called Deep Visual Proteomics (DVP). The approach allows besides the functional characterization of the heterogeneity of single cells also the implementation of a spatial proteomic characterization of tissues and is potentially superior to classical single-cell approaches at the DNA and RNA level. DVP uses high-resolution imaging, artificial intelligence (AI) - namely Biology Image Analysis Software (BIAS) - based image analysis approach for single-cell phenotyping and isolation with a novel ultrasensitive proteomics workflow. Specifically, we aim to characterize and study in depth the biological relevance of somatic mutations in plaque infiltrating leukocytes and smooth muscle cells (SMCs) combining powerful imagining technologies with unbiased proteomics. We hypothesize that somatic mutations might mediate proatherosclerotic effects in atherosclerotic plaques after invasion of mutated leukocytes. Very similar, we speculate that comparable to mutated leukocytes, SMCs affected by somatic mutations might support clonal expansion, reprogramming of SMCs, impact SMC phenotype transitions and finally affect plaque stability. DVP brings together protein abundance with complex cellular and subcellular phenotypes while maintaining spatial context. The data generated by DVP will assist us in discovering novel protein signatures that will provide, for the first time, molecular insights into proteome variation at the phenotypic level with complete spatial metainformation in human atherosclerotic plaque.