automated slide scanner microscope for bright field and fluorescence microscopy

高分辨率显微镜可以在健康和疾病中进行单细胞表型鉴定。我们的主要兴趣是更好地描述人类动脉粥样硬化斑块的特征。结合激光显微解剖显微镜和超灵敏质谱学，可以首次在单细胞水平上实现蛋白质组学图谱的创新方法，称为深度视觉蛋白质组学(DVP)。该方法除了实现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水平上潜在地优于经典的单细胞方法。DVP使用高分辨率成像，人工智能(AI)，即基于生物图像分析软件(BIAS)的图像分析方法，通过新的超灵敏蛋白质组学工作流程进行单细胞表型和分离。具体地说，我们的目标是将强大的成像技术与无偏见的蛋白质组学相结合，表征和深入研究斑块浸润性白细胞和平滑肌细胞(SMC)中体细胞突变的生物学相关性。我们假设在突变的白细胞入侵后，体细胞突变可能介导动脉粥样硬化斑块中的前动脉粥样硬化效应。非常相似的是，我们推测，与突变的白细胞类似，受体细胞突变影响的SMC可能支持克隆扩张、SMC的重新编程，影响SMC的表型转变，最终影响斑块的稳定性。DVP在保持空间背景的同时，将蛋白质丰度与复杂的细胞和亚细胞表型结合在一起。DVP产生的数据将帮助我们发现新的蛋白质信号，这将首次提供在表型水平上的蛋白质组变异的分子洞察力，以及人类动脉粥样硬化斑块中的完整空间转化。

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.