Valet G
Max-Planck-Institut für Biochemie, Martinsried, Germany.
Cell Prolif. 2005 Aug;38(4):171-4. doi: 10.1111/j.1365-2184.2005.00342.x.
A large amount of structural and functional information is obtained by molecular cell phenotype analysis of tissues, organs and organisms at the single cell level by image or flow cytometry in combination with bioinformatic knowledge extraction (cytomics) concerning nuclei acids, proteins and metabolites (cellular genomics, proteomics and metabolomics) as well as cell function parameters like intracellular pH, transmembrane potentials or ion gradients. In addition, differential molecular cell phenotypes between diseased and healthy cells provide molecular data patterns for (i) predictive medicine by cytomics or for (ii) drug discovery purposes using reverse engineering of the data patterns by biomedical cell systems biology. Molecular pathways can be explored in this way including the detection of suitable target molecules, without detailed a priori knowledge of specific disease mechanisms. This is useful during the analysis of complex diseases such as infections, allergies, rheumatoid diseases, diabetes or malignancies. The top-down approach reaching from single cell heterogeneity in cell systems and tissues down to the molecular level seems suitable for a human cytome project to systematically explore the molecular biocomplexity of human organisms. The analysis of already existing data from scientific studies or routine diagnostic procedures will be of immediate value in clinical medicine, for example as personalized therapy by cytomics.
通过图像或流式细胞术在单细胞水平上对组织、器官和生物体进行分子细胞表型分析，并结合有关核酸、蛋白质和代谢物（细胞基因组学、蛋白质组学和代谢组学）以及细胞功能参数（如细胞内pH值、跨膜电位或离子梯度）的生物信息学知识提取（细胞组学），可获得大量的结构和功能信息。此外，患病细胞与健康细胞之间的差异分子细胞表型为（i）通过细胞组学进行预测医学或（ii）利用生物医学细胞系统生物学对数据模式进行逆向工程以发现药物提供了分子数据模式。通过这种方式可以探索分子途径，包括检测合适的靶分子，而无需事先详细了解特定疾病机制。这在分析感染、过敏、类风湿性疾病、糖尿病或恶性肿瘤等复杂疾病时很有用。从细胞系统和组织中的单细胞异质性到分子水平的自上而下方法似乎适用于人类细胞组计划，以系统地探索人类生物体的分子生物复杂性。分析来自科学研究或常规诊断程序的现有数据在临床医学中将具有直接价值，例如作为通过细胞组学进行的个性化治疗。
