Data from: Enhanced combinatorial analysis of tumor cell-ECM interactions using design-of-experiment optimized microarrays

The dysregulated and fibrotic tumor microenvironment of hepatocellular
carcinoma (HCC) delays diagnosis and presents many complex signals that
drive disease progression. To better recapitulate this microenvironment,
we have enhanced our established protein microarray platform by
integrating the design of experiments (DoE) methodology with
high-throughput cell microarray screening. This innovative approach
systematically interrogates the intricate roles of matrix stiffness
(spanning healthy and fibrotic conditions), extracellular matrix (ECM)
composition, and protein concentration, while simultaneously examining
their interdependent interactions. By leveraging DoE principles, we were
able to explore 117 unique microenvironments on a single microscope slide,
ultimately generating a comprehensive dataset of 234 different
microenvironments without compromising statistical rigor. Our enhanced
screening system enabled the identification of unique microenvironmental
interactions critically significant in dictating cellular responses,
including adhesion, survival, proliferation, epithelial-to-mesenchymal
transition, and drug resistance markers. Utilizing advanced statistical
techniques such as linear models and principal component analysis, we
characterized phenotypic clusters defined by precise microenvironmental
cues. This work presents a robust, high-throughput microarray screening
system that comprehensively explores the contributions of 9
physiologically relevant extracellular matrix proteins and matrix
stiffness in modulating cellular behavior and disease progression through
a methodologically sophisticated and statistically sound approach.

肝细胞癌（hepatocellular carcinoma, HCC）的失调纤维化肿瘤微环境会延缓诊断，并提供诸多驱动疾病进展的复杂信号。为更好地复现该微环境，我们对已建立的蛋白质微阵列平台进行了升级，将实验设计（design of experiments, DoE）方法与高通量细胞微阵列筛选技术相结合。该创新性方法系统地探究了基质刚度（涵盖健康与纤维化状态）、细胞外基质（extracellular matrix, ECM）组成以及蛋白浓度的复杂作用，同时考察了它们之间的相互依赖作用。借助实验设计的原理，我们得以在单张显微镜载玻片上探究117种独特的微环境，最终在不牺牲统计严谨性的前提下，获得了涵盖234种不同微环境的完整数据集。升级后的筛选系统得以识别出对调控细胞反应至关重要的独特微环境相互作用，这些反应包括黏附、存活、增殖、上皮间质转化以及耐药相关标志物的表达。我们利用线性模型、主成分分析等先进统计技术，对由精准微环境线索定义的表型簇进行了表征。本研究提出了一套稳健的高通量微阵列筛选系统，该系统通过方法学严谨且统计可靠的手段，全面探究了9种生理相关细胞外基质蛋白与基质刚度在调控细胞行为与疾病进展中的作用。