Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104.
Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, PA 19104.
Proc Natl Acad Sci U S A. 2024 Sep 24;121(39):e2404586121. doi: 10.1073/pnas.2404586121. Epub 2024 Sep 18.
Developmental biology-inspired strategies for tissue-building have extraordinary promise for regenerative medicine, spurring interest in the relationship between cell biophysical properties and morphological transitions. However, mapping gene or protein expression data to cell biophysical properties to physical morphogenesis remains challenging with current techniques. Here, we present ultiplexed dhesion and raction of ells at igh ield (MATCHY). MATCHY advances the multiplexing and throughput capabilities of existing traction force and cell-cell adhesion assays using microfabrication and a semiautomated computation scheme with machine learning-driven cell segmentation. Both biophysical assays are coupled with serial downstream immunofluorescence to extract cell type/signaling state information. MATCHY is especially suited to complex primary tissue-, organoid-, or biopsy-derived cell mixtures since it does not rely on a priori knowledge of cell surface markers, cell sorting, or use of lineage-specific reporter animals. We first validate MATCHY on canine kidney epithelial cells engineered for rearranged during transfection (RET) tyrosine kinase expression and quantify a relationship between downstream signaling and cell traction. We then use MATCHY to create a biophysical atlas of mouse embryonic kidney primary cells and identify distinct biophysical states along the nephron differentiation trajectory. Our data complement expression-level knowledge of adhesion molecule changes that accompany nephron differentiation with quantitative biophysical information. These data reveal an "energetic ratchet" that accounts for spatial trends in nephron progenitor cell condensation as they differentiate into early nephron structures, which we validate through agent-based computational simulation. MATCHY offers semiautomated cell biophysical characterization at >10,000-cell throughput, an advance benefiting fundamental studies and new synthetic tissue strategies for regenerative medicine.
基于发育生物学的组织构建策略为再生医学带来了非凡的前景，激发了人们对细胞生物物理特性与形态发生之间关系的兴趣。然而，利用当前技术将基因或蛋白质表达数据映射到细胞生物物理特性和物理形态发生仍然具有挑战性。在这里，我们提出了超高通量细胞黏附和力分析（MATCHY）。MATCHY 利用微加工和基于机器学习的半自动计算方案与细胞分割，推进了现有牵引力和细胞-细胞黏附测定的多路复用和通量能力。这两种生物物理测定都与串联的下游免疫荧光相结合，以提取细胞类型/信号状态信息。MATCHY 特别适用于复杂的原代组织、类器官或活检衍生的细胞混合物，因为它不依赖于细胞表面标志物的先验知识、细胞分选或使用谱系特异性报告动物。我们首先在经过转染重排（RET）酪氨酸激酶表达工程化的犬肾上皮细胞上验证了 MATCHY，并量化了下游信号与细胞牵引力之间的关系。然后，我们使用 MATCHY 构建了小鼠胚胎肾原代细胞的生物物理图谱，并确定了沿肾单位分化轨迹的不同生物物理状态。我们的数据补充了伴随肾单位分化的黏附分子变化的表达水平知识，具有定量的生物物理信息。这些数据揭示了一种“能量棘轮”，可以解释肾单位祖细胞在分化为早期肾单位结构时的空间趋势，我们通过基于代理的计算模拟验证了这一点。MATCHY 提供了超过 10000 个细胞/秒的半自动细胞生物物理特性分析，这一进展有利于基础研究和再生医学的新合成组织策略。
