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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 presentmultiplexedadhesion andtraction ofcells athighyield (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.