From Snapshots to Trajectories: Benchmarking Senescence-Conditioned Cell Morphology Generation

Cellular senescence, a state in which cells stop dividing but remain in tissues, drives tissue ageing and is a central readout for the development of new treatments for age-associated diseases. Yet no existing cell morphology benchmark captures this phenotype. We introduce SenoMorph, a senescence-conditioned cell morphology benchmark that pairs each single-cell image with a continuous senescence score derived from its own transcriptome. SenoMorph comprises 81,255 real cross-sectional snapshot cells across three tissues and nine cell types. To our knowledge, SenoMorph is the first public benchmark dedicated to this problem. To understand how existing generators perform on SenoMorph, we assess 9 representative baselines spanning 7 Flow Matching (FM) variants and 2 non-FM generators. Our evaluation shows that these generators, despite attaining competitive distributional scores, reverse the expected morphology trend on individual cell types. We further present SenoFlow, an FM-based generator that enforces two geometric properties under continuous conditions, batch-invariant pairing of source-target supervision and score-Lipschitz, feature-aligned dynamics at integration. SenoFlow consistently outperforms all baselines, lowering FID from 14.2 to 11.6 and raising mean $|\rho |$ from 30.7% to 87.7%. It also follows real trajectories and preserves cell identity on tracked live-cell data. We additionally release a SenoFlow-generated synthetic companion set of 812,550 score-conditioned cells that lift snapshots into trajectories to support data-scarce downstream tasks like senescent-cell classification with +3.9 pp ACC and +1.9 pp AUC gains.