ストレスを受けていない細胞は似ているが、ストレスを受けた細胞は異なる：酵母のストレス応答における環境および単一細胞の不均一性

Cellular stress responses are central to survival, adaptation, and disease pathology, yet much of what we know comes from averages that mask heterogeneity. Using high-throughput single-cell RNA sequencing in Saccharomyces cerevisiae , we profiled approximately 13,000 cells exposed to diverse stresses. By annotating transposable elements (TEs) alongside coding genes, we captured expression across over 6,600 features. Leveraging these rich single-cell data, we redefined what it means to identify a "stress response," shifting from the strongest average signals to the features that best predict whether an individual cell has experienced stress. These distinct definitions revealed different gene sets, underscoring how average versus single-cell views diverge. We found that stress-induced programs are highly condition-specific and perform poorly at identifying whether a cell has experienced stress, even within the environment where they were defined. By contrast, stress-repressed programs, dominated by ribosome-related genes, remain predictive across datasets, revealing a stable signature that distinguishes stressed from unstressed cells. This asymmetry recalls the Anna Karenina principle: Unstressed ("happy") cells resemble one another in shared growth programs, while stressed ("unhappy") cells diverge within and across environments. The divergence we observe does not always manifest as a continuum. We found stressed cells separating into subgroups that pursue alternative transcriptional responses, with some mounting canonical stress programs and others diverting transcription to TEs. Such mutually exclusive behaviors may highlight decision points that may influence survival, genome stability, and evolutionary trajectories. However, at the population level, canonical programs and TEs appear positively correlated since both increase under stress. At the single-cell level, they are inversely related, with cells seemingly committing to one program or the other at any given time. This false signal illustrates how averaging can be not only obscuring but also misleading and why single-cell resolution is required to understand stress responses.