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Temperature can impact every reaction and molecular interaction essential to a cell. For organisms that cannot regulate their own temperature, a major challenge is how to adapt to temperatures that fluctuate unpredictability and on variable timescales. Biomolecular condensation offers a possible mechanism for encoding temperature-responsiveness and robustness into cell biochemistry and organization. To explore this idea, we examined temperature adaptation in a filamentous-growing fungus called Ashbya gossypii that engages biomolecular condensates containing the RNA-binding protein Whi3 to regulate mitosis and morphogenesis. We collected wild isolates of Ashbya that originate in different climates and found that mitotic asynchrony and polarized growth, which are known to be controlled by the condensation of Whi3, are temperature sensitive. Sequence analysis in the wild strains revealed changes to specific domains within Whi3 known to be important in condensate formation. Using an in vitro condensate reconstitution assay we found that temperature impacts the relative abundance of protein to RNA within condensates and that this directly impacts the material properties of the droplets. Finally, we found that exchanging Whi3 genes between warm and cold isolates was sufficient to rescue some, but not all, condensate-related phenotypes. Together these data demonstrate that material properties of Whi3 condensates are temperature sensitive, that these properties are important for function, and that sequence optimizes properties for a given climate., Competing Interests: Declaration of interests: Authors declare no competing interests.

Assessing dynamic processes at single molecule scales is key toward capturing life at the level of its molecular actors. Widefield superresolution methods, such as STORM, PALM, and PAINT, provide nanoscale localization accuracy, even when distances between fluorescently labeled single molecules ("emitters") fall below light's diffraction limit. However, as these superresolution methods rely on rare photophysical events to distinguish emitters from both each other and background, they are largely limited to static samples. In contrast, here we leverage spatiotemporal correlations of dynamic widefield imaging data to extend superresolution to simultaneous multiple emitter tracking without relying on photodynamics even as emitter distances from one another fall below the diffraction limit. We simultaneously determine emitter numbers and their tracks (localization and linking) with the same localization accuracy per frame as widefield superresolution does for immobilized emitters under similar imaging conditions (≈50nm). We demonstrate our results for both in cellulo data and, for benchmarking purposes, on synthetic data. To this end, we avoid the existing tracking paradigm relying on completely or partially separating the tasks of emitter number determination, localization of each emitter, and linking emitter positions across frames. Instead, we develop a fully joint posterior distribution over the quantities of interest, including emitter tracks and their total, otherwise unknown, number within the Bayesian nonparametric paradigm. Our posterior quantifies the full uncertainty over emitter numbers and their associated tracks propagated from origins including shot noise and camera artefacts, pixelation, stochastic background, and out-of-focus motion. Finally, it remains accurate in more crowded regimes where alternative tracking tools cannot be applied.