TY - JOUR TI - Recovering mixtures of fast-diffusing states from short single-particle trajectories AU - Heckert, Alec AU - Dahal, Liza AU - Tjian, Robert AU - Darzacq, Xavier A2 - Smal, Ihor A2 - Akhmanova, Anna A2 - Paul, Maarten W VL - 11 PY - 2022 DA - 2022/09/06 SP - e70169 C1 - eLife 2022;11:e70169 DO - 10.7554/eLife.70169 UR - https://doi.org/10.7554/eLife.70169 AB - Single-particle tracking (SPT) directly measures the dynamics of proteins in living cells and is a powerful tool to dissect molecular mechanisms of cellular regulation. Interpretation of SPT with fast-diffusing proteins in mammalian cells, however, is complicated by technical limitations imposed by fast image acquisition. These limitations include short trajectory length due to photobleaching and shallow depth of field, high localization error due to the low photon budget imposed by short integration times, and cell-to-cell variability. To address these issues, we investigated methods inspired by Bayesian nonparametrics to infer distributions of state parameters from SPT data with short trajectories, variable localization precision, and absence of prior knowledge about the number of underlying states. We discuss the advantages and disadvantages of these approaches relative to other frameworks for SPT analysis. KW - mammalian cell culture KW - U2OS osteosarcoma KW - single-particle tracking JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -