Machine Learning Establishes Single-Cell Calcium Dynamics as an Early Indicator of Antibiotic Response.

ABSTRACT: Changes in bacterial physiology necessarily precede cell death in response to antibiotics. Herein we investigate the early disruption of Ca2+ homeostasis as a marker for antibiotic response. Using a machine learning framework, we quantify the temporal information encoded in single-cell Ca2+ dynamics. We find Ca2+ dynamics distinguish kanamycin sensitive and resistant cells before changes in gross cell phenotypes such as cell growth or protein stability. The onset time (pharmacokinetics) and probability (pharmacodynamics) of these aberrant Ca2+ dynamics are dose and time-dependent, even at the resolution of single-cells. Of the compounds profiled, we find Ca2+ dynamics are also an indicator of Polymyxin B activity. In Polymyxin B treated cells, we find aberrant Ca2+ dynamics precedes the en

SUBMITTER: Meyer CT 

PROVIDER: S-EPMC8148219 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature