Molecular machines stimulate intercellular calcium waves and cause muscle contraction.

Intercellular calcium waves (ICW) are complex signalling phenomena that control many essential biological activities, including smooth muscle contraction, vesicle secretion, gene expression and changes in neuronal excitability. Accordingly, the remote stimulation of ICW could result in versatile biomodulation and therapeutic strategies. Here we demonstrate that light-activated molecular machines (MM)-molecules that perform mechanical work on the molecular scale-can remotely stimulate ICW. MM consist of a polycyclic rotor and stator that rotate around a central alkene when activated with visible light. Live-cell calcium-tracking and pharmacological experiments reveal that MM-induced ICW are driven by the activation of inositol-triphosphate-mediated signalling pathways by unidirectional, fast-rotating MM. Our data suggest that MM-induced ICW can control muscle contraction in vitro in cardiomyocytes and animal behaviour in vivo in Hydra vulgaris. This work demonstrates a strategy for directly controlling cell signalling and downstream biological function using molecular-scale devices.

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning.

A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource.