Unicellular marine microalgae are responsible for one of the largest carbon sinks on Earth. This is in part due to intracellular formation of calcium carbonate scales termed coccoliths. ...Traditionally, the influence of changing environmental conditions on this process has been estimated using poorly constrained analogies to crystallization mechanisms in bulk solution, yielding ambiguous predictions. Here, we elucidated the intracellular nanoscale environment of coccolith formation in the model species
using cryoelectron tomography. By visualizing cells at various stages of the crystallization process, we reconstructed a timeline of coccolith development. The three-dimensional data portray the native-state structural details of coccolith formation, uncovering the crystallization mechanism, and how it is spatially and temporally controlled. Most strikingly, the developing crystals are only tens of nanometers away from delimiting membranes, resulting in a highly confined volume for crystal growth. We calculate that the number of soluble ions that can be found in such a minute volume at any given time point is less than the number needed to allow the growth of a single atomic layer of the crystal and that the uptake of single protons can markedly affect nominal pH values. In such extreme confinement, the crystallization process is expected to depend primarily on the regulation of ion fluxes by the living cell, and nominal ion concentrations, such as pH, become the result, rather than a driver, of the crystallization process. These findings call for a new perspective on coccolith formation that does not rely exclusively on solution chemistry.
Cryo-electron tomograms capture a wealth of structural information on the molecular constituents of cells and tissues. We present DeePiCt (deep picker in context), an open-source deep-learning ...framework for supervised segmentation and macromolecular complex localization in cryo-electron tomography. To train and benchmark DeePiCt on experimental data, we comprehensively annotated 20 tomograms of Schizosaccharomyces pombe for ribosomes, fatty acid synthases, membranes, nuclear pore complexes, organelles, and cytosol. By comparing DeePiCt to state-of-the-art approaches on this dataset, we show its unique ability to identify low-abundance and low-density complexes. We use DeePiCt to study compositionally distinct subpopulations of cellular ribosomes, with emphasis on their contextual association with mitochondria and the endoplasmic reticulum. Finally, applying pre-trained networks to a HeLa cell tomogram demonstrates that DeePiCt achieves high-quality predictions in unseen datasets from different biological species in a matter of minutes. The comprehensively annotated experimental data and pre-trained networks are provided for immediate use by the community.
