RESUMEN
3D single-molecule tracking microscopy has enabled measurements of protein diffusion in living cells, offering information about protein dynamics and cellular environments. For example, different diffusive states can be resolved and assigned to protein complexes of different size and composition. However, substantial statistical power and biological validation, often through genetic deletion of binding partners, are required to support diffusive state assignments. When investigating cellular processes, real-time perturbations to protein spatial distributions is preferable to permanent genetic deletion of an essential protein. For example, optogenetic dimerization systems can be used to manipulate protein spatial distributions that could offer a means to deplete specific diffusive states observed in single-molecule tracking experiments. Here, we evaluate the performance of the iLID optogenetic system in living E. coli cells using diffraction-limited microscopy and 3D single-molecule tracking. We observed a robust optogenetic response in protein spatial distributions after 488 nm laser activation. Surprisingly, 3D single-molecule tracking results indicate activation of the optogenetic response when illuminating with high-intensity light with wavelengths at which there is minimal photon absorbance by the LOV2 domain. The preactivation can be minimized through the use of iLID system mutants, and titration of protein expression levels.
Asunto(s)
Escherichia coli , Optogenética , Dimerización , Imagen Individual de Molécula/métodos , MicroscopíaRESUMEN
Fluorescence microscopy enables spatial and temporal measurements of live cells and cellular communities. However, this potential has not yet been fully realized for investigations of individual cell behaviors and phenotypic changes in dense, three-dimensional (3D) bacterial biofilms. Accurate cell detection and cellular shape measurement in densely packed biofilms are challenging because of the limited resolution and low signal to background ratios (SBRs) in fluorescence microscopy images. In this work, we present Bacterial Cell Morphometry 3D (BCM3D), an image analysis workflow that combines deep learning with mathematical image analysis to accurately segment and classify single bacterial cells in 3D fluorescence images. In BCM3D, deep convolutional neural networks (CNNs) are trained using simulated biofilm images with experimentally realistic SBRs, cell densities, labeling methods, and cell shapes. We systematically evaluate the segmentation accuracy of BCM3D using both simulated and experimental images. Compared to state-of-the-art bacterial cell segmentation approaches, BCM3D consistently achieves higher segmentation accuracy and further enables automated morphometric cell classifications in multi-population biofilms.
Asunto(s)
Bacterias/citología , Biopelículas , Imagenología Tridimensional/métodos , Microscopía Fluorescente/métodos , Bacterias/química , Bacterias/crecimiento & desarrollo , Fenómenos Fisiológicos Bacterianos , Aprendizaje Profundo , Análisis de la Célula Individual/métodosRESUMEN
Bacterial cell walls contain peptidoglycan (PG), a scaffold that provides proper rigidity to resist lysis from internal osmotic pressure and a barrier to protect cells against external stressors. It consists of repeating sugar units with a linkage to a stem peptide that becomes cross-linked by cell wall transpeptidases (TP). While synthetic PG fragments containing l-lysine in the third position on the stem peptide are easier to access, those with meso-diaminopimelic acid (m-DAP) pose a severe synthetic challenge. Herein, we describe a solid phase synthetic scheme based on widely available building blocks to assemble meso-cystine (m-CYT), which mimics key structural features of m-DAP. To demonstrate proper mimicry of m-DAP, cell wall probes were synthesized with m-CYT in place of m-DAP and evaluated for their metabolic processing in live bacterial cells. We found that m-CYT-based cell wall probes were properly processed by TPs in various bacterial species that endogenously contain m-DAP in their PG. Additionally, we have used hybrid quantum mechanical/molecular mechanical (QM/MM) and molecular dynamics (MD) simulations to explore the influence of m-DAP analogs on the PG cross-linking. The results showed that the cross-linking mechanism of transpeptidases occurred through a concerted process. We anticipate that this strategy, which is based on the use of inexpensive and commercially available building blocks, can be widely adopted to provide greater accessibility of PG mimics for m-DAP containing organisms.