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.

The N-terminally myristoylated matrix (MA) domain of the HIV-1 Gag polyprotein promotes virus assembly by targeting Gag to the inner leaflet of the plasma membrane. Recent studies indicate that, prior to membrane binding, MA associates with cytoplasmic tRNAs (including tRNA), and in vitro studies of tRNA-dependent MA interactions with model membranes have led to proposals that competitive tRNA interactions contribute to membrane discrimination. We have characterized interactions between native, mutant, and unmyristylated (myr-) MA proteins and recombinant tRNA by NMR spectroscopy and isothermal titration calorimetry. NMR experiments confirm that tRNA interacts with a patch of basic residues that are also important for binding to the plasma membrane marker, phosphatidylinositol-4,5-bisphosphate [PI(4,5)P]. Unexpectedly, the affinity of MA for tRNA (K = 0.63 ± 0.03 μM) is approximately 1 order of magnitude greater than its affinity for PI(4,5)P-enriched liposomes (K = 10.2 ± 2.1 μM), and NMR studies indicate that tRNA binding blocks MA association with liposomes, including those enriched with PI(4,5)P, phosphatidylserine, and cholesterol. However, the affinity of MA for tRNA is diminished by mutations or sample conditions that promote myristate exposure. Since Gag-Gag interactions are known to promote myristate exposure, our findings support virus assembly models in which membrane targeting and genome binding are mechanistically coupled.

Protein-membrane reversible association is critical to many cell and physiological functions. In most cases, protein association to the membrane is not stoichiometric and is best described by the partition coefficient rather than the dissociation constant. Fluorescence Correlation Spectroscopy (FCS) is a suitable methodology for the determination of the partition coefficient (K p ) of proteins into membranes (1, 2). Using FCS, K p is typically determined from the diffusion of lipid vesicles loaded with fluorescently-tagged proteins. However, the uneven distribution of protein onto vesicles and the presence of unbound fluorescently-tagged protein due to dissociation events or the coexistence of a non-membrane competent protein conformer biases the retrieved quantities. In this work we investigate the effects of unbound protein in K p quantification by FCS using antibody-membrane association as a model. In addition, we discuss the likely distribution of proteins onto vesicles in the case of reversible protein-membrane association, when the conventionally used Poisson distribution does not fully describe association/dissociation events. We compare the experimentally determined K p against the results obtained upon simulating the diffusion of vesicles loaded with fluorescently-tagged antibodies following different statistical distributions. Finally, we discuss the conditions in which the Photon Counting Histogram (PCH) can be used to quantify the distribution of proteins associated to the vesicles. (1.) Melo, A. M., M. Prieto, and A. Coutinho. 2011. The effect of variable liposome brightness on quantifying lipid-protein interactions using fluorescence correlation spectroscopy.

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 highly crosslinked by cell wall transpeptidases (TP). While synthetic PG fragments containing L-Lysine in the 3 rd 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 crosslinking. The results showed that crosslinking 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.