We introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional β-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to predict plausible locations of structures in cells where these structures were not imaged. The trained model can also be used to quantify the variation in the location of subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs.

The outer membrane (OM) of Gram-negative bacteria is a complex bilayer composed of proteins, phospholipids, lipoproteins, and lipopolysaccharides. Despite recent advances revealing the molecular pathways underlying protein and lipopolysaccharide incorporation into the OM, the spatial distribution and dynamic regulation of these processes remain poorly understood. Here, we used sequence-specific fluorescent labeling to map the incorporation patterns of an OM-porin protein, LamB, by labeling proteins only after epitope exposure on the cell surface. Newly synthesized LamB appeared in discrete puncta, rather than evenly distributed over the cell surface. Further growth of bacteria after labeling resulted in divergence of labeled LamB puncta, consistent with a spatial pattern of OM growth in which new, unlabeled material was also inserted in patches. At the poles, puncta remained relatively stationary through several rounds of division, a salient characteristic of the OM protein population as a whole. We propose a biophysical model of growth in which patches of new OM material are added in discrete bursts that evolve in time according to Stokes flow and are randomly distributed over the cell surface. Simulations based on this model demonstrate that our experimental observations are consistent with a bursty insertion pattern without spatial bias across the cylindrical cell surface, with approximately one burst of ≈ 10(-2) µm(2) of OM material per two minutes per µm(2). Growth by insertion of discrete patches suggests that stochasticity plays a major role in patterning and material organization in the OM.

MOTIVATION: Many proteins with vastly dissimilar sequences are found to share a common fold, as evidenced in the wealth of structures now available in the Protein Data Bank. One idea that has found success in various applications is the concept of a reduced amino acid alphabet, wherein similar amino acids are clustered together. Given the structural similarity exhibited by many apparently dissimilar sequences, we undertook this study looking for improvements in fold recognition by comparing protein sequences written in a reduced alphabet. RESULTS: We tested over 150 of the amino acid clustering schemes proposed in the literature with all-versus-all pairwise sequence alignments of sequences in the Distance mAtrix aLIgnment database. We combined several metrics from information retrieval popular in the literature: mean precision, area under the Receiver Operating Characteristic curve and recall at a fixed error rate and found that, in contrast to previous work, reduced alphabets in many cases outperform full alphabets. We find that reduced alphabets can perform at a level comparable to full alphabets in correct pairwise alignment of sequences and can show increased sensitivity to pairs of sequences with structural similarity but low-sequence identity. Based on these results, we hypothesize that reduced alphabets may also show performance gains with more sophisticated methods such as profile and pattern searches. AVAILABILITY: A table of results as well as the substitution matrices and residue groupings from this study can be downloaded from (http://www.rpgroup.caltech.edu/publications/supplements/alphabets).

We present a noniterative image cross-correlation approach to track translation and rotation of crawling cells in time-lapse video microscopy sequences. The method does not rely on extracting features or moments, and therefore does not impose specific requirements on the type of microscopy used for imaging. Here we use phase-contrast images. We calculate cell rotation and translation from one image to the next in two stages. First, rotation is calculated by cross correlating the images' polar-transformed magnitude spectra (Fourier magnitudes). Rotation of the cell about any center in the original images results in translation in this representation. Then, we rotate the first image such that the cell has the same orientation in both images, and cross correlate this image with the second image to calculate translation. By calculating the rotation and translation over each interval in the movie, and thereby tracking the cell's position and orientation in each image, we can then map from the stationary reference frame in which the cell was observed to the cell's moving coordinate system. We describe our modifications enabling application to nonidentical images from video sequences of moving cells, and compare this method's performance with that of a feature extraction method and an iterative optimization method.