An Algorithm to Automate Yeast Segmentation and Tracking

Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation.     

Comprehensive homing endonuclease target site specificity profiling reveals evolutionary constraints and enables genome engineering applications

Homing endonucleases (HEs) promote the evolutionary persistence of selfish DNA elements by catalyzing element lateral transfer into new host organisms. The high site specificity of this lateral transfer reaction, termed homing, reflects both the length (14–40 bp) and the limited tolerance of target or homing sites for base pair changes. In order to better understand molecular determinants of homing, we systematically determined the binding and cleavage properties of all single base pair variant target sites of the canonical LAGLIDADG homing endonucleases I-CreI and I-MsoI. These Chlorophyta algal HEs have very similar three-dimensional folds and recognize nearly identical 22 bp target sites, but use substantially different sets of DNA-protein contacts to mediate site-specific recognition and cleavage. The site specificity differences between I-CreI and I-MsoI suggest different evolutionary strategies for HE persistence. These differences also provide practical guidance in target site finding, and in the generation of HE variants with high site specificity and cleavage activity, to enable genome engineering applications.     

Comprehensive computational design of mCreI homing endonuclease cleavage specificity for genome engineering

Homing endonucleases (HEs) cleave long (～ 20 bp) DNA target sites with high site specificity to catalyze the lateral transfer of parasitic DNA elements. In order to determine whether comprehensive computational design could be used as a general strategy to engineer new HE target site specificities, we used RosettaDesign (RD) to generate 3200 different variants of the mCreI LAGLIDADG HE towards 16 different base pair positions in the 22 bp mCreI target site. Experimental verification of a range of these designs demonstrated that over 2/3 (24 of 35 designs, 69%) had the intended new site specificity, and that 14 of the 15 attempted specificity shifts (93%) were achieved. These results demonstrate the feasibility of using structure-based computational design to engineer HE variants with novel target site specificities to facilitate genome engineering.     

Unsupervised Feature Learning Improves Prediction of Human Brain Activity in Response to Natural Images

Encoding and decoding in functional magnetic resonance imaging has recently emerged as an area of research to noninvasively characterize the relationship between stimulus features and human brain activity. To overcome the challenge of formalizing what stimulus features should modulate single voxel responses, we introduce a general approach for making directly testable predictions of single voxel responses to statistically adapted representations of ecologically valid stimuli. These representations are learned from unlabeled data without supervision. Our approach is validated using a parsimonious computational model of (i) how early visual cortical representations are adapted to statistical regularities in natural images and (ii) how populations of these representations are pooled by single voxels. This computational model is used to predict single voxel responses to natural images and identify natural images from stimulus-evoked multiple voxel responses. We show that statistically adapted low-level sparse and invariant representations of natural images better span the space of early visual cortical representations and can be more effectively exploited in stimulus identification than hand-designed Gabor wavelets. Our results demonstrate the potential of our approach to better probe unknown cortical representations.     

Unfolded Protein Response Signaling and Metabolic Diseases

The endoplasmic reticulum (ER) is a central organelle for protein biosynthesis, folding, and traffic. Perturbations in ER homeostasis create a condition termed ER stress and lead to activation of the complex signaling cascade called the unfolded protein response (UPR). Recent studies have documented that the UPR coordinates multiple signaling pathways and controls various physiologies in cells and the whole organism. Furthermore, unresolved ER stress has been implicated in a variety of metabolic disorders, such as obesity and type 2 diabetes. Therefore, intervening in ER stress and modulating signaling components of the UPR would provide promising therapeutics for the treatment of human metabolic diseases.