We present a method for automatic full-precision alignment of the images in a tomographic tilt series. Full-precision automatic alignment of cryo electron microscopy images has remained a difficult challenge to date, due to the limited electron dose and low image contrast. These facts lead to poor signal to noise ratio (SNR) in the images, which causes automatic feature trackers to generate errors, even with high contrast gold particles as fiducial features. To enable fully automatic alignment for full-precision reconstructions, we frame the problem probabilistically as finding the most likely particle tracks given a set of noisy images, using contextual information to make the solution more robust to the noise in each image. To solve this maximum likelihood problem, we use Markov Random Fields (MRF) to establish the correspondence of features in alignment and robust optimization for projection model estimation. The resulting algorithm, called Robust Alignment and Projection Estimation for Tomographic Reconstruction, or RAPTOR, has not needed any manual intervention for the difficult datasets we have tried, and has provided sub-pixel alignment that is as good as the manual approach by an expert user. We are able to automatically map complete and partial marker trajectories and thus obtain highly accurate image alignment. Our method has been applied to challenging cryo electron tomographic datasets with low SNR from intact bacterial cells, as well as several plastic section and X-ray datasets.

Analyzing immune cell interactions in the bone marrow is vital for understanding hematopoiesis and bone homeostasis. Three-dimensional analysis of the complete, intact bone marrow within the cortex of whole long bones remains a challenge, especially at subcellular resolution. We present a method that stabilizes the marrow and provides subcellular resolution of fluorescent signals throughout the murine femur, enabling identification and spatial characterization of hematopoietic and stromal cell subsets. By combining a pre-processing algorithm for stripe artifact removal with a machine-learning approach, we demonstrate reliable cell segmentation down to the deepest bone marrow regions. This reveals age-related changes in the marrow. It highlights the interaction between CXCR1 cells and the vascular system in homeostasis, in contrast to other myeloid cell types, and reveals their spatial characteristics after injury. The broad applicability of this method will contribute to a better understanding of bone marrow biology.

A waveform isolation method is described for the mass-selective transmission of ions through quadrupole mass filters, and it is implemented on a new tandem mass analyzer instrument. The method features the application of broad-band waveforms comprising appropriate frequencies to cause mass-selective instability in ions of particular mass-to-charge (m/z) and to transmit all others. The experiment is implemented in a tandem quadrupole system in which the first mass filter is a rectilinear ion trap (RIT) operated in a continuous mass-selective mode to transmit ions of ions of one or more arbitrarily selected m/z value(s). The second analyzer was used to verify the quality of the mass selection achieved using the first analyzer via conventional quadrupole ion trap mass-selective instability scanning. A new subtype of product ion tandem mass spectrometry (MS/MS) scan, termed the summed product ion scan, is demonstrated with a mixture of biological compounds. It is used to characterize product ions arising after simultaneous isolation and collisional activation of multiple precursor species, in this case ions of the same analyte generated in different charge states. The summed product ion scan can be useful for enhancing sensitivity for the analyte of interest or for providing more comprehensive information about an analyte than is available by monitoring a single ionized form of the analyte. The analytical performance of the waveform isolation method is tested using simple drug mixtures, and its potential for increasing overall yields in preparative mass spectrometry is explored briefly. It is shown that efficiencies of ca. 70% of ion transfer to a surface for ion soft landing surface can be achieved. The upper mass range is limited by axial acceleration arising from the stretched geometry, and one solution to this problem is provided.

Bacteriophage HK97 maturation involves discrete intermediate particle forms, comparable to transitional states in protein folding, before reaching its mature form. The process starts by formation of a metastable prohead, poised for exothermic expansion triggered by DNA packaging. During maturation, the capsid subunit transitions from a strained to a canonical tertiary conformation and this has been postulated to be the driving mechanism for initiating expansion via switching hexameric capsomer architecture from skewed to 6-fold symmetric. We report the subnanometer electron-cryomicroscopy reconstruction of the HK97 first expansion intermediate before any crosslink formation. This form displays 6-fold symmetric hexamers, but capsid subunit tertiary structures exhibit distortions comparable to the prohead forms. We propose that coat subunit strain release acts in synergy with the first crosslinks to drive forward maturation. Finally, we speculate that the energetic features of this transition may result from increased stability of intermediates during maturation via enhanced inter-subunit interactions.

Images can be segmented by first using a classifier to predict an affinity graph that reflects the degree to which image pixels must be grouped together and then partitioning the graph to yield a segmentation. Machine learning has been applied to the affinity classifier to produce affinity graphs that are good in the sense of minimizing edge misclassification rates. However, this error measure is only indirectly related to the quality of segmentations produced by ultimately partitioning the affinity graph. We present the first machine learning algorithm for training a classifier to produce affinity graphs that are good in the sense of producing segmentations that directly minimize the Rand index, a well known segmentation performance measure. The Rand index measures segmentation performance by quantifying the classification of the connectivity of image pixel pairs after segmentation. By using the simple graph partitioning algorithm of finding the connected components of the thresholded affinity graph, we are able to train an affinity classifier to directly minimize the Rand index of segmentations resulting from the graph partitioning. Our learning algorithm corresponds to the learning of maximin affinities between image pixel pairs, which are predictive of the pixel-pair connectivity.

The shapes of dendritic arbors are fascinating and important, yet the principles underlying these complex and diverse structures remain unclear. Here, we analyzed basal dendritic arbors of 2,171 pyramidal neurons sampled from mammalian brains and discovered 3 statistical properties: the dendritic arbor size scales with the total dendritic length, the spatial correlation of dendritic branches within an arbor has a universal functional form, and small parts of an arbor are self-similar. We proposed that these properties result from maximizing the repertoire of possible connectivity patterns between dendrites and surrounding axons while keeping the cost of dendrites low. We solved this optimization problem by drawing an analogy with maximization of the entropy for a given energy in statistical physics. The solution is consistent with the above observations and predicts scaling relations that can be tested experimentally. In addition, our theory explains why dendritic branches of pyramidal cells are distributed more sparsely than those of Purkinje cells. Our results represent a step toward a unifying view of the relationship between neuronal morphology and function.

Command-like descending neurons can induce many behaviors, such as backward locomotion, escape, feeding, courtship, egg-laying, or grooming (we define 'command-like neuron' as a neuron whose activation elicits or 'commands' a specific behavior). In most animals it remains unknown how neural circuits switch between antagonistic behaviors: via top-down activation/inhibition of antagonistic circuits or via reciprocal inhibition between antagonistic circuits. Here we use genetic screens, intersectional genetics, circuit reconstruction by electron microscopy, and functional optogenetics to identify a bilateral pair of larval 'mooncrawler descending neurons' (MDNs) with command-like ability to coordinately induce backward locomotion and block forward locomotion; the former by stimulating a backward-active premotor neuron, and the latter by disynaptic inhibition of a forward-specific premotor neuron. In contrast, direct monosynaptic reciprocal inhibition between forward and backward circuits was not observed. Thus, MDNs coordinate a transition between antagonistic larval locomotor behaviors. Interestingly, larval MDNs persist into adulthood, where they can trigger backward walking. Thus, MDNs induce backward locomotion in both limbless and limbed animals.

Mean-Field theory is extended to recurrent networks of spiking neurons endowed with short-term depression (STD) of synaptic transmission. The extension involves the use of the distribution of interspike intervals of an integrate-and-fire neuron receiving a Gaussian current, with a given mean and variance, in input. This, in turn, is used to obtain an accurate estimate of the resulting postsynaptic current in presence of STD. The stationary states of the network are obtained requiring self-consistency for the currents-those driving the emission processes and those generated by the emitted spikes. The model network stores in the distribution of two-state efficacies of excitatory-to-excitatory synapses, a randomly composed set of external stimuli. The resulting synaptic structure allows the network to exhibit selective persistent activity for each stimulus in the set. Theory predicts the onset of selective persistent, or working memory (WM) activity upon varying the constitutive parameters (e.g. potentiated/depressed long-term efficacy ratio, parameters associated with STD), and provides the average emission rates in the various steady states. Theoretical estimates are in remarkably good agreement with data "recorded" in computer simulations of the microscopic model.

We use super-resolution interferometric photoactivation and localization microscopy (iPALM) and a constrained photoactivatable fluorescent protein integrin fusion to measure the displacement of the head of integrin lymphocyte function-associated 1 (LFA-1) resulting from integrin conformational change on the cell surface. We demonstrate that the distance of the LFA-1 head increases substantially between basal and ligand-engaged conformations, which can only be explained at the molecular level by integrin extension. We further demonstrate that one class of integrin antagonist maintains the bent conformation, while another antagonist class induces extension. Our molecular scale measurements on cell-surface LFA-1 are in excellent agreement with distances derived from crystallographic and electron microscopy structures of bent and extended integrins. Our distance measurements are also in excellent agreement with a previous model of LFA-1 bound to ICAM-1 derived from the orientation of LFA-1 on the cell surface measured using fluorescence polarization microscopy.

Among the proteins required for lipid metabolism in Mycobacterium tuberculosis are a significant number of uncharacterized serine hydrolases, especially lipases and esterases. Using a streamlined synthetic method, a library of immolative fluorogenic ester substrates was expanded to better represent the natural lipidomic diversity of Mycobacterium. This expanded fluorogenic library was then used to rapidly characterize the global structure activity relationship (SAR) of mycobacterial serine hydrolases in M. smegmatis under different growth conditions. Confirmation of fluorogenic substrate activation by mycobacterial serine hydrolases was performed using nonspecific serine hydrolase inhibitors and reinforced the biological significance of the SAR. The hydrolases responsible for the global SAR were then assigned using gel-resolved activity measurements, and these assignments were used to rapidly identify the relative substrate specificity of previously uncharacterized mycobacterial hydrolases. These measurements provide a global SAR of mycobacterial hydrolase activity, a picture of cycling hydrolase activity, and a detailed substrate specificity profile for previously uncharacterized hydrolases.