Imaging large samples at the resolution offered by electron microscopy is typically achieved by sequentially recording overlapping tiles that are later combined to seamless mosaics. Mosaics of serial sections are aligned to reconstruct three-dimensional volumes. To achieve this, image distortions and artifacts as introduced during sample preparation or imaging need to be removed.

In this chapter, we will discuss typical sources of artifacts and distortion, and we will learn how to use the open source software TrakEM2 to correct them.

The endosomal sorting complex required for transport (ESCRT)-III complex, capable of polymerization and remodeling, participates in abscission of the intercellular membrane bridge connecting two daughter cells at the end of cytokinesis. Here, we integrate quantitative imaging of ESCRT-III during cytokinetic abscission with biophysical properties of ESCRT-III complexes to formulate and test a computational model for ESCRT-mediated cytokinetic abscission. We propose that cytokinetic abscission is driven by an ESCRT-III fission complex, which arises from ESCRT-III polymerization at the edge of the cytokinetic midbody structure, located at the center of the intercellular bridge. Formation of the fission complex is completed by remodeling and breakage of the ESCRT-III polymer assisted by VPS4. Subsequent spontaneous constriction of the fission complex generates bending deformation of the intercellular bridge membrane. The related membrane elastic force propels the fission complex along the intercellular bridge away from the midbody until it reaches an equilibrium position, determining the scission site. Membrane attachment to the dome-like end-cap of the fission complex drives membrane fission, completing the abscission process. We substantiate the model by theoretical analysis of the membrane elastic energy and by experimental verification of the major model assumptions.

The brain is worthy of study because it is in charge of behavior. A flurry of recent technical advances in measuring and quantifying naturalistic behaviors provide an important opportunity for advancing brain science. However, the problem of understanding unrestrained behavior in the context of neural recordings and manipulations remains unsolved, and developing approaches to addressing this challenge is critical. Here we discuss considerations in computational neuroethology-the science of quantifying naturalistic behaviors for understanding the brain-and propose strategies to evaluate progress. We point to open questions that require resolution and call upon the broader systems neuroscience community to further develop and leverage measures of naturalistic, unrestrained behavior, which will enable us to more effectively probe the richness and complexity of the brain.

Electrophysiology has long been the workhorse of neuroscience, allowing scientists to record with millisecond precision the action potentials generated by neurons in vivo. Recently, calcium imaging of fluorescent indicators has emerged as a powerful alternative. This technique has its own strengths and weaknesses and unique data processing problems and interpretation confounds. Here we review the computational methods that convert raw calcium movies to estimates of single neuron spike times with minimal human supervision. By computationally addressing the weaknesses of calcium imaging, these methods hold the promise of significantly improving data quality. We also introduce a new metric to evaluate the output of these processing pipelines, which is based on the cluster isolation distance routinely used in electrophysiology.

The perforant-path projection to the hippocampus forms synapses in the apical tuft of CA1 pyramidal neurons. We used computer modeling to examine the function of these distal synaptic inputs, which led to three predictions that we confirmed in experiments using rat hippocampal slices. First, activation of CA1 neurons by the perforant path is limited, a result of the long distance between these inputs and the soma. Second, activation of CA1 neurons by the perforant path depends on the generation of dendritic spikes. Third, the forward propagation of these spikes is unreliable, but can be facilitated by modest activation of Schaffer-collateral synapses in the upper apical dendrites. This 'gating' of dendritic spike propagation may be an important activation mode of CA1 pyramidal neurons, and its modulation by neurotransmitters or long-term, activity-dependent plasticity may be an important feature of dendritic integration during mnemonic processing in the hippocampus.

The homeodomain-containing transcription factor NKX3.1 is a putative prostate tumor suppressor that is expressed in a largely prostate-specific and androgen-regulated manner. Loss of NKX3.1 protein expression is common in human prostate carcinomas and prostatic intraepithelial neoplasia (PIN) lesions and correlates with tumor progression. Disruption of the murine Nkx3.1 gene results in defects in prostate branching morphogenesis, secretions, and growth. To more closely mimic the pattern of NKX3.1 loss that occurs in human prostate tumors, we have used Cre- and loxP-mediated recombination to delete the Nkx3.1 gene in the prostates of adult transgenic mice. Conditional deletion of one or both alleles of Nkx3.1 leads to the development of preinvasive lesions that resemble PIN. The pattern of expression of several biomarkers (Ki-67, E-cadherin, and high-molecular-weight cytokeratins) in these PIN lesions resembled that observed in human cases of PIN. Furthermore, PIN foci in mice with conditional deletion of a single Nkx3.1 allele lose expression of the wild-type allele. Our results support the role of NKX3.1 as a prostate tumor suppressor and indicate a role for this gene in tumor initiation.

The spatiotemporal activities of astrocyte Ca(2+) signaling in mature neuronal circuits remain unclear. We used genetically encoded Ca(2+) and glutamate indicators as well as pharmacogenetic and electrical control of neurotransmitter release to explore astrocyte activity in the hippocampal mossy fiber pathway. Our data revealed numerous localized, spontaneous Ca(2+) signals in astrocyte branches and territories, but these were not driven by neuronal activity or glutamate. Moreover, evoked astrocyte Ca(2+) signaling changed linearly with the number of mossy fiber action potentials. Under these settings, astrocyte responses were global, suppressed by neurotransmitter clearance, and mediated by glutamate and GABA. Thus, astrocyte engagement in the fully developed mossy fiber pathway was slow and territorial, contrary to that frequently proposed for astrocytes within microcircuits. We show that astrocyte Ca(2+) signaling functionally segregates large volumes of neuropil and that these transients are not suited for responding to, or regulating, single synapses in the mossy fiber pathway.

Evaluation of confidence about one's knowledge is key to the brain's ability to monitor cognition. To investigate the neural mechanism of confidence assessment, we examined a biologically realistic spiking network model and found that it reproduced salient behavioral observations and single-neuron activity data from a monkey experiment designed to study confidence about a decision under uncertainty. Interestingly, the model predicts that changes of mind can occur in a mnemonic delay when confidence is low; the probability of changes of mind increases (decreases) with task difficulty in correct (error) trials. Furthermore, a so-called "hard-easy effect" observed in humans naturally emerges, i.e., behavior shows underconfidence (underestimation of correct rate) for easy or moderately difficult tasks and overconfidence (overestimation of correct rate) for very difficult tasks. Importantly, in the model, confidence is computed using a simple neural signal in individual trials, without explicit representation of probability functions. Therefore, even a concept of metacognition can be explained by sampling a stochastic neural activity pattern.

We recently identified ten novel SLE susceptibility loci in Asians and uncovered several additional suggestive loci requiring further validation. This study aimed to replicate five of these suggestive loci in a Han Chinese cohort from Hong Kong, followed by meta-analysis (11,656 cases and 23,968 controls) on previously reported Asian and European populations, and to perform bioinformatic analyses on all 82 reported SLE loci to identify shared regulatory signatures. We performed a battery of analyses for these five loci, as well as joint analyses on all 82 SLE loci. All five loci passed genome-wide significance: MYNN (rs10936599, Pmeta = 1.92 × 10-13, OR = 1.14), ATG16L2 (rs11235604, Pmeta = 8.87 × 10 -12, OR = 0.78), CCL22 (rs223881, Pmeta = 5.87 × 10-16, OR = 0.87), ANKS1A (rs2762340, Pmeta = 4.93 × 10-15, OR = 0.87) and RNASEH2C (rs1308020, Pmeta = 2.96 × 10-19, OR = 0.84) and co-located with annotated gene regulatory elements. The novel loci share genetic signatures with other reported SLE loci, including effects on gene expression, transcription factor binding, and epigenetic characteristics. Most (56%) of the correlated (r2 > 0.8) SNPs from the 82 SLE loci were implicated in differential expression (9.81 × 10-198 < P < 5 × 10-3) of cis-genes. Transcription factor binding sites for p53, MEF2A and E2F1 were significantly (P < 0.05) over-represented in SLE loci, consistent with apoptosis playing a critical role in SLE. Enrichment analysis revealed common pathways, gene ontology, protein domains, and cell type-specific expression. In summary, we provide evidence of five novel SLE susceptibility loci. Integrated bioinformatics using all 82 loci revealed that SLE susceptibility loci share many gene regulatory features, suggestive of conserved mechanisms of SLE etiopathogenesis.

The nanoscale connectomics community has recently generated automated and semi-automated "wiring diagrams" of brain subregions from terabytes and petabytes of dense 3D neuroimagery. This process involves many challenging and imperfect technical steps, including dense 3D image segmentation, anisotropic nonrigid image alignment and coregistration, and pixel classification of each neuron and their individual synaptic connections. As data volumes continue to grow in size, and connectome generation becomes increasingly commonplace, it is important that the scientific community is able to rapidly assess the quality and accuracy of a connectome product to promote dataset analysis and reuse. In this work, we share our scalable toolkit for assessing the quality of a connectome reconstruction via targeted inquiry and large-scale graph analysis, and to provide insights into how such connectome proofreading processes may be improved and optimized in the future. We illustrate the applications and ecosystem on a recent reference dataset.Clinical relevance- Large-scale electron microscopy (EM) data offers a novel opportunity to characterize etiologies and neurological diseases and conditions at an unprecedented scale. EM is useful for low-level analyses such as biopsies; this increased scale offers new possibilities for research into areas such as neural networks if certain bottlenecks and problems are overcome.