Although the diversity of cortical interneuron electrical properties is well recognized, the number of distinct electrical types (e-types) is still a matter of debate. Recently, descriptions of interneuron variability were standardized by multiple laboratories on the basis of a subjective classification scheme as set out by the Petilla convention (Petilla Interneuron Nomenclature Group, PING). Here, we present a quantitative, statistical analysis of a database of nearly five hundred neurons manually annotated according to the PING nomenclature. For each cell, 38 features were extracted from responses to suprathreshold current stimuli and statistically analyzed to examine whether cortical interneurons subdivide into e-types. We showed that the partitioning into different e-types is indeed the major component of data variability. The analysis suggests refining the PING e-type classification to be hierarchical, whereby most variability is first captured within a coarse subpartition, and then subsequently divided into finer subpartitions. The coarse partition matches the well-known partitioning of interneurons into fast spiking and adapting cells. Finer subpartitions match the burst, continuous, and delayed subtypes. Additionally, our analysis enabled the ranking of features according to their ability to differentiate among e-types. We showed that our quantitative e-type assignment is more than 90% accurate and manages to catch several human errors.

A large number of degrees of freedom are required to produce a high quality focus through random scattering media. Previous demonstrations based on spatial phase modulations suffer from either a slow speed or a small number of degrees of freedom. In this work, a high speed wavefront determination technique based on spatial frequency domain wavefront modulations is proposed and experimentally demonstrated, which is capable of providing both a high operation speed and a large number of degrees of freedom. The technique was employed to focus light through a strongly scattering medium and the entire wavefront was determined in 400 milliseconds,  three orders of magnitude faster than the previous report.

We demonstrate a high throughput, large compensation range, single-prism femtosecond pulse compressor, using a single prism and two roof mirrors. The compressor has zero angular dispersion, zero spatial dispersion, zero pulse-front tilt, and unity magnification. The high efficiency is achieved by adopting two roof mirrors as the retroreflectors. We experimentally achieved ~ -14500 fs2 group delay dispersion (GDD) with 30 cm of prism tip-roof mirror prism separation, and ~90.7% system throughput with the current implementation. With better components, the throughput can be even higher.

The Caenorhabditis elegans embryo is an important model for analyzing mechanisms of cell fate specification and tissue morphogenesis. Sophisticated lineage-tracing approaches for analyzing embryogenesis have been developed but are labor intensive and do not naturally integrate morphogenetic readouts. To enable the rapid classification of developmental phenotypes, we developed a high-content method that employs two custom strains: a Germ Layer strain that expresses nuclear markers in the ectoderm, mesoderm and endoderm/pharynx; and a Morphogenesis strain that expresses markers labeling epidermal cell junctions and the neuronal cell surface. We describe a procedure that allows simultaneous live imaging of development in 80-100 embryos and provide a custom program that generates cropped, oriented image stacks of individual embryos to facilitate analysis. We demonstrate the utility of our method by perturbing 40 previously characterized developmental genes in variants of the two strains containing RNAi-sensitizing mutations. The resulting datasets yielded distinct, reproducible signature phenotypes for a broad spectrum of genes that are involved in cell fate specification and morphogenesis. In addition, our analysis provides new in vivo evidence for MBK-2 function in mesoderm fate specification and LET-381 function in elongation.

Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set.

To provide a temporal framework for the genoarchitecture of brain development, we generated in situ hybridization data for embryonic and postnatal mouse brain at seven developmental stages for ∼2,100 genes, which were processed with an automated informatics pipeline and manually annotated. This resource comprises 434,946 images, seven reference atlases, an ontogenetic ontology, and tools to explore coexpression of genes across neurodevelopment. Gene sets coinciding with developmental phenomena were identified. A temporal shift in the principles governing the molecular organization of the brain was detected, with transient neuromeric, plate-based organization of the brain present at E11.5 and E13.5. Finally, these data provided a transcription factor code that discriminates brain structures and identifies the developmental age of a tissue, providing a foundation for eventual genetic manipulation or tracking of specific brain structures over development. The resource is available as the Allen Developing Mouse Brain Atlas (http://developingmouse.brain-map.org).

Generating microislands of culture substrate on coverslips by spray application of poly-d lysine is a commonly used method for culturing isolated neurons that form self (autaptic) synapses. This preparation has multiple advantages for studying synaptic transmission in isolation; however, generating microislands by spraying produces islands of non-uniform size and thus cultures vary widely in the number of islands containing single neurons. To address these problems, we developed a high-throughput method for reliably generating uniformly shaped microislands of culture substrate. Stamp molds formed of poly(dimethylsiloxane) (PDMS) were fabricated with arrays of circles and used to generate stamps made of 9.2% agarose. The agarose stamps were capable of loading sufficient poly D-lysine and collagen dissolved in acetic acid to rapidly generate coverslips containing at least 64 microislands per coverslip. When hippocampal neurons were cultured on these coverslips, there were significantly more single-neuron islands per coverslip. We noted that single neurons tended to form one of three distinct neurite-arbor morphologies, which varied with island size and the location of the cell body on the island. To our surprise, the number of synapses per autaptic neuron did not correlate with arbor shape or island size, suggesting that other factors regulate the number of synapses formed by isolated neurons. The stamping method we report can be used to increase the number of single-neuron islands per culture and aid in the rapid visualization of microislands.

Assays that measure morphology, proliferation, motility, deformability, and migration are used to study the invasiveness of cancer cells. However, native invasive potential of cells may be hidden from these contextual metrics because they depend on culture conditions. We created a micropatterned chip that mimics the native environmental conditions, quantifies the invasive potential of tumor cells, and improves our understanding of the malignancy signatures. Unlike conventional assays, which rely on indirect measurements of metastatic potential, our method uses three-dimensional microchannels to measure the basal native invasiveness without chemoattractants or microfluidics. No change in cell death or proliferation is observed on our chips. Using six cancer cell lines, we show that our system is more sensitive than other motility-based assays, measures of nuclear deformability, or cell morphometrics. In addition to quantifying metastatic potential, our platform can distinguish between motility and invasiveness, help study molecular mechanisms of invasion, and screen for targeted therapeutics.

Although most experimentally characterized proteins with similar sequences assume the same folds and perform similar functions, an increasing number of exceptions is emerging. One class of exceptions comprises sequence-similar fold switchers, whose secondary structures shift from α-helix <-> β-sheet through a small number of mutations, a sequence insertion, or a deletion. Predictive methods for identifying sequence-similar fold switchers are desirable because some are associated with disease and/or can perform different functions in cells. Here, we use homology-based secondary structure predictions to identify sequence-similar fold switchers from their amino acid sequences alone. To do this, we predicted the secondary structures of sequence-similar fold switchers using three different homology-based secondary structure predictors: PSIPRED, JPred4, and SPIDER3. We found that α-helix <-> β-strand prediction discrepancies from JPred4 discriminated between the different conformations of sequence-similar fold switchers with high statistical significance (P < 1.8*10 ). Thus, we used these discrepancies as a classifier and found that they can often robustly discriminate between sequence-similar fold switchers and sequence-similar proteins that maintain the same folds (Matthews Correlation Coefficient of 0.82). We found that JPred4 is a more robust predictor of sequence-similar fold switchers because of (a) the curated sequence database it uses to produce multiple sequence alignments and (b) its use of sequence profiles based on Hidden Markov Models. Our results indicate that inconsistencies between JPred4 secondary structure predictions can be used to identify some sequence-similar fold switchers from their sequences alone. Thus, the negative information from inconsistent secondary structure predictions can potentially be leveraged to identify sequence-similar fold switchers from the broad base of genomic sequences.

Animal sensory systems are optimally adapted to those features typically encountered in natural surrounds, thus allowing neurons with limited bandwidth to encode challengingly large input ranges. Natural scenes are not random, and peripheral visual systems in vertebrates and insects have evolved to respond efficiently to their typical spatial statistics. The mammalian visual cortex is also tuned to natural spatial statistics, but less is known about coding in higher order neurons in insects. To redress this we here record intracellularly from a higher order visual neuron in the hoverfly. We show that the cSIFE neuron, which is inhibited by stationary images, is maximally inhibited when the slope constant of the amplitude spectrum is close to the mean in natural scenes. The behavioural optomotor response is also strongest to images with naturalistic image statistics. Our results thus reveal a close coupling between the inherent statistics of natural scenes and higher order visual processing in insects.