News Article | May 8, 2017
Wiseguyreports.Com Adds “UV Lasers -Market Demand, Growth, Opportunities and Analysis of Top Key Player Forecast To 2022” To Its Research Database This report studies UV Lasers in Global market, especially in North America, China, Europe, Southeast Asia, Japan and India, with production, revenue, consumption, import and export in these regions, from 2012 to 2016, and forecast to 2022. This report focuses on top manufacturers in global market, with production, price, revenue and market share for each manufacturer, covering By types, the market can be split into By Application, the market can be split into Hospitals Research Institutes Industrial Manufacturer Others By Regions, this report covers (we can add the regions/countries as you want) North America China Europe Southeast Asia Japan India Global UV Lasers Market Professional Survey Report 2017 1 Industry Overview of UV Lasers 1.1 Definition and Specifications of UV Lasers 1.1.1 Definition of UV Lasers 1.1.2 Specifications of UV Lasers 1.2 Classification of UV Lasers 1.2.1 Solid UV Lasers 1.2.2 Gas UV Lasers 1.3 Applications of UV Lasers 1.3.1 Hospitals 1.3.2 Research Institutes 1.3.3 Industrial Manufacturer 1.3.4 Others 1.4 Market Segment by Regions 1.4.1 North America 1.4.2 China 1.4.3 Europe 1.4.4 Southeast Asia 1.4.5 Japan 1.4.6 India 8 Major Manufacturers Analysis of UV Lasers 8.1 Coherent 8.1.1 Company Profile 8.1.2 Product Picture and Specifications 188.8.131.52 Product A 184.108.40.206 Product B 8.1.3 Coherent 2016 UV Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.1.4 Coherent 2016 UV Lasers Business Region Distribution Analysis 8.2 Rofin 8.2.1 Company Profile 8.2.2 Product Picture and Specifications 220.127.116.11 Product A 18.104.22.168 Product B 8.2.3 Rofin 2016 UV Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.2.4 Rofin 2016 UV Lasers Business Region Distribution Analysis 8.3 Spectra-Physics 8.3.1 Company Profile 8.3.2 Product Picture and Specifications 22.214.171.124 Product A 126.96.36.199 Product B 8.3.3 Spectra-Physics 2016 UV Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.3.4 Spectra-Physics 2016 UV Lasers Business Region Distribution Analysis 8.4 Videojet 8.4.1 Company Profile 8.4.2 Product Picture and Specifications 188.8.131.52 Product A 184.108.40.206 Product B 8.4.3 Videojet 2016 UV Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.4.4 Videojet 2016 UV Lasers Business Region Distribution Analysis 8.5 AMADA 8.5.1 Company Profile 8.5.2 Product Picture and Specifications 220.127.116.11 Product A 18.104.22.168 Product B 8.5.3 AMADA 2016 UV Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.5.4 AMADA 2016 UV Lasers Business Region Distribution Analysis 8.6 Lumentum 8.6.1 Company Profile 8.6.2 Product Picture and Specifications 22.214.171.124 Product A 126.96.36.199 Product B 8.6.3 Lumentum 2016 UV Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.6.4 Lumentum 2016 UV Lasers Business Region Distribution Analysis 8.7 Oxide 8.7.1 Company Profile 8.7.2 Product Picture and Specifications 188.8.131.52 Product A 184.108.40.206 Product B 8.7.3 Oxide 2016 UV Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.7.4 Oxide 2016 UV Lasers Business Region Distribution Analysis 8.8 DPSS Lasers 8.8.1 Company Profile 8.8.2 Product Picture and Specifications 220.127.116.11 Product A 18.104.22.168 Product B 8.8.3 DPSS Lasers 2016 UV Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.8.4 DPSS Lasers 2016 UV Lasers Business Region Distribution Analysis 8.9 ProPhotonix 8.9.1 Company Profile 8.9.2 Product Picture and Specifications 22.214.171.124 Product A 126.96.36.199 Product B 8.9.3 ProPhotonix 2016 UV Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.9.4 ProPhotonix 2016 UV Lasers Business Region Distribution Analysis 8.10 Huaray Laser 8.10.1 Company Profile 8.10.2 Product Picture and Specifications 188.8.131.52 Product A 184.108.40.206 Product B 8.10.3 Huaray Laser 2016 UV Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.10.4 Huaray Laser 2016 UV Lasers Business Region Distribution Analysis 8.11 Delphilaser 8.12 Inngu Laser 8.13 Han’s Laser 8.14 RFH Laser For more information, please visit https://www.wiseguyreports.com/sample-request/1255747-global-uv-lasers-market-professional-survey-report-2017
News Article | May 17, 2017
No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. Adult zebrafish (Danio rerio) for breeding were maintained at 28 °C on a 14 h:10 h light:dark cycle following standard methods31. The Tg(elavl3:GCaMP5G)a4598 transgenic line32 used in this study was of genotype elavl3:GCaMP5G+/+; nacre (mitfa–/–), conveying nearly pan-neuronal expression of the calcium indicator GCaMP5G33 and increased transparency due to the nacre mutation34. The larval zebrafish samples described in this study were raised in filtered fish facility water31 until 5–7 dpf. Mice from which support tissue was collected had been previously killed for other experiments. Only unused, to-be-discarded tissue was collected to serve as support tissue. The Standing Committee on the Use of Animals in Research and Training of Harvard University approved all animal experiments. Larval zebrafish were immobilized by immersion in 1 mg ml–1 α-bungarotoxin (Invitrogen) and mounted dorsum-up in 2% low-melting-temperature agarose in a small dish containing a silicone base (Sylgard 184, Dow Corning). Upon agarose hardening, E3 solution (5 mM NaCl, 0.17 mM KCl, 0.33 mM CaCl and 0.33 mM MgSO ) was added to the dish. In vivo structural imaging of elavl3-driven GCaMP5G signal was conducted with a custom-built two-photon microscope equipped with a Ti:Sapphire laser (Mai Tai, Spectra-Physics) excitation source tuned to 800 nm. Frames with a 764.4 × 509.6 μm2 field of view size (1,200 × 800 pixels2) were acquired at 1-μm intervals (0.637 × 0.637 × 1 μm3 per voxel) at approximately 1 Hz with a scan pattern of four evenly spaced, interlaced passes35. A low-noise anatomical snapshot of brain fluorescence was captured in 300 planes, each the sum of 50 single frames. All light-based imaging was performed without any intentional stimulus presentation. Initial attempts at high-quality larval zebrafish brain preservation were impeded by skin and membranes, which prevented sufficient fixation with whole-fish immersion alone (Extended Data Fig. 1a). To overcome this, skin and membranes covering the brain36 were dissected away. Each larval zebrafish, which had been previously immobilized and embedded for two-photon laser-scanning microscopy, was introduced to a dissection solution (64 mM NaCl, 2.9 mM KCl, 10 mM HEPES, 10 mM glucose, 164 mM sucrose, 1.2 mM MgCl , 2.1 mM CaCl , pH 7.5; ref. 37) containing 0.02% (w/v) tricaine mesylate (MS-222, Sigma-Aldrich). Flow of red blood cells through the vasculature was confirmed before proceeding as an indicator of good health. A portion of agarose was removed to expose the dorsum from the posterior hindbrain to the anterior optic tectum. The dissection was initiated by puncturing the thin epithelial layer over the rhombencephalic ventricle above the hindbrain38 with a sharpened tungsten needle. Small incremental anterior-directed incisions were made along the midline as close to the surface as possible until the brain was exposed from the hindbrain entry site to the anterior optic tectum (Extended Data Fig. 1b). The majority of damage associated with this dissection was restricted to medial tectal proliferation zone progenitor cells39 that are unlikely to have integrated into functional neuronal circuits. Dissections lasted 1–2 min, upon which time the complete dish was immersed in a 2.0% formaldehyde and 2.5% glutaraldehyde fixative solution (Electron Microscopy Sciences) overnight at room temperature (Extended Data Fig. 1d). Following washes, larval zebrafish were cut out from the dish in a block of agarose with a scalpel and moved to a round-bottomed microcentrifuge tube. Specimens were then incubated in post-fixation solution containing 1% osmium tetroxide and 1.5% potassium ferricyanide for 2 h (Extended Data Fig. 1e), washed with water, washed with 0.05 M maleate buffer (pH 5.15), and stained with 1% uranyl acetate in maleate buffer overnight (Extended Data Fig. 1f). During the subsequent wash step with maleate buffer, larval zebrafish were freed from the surrounding agarose block and moved to a new microcentrifuge tube. Next, specimens were washed with water, dehydrated with serial dilutions of acetonitrile in water (25%, 50%, 70%, 70%, 80%, 90%, 95%, 100%, 100%, 100%) for 10 min each, and infiltrated with serial dilutions of a diepoxyoctane-based low viscosity resin40 in acetonitrile (25%, 50%, 75%, 100%) for 1 h each. The samples were then embedded in diepoxyoctane-based resin with surrounding support tissue and hardened for 2–3 days at 60 °C (Extended Data Fig. 1g, h). Aqueous solutions were prepared with water passed through a purification system (typically Arium 611VF, Sartorius Stedim Biotech). This process resulted in high-quality ultrastructure preservation (Extended Data Fig. 1c, i). Additional solution, washing, and timing details were described previously in a step-by-step protocol41. Consistent ultrathin sectioning was difficult to achieve in larval zebrafish samples, which contain heterogeneous tissues, but imperative for reconstructing 3D structure from a series of 2D sections. Tests revealed that errors occurred primarily when the sample composition changed markedly (for example, borders between tissue and empty resin). We overcame this by embedding samples surrounded by a mouse cerebral cortex support tissue (Extended Data Figs 1h, 2f, 3a, 4a). We preferred sectioning perpendicular to most axon and dendrite paths for ease and reliability in reconstructing neuronal morphology. For this reason, our cutting plane was oriented perpendicular to the long (anterior–posterior) axis, which required around 2.5× more sections than alternative orientations. This was made possible by customizing an automated tape-collecting ultramicrotome26, 27 by extending the device’s main mounting plate and enlarging its reels (compare Extended Data Fig. 2a with Fig. 1e from ref. 26) to accommodate one long tape stretch capable of collecting all sections. Sections were continuously cut with a diamond knife (Extended Data Fig. 2b, c) affixed to an ultramicrotome (EM UC6, Leica) and collected onto 8-mm-wide and 50–75-μm-thick tape (Kapton polyimide film, DuPont). Restarts were occasionally required for three reasons: fine-tuning of tape positioning or settings is necessary at the beginning of a run; the ultramicrotome design is constrained by a cutting depth range of about 200 μm; and diamond knives must be shifted after cutting several thousand sections to expose the sample to a fresh edge before dulling impairs sectioning quality. When necessary, restarts were completed as quickly as possible (typically 1–2 min) to minimize possible thermal, electrostatic, or other fluctuations. For the same reason, tape reels were fed continuously without ever being reloaded or exchanged. This combination of fast restarts and continuous tape feeding successfully maintained a steady state across restarts. We sectioned two larval zebrafish specimens. These represent the only two samples we have attempted to cut since adopting the surrounding support tissue approach. The primary focus of this study was a 5.5 dpf larval zebrafish sectioned with a 45° ultra diamond knife (Diatome) and a nominal sectioning thickness that averaged 60 nm with a variable setting ranging from 50–70 nm depending on sectioning consistency. Restarts occurred after sections 276, 3,669, 6,967, 10,346, 12,523, 12,916, and 15,956. Knife shifts occurred after sections 6,967 and 12,916. After sectioning, the tape was cut into segments with a razor blade between collected sections and adhered with double-sided conductive carbon adhesive tape (Ted Pella) to 4-in-diameter silicon wafers (University Wafer), which served as an imaging substrate. A total of 17,963 sections, each approximately 60 nm thick, were spread across 80 wafers (Extended Data Figs 2d, e, 3). One potential limitation of the 5.5 dpf larval zebrafish series is the section thickness. Minimizing section thickness is an important factor in the success of axon and dendrite reconstruction1. Small neuronal processes are difficult to reconstruct in thicker sections, especially when they are running roughly parallel to the plane of the section. To be sure that our approach was not fundamentally limited to thicker sections, we sectioned the second sample—a 7 dpf larval zebrafish—with a nominal sectioning thickness that remained constant at 50 nm throughout the entire cutting session using a 45° histo diamond knife (Diatome). Restarts occurred after sections 296, 312, 4,114, 8,233, and 12,333. Knife shifts occurred after sections 4,114 and 12,333. A total of 15,046 sections, each approximately 50 nm thick, were obtained from 15,052 attempted (Extended Data Fig. 4) and spread across 70 wafers. The thinner sections did not result in more lost material: this series contained 6 losses (0.04%; Extended Data Fig. 4d upper), 25 partial sections (0.17%; Extended Data Fig. 4d middle), no adjacent losses, and 6 adjacent lost–partial or partial–partial events (0.04%; Extended Data Fig. 4d lower). The nominal section thickness of approximately 60 nm made it possible to span the entire 5.5 dpf larval zebrafish brain in about 18,000 sections, as determined by finding the location of the spinal cord–hindbrain boundary42. Although the 7 dpf sample was sectioned at 50 nm, it was not made the focus of subsequent imaging because it contained less of the brain. However, improved reliability for this sample despite a 16% reduction in nominal sectioning thickness suggests that yet higher axial resolution is attainable. A section thickness of ≤30 nm would increase confidence in the ability to reconstruct complete neuronal circuit connectivity, and cutting at such thicknesses is known to be possible for mammalian brain sections of comparable sizes27, 43. Wafers containing tape segments were made hydrophilic by brief glow discharging, post-section stained for 1–2 min inside a chamber containing sodium hydroxide pellets using a stabilized lead citrate solution (UltroStain II, Leica) filtered through a 0.2 μm syringe filter, and then washed thoroughly with water. A thin layer of carbon was then deposited onto each wafer to prevent charging during scanning electron microscopy. WaferMapper software was used with light-based wafer overview images to semi-automatically map the positions of all sections and relate them to fiducial markers. This enabled targeted section overview acquisition (758.8 × 758.8 × 60 nm3 per voxel for 5.5 dpf; 741.5 × 741.5 × 50 nm3 per voxel for 7 dpf). Semi-automated alignment of section overviews in WaferMapper then permitted targeting for imaging at higher resolutions26. Field emission scanning electron microscopy of back-scattered electrons was primarily conducted on a Zeiss Merlin equipped with a large-area imaging scan generator (Fibics) and stock detector. An accelerating voltage of 5.0 kV and beam current of 7–10 nA were used for most acquisition. Imaging of back-scattered electrons at the highest resolution (4.0 × 4.0 × 60 nm3 per voxel) was performed on an FEI Magellan XHR 400L with an accelerating voltage of 5.0 kV and beam current of 1.6–3.2 nA. Field of view sizes acquired from a given section varied depending on the cross-sectional area occupied by tissue. All acquisition was performed with a scan rate at or under 1 megapixel per s. For the 5.5 dpf larval zebrafish, this resulted in overhead-inclusive acquisition times of 5.4 days for section overviews (758.8 × 758.8 × 60 nm3 per voxel), 97 days for isotropic full transverse cross-sections (56.4 × 56.4 × 60 nm3 per voxel), and 100 days for high-resolution brain images (18.8 × 18.8 × 60 nm3 per voxel). Continued development of faster electron microscopy technologies44 will hasten the re-imaging process and permit whole-brain studies to be carried out in a fraction of the time required here. Producing anatomically consistent image registration over about 18,000 sections required control of region of interest drift, over-fitting, magnification changes, and intensities. To quickly assess the quality of the dataset and begin reconstructions, we initially performed affine intra- and inter-section image registrations with Fiji45 TrakEM2 alignment plug-ins46. These results revealed that additional nonlinear registration was required in order to compensate for distortions that were likely caused by section compression during cutting and sample charging during imaging. While the state-of-the-art elastic registration method47 also provided in Fiji45 as a TrakEM2 alignment plug-in achieved excellent local registration, we experienced difficulty—at least without modification to the existing implementation—in achieving an anatomically consistent result that preserved the overall larval zebrafish structure, largely due to struggles with constraining region of interest drift across magnification changes and correcting for shearing caused by sectioning. We also determined that the similar AlignTK9 method, which uses Pearson correlation as the matching criterion coupled with spring mesh relaxation to stabilize the global volume, was likely to suffer from similar problems and would require substantial additional data handling to operate on our multi-resolution dataset. Therefore, in order to preserve the overall larval zebrafish structure and simultaneously achieve high-quality local registration, we turned to a new Signal Whitening Fourier Transform Image Registration (SWiFT-IR) method43, 48. Compared to conventional Pearson or phase correlation-based registration approaches, SWiFT-IR produces more robust image matching by using modulated Fourier transform amplitudes, adjusting its spatial frequency response during matching to maximize a signal-to-noise measure as its indicator of alignment quality. This alignment signal better handles variations in biological content and typical data distortions. Additionally, SWiFT-IR achieves higher precision in block matching as a result of the signal whitening, improves processing speeds with the computational complexity advantages of fast-Fourier transforms, and reduces iterative convergence from thousands to dozens of steps. Together, these capabilities enable a model-driven alignment in place of the usual approach of comparing and aligning a given section to a pre-selected number of adjacent sections. The SWiFT-IR model we used consisted of an estimate of local aligned volume content formed by a windowed average, typically spanning 6 μm along the axis orthogonal to the sectioning plane (z, anterior–posterior). Damaged regions, in particular partial sections, were removed from the model to avoid adversely influencing alignment results. This model then served as a registration template, in which raw images were matched to the current model rather than nearby sections. Alignment proceeded in an iterative fashion starting at 758.8 × 758.8 × 60 nm3 per voxel (section overviews) and progressing incrementally to 56.4 × 56.4 × 60 nm3 per voxel for regions outside the brain and 18.8 × 18.8 × 60 nm3 per voxel for regions inside. At each resolution, source images were iteratively aligned to the current model until no further alignment improvement could be achieved, as indicated by the SWiFT-IR signal-to-noise figure of merit. The model was then transferred to higher resolution data by applying the current warpings to the source data for that scale. Iterative model refinement then continued at this subsequent level. Although most computations were locally affine, residual nonlinear deformations, particularly at the highest resolutions, were represented by a triangulation mesh that deformably mapped raw data onto the model volume. Importantly, access to the lowest resolution section overview data permitted us to build an initial model that constrained subsequent registration steps to the overall larval zebrafish structure. Although their resolution and signal quality were intentionally sacrificed in favour of rapid acquisition, the fact that overviews were quickly captured with the same microscope settings and included support tissue provided key constraints for model refinement that resulted in a more accurate global result. More specifically, the 17,963-section overview image volume was processed using SWiFT-IR to produce an initial model at 564 × 564 × 600 nm3 per voxel. Although the lowest-resolution section overview images were each captured at 758.8 × 758.8 × 60 nm3 per voxel, the relative oversampling orthogonal to the sectioning plane enabled a geometrically accurate model at 564 × 564 × 600 nm3 per voxel. This initial model was then cropped and warped using SWiFT-IR–driven matching across the midline axis to remove cutting compression, rotations, and other systematic variations in the specimen pose. The 16,000-section 56.4 × 56.4 × 60 nm3 per voxel volume was next downsampled to 564 × 564 × 600 nm3 per voxel and aligned to the initial overview model, resulting in an improved model. The matching and remodelling process was iterated at this scale until there was no further improvement in SWiFT-IR match quality. The final model at this scale was then expanded to 282 × 282 × 300 nm3 per voxel and similarly aligned in an iterative fashion. This model volume (about 6 gigavoxels; 1,600 × 1,400 × 2,667 voxels) was convenient for rapid viewing to identify and manually correct defects and refine the pose. Further scales at 169.2 × 169.2 × 180 nm3 per voxel and 56.4 × 56.4 × 60 nm3 per voxel were similarly processed by successively expanding the model and aligning until no meaningful improvement in the figure of merit was reached. The 12,546-section 18.8 × 18.8 × 60 nm3 per voxel image set was then registered using the final 56.4 × 56.4 × 60 nm3 per voxel volume as its model. Image intensity was adjusted across sections to achieve a consistent background level by matching the average over a tissue-free region defined by a 256 × 256 pixels2 area. Many images were acquired at 16-bit depth and were converted in this process to 8-bit depth. The target background level was mapped to intensity 250, which left headroom for bright pixels while keeping most tissue of interest from saturating. Next, a linear intensity fit between the background and a second level, typically the average grey level of a continuous trajectory region on the right side of the brain, was made to adjust the intensity values for each section. Correspondence of individual neurons or functional reference atlas regions across imaging modalities was achieved with landmark-based 3D thin-plate spline warping of each fluorescence dataset to the ssEM dataset using BigWarp49. For matching in vivo two-photon laser-scanning microscopy data from the same specimen, we primarily chose landmarks consisting of distinctive arrangements of low-fluorescence regions where GCaMP5G was excluded and could be easily matched to similar patterns of nuclei in the ssEM dataset. This process was difficult in regions with low fluorescence signal (Extended Data Fig. 7e), where many cells were packed closely together (Extended Data Fig. 7f), and at locations where new neurons were likely to have been added between light microscopy and preparation for ssEM (Extended Data Fig. 7g). In the future, improving the light-level data with specific labelling of all nuclei and faster light-based imaging approaches should improve the ease and accuracy of matching neuron identity. Two functional reference atlases with many separate labels were also registered to the ssEM dataset. For matching the Z-Brain atlas50, we chose landmarks based on identifiable structures in the Z-Brain averaged elavl3:H2B-RFP or anti-tERK fluorescence image stacks that were also observed in the ssEM dataset. These structures primarily consisted of region boundaries, discernable clusters of neurons, midline points, ganglia, and the brain outline. The same Z-Brain landmarks were used for transforming a version51 of the Zebrafish Brain Browser52 that was previously registered to the Z-Brain atlas. Reconstruction across multi-resolution ssEM image volumes profits from being able to simultaneously access and view separate but co-registered datasets. Without this, some of the time benefits of our imaging approach would be offset by the need to register and track each structure across volumes that span both low-resolution, large fields of view and high-resolution, specific regions of interest. With this in mind, we added a feature to the Collaborative Annotation Toolkit for Massive Amounts of Image Data (CATMAID) neuronal circuit mapping software53, 54 to overlay and combine image stacks acquired with varying resolutions in a single viewer (Extended Data Fig. 6). This is made possible by rendering using WebGL. Additionally, this new feature combines stacks via a configurable overlay order, introduces blending operations for each overlaid stack, and enables programmatic shaders for dynamic image processing. When overlaid stacks’ resolutions differ, the nearest available zoom level for each stack is interpolated. Missing data regions can be omitted or rendered with interpolation. To account for the increase in data storage and bandwidth when viewing multiple image stacks, the CATMAID image data hierarchy was extended with a shared graphics card memory cache of image tiles using a least-recently-used replacement policy. All additions and modifications to the CATMAID software are now incorporated into the main open-source release. Manual reconstruction was conducted using our modified CATMAID version by placing nodes near the centre of each neuronal structure on every section in which it could be clearly identified. This led to a wire-frame model (‘skeleton’) for each annotated structure. Starting points for reconstruction (‘seeds’) of myelinated processes were manually identified by searching for profiles surrounded by the characteristic thick, densely stained outline associated with staining of the myelin sheath55 (Fig. 1e–g, i). The search protocol consisted of viewing all tissue on a given section from the upper-left to lower-right corner at the highest available resolution. To obtain seeds for the projectome reconstruction, searching was repeated every 50 sections throughout the 16,000 sections acquired at or higher than 56.4 × 56.4 × 60 nm3 per voxel. Many annotations were produced in an affine-only alignment space before being mapped into the final SWiFT-IR alignment space. The reconstructions reported here represent about 450 days of uninterrupted (24 h per day) human annotation. For visualization and reported length measurements, each skeleton was smoothed using a custom python-based implementation of a Kalman smoothing algorithm on a space defined by manually annotated points within unique segments. The initial state variables for smoothing were derived by an optimization of point-to-line distance to connected reconstruction segments. Other variables were tuned with the Estimation Maximization algorithm of the pykalman library to compensate for a lack of human input where data was unavailable because of lost or partial sections. Because the final image alignment was of good quality, smoothing in this manner should produce a slight underestimate in reported reconstruction path lengths. Neurons with known projection patterns or identities were named in the CATMAID database. For example (and subject to change), the reconstruction of a neuron innervating the right anterior macula (utricle) might be named as ‘Ear_AnteriorMacula_R_01’, while an identified neuron such as the left Mauthner neuron was named ‘Mauthner_L’. Two identifiable left–right reticulospinal neuron pairs belonged to the MeM class, which emanates from the nucleus of the medial longitudinal fasciculus (nucMLF). On each side, these were differentiated into dorsal (MeMd) and ventral (MeMv) subclasses based on consistent soma positioning. Image volumes and reconstructions were primarily visualized using Vivaldi56, a domain-specific language for rendering and processing on distributed systems, because it provides access to the parallel computing power of multi-GPU systems with language syntax similar to python. For volume visualizations, we used a direct volume rendering ray-casting technique in which an orthogonal or perspective iterator was marched along a viewing ray while sampled voxel colours were accumulated using an alpha compositing algorithm. We screened out regions containing only support tissue during rendering with labelled volumes constructed by interpolating between manually produced masks indicating which image voxels belong to each separate tissue region. In cases where separate image volumes of the same region were rendered together (for example, ssEM and fluorescence combined), direct volume rendering was performed by combining front-to-back colour and alpha compositions formed from the different transfer function belonging to each image volume. For volume visualizations including reconstructions, direct volume rendering of image data was combined with streamline rendering of reconstructions using two different techniques. The first combined an OpenGL framebuffer with the Vivaldi volume rendering. In this case, each streamline was rendered using OpenGL as a tube into an off-screen buffer (that is, Framebuffer Object). Vivaldi then compared the resulting render and depth buffers to perform direct volume rendering of only the image data above the streamline depth value. This made it possible to ignore image voxels obscured by streamlines, which were treated as opaque. The second technique involved generating a complete streamline volume by 3D rasterization. This streamline volume was then combined with the image volume for direct volume rendering. The former technique is faster and can cope with dynamic streamline changes, but the latter was found to yield better overall rendering quality for our purposes. Visualizations of reconstructions without the image volume context were rendered either in the CATMAID 3D WebGL viewer or plotted in MATLAB. When reconstructions are shown without specific labelling, colours were assigned randomly from a custom palette. Reference plane (for example, horizontal, sagittal, and section) indicators were rendered with Vivaldi by detecting the zero-crossing of each viewing ray and the plane. Support for viewing opaque data views in some spatial regions alongside the semi-transparent volume visualization views in other regions was introduced as a new Vivaldi function, clipping_plane. Similarly, contour (nonplanar) reslice support was added to illustrate a flattened view along a specific reconstruction path consisting of vertical line segments extracted from the image volume. For many cases, the size of the volume being rendered was larger than available memory. In order to support out-of-core processing, we developed and integrated into Vivaldi a slice-based streaming computing framework using the Hadoop distributed file system (HDFS) that will be reported elsewhere. Initial observations of apparent myelinated axon symmetry were found during visual inspection (Fig. 3; Supplementary Videos 8, 9). To quantitatively assess the extent of symmetry, we developed a 3D symmetry plane fitting method and two symmetry analyses: one that produces a cost associated with the 3D shape and position similarity between reconstructed structures and another that compares the relative 2D (cross-sectional) positioning of two identified neuron axons on one side with that for the contralateral axons with the same identities. Only the longest reconstructed path from the soma through the myelinated axon projection was considered in plane fitting and symmetry analyses. Dendrites or short axonal branches were ignored. Each resulting reconstruction path (skeleton) was represented as an ordered list of nodes (points) taken directly from manual reconstructions. Sidedness (left or right) was determined by soma position. The new 3D symmetry plane fitting and 3D symmetry comparison analysis approaches have been described elsewhere57. The symmetry plane fitting, in brief, involves choosing an approximate symmetry plane, reflecting the complete set of points belonging to the reconstruction subset of interest with respect to this plane, registering the original and reflected point clouds with an iterative closest point algorithm, and inferring the optimal symmetry plane from the reflection and registration mappings. The subset of reconstructions from which this plane fitting was performed consisted entirely of identified neurons whose axon projections formed part of the approximately 30-μm-diameter MLF, recognized with the help of refs 29, 58. The 3D symmetry comparison for each template reconstruction on one side, in brief, involved reflecting all contralateral skeletons and computing a matching cost via dynamic time warping (DTW) between the template and each reflected skeleton. The reconstruction subset analysed in this fashion was restricted to identified neuron classes with 1–2 members per side whose axons formed part of the MLF. For our purposes, the DTW cost was taken as the sum of the Euclidian distances between all matched points normalized to the number of matched point pairs (Extended Data Fig. 9a–c). The DTW gap cost parameter for matching a point in one sequence with a gap in another was set to zero because our data was sampled at a nearly constant rate and we sought the optimal subsequence match even in cases where one is shorter than or offset with respect to the other. To compensate for unmatched regions (that is, overhangs), the DTW cost was then multiplied by a penalty factor proportional to the sequence lengths remaining unmatched (total length divided by matched length). Comparing each reconstruction on one side to all reconstructions from the opposite side formed a cost matrix (Fig. 4b) from which an optimal pairwise assignment could be determined without any bias introduced from the previously determined identities. The Munkres algorithm59 was then used to compute a globally optimal pairwise assignment. We also sought to compare the relative 2D positioning for each set of two axons on one side with the contralateral set that had the same identities. The reconstruction subset analysed in this fashion was restricted either to the Mauthner cells and nucMLF neurons (Fig. 3c, 4e, g) or the larger set of 44 identified reticulospinal neurons (Fig. 4b; Extended Data Fig. 9i). To start, we compensated for a small angle offset in the sectioning plane relative to the true transverse plane by projecting the point coordinates of reconstructions such that the previously computed symmetry plane became the plane x = 0. Given the transverse planes z , z and a projected skeleton S containing points s = (s , s , s ), we let . That is, was taken as the subset of points from S whose coordinates are contained in the interval . We refer to the subset of ℝ3 bounded by as the slice . For each slice and skeleton S, we defined as the mean of the elements in . This mean was then taken as representative of the skeleton S in slice for analysis and plotting. Note that all analysis and plotting presented in static form was based on a slice thickness corresponding to a single section (approximately 60 nm), where each slice consisted simply of adjacent sections. Larger slice sizes were used for dynamic presentation (Supplementary Video 10) in order to reduce video duration and size. For comparing a set of two axons with its contralateral counterpart, we then took s ,..., s to be the set of representative points in a fixed slice for skeletons S ,..., S and took t ,..., t to be the representative points (for the same slice) of the respective skeletons T ,..., T that were previously matched to S ,..., S by the Munkres algorithm assignment after 3D symmetry analysis. To quantify the degree of similarity, we devised two measures (Extended Data Fig. 9e). The first, termed the angle difference, a , between a set of two axons and their contralateral counterparts, was defined as: where i, j were skeleton indices, was the reflections of with respect to the computed plane of symmetry, was the dot product between x and y, and was the norm of x. The second, termed the distance difference, d , between a set of two axons and their contralateral counterparts, was defined as: where i, j were skeleton indices, was the reflections of with respect to the computed plane of symmetry, was the absolute value of x, was the norm of x, and M was the maximum of across all axon sets and all slices. Note that a and d were normalized such that they could vary from 0 (no difference, 0° or 0 μm) to 1 (maximum difference, 180° or 8 μm). Further, when the points s and s were perfectly symmetrical with respect to points t and t , then a = 0 and d = 0. To visualize this quantification, a difference matrix, D, was generated for each slice such that D(i,j) = a if j > i and D(i,j) = d if j < i (Fig. 4f; Extended Data Fig. 9f; Supplementary Video 10). Calculating the variance for each element in D across all slices showed which axon sets deviated most with respect to the reflection of their contralateral counterparts (Fig. 4k). Heatmaps of the vectorized upper (j > i) and lower (j < i) triangles of D across slices additionally revealed locations with differences between axon sets and their contralateral counterparts (Fig. 4h, i; Extended Data Fig. 9h, j, k; Supplementary Video 10), with black values representing insufficient data at slice positions where at least one of the compared axons was not annotated. Plotting the sum of all a and d values for a given slice further illustrated positions where differences were present (Fig. 4j). Finally, the same analysis was performed after artificially swapping the identities (assignment) of the two axon reconstructions with the lowest 3D symmetry analysis costs (MeLc and Mauthner) to provide a basis for comparison (Fig. 4j). Custom software tools generated for data handling, visualization, and analysis are publicly available (http://zebrafish.link/hildebrand16/code). Our modifications to CATMAID53, 54 software are included in the main open-source release (http://github.com/catmaid/catmaid). More information on SWiFT-IR alignment software is publicly available (http://www.mmbios.org/swift-ir-home). All aligned ssEM data, reconstructions, transformed functional reference atlases, and an introductory guide are publicly available (hosted by NeuroData at http://neurodata.io/data/hildebrand16 and http://zebrafish.link/hildebrand16). Image data are served as a collection of 8-bit 1,024 × 1,024 pixel2 PNG images with an optional tRNS value of 255 specified to enable transparency. The original resolution for each image stack was downsampled multiple times to create a resolution hierarchy that provides a smooth visualization experience. The entire aligned image dataset requires about 2.7 terabytes of disk space as compressed PNG images (607 gigabytes for 56.4 × 56.4 × 60 nm3 per voxel ssEM data, 1,824 gigabytes for 18.8 × 18.8 × 60 nm3 per voxel ssEM data, 355 gigabytes for 4.0 × 4.0 × 60 nm3 per voxel ssEM of dorsal neuromasts, 1 gigabyte for 600 × 600 × 1200 nm3 per voxel Z-Brain data, and 3 gigabytes for 600 × 600 × 1200 nm3 per voxel Zebrafish Brain Browser data). Data and reconstructions are served to end users via Amazon Web Services (AWS), with an instance of our modified CATMAID53, 54 software deployed on the Elastic Compute Cloud (EC2) that points to static images hosted by the Simple Storage Service (S3) built-in web server.
News Article | August 31, 2016
Sample size was not predetermined. For cell electron microscopy, samples were double-blind examined. Other experiments were not randomized or blinded. Box–whisker plots all show median, 25/75 quartiles by box boundaries and minimum/maximum values by errors, with the exception of Fig. 3 and Extended Data Fig. 7 which use Tukey-defined error bars. Human Rab5-6×His and GFP–Rab5-6×His were expressed and purified essentially as previously described in the Escherichia coli expression system6. Human Rabex-5 amino-acid residues 131–394 were PCR and restriction cloned into a pGST-parallel2 vector containing a TEV cleavable N-terminal glutathione-S-transferase (GST)29, 30. Expression and purification was performed essentially as described31. Briefly, E. coli-expressed proteins were transformed into BL21(DE3) cells and grown at 37 °C until absorbance at 600 nm (A ) of 0.8, whereupon the incubator was reduced to 18 °C. After 30 min, cultures were induced with 0.1 mM IPTG and grown overnight (16 h). Cell pellets were resuspended in standard buffer (20 mM Tris pH7.4, 150 mM NaCl, 0.5 mM TCEP) and flash frozen in liquid nitrogen. All subsequent steps performed at 4 °C or on ice. Cell pellets were resuspended in standard buffer supplemented with 1 mM MgCl for GTPases, and protease inhibitor cocktail (chymostatin 6 μg/ml, leupeptin 0.5 μg/ml, antipain-HCl 10 μg/ml, aprotinin 2 μg/ml, pepstatin 0.7 μg/ml, APMSF 10 μg/ml), homogenized and lysed by sonication. Histidine-tagged proteins were bound in batch to Ni-NTA resin in the presence of 20 mM imidazole, and eluted with 200 mM imidazole. GST-tagged proteins were purified on GS resin (GS-4B, GE Healthcare) by binding for 2 h followed by stringent washing, and cleavage from resin overnight. Imidazole-containing samples were immediately diluted after elution and tags cleaved during overnight dialysis. Following dialysis and tag cleavage, samples were concentrated and TEV or HRV 3C protease was removed by reverse purification through Ni-NTA or GS resin. Samples were then purified by size-exclusion chromatography on Superdex 200 columns in standard buffer. Human EEA1 was purified as a GST fusion in a pOEM series vector (Oxford Expression Technologies) modified to contain a HRV 3C-cleavable N-terminal GST and protease cleavage site or from a modified pFastbac1 vector (Thermo Fisher Scientific)23. Some samples were also purified as 6×His-MBP and 10×His fusions from a modified pOEM vector (rotary shadowing for N-to-C terminus alignment, and optical tweezer control, respectively; all other experiments performed with tags removed). Mutants were purified identically to wild-type EEA1. SF9 cells growing in ESF921 media (Expression Systems) were co-transfected with linearized viral genome and the expression plasmid and selected for high infectivity. P1 and P2 virus was generated according to the manufacturer’s protocol, and expression screens and time courses performed to optimize expression yield. Best viruses were used to infect 1–2 l SF9 cells at 106 cells/ml at 1% vol/vol and routinely harvested after 40–48 h at about 1.5 × 106 cells/ml, suspended in standard buffer and flash frozen in liquid nitrogen. Pellets were thawed on ice and lysed by Dounce homogenizer. Purification took place rapidly in standard buffer at 4 °C on GS resin in batch format. Bound protein was washed thoroughly and cleaved from resin by HRV 3C protease overnight. Proteins retaining 6×His-MBP tags were purified on amylose resin and eluted with 10 mM maltose. Protein retaining 10×His were eluted from Ni-NTA resin in standard buffer supplemented with 200 mM imidazole. All EEA1 and mutants were immediately further purified by Superose 6 size-exclusion chromatography where they eluted as a single peak. All experiments were performed with a preparation confirmed for Rab5 and PI(3)P binding. Concentrations were determined by UV280 and Bradford assay. All proteins were aliquoted and flash frozen in liquid nitrogen and stored at −80 °C. EEA1 variants extended and swapped were synthesized genes optimized for insect cell expression (Genscript). The extended mutant has regions of low coiled-coil prediction removed, resulting in an EEA1 construct 1,286 amino acids in length (versus 1,411 in wild-type EEA1) (see Extended Data Fig. 3). The swapped mutant has the C-terminal portion of the coiled-coil rearranged to follow the N-terminal Zn2+-finger domains, and the N-terminal portion of the coiled-coil therefore rearranged to the C-terminal region of EEA1. Variants were treated identically to wild-type EEA1 in purification. An autosampler equipped Viskotek TDAMax system was used to analyse the light-scattering from purified EEA1. Sample was loaded the autosampler and passed through a TSKGel G5000PW column (Tosoh Biosciences) and fractions were subjected to scattering data acquisition. Data obtained were averaged across the protein elution volume and molecular masses determined in OmniSEC software package. The following lipids were purchased and used directly: DOPC, DOPS, DOGS-NiNTA, RhoDPPE (Avanti), DiD (Invitrogen) and PI(3)P (Echelon Biosciences). Lipids were dissolved in chloroform, except PI(3)P in 1:2:0.8 CHCl :MeOH:H O. All were stored at −80 °C. Early endosome fusion assay was performed as previously described32. To assess the ability of EEA1 to bind competently in a GTP-dependent manner to Rab5, Rab5 was bound to GS resin and subsequently loaded with nucleotide (GDP, GTP-γS) as previously described6. Binding of EEA1 and all variants to immobilized Rab5 proceeded for1 h at room temperature, and the washed Rab5 resin was evaluated for EEA1 binding by western blot. Similarly, the binding of EEA1 to PI(3)P containing liposomes was evaluated as previously described by formation of liposomes composed of DOPC:DOPS or DOPC:DOPS:PI(3)P (85:15 or 80:15:5 respectively)33. Briefly, liposomes were formed from the hydration of lipids at 1 mM in standard buffer, and combined with EEA1 for 1 h before ultracentrifugation to separate supernatant and pellet for western blotting to evaluate EEA1 sedimentation. Rabbit anti-EEA1 antibody was made in our laboratory. Liposomes were formed by extrusion as previously described34. Liposome compositions for fluorescence microscopy tethering assays were DOPC:DOPS:DOGS-NiNTA, DOPC:DOPS:PI(3)P, DOPC:DOPS:biotin-DPPE, with RhoDPPE and DiD where applicable. Liposome compositions for bead-supported membranes were DOPC:DOPS:DOGS-NiNTA, DOPC:DOPS:PI(3)P. Solvent was evaporated under nitrogen and vacuum overnight. The resulting residue was suspended in standard buffer, rapidly vortexed, freeze-thawed five times by submersion in liquid N2 followed by water at 40 °C, and extruded by 11 passes through two polycarbonate membranes with a pore diameter of 100 nm (Avestin). Vesicles stored at 4 °C were used within 5 days. Silica beads (2 μm NIST-traceable size-standards for optical tweezers, or 10 μm standard microspheres for microscopy; Corpuscular) were thoroughly cleaned in pure ethanol and Hellmanex (1% sol., Hellma Analytics) before storage in water. Supported bilayers were formed as previously described with modifications35. Liposomes composed of DOPC:DOPS 85:15 (with 5% PI(3)P and DOGS-NiNTA where applicable) were added to a solution containing 250 mM NaCl for tethering assays (10 μm) and 100 mM for optical tweezers (2 μm), and 5 × 106 beads. Liposomes were added to final concentration of 100 μM and incubated for 30 min (final volume 100 μl). Samples were washed with 20 mM Tris pH7.4 three times by addition of 1 ml followed by gentle centrifugation (at 380g). Final wash was with standard buffer. Salt concentrations were optimized by examination of homogeneity at the transverse plane followed by examination of the excess membrane at the coverslip plane (see Extended Data Fig. 2a–d). We found that the membranes were extremely robust in conditions where the bilayer is fully formed, and could be readily pipetted and washed, consistent with previous reports36. Membrane-coated beads were used within 1 h of production and always stored before use on a rotary suspension mixer. Glass coverslips were cleaned in ethanol, Hellmanex and thoroughly rinsed in water. In these experiments, the following concentrations were used: 1 nM Rabex-5 (131–394), 100 nM Rab5-6×His, 120 nM EEA1. Experiments were performed in standard buffer with 5 mM MgCl and 1 μM nucleotide. Liposomes and proteins were pre-mixed in low-binding tubes at concentrations indicated, incubated for 5 min and imaged immediately upon addition to the coverslip. Images were acquired with a Nikon TiE equipped with a 60× plan-apochromat 1.2 numerical aperture W objective and Yokagawa CSU-X1 scan head. Images were acquired on an Andor DU-897 back-illuminated CCD. Acquired images were processed by the SQUASH package for Fiji37. A 200 μl observation chamber (μ-Slide 8 well, uncoated, #1.5, ibidi) was pre-blocked with BSA (1 mg/ml in standard buffer) for 1.5–2 h and washed thoroughly. Finally, 180 μl of standard buffer containing beads was added to the sample chamber. In these experiments, the following concentrations were used: 1 nM Rabex-5 (131–394), 100 nM GFP–Rab5-6×His, and the given EEA1 concentrations (between 30 and 400 nM). Nucleotide control experiments were performed at 190 nM EEA1. Experiments were performed in standard buffer with 2 mM MgCl and 1 mM nucleotide. Altogether Rab5, Rabex5, nucleotide, EEA1 and buffer were mixed in low-binding tubes at concentrations indicated, and were added to 240 μl final volume to assure mixing throughout the chamber volume. Images for co-localization analysis were acquired with a Nikon TiE equipped with a 60× plan-apochromat 1.2 numerical aperture W objective and Yokagawa CSU-X1 scan head. Images were acquired on an Andor DU-897 back-illuminated CCD. Acquired images were processed by the SQUASH package for Fiji37. Data obtained for distance measurements were acquired in the same way and processed in Fiji by determining line profiles eight pixels wide from the centre of the bead outwards over an observed vesicle. These profiles were fitted with a Gaussian distribution. The alignment of the microscope was confirmed by imaging of sub-diffraction beads, revealing no clear systematic shift and a maximum positional error of 21 nm determined in Motion Tracking16. Controls with sub-diffraction-sized multicolour particles (Methods) and distance measurements between Rab5 itself and its resident membrane were within the measurement error of the technique (approximately 15 nm)38. HeLa cells were stained using primary antibodies against EEA1 N terminus (610457, prepared in mouse, BD Biosciences) and EEA1 C terminus (2900, prepared in rabbit, Abcam). The secondary antibodies were anti-mouse Alexa568 antibody (A-11004, prepared in goat, Life Technologies) and anti-rabbit Alexa647 (A-21244, prepared in goat, Life Technologies). Coverslips were mounted in STORM buffer (100 mM Tris-HCl pH8.7, 10 mM NaCl, 10% glucose, 15% glycerol, 0.5 mg/ml glucose oxidase, 40 μg/ml catalase, 1% BME) and sealed with nail polish. Cells were imaged on a Zeiss Eclipse Ti microscope equipped with a 150 mW 561 nm laser and a 300 mW 647 laser. For imaging, lasers intensities were set to achieve 50 mW at the rear lens of the objective. Illumination was applied at a sub-TIRF angle through the objective to improve the signal to noise ratio. Videos of 24,000 frames (12,000 frames per channel) were acquired by groups of 6 consecutive frames using the NIS Elements software (Nikon). Images were aligned using 100 nm Tetraspeck beads (Thermo Fisher). This software was also used for peak detection and image reconstruction. The localization of the EEA1 termini could be distorted a maximum of approximately 20 nm owing to the size of the antibodies. The localization accuracy of the secondary antibody was ~25 nm. Measured distances were determined in Fiji and represent distances between respective centres-of-mass. Representative experiment is shown, n = 3. Bead-supported membranes were prepared as described. The concentrations used were as in the microscopy experiments: 1 nM Rabex-5 (131–394), 100 nM Rab5-6×His and EEA1 concentrations (between 30 and 400 nM). Most experiments were performed at 40 nM EEA1, with additional trials taking place at 4 and 400 nM. At lowest concentrations, single transient events became difficult to observe (<5% had interactions). At the highest concentrations, events were often non-transient or repeated. Samples were rotary-shadowed essentially as described39. Briefly, samples were diluted in a spraying buffer, consisting of 100 mM ammonium acetate and 30% glycerol. Diluted samples were sprayed via a capillary onto freshly cleaved mica chips. These mica chips were mounted in the high vacuum evaporator (MED 020, Baltec) and dried. Specimens were platinum coated (5–7.5 nm) and carbon was evaporated. Following deposition, the replica was floated off and examined at 71,000× magnification and imaged onto a CCD (Morgagni 268D, FEI; Morada G2, Olympus). Images obtained were processed in ImageJ by skeletonizing the particles. Lengths were determined directly from these data and represent an overestimation due to the granularity of the platinum shadowing (5–7.5 nm granules). The bouquet plots were generated by aligning the initial five segments of the molecules and the entire population set was plotted. To determine the curvature measure, we first took the skeletonized curves and smoothed them with a window of 8.2 nm. These curves were then segmented with 301 equally spaced points, and these smoothed curves were used for the curvature calculation. We first attempted to define curvature at one segment length (~0.75 nm) but this analysis was too noisy to obtain meaningful description of the curves. We therefore determined the curvature by taking the difference of the tangents and diving it by the arc length at a distance of ~15 nm (20 points). The variance of this measure was determined, and bootstrapping with resampling was used to determine errors over the whole population and for 1,000 iterations. Although proteins are not homogeneous polymers, the WLC model captures essential aspects of the physics underlying their shape fluctuations40, 41. Calculation of fits to all mean tangent-correlations and the equilibration analysis were performed using Easyworm source code in Matlab42. First, the original skeletonized curves were segmented with 301 equally spaced points. These data were then used to calculate the tangent-correlations and the kurtosis plots. We fitted the regime whereby the kurtosis measurement defined that the molecules were equilibrated18, 43, 44. This distance therefore varied (see Extended Data Fig. 6, kurtosis plots), but the estimation of persistence length was only weakly dependent on this distance. The fitting routines were then implemented up to the thermal equilibration distance with bootstrapping with resampling, which was run for the whole population and 1,000 times to obtain errors. These are given as mean ± standard deviation. For values and fit statistics, please refer to Supplementary Data Table. We did not apply the WLC model to the swapped mutant (Extended Data Fig. 4h) because of the lack of significant structural changes upon Rab5 binding (Fig. 2f and Extended Data Fig. 4f). The analytical fitting to the radial distribution functions was performed in Python18. The radial distribution function for a worm-like chain is the probability density for finding the end points of the polymer. The polymers are considered as embedded in a two-dimensional space in this scheme. This treatment adopts the continuum model of the polymer, thereby defining the statistical properties via free energy calculation. Fitting to analytical solution of the WLC yielded a mean effective persistence length of 270 ± 14 nm for EEA1 alone (mean ± error of fit), and two populations of effective persistence lengths (26 ± 2 nm (67%) and 300 ± 14 nm (33%)) for EEA1 in the presence of Rab5:GTP-γS. A custom-built high-resolution dual-trap optical tweezer microscope was used45, 46. A single stable solid-state laser (Spectra-Physics, 5 W) was split by polarization into two traps that could be independently manoeuvred. Forces were measured independently in both traps by back-focal plane interferometry. Absolute distances between the two traps were determined by template-based video microscopy analysis (43 ± 2 nm per pixel) and offset-corrected for each microsphere pair by repeatedly contacting the microspheres after each experiment. The template detection algorithm had subpixel accuracy, at an estimated uncertainty in absolute distance measurements to be not more than ± 20 nm. Bead displacement was calculated according to ΔF = −κΔy. Extended Data Fig. 7g demonstrates the sensitivity of the instrument via the Allan deviation47 for averaging times greater than 100 ms. All optical tweezer experiments were performed with 2 μm silica size-standard microspheres (Corpuscular), at a temperature of 26 ± 2 °C in a laminar flow chamber with buffers containing 35% glycerol to prevent sedimentation of the silica microspheres. Thermal calibration of the optical traps was performed with the power spectrum method using a dynamic viscosity of 3.1 mPas (ref. 48) (mean trap stiffness: trap 1, κ = 0.035 ± 0.007 pN/nm; trap 2, κ = 0.029 ± 0.007 pN/nm), leading to an overall trap stiffness of κ = 0.0159 pN/nm (yellow response curve in Extended Data Fig. 7h). Data were acquired at 1 kHz and further processed using custom-written software in R. Spurious electronic noise at 50 Hz was filtered using a fifth-order Butterworth notch filter from 49 to 51 Hz. For probing the interactions of EEA1 with Rab5 without any assumptions on the shape of EEA1, a distance agnostic protocol with consecutive cycles of approaching, waiting (20 s) and retraction was used, approaching closer in each iteration (Fig. 3b). The stationary segments were then subjected to automatic change-point analysis to identify regions of the time series longer than 100 ms with significantly different mean and variance49. Events thus identified were classified as transient if the mean and variance went back to base levels within the stationary segment (see examples in force traces in Fig. 3c and Extended Data Fig. 7). Mean times of interactions were 3.4 ± 0.6 s for GTP-γS and 0.9 ± 0.2 s for GTP. A fluctuation analysis of the differential distance signal during these events gave an estimated tether misalignment of less than 30° in all interactions50. Only transient events were further processed. Silica beads alone as a negative control measured a mean contact distance of 22 nm (Fig. 3d, grey). To calculate the persistence length for individual captured molecules we determined the equilibrium extension, z , from the capture distance D (nm), the average measured force increase upon tethering ΔF (pN) and the known displacements from each trap Δx = ΔF/κ and Δx = ΔF/κ as z = D − Δx − Δx . With this distance, the persistence length was calculated according to51 Similarly, to estimate the magnitude of the entropic collapse force, this formula was applied to the equilibrium extensions of EEA1, as estimated by the end-to-end distances of the molecules from electron microscopy. Values determined were (median and bounds at (2.5%, 97.5%)) EEA1, 23 (14, 33) nm; extended, 73 (60, 88) nm; swapped, 26 (21, 30) nm; 10×His, 78 (35, 140) nm. Values reported are medians and 95% confidence intervals determined from bootstrapping. HeLa EEA1-KO lines were generated using CRISPR-Cas9 technology52 on HeLa-Kyoto cell lines obtained from the BAC recombineering facility at the Max Planck Institute of Molecular Cell Biology and Genetics. Cell lines were tested for mycoplasma and authenticated (Multiplexion, Heidelberg). pSpCas9(BB-2A–GFP (PX458) and pSpCas9(BB)-2A-Puro (PX459) were a gift from F. Zhang (Addgene plasmid 48138, 48139). A PX458 plasmid encoding a GFP–labelled Cas9 nuclease and the sgRNA sequence (from GECKO52 library 17446, GTGGTTAAACCATGTTAAGG, targeting first exon) was transfected into standard HeLa Kyoto cells with Lipofectamine 2000 following the manufacturer’s instructions. Cells were cultured in DMEM media supplemented with 10% FBS and 1% penicillin-streptomycin at 37 °C and 5% CO . After 3 days, the transfected cells were FACS sorted by their GFP fluorescence into 96-well plates to obtain single clones and visually inspected53. These clones were then screened by western blotting and in-del formation confirmed sequencing of genomic DNA (primer forward, AGCGGCCGTCGCCACCG; reverse, TAAGCGCCTGCCGGGCTG). Note the region is extremely GC-rich (75%, ± 250 nt from targeted indel region). Additionally, a mixed-clonal line was obtained by transfection of HeLa Kyoto with PX459 with the above sgRNA sequence. After 72 h from transfection, cells were exchanged into media supplemented with 0.5 μg/ml puromycin (concentration determined in separated experiment) and selected for 3 days. All imaging experiments were confirmed on this secondary line. Wild-type EEA1 and the extended and swapped variants (Extended Data Fig. 3) were cloned into customized mammalian expression plasmids under the CMV promoter resulting in untagged proteins. HeLa or HeLa EEA1-KO cells were seeded into 96-well plates and transfected (or mock transfected) after 48 h. Following 48 h after transfection, cells were exchanged into serum-free media containing 8.2 μg/ml LDL-Alexa 488 (prepared as previously described16) or 100 ng/ml EGF-Alexa 488 (E13345, Thermo Fisher) for 10 min at 37 °C, and washed in PBS then fixed in 4% paraformaldehyde. Fixed cells were stained with antibodies against EEA1 (laboratory-made rabbit) and Rab5 (610724, prepared in mouse, BD Biosciences) as previously described24. DAPI was used to stain the nuclei. Not all early endosomes harbour EEA1 (ref. 54) and other tethering factors could compensate for EEA1 (refs 24, 55). All imaging was performed on a Yokogawa CV7000 s automated spinning disc confocal using a 60× 1.2 numerical aperture objective. Fifteen images were acquired per well and each condition was duplicated at least twice per plate, resulting in 30 or more images per condition. Image analysis used home-made software, MotionTracking, as previously described56, 57. Images were first corrected for illumination, chromatic aberration and physical shift using multicolour beads. All cells, nuclei and cell objects in corrected images were then segmented and their size, content and complexity calculated. The intensity of EEA1 in wild-type HeLa cells was measured to determine a wild-type intensity distribution. In the rescue experiments, an intensity threshold for the transfections was set at about two times the mean of wild-type cells (Extended Data Fig. 8i). Experiments were repeated at different seeding densities with similar results. Given a cell density threshold between 10 and 100 per image, we obtained an average of more than 300 cells per condition after filtering for the transfection level of EEA1, and more than 15,000 endosomes per experiment. A two-tailed t-test was used for significance calculations. Cells in 3 cm diameter plastic dishes were processed for electron microscopy using a method58 to provide particularly heavy staining of cellular components. Briefly, cells were fixed by addition of 2.5% glutaraldehyde in PBS for 1 h at room temperature and then washed with PBS. The cells were then processed as described58 with sequential incubations in solutions containing potassium ferricyanide/osmium tetroxide, thiocarbohydrazide, osmium tetroxide, uranyl acetate and lead nitrate in aspartic acid before dehydration and flat embedding in resin. Sections were cut parallel to the substratum and analysed unstained in a JEOL 1011 transmission electron microscope (Tokyo, Japan). Images for quantitation were collected from coded samples (double blind) to avoid bias. Distance analysis used ImageJ. To correct for thickness of slices (60 nm), the following equation was used: where P (r) is the apparent 2D distance distribution, R is the 3D distance, H is the thickness of the slice and Z is the normalization constant. Uncorrected distance was measured at 119.8 ± 78.2 nm (mean ± s.d.), which resulted in 130.0 ± 76.8 nm corrected.
News Article | April 20, 2016
We imaged the primary visual cortex of an awake 9-month-old C57BL/6 male mouse, as described previously10, 13, with a custom-built two-photon microscope12. Using volumetric in vivo two-photon calcium imaging of a genetically encoded calcium indicator (GCaMP3), we measured the time-resolved responses of a population of identified neurons to a wide array of stimuli including drifting gratings (up to 16 directions, 3 spatial, and 2 temporal frequencies). Following 12 days of imaging calcium responses in the same cohort of neurons, we labelled blood vessels with a tail vein injection (rhodamine B-conjugated dextran) and acquired an in vivo fluorescence volume. The animal’s brain was then prepared for large-scale transmission EM as described previously8. 3,700 serial sections (<50 nm thick) were cut and imaged spanning a 450 μm × 450 μm × 150 μm volume at 4 nm × 4 nm × 40 nm per voxel resolution. Sections representing the middle third of the EM volume were aligned and imported into CATMAID16 for distributed, online, manual reconstruction and targeted volumes around identified synapses were exported for volumetric segmentation and PSD analysis. EM reconstructed neurons were identified in the in vivo stack by using the blood vessels as landmarks. Apical dendrites originating from deeper neocortical lamina were similarly identified and corresponded by location and branching geometry of their apical tufts. Permutation tests were used in statistical analyses, unless otherwise noted. All procedures were conducted in accordance with the ethical guidelines of the NIH and approved by the IACUC at Harvard Medical School. For cranial window implant surgery the mouse was anesthetized with isoflurane (1.2–2% in 100% O ). Dexamethasone (3.2 mg per kg body weight, intramuscular) was administered on the day before surgery and atropine (0.2 mg per kg body weight, intraperitoneally) at the beginning of surgery. Using aseptic technique, we secured a headpost in place using cyanoacrylate, dental acrylic, and C&B Metabond (Parkell), and made a 5 mm craniotomy over the left visual cortex (centre: ~2.8 mm lateral, 0.5 mm anterior to lambda) as described previously32. A 5 mm glass cranial window was implanted consisting of an 8 mm coverslip cured to two 5 mm coverslips (Warner #1; total thickness: ~0.5 mm; thickness below skull: ~200 mm) using index-matched adhesive (Norland #71). We secured the window in place using cyanoacrylate and dental acrylic. We habituated the mouse with water scheduling so that water was delivered only during and immediately after head restraint training. We increased the duration of head restraint sessions over the course of 2 weeks, from 3 min to 2 h32. We then performed retinotopic mapping of visual cortical areas using widefield intrinsic autofluorescence imaging, measuring autofluorescence produced by blue excitation (470 nm centre, 40 nm band, Chroma) through a green/red emission filter (longpass, 500 nm cutoff). We collected images using a CCD camera (Sensicam, Cooke, 344 × 260 pixels spanning 4 mm × 3 mm; 2 Hz acquisition rate) through a 5× air objective (0.14 NA, Mitituyo) using ImageJ acquisition software. For retinotopic mapping, stimuli were presented at 2–6 retinotopic positions for 10 s each, with 10 s of mean luminance between trials. GCaMP3 expression was targeted by viral injection. Dexamethasone (3.2 mg per kg body weight, intramuscular) was administered at least 2 h before coverslip removal. The mouse was anesthetized (isoflurane, 1–1.5%) and the cranial window was sterilized with alcohol and the coverslip removed. We then volume injected (50–100 ml min−1, Stoelting) 30–100 nl of a 10:1 mixture of AAV2/1.hSynap.GCaMP3.3.SV4033 (Penn Vector Core) and 1 mM sulforhodamine-101 (Invitrogen) to visualize the injection. Using the blood vessel pattern observed during widefield imaging as a guide, we made an injection in the posterior part of primary visual cortex at a depth of ~250 μm below the pial surface. After injection, a new cranial window was sealed in place and the mouse recovered. A 120 Hz LCD monitor (Samsung 2233RZ, 2200) was calibrated at each temporal frequency using a spectrophotometer (Photoresearch PR-650). We confirmed waveforms were sinusoidal by measuring luminance fluctuations of a full-field sinusoidally modulated stimulus (using a photomultiplier tube, Hamamatsu). The monitor was positioned so that the stimulus patch was 21 cm from the contralateral eye. Local 40° Gabor-like circular patches (sigmoidal 10–90% falloff in 10°) containing either square-wave (for mapping retinotopy with widefield intrinsic autofluorescence and targeting GCaMP3 injections) or sine-wave (for mapping position of receptive fields with two-photon imaging) drifting gratings (80% contrast) were alternated with periods of uniform mean luminance (59 cd m−2). In an effort to increase the population of responsive cells and explore receptive field parameters we presented gratings of varying directions at multiple spatial and temporal frequencies or at different positions in the visual field. We presented either 8 directions at 3 spatial frequencies (0.06, 0.12, and 0.24 cycles per degree (cpd)) and 2 temporal frequencies (2 and 8 Hz), 16 directions at 2 spatial frequencies (0.04 and 0.16 cpd) and 2 temporal frequencies (2 and 8 Hz), 8 directions at 6 positions, or 16 directions at 4 positions (45–115° eccentricity and −5–25° elevation), for a total of 64 stimulus types plus 10% blank trials. Stimuli were centred on the location eliciting maximum calcium responses in the imaged field (monocular cortex), which most effectively drove responses in the population for experiments that did not vary stimulus position. All stimuli in a given protocol were presented in a pseudo-random order (sampling without replacement), and presented 3 times per volume experiment with 2–4 experiments per volume per day. Imaging was performed with a custom-designed two-photon laser-scanning microscope12. Excitation light from a Mai Tai HP DeepSee laser (Spectra-Physics) with dispersion compensation was directed into a modulator (Conoptics) and a beam expander (Edmund Optics). The expanded beam was raster scanned into the brain with a resonant (4 kHz, Electro-Optical Products) and a conventional galvanometer (Galvoline) (240 line frames, bidirectional, 31 Hz) through a 0.8 numerical aperture (NA) 16× objective lens (Nikon). Emitted photons were directed through a green filter (centre: 542 nm; band: 50 nm; Semrock) onto GaAsP photomultipliers (no cooling, 50 μA protection circuit, H7422P-40MOD, Hamamatsu). The photomultiplier signals were amplified (DHPCA-100, Femto), and low-pass filtered (cutoff frequency = ~700 kHz). These and the mirror driver signals were acquired at 3.3 MHz using a multifunction data acquisition board (PCI-6115, National Instruments). Images were reconstructed in MATLAB (MathWorks) and continuously streamed onto a RAID array. Microscope control was also performed in MATLAB using an analogue output board (PCI-6711, National Instruments). The laser’s dispersion compensation was adjusted to maximize collected fluorescence. A piezoelectric objective translator on the microscope enabled imaging multiple 300 × 300 × 100 μm volumes with 8 planes at 4 Hz separated by ~12.5 μm allowing us to capture the response properties of many cells through the depth of L2/3. The imaged field of view was 200–300 μm on a side at resolution of 0.8–1.2 μm per pixel (dwell-time ~2.7 μs). GCaMP3 was excited at 920 nm. Laser power was automatically adjusted as a function of imaging depth at the modulator with power exiting the objective ranging from 30–60 mW. During imaging, the mouse was placed on 6-inch diameter foam ball that could spin noiselessly on an axel (Plasteel). We monitored trackball revolutions using a custom photodetector circuit and recorded eye movements using an IR-CCD camera (Sony xc-ei50; 30 Hz) and infrared illumination (720–2,750 nm bandpass filter, Edmund). Visual stimuli were presented for 4 s with 4 s of mean luminance between trials. Recording sessions were 2–6 h in duration. Use of the genetically encoded calcium indicator GCaMP3, permitted recording from the same neurons over multiple days with the selectivity of calcium signals stable over several days of imaging (Extended Data Fig. 1)32, 34, 35. Within this volume we obtained calcium signals from cell bodies of superficial layer (L2/3) neurons and large calibre apical dendrites that continued beyond the depth of our imaging volume and had branching morphologies consistent with deep layer pyramidal cells. These were likely from L5 neurons because of their large calibre, and because most L6 pyramidal cells do not project their apical dendrites more superficially than L436, 37. The calcium signals from these deep layer apical dendrites stem from either forward-38, 39 or back-propagating action potentials40, are consistent across days (Extended Data Fig. 1) and along the length of the deep layer apical dendritic trunks (Extended Data Fig. 2), and therefore most likely reflect the response properties of the soma. We relocated the cohort of neurons daily by using the vasculature’s negative staining as fiducial landmarks. For the final in vivo imaging session, we injected the tail vein with a fluorescent dye to label blood vessels (rhodamine B isothiocyanate–Dextran (MW ~70k), 5% v/v, Sigma) and acquired a fluorescence stack to correspond the calcium-imaged neurons in vivo with their identities in the EM volume ex vivo8 (see below, and Extended Data Fig. 4). Following in vivo two-photon imaging the animal was perfused transcardially (2% formaldehyde/2.5% glutaraldehyde in 0.1 M cacodylate buffer with 0.04% CaCl ) and the brain was processed for serial-section TEM. 200 μm thick coronal vibratome sections were cut, post-fixed, and en bloc stained with 1% osmium tetroxide/1.5% potassium ferrocyanide followed by 1% uranyl acetate, dehydrated with a graded ethanol series, and embedded in resin (TAAB 812 Epon, Canemco). We located the calcium-imaged region by matching vasculature between in vivo fluorescence and serial thick (1 μm) toluidine blue (EMS) sections cut from an adjacent vibratome sections, then cut ~3,700 serial (<50 nm) sections on an ultramicrotome (Leica UC7) using a 35 degree diamond knife (EMS-Diatome) and manually collected sections on 1 mm × 2 mm dot-notch slot grids (Synaptek) that were coated with a pale gold Pioloform (Ted Pella) support film, carbon coated, and glow-discharged. Following section pickup, we post-stained grids with uranyl acetate and lead citrate. Using the custom-built transmission electron microscope camera array (TEMCA)8 we imaged the ~3,700 section series, targeting a ~450 μm × 450 μm region for each section (Fig. 1c). Acquired at 4 nm per pixel in plane, this amounted to ~100 terabytes of raw data to date comprising 30 million cubic microns of brain and >10 million (4,000 × 2,672 pixel) camera images. Magnification at the scope was 2,000×, accelerating potential was 120 kV, and beam current was ~90 microamperes through a tungsten filament. Images suitable for circuit reconstruction were acquired at a net rate of 5–8 million pixels s−1. Approximately the middle third of the series (sections 2,281–3,154) was aligned using open source software developed at Pittsburgh Supercomputing Center (AlignTK)8 and imported into CATMAID16 for distributed online visualization and segmentation. Within the analysed EM series there were 51 missing sections. Nineteen were single section losses. There were 2 instances each of missing 2, 3, and 4; and 1 instance each of missing 6 or 8 consecutive sections near the series boundaries. Folds, staining artefacts, and sometimes cracks occurred during section processing, but were typically isolated to edges of our large sections and therefore did not usually interfere with manual segmentation. To find the correspondence between the cells imaged in vivo with those in the EM data set, a global 3D affine alignment was used with fiducial landmarks manually specified at successively finer scales of vasculature and then cell bodies to re-locate the calcium-imaged neurons in the EM-imaged volume (Extended Data Fig. 4). Apical dendrites arising from deep layer (putative L5) pyramidal neurons were identified by their characteristic morphology36, 41, 42 (also see below). Their correspondence was facilitated by the unique branching patterns of their apical tufts and those that could not be unambiguously identified were not included in the functional analysis. We first traced the axonal and dendritic arbors of the functionally characterized neurons in the EM data set by manually placing a series of marker points down the midline of each process to generate a skeletonized model of the arbors using CATMAID16 (Figs 1d, 2a, 3a, Extended Data Fig. 6, Supplementary Data 1–3). We identified synapses using classical criteria42. For each synapse on the axon of a functionally characterized cell, dendrites of postsynaptic excitatory neurons were traced either to the boundaries of the aligned volume or centripetally back to the cell body8. We identified deep layer apical dendrites of (putative L5) pyramidal cells by their large calibre, high spine density, and their continuation beyond the bottom border of the EM volume, which spans from the pial surface through L4. For each neuronal target reconstruction included in the analysis, a second independent annotator verified the tracing by working backwards from the most distal end of every process. An additional round of validation was done for each synapse between functionally characterized cells where a third annotator who had not previously traced the pre- or post-synaptic process, independently verified the anatomical connectivity blind to previous tracing work. We began this independent round of validation at each synapse and traced the pre- and postsynaptic processes centripetally. If the initial reconstruction and subsequent verification of the reconstruction diverged, that connection and the segmentation work distal from the point of divergence was excluded from further analysis. EM reconstruction and validation was performed blind to cells’ functional characteristics and targeted cells were initially assigned to individual annotators pseudo-randomly weighted by tracing productivity. We performed targeted volumetric reconstructions of synapses connecting functionally characterized cells by developing tools to interface with CATMAID cutout, locally align, and catalogue volumes of interest based on location (Fig. 4a; for example, 400 pixels × 400 pixels × 41 sections or 3.2 μm × 3.2 μm × 1.64 μm volumes centred on synapses represented by CATMAID connectors). Presynaptic boutons, postsynaptic spines, their parent axons and dendrites, and postsynaptic density (PSD) areas were manually segmented with itk-SNAP (http://www.itksnap.org/). PSD areas were calculated as described previously43 with obliquely cut or en face synapse areas measured using their maximum z-projection. En face or obliquely cut synapses were identified by serial sections that starkly transitioned from a clear presynaptic specialization hosting a vesicle pool, to a distinctly different postsynaptic cell, typically with an intervening section of electron dense area representing the postsynaptic density and/or synaptic cleft (for example, Extended Data Fig. 5). In vivo calcium imaging data was analysed in MATLAB and ImageJ (NIH) as described previously12, 13. To correct for motion along the imaged plane (x–y motion), the stack for each imaging plane was registered to the average field of view using TurboReg44. A 5 pixel border at each edge of the field of view was ignored to eliminate registration artefacts. Masks for analysing fluorescence signal from neurons were manually generated corresponding to cells in the EM volume, registered to the in vivo anatomical fluorescence stack, and to individual physiological imaging planes. Time courses of cells spanning multiple physiological imaging planes were weighted by dwell time in each plane and averaged across planes. Evoked responses for each EM identified cells were measured for each stimulation epoch as the difference in fractional fluorescence (% ΔF/F ) between the 5 s after and the 2.5 s before stimulus onset (pre-stimulus activity), and averaged across stimulus repetitions. We quantified visual responsiveness of each cell by calculating the average Pearson correlation coefficient of the responses to all stimuli across repetitions (average trial-to-trial correlation). We defined the significance of visual responses as the probability (P value) that the observed trial-to-trial correlation is larger than the correlation obtained from a full random permutation of the data for spatial and temporal frequency experiments (P < 0.05) and experiments where stimulus position was varied (P < 0.01). In retinotopic experiments designed to increase the number of characterized neurons, we found cells that did not reliably respond to stimuli ± 20° from the centre of the display. These cells that either had receptive fields smaller than our stimuli or stimuli were positioned at the at the edge of their receptive fields. We considered these cells as potentially driven by stimulus edge effects and therefore excluded such experiments from further analysis. To estimate the preferred orientation, direction, and spatiotemporal frequency, we modelled responses with a combination of a multivariate Gaussian with spatial frequency (x and y, deg), temporal frequency (Hz) and position (x and y, deg) as independent dimensions, a constant gain factor, and a static exponent. We fit the model to data using a large-scale nonlinear optimization algorithm (Trust Region Reflective, MATLAB Optimization Toolbox, MathWorks Inc.), generating multiple fits from randomly selected starting points and selected the best fit (least-square criterion). The quality of model fits was inspected visually for all neurons included in the data set. EM connectivity was analysed using custom written software in MATLAB and Python. Connectivity analysis that did not utilize functional information (Figs 1e and 3, Extended Data Fig. 7) started with the entire population of excitatory neuronal targets in the reconstructed network. Network modularity and neuron connectivity motifs (Fig. 1e and Extended Data Fig. 7) were analysed with code modified from the Brain Connectivity Toolbox45. We used an implementation of the Louvain method17 followed by consensus portioning46 for weighted and directed graphs to detect communities, or interconnected pyramidal neuron targets, from our EM reconstructed network purely by anatomical connectivity. For this analysis we included only the 201 traced neurons having multiple synaptic partners (degree ≥ 2). The number of synapses reconstructed between neurons was used as weights for all analyses. Modularity Q was given by the standard equation: where l is the total number of edges, given by where N is the total set of nodes, a is the (i,j) element of the weighted adjacency matrix, δ(m ,m ) is 1 if i and j are in the same community and 0 otherwise, and are the in and out degrees of the jth and ith nodes respectively, calculated by To generate null models of connectivity matrices for hypothesis testing, we shuffled the reconstructed adjacency conditioned on our sample degree, weight and strength distributions (Extended Data Fig. 7)31, 47. Analysis of connectivity with neuronal function restricted our sample population to those cell pairs where both pre- and post-synaptic cells were functionally characterized. For orientation tuning (Figs 1d, f, 2, 4a–c, Extended Data Figs 5, 6, 8, 9), between 50 neurons, there were 29 connected pairs. On average, we detected 1.3 synapses per connected pair where we measured orientation selectivity for both cells. We varied retinotopic position and spatial and temporal frequencies of the grating stimulus with the goal of improved measurement of orientation preference for more cells. The sensory physiology of a subset of cells were simultaneously recorded across multiple stimulus parameters. These 120 cells were used for signal correlation analysis (Extended Data Fig. 10). Potential synapse length (L ) represents the degree to which pairs of neurons’ axonal and dendritic arbors come sufficiently close to make a synapse (Fig. 2a, c–f, 3b, d, Extended Data Figs 9, 10). For excitatory pyramidal cells, we computed this length of potential synaptic connectivity between all pairs by first resampling the dendritic and axonal arbor skeletons to a maximum segment length of 40 nm (the average thickness of the EM sections) and summing the length of all dendrite segments within a maximum spine length distance of the axon (s = 5 μm: Figs 2, 3 and Extended Data Fig. 10; s = 1 μm: Extended Data Fig. 9). We use s = 5 μm based the longest spine connecting functionally connected neurons (~ 5 μm). Analysis of neurons connected by multiple synapses (Fig. 3) was not restricted to cell pairs where both pre and post-synaptic cells were physiologically characterized. This population included 137 neurons connected by 267 synapses in 115 multi-synapse cell pairs whose axonal and dendritic arbors were traced exhaustively in the aligned volume. As a comparison population, we used 25 unique pairs connected by one synapse from the functionally characterized population described above, because they were also reconstructed throughout the aligned volume. To examine whether poly-synaptic connectivity occurs greater than random, we first computed a population average synapse rate (λ ) normalized by potential synapse length, by dividing the total number of synapses reconstructed from the set of 50 functionally characterized neurons by their total pairwise L . We next compared λ for individual neuron pairs each connected by different numbers of synapses (Fig. 3b). This was used to assess whether multiple synapses occurred more often than predicted from a simple Poisson model. We examined the frequency of clustered vs distant synapses by comparing synapse pairs that were separated by >20 μm or <20 μm. For each synapse from each pair of neurons connected by n synapses, we computed the total L within 20 μm or beyond 20 μm from that synapse. We then took the fraction of the overlap beyond 20 μm: as the expected probability that each of the (n − 1) other synapses will occur >20 μm away. The expected number of distant synapse was taken as (n − 1) times the fraction of overlap beyond 20 μm, which was compared with the actual number of distant synapses observed (Fig. 3d). 3D renderings were generated using Blender (http://www.blender.org/) (Figs 1d, 2a, 3a, Extended Data Fig. 6, Supplementary Data 1–3), Imaris (Bitplane) (Extended Data Fig. 4 and Supplementary Video 1), and itk-SNAP (Fig. 4a). Cytoscape (http://www.cytoscape.org/) was used for network graph layouts (Figs 1f). Statistical methods were not used to predetermine sample sizes. Statistical comparisons between sample distributions were done with Permutation tests (that is, Monte Carlo-based Randomization tests) unless otherwise noted. Permutation tests were ideal as we do not assume the underlying distributions are normal, nor need the observations to be independent. For Permutation tests, we computed the incidence of differences between means or Pearson’s linear correlation coefficient of randomly drawn samples from combined sample distributions exceeding the empirical difference (Figs 2b–d, f, 4c and Extended Data Figs 7b, 9a, b, 10c, d). Cochran-Armitage two-sided tests for trend were used on proportional binned data with linear weights (Fig. 2b, f). Standard errors were calculated from bootstrapped sample distributions. For cumulative distributions (Figs 2c, d, and Extended Data Figs 9a, b, 10c, d), we repeatedly resampled by randomly drawing with replacement from the sample distribution the number of observed values 1,000–10,000 times and extracted the standard deviation at each step of the empirical CDF. For binned data (Fig. 2b, f, and Extended Data Fig. 9d), each resampled distribution was binned and the standard deviation was computed from the resampled probabilities or rates within each bin. Custom code is available upon request.
News Article | February 22, 2017
All animal procedures adhered to the laws governing animal experimentation issued by the German Government. For all experiments, we used 3- to 12-week-old C57Bl/6 (n = 3), Chattm2(cre)Lowl (n = 34; ChAT:Cre, JAX 006410, The Jackson Laboratory), and Tg(Pcp2-cre)1Amc (n = 5; Pcp2, JAX 006207) mice of either sex. The transgenic lines were cross-bred with the Cre-dependent red fluorescence reporter line Gt(ROSA)26Sortm9(CAG-tdTomato)Hze (Ai9tdTomato, JAX 007905) for a subset of experiments. Owing to the explanatory nature of our study, we did not use randomization and blinding. No statistical methods were used to predetermine sample size. Animals were housed under a standard 12-h day–night rhythm. For recordings, animals were dark-adapted for ≥ 1 h, then anaesthetized with isoflurane (Baxter) and killed by cervical dislocation. The eyes were removed and hemisected in carboxygenated (95% O , 5% CO ) artificial cerebral spinal fluid (ACSF) solution containing (in mM): 125 NaCl, 2.5 KCl, 2 CaCl , 1 MgCl , 1.25 NaH PO , 26 NaHCO , 20 glucose, and 0.5 l-glutamine (pH 7.4). Then, the tissue was moved to the recording chamber of the microscope, where it was continuously perfused with carboxygenated ACSF at ~37 °C. The ACSF contained ~0.1 μM sulforhodamine-101 (SR101, Invitrogen) to reveal blood vessels and any damaged cells in the red fluorescence channel. All procedures were carried out under very dim red (>650 nm) light. A volume of 1 μl of the viral construct (AAV9.hSyn.iGluSnFR.WPRE.SV40 or AAV9.CAG.Flex.iGluSnFR.WPRE.SV40 (AAV9.iGluSnFR) or AAV9.Syn.Flex.GCaMP6f.WPRE.SV40, Penn Vector Core) was injected into the vitreous humour of 3- to 8-week-old mice anaesthetized with 10% ketamine (Bela-Pharm GmbH & Co. KG) and 2% xylazine (Rompun, Bayer Vital GmbH) in 0.9% NaCl (Fresenius). For the injections, we used a micromanipulator (World Precision Instruments) and a Hamilton injection system (syringe: 7634-01, needles: 207434, point style 3, length 51 mm, Hamilton Messtechnik GmbH). Owing to the fixed angle of the injection needle (15°), the virus was applied to the ventronasal retina. Imaging experiments were performed 3–4 weeks after injection. Sharp electrodes were pulled on a P-1000 micropipette puller (Sutter Instruments) with resistances >100 MΩ. Single cells in the inner nuclear layer were dye-filled with 10 mM Alexa Fluor 555 (Life Technologies) in a 200 mM potassium gluconate (Sigma-Aldrich) solution using the buzz function (50-ms pulse) of the MultiClamp 700B software (Molecular Devices). Pipettes were carefully retracted as soon as the cell began to fill. Approximately 20 min were allowed for the dye to diffuse throughout the cell before imaging started. After recording, an image stack was acquired to document the cell’s morphology, which was then traced semi-automatically using the Simple Neurite Tracer plugin implemented in Fiji (https://imagej.net/Simple_Neurite_Tracer). All drugs were bath applied for at least 10 min before recordings. The following drug concentrations were used (in μM): 10 gabazine (Tocris Bioscience)50, 75 TPMPA (Tocris Bioscience)50, 50 l-AP4 (l-(+)-2-amino-4-phosphonobutyric acid, Tocris Bioscience) and 0.5 strychnine (Sigma-Aldrich)51. Drug solutions were carboxygenated and warmed to ~37 °C before application. Pharmacological experiments were exclusively performed in the On and Off ChAT-immunoreactive bands, which are labelled in red fluorescence in ChAT:Cre × Ai9tdTomato crossbred animals. We used a MOM-type two-photon microscope (designed by W. Denk, MPI, Heidelberg; purchased from Sutter Instruments/Science Products). The design and procedures have been described previously52. In brief, the system was equipped with a mode-locked Ti:Sapphire laser (MaiTai-HP DeepSee, Newport Spectra-Physics), two fluorescence detection channels for iGluSnFR or GCaMP6f (HQ 510/84, AHF/Chroma) and SR101/tdTomato (HQ 630/60, AHF), and a water immersion objective (W Plan-Apochromat 20×/1.0 DIC M27, Zeiss). The laser was tuned to 927 nm for imaging iGluSnFR, GCaMP6f or SR101, and to 1,000 nm for imaging tdTomato. For image acquisition, we used custom-made software (ScanM by M. Müller and T.E.) running under IGOR Pro 6.3 for Windows (Wavemetrics), taking time-lapsed 64 × 16 pixel image scans (at 31.25 Hz) for glutamate and 32 × 32 pixel image scans (at 15.625 Hz) for calcium imaging. For visualizing morphology, 512 × 512 pixel images were acquired. For light stimulation, we focused a DLP projector (K11, Acer) through the objective, fitted with band-pass-filtered light-emitting diodes (LEDs) (green, 578 BP 10; and blue, HC 405 BP 10, AHF/Croma) to match the spectral sensitivity of mouse M- and S-opsins. LEDs were synchronized with the microscope’s scan retrace. Stimulator intensity (as photoisomerization rate, 103 P* per s per cone) was calibrated as described previously52 to range from 0.6 and 0.7 (black image) to 18.8 and 20.3 for M- and S-opsins, respectively. Owing to technical limitations, intensity modulations were weakly rectified below 20% brightness. An additional, steady illumination component of ~104 P* per s per cone was present during the recordings because of two-photon excitation of photopigments (for detailed discussion, see refs 52 and 53). The light stimulus was centred before every experiment, such that its centre corresponded to the centre of the recording field. For all experiments, the tissue was kept at a constant mean stimulator intensity level for at least 15 s after the laser scanning started and before light stimuli were presented. Because the stimulus was projected though the objective lens, the stimulus projection plane shifted when focusing at different IPL levels. We therefore quantified the resulting blur of the stimulus at the level of photoreceptor outer segments. We found that a vertical shift of the imaging plane by 50 μm blurred the image only slightly (2% change in pixel width), indicating that different IPL levels (total IPL thickness = 41.6 ± 4.8 μm, mean ± s.d., n = 20 scans) can be imaged without substantial change in stimulus quality. Four types of light stimuli were used (Fig. 1): (i) full-field (600 × 800 μm) and (ii) local (100 μm in diameter) chirp stimuli consisting of a bright step and two sinusoidal intensity modulations, one with increasing frequency (0.5–8 Hz) and one with increasing contrast; (iii) 1-Hz light flashes (500 μm in diameter, 50% duty cycle); and (iv) binary dense noise (20 × 15 matrix of 20 × 20 μm pixels; each pixel displayed an independent, balanced random sequence at 5 Hz for 5 min) for space–time receptive field mapping. In a subset of experiments, we used three additional stimuli: (v) a ring noise stimulus (10 annuli with increasing diameter, each annulus 25 μm wide), with each ring’s intensity determined independently by a balanced 68-s random sequence at 60 Hz repeated four times; (vi) a surround chirp stimulus (annulus; full-field chirp sparing the central 100 μm corresponding to the local chirp); and (vii) a spot noise stimulus (100 or 500 μm in diameter; intensity modulation like ring noise) flickering at 60 Hz. For all drug experiments, we showed in addition: (viii) a stimulus consisting of alternating 2-s full-field and local light flashes (500 and 100 μm in diameter, respectively). All stimuli were achromatic, with matched photo-isomerization rates for mouse M- and S-opsins. For each scan field, we used the relative positions of the inner (ganglion cell layer) and outer (inner nuclear layer) blood vessel plexus to estimate IPL depth. To relate these blood vessel plexi to the ChAT bands, we performed separate experiments in ChAT:Cre × Ai9tdTomato mice. High-resolution stacks throughout the inner retina were recorded in the ventronasal retina. The stacks were then first corrected for warping of the IPL using custom-written scripts in IGOR Pro. In brief, a raster of markers (7 × 7) was projected in the x–y plane of the stack and for each marker the z positions of the On ChAT band were manually determined. The point raster was used to calculate a smoothed surface, which provided a z offset correction for each pixel beam in the stack. For each corrected stack, the z profiles of tdTomato and SR101 labelling were extracted by manually drawing ROIs in regions where only blood vessel plexi or the ChAT bands were visible. The two profiles were then matched such that 0 corresponded to the inner vessel peak and 1 corresponded to the outer vessel peak. We averaged the profiles of n = 9 stacks from three mice and determined the IPL depth of the On and Off ChAT bands to be 0.48 ± 0.011 and 0.77 ± 0.014 AU (mean ± s.d.), respectively. The s.d. corresponds to an error of 0.45 and 0.63 μm for the On and Off ChAT bands, respectively. In the following, recording depths relative to blood vessel plexi were transformed into IPL depths relative to ChAT bands for all scan fields (Fig. 1b), with 0 corresponding to the On ChAT band and 1 corresponding to the Off ChAT band. Data analysis was performed using Matlab 2014b/2015a (Mathworks Inc.) and IGOR Pro. Data were organized in a custom written schema using the DataJoint for Matlab framework (github.com/datajoint/datajoint-matlab)54. Regions-of-interest (ROIs) were defined automatically by a custom correlation-based algorithm in IGOR Pro. First, the activity stack in response to the dense noise stimulus (64 × 16 × 10,000 pixels) was de-trended by high-pass filtering the trace of each individual pixel above ~0.1 Hz. For the 100 best-responding pixels in each recording field (highest s.d. over time), the trace of each pixel was correlated with the trace of every other pixel in the field. Then, the correlation coefficient (ρ) was plotted against the distance between the two pixels and the average across ROIs was computed (Extended Data Fig. 1a). A scan field-specific correlation threshold (ρ ) was determined by fitting an exponential between the smallest distance and 5 μm (Extended Data Fig. 1b). ρ was defined as the correlation coefficient at λ, where λ is the exponential decay constant (space constant; Extended Data Fig. 1b). Next, we grouped neighbouring pixels with ρ > ρ into one ROI (Extended Data Fig. 1c–e). To match ROI sizes with the sizes of BC axon terminals, we restricted ROI diameters (estimated as effective diameter of area-equivalent circle) to range between 0.75 and 4 μm (Extended Data Fig. 1b, g). For validation, the number of ROIs covering single axon terminals was quantified manually for n = 31 terminals from n = 5 GCaMP6-expressing BCs (Extended Data Figs 1g, 2a–c). The glutamate (or calcium) traces for each ROI were extracted (as ΔF/F) using the image analysis toolbox SARFIA for IGOR Pro55 and resampled at 500 Hz. A stimulus time marker embedded in the recorded data served to align the traces relative to the visual stimulus with 2 ms precision. For this, the timing for each ROI was corrected for sub-frame time-offsets related to the scanning. Stimulus-aligned traces for each ROI were imported into Matlab for further analysis. For the chirp and step stimuli, we down-sampled to 64 Hz for further processing, subtracted the baseline (median of first 20–64 samples), computed the median activity r(t) across stimulus repetitions (5 repetitions for chirp, >30 repetitions for step) and normalized it such that . For dye-injected BCs, axon terminals were labelled manually using the image analysis toolbox SARFIA for IGOR Pro. Then, ROIs were estimated as described above and assigned to the reconstructed cell, if at least two pixels overlapped with the cell´s axon terminals. We mapped the receptive field from the dense noise stimulus and the response kernel to the ring noise stimulus by computing the glutamate/calcium transient-triggered average. To this end, we used Matlab’s findpeaks function to detect the times t at which transients occurred. We set the minimum peak height to 1 s.d., where the s.d. was robustly estimated using: We then computed the glutamate/calcium transient-triggered average stimulus, weighting each sample by the steepness of the transient: Here, is the stimulus, τ is the time lag and M is the number of glutamate/calcium events. For the receptive field from the dense noise stimulus, we smoothed this raw receptive field estimate using a 3 × 3-pixel Gaussian window for each time lag separately and used singular value decomposition (SVD) to extract temporal and spatial receptive field kernels. To extract the receptive field’s position and scale, we fitted it with a 2D Gaussian function using Matlab’s lsqcurvefit. Receptive field quality (Qi ) was measured as one minus the fraction of residual variance not explained by the Gaussian fit , Response quality index. To measure how well a cell responded to a stimulus (local and full-field chirp, flashes), we computed the signal-to-noise ratio where C is the T by R response matrix (time samples by stimulus repetitions), while and denote the mean and variance across the indicated dimension, respectively2. For further analysis, we used only cells that responded well to the local chirp stimulus (Qi > 0.3) and resulted in good receptive fields (Qi > 0.2). Polarity index. To distinguish between On and Off BCs, we calculated the polarity index (POi) from the step response to local and full-field chirp, respectively, as where b = 2 s (62 samples). For cells responding solely during the On-phase of a step of light POi = 1, while for cells only responding during the step’s Off-phase POi = −1. Opposite polarity index. The number of opposite polarity events (OPi) was estimated from individual trials of local and full-field chirp step responses (first 6 s) using IGOR Pro’s FindPeak function. Specifically, we counted the number of events that occurred during the first 2 s after the step onset and offset for Off and On BCs, respectively. For each trial the total number of events was divided by the number of stimulus trials. If OPi = 1, there was on average one opposite polarity event per trial. High frequency index. The high frequency index (HFi) was used to quantify spiking (compare with ref. 28) and was calculated from responses to individual trials of the local and full-field chirps. For the first 6 s of each trial, the frequency spectrum was calculated by fast Fourier transform (FFT) and spectra were averaged across trials for individual ROIs. Then, HFi = log(F /F ), where F and F are the mean power between 0.5–1 Hz and 2–16 Hz, respectively. Response transience index. The step response (first 6 s) of local and full-field chirps was used to calculate the response transience (RTi). Traces were up-sampled to 500 Hz and the response transience was calculated as where α = 400 ms is the read-out time following the peak response t . For a transient cell with complete decay back to baseline RTi = 1, whereas for a sustained cell with no decay RTi = 0. Response plateau index. Local and full-field chirp responses were up-sampled to 500 Hz and the plateau index (RPi) was determined as: with the read-out time α = 2 s. A cell showing a sustained plateau has an RPi = 1, while for a transient cell RPi = 0. Tonic release index. Local chirp frequency and contrast responses were up-sampled to 500 Hz and the baseline (response to 50% contrast step) was subtracted. Then, the glutamate traces were separated into responses above (r ) and below (r ) baseline and the tonic release index (TRi) was determined as: For a cell with no tonic release TRi = 0, whereas for a cell with maximal tonic release TRi = 1. Response delay. The response delay (t ) was defined as the time from stimulus onset/offset until response onset and was calculated from the up-sampled local chirp step response. Response onset (t ) and delay (t ) were defined as and , respectively. We used sparse principal component analysis, as implemented in the SpaSM toolbox by K. Sjöstrang et al. (http://www2.imm.dtu.dk/projects/spasm/), to extract sparse response features from the mean responses across trials to the full-field (12 features) and local chirp (6 features), and the step stimulus (6 features) (as described in ref. 2; see Extended Data Fig. 4b). Before clustering, we standardized each feature separately across the population of cells. BC-terminal volume profiles were obtained from electron microscopic reconstructions of the inner retina6, 10. To isolate synaptic terminals, we removed those parts of the volume profiles that probably corresponded to axons. We estimated the median axon density for each type from the upper 0.06 units of the IPL and subtracted twice that estimate from the profiles, clipping at zero. Profiles were smoothed with a Gaussian kernel (s.d. = 0.14 units IPL depth) to account for jitter in depth measurements of two-photon data. For the GluMI cell, we assumed the average profile of CBC types 1 and 2. We used a modified mixture of Gaussian model56 to incorporate the prior knowledge from the anatomical BC profiles. For each ROI i with IPL depth , we define a prior over anatomical types c as Where IPL(d,c) is the IPL terminal density profile as a function of depth and anatomical cell type. For example, all ROIs of a scan field taken at an IPL depth of 1.7 were likely to be sorted into clusters for CBC types 1 and 2, while a scan field taken at a depth of 0 received a bias for CBC types 5–7 (Extended Data Fig. 4a). The parameters of the mixture of Gaussian model are estimated as usual, with the exception of estimating the posterior over clusters. Here, the mixing coefficients are replaced by the prior over anatomical types, resulting in a modified update formula for the posterior: All other updates remain the same as for the standard mixture of Gaussians algorithm57. We constrained the covariance matrix for each component to be diagonal, resulting in 48 parameters per component (24 for the mean, 24 for the variances). We further regularized the covariance matrix by adding a constant (10−5) to the diagonal. The clustering was based on a subset (~83%) of the data (the first 11,101 recorded cells). The remaining ROIs were then automatically allocated to the established clustering (n = 2,210 ROIs). For each pair of clusters, we computed the direction in feature space that optimally separated the clusters , where are the cluster means in feature space and is the pooled covariance matrix. We projected all data on this axis and standardized the projected data according to cluster 1 (that is, subtracted the projected mean of cluster 1 and divided by its s.d.). We computed d′ as a measure of the separation between the clusters: , where are the means of the two clusters in the projected, normalized space. We also performed a more constrained clustering in which we divided the IPL into five portions without overlap based on stratification profiles. We then clustered each zone independently using a standard mixture of Gaussian approach and a cluster number determined by the number of BC types expected in each portion. The correlation between the cluster means of our clustering and the more constrained clustering was 0.97 for the full-field chirp traces, indicating high agreement. Field entropy. Field entropy (S ) was used as a measure of cluster heterogeneity within single recording fields and was defined as , where i is the number of clusters in one recording field and p corresponds to the number of ROIs assigned to the ith cluster. S = 0 if all ROIs of one recording field are assigned to one cluster and S increases if ROIs are equally distributed across multiple clusters. In general, high field-entropy indicates high cluster heterogeneity within a single field. Analysis of response diversity. To investigate the similarity of local and full-field chirp responses across clusters (Fig. 3), we determined the linear correlation coefficient between any two cluster pairs. The analysis was performed on cluster means. For every cluster, correlation coefficients were averaged across clusters with the same and opposite response polarity, respectively. We used principal component analysis (using Matlab’s pca function) to obtain a 2D embedding of the mean cluster responses. The principal component analysis was computed on all 14 local and 14 full-field cluster means. If not stated otherwise, the non-parametric Wilcoxon signed-rank test was used for statistical testing. Pharmacology. To analyse drug-induced effects on BC clusters (Fig. 4, Extended Data Figs 7, 8), response traces and receptive fields of ROIs in one recording field belonging to the same cluster were averaged if there were at least 5 ROIs assigned to this cluster. Spatial receptive fields were aligned relative to the pixel with the highest s.d. before averaging. Centre-surround properties. To estimate the signal-to-noise ratio of ring maps of single ROIs, we extracted temporal centre and surround kernels and normalized the respective kernel to the s.d. of its baseline (first 50 samples). For further analysis, we included only ROIs with |Peak | > 12 s.d. and |Peak | > 7 s.d. Ring maps of individual ROIs were then aligned relative to its peak centre activation and averaged across ROIs assigned to one cluster. To isolate the BC surround, the centre rings (first two rings) were cut and the surround time and space components were extracted by singular value decomposition (SVD). The surround space component was then extrapolated across the centre by fitting a Gaussian and an extrapolated surround map was generated. To isolate the BC centre, the estimated surround map was subtracted from the average map and centre time and space components were extracted by SVD. The estimated centre and surround maps were summed to obtain a complete description of the centre–surround structure of BC receptive fields. Across clusters, the estimated centre–surround maps captured 92.5 ± 1.9% of the variance of the original map. Owing to the low signal-to-noise ratio, the temporal centre–surround properties of individual ROIs were extracted as described above using the centre and surround space kernels obtained from the respective cluster average. The 1D Gaussian fits of centre and surround space activation were used to calculate centre and surround ratios (CSRs) for various stimulus sizes. Specifically, the CSR was defined as where S corresponds to the stimulus radius and ranged from 10 to 500 μm, with a step size dx of 1 μm. Time kernels for different stimulus sizes were generated by linearly mixing centre and surround time kernels, weighted by the respective CSR. BC spectra. The temporal spectra of BC clusters were calculated by Fourier transform of the time kernels estimated for a local (100 μm in diameter) and full-field (500 μm in diameter) light stimulus (see centre–surround properties). Owing to the lower SNR of time kernels estimated for the full-field stimulus, kernels were cut 100 ms before and at the time point of response, still capturing 86.7 ± 14.7% of the variance of the original kernel. The centre of mass (Centroid) was used to characterize spectra of different stimulus sizes and was determined as where x(n) corresponds to the magnitude and f(n) represents the centre frequency of the nth bin. Surround chirp and spot noise data. To investigate the effects of surround-only activation and stimulus size on temporal encoding properties across BC clusters, response traces and estimated kernels of ROIs in one recording field belonging to the same cluster were averaged if there were at least five ROIs assigned to this cluster. The spectra for kernels estimated from local and full-field spot noise stimuli were calculated as described above. Time kernel correlation. To analyse the similarity of temporal kernels estimated for a specific stimulus size (Fig. 5i, j), we computed the linear correlation coefficient of each kernel pair from clusters with the same response polarity. We then calculated the average correlation coefficient for every cluster (Fig. 5i) and across all cluster averages (Fig. 5j). Data (original data and clustering results) as well as Matlab code are available from http://www.retinal-functomics.org.
Spectra - Physics | Date: 2013-02-07
The device (3) comprises a frame (6), a support piece (7) which is movable with respect to said frame (6) and which exhibits a master side provided with a rigid blade (9), on which may be fixed a first beam (2), and a slave side provided with two rollers, on which can rest an element for holding a second beam (2), and a set of actuators (13), which are able to position the movable support piece (7) in translation along two axes, termed the vertical axis and transverse axis respectively, which are perpendicular to one another and to a longitudinal axis, and in rotation about said longitudinal axis, so as to adjust the position of the first beam, the second beam following the translational motion along said two vertical and transverse axes.
Spectra - Physics | Date: 2012-07-02
Device for the translational guidance of a load and method for creating such a device. The guide device (1) comprises an elongate bed plate (2), a mobile carriage (6) which serves to support the load, and guide means (7) that allow the carriage (6) to move longitudinally in relation to the bed plate (2), said carriage (6) comprising a bottom plate (9) which is provided with through-cuts (10) intended to give it at least some lateral elasticity, and which is mounted on the bed plate (2) under lateral stress, as well as a platform (13) which is fixed to the top face of this bottom plate (9) at fixing points (14).
Spectra - Physics | Date: 2014-01-23
A driving device for driving in rotation a toothed wheel, in particular a turntable, has a worm intended to mesh with the toothed wheel, a motor to drive the worm in rotation, a flexible sleeve that partially surrounds the worm in such a way as to form an assembly described as a worm/sleeve assembly, and a pre-stressing unit. The motor is arranged in a structure that is fitted pivotably relative to the worm/sleeve assembly, and the driving device also has a force transfer unit connecting the motor to the sleeve at a second extremity of the worm.
Spectra - Physics | Date: 2016-03-16
A system provides for relative movement between two plates that are substantially parallel to a plane defined by a first direction and a second direction. The system includes a first unit that is configured to allow a relative movement between the two plates in a third direction that is orthogonal to the plane. The first unit also independently prevents a relative movement between the two plates in the plane. The system further includes a second unit that is configured to allow one plate to relatively travel with respect to the other plate about the first and second directions.
News Article | December 2, 2016
This report studies Industrial Fiber Lasers in Global market, especially in North America, Europe, China, Japan, Southeast Asia and India, with production, revenue, consumption, import and export in these regions, from 2011 to 2015, and forecast to 2021. This report focuses on top manufacturers in global market, with production, price, revenue and market share for each manufacturer, covering TRUMPF(SPI) IPG Photonics NLIGHT Corporation Raycus Rofin Spectra-Physics Coherent GSI Nufern Fujikura Vytek Xi’an Sino-Meiman Laser Tech By types, the market can be split into Type I Type II Type III By Application, the market can be split into Industrial Cutting?Drilling & Welding Laser Hardening & Cladding Scientific Research By Regions, this report covers (we can add the regions/countries as you want) North America China Europe Southeast Asia Japan India 1 Industry Overview of Industrial Fiber Lasers 1.1 Definition and Specifications of Industrial Fiber Lasers 1.1.1 Definition of Industrial Fiber Lasers 1.1.2 Specifications of Industrial Fiber Lasers 1.2 Classification of Industrial Fiber Lasers 1.2.1 Type I 1.2.2 Type II 1.2.3 Type III 1.3 Applications of Industrial Fiber Lasers 1.3.1 Industrial Cutting?Drilling & Welding 1.3.2 Laser Hardening & Cladding 1.3.3 Scientific Research 1.4 Market Segment by Regions 1.4.1 North America 1.4.2 China 1.4.3 Europe 1.4.4 Southeast Asia 1.4.5 Japan 1.4.6 India 2 Manufacturing Cost Structure Analysis of Industrial Fiber Lasers 2.1 Raw Material and Suppliers 2.2 Manufacturing Cost Structure Analysis of Industrial Fiber Lasers 2.3 Manufacturing Process Analysis of Industrial Fiber Lasers 2.4 Industry Chain Structure of Industrial Fiber Lasers 3 Technical Data and Manufacturing Plants Analysis of Industrial Fiber Lasers 3.1 Capacity and Commercial Production Date of Global Industrial Fiber Lasers Major Manufacturers in 2015 3.2 Manufacturing Plants Distribution of Global Industrial Fiber Lasers Major Manufacturers in 2015 3.3 R&D Status and Technology Source of Global Industrial Fiber Lasers Major Manufacturers in 2015 3.4 Raw Materials Sources Analysis of Global Industrial Fiber Lasers Major Manufacturers in 2015 4 Global Industrial Fiber Lasers Overall Market Overview 4.1 2011-2016E Overall Market Analysis 4.2 Capacity Analysis 4.2.1 2011-2016E Global Industrial Fiber Lasers Capacity and Growth Rate Analysis 4.2.2 2015 Industrial Fiber Lasers Capacity Analysis (Company Segment) 4.3 Sales Analysis 4.3.1 2011-2016E Global Industrial Fiber Lasers Sales and Growth Rate Analysis 4.3.2 2015 Industrial Fiber Lasers Sales Analysis (Company Segment) 4.4 Sales Price Analysis 4.4.1 2011-2016E Global Industrial Fiber Lasers Sales Price 4.4.2 2015 Industrial Fiber Lasers Sales Price Analysis (Company Segment) 6 Global 2011-2016E Industrial Fiber Lasers Segment Market Analysis (by Type) 6.1 Global 2011-2016E Industrial Fiber Lasers Sales by Type 6.2 Different Types of Industrial Fiber Lasers Product Interview Price Analysis 6.3 Different Types of Industrial Fiber Lasers Product Driving Factors Analysis 6.3.1 Type I Industrial Fiber Lasers Growth Driving Factor Analysis 6.3.2 Type II Industrial Fiber Lasers Growth Driving Factor Analysis 6.3.3 Type III Industrial Fiber Lasers Growth Driving Factor Analysis 7 Global 2011-2016E Industrial Fiber Lasers Segment Market Analysis (by Application) 7.1 Global 2011-2016E Industrial Fiber Lasers Consumption by Application 7.2 Different Application of Industrial Fiber Lasers Product Interview Price Analysis 7.3 Different Application of Industrial Fiber Lasers Product Driving Factors Analysis 7.3.1 Industrial Cutting?Drilling & Welding of Industrial Fiber Lasers Growth Driving Factor Analysis 7.3.2 Laser Hardening & Cladding of Industrial Fiber Lasers Growth Driving Factor Analysis 7.3.3 Scientific Research of Industrial Fiber Lasers Growth Driving Factor Analysis 8 Major Manufacturers Analysis of Industrial Fiber Lasers 8.1 TRUMPF(SPI) 8.1.1 Company Profile 8.1.2 Product Picture and Specifications 220.127.116.11 Type I 18.104.22.168 Type II 22.214.171.124 Type III 8.1.3 TRUMPF(SPI) 2015 Industrial Fiber Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.1.4 TRUMPF(SPI) 2015 Industrial Fiber Lasers Business Region Distribution Analysis 8.2 IPG Photonics 8.2.1 Company Profile 8.2.2 Product Picture and Specifications 126.96.36.199 Type I 188.8.131.52 Type II 184.108.40.206 Type III 8.2.3 IPG Photonics 2015 Industrial Fiber Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.2.4 IPG Photonics 2015 Industrial Fiber Lasers Business Region Distribution Analysis 8.3 NLIGHT Corporation 8.3.1 Company Profile 8.3.2 Product Picture and Specifications 220.127.116.11 Type I 18.104.22.168 Type II 22.214.171.124 Type III 8.3.3 NLIGHT Corporation 2015 Industrial Fiber Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.3.4 NLIGHT Corporation 2015 Industrial Fiber Lasers Business Region Distribution Analysis 8.4 Raycus 8.4.1 Company Profile 8.4.2 Product Picture and Specifications 126.96.36.199 Type I 188.8.131.52 Type II 184.108.40.206 Type III 8.4.3 Raycus 2015 Industrial Fiber Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.4.4 Raycus 2015 Industrial Fiber Lasers Business Region Distribution Analysis 8.5 Rofin 8.5.1 Company Profile 8.5.2 Product Picture and Specifications 220.127.116.11 Type I 18.104.22.168 Type II 22.214.171.124 Type III 8.5.3 Rofin 2015 Industrial Fiber Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.5.4 Rofin 2015 Industrial Fiber Lasers Business Region Distribution Analysis 8.6 Spectra-Physics 8.6.1 Company Profile 8.6.2 Product Picture and Specifications 126.96.36.199 Type I 188.8.131.52 Type II 184.108.40.206 Type III 8.6.3 Spectra-Physics 2015 Industrial Fiber Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.6.4 Spectra-Physics 2015 Industrial Fiber Lasers Business Region Distribution Analysis 8.7 Coherent 8.7.1 Company Profile 8.7.2 Product Picture and Specifications 220.127.116.11 Type I 18.104.22.168 Type II 22.214.171.124 Type III 8.7.3 Coherent 2015 Industrial Fiber Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.7.4 Coherent 2015 Industrial Fiber Lasers Business Region Distribution Analysis 8.8 GSI 8.8.1 Company Profile 8.8.2 Product Picture and Specifications 126.96.36.199 Type I 188.8.131.52 Type II 184.108.40.206 Type III 8.8.3 GSI 2015 Industrial Fiber Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.8.4 GSI 2015 Industrial Fiber Lasers Business Region Distribution Analysis 8.9 Nufern 8.9.1 Company Profile 8.9.2 Product Picture and Specifications 220.127.116.11 Type I 18.104.22.168 Type II 22.214.171.124 Type III 8.9.3 Nufern 2015 Industrial Fiber Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.9.4 Nufern 2015 Industrial Fiber Lasers Business Region Distribution Analysis 8.10 Fujikura 8.10.1 Company Profile 8.10.2 Product Picture and Specifications 126.96.36.199 Type I 188.8.131.52 Type II 184.108.40.206 Type III 8.10.3 Fujikura 2015 Industrial Fiber Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.10.4 Fujikura 2015 Industrial Fiber Lasers Business Region Distribution Analysis 8.11 Vytek 8.11.1 Company Profile 8.11.2 Product Picture and Specifications 220.127.116.11 Type I 18.104.22.168 Type II 22.214.171.124 Type III 8.11.3 Vytek 2015 Industrial Fiber Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.11.4 Vytek 2015 Industrial Fiber Lasers Business Region Distribution Analysis 8.12 Xi’an Sino-Meiman Laser Tech 8.12.1 Company Profile 8.12.2 Product Picture and Specifications 126.96.36.199 Type I 188.8.131.52 Type II 184.108.40.206 Type III 8.12.3 Xi’an Sino-Meiman Laser Tech 2015 Industrial Fiber Lasers Sales, Ex-factory Price, Revenue, Gross Margin Analysis 8.12.4 Xi’an Sino-Meiman Laser Tech 2015 Industrial Fiber Lasers Business Region Distribution Analysis 9 Development Trend of Analysis of Industrial Fiber Lasers Market 9.1 Global Industrial Fiber Lasers Market Trend Analysis 9.1.1 Global 2016-2021 Industrial Fiber Lasers Market Size (Volume and Value) Forecast 9.1.2 Global 2016-2021 Industrial Fiber Lasers Sales Price Forecast 9.2 Industrial Fiber Lasers Regional Market Trend 9.2.1 North America 2016-2021 Industrial Fiber Lasers Consumption Forecast 9.2.2 China 2016-2021 Industrial Fiber Lasers Consumption Forecast 9.2.3 Europe 2016-2021 Industrial Fiber Lasers Consumption Forecast 9.2.4 Southeast Asia 2016-2021 Industrial Fiber Lasers Consumption Forecast 9.2.5 Japan 2016-2021 Industrial Fiber Lasers Consumption Forecast 9.2.6 India 2016-2021 Industrial Fiber Lasers Consumption Forecast 9.3 Industrial Fiber Lasers Market Trend (Product Type) 9.4 Industrial Fiber Lasers Market Trend (Application)