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News Article | April 20, 2016
Site: www.nature.com

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 | August 31, 2016
Site: www.nature.com

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 | November 10, 2016
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All animal experiments were carried out in compliance with the institutional guidelines of the Max Planck Society, the Ludwig-Maximilians-University and the local government (Government of Upper Bavaria). Data for this study are derived from a total of 38 adult mice of both sexes. For the characterization of the lesion model, the cells’ identity and the afferent/efferent connectivity we used C57BL/6J mice. For cell fusion controls and structural in vivo two-photon imaging we used Emx1-Cre13 crossed to CAG-CAT-GFP42 donor cells and Ai9 (Rosa-CAG-LSL-tdTomato reporter mice)14 host mice. For functional in vivo two-photon imaging we used Emx1-Cre13 crossed to Ai9 donor cells and C57BL/6J host mice. No a priori determination of sample size was conducted, experiments were not randomized, and investigators were not blinded to experimental conditions, except where noted. Mice were housed in a 12:12 h light–dark cycle. All mice were 2–4 months old at the time of the first surgery. Surgeries were performed aseptically under anaesthesia with a mixture of fentanyl (0.05 mg kg−1, Hexal or Janssen), midazolam (5 mg kg−1, Ratiopharm or Roche) and medetomidine (0.5 mg kg−1, Orion Corp. or Fort Dodge). After surgery, anaesthesia was terminated with atipamezol (2.5 mg kg−1, Orion Corp.), flumazenil (0.5 mg kg−1, Hexal) and naloxone (1.2 mg kg−1, Ratiopharm) or buprenorphin (0.1 mg kg−1, Essex). Carprofen (5 mg kg−1, Pfizer) or meloxicam (1 mg kg−1, Boehringer Ingelheim) was administered as a postoperative analgesic. Rhodamine-labelled latex beads (Lumafluor) were conjugated with chlorine e6 (Ce6, Frontier Scientific) as described previously43, with minor modifications. In brief, 1.5 ml of Ce6 solution (0.597 mg ml−1 in 0.01 M phosphate buffer (PB), pH 7.4) were added to 5 mg of 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide (MP Biomedicals) and kept at 4 °C for 30 min. Then 12.5 μl of rhodamine latex beads were diluted in 100 μl PB, mixed with 750 μl of activated Ce6 solution, and incubated on an orbital shaker for 1 h at room temperature. The reaction was stopped by adding 335 μl of 0.1 M glycine buffer (pH 8.0). The conjugated latex beads were washed at least three times (30 min, 100,000g) with 0.01 M PB and finally resuspended in 50 μl PB. Beads were stored at 4 °C and used within 1 month. For beads injection, mice were anaesthetized, and a unilateral craniotomy (2.5 mm diameter) was performed at the posterior end of the parietal bone, centred at 2.5 mm lateral from lambda, to expose the visual cortex. Ce6-conjugated beads (9 μg μl−1) were injected at 5–10 locations to reach a total volume of 0.5–1 μl, at a depth of 0.5 mm into the primary visual cortex (V1). Injections were restricted to the binocular zone of V1 with a total volume of 0.5 μl in structural and functional in vivo imaging experiments. The location of V1 and the binocular zone was identified using intrinsic optical imaging16 and/or the characteristic blood vessel pattern. One to two weeks after the beads injection, a second craniotomy was performed in order to expose the contralateral V1 that was non-invasively subjected to laser-photoactivation of Ce6 for 4 min (670 nm, 27 mW; laser and beam shaping optics for a collimated parallel beam of 1 mm diameter, Schäfter+Kirchhoff) to induce apoptotic cell death of callosal projection neurons (CPNs). This lesion model thus targets only a fraction of neurons in an area and layer-specific manner, while the overall tissue structure is preserved. For monosynaptic rabies virus tracing experiments, mouse cortical cells were labelled via in utero electroporation44. In brief, at embryonic day E14.5–E16.5, pregnant mice were anaesthetized and the uterine horns were exposed by laparotomy. The plasmid pDsRedExpress2-2A-Glyco-IRES2-TVA or pDsRedExpress2-IRES2-TVA (1 μg μl−1) was mixed with 2.5 μg μl−1 Fast Green dye (Sigma-Aldrich) and 1 μl of the solution was injected into one of the lateral ventricles of each embryo using a glass capillary. Tweezer-type circular electrodes oriented with a ~45° angle for cortical targeting were used to hold the head of the embryo and deliver electrical pulses (5 pulses, 35 V, 100 ms) produced by a square-wave electroporation generator (ECM 830, BTX, Harvard Apparatus). Uterine horns were returned into the abdominal cavity and embryos were allowed to continue their normal development. For transplantation, E18.5 embryos previously electroporated at E14.5 were euthanized and cerebral cortices were collected for cell dissociation. Some cells were plated directly after dissociation at a density of 500,000 cells per well in 24-well plates in B27-containing DMEM high glucose (4.5 g l−1) with glutamax, plus penicillin–streptomycin, and fixed after 2h for subsequent immunocytochemistry (see below). For tracing of the afferents to endogenous upper layer neurons, electroporation was performed at E15.5–E16.5, and animals were allowed to develop until adulthood, when the RABV was then injected into V1 of the electroporated hemisphere. In a subset of experiments, in vitro transduction with Moloney murine leukaemia virus (MMuLV)-derived retroviral vectors (n = 10 host mice) or adeno-associated virus (AAV; n = 2 host mice) was performed to label donor cells. In brief, neocortical tissue from E14.5/E15.5 embryos was mechanically dissociated in HBSS (Invitrogen) buffered with 10 mM HEPES, or in EBSS with papain (20 U ml−1, 0.005% DNase, 1 mM l-cysteine, 0.5 mM EDTA; Papain dissociation system, Worthington) after 45 min of enzymatic treatment at 37 °C. Enzymatic activity was stopped by protease inhibitors (10 mg ml−1 ovomucoid). Cells were plated in 20 μg ml−1 poly-d-lysine (Sigma-Aldrich)-coated 24-well plates, at a density of 300,000 cells per well. Cells were initially kept in 10% FBS-containing DMEM high glucose (4.5 g l−1) with glutamax, plus penicillin–streptomycin (all from Invitrogen), and transduced after 2–4 h with viral vectors (1–2 μl per well; titres typically ranged from 107 to 1011 transducing units ml−1). For monosynaptic rabies tracing experiments MMuLV-derived retroviral vectors (CAG-DsRedExpress2-2A-Glyco-IRES2-TVA or CAG-DsRedExpress2-IRES2-TVA) and for functional in vivo imaging experiments an AAV vector (AAV2/1-hSyn1-flex-mRuby2-P2A-CGaMP6 s-WPRE-SV40) was used. Serum was gradually removed by replacing half of the medium with B27-containing DMEM high glucose (4.5 g l−1) with glutamax plus penicillin-streptomycin on each of the following 2 days. Cells were collected for transplantation after 2–5 days in vitro. Embryonic cells for transplantation were fluorescently labelled either in the above mentioned mouse lines, by in utero electroporation of DNA plasmids, or via in vitro viral transduction. Seven to ten days after laser-photoactivation, embryonic cells were transplanted into the previously illuminated area. In short, E18.5 embryos were euthanized, and fluorescently labelled cortical hemispheres were collected for dissociation. Cortical tissue was mechanically dissociated either in HBSS buffered with 10 mM HEPES, or in EBSS with papain (20 U ml−1 papain, 0.005% DNase, 1 mM l-cysteine, 0.5 mM EDTA; Papain dissociation system, Worthington) after 45 min of enzymatic treatment at 37 °C. In case of papain treatment, enzyme activity was stopped by protease inhibitors (10 mg ml−1 ovomucoid). A cell suspension (50 million cells ml−1) was prepared in neurobasal medium (NB) or DMEM high glucose (4.5 g l−1), both supplemented with B27, glutamax and penicillin–streptomycin. Donor cells labelled via in vitro viral transduction were washed at least 5 times with pre-warmed PBS to remove any remaining viral particles. Gentle trypsinization (0.025%, 10 min at 37 °C) was performed and a cell suspension (25 million–50 million cells ml−1) was prepared in B27-containing DMEM high glucose (4.5 g l−1) with glutamax, plus penicillin–streptomycin. Between 25,000 and 80,000 donor cells were transplanted into V1 of adult mice. A total volume of 1 μl of cell suspension was injected with a syringe (gauge 31–33, Hamilton) within the area previously illuminated to induce neuronal death, at 2.5 ± 0.2 mm medio-lateral, 0.0 ± 0.2 mm antero-posterior relative to lambda, and distributed dorso-ventrally over 200–300 μm at a depth between 0.5 and 0.2 mm. The exact injection coordinates and pattern of pial vasculature were noted for later injection of the RABV. For two-photon imaging a cranial glass window was implanted, otherwise the bone lid was repositioned and the skin was sutured. For chronic structural and functional in vivo imaging experiments a cranial glass window was implanted on top of V1 (ref. 15) after cell transplantation. In brief, a coverslip (5 mm diameter, #1 thickness, EMS) was loosely placed on the dura, resting on the edge of the craniotomy, and sealed to the bone with cyanoacrylate. A small metal bar with screw holes (5 × 8 mm) for head fixation during image acquisition was attached to the skull medial to the window implant. Skin margins, cover-glass and metal bar were embedded in dental acrylic (Heraeus Kulzer) mixed with black pigment (Kremer). In a subset of mice (n = 16), the location of cell transplantation was verified with optical imaging of intrinsic signals. In short, anaesthetized mice were presented with square wave drifting gratings (4 orientations, 600 ms stimulus duration; 0.03 cycles degree−1, 2 cycles s−1) in a 2 × 2 array covering approximately −10° to 70° azimuth, −20° to 40° elevation of the ipsi- or contralateral visual field, respectively. For identification of the binocular zone, visual stimuli were presented to the ipsilateral eye while the contralateral eye was covered. The cortical surface was evenly illuminated through the cranial window with monochromatic light at 707 nm. A cooled, slow scan CCD camera (12 bit, Optical Imaging Inc.) was focused 200–300 μm below the cortical surface and frames were recorded with 600 ms exposure time. Images of average responses (3 repetitions of 12 stimulus frames per location) were blank-corrected, range-fitted and low-pass-filtered24. The false colour-coded maximum projection of visual responses is mapped on top of the blood vessel image acquired before visual stimulation. In vivo two-photon imaging was carried out on an Olympus FV1000BX61 system equipped with a mode-locked Ti:sapphire laser (Mai Tai DeepSee, Spectra-Physics) through a 25× water immersion objective (1.05NA, Olympus). Laser settings and image acquisition were controlled by Fluoview software (Olympus). For structural in vivo imaging mice were anaesthetized with fentanyl based anaesthesia (see above) and placed on a feedback controlled heating pad. Data were acquired at 910 nm with an average laser power of <30 mW below the objective, and the emission signal was directed through a dichroic mirror (DM570) and red/green bandpass emission filters (BA495-540HQ and BA570-625HQ, all Olympus); a typical imaging session lasted 2 h. Host mice (n = 11) were imaged as early as 3 dpt. Individual transplanted cells were identified and followed in short, increasing intervals (2–5 days) within the first 4 weeks and weekly thereafter (up to 12 wpt; n = 6 mice). In two mice we acquired late time points at 9–11 mpt. In each imaging session, high-resolution tiled volume stacks (510 × 510 μm field of view; 0.33 μm per pixel; 1–3 μm z-steps) were acquired up to a depth of 350 μm from the pial surface for overview and reconstruction of whole cell morphology of transplanted cells. In addition, high-resolution close-up stacks (73 × 73 μm; 0.14 μm per pixel; 0.5–1.0 μm z-steps) of typically three individual dendritic and axonal processes were acquired at various depths between 50 and 300 μm. For the analysis presented in Fig. 2, we included both basal and apical dendritic processes. For functional in vivo imaging experiments, donor cells were labelled with the GECIs CGaMP6s25 or Twitch2B26 (a FRET-based sensor). Emx1-Cre × Ai9 donor cells were mixed with AAV encoding a double-floxed inverted open reading frame version of either GECI before transplantation (AAV2/1-hSyn1-flex-CGaMP6s-WPRE-SV40; AAV2/1-CAG-flex-Twitch2B-WPRE-SV40). In a subset of experiments (n = 2 host mice) donor cells were labelled in vitro (AAV2/1-hSyn1-flex-mRuby2-P2A-CGaMP6s-WPRE-SV40, see above). In vivo imaging was performed under light anaesthesia; mice received 0.4× dose for initial anaesthesia and a subsequent 0.2× dose every 90 min. Mice were kept on a feedback-controlled heating pad. For ipsi- and contralateral visual stimulation, either the left or the right eye was occluded, respectively, and full field moving gratings (square wave, high contrast; 0.04 cycles degree−1, 1.5 cycles s−1; 4 orientations, 8 directions) were presented to the open eye (30 cm distance monitor to eye). The 8 directions were presented in random order, each displayed for 3 s, followed by 3 s of an isoluminant grey screen. Presentation of 3 × 8 directions was flanked by 10 s of grey screen (constituting one stimulus sequence of 3 repeats). Typically, 2–3 stimulus sequences were presented per imaging plane (altogether 6–9 repeats). Data were acquired either at 940 nm (GCaMP6) or at 860 nm (Twitch2B) with an average laser power of <30 mW; a typical imaging session lasted 2–3 h. Emitted light was directed through a longpass dichroic mirror (DM570, GCaMP6; 505DCXR, Twitch2B) and recorded through emission filters (BA495-540HQ and BA570-625HQ, GCaMP6; ET480/40M and ET535/30M, Twitch2B). In each imaging session a tiled volume stack (760 × 760 μm field of view; 0.49 μm per pixel; 3–5 μm z-steps) was acquired up to a depth of 350 μm from the pial surface for an overview of transplanted (RFP+) neurons. Five to ten candidate areas with RFP+/GECI+ neurons were recorded at a frame rate of 2.4 Hz during visual stimulation (73 × 73 μm field of view; 0.28 μm per pixel). Host mice were imaged starting at 4 wpt, and individual responsive neurons were repeatedly recorded up to 15 wpt. In a subset of experiments (n = 2 mice, see above), we recorded late time points at 11–15 wpt. Image stacks were processed using the Fiji package of ImageJ (US National Institutes of Health) as follows. Fluorescence signals of rhodamine-coupled Ce6 beads were removed by channel subtraction. Images were converted to 8-bit and subjected to a small 2D Gaussian filter (σ < 0.6 pixels). For display purposes only, maximum intensity z-projections are shown with adjusted brightness/contrast. Whole-cell morphology was reconstructed using Simple Neurite Tracer. In brief, apical and basal dendrites were semi-automatically traced through the high-resolution tiled volume stack. On the basis of the traced skeleton, a single-cell 3D volume model was rendered. To determine spine and bouton densities, dynamics and survival, putative synaptic structures were identified15 in image stacks at each recorded time point. We included all clearly visible structures in x, y and z. An ID was assigned to each individual identified structure at the time point of its first appearance and registered across time points. Initial identification of spines and boutons was performed blinded to the time point of recording. Registration across time points was performed sequentially. Density is reported as structures per μm, and turnover is calculated as fraction of structures (gained + lost)/(total t1 + t2). We calculated the average survival fraction of gained structures dependent on the time point of their first appearance (newly formed structures at binned time points: 3–4, 5–6, 7–9, 12–13, 17–19 and 22–24 dpt and weekly bins between 4 and 9 wpt) according to the non-parametric Kaplan–Meier estimator (using GraphPad PRISM). This method takes into account some uncertainty of the actual survival of spines present at (and presumably longer than) the last experimental time point. Hazard ratios compare the rate of structure loss between structures that were gained at different time points; median survival ratios compare the relative median survival of gained structures at different time points. In total, 16 dendritic stretches from 5 mice, and 33 axonal stretches from 6 mice were analysed (average dendritic length: 50.5 ± 12.4 μm; average axonal length: 77.7 ± 25.3 μm). A total of 13,251 dendritic spines and 6,266 axonal boutons across all time points were identified and registered to 2,493 individual dendritic spines and 1,600 individual axonal boutons on 0.8 mm total dendritic and 2.6 mm total axonal length. Functional imaging data were analysed using Fiji and Matlab (Mathworks), and the investigator was blinded to the recording time points analysed. Individual frames were background subtracted using a rolling ball algorithm (>100 pixels radius), converted to 8 bit and subjected to a small 2D Gaussian filter (σ < 0.8 pixels). Stacks were full-frame aligned using linear transformations (StackReg, P. Thévenaz, EPFL; http://bigwww.epfl.ch/thevenaz/stackreg) and regions of interest were selected manually based on the aligned maximum intensity projection across a stimulation sequence. The fluorescence signal (F) was calculated as the average fluorescence of all pixels within a given region of interest (Twitch2B: R = av.F /av.F ). Neuronal activity was measured as the normalized change in fluorescence signal over time: (F − F )/F (Twitch2B: R − R /R ). The baseline (F , R ) was calculated as the average signal over typically 10 s before and after each stimulation sequence (see above). We classified neurons as visually responsive if the average ΔF/F  > 3σ (Twitch2B: ΔR/R  > 0.05; ref. 33) for at least one stimulus direction. Tuning properties of each neuron were depicted in complex space using the normalized average peak response for each direction expressed in polar coordinates. Orientation and direction tuning preference was expressed as orientation and direction selectivity index (OSI and DSI), respectively, and calculated as follows: OSI = (R − R )/(R + R ); and DSI = (R − R )/(R + R ). R is the average peak response to the preferred direction (R ), to the mean of the orthogonal directions (R ) and the opposing direction (R ). As ratio based tuning properties do not take into account the distribution of responses across all tested directions, we also calculated single and double Gaussian fits45. Following the assumption that an ideal orientation (or direction) tuned neuron would be perfectly described by a double (or single) Gaussian fit, the goodness of fit (R2) serves as a measure of tuning quality. In short, curves were fit with nonlinear regression using PRISM (GraphPad). Single Gaussian fits were calculated according to y = a + amp × exp(−0.5 × ((x − x )/σ)2), with a = offset from x-axis, amp = peak amplitude, x = stimulus directions in degrees, x  = x value at peak amplitude, and the following constraints: a > 0, amp = 0–1, x  = 180°, σ ≥ 22.5°. Double Gaussian fits were calculated according to y = a + amp1 × exp(−0.5 × ((x − x )/σ )2) + amp2 × exp(−0.5 × ((x − x )/σ )2), with a = offset from x-axis, amp1 = peak amplitude, amp2 = amplitude at opposing direction, x = stimulus directions in degree, x  = x value at peak amplitude, x  = x value at amplitude of opposing direction, and the following constraints: a > 0, amp1 = 0–1, amp2< amp1, Δx  = 180°, σ  ≥ 22.5°. R2 is computed as the normalized sum of least squares. To describe further the changes in tuning of individual neurons over time, we calculated the difference in preferred orientation between successive imaging time points for all neurons recorded at least twice without interruption (14 out of 27 cells, n = 4 mice). With 4 orientations, the individual difference was limited to discrete values of Δ45° between 0° and 90°. Note, that changes in preference and specificity reported here are unlikely to arise from nonlinearities of the calcium indicators, as we see both, marked changes in tuning preference at consistent average peak amplitudes across time points (Fig. 6d, Extended Data Fig. 11e, f), as well as stable tuning at late time points (Fig. 6b). Changes in the reliability of responses over time were assessed for all neurons recorded at least twice (15 out of 27 cells, n = 5 mice) by calculating the average correlation coefficients (Pearson correlation) of trial-to-trial responses during visual stimulation at the preferred direction across time points. As an additional measure of reliability, we calculated the average coefficient of variation (CV = σ/μ) for GCaMP6s+ cells recorded at 6, 9 and 15 wpt (15 out of 27 cells, n = 3 mice; 5–7 cells per time point). Note that owing to the distinct ranges of peak amplitudes of GCaMP6s and Twitch2B it is not possible to calculate a meaningful CV value combining cells labelled with either GECI, and thus only GCaMP6s+ cells were included. We examined the brain-wide synaptic input to transplanted cells by retrograde monosynaptic tracing with a modified rabies virus (EnvA-ΔG-RABV)18. RABV particles are pseudotyped to specifically infect cells expressing TVA-class receptors and express eGFP instead of their own glycoprotein (G). Transsynaptic spread to pre-synaptic partners is only possible after complementing the RABV with its G-protein coat by the G-TVA expressing cells and is therefore limited to one monosynaptic jump. G-TVA expressing cells also encode for DsRed, while their pre-synaptic partners will be eGFP+ only. DsRed+/GFP+ neurons are termed starter cells. In brief, EnvA-ΔG-RABV was injected 4 or 12 weeks after transplantation of embryonic cells expressing DsRedExpress2-2A-Glyco-IRES2-TVA or DsRedExpress2-IRES2-TVA, in three locations surrounding the transplantation site (200 nl per location). To map the pre-synaptic connectivity of upper layer neurons in adult V1, E14.5–E16.5 embryos were in utero electroporated with pDsRedExpress2-2A-Glyco-IRES2-TVA or pDsRedExpress2-IRES2-TVA (see above). Embryos were allowed to develop and as adult mice received 3 injections of RABV within V1 of the electroporated hemisphere (200 nl per injection). In both experimental groups, animals were euthanized 7–9 days later for immunostainings and circuit analysis. Plated cells were fixed in 4% paraformaldehyde (PFA) for 30 min, washed and incubated in blocking and permeabilizing solution for 30 min (3% bovine serum albumin; 0.5% Triton X-100) before applying the primary antibodies goat mCherry (1:500; Sicgen), rabbit anti-Cux1 (1:500; Santa Cruz), for overnight incubation at 4 °C. After washing, cells were incubated with species- and subclass-specific secondary antibodies conjugated to Cy3 and Cy5 (Dianova) used at 1:500 for 2 h at room temperature. Nuclei were stained with 1 μg ml−1 4,6-diamidino-2-phenylindole (DAPI; Sigma-Aldrich; 5 min at room temperature) and coverslips were mounted on glass slides with Aqua-Poly/Mount (Polysciences). For immunohistochemistry, brains were collected after transcardial perfusion of deeply anaesthetized mice with PBS (5 min) followed by 4% PFA, 30–40 min. Brains were then post-fixed in 4% PFA overnight, at 4 °C, serially cut on a vibratome into 60–70 μm sagittal or coronal sections, which were kept free-floating for further processing. TUNEL staining was performed according to the manufacturer’s instructions (Roche). Immunohistochemistry was carried out using the following primary antibodies: chicken anti-GFP (1:1,000; Aves Labs), rabbit anti-RFP (1:1,000; Rockland), goat anti-mCherry (1:200; Sicgen), rabbit anti-Cux1 (1:200; Santa Cruz). After washing, sections were incubated with species- and subclass-specific secondary antibodies conjugated to Cy3 or Cy5 (Dianova) or Alexa Fluor 488 or 647 (Invitrogen), used at 1:500 or 1:1,000 depending on high (≥1:500) or low (<1:500) concentration of the primary antibody. Sections were incubated for 10 min with 1 μg ml−1 DAPI for nuclear labelling and mounted on glass slides with Aqua-Poly/Mount. Images were acquired using a laser-scanning confocal microscope (Zeiss, LSM 710), and analysed with ZEN 2012 (Zeiss) and ImageJ 1.48p software. Quantitative analysis of Cux1 expressing cells was done by counting positive cells among all RFP+ donor cells in the mouse. Cell countings were performed with the Cell Counter plug-in for Image J 1.48p, by careful inspection across serial optical sections (spaced at 1 μm) of confocal Z-stacks acquired with a 40× objective (NA 1.1). Results are represented as mean ± s.e.m. calculated between different mice. Image processing was performed with ImageJ and Adobe Photoshop/Illustrator (Adobe Systems) for preparation of multipanel figures. For circuits analysis complete brains were carefully removed from the skull following perfusion (see above). After cutting, brain sections were kept in serial order and stained for GFP and RFP. Subsequently, sections with transplanted cells were selected and further stained for cortical layer markers. Of all sections, those with RFP-labelled axonal fibres and/or GFP-labelled cell somas were scanned using an epifluorescence microscope with a motorized stage (Zeiss, Axio ImagerM2) equipped with a 10× objective (NA 0.3). We used automatic scanning and alignment of individual tiles, followed by image stitching to create a high resolution image of the whole section. These images were used to identify brain regions where RFP+ axons or GFP+ cells were found, by comparison with the corresponding sections of the Allen Reference Atlas of the adult mouse brain46 (version 2, 2011; website: © 2015 Allen Institute for Brain Science, Allen Mouse Brain Atlas, available from: http://mouse.brain-map.org). Some sections of interest are not available in the reference atlas, namely in the sagittal atlas, which displays 21 sections spaced at 200-μm intervals, and only up to 4.0 mm lateral from bregma. In these cases, the Brain Explorer 2 3D viewer (version 2.3.5, Allen Institute for Brain Science, as referenced before) was used to retrieve the corresponding annotated section to overlap it with the experimental section and identify the anatomical location of the labelled cells or axonal fibres. In sections with unclear cell numbers due to close apposition of two GFP somata or with high densities of GFP cells, scanning of confocal Z-stacks with a 40× objective (numerical aperture (NA) 1.1) was carried out, and quantification was performed by careful inspection through serial optical sections spaced at 1-μm intervals. In sections containing transplanted cells, four categories were considered for counting: GFP-only cells with neuronal morphology, GFP-only cells with glial morphology, GFP/RFP (or mCherry) cells with neuronal morphology, GFP/RFP (or mCherry) cells with glial morphology. Connectivity ratio was calculated by dividing the number of monosynaptically connected neurons (GFP-only cells with neuronal morphology) in a given region by the number of starter neurons (GFP/RFP or mCherry with neuronal morphology) amongst the transplanted neurons in V1. Results are represented as mean ± s.e.m. calculated between different mice. To assess the efferent projections of transplanted neurons, all brain slices were inspected under an epifluorescence microscope (Zeiss, Axio ImagerM2) (n = 3 mice, ~134 slices per brain) using a 20× objective (NA 0.8), and the total number of RFP-labelled neurites was counted in each anatomical structure or white matter tract. In a small number of slices per mouse (4–9) quantification of individual fibres could not be performed reliably owing to high background of mCherry staining (as compared to RFP staining) or in densely labelled parts of Vis, cc and ectorhinal cortices, thus the numbers for those structures were expressed as ‘greater than’ the number of counted fibres. For these reasons, we express the quantification in categories which we present colour-coded and, which reflect the relative amounts of RFP-labelled neurites in different structures. To determine the normal projectome of upper layer V1 neurons we used data from the Allen Mouse Brain Connectivity Atlas (Allen Brain Atlas Data Portal) that were generated by anterograde tracing using AAV injections in V1. We used data from the Cux2-IRES-Cre mouse line in combination with Cre-dependent AAVs (rAAV2/1-pCAG-flex-eGFP-WPRE-bGH or rAAV2/1-pCAG-flex-synaptophysin-eGFP-WPRE-bGH), to limit the analysis to the projections of upper layer neurons. Projection volumes per structure (sum of detected signal in mm3) for five individual experiments (501116471, 263780729, 293472335, 483013787 and 501117182; injection volume 0.179–0.384 mm3, 98.9–100% in V1) were averaged and values below the threshold of 0.003 mm3 were not considered. The data were then assembled per anatomical group by summing the projection volumes of the respective structures. Brain Explorer 2 software was used to retrieve a total of 222 images corresponding to consecutive sagittal sections of an entire hemisphere from the adult mouse brain, where V1 and dLGN volumes were highlighted, using the Allen Mouse Brain Atlas data set. Given the width of a hemisphere (4.2–4.9 mm), each image represents a ~20.5 μm sagittal slice. Thus, to represent the 60–70 μm thick experimental slices containing labelled neurons, one in every third image was used thereafter. Using Free-D software47 the V1 and dLGN volumes were reconstructed as 3D surface or wireframe models by manually delineating these structures boundaries as closed contours in the 2D images comprising the stack. For each experimental slice containing starter neurons or pre-synaptic dLGN neurons traced with the RABV, the positions of these neurons were obtained from the Image J Cell Counter images, and scaled down to the whole slice tiled fluorescence images. These were overlapped and aligned with the corresponding anatomical slices of the Allen Brain Atlas data set, and the positions of individual neurons were rendered as points in the Free-D stacks (starter cells in the V1 stack; dLGN neurons in the dLGN stack). To explore the topographic arrangement of dLGN-V1 connections, we plotted the points, starter and dLGN neurons, in a different colour for each mouse (n = 8 mice), in the V1 or dLGN stack, respectively, and rendered them into a 3D representation. For statistical analysis of the topographic relationships we retrieved the x, y, z coordinates of each point (cells) as well as each of the points composing the V1 and dLGN contours. Using Matlab R2016a (Mathworks), we calculated the centroid of each group of points (V1, dLGN, V1 starters, dLGN pre-synaptic cells), and plotted the centroids of the V1 or dLGN cell clusters relative to the centroids of each structure, respectively, in order to assess correlations between anterior-posterior, dorso-ventral and medio-lateral dimensions (n = 8 experimental mice, including mice where RABV tracing was performed at 4 wpt (4 mice) or 12 wpt (4 mice), and n = 4 control mice, in which the native circuitry was traced by in utero electroporation of the G-TVA plasmid and RABV injection in the adult mouse V1). Statistics were performed using PRISM (Graphpad). Appropriate statistical tests were chosen dependent on sample size, data distribution and number of comparisons. Spine and bouton data were analysed with one-way ANOVA and Tukey post-tests for multiple comparisons. Survival curves were analysed pairwise using the Gehan–Breslow–Wilcoxon test, and P-value thresholds were adjusted for multiple comparisons applying a Bonferroni correction. dLGN-V1 topographic relationships were analysed by linear regression and correlative analysis between each two dimensions in space (R2 reflects the goodness-of-fit; P < 0.05). Functional data were subjected to non-parametric tests using Kruskal–Wallis with Dunn’s post-tests. In addition, the tuning of individual neurons across time points, and the average normalized tuning of neurons at 6, 9 and 15 wpt were compared with two-way ANOVA and Tukey post-tests for multiple comparisons. The minimum level of significance was defined as P < 0.05 and all values are reported as mean ± s.e.m., if not stated otherwise. The data that support the findings of this study are available from the corresponding author upon reasonable request.


News Article | February 22, 2017
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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.


News Article | January 20, 2016
Site: www.nature.com

No statistical methods were used to predetermine sample size. All procedures were performed in accordance with the law on animal protection issued by the German Federal Government (Tierschutzgesetz) and approved by the institutional animal welfare committee of the University of Tübingen. For all experiments, we used 4- to 8-week-old mice of either sex. In addition to C57Bl6 (wild-type) mice, we used the transgenic lines PvalbCre (‘PV’, JAX 008069, The Jackson Laboratory; ref. 43), Pcp2Cre (‘Pcp2’, JAX 006207; ref. 44) and ChATCre (JAX 006410; ref. 51), cross-bred with the red fluorescence Cre-dependent reporter line Ai9:tdTomato (JAX 007905). Owing to the exploratory nature of our study, we did not use randomization and blinding. Animals were housed under a standard 12 h day/night rhythm. For activity recordings, animals were dark-adapted for ≥1 h, then anaesthetized with isoflurane (Baxter) and killed by cervical dislocation. The eyes were enucleated 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). Bulk electroporation of the fluorescent Ca2+ indicator Oregon-Green BAPTA-1 (OGB-1) into the ganglion cell layer (GCL) was carried out as described before19, 47. In brief, the retina was dissected from the eyecup, flat-mounted onto an Anodisc (#13, 0.2 μm pore size, GE Healthcare) with the GCL facing up, and placed between two 4-mm horizontal plate electrodes (CUY700P4E/L, Nepagene/Xceltis). A 10 μl drop of 5 mM OGB-1 (hexapotassium salt; Life Technologies) in ACSF was suspended from the upper electrode and lowered onto the retina. After application of 10–12 pulses (+9 V, 100 ms pulse width, at 1 Hz) from a pulse generator/wide-band amplifier combination (TGP110 and WA301, Thurlby handar/Farnell), the tissue was moved to the recording chamber of the microscope, where it was continuously perfused with carboxygenated ACSF at ~37 °C and left to recover for ~60 min before the recordings started. In all experiments with wild-type mice, ACSF contained ~0.1 μM Sulforhodamine-101 (SR101, Invitrogen) to reveal blood vessels and any damaged cells in the red fluorescence channel20. All procedures were carried out under very dim red (>650 nm) light. We used a MOM-type two-photon microscope (designed by W. Denk, MPI, Martinsried; purchased from Sutter Instruments/Science Products). Design and procedures were described previously20. In brief, the system was equipped with a mode-locked Ti:Sapphire laser (MaiTai-HP DeepSee, Newport Spectra-Physics) tuned to 927 nm, two fluorescence detection channels for OGB-1 (HQ 510/84, AHF/Chroma) and SR101 (HQ 630/60, AHF), and a water immersion objective (W Plan-Apochromat 20x/1,0 DIC M27, Zeiss). For image acquisition, we used custom-made software (ScanM, by M. Müller, MPI, Martinsried, and T.E.) running under IGOR Pro 6.3 for Windows (Wavemetrics), taking 64 × 64 pixel image sequences (7.8 frames per s) for activity scans or 512 × 512 pixel images for high-resolution morphology scans. 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) that roughly match the spectral sensitivity of moose M- and S-opsins. LEDs were synchronized with the microscope’s scan retrace. Stimulator intensity (as photoisomerisation 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 two-photon excitation of photopigments, an additional, steady illumination component of ~104 P* per s per cone was present during the recordings (for detailed discussion, see ref. 22). For all experiments, the tissue was kept at a constant intensity level (see stimuli below) for at least 30 s after the laser scanning started before light stimuli were presented. Four types of light stimulus were used (Fig. 1b, top): (i) full-field ‘chirp’ stimulus consisting of a bright step and two sinusoidal intensity modulations, one with increasing frequency and one with increasing contrast; (ii) 0.3 × 1 mm bright bar moving at 1 mm s−1 in eight directions19; (iii) alternating blue and green 3-s flashes; and (iv) binary dense noise, a 20 × 15 matrix with 40 μm pixel-side length; each pixel displayed an independent, perfectly balanced random sequence at 5 Hz yielding a total running time of 5 min for receptive field (RF) mapping. In some experiments, we used in addition dark moving bars (like (ii) but contrast-inversed), and stationary bright or dark 0.2 × 0.8 mm bars flashed for 1 s in six orientations (see Extended Data Fig. 7h–s). Except for (iii), stimuli were achromatic, with matched photo-isomerization rates for M- and S-opsins. TdTomato- or OGB-1-labelled RGCs were targeted using two-photon imaging for juxtacellular recordings using borosilicate electrodes (4–6 MΩ) filled with ACSF with added SR101 (250 μM). Data were acquired using an Axoclamp-900A or Axopatch 200A amplifier in combination with a Digidata 1440 (all: Molecular Devices), digitized at 10 kHz and analysed offline using IGOR Pro. We presented the same light stimuli as for the Ca2+ imaging. In some experiments, electrical recordings and Ca2+ imaging was performed simultaneously. After the recording, the membrane under the electrode was opened using a voltage ‘buzz’ to let the cell fill with dye by diffusion for approximately 30 min; then two-photon image stacks were acquired to document the cell’s morphology. Filled cells were traced semi-automatically offline using the Simple Neurite Tracer plugin implemented in Fiji (http://fiji.sc/Fiji), yielding cell skeletons. If necessary, we used the original image stack to correct the skeletons for any warping of the IPL using custom-written scripts in IGOR Pro. To this end, we employed the SR101-stained blood vessel plexuses on either side of the IPL as landmarks to define the IPL borders (see below). Only cells where the filling quality allowed full anatomical reconstruction were used for analysis (see below). Following Ca2+ imaging, retinas were mounted onto filter paper (0.8 μm pore size, Millipore) and fixed in 4% paraformaldehyde (in PBS) for 15 min at 4 °C. Immunolabelling was performed using antibodies against ChAT (choline-acetyltransferase; goat anti-ChAT, 1:100, AB144P, Millipore), GAD67 (glutamate decarboxylase; mouse anti-GAD67, 1:100, MAB5046, Millipore), SMI-32 (mouse anti-SMI32, 1:100, SMI-32R-100, Covance), and melanopsin (rabbit anti-melanopsin, 1:4000, AB-N38, Advanced Targeting Systems) for 4 days. Secondary antibodies were Alexa Fluor conjugates (1:750, 16 h, Life Technologies). For each retina, the recorded region was identified by the local blood vessel pattern and confirmed by comparing size and position of individual somata in the GCL. Image stacks were acquired on a confocal microscope (Nikon Eclipse C1) equipped with a ×60 oil objective (1.4 NA). The degree of immunolabelling of GCL cells was evaluate and rated (from 0, negative, to 4 positive) using z-stacks. Attribution of labelled somata to recorded ones was performed manually using ImageJ (http://imagej.nih.gov/ij) and IGOR Pro. Data analysis was performed using Matlab 2012 and 2014a (The Mathworks Inc.), and IGOR Pro. Data were organized in a custom written schema using the DataJoint for Matlab framework (http://datajoint.github.io/; D. Yatsenko, Tolias lab, Baylor College of Medicine). Regions of interest (ROIs), corresponding to somata in the GCL, were defined semi-automatically by custom software (D. Velychko, CIN) based on a high-resolution (512 × 512 pixels) image stack of the recorded field. Then, the Ca2+ traces for each ROI were extracted (as ΔF/F) using the IGOR Pro-based image analysis toolbox SARFIA (http://www.igorexchange.com/project/SARFIA). A stimulus time marker embedded in the recording data served to align the Ca2+ traces relative to the visual stimulus with a temporal precision of 2 ms. Stimulus-aligned Ca2+ traces for each ROI were imported into Matlab for further analysis. First, we de-trended the Ca2+ traces by high-pass filtering above ~0.1 Hz. For all stimuli except the dense noise (for RF mapping), we then subtracted the baseline (median of first eight samples), computed the median activity r(t) across stimulus repetitions (typically three to five repetitions) and normalized it such that . We mapped the linear RFs of the neurons by computing the Ca2+ transient-triggered average. To this end, we resampled the temporal derivative of the Ca2+ response ċ(t) at 10-times the stimulus frequency and used Matlab’s findpeaks function to detect the times t at which Ca2+ transients occurred. We set the minimum peak height to 1 s.d., where the s.d. was robustly estimated using: We then computed the Ca2+ transient-triggered average stimulus, weighting each sample by the steepness of the transient: Here, S(x, y, t) is the stimulus, τ is the time lag (ranging from approximately −320 to 1,380 ms) and M is the number of Ca2+ events. We smoothed this raw RF estimate using a 5 × 5 pixel Gaussian window for each time lag separately. RF maps shown correspond to a s.d. map, where the s.d. is calculated over time lags τ: To extract the RF’s position and scale, we fitted it with a 2D Gaussian function using Matlab’s lsqcurvefit. The time course of the receptive field F (τ) was estimated by the average of the eight pixels closest to the fitted RF centre (according to the Mahalanobis distance) weighted by a Gaussian profile. RF quality (QI ) was measured as one minus the fraction of variance explained by the Gaussian fit To extract time course and directional tuning of the Ca2+ response to the moving bar stimulus, we performed a singular value decomposition (SVD) on the T by D normalized mean response matrix M (times samples by number of directions; T = 32; D = 8; Extended Data Fig. 7a, b): This procedure decomposes the response into a temporal component in the first column of U and a direction dependent component or tuning curve in the first column of V, such that the response matrix can be approximated as an outer product of the two: An advantage of this procedure is that it does not require manual selection of time bins for computing direction tuning, but extracts the direction tuning curve given the varying temporal dynamics of different neurons. To measure direction selectivity (DS) and its significance, we projected the tuning curve V on a complex exponential , where α is the direction in the kth condition: This is mathematically equivalent to computing the vector sum in the 2D plane or computing the power in the first Fourier component. We computed a DS index as the resulting vector length correcting for the direction spacing. We additionally assessed the statistical significance of direction tuning using a permutation test53. To this end, we created surrogate trials (that is, stimulus repetitions) by shuffling the trial labels (that is, destroying any relationship between condition and response), computed the tuning curve for each surrogate trial and projected it on the complex exponential ϕ. Carrying out the procedure 1,000 times generated a null distribution for K, assuming no direction tuning. We used the percentile of the true K as the P value for direction tuning (Extended Data Fig. 7c). Importantly, a large DSi does not necessarily result in a small P value, for example, in the case of large trial to trial variability. As a result, the DSi distributions of significantly and not significantly direction tuned neurons show substantial overlap (Extended Data Fig. 7d, e). Therefore, a simple threshold as a DS criterion (for example, DSi > 0.4) does not provide a good separation into direction selective cell types and others. Orientation selectivity (OS) was assessed in an analogous way. However, we used the complex exponential , corresponding to the second Fourier component. To measure how well a cell responded to a stimulus (chirp, moving bar, colour), we computed the signal-to-noise ratio where C is the T by R response matrix (time samples by stimulus repetitions) and 〈 〉 and Var[ ] denote the mean and variance across the indicated dimension, respectively. If all trials are identical such that the mean response is a perfect representative of the response, QI is equal to 1. If all trials are completely random with fixed variance (so that the mean response is not informative about the individual trial responses at all), QI is proportional to 1/R. For further analysis, we used only cells that responded well to the chirp and/or the moving bar stimulus (QI  > 0.45 or QI  > 0.6). The full-field index was computed as comparing the response quality to a local stimulus (moving bar) and a global stimulus (chirp). where r and r are defined as the activity during the response to the leading edge of the moving bar (the first 400 ms of the ON response) and the trailing edge of the moving bar (the first 400 ms of the OFF response). Colour selectivity was measured for the ON response using and for the OFF response using an analogous definition. Here, r and r are the responses in a time window of 1,280 ms after onset of the green and blue stimulus, respectively. We used sparse principle component analysis54, 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 responses to the chirp, colour, and moving bar stimulus, resulting in features which use only a small number of time bins. The extracted features are localized in time and therefore readily interpretable (for example, ‘high-frequency feature’), although this constraint was not explicitly enforced by the algorithm (Extended Data Fig. 2e). We also explored alternative feature extraction techniques such as regular PCA, but these resulted in inferior cluster quality. In addition, they required manually defining regions corresponding to specific parts of the stimulus (for example, frequency chirp) to yield localized and interpretable features. We extracted 20 features with 10 non-zero time bins from the mean response to the chirp (averaging across trials) and 6 features with 10 non-zero time bins from the mean response to the colour stimulus. For the moving bar stimulus, we extracted 8 features with 5 non-zero time bins from the response time course (see above) and 4 features with 6 non-zero time bins from its temporal derivative. All features were in the temporal domain, ensuring spatial invariance. In addition, we used two features from the time course of the RF, extracted with regular PCA. Overall, this procedure resulted in a 40 dimensional feature vector for each cell. Before clustering, we standardized each feature separately across the population of cells. DS and non-DS cells were clustered independently, classifying cells as DS if the permutation test resulted in P < 0.05 (see above). We fit each data set with a Mixture of Gaussians model using the expectation-maximization algorithm (Matlab’s gmdistribution object). We constrained the covariance matrix for each component to be diagonal, resulting in 80 parameters per component (40 for the mean, 40 for the variances). We further regularized the covariance matrix by adding a constant (10−5) to the diagonal. To find the optimal number of clusters, we evaluated the Bayesian information criterion55 where L is the log-likelihood of the model, N is the number of cells and M is the number of parameters in the model, that is, M = 81C − 1 where C is the number of clusters and the contributions that arose from means, variances and mixture proportions (which have to add to 1). Although other choices such as the Aikaike information criterion (AIC) would have been possible, we found the BIC to yield a good compromise between model complexity and quality, since the AIC is known to find too many clusters for large sample sizes. We also computed log Bayes factors as 2ΔBIC for each candidate cluster number to test how strong the evidence for further splitting is. Values >6 were treated as strong evidence in favour of further splitting. The minimum of the BIC coincided well with the number of clusters after which there was no strong evidence for further adding more clusters. To avoid local minima, we restarted the EM algorithm 20 times per candidate number of clusters and used the solution with the largest likelihood. This procedure resulted in 24 and 48 clusters for DS and non-DS cells, respectively (Extended Data Fig. 2a). To evaluate cluster quality, we rank-ordered the posterior probabilities for cluster assignment for each cluster, normalized for cluster size and averaged across clusters for non-DS and DS cells separately (Extended Data Fig. 2b). The steep decays of the sigmoidal functions indicate good cluster separability. To check how consistent the clustering was against subsampling of the data, we created 20 surrogate data sets containing random selections of 90% of the cells. We fit these surrogate data sets with a Mixture of Gaussians model with the optimal number of clusters determined on the original data set. For each cluster mean in these models, we computed the correlations with the most similar cluster for the model fit on the original data set. To summarize the similarity of clusterings, we computed the median correlation across clusters (Extended Data Fig. 2c). On average, the clusterings obtained on the surrogate and the original data set were very similar (mean median correlation: 0.96 ± 0.19 and 0.97 ± 0.01; mean ± s.d.; for DS and non-DS cells separately). In addition, we performed an alternative clustering version, where we did not split the data in DS and non-DS cells but added DSi, OSi, soma and receptive field size as features. The identified clusters were very similar, but this strategy failed to identify most DS types as separate clusters, except for the ON–OFF DS cell. Therefore, we decided to first isolate significant DS cells and cluster them separately, before merging similarly responding DS and non-DS clusters (see below), if we did not find a reason to keep the DS group as a separate RGC type. Nevertheless, a strategy equally justified as ours could start with the alternative clustering and then split those clusters containing large fractions of DS cells. A subset of cells was stained against GAD67 to identify dACs (see above). The intensity of this staining was manually rated as follows: −2 (absent), −1 (probably absent), 0 (uncertain), 1 (probably present), and 2 (present). For each cluster, we computed the average staining from the labelled cells (average number of cells with GAD67 information per cluster: 16.8 ± 10.0, mean ± s.d.). Clusters with an average staining <-0.2 were labelled RGCs (n = 30 clusters), those with average staining >0.2 were labelled ACs (n = 26). Clusters with average staining in-between those values (n = 5), or those that contained 6 or less cells with GAD67 information (n = 8) were labelled as uncertain, unless other clear criteria such as soma size or genetic labels indicated that they are ACs or RGCs. In this case they were manually allocated to RGC or AC (n = 3 and n = 2, respectively). Two clusters automatically classified as AC were included in the uncertain group due to their functional similarity with the OFF-suppressed types (G ). This procedure resulted in 33 RGC clusters, 10 uncertain clusters and 26 AC clusters. We extracted all cells with large cell bodies (>136 μm2; mean + 1 s.d. of total soma size distribution; Extended Data Fig. 2i, j) from RGC and uncertain clusters. Predominantly, these cells had been assigned to nine of the clusters. We re-clustered those cells using a Mixture of Gaussians model as described above, resulting in 16 clusters (Extended Data Fig. 2j). Receptive field size was not used in this process. Five of these clusters could be clearly associated with the three known alpha-RGC types and their response profiles31 (trans. OFF alpha, 2; sust. OFF alpha, 2; ON alpha, 1). Cells in these clusters were SMI-32-positive, as expected from alpha RGCs (Fig. 3i, k). Probably, this procedure missed some alpha cells, as somata close to the edge of scan fields were cut and we thus underestimate the soma size of these cells (for example, G c, see Fig. 2a–c). Remaining cells were kept in their original cluster. Logistic regression was used to assess the effect cell type (alpha vs. mini) on SMI-32 staining (absent vs. present). We used the Matlab implementation fitglm with a binomial nonlinearity. 95%-confidence intervals on the proportion of SMI32-positive cells were computed using bootstrapping with 1,000 samples. We used a standard linkage algorithm on the means of the RGC groups in the standardized feature space with correlation distance and average unweighted distance and plotted the result as a dendrogram (using Matlab functions linkage and dendrogram). The leaf order was optimized using the Matlab function optimalleaforder and modified for clarity of presentation. with the number of cells in a group (n ), the median RF size (A ) within a group counting only cells that surpassed a RF quality criterion of 0.3, and the total scan area across all experiments (A ). We corrected n for 29% cells discarded by our quality criterion as well as an empirically estimated 8% of cells that did not yield a ROI in the first place due to weak or absent labelling. In addition, A was corrected for an empirically estimated 34.8% RF overhang (that is, where a cell’s RF exceeds the scan field edge). This procedure yielded a CF of 2.0 ± 0.7 for most RGC groups (Gaussian fit; see Fig. 2e, right). However, differences between studies in approaches to measure RFs (for example, checkerboards vs. bars), in the assumptions used for RF fitting (for example, homogenous RFs best fitted by Gaussians), or in the methods to estimate dendritic arbor area can easily yield different absolute estimates of CF (see also Supplementary Table 1). To determine a cell’s IPL stratification profile, we calculated dendritic density as described previously10 with spatial smoothing of 1 μm3. The resultant 3D density cloud was projected on the z axis to estimate the mean IPL depth profile. The relationship between the depth profiles and the two ChAT bands was estimated in independent experiments using mice that express tdTomato in cholinergic ACs (ChATCre × Ai9:tdTomato). We compared the IPL depth of the tdTomato-labelled dendritic plexi to the two SR101-labelled blood vessel planes that line the inner retina. We estimated the error to be ~1.5 μm (s.d.), corresponding to 3–4% IPL depth (n = 13 measurements in 2 mice). To relate each cell’s IPL profile to functional groups we calculated the mean correlation coefficient between a cell’s response to the chirp and moving bar stimuli and each group’s mean response. The correlation coefficient (−1…1) for each pair was then multiplied with the cell’s depth profile and a correlation-rank based weighting factor W = 0.9rank −1. Thus, each individual recording yielded a complete two-dimensional map, with IPL depth on one axis and functional group on the other. Next, we averaged across the maps for those cells that passed our response quality criterion (n  = 31/51; n  = 24/33; see above). The resultant matrices were normalized in two steps: First, we divided each group’s IPL depth profile by the mean depth profile of all included cells to eliminate any bias in sampling depth. Second, we divided each depth profile by its own maximum. This resulted in an automatic and unbiased estimate of dendritic stratification depth for all RGC groups (Fig. 5). Note that this automated approach is based on a relatively small sample of reconstructed cells and therefore can only provide an approximate prediction of stratification levels. This approach is invariant to differences in lateral dendritic field dimensions that may be associated with retinal position (for example, refs 10,23).


Patent
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.


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).


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.


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
Site: www.newsmaker.com.au

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  8.1.2.1 Type I  8.1.2.2 Type II  8.1.2.3 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  8.2.2.1 Type I  8.2.2.2 Type II  8.2.2.3 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  8.3.2.1 Type I  8.3.2.2 Type II  8.3.2.3 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  8.4.2.1 Type I  8.4.2.2 Type II  8.4.2.3 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  8.5.2.1 Type I  8.5.2.2 Type II  8.5.2.3 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  8.6.2.1 Type I  8.6.2.2 Type II  8.6.2.3 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  8.7.2.1 Type I  8.7.2.2 Type II  8.7.2.3 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  8.8.2.1 Type I  8.8.2.2 Type II  8.8.2.3 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  8.9.2.1 Type I  8.9.2.2 Type II  8.9.2.3 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  8.10.2.1 Type I  8.10.2.2 Type II  8.10.2.3 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  8.11.2.1 Type I  8.11.2.2 Type II  8.11.2.3 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  8.12.2.1 Type I  8.12.2.2 Type II  8.12.2.3 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)

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