A quantitative method for the characterization of nanoscale 3D morphology is applied to the investigation of a hybrid solar cell based on a novel hierarchical nanostructured photoanode. A cross section of the solar cell device is prepared by focused ion beam milling in a micropillar geometry, which allows a detailed 3D reconstruction of the titania photoanode by electron tomography. It is found that the hierarchical titania nanostructure facilitates polymer infiltration, thus favoring intermixing of the two semiconducting phases, essential for charge separation. The 3D nanoparticle network is analyzed with tools from stochastic geometry to extract information related to the charge transport in the hierarchical solar cell. In particular, the experimental dataset allows direct visualization of the percolation pathways that contribute to the photocurrent. Hybrid solar cells, e.g., photovoltaic devices based on heterojunctions between polymers and metal oxide nanostructures, have been proposed as viable technologies for large scale, inexpensive production of electricity from sunlight.–3 Even tThe morphology of the nanostructured photoanodes used in hybrid solar cells is one of the main properties influencing their behavior, by affecting processes such as light scattering and charge generation, injection and transport.,4,5 Rece2 films as photoanodes, where the nano- and meso-scale porosity is obtained by tuning macroscopic growth parameters in pulsed laser deposition.[Polymer infiltration into the nanostructured inorganic component is generally difficult to achieve because of the small pore size and complex, random 3D nanostructure. Solutions to this problem include the co-processing of organic and inorganic precursors to obtain interconnected networks, demonstrated for semiconducting sulfides.,9 Here wposition. The noveposition.Conventional studies of hybrid solar cells investigate optical and electrical properties, either through bulk measurements or through microtips that allow analysis of selected areas. This appElectron tomography can be used to produce unambiguous 3D representations of nanostructures materials, where individual particles and interfaces can be distinguished and analyzed with statistical tools. SuccessfIn order to produce electron transparent cross-sectional specimens of hybrid solar cells, focused ion beam (FIB) milling is generally used to create lamellar sections of the device that are thus transferred to a TEM support to be analyzed in transmission.,22 This It is important to note that this specimen geometry is ideal for electron tomography because the cylindrical symmetry and constant section of the specimen maximize the available tilt range (in principle up to 360 degrees with purpose-built specimen holders) and considerably improve the quality and reproducibility of the 3D reconstruction over established lamella-based tomography.2 phase (which are related to exciton quenching efficiency) as well as geometric tortuosity[2 phase which describes the windedness or the detours of the percolation pathways, respectively. The results indicate that the tree-like structure of the photoanode is beneficial for electron transport.Our analysis of the hierarchical device uses 2D and 3D nanoscale characterization tools to evaluate the physical properties of the device. In particular we prepare a cross section of the hierarchical solar cell in the micropillar geometry, and produce a 3D reconstruction of the device using electron tomography. We employ statistical analysis and tools from stochastic geometry to extraortuosity of the T2 film. The hierarchical photoanode, labeled here as h-TiO2, exhibits a forest-like architecture obtained by pulsed laser deposition (PLD).[2 target is ablated by ns laser pulses, producing a plasma that expands in an oxygen atmosphere. Upon collision among the ablated species and the background gas, nano-aggregates form in the gas phase. In a pressure range from about 5 to 40 Pa, particles of around 10–20 nm in size self-assemble on the substrate in hierarchical quasi-1D structures resembling trees.[2/g depending on deposition pressure[In the present work, we analyze a solar cell photoanode based on a hierarchical nanoparticle-assembled TiOon (PLD). In this  pressure with a s2. High resolution transmission electron microscopy  images in channels  that are2 nanoparticle pastes, and a better interdiffusion of ionic liquid electrolytes due to its mesoporous structure.[Thanks to these features, this structure has demonstrated potential advantages when employed in liquid dye-sensitized solar cells, showing an electron lifetime more than 1 order of magnitude longer in comparison to standard, randomly assembled TiOtructure.2 behaviour in solid-state hybrid cells. The prototype cell presented here comprises an ITO-coated glass substrate, a 1 μm thick h-TiO2 film, a spin coated P3HT layer (molecular weight 80 kg/mol) and an Ag back electrode. The thickness of the active layer was chosen as the best performing in the 0.4–1.6 μm range .Due to the interplay between nanoscale morphology and potential benefits to the photovoltaic performance, we set out to investigate the h-TiO2 prototype . The h-TiO2 device shows a 50% improvement respect to that of TiO2/P3HT devices based on mesoporous photoanodes made by doctor blading deposition of commercial pastes of comparable thickness. Further improvements can be expected by optimizing the deposition and packaging process, which is beyond the scope of the present study. It is worth noting that the observed improvement is mainly driven by an enhancement in the generated photocurrent . Both the series and shunt resistance are lower for the h-TiO2 device, with a combined result of a lower fill factor.Table 2 has a slightly lower optical density than TiO2 paste of comparable thickness. This suggests that the improvement in photogenerated current observed for h-TiO2 is even more significant.Time resolved photoluminescence spectroscopy did not show any significant change in the exciton quenching dynamics, indicating that the charge generation mechanism at the interface is not affected by the new architecture, at least in a scale comparable to the exciton-diffusion length. UV-vis absorption measuraments show that, in the range 400–650 nm, the h-TiO2/P3HT cell by using the FIB to prepare a cross sectional lamella of the solar cell (about 150 nm thick) to investigate the polymer infiltration. We verify P3HT infiltration in the h-TiOWe applied HAADF STEM tomography to further investigate the 3D morphology of the solar cell. The geometry of the specimen is a crucial parameter in this technique, as it determines the effective available tilt range during acquisition, and affects the quality of the dataset. Since our aim was to obtain high quality quantifiable data with minimal operator input and bias, we adopted a micropillar geometry for the cross sectional specimen preparation, rather than the conventional lamella geometry.2/P3HT device (except for the Ag electrode) we select a feature on the film and prepare a tapered micropillar sample (6 μm long and 300 nm thick at the base). From the complete h-TiOThe micropillar/pin assembly is then mounted onto a tomography pin holder. We acquire images in HAADF STEM mode, collecting a signal which is sensitive to the atomic number of the specimen. Given the composite nature of the specimen and the large variation in average Z-number for different regions, care must be taken to optimize the brightness and contrast levels in the high angle annular dark field images, thus allowing for efficient discrimination of all the solar cell components. To fully determine how the hierarchical structure develops across the thickness of the device, we select a magnification value (80k×) that allows us capture the whole micropillar. A series of images is acquired at various degrees of tilt along the micropillar's longitudinal axis . 3D reconstruction and image segmentation, usually some of the most tedious and potentially operator-biased steps of electron tomography, are relatively straightforward on our dataset thanks to the quality of the tilt series obtainable from a sample with cylindrical symmetry.,24 A rec2 particles aggregate to form a central column 100 to 150 nm wide surrounded by side branches pointing upwards (away from the ITO layer), and is therefore representative of the photoanode . This value is in good agreement with BET measurements on PLD TiO2 hierarchical films before polymer infiltration.[2 film, which indicated a size of 12.8 ± 4.0 nm .The TiOltration. Since BE2 particles, i.e., the fraction of TiO2 connected with the electrode.[2–P3HT interface. The electrons are injected into the titania structure and have to travel to the transparent electrode, shown at the bottom in our images. Therefore, the efficiency depends on the existence of unhindered percolation pathways to transport charges towards the electrodes. It is found that 98.5% of the TiO2 is connected with both electrodes (located at the top and bottom). Moreover, these pathways should preferably be monotonic; this measure of the connectivity is therefore direction-dependent, particularly in a branched or dendritic structure . For electron transport, the monotonic connectivity of h-TiO2, defined as the fraction of TiO2 material connected to the anode through a path with strictly decreasing distance, is found to be 89.5%  for the volume considered in this calculation. When the conductive paths are evaluated in the opposite direction, only 69.6% of the material is monotonically connected (towards the top). Such a difference suggests that the hierarchical titania structure favors electron transport towards the anode, thus minimizing losses due to recombination. The h-TiO2 film can be considered as the multibranched analogue of TiO2 rods. Hence, a type of asymmetrical, channeled conduction is expected, different from the symmetrical percolative conduction typical of photoanodes based on randomly assembled nanoparticles.Subsequently we apply descriptive statistical analysis,25 to anlectrode. In this 2 particles can be analyzed to determine the possibility for charge injection into the semiconducting layer. In particular, in the 3D reconstruction it is possible to measure the distance between any point in the pores and the nearest TiO2 particle. In case of a perfect infiltration, in which P3HT entirely fills the pores in the TiO2 film, this corresponds to determining how much polymer is close enough to the P3HT/TiO2 interface to inject a photo-generated electron. To optimize the performance of the solar cell, this distance has to be smaller than, or at least comparable with, the exciton diffusion length in P3HT  line. This yields the geometric tortuosity,[In the following, we analyze the geometry of the electrons pathways along the TiOrtuosity, defined rtuosity, where weGeometric tortuosity is equal to 1 for systems in which there are ideal vertical paths for charge collection ; higher values reflect a more complex geometry, where flow occurs through convoluted paths.2 in terms of electronic transport. We find values that are close to those observed in micro-porous systems with comparable porosity (around 1.0–2.0);[This characteristic parameter provides another indication of the behavior of h-TiO1.0–2.0);,29 howev2 photoanode produced via pulsed laser deposition, featuring large surface area and a hierarchical structure, properties of paramount importance for solar cell photoanodes. Polymer infiltration is verified at the nanoscale. We demonstrate a quantitative assessment of the polymer infiltration in the hierarchical TiO2 film, showing how electron tomography performed on a FIB-prepared micropillar specimen provides complete morphological information on a nanostructured composite device, with minimal user input. We also demonstrate how statistical analysis of the electron tomography dataset using tools from stochastic geometry provides quantitative information on transport pathways towards the anode, and on the geometry of the interface between the organic and inorganic phases in the hybrid solar cell.We characterized a promising model hybrid solar cell system, based on a P3HT-infiltrated TiOThe methodology for specimen preparation, characterization, and analysis, can be applied to other systems, particularly in the field of photovoltaics and catalysis. We expect the combination of FIB micropillar sample preparation, electron tomography and statistical analysis to become a standard tool to study solar cell model systems and contribute to the design of novel groundbreaking devices.Synthesis via PLD and Device Assembly: Pulsed laser deposition (PLD) of titanium oxide was accomplished by ablating a 99.99% pure TiO2 target with laser pulses from a Nd:Yag in 4th armonic  in 7 Pa O2 background pressure. PLD in a background atmosphere can lead to cluster nucleation in the ablation plasma plume and soft landing of clusters on the substrate, depending on deposition parameters, as extensively described in previous articles.[articles.–322 films were grown on ITO-glass substrates at room temperature, yielding amorphous films. Post-deposition annealing in air in a muffle oven at 500 °C for 2 h was performed in order to obtain crystalline films, mainly composed of anatase. Crystallinity was characterized (not shown) by X-ray diffraction  and Raman spectroscopy .TiOSample Preparation: We used a FEI Helios Nanolab dual beam machine, with a Ga+ ion beam for selectively milling areas of the specimen and a FEG emitter for electron imaging during the milling process. In this way we first surveyed the solar cell structure and isolated the representative features on the film. We first deposited a protective layer of platinum on the area of interest and applied a rough cut using annular milling with a current of 2.8 nA at 30 kV, isolating a rod of about 5 μm in diameter. It is important in this process to mill through to the glass substrate, so that the entire device section is extracted. We then used the Omniprobe micromanipulator to move the rod from the device to the apex of a TEM-compatible tungsten pin. Ion-beam Pt deposition was used to secure the rod to the W pin, and then the Ga ion beam was used to thin the rod by annular milling, progressively decreasing the beam current to reduce ion-beam damage, ending at 9 pA. Finally, a micropillar of about 300 nm in diameter was obtained . This milling procedure ensures that the long axis of the pillar is parallel to the main axis of the W pin, and it is essential to minimize misalignments in the tomographic acquisition.Electron Microscopy: A tilt series was acquired in a FEI Tecnai F20 (200 kV acceleration voltage) using a Fischione on-axis pin holder. The tilt range is from –72 to +72 degrees, with an image acquired every 2 degrees. A tilt range of 140 degrees or more has been shown to limit “missing wedge” artifacts, resulting in a good spatial resolution and low elongation of features.[features. Althoughfeatures. This cauThe imaging signal was collected using a high-angle annular dark field (HAADF) detector, providing atomic-number contrast. The magnification is 80 kx and the pixel size is 0.77 nm. The images are aligned using Inspect3D and the reconstruction (shown in Supporting Information Video S4) is computed with the same software using a weighted back projection (WBP) algorithm. It is known that, as a general rule, iterative algorithms (like SIRT) yield a more accurate reconstruction, particularly in the case of a large missing wedge. However, such algorithms require a large number of iterations (>30) to correctly resolve high spatial frequencies, and sometime present convergence problems. Therefore, for some datasets, WBP can return sharper reconstructions than SIRT. This is the case for the system under analysis here, composed by a network of particles which are very small compared to the overall volume.2 voxels respectively. The image segmented from the WBP dataset preserves most of the fine features, while the segmentation on the SIRT dataset fails at resolving fine details.SIRT also tends to smooth the signal, resulting in slightly smeared features compared to WBP. This makes segmentation more ambiguous, since the range of values that can be selected by an operator for a threshold in SIRT is quite broad. On the other hand, WBP results in sharp features, thus making segmentation less arbitrary. Supporting Figure 5 shows the difference in reconstruction for a slice taken along the pillar axis, including a histogram of the grayscale values and the resulting segmented dataset . The histograms are divided in three regions, corresponding to void, P3HT and TiOThe quality of the reconstructions obtained with different algorithms was evaluated by comparison with the high resolution TEM data, finding a better match with the WBP algorithm.We then processed the reconstructed volume with ImageJ to remove background fluctuations and optimize the contrast between the different components of the film. We finally applied a threshold in ImageJ to obtain a segmented dataset and use ResolveRT to visualize the reconstructed 3D volume.Statistical Data Analysis: The data was smoothed using a Gaussian filter in Fiji with variance σ2 = 0.25, which is a very light smoothing operator and subsequently binarized by global thresholding. The skeletonization was performed using Avizo 6.3, using the default settings.2 was estimated from a sub-volume (2 and P3HT) using the respective densities. The specific surface area was calculated by dividing the surface measured with Avizo by the mass of the sample. The particle size was extracted by iterative erosion of the segmented dataset until the centers were isolated. The number of erosion steps, for spherical particles, is an estimate of the radius; the erosion procedure is able to resolve partially overlapping features.The content of TiOb-volume c of the i ∈{1,…, n} individually by z describes the z-coordinate in the 3D image. The shortest paths along the graph are determined using Dijkstra's algorithm.[X be the shortest distance from a randomly chosen point in the polymer phase to the TiO2 phase. Then the spherical contact distribution function D is given by D(r) = P(X ≤ r). The monotonous connectivity monotonc for the TiO2 phase is defined by The geometric tortuosity is calculated for each node lgorithm.,34 Let X


It is known that type 1 diabetes mellitus (type 1 DM) maybe associated with other autoimmune diseases, such as autoimmune thyroid disease (ATD), celiac disease (CD) and Addison's disease (AD). We report the case of a patient with type 1 DM associated with familial mediterranean fever (FMF). It is the third association of these two diseases described in the medical literature to our knowledge so far.Our aim is to emphasize that FMF must be taken into consideration as a possible disease associated with type 1 DM in the presence of suggestive findings.A 13 year old boy already suffering from diabetes mellitus type 1 since the age of 4 years and 3 months, came to our attention because of the presence of periodic fever associated with abdominal pain, oral ulcers, chest pain and diffuse arthralgia. The fever appeared every 15-30 days with peaks that reached 40°C and lasted 24-48 hours. Blood tests , instrumental examinations (ultrasound of the abdomen and chest x-ray) and the rest of laboratory investigations  were normal in the interval between febrile episodes, but during the attacks revealed an increase in inflammatory markers . For the clinical suspicion of FMF we requested that the genetic investigation was performed.The molecular analysis of the nucleotide sequence of exon 2 of MEFV gene, performed in genomic DNA extracted from peripheral blood leukocytes, showed the mutation c.442G> C (p.E148Q) in the heterozygous state. The colchicine therapy, started 20 months ago at a dose of 1 mg/day, is well tolerated and has determined the immediate disappearance of the symptoms so far. Renal biopsy, performed at 14 years and 6 months of age because of persistent proteinuria, showed the absence of amyloidosis but a slight and irregular thickening of the lamina densa of some glomerular capillaries presumably due to diabetes. The serological tests have ruled out so far CD, AD, ATD and connective tissue diseases (CTD). Until now, the eye examination did not detect the presence of iridocyclitis.The coexistence of FMF and type 1 DM is a very rare finding. FMF heterozygotes tend to have a milder course of the disease and are less prone to experience new clinical manifestations than homozygotes. Moreover, at puberty, their symptomatology could disappear allowing to cease colchicine without relapses or an increase of inflammatory markers. Recently, a case of simultaneous protracted febrile myalgia syndrome (PFMS) preceded by diabetic ketoacidosis (DKA) has been described for which the authors have suggested that DKA-associated cytokine release could be a predisposing factor or a trigger for FMF-associated PFMS. Conversely, it was hypothesized that the immune dysregulation in FMF could be involved in the autoimmune mechanism that leads to type 1 DM. Finally, a prolonged follow up is needed to verify the long-term necessity of colchicine for our patient and further studies are required to reveal any possible shared molecular mechanisms that are responsible for these two diseases.None declared.


We generated a similar human brain network using microarray and RNA-seq data (BrainSpan Atlas) and identified 407 genes with high expression in the developing human VZ and subventricular zone (SVZ) at 8–9 post-conception weeks. Seven of the human genes were also present in the mouse VZ network. The human and mouse networks were extended using available genetic and proteomic datasets through GeneMANIA. A gene ontology search of the mouse and human networks indicated that many of the genes are involved in the cell cycle, DNA replication, mitosis and transcriptional regulation. The reported involvement of Cdon, Celsr1, Dbi, Eomes, Neurog2, Notch1, Pcnt, Sox3, Tead2, and Tgif2 in neural development or diseases resulting from the disruption of neurogenesis validates these candidate genes. Taken together, our knowledge-based discovery method has validated the involvement of many genes already known to be involved in neocortical development and extended the potential number of genes by 100's, many of which are involved in functions related to cell proliferation but others of which are potential candidates for involvement in the regulation of neocortical development.Neurons of the mammalian neocortex are produced by proliferating cells located in the ventricular zone (VZ) lining the lateral ventricles. This is a complex and sequential process, requiring precise control of cell cycle progression, fate commitment and differentiation. We have analyzed publicly available databases from mouse and human to identify candidate genes that are potentially involved in regulating early neocortical development and neurogenesis. We used a mouse  The neurons of the mammalian neocortex originate from neural progenitor cells  . This atlas provides spatial expression patterns from in situ hybridization (ISH) images for 2104 genes related to brain development at multiple stages ranging between embryonic day 11.5 (E11.5) and postnatal day 56 (P56) of C57BL/6J mice. Specifically, we made use of the Allen Brain Atlas application programming interface (API) (http://www.brain-map.org/api/index.html) to identify genes that are: (1) expressed in the telencephalon at E15.5, and (2) highly expressed in the VZ. The API's correlation search service yielded correlation scores for these genes. The most tightly correlated genes (r > 0.7) were used to create a Neocortex VZ Network. We analyzed expression of these genes in a qualitative manner by downloading high resolution ISH images for these genes from the Allen Developing Mouse Brain Atlas (http://help.brain-map.org/download/attachments/4325389/DevMouse_Overview.pdf).To identify genes with high differential expression in the VZ of the developing mouse neocortex, we used the Allen Developing Mouse Brain Atlas . Specifically, we used the developmental transcriptome dataset, which is comprised of RNA sequencing and exon microarray data of ~52,400 genes from brains at different developmental stages [8 post-conceptual weeks (pcw) up to 39 years of age] to identify genes differentially expressed at 8–9 pcw vs. all later stages. We used the prenatal laser microdissection (LMD) microarray dataset  to find genes differentially expressed in the VZ and SVZ relative to the rest of the brain. For both datasets  we used the BrainSpan web interface to download fold-change and p-values for differentially expressed genes. Only those genes with at least six-fold higher expression  were used. Subsequently we corrected p-values using the Holm-Bonferroni method and set the significant p-value at 1 × 10−9. We then compared the resulting lists for overlapping genes and used these genes for further analysis.We identified genes differentially expressed in the VZ and SVZ of the human brain early in development using the BrainSpan Atlas of the Developing Human Brain   and the 407 human network genes . GeneMANIA uses functional association data including protein and genetic interactions, pathways, co-expression, co-localization, protein domain similarity and predicted interactions (based on organisms other than the one searched). GeneMANIA recommends using gene lists with no more than 100 genes for a search. Therefore, we used the top 100 genes from the human network plus the 7 mouse genes that overlapped between the mouse and human lists. All 13 mouse genes were searched through GeneMANIA. An additional 50 genes were shown in each network that are associated with the original 13 VZ network genes or the 407 human network genes. The parameters used for the search, genes queried, associated gene lists, references used to generate the associations and all other outputs from GeneMANIA can be found in Tables GeneMANIA  was used to identify GO terms that are significantly overrepresented in the list of 63 mouse extended network genes and 407 human SVZ and VZ genes. The mouse search was relative to a background set of the mouse genome and the human search was relative to approximately 18,000 human genes that could be associated with any GO term. GOrilla results were visualized as “TreeMaps” generated in REViGO . A PubMed search was carried out using the keywords: (1) 13 gene symbols respectively; (2) combination of any two of the 13 gene symbols . Specifically, “Cdo” and “Cdon” were used for Cdon studies, as “Cdo” was used as the gene symbol previously. Advanced search by “[Title/Abstract]” was performed for Notch1 because the number of articles was 3269 (indicated with “*”). Numbers of articles retrieved are listed in Table *”). Non-mammalian or non-neuronal studies were excluded by scrutinizing titles, abstracts and “Materials and Methods.” The hypothetical functional gene network contains the 13 candidates and a subset of the 28 genes that interacted with more than one gene found through the literature mining approach . However, for some correlations more than one article is accessible.To validate the potential interaction among the 13 VZ network genes, we performed a literature mining approach on published studies to generate a hypothetical functional gene network (http://developingmouse.brain-map.org/). This atlas has ISH data for over 2100 developmentally important genes during seven stages of development beginning at E11.5 through P56. We specifically analyzed data from E15.5, which is during the peak of neurogenesis for neocortex development. We graphed the most tightly correlated genes that are differentially expressed in the VZ of the neocortex at that time point  compared to all other time points and brain areas  is expressed on the surface of the lateral ventricle  and Neurog2 are additionally expressed in the SVZ and intermediate zone , a web-based interface that searches a large set of functional association data to return related genes based on available genomic and proteomic data. The association data include protein, DNA and genetic interactions, pathways, gene and protein expression data, phenotypic screens and shared protein domains  and REViGO (http://revigo.irb.hr/). We found a significant enrichment in genes involved in transcriptional regulation, anatomical structure development and macromolecule metabolism . We started by identifying genes with strongest expression in the SVZ and VZ from the BrainSpan LMD microarray dataset (comprised of microarray profiles of samples taken from ~300 different brain structures at 15–21 pcw). Using the web interface of the BrainSpan atlas we identified data from 24,084 probes with at least the same level of expression  in the VZ and SVZ, compared to all other areas. From this list we removed all data points with less than a six-fold difference in expression in the VZ and SVZ compared to all other areas, resulting in a list of data points from 2746 remaining probes. After Holm-Bonferroni correction the p-values of all data points remained below the significant p-value of 1×10−9. This set of 2746 data points contained 1781 different genes. Ten of the genes from this list  were among the 13 mouse genes in the network described above  were also present in the mouse network described above  compared to later time points. Again we employed the BrainSpan web interface to access the transcriptome dataset [comprised of RNA sequencing and exon microarray data from brains at different developmental stages (8 pcw up to 39 years of age)]. Our query resulted in a list of 18,105 genes with = one-fold expression at pcw 8–9 compared to all later stages. This list was narrowed down to 1176 after removing all genes with less than a six-fold expression difference at pcw 8–9 compared to all later stages. After Holm-Bonferroni correction 1048 of these genes remained below the significant CELSR1, HMGN2, NEUROG2, NOTCH1, SOX3, TEAD2, TGIF2) were also present in the mouse VZ network. An additional six genes overlap with the mouse extended network . The overlap between these networks is a further validation of the publically available datasets and methods used to generate the networks.We then compared the resulting gene lists from the LMD microarray dataset and the developmental transcriptome, and found that 407 genes were overlapping between these two lists Table . From thMCM6, KIF18B, FEN1 and CCNF) are known to be involved in DNA replication, mitosis and cell cycle specific proteasome degradation.To further evaluate the genes involved in human VZ and SVZ development, we generated an extended human network using GeneMANIA Figure . Due to http://bioinfo.vanderbilt.edu/webgestalt/), which has several enrichment analysis tools  we also used WebGestalt  specifically reduced in the mouse GE compared with the VZ of the neocortex   during early neural development. Tead2 is a transcription factor that is essential for neural tube closure  is still controversial     Tables ,S6, and ) Tables ,S6. The Hmgn2 and Ssrp1) from our mouse network not previously known to be involved in neocortical development. In addition, the data from the human indicates 100's of genes potentially involved in ventricular zone development that could be the basis for future investigations.Taken together, our proposed gene networks provide a comprehensive view to discover genes involved in complex developmental processes, to reveal the relationships among them, and to help understand genetic modulation at a systematic level. Specifically, through the use of databases that incorporate multiple datasets from both human and mouse studies (Allen Brain Atlas and GeneMANIA) and the analysis of the extensive literature on the developing neocortex, we have uncovered at least two genes (The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In addition to being superior to earlier methods based on statistical inferences, in comparison to state of the art methods using machine learning, PconsC2 is superior for families with more than 100 effective sequence homologs. The improved contact prediction enables improved structure prediction.Given sufficient large protein families, and using a global statistical inference approach, it is possible to obtain sufficient accuracy in protein residue contact predictions to predict the structure of many proteins. However, these approaches do not consider the fact that the contacts in a protein are neither randomly, nor independently distributed, but actually follow precise rules governed by the structure of the protein and thus are interdependent. Here, we present PconsC2, a novel method that uses a deep learning approach to identify protein-like contact patterns to improve contact predictions. A substantial enhancement can be seen for all contacts independently on the number of aligned sequences, residue separation or secondary structure type, but is largest for  β-sheet containing proteins. Most importantly the improvement brings for the first time sufficiently accurate predictions to some protein families with less than 1000 homologous sequences. PconsC2 outperforms as well state of the art machine learning based predictors for protein families larger than 100 effective sequences. PconsC2 is licensed under the GNU General Public License v3 and freely available from http://c2.pcons.net/.Here, we introduce a novel protein contact prediction method PconsC2 that, to the best of our knowledge, outperforms earlier methods. PconsC2 is based on our earlier method, PconsC, as it utilizes the same set of contact predictions from plmDCA and PSICOV. However, in contrast to PconsC, where each residue pair is analysed independently, the initial predictions are analysed in context of neighbouring residue pairs using a deep learning approach, inspired by earlier work. We find that for each layer the deep learning procedure improves the predictions. At the end, after five layers of deep learning and inclusion of a few extra features provides the best performance. An improvement can be seen for all types of proteins, independent on length, number of homologous sequences and structural class. However, the improvement is largest for  PLOS Computational Biology Methods article.This is a α-helical proteins De novo protein structure prediction is a long-standing problem in protein bioinformatics. For many years the focus was to develop methods that accurately describe the free energy landscape of a protein and then use a search strategy to identify the conformation with the lowest free energy. Using this strategy, methods based on fragment-based assembly of proteins can sometimes produce surprisingly accurate models, in particular for small Knowing which residues in a protein interact with each other provides sufficient information to predict the structure of a protein In addition to utilising correlated mutations, earlier contact prediction methods used various machine learning approaches to improve the predictions Deep learning is a sub-field of machine learning in which multiple layers of non-linear processing are used to learn nonlinear mappings and abstract complex features through the subsequent layers of the deep architecture, thereby obtaining a hierarchical abstraction of the input data The method is based on the fact that in proteins residue-residue contacts are often found in proximity of other contacts, while a non-contact is often found next to other non-contacts. In a 3×3 contact matrix with either a contact or a non-contact in the central position the frequency of contacts in the other positions differs significantly, see i and j in two α-helices are in contact, residues i and j in two parallel β-strands implies strongly that the neighbouring residues, Moreover, the distribution of contacts follows characteristic patterns and recurring ones can be visually recognised and therefore used for constraining the prediction of nearby contacts. For instance if residues β-sheet containing proteins, but exist for all type of proteins. Further, in comparison to state of the art other machine learning based methods contact prediction methods PconsC2 is superior for proteins with more than 100 effective sequence homologs.Here, we show that a deep learning pattern recognition-based approach improves the accuracy significantly for all types of proteins, residue separations and numbers of homologous sequences. The obtained improvement is largest for We recently developed PconsC, an ensemble method reconciling predictions across different contact inference methods and a range of multiple sequence alignments The development of PconsC2 was performed using five-fold cross-validation on 150 proteins reported in the PSICOV paper β-Cβ contacts used here, and due to differences in the training, as well as inherent noise in the training of the underlying machine learning method.First, we examined if a deep learning approach improves predictions using identical inputs as in PconsC receptive field, providing information about the neighbourhood of predicted contacts around each residue pair. Already using a receptive field of the size 3×3 provides some improvement, see In layers Using a window of 11×11 and five layers of deep learning and evaluating the predictions at one prediction per residue (L = 1), a common cutoff used in earlier studies, the relative improvement between first and last layer in PPV is 54% (18% in terms of absolute PPV), see Next, we examined if it was possible to further improve the accuracy by including additional features. Inspired by current literature, we included four different features: Separation of contacting residues in the sequence (Separation), predicted secondary structures of residues surrounding the potentially contacting residues (SS), predicted solvent accessible surface area (RSA) and sequence profiles in the form of PSSMs (PSSM). All features improve the accuracy with a few percentage points both at the first and final layer, see To further evaluate the performance of PconsC2 to other methods we have used three different datasets, see Contacts at different separation provide different types of information and the underlying contact prediction methods; plmDCA and PSICOV, behave quite differently in this aspect. Both methods predict a lower fraction of long-range contacts than observed in proteins, but PSICOV predicts more long-range contacts among its top-ranked predictions than plmDCA, see The separation distribution for the top ranked contacts for PconsC2 resembles the distribution of contacts from PSICOV. However, the accuracy for contacts predicted by PconsC2 is higher and not strongly dependent on residue separation, see In a comparison to other contact prediction methods PconsC2 exhibits greater accuracy at all sequence separations, but the performance improvement is most prominent for the contacts with sequence separation of 12 amino acids or more, see β-sheets are easily recognisable in a contact map because of their dense visual patterns. On the other hand contacts between β-sheets are primarily mediated through backbone hydrogen bonds, suggesting that these might not be under the same co-evolutionary pressure as contacts mediated by sidechains, that is they might be harder to identify using methods based on co-variation of the sidechains. To analyse the effect of secondary structure, all proteins in the independent dataset were classified into their fold classes according to CATH The interaction patterns between different secondary structure elements vary significantly. In particular, parallel and anti-parallel β proteins and least for all α proteins. The overall performance is highest for the mixed α/β proteins followed by all β proteins.In In The success of statistical inference contact prediction methods, such as PSICOV and plmDCA, depends strongly on the availability of thousands of aligned sequences. Today the genome projects have already provided a sufficient number of homologous sequences for more than one hundred protein domain families We examined to what extent PconsC2 could increase the performance for smaller families. Due to the way the input alignments are constructed, the total count of sequences in the alignment is not the most suitable metric for the information content, as a high number of close homologs do not provide as much co-variation as comparable number of more distantly related proteins. As suggested before a better measure is to use the number of efficient sequences to take redundancy into account β-proteins the plateau performance is reached at a lower number of sequences than for earlier methods. Although the average PPV for PconsC2, as well as earlier statistical inference methods, depend strongly on the number of efficient sequences in the alignment it is clear that PPV values of 0.5 or even higher are reached even for protein families with fewer than 100 effective sequences, see In In addition we compare PconsC2 to a set of alternative contact prediction methods, including mutual information (MI) and MI with phylogenetic correction (CMIr), and two recent machine learning based methods, PhyCMAP and CMAPpro.In all three datasets PconsC2 shows a higher performance than all the other methods, see Next, we compared the performance of the different methods given the number of effective sequences in the alignments. For PconsC and PconsC2, which use several multiple alignments, we used HHblits with an E-value cutoff There is a difference in performance on the PSICOV set versus the other two dataset given the same the number of effective, see To highlight the behaviour of PconsC2 in comparison to PconsC we selected a few examples to highlight when PconsC2 significantly improves the predictions, see P. furiosus DNA polymerase sliding clamp (1iz4A), contains two DNA clamp domains, which are in a predominantly β conformation. In comparison to PconsC, PconsC2 filters out spurious short- and medium-range predictions, located at the domain termini. In addition, PconsC2 increases the predicted contact density in the β-sheet regions of both domains.The first example, R. opacus 1CP chloromuconolactone dehalogenase ClcF (3znuG), is a homo-10-meric protein. Here, we only predict contacts within one chain and the prediction performance of PconsC is poorer (PPV = 0.18) than of the constituting methods . This is due to a limited overlap between the individual predictions, resulting in a nearly random distribution of predicted contacts. However, the use of additional information, as well as pattern recognition in PconsC2 allows for reconciling the conflicting predictions and attaining a better prediction than any of the individual methods (PPV = 0.56).The second example, Obviously, there also exist proteins for which the PconsC2 methodology does not work and occasionally PconsC2 performs worse than PconsC. In general these cases can be divided into two categories. PconsC2 tends to perform badly, when the underlying predictors provide conflicting or low quality information. PconsC2 also sometimes fails when the deep learning methodology introduces artefacts into the contact map. We have paid close attention to reducing the amount of artefacts introduced by deep learning, which is one of the main reasons behind limiting the number of prediction layers.The first example of PconsC2 failing with respect to PconsC is the LIM domain of RSGI RUH-019 (1wigA), which is a relatively short (73 amino acids) protein that contains few secondary structures. Here, PconsC2 produces a spurious cluster of predictions between the N- and C-termini, reducing the PPV from 0.49 to 0.40, see see The second example is the human insulin-like growth factor 1A (1imxA), for which neither PconsC nor PconsC2 make usable predictions. It is worth noting, that the contact patterns predicted by both approaches are quite different, with PconsC predicted contacts being more uniformly distributed along the sequence, while the PconsC2 attempts to create incorrect interactions between secondary structure elements, see ab-initio protein structure prediction. To test if the improved contact predictions from PconsC2 indeed are more useful for protein modelling, we built models for all proteins in the independent test set using both PconsC and PconsC2 predicted contacts, using the PconsFold pipeline β-sheet containing proteins, see One of the ultimate goals of contact prediction is to facilitate The average quality increases with the effective number of sequences in the protein family, see One likely reason why the improvement of model qualities not is larger might be that our folding protocol cannot fully utilize the improved contacts. In β-sheets, which can be recognised. When a hypothetical contact is predicted between residue i and j it is intrinsically conditioned that the contact likelihood of – at least – the pairs of residues close to i, j are affected. Such rules have been used in other contact prediction methods, not using the statistical inference approach, either by satisfying specific constraints Earlier statistical inference co-evolution based contact prediction methods assume independence between the couplings calculated among pairs of residues, but contacts in a protein contact map are neither randomly, nor independently distributed. Their distribution clearly follows characteristic patterns for certain types of interactions, e.g. hydrogen bonding ladders between β-sheet containing proteins. Perhaps most importantly, already at 100 effective sequence homologs the average prediction accuracy from PconsC2 rivals the maximum accuracies obtained from earlier methods and for larger families PconsC2 outperforms all methods. However, other machine learning approaches outperform PconsC2 for smaller families indicating room for improvement. The fact that PconsC2 predicts a larger fraction of contacts separated by many residues contributes to that models produced using contacts from PconsC2 instead of PconsC are on average 9% better.PconsC2 improves the prediction for all proteins and all types of contacts, but the improvement is most significant for α-Cα, Cβ-Cβ or the closest heavy atom distances in sidechains. The relative improvement of PconsC2 is independent on the definition and therefore we chose to present only the results using the same contact definition as used in CASP , that is Cβ-Cβ, Cα for Glycine, distance between the amino-acids ≤8Å There exist different possible definitions for residue contacts. The most commonly used are based on Chttps://c2.pcons.net/.PconsC2 has been trained on a set of 150 non-large, single-domain proteins used for evaluation of PSICOV −3 to any of the proteins in the training set or the proteins in testing set. Homology reduction was done by a jackhmmer search against UniRef100. Finally proteins with very few homologs and not compact were rejected. We considered proteins to be not compact enough if they have a small amount of long-range contacts, long intrinsic disordered regions, or large unstructured termini.The final evaluation was conducted on a set of 383 proteins, which are not evolutionarily related to the proteins in the training set. This set was constructed starting from approximately 650 proteins of known structure, including proteins representing the most common folds. This set was then homology reduced to ensure that no protein in this set has detectable hits with E-value lower than 10For the CASP10 datasets all protein sequences were downloaded from the CASP website. Comparisons were performed both against the predictions submitted to CASP and by running all predictors again, taking into account increases in databases' size etc. An overview of the three datasets is presented in The number of sequences included in an alignment can be used as a rough indicator of the expected accuracy of the contact predictions. However, a better correlation is obtained when effective number of aligned sequences is used, i.e taking the redundancy into account. Here, we have used the same definition as used in the PSICOV article Then all sequences that have fewer than For benchmarking, predictions from PconsC2 are compared with predictions from a number of other methods. These can basically be divided into three groups, mutual information methods that only use mutual information, global information methods that infer the observed mutual information from estimated direct interactions, and machine learning based methods that use mutual information as well as other information to predict contacts.Here, Mutual Information (MI) and Corrected Mutual Information (CMIr) are calculated using a freely available package developed by Bahar Further three statistical inference methods; plmDCA Finally, we also tested three machine learning based contact predictors; PhyCMAP deep neural networks architectures (with many hidden layers of neurons) and are particularly useful in structured problems. It is because the hidden layers can learn and store complex higher level feature representations for the activation signals provided by the input layer, thus mimicking in a certain sense, the concept of high-level concepts. This behaviour can be extremely valuable for solving structured problems in which some high-level structure underlies the input data, such as image, speech and handwriting recognition. Deep learning architectures based on neural networks have some drawbacks: for example they are computationally demanding and require careful tuning abstract higher level features from the data . We implemented a multilayer feed-forward stack of random forest learners, but in principle any machine learning algorithm could be used in each layer; for this reason, during the description of the architecture, we refer with the generic term of “learner” to the algorithm used in each layer.The machine learning architecture implemented in this paper is inspired by deep learning literature in the sense that it is a feed-forward stack of learners, in which each layer receptive fieldLet positional neural networks, one for each position The approach described here is conceptually different from the one adopted by Di Lena et al. The architecture can be described with the following recurrent formalisation:Layer Layers where Here, the receptive field is a square matrix of During the development of PconsC2, we observed certain inherent characteristics of input data and the contact prediction problem, which can be summarised in the following three points:L-residue long protein, of a number of vectors in the order of The contact prediction problem requires the elaboration, for each The number of contacts increases almost linearly with protein length, but since the number of possible contacts increases quadratically, the contact density of a protein decreases with the inverse of the protein length. This means that the set of vectors for training is heavily unbalanced.The dataset (consisting of predictions from the single coevolution based methods) is quite noisy and might contain artefacts, as certain strong couplings detected by these methods may have arisen for reasons other than intra-chain spatial proximity.We found, in spirit of PconsC In PconsC2 each layer of the deep learning architecture uses a Random Forest Classifier ordinary features and the receptive field, concatenated. The ordinary features are identical for each residue pair in every layer, while the receptive field As described above, each layer −40, 10−10, 10−4 and 1) to construct alignments by HHblits (from HHsuite 2.0.16) and jackhmmer from HMMER 3.0. The HHblits alignments used the bundled uniprot20_2013_03 database. Disabling both filtering the resulting MSA  and limiting the amount of sequences, that are allowed to pass the second pre-filter, as well as allowing for realigning all the hits in HHblits lead to the construction of the most information-rich and accurate alignment at cost of slightly increased running time. Jackhmmer alignments were constructed with UniRef100, based on UniProt 2013_06 release, with at most 5 rounds of iterative search and inclusion threshold (incE) set to the same value as the E-value threshold.PconsC2 uses the same combination of predictions from PSICOV In addition to the 16 contact predictions we examined the possibility to use additional information in the form of sequence separation, PSSMs as well as predicted structural features.Sequence separation is the distance between amino acids in sequence space, or differently formulated, the absolute value of difference between amino acid indices.1 was calculated. In the feature vector we represent each position i of the protein sequence with 21 real values, denoting the logarithm of ratio of frequency of an amino acid at this position to the background frequency of this amino acid in UniRef50. As the predictions take into account two positions in the sequence at the time, this results in For each protein in the dataset, a sequence profile starting from the MSA obtained with HHblits with E-value cut-off hhfilter, as implemented in addss.pl script of HHsuite. The secondary structure prediction for each residue was encoded using a vector of probabilities for each of the structural classes . Here, we used a window of 9 residues  providing The secondary structure prediction was calculated feeding PSIPRED with the alignment obtained by running HHblits with E-value cut-off Predicted relative solvent accessibility information calculated with NetSurfP The secondary structure features predicted by NetSurfP and PSIPRED may seem redundant, though methods used are not the same. Predictions by PSIPRED come at nearly negligible computational cost, as they use pre-computed alignments, whereas NetSurfP predictions come “free” with predicted RSA and the inclusion of both provided a slight improvement.In summary, the additional features are:PSICOV and plmDCA are, respectively, the 8 predictions from PSICOV and from plmDCA Click here for additional data file.Table S1PDB IDs and sequences of the training- and testsets used in this study.(TXT)Click here for additional data file.


In this communication we report a direct immunoassay for detecting dengue virus by means of a label-free interferometric optical detection method. We also demonstrate how we can optimize this sensing response by adding a blocking step able to significantly enhance the optical sensing response. The blocking reagent used for this optimization is a dry milk diluted in phosphate buffered saline. The recognition curve of dengue virus over the proposed surface sensor demonstrates the capacity of this method to be applied in Point of Care technology. The number of dengue virus cases has grown dramatically in recent decades: 2.5 billion people in more than 100 countries are at risk of acquiring dengue viral infection. There are more than 50 million new infections annually, with a number of deaths ranging between 20,000 and 25,000, mainly in children [Dengue is an arthropod-borne flavivirus that comprises four distinct serotypes  that constitute an antigenic complex of the genus flavivirus, family Flaviviridae. The infections can result in a range of clinical manifestations from asymptomatic infection to dengue fever (DF) and the severe disease dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS).The conventional techniques for dengue detection such as virus isolation, nucleic acid or antigen detection test involve expensive complex procedures ,3 and seCurrently, immunoassay based Enzyme linked ImmunoSorbent Assay (ELISA) tests are commercially available for qualitative or semi-quantitative measurement of dengue virus  and theyIn this work we report an optimization of a direct immunoassay for dengue virus detection employing a recent label-free interferometric optical detection method . This op2.2.1.2 and on SU8 polymeric thin film that exhibits a reliable optical label-free biosensing capability. For the BICELL fabrication we use SU8 2000.5  diluted 1:10 in cyclopentanone; this SU8 was deposited by spinning and then soft-baked at 70 °C for 1 min. An exposure to UV light process was then carried out, followed by a post-bake step at 70 °C for 5 min to give a stable thin film. Finally, an acid catalysis step was carried out in order to increase the hydrophilicity of the film. The label-free optical biosensing is carried out by monitoring the changes in the optical mode response provoked by the occurence of both the immobilization of the anti-dengue bioreceptors and the recognition of dengue virus antigen.For this experimental work we used as photonic transducer a Biophotonic Sensing Cell (BICELL) –10 based2.2.Optical read-out of the biosensor is based on the Interferometric Optical Detection Method (IODM), an advantageous optical label-free biosensing interferometric read-out. This IODM is characterized by the use of two interferometric signals, which allows for the optical reading system to convert the changes caused by the optical transduction into a unique, sensitive variable of detection . Thus, b2.3.The Ab-dengue/dengue direct immunoassay  was carried out on the SU8 polymeric sensing surface of the BICELLs, allowing label free detection by the accumulation or recognition of biomolecules. Since it is important to avoid nonspecific adsorption, the biosensing response was tested against two types of blocking agents for this immunoassay from Vircell: 5% BSA and 5% skim milk. Incubating the antibody dengue onto the sensing surface until saturation permitted carrying out the recognition experiments and assessment of the blocking step performance. Thus, in a second phase, the two types blocking agents were studied.2.4.After the blocking step we proceeded to make a direct immunoassay with the samples of dengue virus without purification containing other types of proteins and biomolecules provided by Vircell.3.Several steps where carried for the optimization of the dengue immunoassay: we firstly incubated the dengue antibody onto the sensor surface until saturation, and then we performed and studied the blocking agent to avoid nonspecific adsorption to optimize the immunoassay. The blocking agents studied were BSA and skim milk, being the latter the one which offered a better response see .The study of the two types of blocking agents on the proposed sensor allowed us to select the most suitable one for our immunoassay. In Since 5% skim milk was the most suitable blocking agent for our immunoassay we studied the response of dengue virus without purification over the surface sensor with and without blocking agent.Thus, after selecting the most suitable blocking agent, a performance comparison with and without blocking agent was carried out. 4.We have developed a sensor based on SU8 polymer able to detect dengue virus through an optical label-free biosensing technique. The optical read-out for the characterization of the biosensor is based on the Interferometric Optical Detection Method. We efficiently optimized the dengue immunoassay by means of an appropriate blocking agent (5% skim milk), thus avoiding non-specific absorption, and therefore obtaining a high affinity reaction between antigen and antibody.in-vitro dengue diagnostis as a good approach for optical Point of Care technologies. Moreover, with this biosensing systems a competitive limit of detection can be achieved in comparison with other optical label free biosensors [The response curve of the dengue Virus shows that the proposed sensing system it is a good alternative for cost-effective osensors ,8. In fa


Duplication of the yeast centrosome  is thought to occur through a series of discrete steps that culminate in insertion of the new SPB into the nuclear envelope (NE). To better understand this process, we developed a novel two-color structured illumination microscopy with single-particle averaging (SPA-SIM) approach to study the localization of all 18 SPB components during duplication using endogenously expressed fluorescent protein derivatives. The increased resolution and quantitative intensity information obtained using this method allowed us to demonstrate that SPB duplication begins by formation of an asymmetric Sfi1 filament at mitotic exit followed by Mps1-dependent assembly of a Spc29- and Spc42-dependent complex at its tip. Our observation that proteins involved in membrane insertion, such as Mps2, Bbp1, and Ndc1, also accumulate at the new SPB early in duplication suggests that SPB assembly and NE insertion are coupled events during SPB formation in wild-type cells.DOI:http://dx.doi.org/10.7554/eLife.08586.001 Cells divide to produce two new daughter cells that each contain the same genetic material. First, the DNA of the parent cell is copied, then it must be physically separated into the daughter cells by a structure made of filaments called microtubules. To ensure that the DNA is separated into two equal parts, the microtubules must emerge from two points in the cell, known as spindle poles.Each spindle pole is made of a group (or ‘complex’) of proteins and these have to be copied before the cell can divide. While we understand how DNA is copied, we do not know how cells copy proteins. The spindle pole in yeast—known as the spindle pole body—is an ideal model to study this problem because the proteins that form it have already been identified and it is easy to study yeast in the laboratory.Burns et al. developed a new method to study the spindle pole body using fluorescent protein tags and a sophisticated microscopy technique. The experiments mapped the positions of 18 proteins within the spindle pole body during its duplication. Some of these proteins enable the spindle pole to insert into the membrane that surrounds the cell's nucleus. Unexpectedly, Burns et al. observed that this set of proteins interact with the new spindle pole as it forms, instead of afterwards as was previously believed.Burns et al.'s findings suggest that the spindle pole body assembles into the membrane surrounding the nucleus at the same time as it is copied. The next challenges are to understand the details of how this works and to use the same method to study other large protein complexes in cells. Until now, highly detailed surveys of protein structures have been limited to a handful of proteins and conditions. The method developed by Burns et al. makes it possible to carry out studies that examine the movements of whole protein complexes during cell division.DOI:http://dx.doi.org/10.7554/eLife.08586.002 The goal of molecular, biochemical and cell biological studies is to elucidate the function of cellular components and understand how proteins and the complexes they form interact in vivo. Few methods are available to examine large protein structures such as microtubule-organizing centers (MTOCs) in intact cells. However, elucidating the mechanism of MTOC assembly is important since MTOCs perform a variety of functions in the cell, including cell signaling, cilia assembly, intraflagellar transport, and chromosome segregation. Duplication of a class of MTOCs known as centrosomes (in metazoans) or spindle pole bodies  once per cell cycle is essential to facilitate the formation of a bipolar spindle . DefectsSaccharomyces cerevisiae. Electron microscopy (EM) studies show that the SPB is a multilayered cylindrical organelle that is embedded in the nuclear envelope (NE) throughout the yeast lifecycle  and single particle averaging (SPA) to all 18 subunits of the SPB expressed at endogenous levels as fusions to green fluorescent protein (GFP), yellow fluorescent protein (YFP), mCherry (a red fluorescent protein derivative), cyan fluorescent protein (CFP), or mTurquoise2 (a CFP derivative). In contrast to SPA approaches used in previous studies with super-resolution microscopy data , we emplThe small size of the budding yeast SPB  filaments in vitro, John Kilmartin proposed that elongation of the half-bridge may occur through end-to-end association of Sfi1 C-termini; this would generate a free Sfi1 N-terminus that could seed satellite assembly  Li et a. Using CThree important and novel observations were made regarding the distribution of Sfi1 on the extended half-bridge. Unlike Spc42-mCherry intensity, which is greatest at the mother SPB and shows reduced levels at the satellite, many cells exhibit increased GFP-Sfi1 fluorescence at the distal end near the satellite compared with the proximal end , suggestTo compare the positions of Sfi1 N- and C-termini along with other half-bridge components, we developed computational methods to align dual color SIM images based on Spc42 fluorescence at the mother SPB and the satellite . Conceptually similar to single particle analysis used in many cryoEM studies, SPA-SIM involves the generation of probability profiles depicting the average position of the second SPB component after alignment of many images using Spc42. This type of analysis incorporating a common fiducial marker is advantageous because it shows the likelihood that a protein is present in a given location based on many cells and allows for positional comparison between different proteins.As a proof-of-principle, we first aligned seventeen images from α-factor arrested cells containing a version of Spc42 fused at its N-terminus to YFP and at its C-terminus to mTurquoise2 (YFP-Spc42-mTurquoise2). Based on EM, the C-terminus of Spc42 is above the NE in intermediate layer 2 of the SPB, which is located between the central and outer plaque; the Spc42 N-terminus is located in the central plaque, which is spaced 10.8 nm from intermediate layer 2 towards the NE . BecauseAlignment of GFP-Sfi1 and Sfi1-GFP images using Spc42-mCherry further demonstrated the validity of this approach and provided additional insights into its organization at the extended half-bridge that were not ascertained by inspection of individual images or by high resolution EM, PALM and stochastic optical reconstruction microscopy (STORM) analysis . Not onlUnlike Sfi1, which appeared to form a filament with a distinct polarity, Mps3 and Kar1 molecules were distributed along the half-bridge in asynchronous and α-factor arrested cells . ProbabiCDC31 were lethal when expressed under the endogenous promoter; however a cdc31∆::MET-YFP-CDC31 strain was viable and YFP-Cdc31 was detected as one or two foci in most α-factor arrested cells  large budded cells and at the pole in nearly all early G1 cells  . In thesmps1-1 arrest with an elongated bridge lacking a satellite, which is reminiscent of certain SFI1 mutants  or one Spc42-mCherry focus (representing a SPB with an elongated bridge lacking a satellite)  . The mixtellite) . CollectIn addition to Spc42, the satellite is predicted by immunoEM to contain three additional components of the core SPB: Spc29, Nud1, and Cnm67 . To testAs shown in Nud1-mTurquoise2 was observed at the satellite in many  but not all α-factor arrested cells . The intCnm67 showed high variability at the satellite (ranging from 0–43%), even using five different strains and three distinct fluorescent protein tags . This vaTo effectively examine the timing at which individual components assemble into the satellite, we started with a highly synchronized mitotic cell population by employing a metaphase arrest/release protocol involving depletion of the anaphase activator Cdc20 . BecauseIn cells containing Spc42-mTurquoise2 and Nud1-YFP, we observed two foci of Spc42-mTurquoise2 as early as 45 min following release from metaphase, at about the same time a very small bud was detected . Of the MET-CDC20/SPC42-YFP/SPC29-mTurquoise2. However, the observation that the percentage of SPBs containing two Spc29-YFP foci in addition to two foci of Spc42-mTurquoise2 increased from 15% at 30 min to 47% at 40 min supports that Spc42 assembles prior to Spc29  of cells 30 min following release from metaphase . Of thesto Spc29 .cdc20-1 mutant, SPB size is over twice the size as in wild-type cells in mitosis , we exploited the fact that SPB diameter, and thus pore diameter, increases with ploidy . LocalizA complex set of genetic and physical interactions suggests that Mps2 and Ndc1 anchor the SPB in the NE via binding to the soluble proteins Bbp1 and Nbp1, respectively, which associate with components of the core SPB such as Spc29 . Based oTo test the idea that components of the pore membrane localize to the newly forming SPB, we examined the localization of Bbp1, Mps2, Ndc1, and Nbp1 fused to mTurquoise2 in α-factor arrested cells containing Spc110-YFP by SIM . Under tWe used asynchronously dividing cells to examine the distribution of pore membrane proteins throughout the cell cycle  to understand their distribution during SPB duplication and to provide insights into the temporal control of pore membrane assembly. As shown in From our cell cycle analysis of Ndc1-Turquoise2 and Spc42-YFP , we notiAs shown in Analysis of positional information obtained from the Gaussian fits used for SPA-SIM  showed tRelease of cells from α-factor into the cell cycle allowed us to compare the kinetics of Spc110 acquisition (which occurs at the end of duplication after the new SPB is inserted into the NE) with that of incorporation of Nbp1 or Ndc1 to determine if Nbp1 is indeed a limiting factor for SPB duplication as our SPA-SIM analysis suggested. Continued growth of cells allowed us to watch NE insertion and protein incorporation during the next cell cycle to ensure the α-factor arrest did not affect recruitment of Nbp1 or Ndc1.A single Spc110-YFP focus and a single Ndc1-mTurquoise2 or Nbp1-mTurquoise2 focus was observed in Ndc1-mTurquoise2/Spc110-YFP or Nbp1-mTurquoise2/Spc110-YFP strains arrested in α-factor  and at tsfi1+ and cdc31+ in fission yeast   was integrated into strains by digestion with AflII and transformants were selected on plates containing G418. The N- and C-terminal-tagged versions of SFI1 were gifts of John Kilmartin (University of Cambridge) and were integrated into strains as described .Yeast strains are derivatives of W303 and are listed in /ura3-1) . Haploidescribed . mps1-1   in 100 mM sucrose and then washed two times in phosphate-buffered saline, pH 7.4. An aliquot of cells was placed on a glass slide and covered with a number 1.5 coverslip. SIM images were acquired with an Applied Precision OMX Blaze . A 60× 1.42 NA Plan Apo oil objective was used, and emission was collected onto two PCO Edge sCMOS cameras  with each camera dedicated to one specific channel. For CFP/mTurqouise2/YFP experiments, a 440/514/561 dichroic was used with 460–485 nm and 530–552 nm emission filters for CFP/mTurqouise2 and YFP, respectively. The 440 nm line and 514 nm line were used for excitation of CFP/mTurqouise2 and YFP, respectively. For GFP/mCherry experiments, a 405/488/561/640 dichroic was used with 504–552 nm and 590–628 nm emission filters for GFP and mCherry, respectively, using a 488 nm laser line (GFP) and 561 nm laser line (mCherry). To limit spectral cross-talk, all SIM data was acquired in alternating excitation mode. SIM reconstruction was performed with the Applied Precision software Softworx with a Wiener filter of 0.001. Color alignment from different cameras in the radial plane was performed using the color alignment slide from GE Healthcare . In the axial direction, color alignment was performed using 100 nm tetraspeck beads . For image preparation, the SIM reconstructed images were scaled 2 × 2 with bilinear interpolation then smoothed with a Gaussian blur of pixel radius 0.8. In many cases, for illustration purposes, a max projection in z over the relevant slices was done.http://research.stowers.org/imagejplugins/. In cases where the protein of interest represents two distinct spots corresponding to the mother SPB and satellite, the spots were fitted to two 3D Gaussian functions (see below) and realigned along the axis between these functions for further analysis and visualization. In cases where the protein represents a less distinct distribution or when the distribution along the mother-satellite axis is being queried, a secondary protein  was fit to two 3D Gaussian functions for further analysis and visualization.Three-dimensional analysis of SIM images was performed with custom written macros and plugins in the open source software, ImageJ . All plugins and their source code are available at SPBs were identified in a semi-automated fashion by sum projecting the raw SIM images, blurring them with Gaussian blur with a standard deviation of 1 pixel and progressively finding all maxima with an intensity above 15% of the image maximum intensity and a minimum distance of 30 pixels from other maxima. Next 30 × 30 pixel intensity profiles along the z axis were created. SPBs with fitted channel profile maxima in the first or last slice were eliminated. Each selected SPB was then visually inspected in three dimensions for counts of those containing two spots. This methodology underestimates the number of two spot images given the lower z resolution and thus the inability of the microscope to distinguish spots where the mother-satellite axis is oriented vertically. Nevertheless, yeast nuclei rarely orient themselves this way and this method provides the best estimate of satellite formation and/or incorporation.SPBs containing two visible spots were selected for fitting initialization with the mother selected first. In samples with Spc110 labeling, the spot closest to the Spc110 was identified as the mother SPB. In samples where Spc42 was used for fitting, the brighter Spc42 spot was considered the mother SPB, as was shown in Spc42/Spc110 dual labeling experiments see . While tAfter manual spot selection, the spots were fit to the sum of two 3D Gaussian functions by Levenberg–Marquardt non-linear least squares . Spot ceAfter fitting, a two dimensional plane profile in both channels was created containing the fitted centers of the spots and oriented horizontally along the axis perpendicular to the mother-satellite axis. Intensities were obtained by tri-linear interpolation along this plane with a pixel size four times smaller than that of the reconstructed SIM image. These realigned profiles were summed together to create dual-color probability maps for the intensity profiles of the two proteins under study. In cases where Spc110 is labeled in the second channel, profiles were rotated so that the side of the profile with the higher Spc110 intensity (the mother SPB) was oriented to one side. In that way it was possible to assess the shift of the mother-satellite profile relative to the pole axis. In the same way, half-bridge profiles collected with Spc42 as a reference were oriented to one side based on the non-Spc42 signal (or YFP in the YFP-Spc42-mTurquoise2 strain). Typically images are shown with the aligned channel above the non-aligned channel since this is most consistent with available structural and molecular information. The exceptions to this are Mps3 and N-terminus of Spc42, where EM and/or FRET data demonstrates positioning below the mother-satellite axis .-spindle axis was centered slightly differently for different probability maps. For creation of overlayed contour maps, these differences were eliminated by vertically shifting the probability maps with bilinear interpolation. Contour maps were then generated by thresholding each channel at 75% of its maximum intensity and outlining the resulting mask. For proteins present at both the mother and satellite, each distribution was outlined independently to avoid bias due to the intensity differences between these distributions.Analysis of probability map centers for distance and angle measurements were made by multi-Gaussian fitting of average intensity profiles along either axis. Errors were estimated by standard Monte Carlo analysis of 100 randomly simulated data sets with random Gaussian noise corresponding to the variance indicated by the fit residuals . Given tCenters and FWHM values for the probability distributions were generated by fitting average horizontal and vertical profiles to one-dimensional Gaussian functions. For horizontal (x direction) half-bridge profiles with a single peak, 30 pixels at either end of the distribution were removed to prevent mis-fitting due to variable background signals in that region. For pore protein distributions in that same direction, a third broad Gaussian function centered on the distribution was required for reasonable fitting of the distribution.Log-phase cells were high-pressure frozen in a Wohlwend Compact 02 high pressure freezer, freeze-substituted in 0.25% glutaraldehyde, 0.1% uranyl acetate in acetone, embedded in Lowicryl HM20, and processed for immunoEM as previously described . The aff review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent , Karsten Weis (Reviewing Editor), and two reviewers.Thank you for submitting your work entitled “Structured-illumination with particle averaging reveals novel roles for yeast centrosome components during duplication” for peer review at The reviewers have discussed the reviews with one another, and the Reviewing Editor has drafted this decision to help you prepare a revised submission.In this manuscript, Burns et al. use SIM and single particle averaging (SPA-SIM) in order to characterize molecular events associated with SPB duplication. Overall, the reviewers felt that this is an excellent manuscript employing state-of-the-art imaging to gain insight into the structure and assembly of spindle pole bodies. The article is well-written and, in general, the data support the authors’ conclusions. However, there were some concerns that the results remain somewhat descriptive and it was concluded that additional experiments would be needed.1) The authors propose that Nbp1 is a limiting component for SPB insertion. The study would be tremendously strengthened if they could actually show this using mutants or degron-fusion proteins for pore components and analyzing the recruitment and appearance of other pore and satellite proteins.CDC20 to arrest and then release cells. While this is a commonly employed technique for biochemical analyses in the field, it is not clear why this is important for single cell analysis. The authors could use a morphological marker (such as pole separation) to tell them exactly where they are within the cell cycle. The issue here is how representative arrest and release is relative to “normal” cell cycle progression. One might imagine a build-up of certain products in the arrest that might be different from normal progression. It is not necessary that the authors repeat their entire data set but they should validate for a few key proteins that the changes they see are representative of unperturbed cell cycles.2) The authors use 3) The description of the methods is often not sufficient to understand the details. For example, while it is clear how the y-axis is determined, it remains obscure how the authors establish the x-axis. Here, more explanation is required.4) For the position of proteins from the single particle averaging, the distribution of maximum intensity is provided, with the standard error of the mean indicated. The authors need to include a table indicating additional structural information. This should include the FWHM measurement for each protein. Note in 5) The manuscript lacks precision in several places and the authors should carefully edit their Results section. For example, in Also, in the main text, the description of the data in mps1-1 arrested cells, cells show many more configurations. Does it mean that the cells arrest at different points in the duplication cycle?In In  In this manuscript, Burns et al. use SIM and single particle averaging (SPA-SIM) in order to characterize molecular events associated with SPB duplication. Overall, the reviewers felt that this is an excellent manuscript employing state-of-the-art imaging to gain insight into the structure and assembly of spindle pole bodies. The article is well-written and, in general, the data support the authors’ conclusions. However, there were some concerns that the results remain somewhat descriptive and it was concluded that additional experiments would be needed.We thank the reviewers and editors for their consideration of our manuscript and their positive comments. We have revised the paper to ensure that our methods and results are clearly and accurately presented to justify our conclusions. We recognize that the work may seem descriptive, but analysis of 18 proteins in wild-type cells resulted in findings that we believe significantly advance our understanding of SPB duplication.1) The authors propose that Nbp1 is a limiting component for SPB insertion. The study would be tremendously strengthened if they could actually show this using mutants or degron-fusion proteins for pore components and analyzing the recruitment and appearance of other pore and satellite proteins.mps1-1 in We agree with the reviewers about the value of mutants, and we have included analysis of mps2 mutant strains.Previously, we have examined protein localization in the Nbp1 degron strain, finding that in addition to Nbp1, Bbp1 and Mps2 are lost at a fraction of SPBs; Cdc31, Kar1, Mps3, Sfi1, Spc42 and Spc29 are unaffected . SIM is unlikely to tell us more than this. We also showed in this same paper that Nbp1 localization is affected in some Unfortunately, degrons and many mutant alleles in SPB components destroy SPB structure, and it is challenging or impossible to sort out primary versus secondary effects based solely on localization data. Precision tools that disrupt individual interactions between pore proteins and each other or satellite components do not exist. Mutants with specific defects defining a single function of Nbp1 would require extensive work to produce and characterize, warranting a separate report beyond the scope of this paper. We feel our solid, systematic characterization of the 18 components in wild-type cells significantly raises the knowledge base in the field and will be a springboard for many future studies.2) The authors use CDC20 to arrest and then release cells. While this is a commonly employed technique for biochemical analyses in the field, it is not clear why this is important for single cell analysis. The authors could use a morphological marker (such as pole separation) to tell them exactly where they are within the cell cycle. The issue here is how representative arrest and release is relative to “normal” cell cycle progression. One might imagine a build-up of certain products in the arrest that might be different from normal progression. It is not necessary that the authors repeat their entire data set but they should validate for a few key proteins that the changes they see are representative of unperturbed cell cycles.cdc20 arrest , however, we are using this experiment to study protein accumulation at the new SPB. Because cells released from the CDC20 arrest in a highly synchronized manner, we know we are examining the earliest duplication structures and comparing equivalent intermediates. In addition, its use does not rely on any preconceived ideas about the hierarchical order of SPB assembly that we may have, so it is ideal to ask the basic question: what protein is recruited first, in an unbiased manner?While we agree that the distance between spindle pole bodies is a useful method to determine the position the cell cycle, our study is the first to examine early SPB duplication structures using fluorescence microscopy, so what to use as a reference point to stage this period of pole duplication was unknown. There is certainly a precedent that SPB components may build up at the pole during a CDC20 release, suggesting the Spc42 is the first of the known satellite components to be assembled.However, to alleviate concerns over the possibility of a build-up of proteins due to the arrest, we have gone back and quantified images from unbudded cells in an asynchronous population, so it is clear that the temporal order of assembly shown in  is not aCDC20 release was used. We have revised the text to ensure how cells were grown and arrested throughout the paper to make this clear.The satellite assembly experiment in 3) The description of the methods is often not sufficient to understand the details. For example, while it is clear how the y-axis is determined, it remains obscure how the authors establish the x-axis. Here, more explanation is required.We agree that we could have explained orientation of axes more thoroughly. In In 4) For the position of proteins from the single particle averaging, the distribution of maximum intensity is provided, with the standard error of the mean indicated.Throughout the paper, we have provided average intensity distributions as images and in plots rather than maximum intensity distributions because we didn’t want to make assumptions about protein localization variability versus the underlying size in the SPB substructure. We have added text to clarify this in the paper.The authors need to include a table indicating additional structural information. This should include the FWHM measurement for each protein. Note inthat the distribution of Sfi1 is greater (in width) than Kar1. It is important to have a table with these statistics. In addition, the area over which these proteins occupy could be expressed as the area in which 95% of fluorescence is contained. This would help the reader appreciate the distribution, perhaps with more insight than simply the maximum intensity.We have provided full-width half-maximum values and errors for each protein in 5) The manuscript lacks precision at several places and the authors should carefully edit their Results section. For example, inaround 70% of pheromone arrested cells show two Nud1 foci (SPB and satellite), whereas in, this number drops to less than 10%. Why? Is this due to a different duration of pheromone arrest?CDC20 into α-factor for 3 hours, as we did in We understand how these differences led to confusion. We believe that time and fluorophores are the key factors leading to this difference. We have acknowledged and discussed the differences between these datasets in the revised text and added an additional time point that illustrates this to The authors should give this duration for 5A and B either in the main text or in the figure legend. Also, in the main text, the description of the data indoes not mention that the cells were arrested with pheromone, which makes the reading confusing.We apologize for this oversight. We have revised the text and legends to make it clear how cells were grown and/or arrested. The Materials and methods list the arrest time, temperature, media and amounts of pheromone used in all of our experiments.Furthermore in 5A and B, while Cnm67 is thought to link Nud1 to the SPB, it is absent from the satellite in more than 60% of the cells where Nud1 is present around 70% of the time. What do the authors make of that?We have two lines of thinking, which we added to the Discussion.CNM67 and the fact that cnm67∆ cells are viable, it is reasonable to consider the idea that Nud1 may associate with the SPB in a Cnm67-independent manner. This likely involves the half-bridge where Spc72 binds, but to our knowledge, no physical evidence places Nud1 here except its ability to associate with Spc72. Nud1 has no known function in SPB duplication, but instead serves as a signaling platform for the mitotic exit network.Based on data from Adams and Kilmartin  showing that Nud1-GFP localizes to the SPB in some cells lacking However, we also fully acknowledge that detection of Cnm67 at the satellite is challenging and varied considerably from strain to strain. Thus, it is entirely possible that we are underestimating the amount of Cnm67 at the satellite. The same difficulties were also previously reported using anti-Cnm67 antibodies and immuno-EM .There are many examples of similar issues.We have carefully scrutinized our text and made changes in the revised version to ensure that the text accurately reflects our data and the published literature.In, the vast majority of non-arrested cells show a single SPC42 focus associated with a single Sfi1 focus. Does that mean that ∼90% of these cells are in metaphase? In the mps1-1 arrested cells, cells show many more configurations. Does it mean that the cells arrest at different points in the duplication cycle?mps1-1 cells. We have revised the text and figure legend to ensure this is clear.We apologize for our lack of clarity in the figure legend and text. We have included only large budded cells that have not gone into anaphase in our analysis of wild-type and the mutant at the permissive temperature, since, as the reviewers indicate, these would be the most relevant/comparable to the arrested In, the authors suggest the existence of a 17° kink in the bridge. Along which axis is this kink? Could this be simply due to the fact that the bridge is on the membrane while Spc42 is inside the pore?Bending occurs in the y-axis, which we have defined as the pole axis that is perpendicular to the nuclear membrane. Based on the distance between Spc42 or Sfi1 foci, and assuming that the G1 haploid nucleus is approximately 1µm in diameter, the natural curvature in the membrane would result in a ∼7° bend in the elongated bridge. Since the 17° bend we observed is greater, we believe there is additional bending. We have added the predicted position of the Sfi1 C-terminus to our plot of bridge coordinates in