Project description:BackgroundThe analysis of co-localized protein expression in a tissue section is often conducted with immunofluorescence histochemical staining which is typically visualized in localized regions. On the other hand, chromogenic immunohistochemical staining, in general, is not suitable for the detection of protein co-localization. Here, we developed a new protocol, based on chromogenic immunohistochemical stain, for system-wide detection of protein co-localization and differential expression.Methodology/principal findingsIn combination with a removable chromogenic stain, an efficient antibody stripping method was developed to enable sequential immunostaining with different primary antibodies regardless of antibody's host species. Sections were scanned after each staining, and the images were superimposed together for the detection of protein co-localization and differential expression. As a proof of principle, differential expression and co-localization of glutamic acid decarboxylase67 (GAD67) and parvalbumin proteins was examined in mouse cortex.Conclusions/significanceAll parvalbumin-containing neurons express GAD67 protein, and GAD67-positive neurons that do not express parvalbumin were readily visualized from thousands of other neurons across mouse cortex. The method provided a global view of protein co-localization as well as differential expression across an entire tissue section. Repeated use of the same section could combine assessments of co-localization and differential expression of multiple proteins.
Project description:Genomic studies have been a major approach to elucidating disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the nature of dependence among genetic markers. This situation becomes even more complicated when multi-omics data are available. To integrate the data from different platforms, various integrative analyses have been adopted, ranging from the direct union or intersection operation on sets derived from different single-platform analysis to complex hierarchical multi-level models. The former ignores the biological relationship between molecules while the latter can be hard to interpret. We propose in this study an integrative approach that combines both single nucleotide variants (SNVs) and copy number variations (CNVs) in the same genomic unit to co-localize the concurrent effect and to deal with the sparsity due to rare variants. This approach is illustrated with simulation studies to evaluate its performance and is applied to low-density lipoprotein cholesterol and triglyceride measurements from Taiwan Biobank. The results show that the proposed method can more effectively detect the collective effect from both SNVs and CNVs compared to traditional methods. For the biobank analysis, the identified genetic regions including the gene VNN2 could be novel and deserve further investigation.
Project description:This article describes data related to a research article titled "Comprehensive analysis of the dynamic structure of nuclear localization signals" by Yamagishi et al. [1]. In this article, we provide the data covering wider range of the mammalian NLSs in UniProt (Universal Protein Resource) [2] regardless of their conformations. To be more specific as follows: We have extracted all NLSs which are clearly indicated as "NLS" with evidence type (a code from the Evidence Codes Ontology) [3] in UniProt. A total of 1364 NLSs in 1186 proteins were extracted from UniProt. The number of NLSs found in each protein (UniProt ID), the sequence length of NLSs and their distribution are shown.
Project description:Spatial data from high-resolution images abound in many scientific disciplines. For example, single-molecule localization microscopy, such as stochastic optical reconstruction microscopy, provides super-resolution images to help scientists investigate co-localization of proteins and hence their interactions inside cells, which are key events in living cells. However, there are few accurate methods for analyzing co-localization in super-resolution images. The current methods and software are prone to produce false-positive errors and are restricted to only 2-dimensional images. In this paper, we develop a novel statistical method to effectively address the problems of unbiased and robust quantification and comparison of protein co-localization for multiple 2- and 3-dimensional image datasets. This method significantly improves the analysis of protein co-localization using super-resolution image data, as shown by its excellent performance in simulation studies and an analysis of co-localization of protein light chain 3 and lysosomal-associated membrane protein 1 in cell autophagy. Moreover, this method is directly applicable to co-localization analyses in other disciplines, such as diagnostic imaging, epidemiology, environmental science, and ecology.
Project description:This paper undertakes a systematic assessment of the extent to which factor analysis the correct number of latent dimensions (factors) when applied to ordered-categorical survey items (so-called Likert items). We simulate 2400 data sets of uni-dimensional Likert items that vary systematically over a range of conditions such as the underlying population distribution, the number of items, the level of random error, and characteristics of items and item-sets. Each of these datasets is factor analysed in a variety of ways that are frequently used in the extant literature, or that are recommended in current methodological texts. These include exploratory factor retention heuristics such as Kaiser's criterion, Parallel Analysis and a non-graphical scree test, and (for exploratory and confirmatory analyses) evaluations of model fit. These analyses are conducted on the basis of Pearson and polychoric correlations. We find that, irrespective of the particular mode of analysis, factor analysis applied to ordered-categorical survey data very often leads to over-dimensionalisation. The magnitude of this risk depends on the specific way in which factor analysis is conducted, the number of items, the properties of the set of items, and the underlying population distribution. The paper concludes with a discussion of the consequences of over-dimensionalisation, and a brief mention of alternative modes of analysis that are much less prone to such problems.
Project description:Enamelin and amelogenin are vital proteins in enamel formation. The cooperative function of these two proteins controls crystal nucleation and morphology in vitro. We quantitatively analyzed the co-localization between enamelin and amelogenin by confocal microscopy and using two antibodies, one raised against a sequence in the porcine 32 kDa enamelin region and the other raised against full-length recombinant mouse amelogenin. We further investigated the interaction of the porcine 32 kDa enamelin and recombinant amelogenin using immuno-gold labeling. This study reports the quantitative co-localization results for postnatal days 1-8 mandibular mouse molars. We show that amelogenin and enamelin are secreted into the extracellular matrix on the cuspal slopes of the molars at day 1 and that secretion continues to at least day 8. Quantitative co-localization analysis (QCA) was performed in several different configurations using large (45 μm height, 33 μm width) and small (7 μm diameter) regions of interest to elucidate any patterns. Co-localization patterns in day 8 samples revealed that enamelin and amelogenin co-localize near the secretory face of the ameloblasts and appear to be secreted approximately in a 1:1 ratio. The degree of co-localization decreases as the enamel matures, both along the secretory face of ameloblasts and throughout the entire thickness of the enamel. Immuno-reactivity against enamelin is concentrated along the secretory face of ameloblasts, supporting the theory that this protein together with amelogenin is intimately involved in mineral induction at the beginning of enamel formation.
Project description:Quantifying the extent to which points are clustered in single-molecule localization microscopy data is vital to understanding the spatial relationships between molecules in the underlying sample. Many existing computational approaches are limited in their ability to process large-scale data sets, to deal effectively with sample heterogeneity, or require subjective user-defined analysis parameters. Here, we develop a supervised machine-learning approach to cluster analysis which is fast and accurate. Trained on a variety of simulated clustered data, the neural network can classify millions of points from a typical single-molecule localization microscopy data set, with the potential to include additional classifiers to describe different subtypes of clusters. The output can be further refined for the measurement of cluster area, shape, and point-density. We demonstrate this approach on simulated data and experimental data of the kinase Csk and the adaptor PAG in primary human T cell immunological synapses.
Project description:Single molecule localization microscopy (SMLM) methods produce data in the form of a spatial point pattern (SPP) of all localized emitters. Whilst numerous tools exist to quantify molecular clustering in SPP data, the analysis of fibrous structures has remained understudied. Taking the SMLM localization coordinates as input, we present an algorithm capable of tracing fibrous structures in data generated by SMLM. Based upon a density parameter tracing routine, the algorithm outputs several fibre descriptors, such as number of fibres, length of fibres, area of enclosed regions and locations and angles of fibre branch points. The method is validated in a variety of simulated conditions and experimental data acquired using the image reconstruction by integrating exchangeable single-molecule localization (IRIS) technique. For this, the nanoscale architecture of F-actin at the T cell immunological synapse in both untreated and pharmacologically treated cells, designed to perturb actin structure, was analysed.
Project description:Since the first experimental observation of all-optical switching phenomena, intensive research has been focused on finding suitable magnetic systems that can be integrated as storage elements within spintronic devices and whose magnetization can be controlled through ultra-short single laser pulses. We report here atomistic spin simulations of all-optical switching in multilayered structures alternating n monolayers of Tb and m monolayers of Co. By using a two temperature model, we numerically calculate the thermal variation of the magnetization of each sublattice as well as the magnetization dynamics of [[Formula: see text]/[Formula: see text]] multilayers upon incidence of a single laser pulse. In particular, the condition to observe thermally-induced magnetization switching is investigated upon varying systematically both the composition of the sample (n,m) and the laser fluence. The samples with one monolayer of Tb as [[Formula: see text]/[Formula: see text]] and [[Formula: see text]/[Formula: see text]] are showing thermally induced magnetization switching above a fluence threshold. The reversal mechanism is mediated by the residual magnetization of the Tb lattice while the Co is fully demagnetized in agreement with the models developed for ferrimagnetic alloys. The switching is however not fully deterministic but the error rate can be tuned by the damping parameter. Increasing the number of monolayers the switching becomes completely stochastic. The intermixing at the Tb/Co interfaces appears to be a promising way to reduce the stochasticity. These results predict for the first time the possibility of TIMS in [Tb/Co] multilayers and suggest the occurrence of sub-picosecond magnetization reversal using single laser pulses.