Assessing the capabilities of a 4kx4k CCD camera for electron cryo-microscopy at 300kV

CCD cameras have numerous advantages over photographic film for detecting electrons; however the point spread function of these cameras has not been sufficient for single particle data collection to subnanometer resolution with 300kV microscopes. We have adopted spectral signal to noise ratio (SNR) as a parameter for assessing detector quality for single particle imaging. The robustness of this parameter is confirmed under a variety of experimental conditions. Using this parameter, we demonstrate that the SNR of images of either amorphous carbon film or ice embedded virus particles collected on a new commercially available 4kx4k CCD camera are slightly better than photographic film at low spatial frequency (<1/5 Nyquist frequency), and as good as photographic film out to half of the Nyquist frequency. In addition it is slightly easier to visualize ice embedded particles on this CCD camera than on photographic film. Based on this analysis it is realistic to collect images containing subnanometer resolution data (6-9A) using this CCD camera at an effective magnification of approximately 112000x on a 300kV electron microscope.     

TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data

Background

Time series single-cell RNA sequencing (scRNA-seq) data are emerging. However, the analysis of time series scRNA-seq data could be compromised by 1) distortion created by assorted sources of data collection and generation across time samples and 2) inheritance of cell-to-cell variations by stochastic dynamic patterns of gene expression. This calls for the development of an algorithm able to visualize time series scRNA-seq data in order to reveal latent structures and uncover dynamic transition processes.

Results

In this study, we propose an algorithm, termed time series elastic embedding (TSEE), by incorporating experimental temporal information into the elastic embedding (EE) method, in order to visualize time series scRNA-seq data. TSEE extends the EE algorithm by penalizing the proximal placement of latent points that correspond to data points otherwise separated by experimental time intervals. TSEE is herein used to visualize time series scRNA-seq datasets of embryonic developmental processed in human and zebrafish. We demonstrate that TSEE outperforms existing methods (e.g. PCA, tSNE and EE) in preserving local and global structures as well as enhancing the temporal resolution of samples. Meanwhile, TSEE reveals the dynamic oscillation patterns of gene expression waves during zebrafish embryogenesis.

Conclusions

TSEE can efficiently visualize time series scRNA-seq data by diluting the distortions of assorted sources of data variation across time stages and achieve the temporal resolution enhancement by preserving temporal order and structure. TSEE uncovers the subtle dynamic structures of gene expression patterns, facilitating further downstream dynamic modeling and analysis of gene expression processes. The computational framework of TSEE is generalizable by allowing the incorporation of other sources of information.

     

Rapid Simulation of Unprocessed DEER Decay Data for Protein Fold Prediction

Despite advances in sampling and scoring strategies, Monte Carlo modeling methods still struggle to accurately predict de novo the structures of large proteins, membrane proteins, or proteins of complex topologies. Previous approaches have addressed these shortcomings by leveraging sparse distance data gathered using site-directed spin labeling and electron paramagnetic resonance spectroscopy to improve protein structure prediction and refinement outcomes. However, existing computational implementations entail compromises between coarse-grained models of the spin label that lower the resolution and explicit models that lead to resource-intense simulations. These methods are further limited by their reliance on distance distributions, which are calculated from a primary refocused echo decay signal and contain uncertainties that may require manual refinement. Here, we addressed these challenges by developing RosettaDEER, a scoring method within the Rosetta software suite capable of simulating double electron-electron resonance spectroscopy decay traces and distance distributions between spin labels fast enough to fold proteins de novo. We demonstrate that the accuracy of resulting distance distributions match or exceed those generated by more computationally intensive methods. Moreover, decay traces generated from these distributions recapitulate intermolecular background coupling parameters even when the time window of data collection is truncated. As a result, RosettaDEER can discriminate between poorly folded and native-like models by using decay traces that cannot be accurately converted into distance distributions using regularized fitting approaches. Finally, using two challenging test cases, we demonstrate that RosettaDEER leverages these experimental data for protein fold prediction more effectively than previous methods. These benchmarking results confirm that RosettaDEER can effectively leverage sparse experimental data for a wide array of modeling applications built into the Rosetta software suite.     

isoSTED microscopy with water-immersion lenses and background reduction

Fluorescence microscopy is an excellent tool to gain knowledge on cellular structures and biochemical processes. Stimulated emission depletion (STED) microscopy provides a resolution in the range of a few 10 nm at relatively fast data acquisition. As cellular structures can be oriented in any direction, it is of great benefit if the microscope exhibits an isotropic resolution. Here, we present an isoSTED microscope that utilizes water-immersion objective lenses and enables imaging of cellular structures with an isotropic resolution of better than 60 nm in living samples at room temperature and without CO₂ supply or another pH control. This corresponds to a reduction of the focal volume by far more than two orders of magnitude as compared to confocal microscopy. The imaging speed is in the range of 0.8 s/μm³. Because fluorescence signal can only be detected from a diffraction-limited volume, a background signal is inevitably observed at resolutions well beyond the diffraction limit. Therefore, we additionally present a method that allows us to identify this unspecific background signal and to remove it from the image.     

Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization

Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on many factors such as the number of coils and the methodology used to combine multichannel MRI signals. Indeed, the two prevailing methods for multichannel signal combination lead to noise patterns better described by Rician and noncentral Chi distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in human brain data.     

Signal detection in high-resolution mass spectrometry data

Mass spectrometry data from high-resolution time-of-flight instruments often contain a vast number of noninformative background-ion peaks whose signal is similar to that of peptide peaks. Consequently, seeking peptide signal in these spectra based on a signal-to-noise ratio will remove signal peaks as well as noise. This work characterizes the background as a precursor to seeking peptide-related features. Robust-regression methods are used to estimate distributions for null (background) peak intensities and locations. Defining signal peaks as outliers with respect to these distributions leads to more precision in detecting the isotopic envelope of peaks from low-abundance peptides in high-resolution spectra.     

Gas phase activity of anti-FITC antibodies immobilized on a surface acoustic wave resonator device

In this paper we present the results of a series of experiments on the activity of antibodies in a vapor phase sensor. For these experiments the sensor component was a ST-Quartz resonator with a center frequency of approximately 250 MHz. Anti-FITC antibodies were attached to the electrodes on the device surface via a protein-A crosslinker. Surface acoustic wave (SAW) resonator devices with various coatings were mounted in TO-8 packages, inserted into our sensor head module and subjected to various fluorescent analyte gases. Numerous controls were performed including the use of coated and uncoated devices along with devices coated with antibodies which were not specific for the target analyte. The SAW immunosensor response was monitored and a baseline frequency shift was observed when the analyte being presented was the antigen for the immobilized antibody. To provide an independent measure of antibody/antigen binding, the devices were removed from the sensor head, washed with a buffer solution to remove any unbound analyte, and then inspected using a confocal laser scanning microscope (CLSM). Since all the analytes being used in these experiments were fluorescent this afforded us the opportunity to visualize the attachment of the analyte to the antibody film. Given the high resolution of the CLSM, we were able to identify the location of the attachment of the fluorescent analytes relative to the 1.5 microm wide electrodes of the SAW device. We believe that these experiments demonstrate that we have achieved real time molecular recognition of these small molecules in the vapor phase.     

Enhanced background rejection in thick tissue with differential-aberration two-photon microscopy

When a two-photon excited fluorescence (TPEF) microscope is used to image deep inside tissue, out-of-focus background can arise from both ballistic and nonballistic excitation. We propose a solution to largely reject TPEF background in thick tissue. Our technique is based on differential-aberration imaging with a deformable mirror. By introducing extraneous aberrations in the excitation beam path, we preferentially quench in-focus TPEF signal while leaving out-of-focus TPEF background largely unchanged. A simple subtraction of an aberrated, from an unaberrated, TPEF image then removes background while preserving signal. Our differential aberration (DA) technique is simple, robust, and can readily be implemented with standard TPEF microscopes with essentially no loss in temporal resolution when using a line-by-line DA protocol. We analyze the performance of various induced aberration patterns, and demonstrate the effectiveness of DA-TPEF by imaging GFP-labeled sensory neurons in a mouse olfactory bulb and CA1 pyramidal cells in a hippocampus slice.     

AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays

Microarray-based analysis of single nucleotide polymorphisms (SNPs) has many applications in large-scale genetic studies. To minimize the influence of experimental variation, microarray data usually need to be processed in different aspects including background subtraction, normalization and low-signal filtering before genotype determination. Although many algorithms are sophisticated for these purposes, biases are still present. In the present paper, new algorithms for SNP microarray data analysis and the software, AccuTyping, developed based on these algorithms are described. The algorithms take advantage of a large number of SNPs included in each assay, and the fact that the top and bottom 20% of SNPs can be safely treated as homozygous after sorting based on their ratios between the signal intensities. These SNPs are then used as controls for color channel normalization and background subtraction. Genotype calls are made based on the logarithms of signal intensity ratios using two cutoff values, which were determined after training the program with a dataset of approximately 160,000 genotypes and validated by non-microarray methods. AccuTyping was used to determine >300,000 genotypes of DNA and sperm samples. The accuracy was shown to be >99%. AccuTyping can be downloaded from http://www2.umdnj.edu/lilabweb/publications/AccuTyping.html.     

A comparison of background correction methods for two-colour microarrays

MOTIVATION: Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments. RESULTS: Using data where some independent truth in gene expression is known, eight different background correction alternatives are compared, in terms of precision and bias of the resulting gene expression measures, and in terms of their ability to detect differentially expressed genes as judged by two popular algorithms, SAM and limma eBayes. A new background processing method (normexp) is introduced which is based on a convolution model. The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates. Methods which stabilize the variances of the log-ratios along the intensity range perform the best. The normexp+offset method is found to give the lowest false discovery rate overall, followed by morph and vsn. Like vsn, normexp is applicable to most types of two-colour microarray data. AVAILABILITY: The background correction methods compared in this article are available in the R package limma (Smyth, 2005) from http://www.bioconductor.org. SUPPLEMENTARY INFORMATION: Supplementary data are available from http://bioinf.wehi.edu.au/resources/webReferences.html.     

Automatic quantitative evaluation of autoradiographic band films by computerized image analysis

The present paper describes a new image processing method for automatic quantitative analysis of autoradiographic band films. It was developed in a specific image analysis environment (IBAS 2.0), but the algorithms and methods can be utilized elsewhere. The program is easy to use and presents some particularly useful features for evaluation of autoradiographic band films, such as the choice of whole film or single lane background determination; the possibility of evaluating bands with film scratch artifacts and the quantification in absolute terms or relative to reference values. The method was tested by comparison with laser-scanner densitometric quantifications of the same autoradiograms. The results show the full compatibility of the two methods and demonstrate the reliability and sensitivity of image analysis. The method can be used not only to evaluate autoradiographic band films, but to analyze any type of signal bands on other materials (e.g. electrophoresis gel, chromatographic paper, etc.).     

NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data

A key benefit of long-read nanopore sequencing technology is the ability to detect modified DNA bases, such as 5-methylcytosine. The lack of R/Bioconductor tools for the effective visualization of nanopore methylation profiles between samples from different experimental groups led us to develop the NanoMethViz R package. Our software can handle methylation output generated from a range of different methylation callers and manages large datasets using a compressed data format. To fully explore the methylation patterns in a dataset, NanoMethViz allows plotting of data at various resolutions. At the sample-level, we use dimensionality reduction to look at the relationships between methylation profiles in an unsupervised way. We visualize methylation profiles of classes of features such as genes or CpG islands by scaling them to relative positions and aggregating their profiles. At the finest resolution, we visualize methylation patterns across individual reads along the genome using the spaghetti plot and heatmaps, allowing users to explore particular genes or genomic regions of interest. In summary, our software makes the handling of methylation signal more convenient, expands upon the visualization options for nanopore data and works seamlessly with existing methylation analysis tools available in the Bioconductor project. Our software is available at https://bioconductor.org/packages/NanoMethViz.     

Employing site-specific recombination for conditional genetic analysis in Sinorhizobium meliloti

The ability to remove a genetic function from an organism with good temporal resolution is crucial for characterizing essential genes or genes that act in complex developmental programs. The rhizobium-legume symbiosis involves an elaborate two-organism interaction requiring multiple levels of signal exchange. As an important step toward probing rhizobium genetic functions with temporal resolution, we present the development of a conditional gene deletion system in Sinorhizobium meliloti that employs Cre/loxP site-specific recombination. This system enables chemically inducible and irreversible gene deletion or gene upregulation. Recombinase-mediated excision events can be positively or negatively selected or monitored by a colorimetric assay. The system may be adaptable to various bacterial species, in which recombinase activity may be placed under the control of diverse user-defined promoters. This system also shows promise for uses in promoter trapping and biosensing applications.     

The protein-proteinase complex of the barley grown on a different agronomic background using regulatory preparations

An integrated effect of agronomic background (the degree of soil fertility and the dose of applied fertilizers) and the regulatory preparations of phenolic and terpene natures on the protein-proteinase complex of brewer's barley grown under conditions of the nonchernozem zone was studied. Gel chromatography methods were used to demonstrate that the treatment with preparations of regulatory substances increased the degree and depth of storage protein hydrolysis during malting and provided the necessary degree of hydrolysis of the endosperm proteins, an important characteristic of the ready malt. Neutral proteinases hydrolyzed proteins forming intermediate products with various molecular weights, thereby indicating their sufficiently narrow specificity. Acid proteinases generated a large amount of low-molecular-weight products, confirming a wide specificity of their action and their ability to hydrolyze the peptide bonds formed by various amino acid residues.     

乳杆菌在模拟肉制品条件下结合苯并芘的效果

【背景】乳杆菌对众多致癌物具有吸附作用,但关于乳杆菌结合吸附苯并芘特性的研究并不多。【目的】探讨戊糖乳杆菌(Lactobacillus pentosus) ML32和植物乳杆菌(Lactobacillus plantarum)121对加工肉制品中苯并芘的吸附能力与吸附机制。【方法】基于HPLC检测菌体对不同模拟加工处理方式肉品中的苯并芘的吸附率。【结果】植物乳杆菌121和戊糖乳杆菌ML32对模拟油炸、烟熏或烧烤方式处理肉中苯并芘的吸附率均在30%以上。菌株121对直接烟熏肉中的苯并芘吸附率为41.21%,直接油炸肉中吸附率为38.71%,直接烧烤肉中吸附率为37.51%;菌株ML32对间接烟熏肉中的苯并芘吸附率为40.02%,间接烧烤肉中吸附率为38.01%。植物乳杆菌121适合于去除高温长时间加工肉中的苯并芘,戊糖乳杆菌ML32则相反。另外,乳杆菌细胞壁中的肽聚糖或许在吸附过程中发挥了主要作用。【结论】两株乳杆菌121和ML32具有吸附某些加工肉制品中苯并芘的效果,或许可以作为一种方法用于消除某些肉制品中因苯并芘过量带来的风险。     

NetMHCIIpan-2.0 - Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure

Background

Macrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively.

Results

We show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest.

Conclusions

The computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without compromise in image quality or information loss in associated spectra. These results motivate further use of label free microscopy techniques in real-time imaging of live immune cells.     

The protein-proteinase complex of the barley grown on a different agronomic background using regulatory preparations

An integrated effect of agronomic background (the degree of soil fertility and the dose of applied fertilizers) and the regulatory preparations of phenolic and terpene natures on the protein-proteinase complex of brewer’s barley grown under conditions of the nonchernozem zone was studied. Gel chromatography methods were used to demonstrate that the treatment with preparations of regulatory substances increased the degree and depth of storage protein hydrolysis during malting and provided the necessary degree of hydrolysis of the endosperm proteins, an important characteristic of the ready malt. Neutral proteinases hydrolyzed proteins forming intermediate products with various molecular weights, thereby indicating their sufficiently narrow specificity. Acid proteinases generated a large amount of low-molecular-weight products, confirming a wide specificity of their action and their ability to hydrolyze the peptide bonds formed by various amino acid residues.