Multivariate denoising methods combining wavelets and principal component analysis for mass spectrometry data

The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years, there has been a growing interest in using mass spectrometry for the detection of such biomarkers. The MS signal resulting from MALDI‐TOF measurements is contaminated by different sources of technical variations that can be removed by a prior pre‐processing step. In particular, denoising makes it possible to remove the random noise contained in the signal. Wavelet methodology associated with thresholding is usually used for this purpose. In this study, we adapted two multivariate denoising methods that combine wavelets and PCA to MS data. The objective was to obtain better denoising of the data so as to extract the meaningful proteomic biological information from the raw spectra and reach meaningful clinical conclusions. The proposed methods were evaluated and compared with the classical soft thresholding denoising method using both real and simulated data sets. It was shown that taking into account common structures of the signals by adding a dimension reduction step on approximation coefficients through PCA provided more effective denoising when combined with soft thresholding on detail coefficients.     

Electron microscopy of helical filaments: rediscovering buried treasures in negative stain

Although negative stain electron microscopy is a wonderfully simple way of directly visualizing protein complexes and other biological macromolecules, the images are not really comparable to those of objects seen in everyday life. The failure to appreciate this has recently led to an incorrect interpretation of RecA‐family filament structures.     

Novel mass spectrometry imaging software assisting labeled normalization and quantitation of drugs and neuropeptides directly in tissue sections

MALDI MS imaging has been extensively used to produce qualitative distribution maps of proteins, peptides, lipids, small molecule pharmaceuticals and their metabolites directly in biological tissue sections. There is growing demand to quantify the amount of target compounds in the tissue sections of different organs. We present a novel MS imaging software including protocol for the quantitation of drugs, and for the first time, an endogenous neuropeptide directly in tissue sections. After selecting regions of interest on the tissue section, data is read and processed by the software using several available methods for baseline corrections, subtractions, denoising, smoothing, recalibration and normalization. The concentrations of in vivo administered drugs or endogenous compounds are then determined semi-automatically using either external standard curves, or by using labeled compounds, i.e., isotope labeled analogs as standards. As model systems, we have quantified the distribution of imipramine and tiotropium in the brain and lung of dosed rats. Substance P was quantified in different mouse brain structures, which correlated well with previously reported peptide levels. Our approach facilitates quantitative data processing and labeled standards provide better reproducibility and may be considered as an efficient tool to quantify drugs and endogenous compounds in tissue regions of interest.     

A ferritin-based label for cellular electron cryotomography

Electron cryotomography provides nanometer resolution structural detail of thin biological specimens in a near-native state. Currently, its application is limited by the lack of a specific label for the identification of molecules. Our aim was to develop such a label, analogous to GFP used in fluorescence microscopy. Here, we demonstrate the use of Escherichia coli ferritin FtnA protein as a clonable label for electron cryotomography. Overproduced ferritin is visible in E.?coli cells using cryotomography and fusing this label to a short membrane targeting sequence correctly directs the ferritin fusion to the membrane. Using two proteins with known subcellular localization patterns with this ferritin tag, also including GFP, we obtained essentially the same labeling patterns using electron cryotomography as compared with fluorescence light microscopy. Hence, the ferritin label localizes efficiently and faithfully and it will be a valuable tool for the unambiguous identification of molecules in cellular electron cryotomograms.     

Evaluation of denoising algorithms for biological electron tomography

Tomograms of biological specimens derived using transmission electron microscopy can be intrinsically noisy due to the use of low electron doses, the presence of a "missing wedge" in most data collection schemes, and inaccuracies arising during 3D volume reconstruction. Before tomograms can be interpreted reliably, for example, by 3D segmentation, it is essential that the data be suitably denoised using procedures that can be individually optimized for specific data sets. Here, we implement a systematic procedure to compare various nonlinear denoising techniques on tomograms recorded at room temperature and at cryogenic temperatures, and establish quantitative criteria to select a denoising approach that is most relevant for a given tomogram. We demonstrate that using an appropriate denoising algorithm facilitates robust segmentation of tomograms of HIV-infected macrophages and Bdellovibrio bacteria obtained from specimens at room and cryogenic temperatures, respectively. We validate this strategy of automated segmentation of optimally denoised tomograms by comparing its performance with manual extraction of key features from the same tomograms.     

Fast denoising and lossless spectrum extraction in stimulated Raman scattering microscopy

Stimulated Raman scattering (SRS) microscopy is a nonlinear optical imaging method for visualizing chemical content based on molecular vibrational bonds. However, the imaging speed and sensitivity are currently limited by the noise of the light beam probing the Raman process. In this paper, we present a fast non-average denoising and high-precision Raman shift extraction method, based on a self-reinforcing signal-to-noise ratio (SNR) enhancement algorithm, for SRS spectroscopy and microscopy. We compare the results of this method with the filtering methods and the reported experimental methods to demonstrate its high efficiency and high precision in spectral denoising, Raman peak extraction and image quality improvement. We demonstrate a maximum SNR enhancement of 10.3 dB in fixed tissue imaging and 11.9 dB in vivo imaging. This method reduces the cost and complexity of the SRS system and allows for high-quality SRS imaging without use of special laser, complicated system design and Raman tags.     

A novel wavelet-based thresholding method for the pre-processing of mass spectrometry data that accounts for heterogeneous noise

In recent years there has been an increased interest in using protein mass spectroscopy to discriminate diseased from healthy individuals with the aim of discovering molecular markers for disease. A crucial step before any statistical analysis is the pre-processing of the mass spectrometry data. Statistical results are typically strongly affected by the specific pre-processing techniques used. One important pre-processing step is the removal of chemical and instrumental noise from the mass spectra. Wavelet denoising techniques are a standard method for denoising. Existing techniques, however, do not accommodate errors that vary across the mass spectrum, but instead assume a homogeneous error structure. In this paper we propose a novel wavelet denoising approach that deals with heterogeneous errors by incorporating a variance change point detection method in the thresholding procedure. We study our method on real and simulated mass spectrometry data and show that it improves on performances of peak detection methods.     

The Cell Cycle Ontology: an application ontology for the representation and integrated analysis of the cell cycle process

The Cell Cycle Ontology ( is an application ontology that automatically captures and integrates detailed knowledge on the cell cycle process. Cell Cycle Ontology is enabled by semantic web technologies, and is accessible via the web for browsing, visualizing, advanced querying, and computational reasoning. Cell Cycle Ontology facilitates a detailed analysis of cell cycle-related molecular network components. Through querying and automated reasoning, it may provide new hypotheses to help steer a systems biology approach to biological network building.     

Iterative reconstruction of cryo-electron tomograms using nonuniform fast Fourier transforms

Algorithms for three-dimensional (3D) reconstruction of objects based on their projections are essential in various biological and medical imaging modalities. In cryo-electron tomography (CET) a major challenge for reconstruction is the limited range of projection angles, which manifests itself as a “missing wedge” of data in Fourier space making the reconstruction problem ill-posed. Here, we apply an iterative reconstruction method that makes use of nonuniform fast Fourier transform (NUFFT) to the reconstruction of cryo-electron tomograms. According to several measures the reconstructions are superior to those obtained using conventional methods, most notably weighted backprojection. Most importantly, we show that it is possible to fill in partially the unsampled region in Fourier space with meaningful information without making assumptions about the data or applying prior knowledge. As a consequence, particles of known structure can be localized with higher confidence in cryotomograms and subtomogram averaging yields higher resolution densities.     

Inferring biological structures from super-resolution single molecule images using generative models

Localization-based super resolution imaging is presently limited by sampling requirements for dynamic measurements of biological structures. Generating an image requires serial acquisition of individual molecular positions at sufficient density to define a biological structure, increasing the acquisition time. Efficient analysis of biological structures from sparse localization data could substantially improve the dynamic imaging capabilities of these methods. Using a feature extraction technique called the Hough Transform simple biological structures are identified from both simulated and real localization data. We demonstrate that these generative models can efficiently infer biological structures in the data from far fewer localizations than are required for complete spatial sampling. Analysis at partial data densities revealed efficient recovery of clathrin vesicle size distributions and microtubule orientation angles with as little as 10% of the localization data. This approach significantly increases the temporal resolution for dynamic imaging and provides quantitatively useful biological information.     

A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.     

Time-resolved studies of metalloproteins using X-ray free electron laser radiation at SACLA

BackgroundThe invention of the X-ray free-electron laser (XFEL) has provided unprecedented new opportunities for structural biology. The advantage of XFEL is an intense pulse of X-rays and a very short pulse duration (<10 fs) promising a damage-free and time-resolved crystallography approach.Scope of reviewRecent time-resolved crystallographic analyses in XFEL facility SACLA are reviewed. Specifically, metalloproteins involved in the essential reactions of bioenergy conversion including photosystem II, cytochrome c oxidase and nitric oxide reductase are described.Major conclusionsXFEL with pump-probe techniques successfully visualized the process of the reaction and the dynamics of a protein. Since the active center of metalloproteins is very sensitive to the X-ray radiation, damage-free structures obtained by XFEL are essential to draw mechanistic conclusions. Methods and tools for sample delivery and reaction initiation are key for successful measurement of the time-resolved data.General significanceXFEL is at the center of approaches to gain insight into complex mechanism of structural dynamics and the reactions catalyzed by biological macromolecules. Further development has been carried out to expand the application of time-resolved X-ray crystallography. This article is part of a Special Issue entitled Novel measurement techniques for visualizing ‘live’ protein molecules.     

PseudoViewer: automatic visualization of RNA pseudoknots

MOTIVATION: Several algorithms have been developed for drawing RNA secondary structures, however none of these can be used to draw RNA pseudoknot structures. In the sense of graph theory, a drawing of RNA secondary structures is a tree, whereas a drawing of RNA pseudoknots is a graph with inner cycles within a pseudoknot as well as possible outer cycles formed between a pseudoknot and other structural elements. Thus, RNA pseudoknots are more difficult to visualize than RNA secondary structures. Since no automatic method for drawing RNA pseudoknots exists, visualizing RNA pseudoknots relies on significant amount of manual work and does not yield satisfactory results. The task of visualizing RNA pseudoknots by hand becomes more challenging as the size and complexity of the RNA pseudoknots increase. RESULTS: We have developed a new representation and an algorithm for drawing H-type pseudoknots with RNA secondary structures. Compared to existing representations of H-type pseudoknots, the new representation ensures uniform and clear drawings with no edge crossing for any H-type pseudoknots. To the best of our knowledge, this is the first algorithm for automatically drawing RNA pseudoknots with RNA secondary structures. The algorithm has been implemented in a Java program, which can be executed on any computing system. Experimental results demonstrate that the algorithm generates an aesthetically pleasing drawing of all H-type pseudoknots. The results have also shown that the drawing has high readability, enabling the user to quickly and easily recognize the whole RNA structure as well as the pseudoknots themselves.     

Visualizing normal and defective bone development in zebrafish embryos using the fluorescent chromophore calcein.

Zebrafish have recently become a model of choice among developmental biologists. This unique model enables both modern molecular and genetic studies to be carried out to identify genes involved in a wide variety of developmental processes. The success of the genetic approach depends largely on the application of an easy and effective screening method to identify interesting mutants. In order to develop a method for visualizing skeletal structures in zebrafish embryos that would be suitable for screening skeletal mutants, we investigated the use of the fluorescent chromophore calcein, which binds specifically to calcified skeletal structures. By using this method, we followed the development of the skeletal structures in zebrafish embryos from day 1 to day 21 postfertilization, and analyzed the effect of bone morphogenetic protein-2 (BMP2) on axial skeleton development. We found the development of the calcified skeletal structure to appear in a progressive fashion from head to tail. Calcified structures in the head (i.e., the jaw) developed first, which were then followed by the axial skeleton in the trunk. Interesting to note was that there appeared to be two domains in the calcification of vertebrae within the axial skeleton. The first three vertebrae were in the first domain; the rest being in the second domain. Compared with Alcian blue staining, we found that calcein staining indeed labels calcified skeletal structures, and, moreover, it is a more sensitive and inclusive method for visualizing skeletal structures. To determine whether calcein staining could also be used to detect abnormal bone development, we ectopically expressed BMP2 in zebrafish notochord cells. We demonstrated that ectopic expression of BMP2 in notochord cells inhibited the development of the axial skeleton. Together, these results clearly demonstrated the sensitivity of calcein staining for visualizing bone structures in developing zebrafish embryos and its effectiveness for screening for mutants that have bone structure defects.     

The interactome as a tree--an attempt to visualize the protein-protein interaction network in yeast

The refinement and high-throughput of protein interaction detection methods offer us a protein–protein interaction network in yeast. The challenge coming along with the network is to find better ways to make it accessible for biological investigation. Visualization would be helpful for extraction of meaningful biological information from the network. However, traditional ways of visualizing the network are unsuitable because of the large number of proteins. Here, we provide a simple but information-rich approach for visualization which integrates topological and biological information. In our method, the topological information such as quasi-cliques or spoke-like modules of the network is extracted into a clustering tree, where biological information spanning from protein functional annotation to expression profile correlations can be annotated onto the representation of it. We have developed a software named PINC based on our approach. Compared with previous clustering methods, our clustering method ADJW performs well both in retaining a meaningful image of the protein interaction network as well as in enriching the image with biological information, therefore is more suitable in visualization of the network.     

Cellular signaling: aspects for tumor diagnosis and therapy.

Cells are organic microsystems with functional compartments interconnected by complex signal chains. Intracellular signaling routes and signal reception from the extracellular environment are characterized by redundancy, i.e., parallel pathways exist. If a cell is exposed to an external "signal input", the signal processing elements within the cell provide a response that will be a pattern of reactions manifest as a metabolic, morphologic or electric "signal output". Cell-chip hybrid structures are miniaturized analytical systems with the capability to monitor such cell responses in real time and under continuous control of the environmental conditions. A system analysis approach gives an idea of how the biological component of these hybrid structures works. This is exemplified by the putative role of the microenvironmental pH as a parameter of the utmost importance for the malignant "mode" of tumor cells, which can be monitored and modeled on such hybrid structures.     

A dynamic programming approach for finding common patterns in RNAs.

We developed a dynamic programming approach of computing common sequence structure patterns among two RNAs given their primary sequences and their secondary structures. Common patterns between two RNAs are defined to share the same local sequential and structural properties. The locality is based on the connections of nucleotides given by their phosphodiester and hydrogen bonds. The idea of interpreting secondary structures as chains of structure elements leads us to develop an efficient dynamic programming approach in time O(nm) and space O(nm), where n and m are the lengths of the RNAs. The biological motivation is given by detecting common, local regions of RNAs, although they do not necessarily share global sequential and structural properties. This might happen if RNAs fold into different structures but share a lot of local, stable regions. Here, we illustrate our algorithm on Hepatitis C virus internal ribosome entry sites. Our method is useful for detecting and describing local motifs as well. An implementation in C++ is available and can be obtained by contacting one of the authors.     

一种新的混合震动滤波和各向异性扩散的图像增强和去噪方法研究

针对传统震动滤波和各向异性扩散混合模型存在缺点,提出了一种新的图像增强和去噪方法。该方法将改进的震动滤波项和图像细节保真项同时引入增强和去噪方程,使其根据图像结构信息产生相应变化幅度。通过实验表明,本文提出的方法达到较理想的增强和去噪效果,使得生物医学图像不仅具有很好的平滑效果,而且增强了边缘,同时保留了尽可能多的图像结构和细节信息,并且还很大程度上缩短了计算时间。