Bilateral sensory disturbance after cortical spreading depression revealed by fluorescence imaging of voltage‐sensitive dye

Cortical spreading depression (CSD), a propagation wave of transient neuronal and glial depolarization followed by suppression of spontaneous brain activity, has been hypothesized to be the underlying mechanism of migraine aura and triggers the headache attack. Evidence from various animal models accumulates since its first discovery in 1944 and provides support for this hypothesis. In this paper, alterations of bilateral cortical responses are investigated in a mice migrainous model of CSD using voltage‐sensitive dye imaging under hindlimb and cortical stimulation. After CSD induction in the right hemisphere, bilateral sensory responses evoked by left hindlimb stimulation dramatically decreases, whereas right hindlimb stimulation can still activate bilateral responses with an increased response of the left hemisphere and a well‐preserved response of the right hemisphere. In addition, cortical neural excitability remains after CSD assessed by direct activation of the right hemisphere in spite of the sensory deficit under contralateral hindlimb stimulation. These results depict the sensory disturbance of bilateral hemispheres after CSD, which may be helpful in understanding how sensory disturbance occur during migraine aura.      

Multiple statistical analysis techniques corroborate intratumor heterogeneity in imaging mass spectrometry datasets of myxofibrosarcoma

MALDI mass spectrometry can generate profiles that contain hundreds of biomolecular ions directly from tissue. Spatially-correlated analysis, MALDI imaging MS, can simultaneously reveal how each of these biomolecular ions varies in clinical tissue samples. The use of statistical data analysis tools to identify regions containing correlated mass spectrometry profiles is referred to as imaging MS-based molecular histology because of its ability to annotate tissues solely on the basis of the imaging MS data. Several reports have indicated that imaging MS-based molecular histology may be able to complement established histological and histochemical techniques by distinguishing between pathologies with overlapping/identical morphologies and revealing biomolecular intratumor heterogeneity. A data analysis pipeline that identifies regions of imaging MS datasets with correlated mass spectrometry profiles could lead to the development of novel methods for improved diagnosis (differentiating subgroups within distinct histological groups) and annotating the spatio-chemical makeup of tumors. Here it is demonstrated that highlighting the regions within imaging MS datasets whose mass spectrometry profiles were found to be correlated by five independent multivariate methods provides a consistently accurate summary of the spatio-chemical heterogeneity. The corroboration provided by using multiple multivariate methods, efficiently applied in an automated routine, provides assurance that the identified regions are indeed characterized by distinct mass spectrometry profiles, a crucial requirement for its development as a complementary histological tool. When simultaneously applied to imaging MS datasets from multiple patient samples of intermediate-grade myxofibrosarcoma, a heterogeneous soft tissue sarcoma, nodules with mass spectrometry profiles found to be distinct by five different multivariate methods were detected within morphologically identical regions of all patient tissue samples. To aid the further development of imaging MS based molecular histology as a complementary histological tool the Matlab code of the agreement analysis, instructions and a reduced dataset are included as supporting information.     

Cortical afterdischarges during Leao's cortical spreading depression.

Cortical spreading depression (CSD) has been employed in unanesthetized curarized rats, in order to analyse the role of the cerebral cortex in the generation of epileptic self-sustained parozysms produced by direct cortical electrical stimulation. CSD was preferred because it is reversible and may be repeated several times in the same animal. CSD evoked in the hemisphere contralateral to the stimulated cortex decreased the duration of the afterdischarge by 40% and modified its form and amplitude both at the cortical and reticular levels. The possible role of cortical and subcortical structures in the development of after-discharges is discussed.     

Stopping the clock on proteomic degradation by heat treatment at the point of tissue excision

The effectiveness of rapid and controlled heating of intact tissue to inactivate native enzymatic activity and prevent proteome degradation has been evaluated. Mouse brains were bisected immediately following excision, with one hemisphere being heat treated followed by snap freezing in liquid nitrogen while the other hemisphere was snap frozen immediately. Sections were cut by cryostatic microtome and analyzed by MALDI‐MS imaging and minimal label 2‐D DIGE, to monitor time‐dependent relative changes in intensities of protein and peptide signals. Analysis by MALDI‐MS imaging demonstrated that the relative intensities of markers varied across a time course (0–5 min) when the tissues were not stabilized by heat treatment. However, the same markers were seen to be stabilized when the tissues were heat treated before snap freezing. Intensity profiles for proteins indicative of both degradation and stabilization were generated when samples of treated and nontreated tissues were analyzed by 2‐D DIGE, with protein extracted before and after a 10‐min warming of samples. Thus, heat treatment of tissues at the time of excision is shown to prevent subsequent uncontrolled degradation of tissues at the proteomic level before any quantitative analysis, and to be compatible with downstream proteomic analysis.     

Physiological studies of cortical spreading depression

Cortical spreading depression (CSD) produces propagating waves of transient neuronal hyperexcitability followed by depression. CSD is initiated by K+ release following neuronal firing or electrical, mechanical or chemical stimuli. A triphasic (30-50 s) cortical potential transient accompanies localized transmembrane redistributions of K+, glutamate, Ca2+, Na+, Cl- and H+. Accumulated K+ in the restricted interstitial space can cause both further neuronal depolarisation and inward movement of K+ into astrocytes that buffers this increased extracellular K+ concentration ([K+])o. However, astrocyte interconnections may then propagate the CSD wave by K+ liberation through an opening of remote K+ channels by volume, Ca2+ or N-methyl-D-aspartate receptor activation. Changes in cerebral blood volume and in apparent water diffusion co-efficient (ADC) accompanying CSD were first studied using magnetic resonance imaging (MRI) in whole lissencephalic brains. Diffusion-weighted echoplanar imaging in gyrencephalic brains went on to demonstrate CSD features that paralleled classical migraine aura. The ADC activity persisted minutes/hours post KCl stimulus. Pixelwise analyses distinguished single primary events and multiple, spatially restricted, slower propagating, secondary events whose detailed features varied with the nature of the originating stimulus. These ADC changes varied reciprocally with T2*-weighted (i.e. referring to spin-spin relaxation times) waveforms reflecting local blood flow. There followed prolonged decreases in cerebral blood flow culminating in late cerebrovascular changes blocked by the antimigraine agent sumatriptan. CSD phenomena have possible translational significance for human migraine aura and other cerebral pathologies such as the peri-infarct depolarisation events that follow ischaemia and brain injury.     

大鼠局灶性脑缺血后KCl诱导皮层扩散性抑制的体光学成像

采用线栓法制备大鼠大脑中动脉栓塞(middlecerebralarteryocclusion,MCAO)模型,在额叶皮层用KCl诱导产生皮层扩散性抑制(corticalspreadingdepression,CSD)。MCAO4h后,利用550nm内源信号光学成像(opticalintrin-sicsignalimaging,OISI)监测局灶性脑缺血后大鼠顶-枕叶皮层内源光信号变化。成像1h内观测到一系列诱导CSD波(14±3次),CSD波局限于顶-枕叶皮层中央区域扩展,以光强的显著下降为特征;而旁侧区域光强无明显改变,不具备CSD波特征,表明CSD波未传播到该区域。随后TTC染色证明上述旁侧区域已经梗死。实验表明:MCAO后4h,皮层区域旁侧部分会梗死;CSD波的OIS变化可用来区分缺血梗死区和外周供血较为完整区域(未梗死区)。     

Differential metabolomics software for capillary electrophoresis-mass spectrometry data analysis

In metabolomics, the rapid identification of quantitative differences between multiple biological samples remains a major challenge. While capillary electrophoresis–mass spectrometry (CE–MS) is a powerful tool to simultaneously quantify charged metabolites, reliable and easy-to-use software that is well suited to analyze CE–MS metabolic profiles is still lacking. Optimized software tools for CE–MS are needed because of the sometimes large variation in migration time between runs and the wider variety of peak shapes in CE–MS data compared with LC–MS or GC–MS. Therefore, we implemented a stand-alone application named JDAMP (Java application for Differential Analysis of Metabolite Profiles), which allows users to identify the metabolites that vary between two groups. The main features include fast calculation modules and a file converter using an original compact file format, baseline subtraction, dataset normalization and alignment, visualization on 2D plots (m/z and time axis) with matching metabolite standards, and the detection of significant differences between metabolite profiles. Moreover, it features an easy-to-use graphical user interface that requires only a few mouse-actions to complete the analysis. The interface also enables the analyst to evaluate the semiautomatic processes and interactively tune options and parameters depending on the input datasets. The confirmation of findings is available as a list of overlaid electropherograms, which is ranked using a novel difference-evaluation function that accounts for peak size and distortion as well as statistical criteria for accurate difference-detection. Overall, the JDAMP software complements other metabolomics data processing tools and permits easy and rapid detection of significant differences between multiple complex CE–MS profiles.     

Non-negative matrix factorization for learning alignment-specific models of protein evolution

Models of protein evolution currently come in two flavors: generalist and specialist. Generalist models (e.g. PAM, JTT, WAG) adopt a one-size-fits-all approach, where a single model is estimated from a number of different protein alignments. Specialist models (e.g. mtREV, rtREV, HIVbetween) can be estimated when a large quantity of data are available for a single organism or gene, and are intended for use on that organism or gene only. Unsurprisingly, specialist models outperform generalist models, but in most instances there simply are not enough data available to estimate them. We propose a method for estimating alignment-specific models of protein evolution in which the complexity of the model is adapted to suit the richness of the data. Our method uses non-negative matrix factorization (NNMF) to learn a set of basis matrices from a general dataset containing a large number of alignments of different proteins, thus capturing the dimensions of important variation. It then learns a set of weights that are specific to the organism or gene of interest and for which only a smaller dataset is available. Thus the alignment-specific model is obtained as a weighted sum of the basis matrices. Having been constrained to vary along only as many dimensions as the data justify, the model has far fewer parameters than would be required to estimate a specialist model. We show that our NNMF procedure produces models that outperform existing methods on all but one of 50 test alignments. The basis matrices we obtain confirm the expectation that amino acid properties tend to be conserved, and allow us to quantify, on specific alignments, how the strength of conservation varies across different properties. We also apply our new models to phylogeny inference and show that the resulting phylogenies are different from, and have improved likelihood over, those inferred under standard models.     

Are geometric morphometric analyses replicable? Evaluating landmark measurement error and its impact on extant and fossil Microtus classification

Geometric morphometric analyses are frequently employed to quantify biological shape and shape variation. Despite the popularity of this technique, quantification of measurement error in geometric morphometric datasets and its impact on statistical results is seldom assessed in the literature. Here, we evaluate error on 2D landmark coordinate configurations of the lower first molar of five North American Microtus (vole) species. We acquired data from the same specimens several times to quantify error from four data acquisition sources: specimen presentation, imaging devices, interobserver variation, and intraobserver variation. We then evaluated the impact of those errors on linear discriminant analysis‐based classifications of the five species using recent specimens of known species affinity and fossil specimens of unknown species affinity. Results indicate that data acquisition error can be substantial, sometimes explaining >30% of the total variation among datasets. Comparisons of datasets digitized by different individuals exhibit the greatest discrepancies in landmark precision, and comparison of datasets photographed from different presentation angles yields the greatest discrepancies in species classification results. All error sources impact statistical classification to some extent. For example, no two landmark dataset replicates exhibit the same predicted group memberships of recent or fossil specimens. Our findings emphasize the need to mitigate error as much as possible during geometric morphometric data collection. Though the impact of measurement error on statistical fidelity is likely analysis‐specific, we recommend that all geometric morphometric studies standardize specimen imaging equipment, specimen presentations (if analyses are 2D), and landmark digitizers to reduce error and subsequent analytical misinterpretations.     

Peptide and protein imaging mass spectrometry in cancer research

MALDI mass spectrometry is able to acquire protein profiles directly from tissue that can describe the levels of hundreds of distinct proteins. MALDI imaging MS can simultaneously reveal how each of these proteins varies in heterogeneous tissues. Numerous studies have now demonstrated how MALDI imaging MS can generate different protein profiles from the different cell types in a tumor, which can act as biomarker profiles or enable specific candidate protein biomarkers to be identified.     

Integrated MALDI-MS imaging and LC–MS techniques for visualizing spatiotemporal metabolomic dynamics in a rat stroke model

Spatiotemporal information about biomolecules is indispensable for precise pathological analysis, but it remains largely unclear. Here we show a novel analytical platform combing mass spectrometry imaging (MSI) with its complementary technique, liquid chromatography–mass spectrometry (LC–MS), to elucidate more comprehensive metabolic behaviors, with spatiotemporal information, in tissues. Analysis of a rat transient middle cerebral artery occlusion (MCAO) brain tissue after ischemia–reperfusion was performed to characterize the detailed metabolomic response to pathological alterations. To compare the spatially resolved metabolic state between ischemic and contralateral hemispheres of the MCAO brain, coronally sliced tissues were subjected to MSI. We also measured the metabolites extracted from three different cerebral regions, including whole cortex (CTX), hippocampus (HI) and corpus striatum (CPu), by LC–MS. In the ischemic hemisphere, significant metabolic changes at the CTX and CPu were observed after reperfusion, while not at the HI. A region-specific metabolic behavior was observed in amino acid and nucleotide metabolism, as well as in the TCA cycle. Correlation between MSI and LC–MS data was relatively high in the CTX and CPu. Combination of both MS platforms visualized the diverse spatiotemporal metabolic dynamics during pathological progress. Thus, our proposed strategy will contribute to the understanding of the complex pathogenesis of ischemia–reperfusion.     

Cortical spreading depression aggravates early brain injury in a mouse model of subarachnoid hemorrhage

Cortical spreading depression (CSD) has been observed during the early phase of subarachnoid hemorrhage (SAH). However, the effect of CSD on the cerebral blood flow (CBF) and cerebral oxyhemoglobin (CHbO) during the early phase of SAH has not yet been assessed directly. We, therefore, used laser speckle imaging and optical intrinsic sinal imaging to record CBF and CHbO during CSD and cerebral cortex perfusion (CCP) at 24 hours after CSD in a mouse model of SAH. SAH was induced by blood injection into the prechiasmatic cistern. When CSD occurred, the change trend of CBF and CHbO in Sham group and SAH group was the same, but ischemia and hypoxia in SAH group was more significant. At 24 hours after SAH, the CCP of CSD group was lower than that of no CSD group, and the neurological function score of CSD group was lower. We conclude that induction of CSD further aggravates cerebral ischemia and worsens neurological dysfunction in the early stage of experimental SAH. Our study underscores the consequence of CSD in the development of early brain injury after SAH.     

Non-linear PCA: a missing data approach

MOTIVATION: Visualizing and analysing the potential non-linear structure of a dataset is becoming an important task in molecular biology. This is even more challenging when the data have missing values. RESULTS: Here, we propose an inverse model that performs non-linear principal component analysis (NLPCA) from incomplete datasets. Missing values are ignored while optimizing the model, but can be estimated afterwards. Results are shown for both artificial and experimental datasets. In contrast to linear methods, non-linear methods were able to give better missing value estimations for non-linear structured data.Application: We applied this technique to a time course of metabolite data from a cold stress experiment on the model plant Arabidopsis thaliana, and could approximate the mapping function from any time point to the metabolite responses. Thus, the inverse NLPCA provides greatly improved information for better understanding the complex response to cold stress. CONTACT: scholz@mpimp-golm.mpg.de.     

HiRes--a tool for comprehensive assessment and interpretation of metabolomic data

The increasing role of metabolomics in system biology is driving the development of tools for comprehensive analysis of high-resolution NMR spectral datasets. This task is quite challenging since unlike the datasets resulting from other 'omics', a substantial preprocessing of the data is needed to allow successful identification of spectral patterns associated with relevant biological variability. HiRes is a unique stand-alone software tool that combines standard NMR spectral processing functionalities with techniques for multi-spectral dataset analysis, such as principal component analysis and non-negative matrix factorization. In addition, HiRes contains extensive abilities for data cleansing, such as baseline correction, solvent peak suppression, removal of frequency shifts owing to experimental conditions as well as auxiliary information management. Integration of these components together with multivariate analytical procedures makes HiRes very capable of addressing the challenges for assessment and interpretation of large metabolomic datasets, greatly simplifying this otherwise lengthy and difficult process and assuring optimal information retrieval. AVAILABILITY: HiRes is freely available for research purposes at http://hatch.cpmc.columbia.edu/highresmrs.html     

Automated correlation of single particle tilt pairs for Random Conical Tilt and Orthogonal Tilt Reconstructions

One of the major methodological challenges in single particle electron microscopy is obtaining initial reconstructions which represent the structural heterogeneity of the dataset. Random Conical Tilt and Orthogonal Tilt Reconstruction techniques in combination with 3D alignment and classification can be used to obtain initial low-resolution reconstructions which represent the full range of structural heterogeneity of the dataset. In order to achieve statistical significance, however, a large number of 3D reconstructions, and, in turn, a large number of tilted image pairs are required. The extraction of single particle tilted image pairs from micrographs can be tedious and time-consuming, as it requires intensive user input even for semi-automated approaches. To overcome the bottleneck of manual selection of a large number of tilt pairs, we developed an algorithm for the correlation of single particle images from tilted image pairs in a fully automated and user-independent manner. The algorithm reliably correlates correct pairs even from noisy micrographs. We further demonstrate the applicability of the algorithm by using it to obtain initial references both from negative stain and unstained cryo datasets.     

Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure

Three-dimensional (3D) structural information on many length scales is of central importance in biological research. Excellent methods exist to obtain structures of molecules at atomic, organelles at electron microscopic, and tissue at light-microscopic resolution. A gap exists, however, when 3D tissue structure needs to be reconstructed over hundreds of micrometers with a resolution sufficient to follow the thinnest cellular processes and to identify small organelles such as synaptic vesicles. Such 3D data are, however, essential to understand cellular networks that, particularly in the nervous system, need to be completely reconstructed throughout a substantial spatial volume. Here we demonstrate that datasets meeting these requirements can be obtained by automated block-face imaging combined with serial sectioning inside the chamber of a scanning electron microscope. Backscattering contrast is used to visualize the heavy-metal staining of tissue prepared using techniques that are routine for transmission electron microscopy. Low-vacuum (20–60 Pa H₂O) conditions prevent charging of the uncoated block face. The resolution is sufficient to trace even the thinnest axons and to identify synapses. Stacks of several hundred sections, 50–70 nm thick, have been obtained at a lateral position jitter of typically under 10 nm. This opens the possibility of automatically obtaining the electron-microscope-level 3D datasets needed to completely reconstruct the connectivity of neuronal circuits.     

Absolute protein quantification allows differentiation of cell‐specific metabolic routes and functions

Total protein approach (TPA) is a proteomic method that allows calculation of concentrations of individual proteins and groups of functionally related proteins in any protein mixture without spike‐in standards. Using the two‐step digestion–filter‐aided sample preparation method and LC‐MS/MS analysis, we generated comprehensive quantitative datasets of mouse intestinal mucosa, liver, red muscle fibers, brain, and of human plasma, erythrocytes, and tumor cells lines. We show that the TPA‐based quantitative data reflect well‐defined and specific physiological functions of different organs and cells, for example nutrient absorption and transport in intestine, amino acid catabolism and bile secretion in liver, and contraction of muscle fibers. Focusing on key metabolic processes, we compared metabolic capacities in different tissues and cells. In addition, we demonstrate quantitative differences in the mitochondrial proteomes. Providing insight into the abundances of mitochondrial metabolite transporters, we demonstrate that their titers are well tuned to cell‐specific metabolic requirements. This study provides for the first time a comprehensive overview of the protein hardware mediating metabolism in different mammalian organs and cells. The presented approach can be applied to any other system to study biological processes. All MS data have been deposited in the ProteomeXchange with identifier PXD001352 ( http://proteomecentral.proteomexchange.org/dataset/PXD001352 ).     

An optimized data structure for high-throughput 3D proteomics data: mzRTree

As an emerging field, MS-based proteomics still requires software tools for efficiently storing and accessing experimental data. In this work, we focus on the management of LC–MS data, which are typically made available in standard XML-based portable formats. The structures that are currently employed to manage these data can be highly inefficient, especially when dealing with high-throughput profile data. LC–MS datasets are usually accessed through 2D range queries. Optimizing this type of operation could dramatically reduce the complexity of data analysis. We propose a novel data structure for LC–MS datasets, called mzRTree, which embodies a scalable index based on the R-tree data structure. mzRTree can be efficiently created from the XML-based data formats and it is suitable for handling very large datasets. We experimentally show that, on all range queries, mzRTree outperforms other known structures used for LC–MS data, even on those queries these structures are optimized for. Besides, mzRTree is also more space efficient. As a result, mzRTree reduces data analysis computational costs for very large profile datasets.     

Selective integration of multiple biological data for supervised network inference

MOTIVATION: Inferring networks of proteins from biological data is a central issue of computational biology. Most network inference methods, including Bayesian networks, take unsupervised approaches in which the network is totally unknown in the beginning, and all the edges have to be predicted. A more realistic supervised framework, proposed recently, assumes that a substantial part of the network is known. We propose a new kernel-based method for supervised graph inference based on multiple types of biological datasets such as gene expression, phylogenetic profiles and amino acid sequences. Notably, our method assigns a weight to each type of dataset and thereby selects informative ones. Data selection is useful for reducing data collection costs. For example, when a similar network inference problem must be solved for other organisms, the dataset excluded by our algorithm need not be collected. RESULTS: First, we formulate supervised network inference as a kernel matrix completion problem, where the inference of edges boils down to estimation of missing entries of a kernel matrix. Then, an expectation-maximization algorithm is proposed to simultaneously infer the missing entries of the kernel matrix and the weights of multiple datasets. By introducing the weights, we can integrate multiple datasets selectively and thereby exclude irrelevant and noisy datasets. Our approach is favorably tested in two biological networks: a metabolic network and a protein interaction network. AVAILABILITY: Software is available on request.