Colour model analysis for microscopic image processing

This article presents a comparative study between different colour models (RGB, HSI and CIEL*a*b*) applied to a very large microscopic image analysis. Such analysis of different colour models is needed in order to carry out a successful detection and therefore a classification of different regions of interest (ROIs) within the image. This, in turn, allows both distinguishing possible ROIs and retrieving their proper colour for further ROI analysis. This analysis is not commonly done in many biomedical applications that deal with colour images. Other important aspects is the computational cost of the different processing algorithms according to the colour model. This work takes these aspects into consideration to choose the best colour model tailored to the microscopic stain and tissue type under consideration and to obtain a successful processing of the histological image.     

Non-invasive online detection of nitric oxide from plants and some other organisms by mass spectrometry

As nitric oxide (NO) is a key messenger in many organisms, reliable techniques for the detection of NO are essential. Here, it is shown that a combination of membrane inlet mass spectrometry (MIMS) and restriction capillary inlet mass spectrometry (RIMS) allows for the fast, specific, and non-invasive online detection of NO that has been emitted from tissue cultures of diverse organisms, or from whole plants. As an advantage over other NO assays, MIMS/RIMS discriminates nitrogen isotopes and simultaneously measures NO and O(2) (and other gases) from the same sample. MIMS/RIMS technology may thus help to identify the source of gaseous NO in cells, and elucidate the relationship between primary gas metabolism and NO formation. Using RIMS, it is demonstrated that the novel fungicide F 500((R)) triggers NO production in plants.     

Quantification of steroid conjugates using fast atom bombardment mass spectrometry

Fast atom bombardment/mass spectrometry or liquid secondary ion mass spectrometry provides the capability for direct analysis of steroid conjugates (sulfates, glucuronides) without prior hydrolysis or derivatization. During the analysis of biologic extracts, limitations on the sensitivity of detection arise from the presence of co-extracted material which may suppress or obscure the analyte signal. A procedure is described for the quantitative determination of dehydroepiandrosterone sulfate in serum which achieved selective isolation of the analyte using immunoadsorption extraction and highly specific detection using tandem mass spectrometry. A stable isotope-labeled analog [( 2H2]dehydroepiandrosterone sulfate) was used as internal standard. Fast atom bombardment of dehydroepiandrosterone sulfate yielded abundant [M-H]- ions that fragmented following collisional activation to give HSO4-; m/z 97. During fast atom bombardment/tandem mass spectrometry of serum extracts, a scan of precursor ions fragmenting to give m/z 97 detected dehydroepiandrosterone sulfate and the [2H2]-labeled analog with a selectivity markedly superior to that observed using conventional mass spectrometry detection. Satisfactory agreement was observed between quantitative data obtained in this way and data obtained by gas chromatography/mass spectrometry of the heptafluorobutyrates of dehydroepiandrosterone sulfate and [2H2]dehydroepiandrosterone sulfate obtained by direct derivatization.     

Look@NanoSIMS--a tool for the analysis of nanoSIMS data in environmental microbiology

We describe an open-source freeware programme for high throughput analysis of nanoSIMS (nanometre-scale secondary ion mass spectrometry) data. The programme implements basic data processing and analytical functions, including display and drift-corrected accumulation of scanned planes, interactive and semi-automated definition of regions of interest (ROIs), and export of the ROIs' elemental and isotopic composition in graphical and text-based formats. Additionally, the programme offers new functions that were custom-designed to address the needs of environmental microbiologists. Specifically, it allows manual and automated classification of ROIs based on the information that is derived either from the nanoSIMS dataset itself (e.g. from labelling achieved by halogen in situ hybridization) or is provided externally (e.g. as a fluorescence in situ hybridization image). Moreover, by implementing post-processing routines coupled to built-in statistical tools, the programme allows rapid synthesis and comparative analysis of results from many different datasets. After validation of the programme, we illustrate how these new processing and analytical functions increase flexibility, efficiency and depth of the nanoSIMS data analysis. Through its custom-made and open-source design, the programme provides an efficient, reliable and easily expandable tool that can help a growing community of environmental microbiologists and researchers from other disciplines process and analyse their nanoSIMS data.     

Graph of graphs analysis for multiplexed data with application to imaging mass cytometry

Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous protein targets with 1μm resolution. Each slide is a spatial assay consisting of high-dimensional multivariate observations (m-dimensional feature space) collected at different spatial positions and capturing data from a single biological sample or even representative spots from multiple samples when using tissue microarrays. Often, each of these spatial assays could be characterized by several regions of interest (ROIs). To extract meaningful information from the multi-dimensional observations recorded at different ROIs across different assays, we propose to analyze such datasets using a two-step graph-based approach. We first construct for each ROI a graph representing the interactions between the m covariates and compute an m dimensional vector characterizing the steady state distribution among features. We then use all these m-dimensional vectors to construct a graph between the ROIs from all assays. This second graph is subjected to a nonlinear dimension reduction analysis, retrieving the intrinsic geometric representation of the ROIs. Such a representation provides the foundation for efficient and accurate organization of the different ROIs that correlates with their phenotypes. Theoretically, we show that when the ROIs have a particular bi-modal distribution, the new representation gives rise to a better distinction between the two modalities compared to the maximum a posteriori (MAP) estimator. We applied our method to predict the sensitivity to PD-1 axis blockers treatment of lung cancer subjects based on IMC data, achieving 97.3% average accuracy on two IMC datasets. This serves as empirical evidence that the graph of graphs approach enables us to integrate multiple ROIs and the intra-relationships between the features at each ROI, giving rise to an informative representation that is strongly associated with the phenotypic state of the entire image.     

Immunoaffinity-based mass spectrometric characterization of the FMRFamide-related peptide family in the pericardial organ of Cancer borealis

The tetrapeptide, FMRFamide, was first discovered in 1977 in the molluscan nervous system and was found to affect the contractile force of molluscan cardiac muscle and other muscles [1]. Since then, numerous FMRFamide-related peptides (FaRPs) have been reported in both invertebrate and vertebrate species [2], [3], [4], [5], [6], [7], [8] and [9]. We have previously reported the detection and identification of numerous FaRPs in Cancer borealis pericardial organs (POs), one of the major neurosecretory structures in the crustaceans [2] and [3]. Here, we have developed two immunoaffinity-based methods, immunoprecipitation (IP) and immuno-dot blot screening assay, for the enrichment of FaRPs in C. borealis POs. A combined mass spectrometry (MS)-based approach involving both matrix-assisted laser desorption/ionization Fourier transform mass spectrometry (MALDI-FTMS) and nanoscale liquid chromatography coupled to electrospray ionization quadrupole time-of-flight tandem mass spectrometry (nanoLC-ESI-QTOF MS/MS) is used for a more comprehensive characterization of the FaRP family by utilizing high mass accuracy measurement and efficient peptide sequencing. Overall, 17 FMRFamide-related peptides were identified using these two complementary immuno-based approaches. Among them, three novel peptides were reported for the first time in this study.     

Selective quantitative bioanalysis of proteins in biological fluids by on-line immunoaffinity chromatography-protein digestion-liquid chromatography-mass spectrometry

A quantitative method for the determination of proteins in complex biological matrices has been developed based on the selectivity of antibodies for sample purification followed by proteolytic digestion and quantitative mass spectrometry. An immunosorbent of polyclonal anti-bovine serum albumin (BSA) antibodies immobilized on CNBR agarose is used in the on-line mode for selective sample pretreatment. Next, the purified sample is trypsin digested to obtain protein specific peptide markers. Subsequent analysis of the peptide mixture using a desalination procedure and a separation step coupled, on-line to an ion-trap mass spectrometer, reveals that this method enables selective determination of proteins in biological matrices like diluted human plasma. This approach enhances substantially the selectivity compared to common quantitative analysis executed with immunoassays and colorimetry, fluorimetry or luminescence detection. Hyphenation of the immunoaffinity chromatography with on-line digestion and chromatography-mass spectrometry is performed and a completely on-line quantification of the model protein BSA in bovine and human urine was established. A detection limit of 170 nmol/l and a quantification limit of 280 nmol/l is obtained using 50 microl of either standard or spiked biological matrix. The model system allows fully automated absolute quantitative mass spectrometric analysis of intact proteins in biological matrices without time-consuming labeling procedures.     

Fourier-Transform Raman and Fourier-Transform Infrared Spectroscopy (An Investigation of Five Higher Plant Cell Walls and Their Components)

Infrared and Raman spectra of sequentially extracted primary cell walls and their pectic polymers were obtained from five angiosperm plants. Fourier-transform Raman spectrometry was shown to be a powerful tool for the investigation of primary cell-wall architecture at a molecular level, providing complementary information to that obtained by Fourier-transform infrared microspectroscopy. The use of an extraction procedure using imidazole instead of cyclohexane trans-1,2-N,N,N[prime],N[prime]-diaminotetraacetate allows the extension of the infrared spectral window for data interpretation from 1300 to 800 cm-1, to 2000 to 800 cm-1, and allows us to obtain Raman spectra from extracted cell-wall material. Wall constituents such as pectins, proteins, aromatic phenolics, cellulose, and hemicellulose have characteristic spectral features that can be used to identify and/or fingerprint these polymers without, in most cases, the need for any physical separation. The Gramineae (rice [Oryza sativa], polypogon [Polypogon fugax steud], and sweet corn [Zea mays]) are spectroscopically very different from the nongraminaceous monocotyledon (onion [Allium cepa]) and the dicotyledon (carrot [Daucus carota]); this reflects differences in chemical composition and cross-linking of the walls. The possibility of a taxonomic classification of plant cell walls based on infrared and Raman spectroscopies and the use of spectral fingerprinting for authentication and detection of adulteration of products rich in cell-wall materials are discussed.     

Membrane inlet for mass spectrometric measurement of catalysis by enzymatic decarboxylases

Membrane inlet mass spectrometry (MIMS) uses diffusion across a permeable membrane to detect in solution uncharged molecules of small molecular weight. We point out here the application of MIMS to determine catalytic properties of decarboxylases using as an example catalysis by oxalate decarboxylase (OxDC) from Bacillus subtilis. The decarboxylase activity generates carbon dioxide and formate from the nonoxidative reaction but is accompanied by a concomitant oxidase activity that consumes oxalate and oxygen and generates CO₂ and hydrogen peroxide. The application of MIMS in measuring catalysis by OxDC involves the real-time and continuous detection of oxygen and product CO₂ from the ion currents of their respective mass peaks. Steady-state catalytic constants for the decarboxylase activity obtained by measuring product CO₂ using MIMS are comparable to those acquired by the traditional endpoint assay based on the coupled reaction with formate dehydrogenase, and measuring consumption of O₂ using MIMS also estimates the oxidase activity. The use of isotope-labeled substrate (¹³C₂-enriched oxalate) in MIMS provides a method to characterize the catalytic reaction in cell suspensions by detecting the mass peak for product ¹³CO₂ (m/z 45), avoiding inaccuracies due to endogenous ¹²CO₂.     

On-line mass spectrometry: membrane inlet sampling

Significant insights into plant photosynthesis and respiration have been achieved using membrane inlet mass spectrometry (MIMS) for the analysis of stable isotope distribution of gases. The MIMS approach is based on using a gas permeable membrane to enable the entry of gas molecules into the mass spectrometer source. This is a simple yet durable approach for the analysis of volatile gases, particularly atmospheric gases. The MIMS technique strongly lends itself to the study of reaction flux where isotopic labeling is employed to differentiate two competing processes; i.e., O₂ evolution versus O₂ uptake reactions from PSII or terminal oxidase/rubisco reactions. Such investigations have been used for in vitro studies of whole leaves and isolated cells. The MIMS approach is also able to follow rates of isotopic exchange, which is useful for obtaining chemical exchange rates. These types of measurements have been employed for oxygen ligand exchange in PSII and to discern reaction rates of the carbonic anhydrase reactions. Recent developments have also engaged MIMS for online isotopic fractionation and for the study of reactions in inorganic systems that are capable of water splitting or H₂ generation. The simplicity of the sampling approach coupled to the high sensitivity of modern instrumentation is a reason for the growing applicability of this technique for a range of problems in plant photosynthesis and respiration. This review offers some insights into the sampling approaches and the experiments that have been conducted with MIMS.

     

White blood cell detection using saliency detection and CenterNet: A two-stage approach

White blood cell (WBC) detection plays a vital role in peripheral blood smear analysis. However, cell detection remains a challenging task due to multi-cell adhesion, different staining and imaging conditions. Owing to the powerful feature extraction capability of deep learning, object detection methods based on convolutional neural networks (CNNs) have been widely applied in medical image analysis. Nevertheless, the CNN training is time-consuming and inaccuracy, especially for large-scale blood smear images, where most of the images are background. To address the problem, we propose a two-stage approach that treats WBC detection as a small salient object detection task. In the first saliency detection stage, we use the Itti's visual attention model to locate the regions of interest (ROIs), based on the proposed adaptive center-surround difference (ACSD) operator. In the second WBC detection stage, the modified CenterNet model is performed on ROI sub-images to obtain a more accurate localization and classification result of each WBC. Experimental results showed that our method exceeds the performance of several existing methods on two different data sets, and achieves a state-of-the-art mAP of over 98.8%.     

Analysis of glycerophosphoinositol by liquid chromatography-electrospray ionisation tandem mass spectrometry using a beta-cyclodextrin-bonded column

Glycerophosphoinositol (GroPIns) has been demonstrated to have important roles in many intracellular regulatory processes. GroPIns has been analysed for many years by anion-exchange HPLC after radiolabelling of cells in culture, but no method has been developed, to our knowledge, for the direct detection and quantitation of the unlabelled compound in such biological samples. Here is reported a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the direct quantitative analysis of GroPIns that can indeed be applied to cell extracts. Analyses were performed on a beta-cyclodextrin-bonded HPLC column using a binary mobile phase of acetonitrile and 20 mM ammonium formate in water, which allowed direct on-line detection by tandem mass spectrometry in negative electrospray ionisation (ESI) mode. The method was applied to the quantitative analysis of GroPIns in selected rat cell lines after a two-phase acid extraction of cultured cells using external calibration. The potential matrix signal suppression effects were investigated by the parallel quantitation of GroPIns in extracts of selected cultured cell lines with both external calibration and the standard additions method. The accuracy data obtained demonstrated the feasibility of external calibration, so allowing a simpler and less time-consuming approach than that of the standard additions method.     

Optimized Two-Stage Ensemble Model for Mammography Mass Recognition

ObjectivesMammography mass recognition is considered as a very challenge pattern recognition problem due to the high similarity between normal and abnormal masses. Therefore, the main objective of this study is to develop an efficient and optimized two-stage recognition model to tackle this recognition task.Material and methodsBasically, the developed recognition model combines an ensemble of linear Support Vector Machine (SVM) classifiers with a Reinforcement Learning-based Memetic Particle Swarm Optimizer (RLMPSO) as RLMPSO-SVM recognition model. RLMPSO is used to construct a two-stage of an ensemble of linear SVM classifiers by performing simultaneous SVM parameters tuning, features selection, and training instances selection. The first stage of RLMPSO-SVM recognition model is responsible about recognizing the input ROI mammography masses as normal or abnormal mass pattern. Meanwhile, the second stage of RLMPSO-SVM model used to perform further recognition for abnormal ROIs as malignant or benign masses. In order to evaluate the effectiveness of RLMPSO-SVM, a total of 1187 normal ROIs, 111 malignant ROIs, and 135 benign ROIs were randomly selected from DDSM database images.ResultsReported results indicated that RLMPSO-SVM model was able to achieve performances of 97.57% sensitivity rate with 97.86% specificity rate for normal vs. abnormal recognition cases. For malignant vs. benign recognition performance it was reported of 97.81% sensitivity rate with 96.92% specificity rate.ConclusionReported results indicated that RLMPSO-SVM recognition model is an effective tool that could assist the radiologist during the diagnosis of the presented abnormalities in mammography images. The outcomes indicated that RLMPSO-SVM significantly outperformed various SVM-based models as well as other variants of computational intelligence models including multi-layer perceptron, naive Bayes classifier, and k-nearest neighbor.     

Chemical probes and tandem mass spectrometry: a strategy for the quantitative analysis of proteomes and subproteomes

Quantitative proteome profiling using mass spectrometry and stable isotope dilution is being widely applied for the functional analysis of biological systems and for the detection of clinical, diagnostic or prognostic marker proteins. Because of the enormous complexity of proteomes, their comprehensive analysis is unlikely to be routinely achieved in the near future. However, in recent years, significant progress has been achieved focusing quantitative proteomic analyses on specific protein classes or subproteomes that are rich in biologically or clinically important information. Such projects typically combine the use of chemical probes that are specific for a targeted group of proteins and may contain stable isotope signatures for accurate quantification with automated tandem mass spectrometry and bioinformatics tools for data analysis. In this review, we summarize technical and conceptual advances in quantitative subproteome profiling based on tandem mass spectrometry and chemical probes.     

Combined thin layer chromatography/mass spectrometry: an application of 252Cf plasma desorption mass spectrometry for drug monitoring

The pharmacokinetic analysis is performed in a three-step procedure: sample extraction, sample purification by thin layer chromatography (TLC) and quantitative sample detection by time-of-flight (TOF) mass spectrometry. 252Cf plasma desorption (PD) mass spectrometry utilizing the fission fragment-induced ionization and desorption of non-volatile compounds is suitable as a universal, non-destructive detector in TLC. Here TLC and mass spectrometry are operated in an off-line combination. As an example some pharmacokinetic data for etoposide (VP 16-213) together with calibration data are presented. The new experimental method is discussed in terms of sensitivity and detection limit.     

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.     

高维蛋白质波谱癌症数据特征提取

高维蛋白质波谱癌症数据分析,一直面临着高维数据的困扰。针对高维蛋白质波谱癌症数据在降维过程中的问题,提出基于小波分析技术和主成分分析技术的高维蛋白质波谱癌症数据特征提取的方法,并在特征提取之后,使用支持向量机进行分类。对8-7-02数据集进行2层小波分解时,分别使用db1、db3、db4、db6、db8、db10、haar小波基,并使用支持向量机进行分类,正确率分别达到98.18%、98.35%、98.04%、98.36%、97.89%、97.96%、98.20%。在进一步提高分类识别正确率的同时,提高了时间率。     

Validation of ThermoHuman automatic thermographic software for assessing foot temperature before and after running

The aim of the study was to evaluate an automatic thermographic software package (ThermoHuman®) for assessing skin temperature on the soles of the feet before and after running and to compare it with two manual definitions of the regions of interest (ROIs). 120 thermal images of the soles of the feet of 30 participants, at two measurement points (before and after running 30 min) and on two measurement days were analyzed. Three different models of thermographic image analyses were used to obtain the mean temperature of 9 ROIs: A) ThermoHuman (automatic definition of ROIs using ThermoHuman® software), B) Manual (manual delimitation of ROIs by proportion criteria), and C) Manual-TH (manual delimitation of ROIs in an attempt to replicate the regions analyzed by ThermoHuman). ThermoHuman resulted in an 86% reduction in time involved compared to manual delimitation. Fourteen of the 120 images (12%) presented some error in one or more of the ROI delimitations. Although the three procedures presented significant differences between them (53% in the comparison between ThermoHuman and Manual, 47% between ThermoHuman and Manual-TH, and 28% between Manual and Manual-TH), all differences had a small effect size (ES 0.2–0.4) or lower (ES < 0.2). Bland-Altman plots showed similar 95% limits of agreement between the three procedures before and after running. Intraclass correlation coefficient analysis of the three procedures presented excellent reliability (ICC>0.8). In conclusion, ThermoHuman® software was observed to be time-saving for image analysis with excellent reliability. Although results suggest that ThermoHuman® and manual methods are both valid in themselves, combining them is not recommended due to the differences observed between them.     

Cell population based mass spectrometry using platinum nanodots for algal and fungal studies

For the first time, we applied cell-population based mass spectrometry (CP-MS) for biosensing intact eukaryotic cells of Chlamydomonas reinhardtii and Saccharomyces cerevisiae. Cell counts ranging from 1 × 10(7) to 1.28 × 10(2) were analyzed using MALDI-MS to obtain the threshold detection sensitivity. Platinum nanodots (Pt NDs) were used to enhance the detection sensitivity of CP-MS. Pt NDs were able to improve the detection sensitivity of CP-MS from 3200 cells/mL to 640 cells/mL (5-fold) for Chlamydomonas. For yeast cells, the detection sensitivity was also increased from 400,000 cells/mL to 3200 cells/mL (125-fold) when Pt NDs were used. Using the Clin Pro tool, the obtained results from MALDI-MS data were validated. Statistical analysis of the mass data was performed using MYSTAT software.     

Identification of proteolytic cleavage sites by quantitative proteomics

The identification of natural substrates and their cleavage sites is pivotal to defining proteolytic pathways. Here we report a novel strategy for the identification of the signature of proteolytic cleavage events based on quantitative proteomics. Lysine residues in proteins are blocked by guanidination so that free N-terminals can be labeled with amine-specific iTRAQ reagents. The quantitative nature of iTRAQ reagents allows us to distinguish N-terminals newly formed by proteolytic treatment (neoepitopes) from original N-terminals in proteins. Proteins are digested with trypsin and analyzed using MALDI-TOF/TOF mass spectrometry. Peptides labeled with iTRAQ reagents are distinguished from other peptides by exhibiting intense signature ions in tandem mass spectrometry analysis. A corresponding data acquisition strategy was developed to specifically analyze iTRAQ tagged N-terminal peptides. To validate the procedure, we examined a set of recombinant Escherichia coli proteins that have predicted caspase-3 cleavage motifs. The protein mixture was treated with active or inactive caspase-3 and subsequently labeled with two different iTRAQ reagents. Mass spectrometric analysis located 10 cleavage sites, all corresponding to caspase-3 consensus. Spiking caspase-cleaved substrate into a human cell lysate demonstrated the high sensitivity of the procedure. Moreover, we were able to identify proteolytic cleavage products associated with the induction of cell-free apoptosis. Together, these data reveal a novel application for iTRAQ technology for the detection of proteolytic substrates.