Project description:Chemically synthesized near-infrared aza-BODIPY dyes displayed off-on fluorescence at acidic pH (pKa = 6.2-6.6) through the suppression of the photoinduced electron transfer and/or internal charge transfer process. The apparent pKas of the dyes were shifted well above physiological pH in a hydrophobic microenvironment, which led to "turned-on" fluorescence in micelles and liposomes at neutral and basic pH. Bovine serum albumin also activated the fluorescence, though to a much lesser extent. When these small molecular dyes entered cells, instead of being fluorescent only in acidic organelles, the whole cytoplasm exhibited fluorescence, with a signal/background ratio as high as ?10 in no-wash live-cell imaging. The dye 1-labeled cells remained highly fluorescent even after 3 days. Moreover, slight variations of the dye structure resulted in significantly different intracellular fluorescence behaviors, possibly because of their different cellular uptake and intracellular activation capabilities. After the separation of cellular components, the fraction of plasma membrane and endoplasmic reticulum showed the highest fluorescence, further confirming the fluorescence activation by membrane structures. The fluorescence intensity of these dyes at different intracellular pHs (6.80 and 8.00) did not differ significantly, indicating that intracellular pH did not play a critical role. Altogether, we showed here for the first time that the fluorescence of pH-sensitive aza-BODIPY dyes was switched intracellularly not by acidic pH, but by intracellular membranes (and proteins as well). The excellent membrane permeability, ultrahigh fluorescence contrast ratio, persistent fluorescent signal, and minimal biological interference of dye 1 make it an ideal choice for live-cell imaging and in vivo cell tracking. These findings also imply that the intracellular fluorescence properties of pH-sensitive dyes should be carefully examined before they are used as pH indicators.
Project description:The sense of smell arises from the perception of odors from chemicals. However, the relationship between the impression of odor and the numerous physicochemical parameters has yet to be understood owing to its complexity. As such, there is no established general method for predicting the impression of odor of a chemical only from its physicochemical properties. In this study, we designed a novel predictive model based on an artificial neural network with a deep structure for predicting odor impression utilizing the mass spectra of chemicals, and we conducted a series of computational analyses to evaluate its performance. Feature vectors extracted from the original high-dimensional space using two autoencoders equipped with both input and output layers in the model are used to build a mapping function from the feature space of mass spectra to the feature space of sensory data. The results of predictions obtained by the proposed new method have notable accuracy (R?0.76) in comparison with a conventional method (R?0.61).
Project description:Asara et al. reported the detection of collagen peptides in a 68-million-year-old Tyrannosaurus rex bone by shotgun proteomics. This finding has been called into question as a possible statistical artifact. We reanalyze Asara et al.'s tandem mass spectra using a different search engine and different statistical tools. Our reanalysis shows a sample containing common laboratory contaminants, soil bacteria, and bird-like hemoglobin and collagen.
Project description:Tandem mass spectrometry (MS/MS) experiments often generate redundant data sets containing multiple spectra of the same peptides. Clustering of MS/MS spectra takes advantage of this redundancy by identifying multiple spectra of the same peptide and replacing them with a single representative spectrum. Analyzing only representative spectra results in significant speed-up of MS/MS database searches. We present an efficient clustering approach for analyzing large MS/MS data sets (over 10 million spectra) with a capability to reduce the number of spectra submitted to further analysis by an order of magnitude. The MS/MS database search of clustered spectra results in fewer spurious hits to the database and increases number of peptide identifications as compared to regular nonclustered searches. Our open source software MS-Clustering is available for download at http://peptide.ucsd.edu or can be run online at http://proteomics.bioprojects.org/MassSpec.
Project description:Mass spectra provide the ultimate evidence to support the findings of mass spectrometry proteomics studies in publications, and it is therefore crucial to be able to trace the conclusions back to the spectra. The Universal Spectrum Identifier (USI) provides a standardized mechanism for encoding a virtual path to any mass spectrum contained in datasets deposited to public proteomics repositories. USI enables greater transparency of spectral evidence, with more than 1 billion USI identifications from over 3 billion spectra already available through ProteomeXchange repositories.
Project description:Hydroxylated rhodamines, carbopyronines, silico- and germanorhodamines with absorption maxima in the range of 530-640 nm were prepared and applied in specific labeling of living cells. The direct and high-yielding entry to germa- and silaxanthones tolerates the presence of protected heteroatoms and may be considered for the syntheses of various sila- and germafluoresceins, as well as -rhodols. Application in stimulated emission depletion (STED) fluorescence microscopy revealed a resolution of 50-75 nm in one- and two-color imaging of vimentin-HaloTag fused protein and native tubulin. The established structure-property relationships allow for prediction of the spectral properties and the positions of spirolactone/zwitterion equilibria for the new analogues of rhodamines, carbo-, silico-, and germanorhodamines using simple additive schemes.
Project description:The analytical value of peaks arising by a proximity effect in the electron ionization mass spectra of benzanilides has been established by examining the spectra of numerous examples of general structure XC6H4NHCOC6H4Y. Significant [M-X]+ signals are observed only when X = Cl, Br, I or CH3O in the 2-position. The presence of strong [M-X]+ signals, but negligibly weak [M-Y]+ peaks, even when the C-Y bond would be expected to break more readily than the C-X bond, indicates that these diagnostically useful signals do not arise by simple cleavage. Similarly, the presence of an appreciable [M-Cl]+ signal, but no [M-Br]+ signal, in the spectra of representative examples of 4-Br-2ClC6H3NHCOC6H4Y, reveals that loss of a substituent from the 2-position occurs much more rapidly than fission of a weaker bond to a substituent in the 4-position. These trends are interpreted in terms of cyclization of the ionized 2-substituted benzanilide, followed by elimination of the substituent originally in the 2-position, to form a protonated 2-arylbenzoxazole.
Project description:Mass spectrometry is a valued method to evaluate the metabolomics content of a biological sample. The recent advent of rapid ionization technologies such as Laser Diode Thermal Desorption (LDTD) and Direct Analysis in Real Time (DART) has rendered high-throughput mass spectrometry possible. It is used for large-scale comparative analysis of populations of samples. In practice, many factors resulting from the environment, the protocol, and even the instrument itself, can lead to minor discrepancies between spectra, rendering automated comparative analysis difficult. In this work, a sequence/pipeline of algorithms to correct variations between spectra is proposed. The algorithms correct multiple spectra by identifying peaks that are common to all and, from those, computes a spectrum-specific correction. We show that these algorithms increase comparability within large datasets of spectra, facilitating comparative analysis, such as machine learning.
Project description:The success of high-throughput proteomics hinges on the ability of computational methods to identify peptides from tandem mass spectra (MS/MS). However, a common limitation of most peptide identification approaches is the nearly ubiquitous assumption that each MS/MS spectrum is generated from a single peptide. We propose a new computational approach for the identification of mixture spectra generated from more than one peptide. Capitalizing on the growing availability of large libraries of single-peptide spectra (spectral libraries), our quantitative approach is able to identify up to 98% of all mixture spectra from equally abundant peptides and automatically adjust to varying abundance ratios of up to 10:1. Furthermore, we show how theoretical bounds on spectral similarity avoid the need to compare each experimental spectrum against all possible combinations of candidate peptides (achieving speedups of over five orders of magnitude) and demonstrate that mixture-spectra can be identified in a matter of seconds against proteome-scale spectral libraries. Although our approach was developed for and is demonstrated on peptide spectra, we argue that the generality of the methods allows for their direct application to other types of spectral libraries and mixture spectra.