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Although classical molecular biology assays can provide a measure of cellular response to chemical challenges, they rely on a single biological phenomenon to infer a broader measure of cellular metabolic response. These methods do not always afford the necessary sensitivity to answer questions of subcytotoxic effects, nor do they work for all cell types. Likewise, boutique assays such as cardiomyocyte beat rate may indirectly measure cellular metabolic response, but they too, are limited to measuring a specific biological phenomenon and are often limited to a single cell type. For these reasons, toxicological researchers need new approaches to determine metabolic changes across various doses in differing cell types, especially within the low-dose regime. The data collected herein demonstrate that LC-MS/MS-based untargeted metabolomics with a feature-agnostic view of the data, combined with a suite of statistical methods including an adapted environmental threshold analysis, provides a versatile, robust, and holistic approach to directly monitoring the overall cellular metabolomic response to pesticides. When employing this method in investigating two different cell types, human cardiomyocytes and neurons, this approach revealed separate subcytotoxic metabolomic responses at doses of 0.1 and 1 µM of chlorpyrifos and carbaryl. These findings suggest that this agnostic approach to untargeted metabolomics can provide a new tool for determining effective dose by metabolomics of chemical challenges, such as pesticides, in a direct measurement of metabolomic response that is not cell type-specific or observable using traditional assays. [ABSTRACT FROM AUTHOR]

Field-forward analytical technologies, such as portable mass spectrometry (MS), enable essential capabilities for real-time monitoring and point-of-care diagnostic applications. Significant and recent investments improving the features of miniaturized mass spectrometers enable various new applications outside of small molecule detection. Most notably, the addition of tandem mass spectrometry scans (MS/MS) allows the instrument to isolate and fragment ions and increase the analytical specificity by measuring unique chemical signatures for ions of interest. Notwithstanding these technological advancements, low-cost, portable systems still struggle to confidently identify clinically significant organisms of interest, such as bacteria, viruses, and proteinaceous toxins, due to the limitations in resolving power. To overcome these limitations, we developed a novel multidimensional mass fingerprinting technique that uses tandem mass spectrometry to increase the chemical specificity for low-resolution mass spectral profiles. We demonstrated the method's capabilities for differentiating four different bacteria, including attentuated strains of Yersinia pestis. This approach allowed for the accurate (>92%) identification of each organism at the strain level using de-resolved matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) data to mimic the performance characteristics of miniaturized mass spectrometers. This work demonstrates that low-resolution mass spectrometers, equipped with tandem MS acquisition modes, can accurately identify clinically relevant bacteria. These findings support the future application of these technologies for field-forward and point-of-care applications where high-performance mass spectrometers would be cost-prohibitive or otherwise impractical. [ABSTRACT FROM AUTHOR]

Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis. Microscopy, nevertheless, forms the basis for traditional histopathology and thus contains invaluable morphological information. In this work, we developed a multimodal classification pipeline that uses deep learning, in the form of a pre-trained artificial neural network, to extract the meaningful morphological features from histopathological images, and combine it with the IMS data. To test whether this deep learning-based classification strategy can improve on our previous results in classification of melanocytic neoplasia, we utilized MALDI IMS data with collected serial H&E stained sections for 331 patients, and compared this multimodal classification pipeline to classifiers using either exclusively microscopy or IMS data. The multimodal pipeline achieved the best performance, with ROC-AUCs of 0.968 vs. 0.938 vs. 0.931 for the multimodal, unimodal microscopy and unimodal IMS pipelines respectively. Due to the use of a pre-trained network to perform the morphological feature extraction, this pipeline does not require any training on large amounts of microscopy data. As such, this framework can be readily applied to improve classification performance in other experimental settings where microscopy data is acquired in tandem with IMS experiments. [ABSTRACT FROM AUTHOR]

The preparation of complex biological samples for high-throughput mass spectrometric analyses remains a significant bottleneck, limiting advancement of the capabilities of mass spectrometry (MS) and ultimately limiting development of novel clinical assays. The removal of interfering species (e.g., salts, detergents, and buffers), concentration of dilute analytes, and the reduction of sample complexity are required in order to maximize the quality of resultant MS data. This study describes a novel sample preparation method that makes use of electrophoresis to prepare complex biological samples for high-throughput MS analysis. The method provides for integration of key sample preparation steps, including depletion, fractionation, desalting, and concentration. The prepared samples are captured onto a monolithic reversed-phase capture target that can be analyzed directly by a mass spectrometer. Up to 96 individual samples are simultaneously prepared for MS analysis in under 1 h. For standard proteins added to serum, this method provides femtomole level sensitivity and reproducible label-free detection (coefficient of variation

This study reports the synthesis of the first functional cleavable detergent designed specifically for applications in mass spectrometry. Upon cleavage, two inert compounds and the MALDI matrix are formed, eliminating sources of potential interference originating from traditional cleavable detergents. Analysis of peptides demonstrates that MALDI matrix generated in situ results in MALDI spectra equivalent to those prepared using established protocols. Analysis of the membrane protein diacylgiycerol kinase was accomplished using the combination detergent/MALDI matrix. Applications of the functional cleavable detergents to the profiling of whole cell lysates results in increased signal-to-noise ratios of many ions and the detection of additional proteins previously not observed.

Detergents have been used to enhance the solubility of hydrophobic biomolecules for decades. Despite the widespread use of detergents in biochemistry, the presence of these molecules often complicates further analysis by mass spectrometry. This study presents a solution to this problem by outlining a method utilizing a novel cleavable detergent, 3-[3-(1,1-bisalkyloxyethyl)pyridin- 1 oyl]propane1-sulfonate (PPS). This detergent can be used to extract protein contained within the interior of the cell by disrupting cell membranes. Once the proteins are free from the cell, PPS also assists in protein solubilization by shielding the hydrophobic regions of the newly extracted protein from unfavorable interactions with water. The added advantage of PPS over conventional detergents such as sodium dodecyl sulfate or n-octylglucoside is that the detergent properties that interfere with MALDI mass spectrometry can be eliminated prior to analysis. PPS was found to improve sensitivity in MALDI analyses of both soluble proteins and membrane proteins without degrading spectral quality. The virtues of this strategy were applied to whole cell extracts. Analysis of these extracts resulted in an overall increase in both the number of peaks observed and overall signal intensity.

Lysine residue amino groups in peptides and proteins are modified by reaction with the peroxycarbonate. The findings indicate that with the help of free radical-promoted processes, the strategies for directed fragmentation of peptides are feasible and it can simplify schemes for protein analysis.

Data-independent acquisition (DIA)-based proteomics has become increasingly complicated in recent years because of the vast number of workflows described, coupled with a lack of studies indicating a rational framework for selecting effective settings to use. To address this issue and provide a resource for the proteomics community, we compared 12 DIA methods that assay tryptic peptides using various mass-isolation windows. Our findings indicate that the most sensitive single injection LC-DIA method uses 6 m/z isolation windows to analyze the densely populated tryptic peptide range from 450 to 730 m/z, which allowed quantification of 4465 Escherichia coli peptides. In contrast, using the sequential windowed acquisition of all theoretical fragmentions (SWATH) approach with 26 m/z isolation windows across the entire 400-1200 m/z range, allowed quantification of only 3309 peptides. This reduced sensitivity with 26 m/z windows is caused by an increase in co-eluting compounds with similar precursor values detected in the same tandemMS spectra, which lowers the signal-to-noise of peptide fragment-ion chromatograms and reduces the amount of low abundance peptides that can be quantified from 410 to 920 m/z. Above 920 m/z, more peptides were quantified with 26 m/z windows because of substantial peptide 13C isotope distributions that parse peptide ions into separate isolation windows. Because reproducible quantification has been a long-standing aimof quantitative proteomics, and is a socalled trait of DIA, we sought to determine whether precursor-level chromatograms used in some methods rather than their fragment-level counterparts have similar precision. Our data show that extracted fragment-ion chromatograms are the reason DIA provides superior reproducibility. [ABSTRACT FROM AUTHOR]