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Background: Given the opioid epidemic, fentanyl screening in urine has become increasingly important. Immunoassays remain the most common screening methodology due to the high throughput and ease of integration into automated chemistry systems. The fentanyl ARK II from Ark Diagnostics is a widely used immunoassay, while a novel fentanyl assay called FEN2 by Lin-Zhi has become available on the Roche platform. Here, we evaluate and compare their performance., Methods: Four hundred and thirty-four urine samples were analyzed for fentanyl across the Lin-Zhi FEN2 and ARK II assays on the Cobas c502 platform. Samples were analyzed immediately upon request for drug of abuse screening or frozen for subsequent analysis. For confirmation testing, a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method with a limit of detection of 1 ng/mL for fentanyl/norfentanyl was used. Any sample with either fentanyl or norfentanyl above the LC-MS/MS cutoff was deemed positive., Results: The ARK II had 11 false negatives and 7 false positives, while the Lin-Zhi FEN2 had 12 false negatives and 2 false positives. This resulted in ARK II having a sensitivity and specificity of 90.4% and 97.8% respectively, while Lin-Zhi FEN2 had a sensitivity and specificity of 89.5% and 99.4%., Conclusions: Both the ARK II and Lin-Zhi FEN2 immunoassays detected fentanyl well. Overall, the Lin-Zhi assay had slightly better specificity than ARK II, in our data set. While some discrepant results were observed between the 2 immunoassay systems, most occurred near the immunoassay detection cutoffs., (© Association for Diagnostics & Laboratory Medicine 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Intratumoral heterogeneity can wreak havoc on current precision medicine strategies because of challenges in sufficient sampling of geographically separated areas of biodiversity distributed across centimeter-scale tumor distances. To address this gap, we developed a deep learning pipeline that leverages histomorphologic fingerprints of tissue to create "Histomic Atlases of Variation Of Cancers" (HAVOC). Using a number of objective molecular readouts, we demonstrate that HAVOC can define regional cancer boundaries with distinct biology. Using larger tumor specimens, we show that HAVOC can map biodiversity even across multiple tissue sections. By guiding profiling of 19 partitions across six high-grade gliomas, HAVOC revealed that distinct differentiation states can often coexist and be regionally distributed within these tumors. Last, to highlight generalizability, we benchmark HAVOC on additional tumor types. Together, we establish HAVOC as a versatile tool to generate small-scale maps of tissue heterogeneity and guide regional deployment of molecular resources to relevant biodiverse niches.

The human brain represents one of the most complex biological structures with significant spatiotemporal molecular plasticity occurring through early development, learning, aging, and disease. While much progress has been made in mapping its transcriptional architecture, more downstream phenotypic readouts are relatively scarce due to limitations with tissue heterogeneity and accessibility, as well as an inability to amplify protein species prior to global -OMICS analysis. To address some of these barriers, our group has recently focused on using mass-spectrometry workflows compatible with small amounts of formalin-fixed paraffin-embedded tissue samples. This has enabled exploration into spatiotemporal proteomic signatures of the brain and disease across otherwise inaccessible neurodevelopmental timepoints and anatomical niches. Given the similar theme and approaches, we introduce an integrated online portal, "The Brain Protein Atlas (BPA)" (www.brainproteinatlas.org), representing a public resource that allows users to access and explore these amalgamated datasets. Specifically, this portal contains a growing set of peer-reviewed mass-spectrometry-based proteomic datasets, including spatiotemporal profiles of human cerebral development, diffuse gliomas, clinically aggressive meningiomas, and a detailed anatomic atlas of glioblastoma. One barrier to entry in mass spectrometry-based proteomics data analysis is the steep learning curve required to extract biologically relevant data. BPA, therefore, includes several built-in analytical tools to generate relevant plots (e.g., volcano plots, heatmaps, boxplots, and scatter plots) and evaluate the spatiotemporal patterns of proteins of interest. Future iterations aim to expand available datasets, including those generated by the community at large, and analytical tools for exploration. Ultimately, BPA aims to improve knowledge dissemination of proteomic information across the neuroscience community in hopes of accelerating the biological understanding of the brain and various maladies., (© 2022 Wiley-VCH GmbH.)

Glioblastoma is often subdivided into three transcriptional subtypes (classical, proneural, mesenchymal) based on bulk RNA signatures that correlate with distinct genetic and clinical features. Potential cellular-level differences of these subgroups, such as the relative proportions of glioblastoma's hallmark histopathologic features (e.g. brain infiltration, microvascular proliferation), may provide insight into their distinct phenotypes but are, however, not well understood. Here we leverage machine learning and reference proteomic profiles derived from micro-dissected samples of these major histomorphologic glioblastoma features to deconvolute and estimate niche proportions in an independent proteogenomically-characterized cohort. This approach revealed a strong association of the proneural transcriptional subtype with a diffusely infiltrating phenotype. Similarly, enrichment of a microvascular proliferation proteomic signature was seen within the mesenchymal subtype. This study is the first to link differences in the cellular pathology signatures and transcriptional profiles of glioblastoma, providing potential new insights into the genetic drivers and poor treatment response of specific subsets of glioblastomas., (© 2022. The Author(s).)

Cerebral organoids have emerged as robust models for neurodevelopmental and pathological processes, as well as a powerful discovery platform for less-characterized neurobiological programs. Toward this prospect, we leverage mass-spectrometry-based proteomics to molecularly profile precursor and neuronal compartments of both human-derived organoids and mid-gestation fetal brain tissue to define overlapping programs. Our analysis includes recovery of precursor-enriched transcriptional regulatory proteins not found to be differentially expressed in previous transcriptomic datasets. To highlight the discovery potential of this resource, we show that RUVBL2 is preferentially expressed in the SOX2-positive compartment of organoids and that chemical inactivation leads to precursor cell displacement and apoptosis. To explore clinicopathological correlates of this cytoarchitectural disruption, we interrogate clinical datasets and identify rare de novo genetic variants involving RUVBL2 in patients with neurodevelopmental impairments. Together, our findings demonstrate how cell-type-specific profiling of organoids can help nominate previously unappreciated genes in neurodevelopment and disease., Competing Interests: Declaration of interests S.W.S. is on the Scientific Advisory Committees of Deep Genomics and Population Bio and serves as an academic consultant for the King Abdulaziz University., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Glioblastoma is an aggressive form of brain cancer with well-established patterns of intra-tumoral heterogeneity implicated in treatment resistance and progression. While regional and single cell transcriptomic variations of glioblastoma have been recently resolved, downstream phenotype-level proteomic programs have yet to be assigned across glioblastoma's hallmark histomorphologic niches. Here, we leverage mass spectrometry to spatially align abundance levels of 4,794 proteins to distinct histologic patterns across 20 patients and propose diverse molecular programs operational within these regional tumor compartments. Using machine learning, we overlay concordant transcriptional information, and define two distinct proteogenomic programs, MYC- and KRAS-axis hereon, that cooperate with hypoxia to produce a tri-dimensional model of intra-tumoral heterogeneity. Moreover, we highlight differential drug sensitivities and relative chemoresistance in glioblastoma cell lines with enhanced KRAS programs. Importantly, these pharmacological differences are less pronounced in transcriptional glioblastoma subgroups suggesting that this model may provide insights for targeting heterogeneity and overcoming therapy resistance., (© 2022. The Author(s).)

Cyclosporins are an invaluable class of drug used to prevent the rejection of transplanted tissue. While the most popular drug in this group is cyclosporin A, several other analogues are available, including some enantiomeric and structurally isomeric forms. Unfortunately, the presence of such isomers can make the detection and identification of these drugs by mass spectrometry (MS) alone quite challenging. Here, we demonstrate the separation and analysis of six cyclosporin analogues using liquid chromatography (LC) and differential mobility spectroscopy (DMS) coupled to MS. Using DMS, we demonstrate the separation of three isomers: CycA and CycH (cyclosporin H), which are enantiomers, and isocyclosporin A (a structural isomer of CycA and CycH). For several of the cyclosporins, we can separate different conformers for each isomeric form. After DMS separation, tandem mass spectrometry (MS/MS) analyses of the separated isomers also distinguish these isomeric forms of cyclosporin. In addition, we have probed differences between each isomer by using gas-phase hydrogen-deuterium exchange (HDX) immediately after DMS separation, which reveals differences in the levels of intramolecular hydrogen bonding between each of the cyclosporins.

Molecular characterization of diffuse gliomas has thus far largely focused on genomic and transcriptomic interrogations. Here, we utilized mass spectrometry and overlay protein-level information onto genomically defined cohorts of diffuse gliomas to improve our downstream molecular understanding of these lethal malignancies. Bulk and macrodissected tissues were utilized to quantitate 5,496 unique proteins over three glioma cohorts subclassified largely based on their IDH and 1p19q codeletion status (IDH wild type (IDHwt), n = 7; IDH mutated (IDHmt), 1p19q non-codeleted, n = 7; IDH mutated, 1p19q-codeleted, n = 10). Clustering analysis highlighted proteome and systems-level pathway differences in gliomas according to IDH and 1p19q-codeletion status, including 287 differentially abundant proteins in macrodissection-enriched tumor specimens. IDHwt tumors were enriched for proteins involved in invasiveness and epithelial to mesenchymal transition (EMT), while IDHmt gliomas had increased abundances of proteins involved in mRNA splicing. Finally, these abundance changes were compared with IDH-matched GBM stem-like cells (GSCs) to better pinpoint protein patterns enriched in putative cellular drivers of gliomas. Using this integrative approach, we outline specific proteins involved in chloride transport ( e.g. chloride intracellular channel 1, CLIC1) and EMT ( e.g. procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3, PLOD3, and serpin peptidase inhibitor clade H member 1, SERPINH1) that showed concordant IDH-status-dependent abundance differences in both primary tissue and purified GSC cultures. Given the downstream position proteins occupy in driving biology and phenotype, understanding the proteomic patterns operational in distinct glioma subtypes could help propose more specific, personalized, and effective targets for the management of patients with these aggressive malignancies., (© 2019 Djuric et al.)

Collision-induced dissociation of isotopically labelled protonated pentaglycine produced two abundant [b5]+ ions, the products of the loss of water from the first and second amide groups, labelled [b5]+I and [b5]+II. IRMPD spectroscopy and DFT calculations show that these two [b5]+ ions feature N1-protonated 3,5-dihydro-4H-imidazol-4-one structures. 15N-Labelling established that some interconversion occurs between these two ions but dissociations are preferred. For both ions, DFT calculations show that the barrier to interconversion is slightly higher than those to dissociation. Dehydration of protonated hexaglycine produced three imidazolone ions. Ions [b6]+I and [b6]+II exhibit analogous CID spectra to those from [b5]+I and [b5]+II; however, the spectrum of the [b6]+III ion was dramatically different, showing losses predominantly of a further water molecule or cleavage of the second amide bond to give the glycyloxazolone (a deprotonated [b2]+ ion, labelled GlyGlyox (114 Da)) from the N-terminus. Protonated polyglycines [Glyn + H]+, where n = 7-9, all readily lose at least one water molecule. The corresponding [bn]+ ions lose either a further water molecule, an oxazolone from the N-terminus or a truncated peptide from the C-terminus. The number of amino acid residues in the latter two eliminated neutral molecules provides insight into the location of the imidazolone in the peptide chain and which oxygen was lost in the initial dehydration reaction. From this analysis, it appears that water loss from the longer protonated polyglycines is predominantly from the central residues.

Collision-induced dissociations of isotopically labeled protonated tetraglycines establish that the [b 4 ] + ion formed by loss of water from the second amide bond (structure II) rearranges to form N 1 -protonated 3,5-dihydro-4H-imidazol-4-one (structure I), the product of water loss from the first amide bond. Structure II is slightly higher in energy than I (ΔH at 0 K is 5.1 kJ mol -1 , as calculated at M06-2X/6-311++G-(d,p)), and the barrier to interconversion is 139.8 kJ mol -1 above I. The dominant dissociation pathway is the loss of methanimine (HN=CH 2 ) from ion I with a barrier of 167.1 kJ mol -1 , giving [GlyGlyGlyGly + H - H 2 O - HN=CH 2 ] + , ion III; a minor channel, loss of NH 3 , has a slightly higher barrier (181.5 kJ mol -1 ). Using labeled glycine ( 13 C α ) it was determined that loss of the imine is from the same residue as that from which water was initially lost. The collision-induced dissociation spectra of ion III derived from both I and II were identical, and their energy-resolved curves were also very similar. Ion III fragments by losses of a glycine molecule (the dominant channel), a water molecule, and a glycine residue (57 Da), giving ions IV, V, and VII, respectively. Isotopic labeling established the origins of each of the neutral molecules that are lost. Using glycine (2,2 D 2 ), rapid deuterium exchange was observed for both ions I and II for the α-hydrogens that are from the same residue as that from which the water had been eliminated.