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Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite many solutions being developed, the large data size and high dimensional nature of MSI, especially 3D datasets, still pose computational and memory complexities that hinder accurate identification of biologically relevant molecular patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Therefore, we assess if a probabilistic generative model based on a fully connected variational autoencoder can be used for unsupervised analysis and peak learning of MSI data to uncover hidden structures. The resulting msiPL method learns and visualizes the underlying non-linear spectral manifold, revealing biologically relevant clusters of tissue anatomy in a mouse kidney and tumor heterogeneity in human prostatectomy tissue, colorectal carcinoma, and glioblastoma mouse model, with identification of underlying m/z peaks. The method is applied for the analysis of MSI datasets ranging from 3.3 to 78.9 GB, without prior pre-processing and peak picking, and acquired using different mass spectrometers at different centers. The high dimensional and complex nature of mass spectrometry imaging (MSI) data poses challenges to downstream analyses. Here the authors show an application of artificial intelligence in mining MSI data revealing biologically relevant metabolomic and proteomic information from data acquired on different mass spectrometry platforms.

Could similar changes in SOD1 underlie both familial and sporadic ALS? Here, Bosco et al . find that wild-type SOD1 from sporadic ALS tissues shows conformational changes similar to those seen in familial ALS and that aberrant wild-type SOD1 can be pathogenic, potentially as a result of the same SOD1-dependent mechanism seen in familial ALS. Many mutations confer one or more toxic function(s) on copper/zinc superoxide dismutase 1 (SOD1) that impair motor neuron viability and cause familial amyotrophic lateral sclerosis (FALS). Using a conformation-specific antibody that detects misfolded SOD1 (C4F6), we found that oxidized wild-type SOD1 and mutant SOD1 share a conformational epitope that is not present in normal wild-type SOD1. In a subset of human sporadic ALS (SALS) cases, motor neurons in the lumbosacral spinal cord were markedly C4F6 immunoreactive, indicating that an aberrant wild-type SOD1 species was present. Recombinant, oxidized wild-type SOD1 and wild-type SOD1 immunopurified from SALS tissues inhibited kinesin-based fast axonal transport in a manner similar to that of FALS-linked mutant SOD1. Our findings suggest that wild-type SOD1 can be pathogenic in SALS and identify an SOD1-dependent pathogenic mechanism common to FALS and SALS.

Covalent drug discovery efforts are growing rapidly but have major unaddressed limitations. These include high false positive rates during hit-to-lead identification; the inherent uncoupling of covalent drug concentration and effect [i.e., uncoupling of pharmacokinetics (PK) and pharmacodynamics (PD)]; and a lack of bioanalytical and modeling methods for determining PK and PD parameters. We present a covalent drug discovery workflow that addresses these limitations. Our bioanalytical methods are based upon a mass spectrometry (MS) assay that can measure the percentage of drug-target protein conjugation (% target engagement) in biological matrices. Further we develop an i ntact protein PK/PD model ( i PK/PD) that outputs PK parameters (absorption and distribution) as well as PD parameters (mechanism of action, protein metabolic half-lives, dose, regimen, effect) based on time-dependent target engagement data. Notably, the i PK/PD model is applicable to any measurement (e.g., bottom-up MS and other drug binding studies) that yields % of target engaged. A Decision Tree is presented to guide researchers through the covalent drug development process. Our bioanalytical methods and the Decision Tree are applied to two approved drugs (ibrutinib and sotorasib); the most common plasma off-target, human serum albumin; three protein targets (KRAS, BTK, SOD1), and to a promising SOD1-targeting ALS drug candidates. Robust bioanalytical and modeling methods are needed for covalent drug discovery. Here, the authors demonstrate a mass spectrometry (MS) assay to measure target engagement of any drug-target protein complex, a universal PK/PD model for covalent drugs, and a decision tree to guide research.

A heterologously expressed form of the human Parkinson disease-associated protein α-synuclein with a 10-residue N-terminal extension is shown to form a stable tetramer in the absence of lipid bilayers or micelles. Sequential NMR assignments, intramonomer nuclear Overhauser effects, and circular dichroism spectra are consistent with transient formation of α-helices in the first 100 N-terminal residues of the 140-residue α-synuclein sequence. Total phosphorus analysis indicates that phospholipids are not associated with the tetramer as isolated, and chemical cross-linking experiments confirm that the tetramer is the highest-order oligomer present at NMR sample concentrations. Image reconstruction from electron micrographs indicates that a symmetric oligomer is present, with three- or fourfold symmetry. Thermal unfolding experiments indicate that a hydrophobic core is present in the tetramer. A dynamic model for the tetramer structure is proposed, based on expected close association of the amphipathic central helices observed in the previously described micelle-associated \"hairpin\" structure of α-synuclein.

Therapeutic options for the treatment of glioblastoma remain inadequate despite concerted research efforts in drug development. Therapeutic failure can result from poor permeability of the blood-brain barrier, heterogeneous drug distribution, and development of resistance. Elucidation of relationships among such parameters could enable the development of predictive models of drug response in patients and inform drug development. Complementary analyses were applied to a glioblastoma patient-derived xenograft model in order to quantitatively map distribution and resulting cellular response to the EGFR inhibitor erlotinib. Mass spectrometry images of erlotinib were registered to histology and magnetic resonance images in order to correlate drug distribution with tumor characteristics. Phosphoproteomics and immunohistochemistry were used to assess protein signaling in response to drug, and integrated with transcriptional response using mRNA sequencing. This comprehensive dataset provides simultaneous insight into pharmacokinetics and pharmacodynamics and indicates that erlotinib delivery to intracranial tumors is insufficient to inhibit EGFR tyrosine kinase signaling. Despite major drug discovery efforts, the therapeutic options for glioblastoma (GBM) remain inadequate. Here they analyze patient-derived xenograft model of GBM to quantitatively map distribution and cellular response to the EGFR inhibitor erlotinib, and report heterogeneous erlotinib delivery to intracranial tumors to be inadequate to inhibit EGFR signaling.

The nature of the \"toxic gain of function\" that results from amyotrophic lateral sclerosis (ALS)-, Parkinson-, and Alzheimer-related mutations is a matter of debate. As a result no adequate model of any neurodegenerative disease etiology exists. We demonstrate that two synergistic properties, namely, increased protein aggregation propensity (increased likelihood that an unfolded protein will aggregate) and decreased protein stability (increased likelihood that a protein will unfold), are central to ALS etiology. Taken together these properties account for 69% of the variability in mutant Cu/Zn-superoxide-dismutase-linked familial ALS patient survival times. Aggregation is a concentration-dependent process, and spinal cord motor neurons have higher concentrations of Cu/Zn-superoxide dismutase than the surrounding cells. Protein aggregation therefore is expected to contribute to the selective vulnerability of motor neurons in familial ALS.

PurposeMedulloblastoma, the most common primary pediatric malignant brain tumor, originates in the posterior fossa of the brain. Pineoblastoma, which originates within the pineal gland, is a rarer malignancy that also presents in the pediatric population. Medulloblastoma and pineoblastoma exhibit overlapping clinical features and have similar histopathological characteristics. Histopathological similarities confound rapid diagnoses of these two tumor types. We have conducted a pilot feasibility study analyzing the molecular profile of archived frozen human tumor specimens using mass spectrometry imaging (MSI) to identify potential biomarkers capable of classifying and distinguishing between medulloblastoma and pineoblastoma.MethodsWe performed matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry imaging on eight medulloblastoma biopsy specimens and three pineoblastoma biopsy specimens. Multivariate statistical analyses were performed on the MSI dataset to generate classifiers that distinguish the two tumor types. Lastly, the molecules that were discriminative of tumor type were queried against the Lipid Maps database and identified.ResultsIn this pilot study we show that medulloblastoma and pineoblastoma can be discriminated using molecular profiles determined by MSI. The highest-ranking discriminating classifiers of medulloblastoma and pineoblastoma were glycerophosphoglycerols and sphingolipids, respectively.ConclusionWe demonstrate proof-of-concept that medulloblastoma and pineoblastoma can be rapidly distinguished by using MSI lipid profiles. We identified biomarker candidates capable of distinguishing these two histopathologically similar tumor types. This work expands the current molecular knowledge of medulloblastoma and pineoblastoma by characterizing their lipidomic profiles, which may be useful for developing novel diagnostic, prognostic and therapeutic strategies.

Amyotrophic lateral sclerosis (ALS) is a disorder characterized by the death of both upper and lower motor neurons and by 3- to 5-yr median survival postdiagnosis. The only US Food and Drug Administration-approved drug for the treatment of ALS, Riluzole, has at best, moderate effect on patient survival and quality of life; therefore innovative approaches are needed to combat neurodegenerative disease. Some familial forms of ALS (fALS) have been linked to mutations in the Cu/Zn superoxide dismutase (SOD1). The dominant inheritance of mutant SOD1 and lack of symptoms in knockout mice suggest a \"gain of toxic function\" as opposed to a loss of function. A prevailing hypothesis for the mechanism of the toxicity of fALS-SOD1 variants, or the gain of toxic function, involves dimer destabilization and dissociation as an early step in SOD1 aggregation. Therefore, stabilizing the SOD1 dimer, thus preventing aggregation, is a potential therapeutic strategy. Here, we report a strategy in which we chemically cross-link the SOD1 dimer using two adjacent cysteine residues on each respective monomer (Cys111). Stabilization, measured as an increase in melting temperature, of ∼20 °C and ∼45 °C was observed for two mutants, G93A and G85R, respectively. This stabilization is the largest for SOD1, and to the best of our knowledge, for any disease-related protein. In addition, chemical cross-linking conferred activity upon G85R, an otherwise inactive mutant. These results demonstrate that targeting these cysteine residues is an important new strategy for development of ALS therapies.

One gene can give rise to many functionally distinct proteoforms, each of which has a characteristic molecular mass. Top-down mass spectrometry enables the analysis of intact proteins and proteoforms. Here members of the Consortium for Top-Down Proteomics provide a decision tree that guides researchers to robust protocols for mass analysis of intact proteins (antibodies, membrane proteins and others) from mixtures of varying complexity. We also present cross-platform analytical benchmarks using a protein standard sample, to allow users to gauge their proficiency.

Despite decades of accumulated knowledge about proteins and their post-translational modifications (PTMs), numerous questions remain regarding their molecular composition and biological function. One of the most fundamental queries is the extent to which the combinations of DNA-, RNA- and PTM-level variations explode the complexity of the human proteome. Here, we outline what we know from current databases and measurement strategies including mass spectrometry-based proteomics. In doing so, we examine prevailing notions about the number of modifications displayed on human proteins and how they combine to generate the protein diversity underlying health and disease. We frame central issues regarding determination of protein-level variation and PTMs, including some paradoxes present in the field today. We use this framework to assess existing data and to ask the question, \"How many distinct primary structures of proteins (proteoforms) are created from the 20,300 human genes?\" We also explore prospects for improving measurements to better regularize protein-level biology and efficiently associate PTMs to function and phenotype.