Molecular profiling for advancing precision rheumatology
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Mitochondrial priming and response to BH3 mimetics in “one-two punch” senogenic-senolytic strategies
A one-two punch sequential regimen of senescence-inducing agents followed by senolytic drugs has emerged as a novel therapeutic strategy in cancer. Unfortunately, cancer cells undergoing therapy-induced senescence (TIS) vary widely in their sensitivity to senotherapeutics, and companion diagnostics to predict the response of TIS cancer cells to a specific senolytic drug are lacking. Here, we hypothesized that the ability of the BH3 profiling assay to functionally measure the mitochondrial priming state—the proximity to the apoptotic threshold—and the dependencies on pro-survival BCL-2 family proteins can be exploited to inform the sensitivity of TIS cancer cells to BH3-mimetics. Replicative, mitotic, oxidative, and genotoxic forms of TIS were induced in p16-null/p53-proficient, BAX-deficient, and BRCA1-mutant cancer cells using mechanistically distinct TIS-inducing cancer therapeutics, including palbociclib, alisertib, doxorubicin, bleomycin, and olaparib. When the overall state of mitochondrial priming and competence was determined using activator peptides, the expected increase in overall mitochondrial priming was an exception rather than a generalizable feature across TIS phenotypes. A higher level of overall priming paralleled a higher sensitivity of competent TIS cancer cells to BCL-2/BCL-xL- and BCL-xL-targeted inhibitors when comparing TIS phenotypes among themselves. Unexpectedly, however, TIS cancer cells remained equally or even less overally primed than their proliferative counterparts. When sensitizing peptides were used to map dependencies on anti-apoptotic BCL-2 family proteins, competent TIS cancer cells appeared to share a dependency on BCL-xL. Furthermore, regardless of senescence-inducing therapeutic, stable/transient senescence acquisition, or genetic context, all TIS phenotypes shared a variable but significant senolytic response to the BCL-xL-selective BH3 mimetic A1331852. These findings may help to rethink the traditional assumption of the primed apoptotic landscape of TIS cancer cells. BCL-xL is a conserved anti-apoptotic effector of the TIS BCL2/BH3 interactome that can be exploited to maximize the efficacy of “one-two punch” senogenic-senolytic strategies.
ToF-SIMS sputter depth profiling of interphases and coatings on lithium metal surfaces
Lithium metal as a negative electrode material offers ten times the specific capacity of graphitic electrodes, but its rechargeable operation poses challenges like excessive and continuous interphase formation, high surface area lithium deposits and safety issues. Improving the lithium | electrolyte interface and interphase requires powerful surface analysis techniques, such as ToF-SIMS sputter depth profiling.This study investigates lithium metal sections with an SEI layer by ToF-SIMS using different sputter ions. An optimal sputter ion is chosen based on the measured ToF-SIMS sputter depth profiles and SEM analysis of the surface damage. Further, this method is adapted to lithium metal foil with an intermetallic coating. ToF-SIMS sputter depth profiles in both polarities provide comprehensive insights into the coating structure. Both investigations highlight the value of ToF-SIMS sputter depth profiling in lithium metal battery research and offer guidance for future studies.
Efficient computation using spatial-photonic Ising machines with low-rank and circulant matrix constraints
Spatial-photonic Ising machines (SPIMs) have shown promise as an energy-efficient Ising machine, but currently can only solve a limited set of Ising problems. There is currently limited understanding on what experimental constraints may impact the performance of SPIM, and what computationally intensive problems can be efficiently solved by SPIM. Our results indicate that the performance of SPIMs is critically affected by the rank and precision of the coupling matrices. By developing and assessing advanced decomposition techniques, we expand the range of problems SPIMs can solve, overcoming the limitations of traditional Mattis-type matrices. Our approach accommodates a diverse array of coupling matrices, including those with inherently low ranks, applicable to complex NP-complete problems. We explore the practical benefits of the low-rank approximation in optimisation tasks, particularly in financial optimisation, to demonstrate the real-world applications of SPIMs. Finally, we evaluate the computational limitations imposed by SPIM hardware precision and suggest strategies to optimise the performance of these systems within these constraints.
Spatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics
The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylation and next-generation sequencing (NGS) have become a cornerstone in central nervous system (CNS) tumor classification. A limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity, which restrict its feasibility. Here we demonstrate NePSTA (neuropathology spatial transcriptomic analysis) for comprehensive morphological and molecular neuropathological diagnostics from single 5-µm tissue sections. NePSTA uses spatial transcriptomics with graph neural networks for automated histological and molecular evaluations. Trained and evaluated across 130 participants with CNS malignancies and healthy donors across four medical centers, NePSTA predicts tissue histology and methylation-based subclasses with high accuracy. We demonstrate the ability to reconstruct immunohistochemistry and genotype profiling on tissue with minimal requirements, inadequate for conventional molecular diagnostics, demonstrating the potential to enhance tumor subtype identification with implications for fast and precise diagnostic workup.
Uncovering protein glycosylation dynamics and heterogeneity using deep quantitative glycoprofiling (DQGlyco)
Protein glycosylation regulates essential cellular processes such as signaling, adhesion and cell–cell interactions; however, dysregulated glycosylation is associated with diseases such as cancer. Here we introduce deep quantitative glycoprofiling (DQGlyco), a robust method that integrates high-throughput sample preparation, highly sensitive detection and precise multiplexed quantification to investigate protein glycosylation dynamics at an unprecedented depth. Using DQGlyco, we profiled the mouse brain glycoproteome, identifying 177,198 unique N-glycopeptides—25 times more than previous studies. We quantified glycopeptide changes in human cells treated with a fucosylation inhibitor and characterized surface-exposed glycoforms. Furthermore, we analyzed tissue-specific glycosylation patterns in mice and demonstrated that a defined gut microbiota substantially remodels the mouse brain glycoproteome, shedding light on the link between the gut microbiome and brain protein functions. Additionally, we developed a novel strategy to evaluate glycoform solubility, offering new insights into their biophysical properties. Overall, the in-depth profiling offered by DQGlyco uncovered extensive complexity in glycosylation regulation.
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