High‐precision isotopic analysis of essential mineral elements: capabilities as a diagnostic/prognostic tool

Alzheimer's Disease and Age-Related Changes in the Cu Isotopic Composition of Blood Plasma and Brain Tissues of the APPNL-G-F Murine Model Revealed by Multi-Collector ICP-Mass Spectrometry

Alzheimer's' disease (AD) is characterized by the formation of β-amyloid (Aβ) plaques and neurofibrillary tangles of tau protein in the brain. Aβ plaques are formed by the cleavage of the β-amyloid precursor protein (APP). In addition to protein aggregations, the metabolism of the essential mineral element Cu is also altered during the pathogenesis of AD. The concentration and the natural isotopic composition of Cu were investigated in blood plasma and multiple brain regions (brain stem, cerebellum, cortex, and hippocampus) of young (3-4 weeks) and aged (27-30 weeks) APP^NL-G-F knock-in mice and wild-type controls to assess potential alterations associated with ageing and AD. Tandem inductively coupled plasma-mass spectrometry (ICP-MS/MS) was used for elemental analysis and multi-collector inductively coupled plasma-mass spectrometry (MC-ICP-MS) for high-precision isotopic analysis. The blood plasma Cu concentration was significantly altered in response to both age- and AD-related effects, whereas the blood plasma Cu isotope ratio was only affected by the development of AD. Changes in the Cu isotopic signature of the cerebellum were significantly correlated with the changes observed in blood plasma. The brain stem showed a significant increase in Cu concentration for both young and aged AD transgenic mice compared with healthy controls, whereas the Cu isotopic signature became lighter as a result of age-related changes. In this work, ICP-MS/MS and MC-ICP-MS provided relevant and complementary information on the potential role of Cu in ageing and AD.

High-Precision Isotopic Analysis of Cu and Fe via Multi-Collector Inductively Coupled Plasma-Mass Spectrometry Reveals Lipopolysaccharide-Induced Inflammatory Effects in Blood Plasma and Brain Tissues

The concentration and the isotopic composition of the redox-active essential elements Cu and Fe were investigated in blood plasma and specific brain regions (hippocampus, cortex, brain stem and cerebellum) of mice to assess potential alterations associated with sepsis-associated encephalopathy induced by lipopolysaccharide (LPS) administration. Samples were collected from young (16-22 weeks) and aged (44-65 weeks) mice after intraperitoneal injection of the LPS, an endotoxin inducing neuroinflammation, and from age- and sex-matched controls, injected with phosphate-buffered saline solution. Sector-field single-collector inductively coupled plasma-mass spectrometry was relied upon for elemental analysis and multi-collector inductively coupled plasma-mass spectrometry for isotopic analysis. Significant variations were observed for the Cu concentration and for the Cu and Fe isotope ratios in the blood plasma. Concentrations and isotope ratios of Cu and Fe also varied across the brain tissues. An age- and an inflammatory-related effect was found affecting the isotopic compositions of blood plasma Cu and cerebellum Fe, whereas a regional Cu isotopic redistribution was found within the brain tissues. These findings demonstrate that isotopic analysis of essential mineral elements picks up metabolic changes not revealed by element quantification, making the two approaches complementary.

Identification of two-dimensional copper signatures in human blood for bladder cancer with machine learning

Currently, almost all available cancer biomarkers are based on concentrations of compounds, often suffering from low sensitivity, poor specificity, and false positive or negative results. The stable isotopic composition of elements provides a different dimension from the concentration and has been widely used as a tracer in geochemistry. In health research, stable isotopic analysis has also shown potential as a new diagnostic/prognostic tool, which is still in the nascent stage. Here we discovered that bladder cancer (BCa) could induce a significant variation in the ratio of natural copper isotopes (⁶⁵Cu/⁶³Cu) in the blood of patients relative to benign and healthy controls. Such inherent copper isotopic signatures permitted new insights into molecular mechanisms of copper imbalance underlying the carcinogenic process. More importantly, to enhance the diagnostic capability, a machine learning model was developed to classify BCa and non-BCa subjects based on two-dimensional copper signatures (copper isotopic composition and concentration in plasma and red blood cells) with a high sensitivity, high true negative rate, and low false positive rate. Our results demonstrated the promise of blood copper signatures combined with machine learning as a versatile tool for cancer research and potential clinical application.

The blood Cu concentration and isotopic composition enable new insights into Cu imbalance and diagnosis of bladder cancer with machine learning.

Material-assisted mass spectrometric analysis of low molecular weight compounds for biomedical applications

Low molecular weight compounds play an important role in encoding the current physiological state of an individual. Laser desorption/ionization mass spectrometry (LDI MS) offers high sensitivity with low cost for molecular detection, but it is not able to cover small molecules due to the drawbacks of the conventional matrix. Advanced materials are better alternatives, showing little background interference and high LDI efficiency. Herein, we first classify the current materials with a summary of compositions and structures. Matrix preparation protocols are then reviewed, to enhance the selectivity and reproducibility of MS data better. Finally, we highlight the biomedical applications of material-assisted LDI MS, at the tissue, bio-fluid, and cellular levels. We foresee that the advanced materials will bring far-reaching implications in LDI MS towards real-case applications, especially in clinical settings.

Homeostatic alterations related to total antioxidant capacity, elemental concentrations and isotopic compositions in aqueous humor of glaucoma patients

Glaucoma is a multifactorial eye disease, characterized by progressive optic neurodegeneration. Elevation of the intraocular pressure is the main risk factor for glaucoma and is a consequence of an imbalance in the aqueous humor hydrodynamics, the physiology of which is influenced by the homeostatic equilibrium of essential elements, oxidative stress, and antioxidants. The aim of this work was to study local alterations in glaucomatous patients from two different, but connected, points of view: (i) the total antioxidant capacity (as an indicator of oxidative damage) and (ii) the concentration of mineral elements and their isotopic composition. Such objective was pursued using aqueous humor from patients diagnosed with pseudoexfoliation glaucoma (PEXG, n = 17) and primary open-angle glaucoma (POAG, n = 5) and age-matched control subjects (n = 16). The total antioxidant capacity (TAC) was examined in both aqueous humor and 60 serum samples (n = 20 controls, n = 20 for PEXG, and n = 20 for POAG), both showing higher TAC for the glaucoma population. The concentrations of the essential mineral elements (Cu, Fe, Mg, Na, P, and Zn) and the isotopic compositions of Cu and Zn were determined in aqueous humor using single-collector and multi-collector inductively coupled plasma–mass spectrometry, respectively. Significant differences were established for Mg and P levels when comparing the results for glaucomatous patients with those for the control population (p < 0.01 and p < 0.05 for Mg and P respectively, ANOVA and Kruskal-Wallis). The Zn isotopic composition was significantly shifted from that for the control population for PEXG patients. A significant difference in the isotopic composition of Zn was also established between the PEXG and POAG glaucoma cohorts.

Robust Potassium Isotopic Analysis of Geological and Biological Samples via Multicollector ICP-Mass Spectrometry Using the "Extra-High Resolution Mode"

Potassium isotopic analysis is arousing increasing interest, not only in geochemistry, but also in biomedicine. However, real-life applications are still hindered by the lack of robustness of the methods used. In this work, a novel and robust method for high-precision K isotopic analysis of geological and biological samples was developed, based on the use of a multicollector ICP-mass spectrometer providing a mass resolving power of 15,000 (extra-high resolution mode, XHR). After evaluation of different measurement conditions, i.e., hot vs cold plasma conditions, standard-type vs jet-type sampling cone, and high resolution (HR) vs XHR, a combination of hot plasma conditions, use of the high-transmission jet-type sampling cone, and the XHR mode allowed for high-precision and interference-free K isotopic analysis. Potassium signal monitoring was performed in the ArH+ interference-free 0.006-0.007 amu wide peak shoulder using the XHR mode. The within-run, short-term external, and long-term external precisions for the δ41K value were 0.02‰ (2se, N = 50), 0.03‰ (2SD, N = 7), and 0.06‰ (2SD, N = 163), respectively. A two-stage chromatographic procedure was developed for the isolation of K from both geological and biological samples, and potential matrix effects affecting the K isotope ratio were systematically evaluated. The method was first applied to geological reference materials (RMs) for validation purposes, and the K isotope ratio results were in good agreement with those previously reported. Subsequently, a series of biological RMs, including serum, whole blood, cerebrospinal fluid, bovine muscle, and lobster hepatopancreas, were characterized for their K isotopic composition.

Metal-Organic Framework Hybrids Aid Metabolic Profiling for Colorectal Cancer

Colorectal cancer (CRC) is the third most common fatal cancer worldwide, accounting for ≈10% of cancer-related mortality. Metabolic shift occurs from the very early stage during the development of CRC, which is of significant etiological and diagnostic importance toward precision medicine. Here, an advanced molecular tool to characterize the metabolic alterations in CRC, based on metal-organic framework (MOF) hybrids is reported. Consuming only 500 nL of plasma without any sample pretreatment, MOF hybrids yield direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds. A diagnostic prediction model by a machine learning algorithm is constructed, to discriminate CRC patients from normal controls with an average area under the curve of 0.947 for the discovery cohort and 0.912 for the independent validation cohort. In addition, CRC-specific metabolic signature consisting of 34 potential biomarkers, based on the aforementioned diagnostic model is identified. The results advance the design of nanomaterial-based platforms for metabolic analysis and establish a new liquid biopsy tool for CRC screening compatible with the current clinical workflow in practice.

Plasmonic Alloys Reveal a Distinct Metabolic Phenotype of Early Gastric Cancer

Gastric cancer (GC) is a multifactorial process, accompanied by alterations in metabolic pathways. Non-invasive metabolic profiling facilitates GC diagnosis at early stage leading to an improved prognostic outcome. Herein, mesoporous PdPtAu alloys are designed to characterize the metabolic profiles in human blood. The elemental composition is optimized with heterogeneous surface plasmonic resonance, offering preferred charge transfer for photoinduced desorption/ionization and enhanced photothermal conversion for thermally driven desorption. The surface structure of PdPtAu is further tuned with controlled mesopores, accommodating metabolites only, rather than large interfering compounds. Consequently, the optimized PdPtAu alloy yields direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds, consuming 500 nL of native plasma. A distinct metabolic phenotype is revealed for early GC by sparse learning, resulting in precise GC diagnosis with an area under the curve of 0.942. It is envisioned that the plasmonic alloy will open up a new era of minimally invasive blood analysis to improve the surveillance of cancer patients in the clinical setting.