Data-Driven Soft Independent Modeling of Class Analogy in Paper Spray Ionization Mass Spectrometry-Based Metabolomics for Rapid Detection of Prostate Cancer

Paper spray mass spectrometry combined with machine learning as a rapid diagnostic for chronic kidney disease

A new analytical method for chronic kidney disease (CKD) detection utilizing paper spray mass spectrometry (PS-MS) combined with machine learning is presented. The analytical protocol is rapid and simple, based on metabolic profile alterations in urine. Anonymized raw urine samples were deposited (10 μL each) onto pointed PS-MS sample strips. Without waiting for the sample to dry, 75 μL of acetonitrile and high voltage were applied to the strips, using high resolution mass spectrometry measurement (15 s per sample) with polarity switching to detect a wide range of metabolites. Random forest machine learning was used to classify the resulting data. The diagnostic performance for the potential diagnosis of CKD was evaluated for accuracy, sensitivity, and specificity, achieving results >96% for the training data and >91% for validation and test data sets. Metabolites selected by the classification model as up- or down-regulated in healthy or CKD samples were tentatively identified and in agreement with previously reported literature. The potential utilization of this approach to discriminate albuminuria categories (normo, micro, and macroalbuminuria) was also demonstrated. This study indicates that PS-MS combined with machine learning has the potential to be used as a rapid and simple diagnostic tool for CKD.

Data-Driven Version of Multiway Soft Independent Modeling of Class Analogy (N-Way DD-SIMCA): Theory and Application

One-class classification (OCC) is discussed in the framework of the measurement and processing of multiway data. Data-driven soft independent modeling of class analogy (DD-SIMCA) is applied in the following formats: (1) multiblock and (2) Tucker 3 N-way SIMCA, which are shown to be useful tools for solving classification tasks. A new decision rule for N-way DD-SIMCA is adopted based on the conventional two-way DD-SIMCA model. Multiblock SIMCA is shown to be useful for variable selection, and Tucker 3 SIMCA to select the optimal model complexity when applying multiway data decomposition and to assess the role of individual samples in the classification model. Both approaches, together with the two-way DD-SIMCA version applied to the unfolded data, are compared regarding the analysis of an experimental data set including genuine and adulterated blueberry extract samples. The latter were employed to produce matrix spectral-time data matrices per sample within a flow injection system, taking advantage of the spectral changes in the sample constituents as a function of the pH of the carrier phase. The need to employ the Tucker 3 model instead of a trilinear decomposition is supported by a discussion on the lack of the trilinearity property of the studied data.

Miniature mass spectrometers and their potential for clinical point-of-care analysis

Mass spectrometry (MS) has become a powerful technique for clinical applications with high sensitivity and specificity. Different from conventional MS diagnosis in laboratory, point-of-care (POC) analyses in clinics require mass spectrometers and analytical procedures to be friendly for novice users and applicable for on-site clinical diagnosis. The recent decades have seen the progress in the development of miniature mass spectrometers, providing a promising solution for clinical POC applications. In this review, we report recent advances of miniature mass spectrometers and their exploration in clinical applications, mainly including the rapid analysis of illegal drugs, on-site monitoring of therapeutic drugs, and detection of biomarkers. With improved analytical performance, miniature mass spectrometers are also expected to apply to more and more clinical applications. Some promising POC analyses that can be performed by miniature mass spectrometers in the future are discussed. Lastly, we also provide our perspectives on the challenges in technical development of miniature mass spectrometers for clinical POC analysis.

PAMAM-Functionalized Paper as a New Substrate for the Paper Spray Mass Spectrometry Measurement of Proteins

Paper surface functionalization with polyamidoamine (PAMAM) dendrimers has been developed for increased sensitivity analysis of proteins by paper spray mass spectrometry (PS-MS). PAMAM is a branched polymeric compound with an ethylenediamine core linked to repeating PAMAM units that generates an outer surface rich in primary amines. These positively charged amine groups can interact electrostatically with negatively charged residues (e.g., aspartate, glutamate) on the protein surface. PAMAM inner amide moieties can also promote hydrogen bonding with protein surface oxygens, making PAMAM a useful material for protein extraction. PAMAM-functionalized PS-MS paper strips were used to extract proteins from biofluids, dipped in acetonitrile to remove unbound constituents, dried, and then measured with PS-MS. The use of this strategy was optimized and compared with unmodified paper strips. PAMAM-functionalized paper substrates provided sixfold greater sensitivity for albumin, 11-fold for hemoglobin, sevenfold for insulin, and twofold for lysozyme. The analytical performance of the functionalized paper substrate was evaluated through the analysis of albumin in urine, achieving linearity with R² > 0.99, LOD of 1.1 μg mL^-1, LOQ of 3.8 μg mL^-1, precision better than 10%, and relative recovery 70-83%. The method was applied to quantify urinary albumin from nine anonymous patient samples (concentrations ranged from 6.5 to 77.4 μg mL^-1), illustrating its potential for the diagnosis of microalbuminuria. These data demonstrate the utility of paper modification with the PAMAM dendrimer for sensitive PS-MS analysis of proteins, opening a path for further applications in clinical diagnosis through the analysis of disease-related proteins.

Expanding the DD-SIMCA concept: A case study of precision medicine

It is proposed to use DD-SIMCA method, and, particularly, Full Distancse (FD) as an analytical signal that characterizes each sample in the frame of a classification task. The approach is demonstrated using medical data. FD values help to assess the proximity of each patient to the target class of the control (healthy) subjects. Furthermore, the FD values are used as a response in the PLS model, which predicts the distance of the subject (object) to the target class after a certain treatment and, therefore, the probability of recovery for each person. This enables the application of the personalized medicine. The proposed approach can be used not only in medicine, but also in other fields, e.g., restoration work to preserve and restore cultural heritage sites.

Embossed Paper Platform for Whole Blood Collection, Room Temperature Storage, and Direct Analysis by Pinhole Paper Spray Mass Spectrometry

Dry-state microsampling techniques are convenient and advantageous for sample collection in resource-limited settings, including healthcare systems designed for the underserved population. In this work, a microsampling platform based on an embossed hydrophobic paper substrate is introduced together with three-dimensional (3D) printed cartridges that offer opportunities for rapid (<30 min) drying of the collected samples while also preserving sample integrity when the embossed paper chip is shipped at room temperature. More importantly, a new pinhole paper spray ionization method was developed that facilitates direct mass spectrometry (MS) analysis of the dried blood samples without prior sample preparation. We compared the direct pinhole paper spray MS method with a liquid chromatographic (LC) MS approach that relied upon electrospray ionization (ESI) after analytes present in the blood sample were extracted through liquid-liquid extraction. Limits of detection as low as 0.12 and 0.49 ng/mL were calculated for cocaine and its metabolite benzoylecgonine, respectively, when using the direct pinhole paper spray MS method. Analytical merits such as precision and accuracy, recovery, carryover effects, and analyte stability were all quantified for this new paper spray method and compared to the traditional LC-ESI-MS. Although LC-ESI-MS was observed to be 10× more sensitive, the linear dynamic range for both methods was determined to be the same, in the range of 1-500 ng/mL for both cocaine and benzoylecgonine analytes. When fully developed, the current microsampling strategy could offer an easy-to-use kit that can enable a more effective MS analysis of 20 μL dried blood samples delivered by mail. Both sensitivity (10×) and sample stability are found to be more superior for blood prepared in the embossed hydrophobic paper compared to samples prepared in the planar hydrophilic paper.