Tissue profiling by nanogold-mediated mass spectrometry and artificial neural networks in the mouse model of human primary hyperoxaluria 1

Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models

Pathological pain subtypes can be classified as either neuropathic pain, caused by a somatosensory nervous system lesion or disease, or nociplastic pain, which develops without evidence of somatosensory system damage. Since there is no gold standard for the diagnosis of pathological pain subtypes, the proper classification of individual patients is currently an unmet challenge for clinicians. While the determination of specific biomarkers for each condition by current biochemical techniques is a complex task, the use of multimolecular techniques, such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), combined with artificial intelligence allows specific fingerprints for pathological pain-subtypes to be obtained, which may be useful for diagnosis. We analyzed whether the information provided by the mass spectra of serum samples of four experimental models of neuropathic and nociplastic pain combined with their functional pain outcomes could enable pathological pain subtype classification by artificial neural networks. As a result, a simple and innovative clinical decision support method has been developed that combines MALDI-TOF MS serum spectra and pain evaluation with its subsequent data analysis by artificial neural networks and allows the identification and classification of pathological pain subtypes in experimental models with a high level of specificity.

Dietary Clostridium butyricum supplementation modifies significantly the liver transcriptomic profile in weaned piglets

The addition of probiotics in swine nutrition is known to positively influence both health and growth. The current study investigates differences in the hepatic transcriptome profiles between weaned piglets supplemented with Clostridium butyricum (C. butyricum) and control animals that received no probiotic. The liver is an important metabolic organ that plays a critical role in oxidizing triglycerides for energy production, lipid synthesis and degradation, as well as immune regulation in animals. RNA-Seq analysis was carried out on total RNA harvested from the liver of piglets fed with (n = 3) or without (n = 3) 5 × 10⁵ C. butyricum CFU/g. Compared to the control piglets, 588 of the genes examined (352 up-regulated and 236 down-regulated) were significantly differentially expressed at a fold change > 2 and p < .05 in animals fed with C. butyricum. Quantitative real-time reverse transcription PCR (qRT-PCR) analysis was further used to validate the microarray expression results for 28 genes tested. The functional annotation analyses revealed several genes, processes and pathways with putative involvement in piglet growth and performance. Feeding swine with 5 × 10⁵ C. butyricum CFU/g appears to reinforce their immune status as well as foster the cell cycle and improve the metabolism of carbohydrates, lipids and amino acids. This study provides valuable information about the expression profiles of mRNAs in piglet liver and in-depth functional investigations of these mRNAs that could provide new insights into the molecular networks of growth, immune responses and nutrient metabolism in the porcine liver.

Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma

Multiple myeloma (MM) is a highly heterogeneous disease of malignant plasma cells. Diagnosis and monitoring of MM patients is based on bone marrow biopsies and detection of abnormal immunoglobulin in serum and/or urine. However, biopsies have a single-site bias; thus, new diagnostic tests and early detection strategies are needed. Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF MS) is a powerful method that found its applications in clinical diagnostics. Artificial intelligence approaches, such as Artificial Neural Networks (ANNs), can handle non-linear data and provide prediction and classification of variables in multidimensional datasets. In this study, we used MALDI-TOF MS to acquire low mass profiles of peripheral blood plasma obtained from MM patients and healthy donors. Informative patterns in mass spectra served as inputs for ANN that specifically predicted MM samples with high sensitivity (100%), specificity (95%) and accuracy (98%). Thus, mass spectrometry coupled with ANN can provide a minimally invasive approach for MM diagnostics.

Intact Cell Mass Spectrometry as a Quality Control Tool for Revealing Minute Phenotypic Changes of Cultured Human Embryonic Stem Cells

The stability of in vitro cell cultures is an important issue for any clinical, bio-industrial, or pharmacological use. Embryonic stem cells are pluripotent; consequently, they possess the ability to differentiate into all three germ layers and are inherently prone to respond to differentiation stimuli. However, long-term culture inevitably yields clones that are best adapted to the culture conditions, passaging regimes, or differentiation sensitivity. This cellular plasticity is a major obstacle in the development of bio-industrial or clinical-grade cultures. At present, the quality control of cell cultures is limited by the lack of reliable (epi)genetic or molecular markers or by the focus on a particular type of instability such as karyotype abnormalities or adverse phenotypic traits. Therefore, there is an ongoing need for robust, feasible, and sensitive methods of determining or confirming cell status and for revealing potential divergences from the optimal state. We modeled both intrinsic and extrinsic changes in human embryonic stem cell (hESC) states using different experimental strategies and addressed the changes in cell status by intact cell mass spectrometry fingerprinting. The analysis of spectral fingerprints by methods routinely used in analytical chemistry clearly distinguished the morphologically and biochemically similar populations of hESCs and provided a biomarker-independent tool for the quality control of cell culture. Stem Cells Translational Medicine 2018;7:109-114.

Clusters of Monoisotopic Elements for Calibration in (TOF) Mass Spectrometry

Precise calibration in TOF MS requires suitable and reliable standards, which are not always available for high masses. We evaluated inorganic clusters of the monoisotopic elements gold and phosphorus (Au _n⁺/Au _n^- and P _n⁺/P _n^-) as an alternative to peptides or proteins for the external and internal calibration of mass spectra in various experimental and instrumental scenarios. Monoisotopic gold or phosphorus clusters can be easily generated in situ from suitable precursors by laser desorption/ionization (LDI) or matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Their use offers numerous advantages, including simplicity of preparation, biological inertness, and exact mass determination even at lower mass resolution. We used citrate-stabilized gold nanoparticles to generate gold calibration clusters, and red phosphorus powder to generate phosphorus clusters. Both elements can be added to samples to perform internal calibration up to mass-to-charge (m/z) 10-15,000 without significantly interfering with the analyte. We demonstrated the use of the gold and phosphorous clusters in the MS analysis of complex biological samples, including microbial standards and total extracts of mouse embryonic fibroblasts. We believe that clusters of monoisotopic elements could be used as generally applicable calibrants for complex biological samples. Graphical Abstract ᅟ.

Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks

Cross-contamination of eukaryotic cell lines used in biomedical research represents a highly relevant problem. Analysis of repetitive DNA sequences, such as Short Tandem Repeats (STR), or Simple Sequence Repeats (SSR), is a widely accepted, simple, and commercially available technique to authenticate cell lines. However, it provides only qualitative information that depends on the extent of reference databases for interpretation. In this work, we developed and validated a rapid and routinely applicable method for evaluation of cell culture cross-contamination levels based on mass spectrometric fingerprints of intact mammalian cells coupled with artificial neural networks (ANNs). We used human embryonic stem cells (hESCs) contaminated by either mouse embryonic stem cells (mESCs) or mouse embryonic fibroblasts (MEFs) as a model. We determined the contamination level using a mass spectra database of known calibration mixtures that served as training input for an ANN. The ANN was then capable of correct quantification of the level of contamination of hESCs by mESCs or MEFs. We demonstrate that MS analysis, when linked to proper mathematical instruments, is a tangible tool for unraveling and quantifying heterogeneity in cell cultures. The analysis is applicable in routine scenarios for cell authentication and/or cell phenotyping in general.

Use of flower-like gold nanoparticles in time-of-flight mass spectrometry

RATIONALE

Many kinds of nanoparticles (NPs) have been used for mass spectrometry (MS) so far. Here we report the first use of flower-like gold nanoparticles (AuNPs) as a mediator to enhance ionization in MS of peptides and proteins.

METHODS

Flower-like AuNPs were characterized using transmission and scanning electron microscopy, UV-VIS spectrophotometry, and laser desorption/ionization (LDI)-MS and compared with polyhedral AuNPs. Mass spectra were obtained in positive ion mode using a time-of-flight (TOF) analyzer coupled with either matrix-assisted laser desorption/ionization (MALDI) or surface-assisted laser desorption/ionization (SALDI) methods.

RESULTS

The intensities of peptide peaks (m/z 500-3500) were up to 7.5× and up to 7× higher using flower-like AuNPs and flower-like AuNPs-enriched α-cyano-4-hydroxycinnamic acid (CHCA) matrix respectively, than the classical CHCA matrix. The signals of higher mass peptide/protein peaks (m/z 3600-17000) were up to 2× higher with using flower-like AuNPs-enriched CHCA matrix than conventional CHCA matrix. The signal of profile peaks generated by intact cell MALDI-TOFMS of fibroblast suspension (m/z 4000-20000) was 2× higher with using flower-like AuNPs combined with sinapinic acid (SA) compared to SA matrix alone. The use of flower-like AuNPs as internal calibration standard for the calibration of MS spectra of peptides was performed.

CONCLUSIONS

Flower-like AuNPs and flower-like AuNPs combined with CHCA or SA as combined matrices for MS measurement of peptides and proteins were used. Comparison of the conventional MALDI method and our method with flower-like AuNPs was carried out. In addition, gold clusters generated from flower-like AuNPs by SALDI provide a suitable internal calibration standard for MS analysis of peptides. Copyright © 2015 John Wiley & Sons, Ltd.