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Verfahren narrativer Rekonstruktion im Gespräch
The Nineveh Treatise
A Community Approach

Abstract

Background

In recent years, mass spectrometry (MS) has been applied to clinical microbial biotyping, exploiting the speed of matrix-assisted laser desorption/ionization (MALDI) in recording microbe-specific MS profiles. More recently, liquid atmospheric pressure (AP) MALDI has been shown to produce extremely stable ion flux from homogenous samples and ‘electrospray ionization (ESI)-like’ multiply charged ions for larger biomolecules, whilst maintaining the benefits of traditional MALDI including high tolerance to contaminants, low analyte consumption and rapid analysis. These and other advantages of liquid AP-MALDI MS have been explored in this study to investigate its potential in microbial biotyping.

Methods

Genetically diverse bacterial strains were analyzed using liquid AP-MALDI MS, including clinically relevant species such as Escherichia coli, Staphylococcus aureus and Klebsiella pneumoniae. Bacterial cultures were subjected to a simple and fast extraction protocol using ethanol and formic acid. Extracts were spotted with a liquid support matrix (LSM) and analyzed using a Synapt G2-Si mass spectrometer with an in-house built AP-MALDI source.

Results

Each species produces a unique lipid profile in the m/z range of 400–1100, allowing species discrimination. Traditional (solid) MALDI MS produced spectra containing a high abundance of matrix-related clusters and an absence of lipid peaks. The MS profiles of the bacterial species tested form distinct clusters using principle component analysis (PCA) with a classification accuracy of 98.63% using a PCA-based prediction model.

Conclusions

Liquid AP-MALDI MS profiles can be sufficient to distinguish clinically relevant bacterial pathogens and other bacteria, based on their unique lipid profiles. The analysis of the lipid MS profiles is typically excluded from commercial instruments approved for clinical diagnostics.

Abstract

Mass spectrometry (MS) is the workhorse of metabolomics, proteomics and lipidomics. Mass spectrometry imaging (MSI), its extension to spatially resolved analysis of tissues, is a powerful tool for visualizing molecular information within the histological context of tissue. This review summarizes recent developments in MSI and highlights current challenges that remain to achieve molecular imaging at the cellular level of clinical specimens. We focus on matrix-assisted laser desorption/ionization (MALDI)-MSI. We discuss the current status of each of the analysis steps and remaining challenges to reach the desired level of cellular imaging. Currently, analyte delocalization and degradation, matrix crystal size, laser focus restrictions and detector sensitivity are factors that are limiting spatial resolution. New sample preparation devices and laser optic systems are being developed to push the boundaries of these limitations. Furthermore, we review the processing of cellular MSI data and images, and the systematic integration of these data in the light of available algorithms and databases. We discuss roadblocks in the data analysis pipeline and show how technology from other fields can be used to overcome these. Finally, we conclude with curative and community efforts that are needed to enable contextualization of the information obtained.

Abstract

Common traumas to the skeletal system are bone fractures and injury-related articular cartilage damage. The healing process can be impaired resulting in non-unions in 5–10% of the bone fractures and in post-traumatic osteoarthritis (PTOA) in up to 75% of the cases of cartilage damage. Despite the amount of research performed in the areas of fracture healing and cartilage repair as well as non-unions and PTOA, still, the outcome of a bone fracture or articular cartilage damage cannot be predicted. Here, we discuss known risk factors and key molecules involved in the repair process, together with the main challenges associated with the prediction of outcome of these injuries. Furthermore, we review and discuss the opportunities for mass spectrometry (MS) – an analytical tool capable of detecting a wide variety of molecules in tissues – to contribute to extending molecular understanding of impaired healing and the discovery of predictive biomarkers. Therefore, the current knowledge and challenges concerning MS imaging of bone and cartilage tissue as well as in vivo MS are discussed. Finally, we explore the possibilities of in situ, real-time MS for the prediction of outcome during surgery of bone fractures and injury-related articular cartilage damage.

Abstract

Complexity, cost, and content are three important factors that can impede translation of clinical protein mass spectrometry (MS) tests at a larger scale. Complexity stems from the many components/steps involved in bottom-up protein MS workflows, making them significantly more complicated than enzymatic immunoassays (EIA) that currently dominate clinical testing. This complexity inevitably leads to increased costs, which is detrimental in the price-competitive clinical marketplace. To successfully compete, new clinical protein MS tests need to offer something new and unique that EIAs cannot – a new content of proteoform detection. The preferred method for proteoform profiling is intact protein MS analysis, in which all proteins are measured as intact species thus allowing discovery of new proteoforms. To illustrate the importance of intact proteoform testing with MS and its potential clinical implications, we discuss here recent findings from multiple studies on the distribution of apolipoprotein C-III proteoforms and their correlations with key clinical measures of dyslipidemia. Such studies are only made possible with assays that are low in cost, avoid unnecessary complexity, and are unique in providing the content of proteoforms.

Abstract

Introduction

Element-tagged immunoassay coupled with inductively coupled plasma-mass spectrometry (ICP-MS) detection has the potential to revolutionize immunoassay analysis in clinical detection; however, a systematic evaluation with the standard guidelines of the assay is needed to ensure its performance meets the requirements of the clinical laboratory.

Methods

Carcinoembryonic antigen (CEA) was chosen for analysis using the proposed method. A systematic evaluation of the proposed assay was carried out according to the Clinical and Laboratory Standards Institute (CLSI). The 469 clinical samples were analyzed using the new method and compared with the electrochemiluminescent immunoassay (ECLIA) method.

Results

The measurement range of the assay was 1–900 ng/mL, with a detection limit of 0.83 ng/mL. The inter-assay and intra-assay imprecision were 4.67% and 5.38% with high concentration samples, and 9.27% and 17.64% with low concentration samples, respectively. The cross-reactivity (%) for different antigens was less than 0.05%, and the recovery was between 94% and 108%. Percentage deviation of all the dilutions was less than 12.5% during linearity estimation. The interference bias caused by different substances was less than 10%. The reference interval of the assay was 0–4.442 ng/mL. Comparison with the commercial ECLIA method for clinical sample detection, the proposed method showed a correlation of 0.9878 and no significant differences between the methods were observed (p = 0.6666).

Conclusions

The ICP-MS based immunoassay was successfully developed, and the analytical performance of the assay met the requirements of the CLSI, which fully proved the clinical transferability and application of the new method.

Abstract

Systemic amyloidosis is a serious disease which is caused when normal circulating proteins misfold and aggregate extracellularly as insoluble fibrillary deposits throughout the body. This commonly results in cardiac, renal and neurological damage. The tissue target, progression and outcome of the disease depends on the type of protein forming the fibril deposit, and its correct identification is central to determining therapy. Proteomics is now used routinely in our centre to type amyloid; over the past 7 years we have examined over 2000 clinical samples. Proteomics results are linked directly to our patient database using a simple algorithm to automatically highlight the most likely amyloidogenic protein. Whilst the approach has proved very successful, we have encountered a number of challenges, including poor sample recovery, limited enzymatic digestion, the presence of multiple amyloidogenic proteins and the identification of pathogenic variants. Our proteomics procedures and approaches to resolving difficult issues are outlined.