Quantification of serum apolipoproteins A-I and B-100 in clinical samples using an automated SISCAPA-MALDI-TOF-MS workflow.

A fully automated workflow was developed and validated for simultaneous quantification of the cardiovascular disease risk markers apolipoproteins A-I (apoA-I) and B-100 (apoB-100) in clinical sera. By coupling of stable-isotope standards and capture by anti-peptide antibodies (SISCAPA) for enrichment of proteotypic peptides from serum digests to matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS detection, the standardized platform enabled rapid, liquid chromatography-free quantification at a relatively high throughput of 96 samples in 12h. The average imprecision in normo- and triglyceridemic serum pools was 3.8% for apoA-I and 4.2% for apoB-100 (4 replicates over 5 days). If stored properly, the MALDI target containing enriched apoA-1 and apoB-100 peptides could be re-analyzed without any effect on bias or imprecision for at least 7 days after initial analysis. Validation of the workflow revealed excellent linearity for daily calibration with external, serum-based calibrators (R(2) of 0.984 for apoA-I and 0.976 for apoB-100 as average over five days), and absence of matrix effects or interference from triglycerides, protein content, hemolysates, or bilirubins. Quantification of apoA-I in 93 normo- and hypertriglyceridemic clinical sera showed good agreement with immunoturbidimetric analysis (slope = 1.01, R(2) = 0.95, mean bias = 4.0%). Measurement of apoB-100 in the same clinical sera using both methods, however, revealed several outliers in SISCAPA-MALDI-TOF-MS measurements, possibly as a result of the lower MALDI-TOF-MS signal intensity (slope = 1.09, R(2) = 0.91, mean bias = 2.0%). The combination of analytical performance, rapid cycle time and automation potential validate the SISCAPA-MALDI-TOF-MS platform as a valuable approach for standardized and high-throughput quantification of apoA-I and apoB-100 in large sample cohorts.

Imaging mass spectrometry analysis of renal amyloidosis biopsies reveals protein co-localization with amyloid deposits.

Amyloidosis is a heterogeneous group of protein misfolding diseases characterized by deposition of amyloid proteins. The kidney is frequently affected, especially by immunoglobulin light chain (AL) and serum amyloid A (SAA) amyloidosis as the most common subgroups. Current diagnosis relies on histopathological examination, Congo red staining, or electron microscopy. Subtyping is done by immunohistochemistry; however, commercially available antibodies lack specificity. The purpose of this study was to identify and map amyloid proteins in formalin-fixed paraffin-embedded tissue sections using matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis in an integrated workflow. Renal amyloidosis and non-amyloidosis biopsies were processed for histological and MS analysis. Mass spectra corresponding to the congophilic areas were directly linked to the histological and MS images for correlation studies. Peptides for SAA and AL were detected by MALDI IMS associated to Congo red-positive areas. Sequence determination of amyloid peptides by LC-MS/MS analysis provided protein distribution and identification. Serum amyloid P component, apolipoprotein E, and vitronectin proteins were identified in both AA and AL amyloidosis, showing a strong correlation with Congo red-positive regions. Our findings highlight the utility of MALDI IMS as a new method to type amyloidosis in histopathological routine material and characterize amyloid-associated proteins that may provide insights into the pathogenetic process of amyloid formation.

Imaging mass spectrometry to discriminate breast from pancreatic cancer metastasis in formalin-fixed paraffin-embedded tissues.

Diagnosis of the origin of metastasis is mandatory for adequate therapy. In the past, classification of tumors was based on histology (morphological expression of a complex protein pattern), while supportive immunohistochemical investigation relied only on few "tumor specific" proteins. At present, histopathological diagnosis is based on clinical information, morphology, immunohistochemistry, and may include molecular methods. This process is complex, expensive, requires an experienced pathologist and may be time consuming. Currently, proteomic methods have been introduced in various clinical disciplines. MALDI imaging MS combines detection of numerous proteins with morphological features, and seems to be the ideal tool for objective and fast histopathological tumor classification. To study a special tumor type and to identify predictive patterns that could discriminate metastatic breast from pancreatic carcinoma MALDI imaging MS was applied to multitissue paraffin blocks. A statistical classification model was created using a training set of primary carcinoma biopsies. This model was validated on two testing sets of different breast and pancreatic carcinoma specimens. We could discern breast from pancreatic primary tumors with an overall accuracy of 83.38%, a sensitivity of 85.95% and a specificity of 76.96%. Furthermore, breast and pancreatic liver metastases were tested and classified correctly.

Precision of heavy-light peptide ratios measured by maldi-tof mass spectrometry.

We have investigated the precision of peptide quantitation by MALDI-TOF mass spectrometry (MS) using six pairs of proteotypic peptides (light) and same-sequence stable isotope labeled synthetic internal standards (heavy). These were combined in two types of dilution curves spanning 100-fold and 2000-fold ratios. Coefficients of variation (CV; standard deviation divided by mean value) were examined across replicate MALDI spots using a reflector acquisition method requiring 100 000 counts for the most intense peak in each summed spectrum. The CV of light/heavy peptide centroid peak area ratios determined on four replicate spots per sample, averaged across 11 points of a 100-fold dilution curve and over all six peptides, was 2.2% (ranging from 1.5 to 3.7% among peptides) at 55 fmol total (light + heavy) of each peptide applied per spot, and 2.5% at 11 fmol applied. The average CV of measurements at near-equivalence (light = heavy, the center of the dilution curve) for the six peptides was 1.0%, about 17-fold lower CV than that observed when five peptides were ratioed to a sixth peptide (i.e., a different-sequence internal standard). Response curves across the 100-fold range were not completely linear but could be closely modeled by a power law fit giving R(2) values >0.998 for all peptides. The MALDI-TOF MS method was used to determine the endogenous level of a proteotypic peptide (EDQYHYLLDR) of human protein C inhibitor (PCI) in a plasma digest after enrichment by capture on a high affinity antipeptide antibody, a technique called stable isotope standards and capture by anti-peptide antibodies (SISCAPA). The level of PCI was determined to be 770 ng/mL with a replicate measurement CV of 1.5% and a >14 000-fold target enrichment via SISCAPA-MALDI-TOF. These results indicate that MALDI-TOF technology can provide precise quantitation of high-to-medium abundance peptide biomarkers over a 100-fold dynamic range when ratioed to same-sequence labeled internal standards and enriched to near purity by specific antibody capture. The robustness and throughput of MALDI-TOF in comparison to conventional nano-LC-MS technology could enable currently impractical large-scale verification studies of protein biomarkers.

Top-down label-free LC-MALDI analysis of the peptidome during neural progenitor cell differentiation reveals complexity in cytoskeletal protein dynamics and identifies progenitor cell markers.

In the field of stem cell research, there is a strong requirement for the discovery of new biomarkers that more accurately define stem and progenitor cell populations, as well as their differentiated derivatives. The very-low-molecular-weight (<5 kDa) proteome/peptidome remains a poorly investigated but potentially rich source of cellular biomarkers. Here we describe a label-free LC-MALDI-TOF/TOF quantification approach to screen the very-low-molecular-weight proteome, i.e. the peptidome, of neural progenitor cells and derivative populations to identify potential neural stem/progenitor cell biomarkers. Twelve different proteins were identified on the basis of MS/MS analysis of peptides, which displayed differential abundance between undifferentiated and differentiated cultures. These proteins included major cytoskeletal components such as nestin, vimentin, and glial fibrillary acidic protein, which are all associated with neural development. Other cytoskeletal proteins identified were dihydropyrimidinase-related protein 2, prothymosin (thymosin α-1), and thymosin ß-10. These findings highlight novel stem cell/progenitor cell marker candidates and demonstrate proteomic complexity, which underlies the limitations of major intermediate filament proteins long established as neural markers.

Normalization in MALDI-TOF imaging datasets of proteins: practical considerations.

Normalization is critically important for the proper interpretation of matrix-assisted laser desorption/ionization (MALDI) imaging datasets. The effects of the commonly used normalization techniques based on total ion count (TIC) or vector norm normalization are significant, and they are frequently beneficial. In certain cases, however, these normalization algorithms may produce misleading results and possibly lead to wrong conclusions, e.g. regarding to potential biomarker distributions. This is typical for tissues in which signals of prominent abundance are present in confined areas, such as insulin in the pancreas or ß-amyloid peptides in the brain. In this work, we investigated whether normalization can be improved if dominant signals are excluded from the calculation. Because manual interaction with the data (e.g., defining the abundant signals) is not desired for routine analysis, we investigated two alternatives: normalization on the spectra noise level or on the median of signal intensities in the spectrum. Normalization on the median and the noise level was found to be significantly more robust against artifact generation compared to normalization on the TIC. Therefore, we propose to include these normalization methods in the standard "toolbox" of MALDI imaging for reliable results under conditions of automation.

Towards a routine application of Top-Down approaches for label-free discovery workflows.

Thanks to proteomics investigations, our vision of the role of different protein isoforms in the pathophysiology of diseases has largely evolved. The idea that protein biomarkers like tau, amyloid peptides, ApoE, cystatin, or neurogranin are represented in body fluids as single species is obviously over-simplified, as most proteins are present in different isoforms and subjected to numerous processing and post-translational modifications. Measuring the intact mass of proteins by MS has the advantage to provide information on the presence and relative amount of the different proteoforms. Such Top-Down approaches typically require a high degree of sample pre-fractionation to allow the MS system to deliver optimal performance in terms of dynamic range, mass accuracy and resolution. In clinical studies, however, the requirements for pre-analytical robustness and sample size large enough for statistical power restrict the routine use of a high degree of sample pre-fractionation. In this study, we have investigated the capacities of current-generation Ultra-High Resolution Q-Tof systems to deal with high complexity intact protein samples and have evaluated the approach on a cohort of patients suffering from neurodegenerative disease. Statistical analysis has shown that several proteoforms can be used to distinguish Alzheimer disease patients from patients suffering from other neurodegenerative disease. SIGNIFICANCE: Top-down approaches have an extremely high biological relevance, especially when it comes to biomarker discovery, but the necessary pre-fractionation constraints are not easily compatible with the robustness requirements and the size of clinical sample cohorts. We have demonstrated that intact protein profiling studies could be run on UHR-Q-ToF with limited pre-fractionation. The proteoforms that have been identified as candidate biomarkers in the-proof-of concept study are derived from proteins known to play a role in the pathophysiology process of Alzheimer disease.

Identification of multiple proteoforms biomarkers on clinical samples by routine Top-Down approaches.

Top-Down approaches have an extremely high biological relevance, especially when it comes to biomarker discovery, but the necessary pre-fractionation constraints are not easily compatible with the robustness requirements and the size of clinical sample cohorts. We have demonstrated that intact protein profiling studies could be run on UHR-Q-ToF with limited pre-fractionation (Schmit et al., 2017) [1]. The dataset presented herein is an extension of this research. Proteoforms known to play a role in the pathophysiology process of Alzheimer's disease were identified as candidate biomarkers. In this article, mass spectrometry performance of these candidates are demonstrated.

The Evolution of MALDI-TOF Mass Spectrometry toward Ultra-High-Throughput Screening: 1536-Well Format and Beyond.

Mass spectrometry (MS) offers a label-free, direct-detection method, in contrast to fluorescent or colorimetric methodologies. Over recent years, solid-phase extraction-based techniques, such as the Agilent RapidFire system, have emerged that are capable of analyzing samples in <10 s. While dramatically faster than liquid chromatography-coupled MS, an analysis time of 8-10 s is still considered relatively slow for full-diversity high-throughput screening (HTS). Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) offers an alternative for high-throughput MS detection. However, sample preparation and deposition onto the MALDI target, as well as interference from matrix ions, have been considered limitations for the use of MALDI for screening assays. Here we describe the development and validation of assays for both small-molecule and peptide analytes using MALDI-TOF coupled with nanoliter liquid handling. Using the JMJD2c histone demethylase and acetylcholinesterase as model systems, we have generated robust data in a 1536 format and also increased sample deposition to 6144 samples per target. Using these methods, we demonstrate that this technology can deliver fast sample analysis time with low sample volume, and data comparable to that of current RapidFire assays.