BLT Stages

RNA has a broad range of functions impacting the cell fate. It also carries a lot of chemical modifications, that are responsible for a lot of different functions of RNA. The importance of chemical modifications in physiological processes makes it very interesting for medical purposes. The aim of this study is to produce home-made heavy labelled RNA, to detect by mass spectrometry. The Platform of Clinical Proteomics would use this labelled RNA to develop an absolute quantification of modified bacteria RNA in biological samples. Heavy labelling happens with the incorporation of stable isotopes, heavy atoms C13 and N15. The project team members test this experiment with an easy-to-use cell, the bacteria. RNA is grown in bacteria cells. Then after a certain period RNA is extracted from the bacteria cells and is digested into nucleosides, having separate targets. The targets are detected based on the mass-to-charge ratio with the mass spectrometry. The first step is to inject the same sample in different volumes, to search which modifications are present. The second step is to validate the modifications of the experiment, this happens by extracting the bacteria cells at different times. The bacteria cells grew for 24 hours, with heavy atoms. The results from the experiments confirm that there is an heterogeny incorporation of heavy atoms from the extracted RNA. Sixteen targets are detected, but all targets are not yet fully labelled. After 24 hours, only a mean of 93% incorporation of heavy atoms for adenosine, cytosine or guanosine. Concluding it is insufficient to develop the absolute quantification by mass spectrometry. For future purposes, the growth process can be increased to have fully labelled targets.

Protein biomarkers are biological indicators of normal biological processes in the human body. Besides the monitoring of normal processes, they are also a benchmark for pathogenic processes and the response to therapeutic or pharmaceutical treatments. The goal of this research is to discover new biomarkers and to link them to certain pathologies. Qualification and quantification of proteins is possible with a triple quadrupole tandem mass spectrometer using a bottom-up approach.

Pre-selected peptides from chosen proteins are diluted one hundred times in 0,1 % formic acid in HPLC-grade water and injected in the LC-MS/MS. The column used for separating pure peptides was a ZORBAX RRHD Eclipse Plus C18, 95 Å, 2.1 x 50 mm, 1.8 µm, 1200 bar pressure limit column. The results are analysed using the Skyline software. The injection volume is increased until a good, intense peak is seen in the obtained chromatogram. Depending on the volume necessary to obtain a good result, peptides originating from the same protein are mixed together and analysed using a longer column. The column used for peptide mixes was an AdvanceBio Peptide Mapping 120 Å, 2.1 x 250 mm, 2.7 µm column.

Once the retention time of the peptides are known, a timeframe is set in a window of -1,5 min and +1,5 min of the retention time. This was done to keep the sensitivity at its maximum. After optimizing the method, human plasma samples are analysed. These samples are digested using DL-dithiothreitol, iodoacetamide and trypsin to cut the proteins. After a solid-phase extraction by the Agilent technologies Bravo and drying in an Acid-resistant CentriVap Concentrator, the dried proteins are resuspended in 2 % acetonitrile and 0,1 % formic acid and analysed by the MS. Depending on the transition order obtained in the peptide mix analysis, proteins are determined to be either detectable or not detectable in human plasma samples.

As a result, 33 out of the tested 77 proteins have at least one proteotypic peptide detectable in human plasma. Nineteen proteins are un investigation and hundreds of other proteins shall be analysed during the remaining of the project. In the future, multiplex methods of detectable proteotypic peptides can be used in pathology studies. Besides plasma, other biofluids should be explored for the possible detection and quantification of all proteins.

Narcolepsy is a chronic sleep disorder that affects one in 2000 people, starting from adolescence. The disease is caused by an orexin deficiency because of the destruction of the orexin neurons. Orexin is the biomarker for narcolepsy. Diagnosis is made based on the symptoms in which excessive sleep is the most important. A new method of diagnosing the disease is to quantify the orexin level in cerebrospinal fluid (CSF).

This project aims to confirm whether orexin can present multiple signals in a CSF sample or in the peptide standard after liquid chromatography (LC). These signals are identified with the use of mass spectrometry (MS). Optimal conditions are developed for the detection and identification of the biomarker of narcolepsy in CSF samples or in the peptide standard. Detection and identification of signals are necessary in the process of quantifying orexin. This research is useful for diagnosis in the future. The intention of this research work is to detect an orexin deficiency in narcoleptic patients.

The CSF sample and the peptide standard are separated by LC. All the obtained fractions are analysed by LC-MS. By using the multiple reaction monitoring (MRM)-mode, only the specific molecules are analysed. This project starts with the analysis of a protein standard and a peptide standard in order to set up the LC separation.

Fractionation based on LC separation is performed and optimized. This technique is used on a protein standard, a peptide standard and CSF samples. The capacity of the separation is shown in an ultraviolet (UV)-chromatogram. It is also demonstrated based on matrix-assisted laser desorption/ionization (MALDI) analysis, LC-MS analysis and on a sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE). The UV-detection of the protein standard succeeds. Signals are obtained from the MALDI analysis and LC-MS analysis of the peptide standard. After the separation of CSF and LC-MS detection, orexin was not detected yet.

It is possible to detect the pure product of the peptide standard with mass spectrometry. However, the analysis of a complex CSF sample failed. No single peak is obtained contrary to what the theory claims. This thesis proves that orexin is detectable with mass spectrometry. Because of this, other preliminary preparation steps of the CSF sample are recommended, for example purifying steps. Further research is needed to optimize the conditions for the detection of orexin in CSF samples and to diagnose the disease.

Background: IgA nephropathy is the most common primary glomerulonephritis in the world. It frequently leads to end-stage renal disease, as there is no disease-specific therapy. Henoch Schönlein Purpura is the most common vasculitis in childhood. Histological features of HSP with nephritis are pathologically indistinguishable from those of IgAN, suggesting that the two entities share mechanisms of disease. These diseases are characterized by an aberrant form of IgA1, which has a lack of galactose. The galactose-deficient form of IgA1 in this form is obtained by a wrong O-glycosylation.

Aim: The objective of this study is to use mass spectrometry for the detection of IgA1, with possibly a glycosylation anomaly. This is done in function of the renal impairment of children's Rheumatoid Purpura. The detection of IgA1 that results in interpretable peaks is the main goal of this project, with the prospect of finding IgA1 with glycosylation anomaly in patient samples in the future. This could enable the identification of non-invasive disease-specific biomarkers as well as the development of future disease-specific therapies.

Methods: To obtain peaks that were predominately IgA1, a washing step with capture select is first performed. After this, the proteins in the samples are reduced, alkylated and digested into peptides. The samples with peptides still contains salts and buffers, so these are washed off in a peptide cleanup. After these steps, the samples are clean and ready to inject in the LC-MS. This LC-MS contains a nano LC coupled to a Q-TOF MS system. The experiment was started with BioRad standards and continued with cryo plasma. Also a few patient samples were measured.

Results: A large range of peaks was detected, both in the standard and in the cryo plasma. This confirmed that the conducted protocol could be tested on patient samples. Numerous peaks were found in the plasma of patient samples as well, but no clear link between the characteristics of the O-glycopeptides and the status of the patient samples could be found yet.

Conclusion: The goal to reach a protocol and qualify O-glycopeptides in standards and cryo plasma is succeeded. The O-glycopeptides are also qualified in the patient samples but a biomarker hasn’t been found yet. The continuation of testing on patient samples is needed to find this biomarker for the disease of HSP. This experiment had a high repeatability but a low reproducibility. New experiments are required to discover the source of this low reproducibility

Background: The Alzheimer’s disease (AD) is the most common form of dementia with clinical symptoms normally appearing from the age of 60. AD is being diagnosed by magnetic resonance imaging scans and by the quantification of biomarkers in the cerebrospinal fluid (CSF). Taking a CSF sample is an invasive procedure which cannot be repeated several times. Therefore it is difficult to follow up the disease.

Aim: The aim of this bachelor project is to find good plasmatic biomarkers for the diagnosis of AD. By finding biomarkers in plasma, the diagnosis and follow up could be easier and less invasive for the patients. In this study, Tau and Neurofilament Light (NFL) are being quantified in plasma. This at the time of inclusion and after 24 months to follow up the disease.

Methods: In this bachelor project, 347 patients are included. Each patient has two plasma samples, one at the time of inclusion (M0) and one after 24 months (M24). Tau is being measured in the samples at M0 and NFL in both M0 and M24 samples. To quantify the biomarkers Tau and NFL, a single molecule array (Simoa) is used. This fully automatized technique is based on a digital ELISA.

Results: The results of the quantification of plasmatic Tau showed a significant difference between AD and mild cognitive impairment (MCI). However there was still a big overlap between the different diagnostic groups. The quantification of NFL showed significant differences between patient samples at M0 and M24 in each diagnosis. There is also a clear tendency in the progression of the neurodegeneration in each diagnostic group.

Conclusion: Plasmatic Tau cannot be seen as a good biomarker to differentiate AD from MCI because of the big overlap in results. But it can be interesting to quantify Tau also in plasma samples from after 24 months to see of Tau is a good biomarker for the progression of the disease. NFL is also not a good biomarker to differentiate the diagnoses because of the overlap, but NFL does seem to be a good biomarker to follow up the neurodegeneration that takes place. For in a next study, samples from after 48 months should be included to see the further progression of the disease.

Background: Alzheimer’s disease (AD) is a neurodegenerative disease, which in its early clinical form corresponds to mild cognitive impairment (MCI). Due to an increasing life expectancy, these diseases are rapidly growing socioeconomic and medical problems. Although these diseases are very common, the diagnosis is time-consuming and involves several tests. By identifying new biomarkers, the diagnosis of AD or MCI would be much easier.

Aim: AD is characterized by amyloid plaques (consisting of Aβ-40 and Aβ-42), causing the typical cognitive changes. Three enzymes play an important role in the formation of amyloid: BACE1, TACE and CatD. The aim of this bachelor project is thus to investigate a possible relationship between these enzymes/Aβ-40/Aβ-42 and AD/MCI. This investigation is a part of the Biomarker of AmyLoid pepTide and AlZheimer’s diseAse Risk (BALTAZAR) project, which objectives are to find the relationship between several biomarkers (in plasma and cerebrospinal fluid) and AD/MCI.

Methods: 445 human cerebrospinal fluid (CSF) samples from patients are used in this study. Two different measuring methods are performed: ELISA test for BACE1, Aβ-40 and Aβ-42, and enzymatic activity measurement with a FRET-peptide for TACE and CatD. The tests for BACE1, Aβ-40 and Aβ-42 are fully automatized and executed on the EUROIMMUN® Analyzer I-2P, while tests on TACE and CatD are done manually.

Results: Due to a lack of time, BACE1 is not tested and only 152 samples are investigated for Aβ-40 and Aβ-42. TACE and CatD are first tested on 90 samples to verify if there is a relationship. The results for both TACE and CatD show slightly higher values for samples with AD and MCI with conversion. These results indicate that the enzymes have an influence on the risk of conversion from MCI to AD. For Aβ-40, low values are seen for samples with AD, but identical values for MCI with and without conversion, meaning Aβ-40 has no influence on the risk of conversion. However, for Aβ-42, low values for AD and especially for MCI with conversion are obtained. This difference between MCI with and without conversion indicates Aβ-42 has a strong influence on the risk of conversion. The effect of freezing-thawing is also tested and show that there is an influence on TACE and CatD.

Conclusion: Three potential biomarkers are found to predict the risk of conversion from MCI to AD: TACE, CatD and Aβ-42. However, because of the little difference between MCI with and without conversion for TACE and CatD, these enzymes can not be used as a single biomarker to predict this risk. By testing more enzymes and peptides, a panel can be composed to predict the risk of conversion more accurately. Also, it seems that Aβ-42 is an excellent biomarker to predict the risk, due to the large difference between MCI with and without conversion. Because Aβ-42 is the first Aβ-peptide that is drawn away from the CSF into plaques, it is the earliest biomarker if MCI will converse or not. The process of freezing-thawing has an influence on TACE and CatD because the enzymatic activity is measured. Thus, these enzymes need to be tested first.

Aim + Background – The MS detects peptides but how to ensure the detection and obtained data are correct? Therefore an IQC is spiked in the samples. Ovalbumin is chosen because it is pure, available in large quantities, absent in the human body and has an ideal sequence. The aim is to find a peptide of ovalbumin that can be detected with a good repeatability and a great quantity. To do so, ovalbumin must be validated, on the Q-TOF-MS and QqQ-MS, before it can be used as an IQC. Therefore, ovalbumin is digested to see which peptides can be detected and what kind of modifications are present. Due to the obtained modifications, the complexity of the samples increases, a higher interference is obtained and the sensitivity decreases. This results in a low precision and quantification (bad peptide candidates). Another aim is thus to control and limit the modifications to have better results. The peptides that obtain good results are studied more closely to find the best peptide, which involves changing the digestion time. The best peptide will be used as an IQC in human protein samples (biological fluids).

Methods – The proteomics workflow includes the in-solution digestion procedure (denaturation/alkylation/dilution/digestion/clean-up/speedvac/peptide resuspension/vial transfer/LC injection). This is a necessary manipulation (proteins become peptides) before a MS-based quantification is performed. The digestion is stopped with formic acid, after 30 min, 1 h, 2 h, 4 h, 14 h, 22 h and 48 h incubation. Thereafter, a clean-up from the sample peptides is performed with the Agilent® AssayMAP Bravo. When the pre-treatment of the samples is finished, the samples are analyzed with the Q-TOF-MS or QqQ-MS. Also, the repeatability of the peptides is tested and the detectability in plasma. Therefore, the following concentrations of ovalbumin are tested in 2 µL of plasma: 0,25 ng/µL; 1 ng/µL; 2,5 ng/µL; 10 ng/µL; 25 ng/µL; 50 ng/µL and 75 ng/µL.

Results – Seven peptides are obtained with several modifications, giving a total of twenty possible IQC’s. Thanks to the obtained graphs, a difference between the digestion times can be seen. Results obtained at 22 h or above, mostly give a lot of modifications. However, the quantities of the “no PTM forms” are high enough to be an IQC. A digestion time of 4 h or beneath gives less modifications but the obtained quantities of the “no PTM forms” are not always optimal. The best digestion time is thus at 14 h, which has mostly a great quantity and not much modifications. Thanks to the achieved information of the repeatability of ovalbumin and the detectability in plasma, a peptide selection can be made. The three lowest concentrations of the latter experiment are too low to detect any peptide of ovalbumin in plasma. A higher concentration results in an increased detectability of the peptides. However, 50 ng/µL gives better results than 75 ng/µL.

Conclusion – Thirteen possible IQC’s are not stable or detectable in plasma, which means only seven possible IQC’s are left to be studied. These are further studied in the future on the QqQ-MS, which is able to eliminate more possibilities. However, the best possible IQC at this moment is the “no PTM form” of peptide 123-142 because it is detectable in plasma and the variation percentage is the lowest for this possible IQC. Also, other biological fluids must be tested in the future, such as saliva, urine and serum.

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