Erratum: APOE2, E3, and E4 differentially modulate cellular homeostasis, cholesterol metabolism, and inflammatory response in isogenic iPSC-derived astrocytes

In the initial version of this article, there was an error in the merged image for APOE3 in Figure 1E. While all individual images for S100b and GJA1 were displayed correctly, we accidentally merged APOE3 GJA1 with APOE2 S100b (and not with the S100b image of APOE3). However, this error did not affect the figure’s meaning or conclusion. The correct merged GJA1/S100b staining for APOE3 iAstrocytes has now been included in the article online and below. No correction of the text or figure legend was necessary.ISSN:2213-671

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 β-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

Analysis of sets and collections of Peptide Mass Fingerprint data

Recent advances in genomics, which outstanding achievements were exemplified by the complete sequencing of the human genome provided the infrastructure and information enabling the development of several proteomic technologies. Currently no single proteomic analysis strategy can sufficiently address the question of how the proteome is organised in terms of numerical complexity and complexity generated by the protein-protein interactions forming supramolecular complexes within the cell. In order to bring a detailed structural/functional picture of these complexes in whole genomes, cells, organelles or in normal and pathological states several proteomic strategies can be utilised. Combination of technologies will bring a more detailed answer to what are the components of certain cellular pathways (e.g.: targets of kinases/phosphatases, cytoskeletal proteins, signalling molecules), how do they interconnect, how are they modified in the cell and what are the roles of several complex components in normal and disease conditions. These types of studies depend on fast and high throughput methods of protein identification. One of the most common methods of analysis is mass spectrometric technique called peptide mapping. Peptide mapping is the comparison of mass spectrometrically determined peptide masses of a sequence specific digest of a single protein or peptide of interest with peptide masses predicted from genomic databases. In this work several contributions to the computational analysis of mass spectrometric data are presented. During the course of my studies I looked at the distribution of peptide masses in sequence specific protein sequence digests and developed a simple mathematical model dealing with peptide mass cluster centre location. I have introduced and studied the methods of calibration of mass spectrometric peak-list without resorting to internal or external calibration samples. Of importance is also contribution of this work to the calibration of data produced in high throughput experiments. In addition, I studied how filtering of non-peptide peaks influences the identification rates in mass spectrometric instruments. Furthermore, I focused my studies on measures of spectra similarity which can be used to acquire supplementary information, increasing the sensitivity and specificity of database searches

Analyse von Peptid Massen Fingerabdruck Datensätzen

### Table Of Contents 1. Overview 2. Introduction 3. Biological Mass Spectrometry 4. A mathematical model of the peptide mass rule with applications 5. Calibration of mass spectrometric peptide mass fingerprint data without specific external or internal calibrants 6. Transformation and other factors of the biological mass spectrometry pairwise peak-list comparison process 7. Conclusions 8. ReferencesRecent advances in genomics, which outstanding achievements were exemplified by the complete sequencing of the human genome provided the infrastructure and information enabling the development of several proteomic technologies. Currently no single proteomic analysis strategy can sufficiently address the question of how the proteome is organised in terms of numerical complexity and complexity generated by the protein-protein interactions forming supramolecular complexes within the cell. In order to bring a detailed structural/functional picture of these complexes in whole genomes, cells, organelles or in normal and pathological states several proteomic strategies can be utilised. Combination of technologies will bring a more detailed answer to what are the components of certain cellular pathways (e.g.: targets of kinases/phosphatases, cytoskeletal proteins, signalling molecules), how do they interconnect, how are they modified in the cell and what are the roles of several complex components in normal and disease conditions. These types of studies depend on fast and high throughput methods of protein identification. One of the most common methods of analysis is mass spectrometric technique called peptide mapping. Peptide mapping is the comparison of mass spectrometrically determined peptide masses of a sequence specific digest of a single protein or peptide of interest with peptide masses predicted from genomic databases. In this work several contributions to the computational analysis of mass spectrometric data are presented. During the course of my studies I looked at the distribution of peptide masses in sequence specific protein sequence digests and developed a simple mathematical model dealing with peptide mass cluster centre location. I have introduced and studied the methods of calibration of mass spectrometric peak-list without resorting to internal or external calibration samples. Of importance is also contribution of this work to the calibration of data produced in high throughput experiments. In addition, I studied how filtering of non-peptide peaks influences the identification rates in mass spectrometric instruments. Furthermore, I focused my studies on measures of spectra similarity which can be used to acquire supplementary information, increasing the sensitivity and specificity of database searches.Fortschritte in der Gnomforschung, deren herrausragende Errungenschaften mit der Sequenzierung des Menschlichen Genoms verdeutlicht wurden, stellten die Informationen und Infrastruktur zur Verfügung welche die Entwicklung neuer Methoden der Proteom Forschung ermöglichte. Keine Methode der Proteom Untersuchung alleine ist in der Lage die Frage ausreichend zu Beantworten, wie das Proteom sowohl bezüglich der numerischen Komplexität als auch der Komplexität die sich aus den Protein Protein Interaktionen ergibt, die supermolekulare komplexe bilden, organisiert ist. Um ein detailliertes strukturelle und funktionelle Darstellung dieser Komplexe in Zellen, Organellen, im normalen und in pathologische Zuständen zu gewinnen, können mehrere Techniken der Proteom Analyse verwendet werden. Die Antwort auf die Frage wie zellularer Signalwegen verschaltet sind, wie sie modifiziert werden und was die Funktion von Protein Komplexen, im Normalen und Krankheit- Zustand ist, kann mit Hilfe der Kombination mehrerer Proteom Analyse Techniken bestimmt werden. Die Realisation dieser Studien benötigt Hoch Durchsatz Methoden zur schnellen Proteinidentifizierung. Eines der gebräuchlichsten Analyseverfahren ist die Identifizierung von Proteinen und Peptiden mit Hilfe Massenspektrometrischer Messungen. Massenspektrometrisch bestimmte Peptid-Massen eines Sequenz spezifischen Protein-Verdaus werden mit theoretischen Massen, die anhand einer Protein- Sequenz-Datenbank vorhergesagt wurden, verglichen. In dieser Arbeit werden Beiträge zur computer-unterstützten Analyse von Massenspektrometrischen Daten vorgestellt. Während meiner Studien betrachtete ich die Verteilung der Peptidmassen, wie sie durch einen Sequenz-Spezifischen Protein-Verdauen gebildet werden. Ich entwickelte eine mathematisches Modell um die empirischen Eigenschaften der Verteilung z.B. die Position von Peptide Massen Clustern vorherzusagen. In dieser Arbeit habe ich auch Methoden zur Kalibrierung von Massenspektrometrischen Signalen untersucht, die ohne interne und externe Kalibrierungs-Proben arbeiten. Desweiteren wurde analysiert wie das Entfernen von Nicht-Peptidmassen die Identifizierung-Ergebnisse beeinflusst. Außerdem fokussierte ich meine Studien auf Maße der Spektrenähnlichkeit. Diese Maße können dazu verwendet werden um die Empfindlichkeit und die Genauigkeit der Datenbanksuchen zu erhöhend

rawDiag - an R package supporting rational LC-MS method optimization for bottom-up proteomics

Optimizing methods for liquid chromatography coupled to mass spectrometry (LC-MS) is a non-trivial task. Here we present rawDiag, a software tool supporting rational method optimization by providing MS operator-tailored diagnostic plots of scan level metadata. rawDiag is implemented as R package and can be executed on the command line, or through a graphical user interface (GUI) for less experienced users. The code runs platform independent and can process a hundred raw files in less than three minutes on current consumer hardware as we show by our benchmark. In order to demonstrate the functionality of our package, we included a real-world example taken from our daily core facility business. The R package itself, a shiny demo application, and a Dockerfile that builds the entire architecture from scratch is accessible through a GitHub repository, see https://github.com/protViz/rawDia

Proteomic identification of proliferation and progression markers in human polycythemia vera stem and progenitor cells

Polycythemia vera (PV) is a stem cell disorder characterized by hyperproliferation of the myeloid lineages and the presence of an activating JAK2 mutation. To elucidate mechanisms controlling PV stem and progenitor cell biology, we applied a recently developed highly sensitive data-independent acquisition mass spectrometry workflow to purified hematopoietic stem and progenitor cell (HSPC) subpopulations of patients with chronic and progressed PV. We integrated proteomic data with genomic, transcriptomic, flow cytometry and in vitro colony formation data. Comparative analyses revealed added information gained by proteomic compared with transcriptomic data in 30% of proteins with changed expression in PV patients. Upregulated biological pathways in hematopoietic stem and multipotent progenitor cells (HSC/MPPs) of PV included MTOR, STAT and interferon signalling. We further identified a prominent reduction of clusterin (CLU) protein expression and a corresponding activation of NFĸB signalling in HSC/MPPs of untreated PV patients compared with controls. Reversing the reduction of CLU and inhibiting NFĸB signalling decreased proliferation and differentiation of PV HSC/MPPs in vitro. Upon progression of PV, we identified upregulation of LGALS9 and SOCS2 protein expression in HSC/MPPs. Treatment of patients with hydroxyurea normalized the expression of CLU and NFĸB2, but not of LGALS9 and SOCS2. These findings expand the current understanding of the molecular pathophysiology underlying PV and provide new potential targets (CLU and NFĸB) for antiproliferative therapy in PV patients