Biology subjects

Bludau, I.

Publications and source records attributed to Bludau, I..

At least 19 recordsLinked to original sources

bioRxiv2026

Local translation drives glioblastoma heterogeneity and tumor invasion

O_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC="FIGDIR/small/722387v1_ufig1.gif" ALT="Figure 1"> View larger version (96K): org.highwire.dtl.DTLVardef@1da020corg.highwire.dtl.DTLVardef@1bc9b32org.highwire.dtl.DTLVardef@171198aorg.highwire.dtl.DTLVardef@43ce6f_HPS_FORMAT_FIGEXP M_FIG C_FIG Glioblastoma is characterized by diffuse brain invasion, yet the subcellular mechanisms enabling this aggressive behavior remain poorly understood. A subpopulation of glioblastoma cells forms invasive tumor microtubes (TMs), neurite-like extensions that drive whole-brain colonization. Here, we establish local protein translation as a fundamental driver of TM dynamics and invasive cell states. Developing a subcellular transcriptomics approach - integrating subcellular organelle organization with spatially resolved transcriptomics and functional readouts - we reveal that TM gene expression drives cell state identity. Invasive cells further exhibit significantly elevated local translation in protruding TMs, directly linking subcellular protein synthesis to functional invasive states associated with neurodevelopmental programs of axonal growth cones. Targeted disruption of TM-localized translation via photoswitchable puromycin, and specific knockdowns of the TM-enriched proteins GPM6A and GAP43, impaired TM dynamics, suppressed invasion, and reduced tumor growth. Together, these findings define local translation as a key determinant of tumor heterogeneity and glioblastoma invasion.

cancer biology↗

bioRxiv2026

ProteoPy: an AnnData-based framework for integrated proteomics analysis

SummaryProteoPy is a lightweight Python library for protein- and peptide-level quantitative proteomics analysis, built around the AnnData class as its core data structure. It streamlines data import, preprocessing, and differential analysis while preserving all metadata within a single object. A reimplementation of our previously published COPF algorithm enables proteoform group inference directly from peptide-level data, facilitating the identification of proteoform-specific regulation and isoform usage. Designed for accessibility and flexibility, ProteoPy simplifies analysis for non-specialists and provides an extensible foundation for advanced proteomics workflows, seamlessly integrating with the scanpy and muon ecosystems for reproducible and scalable multi-omics analysis. Availability and implementationProteoPy is implemented in Python 3 and publicly available on GitHub: https://github.com/UKHD-NP/proteopy under the Apache 2.0 license. Contactisabell.bludau@med.uni-heidelberg.de Supplementary informationTutorial notebooks for ProteoPy are included as supplementary data and are also available on GitHub: https://github.com/UKHD-NP/proteopy/tree/main/docs/tutorials.

bioinformatics↗

bioRxiv2025

Complex Assembly and Activity States as Multifaceted Protein Attributes Explaining Phenotypic Variability

Cell function studies primarily focus on measuring overall molecular abundances while often overlooking critical clues--including protein modifications and molecular interaction networks--that critically determine the functional properties of the cell. In prior work, we introduced a suite of methods to reveal context-specific transcription factor-gene regulatory networks, kinase-substrate networks, and protein interaction networks and leveraged them to gain deeper insights into transcriptional regulation and signal transduction. However, the complex interdependencies between these networks are still elusive. To address this challenge, we introduce a multi-omics framework, aimed at harnessing measured or inferred protein activity in context-specific networks, which yields deeper functional insights into mechanisms underlying molecular phenotypes, compared to protein abundance alone. As proof of concept, we utilized progressively differentiated instances of HeLa CCL2 and Kyoto cell lines to explore the role of protein complexes and interactions in cell doubling time and susceptibility to Salmonella Typhimurium infection. Notably, this analysis underscores the pivotal role of protein interaction networks in linking molecular profiles to phenotypic outcomes, thus providing a highly generalizable framework for multi-omics dataset analysis.

systems biology↗

bioRxiv2022

Robust dimethyl-based multiplex-DIA workflow doubles single-cell proteome depth via a reference channel

Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput and robustness, a challenge that we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. In single runs of mammalian cells, a three-plex analysis of tryptic peptides quantified 7,700 proteins per channel. The Lys-N enzyme enables five-plex quantification at MS1 and MS2 level. Because the multiplex channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this feature and confidently quantifies close to 4,000 proteins in single cells with excellent reproducibility, while our workflow currently allows routine analysis of 80 single cells per day. The concept of a stable proteome still holds at this deeper proteome coverage.

systems biology↗

bioRxiv2022

AlphaPeptDeep: A modular deep learning framework to predict peptide properties for proteomics

Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides (https://github.com/MannLabs/alphapeptdeep). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a novel HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition.

bioinformatics↗

bioRxiv2022

Native size exclusion chromatography-based mass spectrometry (SEC-MS) identifies novel components of the Heat Shock Protein 90-dependent proteome

The molecular chaperone heat shock protein 90 (HSP90) works in concert with co-chaperones to stabilize its client proteins, which include multiple drivers of oncogenesis and malignant progression. Pharmacologic inhibitors of HSP90 have been observed to exert a wide range of effects on the proteome, including depletion of client proteins, induction of heat shock proteins, dissociation of co-chaperones from HSP90, disruption of client protein signaling networks, and recruitment of the protein ubiquitylation and degradation machinery--suggesting widespread remodeling of cellular protein complexes. However, proteomics studies to date have focused on inhibitor-induced changes in total protein levels, often overlooking protein complex alterations. Here, we use size-exclusion chromatography in combination with mass spectrometry (SEC-MS) to characterize the changes in native protein complexes following treatment with the HSP90 inhibitor tanespimycin (17-AAG) in the HT29 colon adenocarcinoma cell line. After confirming the signature cellular response to HSP90 inhibition (e.g., induction of heat shock proteins, decreased total levels of client proteins), we were surprised to find only modest perturbations to the global distribution of protein elution profiles in inhibitor-treated cells. Similarly, co-chaperones that co-eluted with HSP90 displayed no clear difference between control and treated conditions. However, two distinct analysis strategies identified multiple inhibitor-induced changes, including several known components of the HSP90 proteome, as well as numerous proteins and protein complexes with no previous links to HSP90. We present this dataset as a resource for the HSP90, proteostasis, and cancer communities (https://www.bioinformatics.babraham.ac.uk/shiny/HSP90/SEC-MS/), laying the groundwork for future mechanistic and therapeutic studies related to HSP90 pharmacology. Data are available via ProteomeXchange with identifier PXD033459.

biochemistry↗

bioRxiv2022

Mitochondrial phosphoproteomes are functionally specialized across tissues

Mitochondria are essential organelles involved in critical biological processes such as energy metabolism and cell survival. Their dysfunction is linked to numerous human pathologies that often manifest in a tissue-specific manner. Accordingly, mitochondrial fitness depends on versatile proteomes specialized to meet diverse tissue-specific requirements. Furthermore, increasing evidence suggests that phosphorylation may also play an important role in regulating tissue-specific mitochondrial functions and pathophysiology. We hypothesized that recent advances in mass spectrometry (MS)-based proteomics would now enable in-depth measurement to quantitatively profile mitochondrial proteomes along with their matching phosphoproteomes across tissues. We isolated mitochondria from mouse heart, skeletal muscle, brown adipose tissue, kidney, liver, brain, and spleen by differential centrifugation followed by separation on Percoll gradients and high-resolution MS analysis of the proteomes and phosphoproteomes. This in-depth map substantially quantifies known and predicted mitochondrial proteins and provides a resource of core and tissue modulated mitochondrial proteins (mitophos.de). We also uncover tissue-specific repertoires of dozens of kinases and phosphatases. Predicting kinase substrate associations for different mitochondrial compartments indicates tissue-specific regulation at the phosphoproteome level. Illustrating the functional value of our resource, we reproduce mitochondrial phosphorylation events on DRP1 responsible for its mitochondrial recruitment and fission initiation and describe phosphorylation clusters on MIGA2 linked to mitochondrial fusion.

biochemistry↗

bioRxiv2022

The structural context of PTMs at a proteome wide scale

The recent revolution in computational protein structure prediction provides folding models for entire proteomes, which can now be integrated with large-scale experimental data. Mass spectrometry (MS)-based proteomics has identified and quantified tens of thousands of post-translational modifications (PTMs), most of them of uncertain functional relevance. In this study, we determine the structural context of these PTMs and investigate how this information can be leveraged to pinpoint potential regulatory sites. Our analysis uncovers global patterns of PTM occurrence across folded and intrinsically disordered regions. We found that this information can help to distinguish regulatory PTMs from those marking improperly folded proteins. Interestingly, the human proteome contains thousands of proteins that have large folded domains linked by short, unstructured regions that are strongly enriched in regulatory phosphosites. These include well-known kinase activation loops that induce protein conformational changes upon phosphorylation. This regulatory mechanism appears to be widespread in kinases but also occurs in other protein families such as solute carriers. It is not limited to phosphorylation but includes ubiquitination and acetylation sites as well. Furthermore, we performed three-dimensional proximity analysis which revealed examples of spatial co-regulation of different PTM types and potential PTM crosstalk. To enable the community to build upon these first analyses, we provide tools for 3D visualization of proteomics data and PTMs as well as python libraries for data accession and processing.

systems biology↗

bioRxiv2021

Rapid profiling of protein complex re-organization in perturbed systems

Protein complexes constitute the primary functional modules of cellular activity. To respond to perturbations, complexes undergo changes in their abundance, subunit composition or state of modification. Understanding the function of biological systems requires global strategies to capture this contextual state information on protein complexes and interaction networks. Methods based on co-fractionation paired with mass spectrometry have demonstrated the capability for deep biological insight but the scope of studies using this approach has been limited by the large measurement time per biological sample and challenges with data analysis. As such, there has been little uptake of this strategy beyond a few expert labs into the broader life science community despite rich biological information content. We present a rapid integrated experimental and computational workflow to assess the re-organization of protein complexes across multiple cellular states. It enables complex experimental designs requiring increased sample/condition numbers. The workflow combines short gradient chromatography and DIA/SWATH mass spectrometry with a data analysis toolset to quantify changes in complex organization. We applied the workflow to study the global protein complex rearrangements of THP-1 cells undergoing monocyte to macrophage differentiation and a subsequent stimulation of macrophage cells with lipopolysaccharide. We observed massive proteome organization in functions related to signaling, cell adhesion, and extracellular matrix during differentiation, and less pronounced changes in processes related to innate immune response induced by the macrophage stimulation. We therefore establish our integrated differential pipeline for rapid and state-specific profiling of protein complex organization with broad utility in complex experimental designs.

systems biology↗

bioRxiv2021

The social architecture of an in-depth cellular protein interactome

Nearly all cellular functions are mediated by protein-protein interactions and mapping the interactome provides fundamental insights into the regulation and structure of biological systems. In principle, affinity purification coupled to mass spectrometry (AP-MS) is an ideal and scalable tool, however, it has been difficult to identify low copy number complexes, membrane complexes and those disturbed by protein-tagging. As a result, our current knowledge of the interactome is far from complete, and assessing the reliability of reported interactions is challenging. Here we develop a sensitive, high-throughput, and highly reproducible AP-MS technology combined with a quantitative two-dimensional analysis strategy for comprehensive interactome mapping of Saccharomyces cerevisiae. We reduced required cell culture volumes thousand-fold and employed 96-well formats throughout, allowing replicate analysis of the endogenous green fluorescent protein (GFP) tagged library covering the entire expressed yeast proteome. The 4159 pull-downs generated a highly structured network of 3,909 proteins connected by 29,710 interactions. Compared to previous large-scale studies, we double the number of proteins (nodes in the network) and triple the number of reliable interactions (edges), including very low abundant epigenetic complexes, organellar membrane complexes and non-taggable complexes interfered by abundance correlation. This nearly saturated interactome reveals that the vast majority of yeast proteins are highly connected, with an average of 15 interactors, the majority of them unreported so far. Similar to social networks between humans, the average shortest distance is 4.2 interactions. A web portal (www.yeast-interactome.org) enables exploration of our dataset by the network and biological communities and variations of our AP-MS technology can be employed in any organism or dynamic conditions.

systems biology↗

bioRxiv2021

AlphaMap: An open-source Python package for the visual annotation of proteomics data with sequence specific knowledge

SummaryIntegrating experimental information across proteomic datasets with the wealth of publicly available sequence annotations is a crucial part in many proteomic studies that currently lacks an automated analysis platform. Here we present AlphaMap, a Python package that facilitates the visual exploration of peptide-level proteomics data. Identified peptides and post-translational modifications in proteomic datasets are mapped to their corresponding protein sequence and visualized together with prior knowledge from UniProt and with expected proteolytic cleavage sites. The functionality of AlphaMap can be accessed via an intuitive graphical user interface or - more flexibly - as a Python package that allows its integration into common analysis workflows for data visualization. AlphaMap produces publication-quality illustrations and can easily be customized to address a given research question. Availability and implementationAlphaMap is implemented in Python and released under an Apache license. The source code and one-click installers are freely available at https://github.com/MannLabs/alphamap. Supplementary informationA detailed user guide for AlphaMap is provided as supplementary data.

bioinformatics↗

bioRxiv2021

AlphaPept, a modern and open framework for MS-based proteomics

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making their efficient analysis a principal challenge. There is a plethora of different computational tools that process the raw MS data and derive peptide and protein identification and quantification. During the last decade, there has been dramatic progress in computer science and software engineering, including collaboration tools that have transformed research and industry. To leverage these advances, we developed AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Using Numba for just-in-time machine code compilation on CPU and GPU, we achieve hundred-fold speed improvements while maintaining clear syntax and rapid development speed. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while providing access to the latest advances in machine learning. We provide an easy on-ramp for community validation and contributions through the concept of literate programming, implemented in Jupyter Notebooks of the different modules. A framework for continuous integration, testing, and benchmarking enforces solid software engineering principles. Large datasets can rapidly be processed as shown by the analysis of hundreds of cellular proteomes in minutes per file, many-fold faster than the data acquisiton. The AlphaPept framework can be used to build automated processing pipelines using efficient HDF5 based file formats, web-serving functionality and compatibility with downstream analysis tools. Easy access for end-users is provided by one-click installation of the graphical user interface, for advanced users via a modular Python library, and for developers via a fully open GitHub repository.

bioinformatics↗

bioRxiv2020

Systematic detection of functional proteoform groups from bottom-up proteomic datasets

The cellular proteome, the ensemble of proteins derived from a genome, catalyzes and controls thousands of biochemical functions that are the basis of living cells. Whereas the protein coding regions of the genome of the human and many other species are well known, the complexity and composition of proteomes largely remains to be explored. This task is challenging because mechanisms including alternative splicing and post-translational modifications generally give rise to multiple distinct, but related proteins - proteoforms - per coding gene that expand the functional capacity of a cell. Bottom-up proteomics is a mass spectrometric method that infers the identity and quantity of proteins from the measurement of peptides derived from these proteins by proteolytic digestion. Whereas bottom-up proteomics has become the method of choice for the detection of translation products from essentially any gene, the inherent missing link between measured peptides and their parental proteins has so far precluded the systematic assessment of proteoforms and thus limited the resolution of proteome maps. Here we present a novel, data-driven strategy to assign peptides to unique functional proteoform groups based on peptide correlation patterns across large bottom-up proteomic datasets. Our strategy does not fully characterize specific proteoforms, as is achievable in top-down approaches. Rather, it clusters peptides into functional proteoform groups that are directly linked to the biological context of the study. This allows the detection of tens to hundreds of proteoform groups in an untargeted fashion from bottom-up proteomics experiments. We applied the strategy to two types of bottom-up proteomic datasets. The first is a protein complex co-fractionation dataset where native complexes across two different cell cycle stages were resolved and analyzed. Here, our approach enabled the systematic detection and evaluation of assembly specific proteoforms at an unprecedented scale. The second is a protein abundance vs. sample data matrix typical for bottom-up cohort studies consisting of tissue samples from the mouse BXD genetic reference panel. In either data type the method detected state-specific proteoform groups that could be linked to distinct molecular mechanisms including proteolytic cleavage, alternative splicing and phosphorylation. We envision that the presented approach lays the foundation for a systematic assessment of proteoforms and their functional implications directly from bottom-up proteomic datasets.

systems biology↗

bioRxiv2020

Phosphoproteome profiling uncovers a key role for CDKs in TNF signaling

Tumor necrosis factor (TNF) is one of the few cytokines successfully targeted by therapies against inflammatory diseases. However, blocking this well studied and pleiotropic ligand can cause dramatic side-effects. We reasoned that a systems-level proteomic analysis of TNF signaling could dissect its diverse functions and offer a base for developing more targeted therapies. Combining phosphoproteomics time course experiments with subcellular localization and kinase inhibitor analysis identifies functional modules of phosphorylations. The majority of regulated phosphorylations could be assigned to an upstream kinase by inhibiting master kinases and spatial proteomics revealed phosphorylation-dependent translocations of hundreds of proteins upon TNF stimulation. Phosphoproteome analysis of TNF-induced apoptosis and necroptosis uncovered a key role for transcriptional cyclin-dependent kinase (CDK) activity to promote cytokine production and prevent excessive cell death downstream of the TNF signaling receptor. Our comprehensive interrogation of TNF induced pathways and sites can be explored at http://tnfviewer.biochem.mpg.de/. Highlights- Distinct phosphorylation events mark early and late TNF signaling - Inhibition of master kinases reveals TNF stimulation dependent kinase-substrate relations - TNF induces phosphorylation-dependent spatial rearrangement of hundreds of proteins - CDK kinase activity promotes TNF-induced cytokine expression and inhibits cell death - CDK12/13 inhibitors have potential as anti-inflammatory agents

systems biology↗

bioRxiv2020

Data-independent acquisition method for ubiquitinome analysis reveals regulation of circadian biology

Protein ubiquitination is involved in virtually all cellular processes. Enrichment strategies employing antibodies targeting ubiquitin-derived diGly remnants combined with mass spectrometry (MS) have enabled investigations of ubiquitin signaling at a large scale. However, so far the power of data independent acquisition (DIA) with regards to sensitivity in single run analysis and data completeness have not yet been explored. We developed a sensitive workflow combining diGly antibody-based enrichment and optimized Orbitrap-based DIA with comprehensive spectral libraries together containing more than 90,000 diGly peptides. This approach identified 35,000 diGly peptides in single measurements of proteasome inhibitor-treated cells - double the number and quantitative accuracy of data dependent acquisition. Applied to TNF-alpha signaling, the workflow comprehensively captured known sites while adding many novel ones. A first systems-wide investigation of ubiquitination of the circadian cycle uncovered hundreds of cycling ubiquitination sites and dozens of cycling ubiquitin clusters within individual membrane protein receptors and transporters, highlighting novel connections between metabolism and circadian regulation.

systems biology↗

bioRxiv2020

Integrative proteomics reveals principles of dynamic phospho-signaling networks in human erythropoiesis

Human erythropoiesis is exquisitely controlled at multiple levels and its dysregulation leads to numerous human diseases. Despite many functional studies focused on classical regulators, we lack a global, system-wide understanding of post-translational mechanisms coordinating erythroid maturation. Using the latest advances in mass spectrometry (MS)-based proteomics we comprehensively investigate the dynamics of protein and post-translational regulation of in vitro reconstituted CD34+ HSPC-derived erythropoiesis. This quantifies and dynamically tracks 7,400 proteins and 27,000 phosphorylation sites. Our data reveals differential temporal protein expression encompassing most protein classes and numerous post-translational regulatory cascades. Drastic cell surface remodeling across erythropoiesis include numerous orchestrated changes in solute carriers, providing new stage-specific markers. The dynamic phosphoproteomes combined with a kinome-targeting CRISPR/Cas9 screen reveal coordinated networks of erythropoietic kinases and downregulation of MAPK signaling subsequent to c-Kit attenuation as key drivers of maturation. Our global view of erythropoiesis establishes a central role of post-translational regulation in terminal differentiation.

systems biology↗

bioRxiv2020

Systematic protein complex profiling and differential analysis from co-fractionation mass spectrometry data

Protein complexes, macro-molecular assemblies of two or more proteins, play vital roles in numerous cellular activities and collectively determine the cellular state. Despite the availability of a range of methods for analysing protein complexes, systematic analysis of complexes under multiple conditions has remained challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise, for instance in the combination of size exclusion chromatography (SEC) with accurate protein quantification by SWATH/DIA-MS. However, most approaches for interpreting co-fractionation datasets to yield complex composition, abundance and rearrangements between samples depend heavily on prior evidence. We introduce PCprophet, a computational framework to identify novel protein complexes from SEC-SWATH-MS data and to characterize their changes across different experimental conditions. We demonstrate accurate prediction of protein complexes (AUC >0.99 and accuracy around 97%) via five-fold cross-validation on SEC-SWATH-MS data, show improved performance over state-of-the-art approaches on multiple annotated co-fractionation datasets, and describe a Bayesian approach to analyse altered protein-protein interactions across conditions. PCprophet is a generic computational tool consisting of modules for data pre-processing, hypothesis generation, machine-learning prediction, post-prediction processing, and differential analysis. It can be applied to any co-fractionation MS dataset, independent of separation or quantitative LC-MS workflow employed, and to support the detection and quantitative tracking of novel protein complexes and their physiological dynamics.

systems biology↗

bioRxiv2019

SECAT: Quantifying differential protein-protein interaction states by network-centric analysis

Protein-protein interactions (PPIs) play critical functional and regulatory roles in virtually all cellular processes. They are essential for the formation of macromolecular complexes, which in turn constitute the basis for extended protein interaction networks that determine the functional state of a cell. We and others have previously shown that chromatographic fractionation of native protein complexes in combination with bottom-up mass spectrometric analysis of consecutive fractions supports the multiplexed characterization and detection of state-specific changes of protein complexes. In this study, we describe a computational approach that extends the analysis of data from the co-fractionation / mass spectrometric analysis of native complexes to the level of PPI networks, thus enabling a qualitative and quantitative comparison of the proteome organization between samples and states. The Size-Exclusion Chromatography Algorithmic Toolkit (SECAT) implements a novel, network-centric strategy for the scalable and robust differential analysis of PPI networks. SECAT and its underlying statistical framework elucidate differential quantitative abundance and stoichiometry attributes of proteins in the context of their PPIs. We validate algorithm predictions using publicly available datasets and demonstrate that SECAT represents a more scalable and effective methodology to assess protein-network state and that our approach thus obviates the need to explicitly infer individual protein complexes. Further, by differential analysis of PPI networks of HeLa cells in interphase and mitotic state, respectively, we demonstrate the ability of the algorithm to detect PPI network differences and to thus suggest molecular mechanisms that differentiate cellular states.

systems biology↗