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Biology subjects

Oeller, M.

Publications and source records attributed to Oeller, M..

16 recordsLinked to original sources

Aggregate Deep Visual Proteomics reveals selective organization of α-synuclein inclusions

-Synuclein inclusions, the lesions typical of Parkinson's disease, have been interpreted as protective deposits, toxic species or failed degradation products, each predicting a different composition and correlating that enrichment with neuron dysfunction and eventual degeneration. Distinguishing them requires measuring what an inclusion concentrates relative to the cell that built it. Here we developed Aggregate Deep Visual Proteomics, which uses ultrasensitive mass spectrometry to profile laser-microdissected inclusions against the remainder of the same cell or a matched host. In HEK293 cells, iPSC-derived dopaminergic neurons and post-mortem patient brain, each inclusion concentrated a selective subset of its cell's proteome, independent of abundance and dominated by membrane, in neurons largely presynaptic. Inclusions took up whole assemblies, with subunits moving together in 421 of 438 complexes, and the V-ATPase alone was enriched in all three systems. Degradation cargo stayed with the receptors that route it, consistent with sequestration and failed degradation acting together. Neuritic and somatic inclusions differed, the former rich in UBQLN2 and the latter in intermediate filaments. In the cell line, phosphorylated -synuclein was tenfold higher in the inclusion than in the rest of the cell, with about one third of the rise already present before an inclusion was visible. Measured against its host, the composition of an inclusion becomes a readout of how it formed and a source of candidate markers and therapeutic targets.

biochemistry↗

In the same cell, the proteome defines cellular state and the transcriptome marks transitions

Bulk transcriptome and proteome correlate only modestly, but this has not been investigated in the same cell or across cell-state changes. Here we introduce a scalable technology that quantifies thousands of proteins and transcripts in the same cell, separating RNA from protein by tip-based C18 capture and pairing full-length RNA sequencing with latest-generation mass spectrometry. In HeLa cells, transcript and protein abundances agree on the broad ranking within a cell (r = 0.45), but do not co-vary across the population (r = 0.038). In pluripotency transitions, only a third of matched transcripts and proteins change synchronously, yet the transcription factors defining each state stay tightly co-regulated. Transcript variance is several-fold larger than protein variance, reflecting transcriptional bursting and mRNA sampling noise. The proteome is thus the stable, low-noise definition of cell state, while the transcriptome marks cellular transitions; consequently, the proteome defines cell-state from far fewer cells.

systems biology↗

A cell type-resolved proteomic atlas of the human body

Proteins define what cells do, yet their systematic quantification across human cell types has remained out of reach. Using Deep Visual Proteomics on tissue from a healthy female donor, we built an atlas of 27 cell types across 14 tissues, quantifying two-thirds of all human protein-coding genes, with up to 8,500 per population. The proteome partitions bimodally into a universal core and highly specialized programs. Integration into the Human Protein Atlas Single Cell Resource enabled comparison of RNA and protein abundance at cell type resolution, revealing that concordance depends on pathway rather than cellular identity. This resolution uncovered cancer-testis antigens in oocytes invisible to bulk profiling. Our openly accessible resource provides a foundation for cell type-resolved proteomics in health and disease.

biochemistry↗

Cell therapy for regeneration of injured donor lungs for transplantation

Donor organ shortage remains the major barrier to transplantation resulting in deaths on the waiting list. For lungs, aspiration-related injury is a common cause of donor organ discard and increases the risk of primary graft dysfunction. Currently, no effective therapies exist to repair damaged donor lungs prior to transplantation. Here, we investigated whether mesenchymal stromal cells (MSCs) from bone marrow or full-term amniotic fluid could restore severely injured donor lungs in a porcine model integrating ex vivo lung perfusion, transplantation and post-transplant follow-up (n=48; 24 donors, 24 recipients). MSCs were administered either once during ex vivo lung perfusion or repeatedly across lung perfusion and the early post-transplant period and compared with placebo treated controls. A single dose conferred only partial benefit, whereas repeated dosing restored graft function, normalized gas exchange and haemodynamics, and prevented graft dysfunction. MSCs from both sources were similarly effective in repeated regimens. These findings identify dosing schedule, rather than cell source, as key determinant of durable organ rescue and support perfusion-guided cell therapy as potentially generalizable regenerative strategy across solid-organ transplantation.

cell biology↗

Scanning DIA on the ZenoTOF 8600 system enables ultra-sensitive and quantitative proteomics from single cells to post-translational modifications in a compact platform

Mass spectrometry-based proteomics increasingly demands platforms that combine quantitative rigor with the discovery capabilities of accurate mass systems. Here we present the ZenoTOF 8600 system, a compact mass spectrometry system that integrates enhanced ion capture and transmission optics with an optical detection system, Zeno trap-enhanced MS/MS, electron-activated dissociation, and scanning quadrupole data-independent acquisition (ZT Scan DIA). We show that ZT Scan DIA outperforms conventional variable-window DIA (Zeno SWATH DIA) in both identifications and quantitative reproducibility, and demonstrate the platforms versatility across proteomics applications: thousands of protein groups from bulk samples at up to 500 samples per day, single-cell proteomics yielding up to 4,700 proteins, accurate ratio recovery in mixed-species quantitative benchmarks, low-attomole targeted quantitation, and detection of disease-relevant phosphorylation in a Parkinsons disease cellular model using complementary CID and EAD fragmentation. The instruments compact footprint makes it attractive for settings where both analytical breadth and operational robustness are required.

biophysics↗

A humanized ossicle model of myelofibrosis reveals THPO-driven fibrosis, osteosclerosis and SPP1-dependent microenvironmental remodeling

Myelofibrosis (MF) is the most severe myeloproliferative neoplasm. Current therapies - except for allogeneic stem cell transplantation - are largely supportive which highlights the need for improved disease models and novel therapeutic targets. Here, we established a humanized MF model by transplanting thrombopoietin (THPO)-overexpressing human bone marrow CD34{square} cells into humanized bone marrow ossicles generated in immunodeficient NSG mice. THPO overexpression induced progressive reticulin fibrosis in vivo, accompanied by myeloid skewing, increased megakaryocyte clustering, and redistribution of human hematopoietic cells to murine spleen and femur, consistent with extramedullary hematopoiesis. THPO-driven ossicles also exhibited features of osteosclerosis, including increased trabecular bone and osteoid formation, indicating active pathological remodeling of the niche. Mechanistically, fibrosis was associated with increased SPP1/OPN expression, which was also observed in bone marrow biopsies from MF patients. Importantly, in vivo neutralization of SPP1 attenuated myeloid skewing, reduced megakaryocyte expansion, and decreased fibrosis severity, highlighting SPP1-driven niche remodeling as a potential therapeutic target in MF. This humanized MF model thus provides a translationally relevant platform to dissect microenvironment-driven MF pathogenesis and evaluate targeted therapies.

cancer biology↗

Deep Learning-Driven Fragment Ion Selection for Improved Quantification in MS based Proteomics

Quantitative proteomics relies on accurate selection of fragment ions for quantification, yet most current algorithms apply simple strategies such as median intensity or single quality filters. Modern data-independent acquisition (DIA) searches generate rich features such as fragment ion correlations, retention time and many others that could be leveraged to assess fragment quality. We introduce QuantSelect, a novel strategy to select optimal fragments by systematically integrating these features via self-supervised deep learning. QuantSelect uses a regularized, weighted-variance loss on intensity traces normalized via our directLFQ algorithm. This allows learning a fragment quality score without ground truth labels, enabling on-the-fly training on label-free DIA datasets. Integrated within our alphaDIA pipeline, QuantSelect significantly improves quantitative accuracy and in some cases substantially corrects protein intensity estimation. Sensitivity in differential expression improved by 68% in a mixed-species benchmarking dataset and by 18% in single-cell data. QuantSelect provides a practical framework for data-driven fragment selection that improves accuracy, precision and downstream inference in DIA proteomics.

bioinformatics↗

A Solid-Phase Extraction Capture (SPEC) workflow in nanoliter volumes for fast, robust and ultrasensitive proteomics

Sample preparation remains a critical bottleneck in mass spectrometry (MS)-based proteomics, particularly for limited sample amounts where surface adsorption and dilution cause substantial losses. Here, we present Solid-Phase Extraction Capture (SPEC), a workflow that confines protein processing to nanoliter volumes within ion-exchange or C18 matrix inside a pipette tip. This achieves near-complete proteolysis within 5 minutes instead of hours and maintains full compatibility with strong detergents without cleanup steps, enabling effective lysis of challenging samples. From 200 ng FFPE tissue, SPEC achieves proteome depth and reproducibility exceeding conventional bulk protocols using 100 {micro}g, critical when sample is irreplaceable. The modular two-tip configuration enables on-tip chemical modifications for mTRAQ labeling and fractionation, while integration with enrichment workflows yields 2-fold improved glycopeptide identifications from plasma and 3-fold enhanced ubiquitin remnant identification at low amounts. SPEC enables nanoPhos for cell-type resolved tissue phosphoproteomics and provides a universal platform for proteomics sample preparation.

systems biology↗

nanoPhos enables ultra-sensitive and cell-type resolved spatialphosphoproteomics

Mass spectrometry (MS)-based phosphoproteomics has transformed our understanding of cell signaling, yet current workflows face limitations in sensitivity and spatial resolution at sub-microgram inputs. Here, we present nanoPhos, a robust method that extends phosphoproteomics to nanogram scale, making it compatible with cell-type-resolved spatial analysis. It employs loss-less solid phase extraction capture (SPEC) for sample preparation, followed by automated phosphopeptide enrichment using Fe(III)-NTA cartridges. nanoPhos identifies over 57,000 unique phosphorylation sites from 1 {micro}g cell lysate and over 4,000 from only 10 ng, a hundred-fold improvement from recent protocols. Combined with Deep Visual Proteomics (DVP), it enables region- and cell-type resolved phosphoproteomics of mouse brain tissue with spatial fidelity and a depth of 13,000 phosphosites from only 1000 cell shapes. This establishes nanoPhos as a versatile and ultra-sensitive platform that extends DVP to post-translational modifications and opens up for cell-type-specific signaling analysis in intact tissue.

systems biology↗

The proteomic landscape of proteotoxic stress in a fibrogenic liver disease

Protein misfolding diseases, including alpha-1 antitrypsin deficiency (AATD), pose significant health challenges, with their cellular progression still poorly understood1-3. We utilize spatial proteomics by mass spectrometry and machine learning to map AATD in human liver tissue. Combining Deep Visual Proteomics (DVP) with single-cell analysis4,5, we probe intact patient biopsies to resolve molecular events during hepatocyte stress in pseudo-time across fibrosis stages. We achieve unprecedented proteome depth of up to 3,800 proteins from a third of a single cell in formalin-fixed, paraffin-embedded (FFPE) tissue. This dataset revealed a potentially clinically actionable peroxisomal upregulation that precedes the canonical unfolded protein response. Our single-cell proteomics data show alpha-1 antitrypsin accumulation is largely cell-intrinsic, with minimal stress propagation between hepatocytes. We integrated proteomic data with AI-guided image-based phenotyping across multiple disease stages, revealing a terminal hepatocyte state characterized by globular protein aggregates and distinct proteomic signatures, notably including elevated TNFSF10/TRAIL expression. This phenotype may represent a critical disease progression stage. Our study offers novel insights into AATD pathogenesis and introduces a powerful methodology for high-resolution, in situ proteomic analysis of complex tissues. This approach holds potential to unravel molecular mechanisms in various protein misfolding disorders, setting a new standard for understanding disease progression at the single-cell level in human tissue.

cell biology↗

Targeting Specific Kinase Substrates Rescues Increased Colitis Severity Induced by the Crohn's Disease-Linked LRRK2-N2081D Variant

LRRK2 contains a kinase domain where both the N2081D Crohns disease (CD) risk and the G2019S Parkinsons disease (PD)-pathogenic variants are located. The mechanisms by which the N2081D variant increase CD risk, and how these adjacent mutations result in distinct diseases, remain unclear. To investigate the pathophysiology of the CD-linked LRRK2 N2081D variant, we generated a knock-in (KI) mouse model and compared its effects to those of the LRRK2-G2019S mutation. We find that Lrrk2N2081D KI mice demonstrate heightened sensitivity to induced colitis, resulting in more severe inflammation and intestinal damage than Lrrk2G2019SKI and wild-type mice. Analysis of Colon tissue revealed distinct mutation-dependent LRRK2 RAB substrate phosphorylation, with significantly elevated phosphorylated RAB10 levels in Lrrk2N2081D mice. In cells, we demonstrate that the N2081D mutation activates LRRK2 through a mechanism distinct from that of LRRK2-G2019S. We further find that proinflammatory stimulation enhances LRRK2 kinase activity, leading to mutation-dependent differences in RAB phosphorylation and inflammatory responses in dendritic cells. Finally, we show that genetic knockout of Rab12, but not pharmacological LRRK2 kinase inhibition, significantly reduced colitis severity in Lrrk2N2081D mice. Our study characterizes the pathogenic mechanisms of LRRK2-linked CD, highlights important structural and functional differences between disease-associated LRRK2 variants, and suggests RAB proteins as promising therapeutic targets for modulating LRRK2 activity in CD treatment.

molecular biology↗

Sequence-based prediction of the solubility of peptides containing non-natural amino acids

Non-natural amino acids are increasingly used as building blocks in the development of peptide-based drugs, as they expand the available chemical space to tailor function, half-life and other key properties. However, while the chemical space of modified amino acids (mAAs) is potentially vast, experimental methods for measuring the developability properties of mAA-containing peptides are expensive and time consuming. To facilitate developability programs through computational methods, we present CamSol-PTM, a method that enables the fast and reliable sequence-based prediction of the solubility of mAA-containing peptides. From a computational screening of 50,000 mAA-containing variants of three peptides, we selected five different mAAs for a total number of 30 peptide variants for experimental validation. We demonstrate the accuracy of the predictions by comparing the calculated and experimental solubility values. Our results indicate that the computational screening of mAA-containing peptides can extend by over four orders of magnitude the ability to explore the solubility chemical space of peptides. This method is available as a web server at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolptm.

biophysics↗

Multi-dimensional protein solubility optimization with an ultra-high-throughput microfluidic platform

Protein-based biologics are highly suitable for drug development, as they exhibit low toxicity and high specificity for their targets. However, for therapeutic applications, biologics must often be formulated to very high concentrations, making insufficient solubility a critical bottleneck in drug development pipelines. Here, we report an ultra-high-throughput microfluidic platform for protein solubility screening. In comparison with previous methods, this microfluidic platform can make, incubate, and measure samples in a few minutes, uses just 20 micrograms of protein (> 10-fold improvement) and yields 10,000 data points (1000-fold improvement). This allows quantitative comparison of formulation additives, such as salt, polysorbate, histidine, arginine and sucrose. Additionally, we can measure how solubility is affected by different concentrations of multiple additives, find a suitable pH for the formulation, and measure the impact of single mutations on solubility, thus enabling the screening of large libraries. By reducing material and time costs, this approach makes detailed multi-dimensional solubility optimization experiments possible, streamlining drug development and increasing our understanding of biotherapeutic solubility and the effects of excipients.

biophysics↗

Automated optimisation of solubility and conformational stability of antibodies and proteins

Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and polyspecificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk

biochemistry↗

Sequence-based pH-dependent prediction of protein solubility using CamSol

Solubility is a property of central importance for the use of proteins in research and in applications in biotechnology and medicine. Since experimental methods for measuring protein solubility are resource-intensive and time-consuming, computational methods have recently emerged to enable the rapid and inexpensive screening of large libraries of proteins, as it is routinely required in development pipelines. Here, we describe the extension of one of such methods, CamSol, to include in the predictions the effect of the pH of the solubility. We illustrate the accuracy of the pH-dependent predictions on a variety of antibodies and other proteins.

biophysics↗

Extracellular vesicles from therapeutic grade allogeneic human placental stromal cells induce angiogenesis and modulate immunity

Nanoparticles can acquire a protein corona defining their biological identity. Corona functions were not yet considered for cell-derived extracellular vesicles (EVs). Here we demonstrate that nanosized EVs from therapy-grade human placental-expanded (PLX) stromal cells are surrounded by an imageable and functional protein corona when enriched with permissive technology. Scalable EV separation from cell-secreted soluble factors via tangential flow-filtration and subtractive tandem mass-tag proteomics revealed significant enrichment of predominantly immunomodulatory and proangiogenic proteins. Western blot, calcein-based flow cytometry, super-resolution and electron microscopy verified EV identity. PLX-EVs protected corona proteins from protease digestion. EVs significantly ameliorated human skin regeneration and angiogenesis in vivo, induced differential signaling in immune cells, and dose-dependently inhibited T cell proliferation in vitro. Corona removal by size-exclusion or ultracentrifugation abrogated angiogenesis. Re-establishing an artificial corona by cloaking EVs with defined proangiogenic proteins served as a proof-of-concept. Understanding EV corona formation will improve rational EV-inspired nanotherapy design.

cell biology↗