Biology subjects

Steigerwald, S.

Publications and source records attributed to Steigerwald, S..

9 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2025

Single cell spatial proteomics maps human liver zonation patterns and their vulnerability to fibrosis

Understanding protein distribution patterns across tissue architecture is crucial for deciphering organ function in health and disease. Here, we applied single-cell Deep Visual Proteomics to perform spatially-resolved proteome analysis of individual cells in native tissue. We combined this with a novel strategic cell selection pipeline and a continuous protein gradient mapping framework to investigate larger clinical cohorts. We generated a comprehensive spatial map of the human hepatic proteome by analyzing hundreds of individual hepatocytes from 18 individuals. Among more than 2,500 proteins per cell about half exhibited zonated expression patterns. Cross-species comparison with mouse data revealed conserved metabolic functions and human-specific features of liver zonation. Analysis of fibrotic samples demonstrated widespread disruption of protein zonation, with pericentral proteins being particularly susceptible. Our study provides a comprehensive resource of human liver organization while establishing a broadly applicable framework for spatial proteomics analyses along tissue gradients.

molecular biology↗

bioRxiv2024

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↗

bioRxiv2024

Deep Visual Proteomics reveals DNA replication stress as a hallmark of Signet Ring Cell Carcinoma

Signet Ring Cell Carcinoma (SRCC) is a rare and highly malignant form of adenocarcinoma with increasing incidence and poor prognosis due to late diagnosis and limited treatment options. We employed Deep Visual Proteomics (DVP), which combines AI directed cell segmentation and classification with laser microdissection and ultra-high sensitivity mass spectrometry, for cell-type specific proteomic analysis of SRCC across the bladder, prostate, liver, and lymph nodes of a single patient. DVP identified significant alterations in DNA damage response (DDR) proteins, particularly within the ATR and mismatch repair (MMR) pathways, indicating replication stress as a crucial factor in SRCC mutagenicity. Additionally, we observed substantial enrichment of immune-related proteins, reflecting high levels of cytotoxic T lymphocyte infiltration and elevated PD-1 expression. These findings suggest that pembrolizumab immunotherapy may be more effective than conventional chemotherapy for this patient. Our results provide novel insights into the proteomic landscape of SRCC, identifying potential targets and open up for personalized therapeutic strategies in managing SRCC.

cancer biology↗

bioRxiv2024

AlphaDIA enables End-to-End Transfer Learning for Feature-Free Proteomics

Mass spectrometry (MS)-based proteomics continues to evolve rapidly, opening more and more application areas. The scale of data generated on novel instrumentation and acquisition strategies pose a challenge to bioinformatic analysis. Search engines need to make optimal use of the data for biological discoveries while remaining statistically rigorous, transparent and performant. Here we present alphaDIA, a modular open-source search framework for data independent acquisition (DIA) proteomics. We developed a feature-free identification algorithm particularly suited for detecting patterns in data produced by sensitive time-of-flight instruments. It naturally adapts to novel, more eTicient scan modes that are not yet accessible to previous algorithms. Rigorous benchmarking demonstrates competitive identification and quantification performance. While supporting empirical spectral libraries, we propose a new search strategy named end-to-end transfer learning using fully predicted libraries. This entails continuously optimizing a deep neural network for predicting machine and experiment specific properties, enabling the generic DIA analysis of any post-translational modification (PTM). AlphaDIA provides a high performance and accessible framework running locally or in the cloud, opening DIA analysis to the community.

bioinformatics↗

bioRxiv2024

A novel hybrid high speed mass spectrometer allows rapid translation from biomarker candidates to targeted clinical tests using 15N labeled proteins

Recent developments in affinity binder or mass spectrometry (MS)-based plasma proteomics are now producing panels of potential biomarker candidates for diagnosis or prognosis. However, clinical validation and implementation of these biomarkers remain limited by the reliance on dated triple quadrupole MS technology. Here, we evaluate a novel hybrid high-speed mass spectrometer, Stellar MS, which integrates the robustness of triple quadrupoles with the enhanced capabilities of an advanced linear ion trap analyzer. This instrument allows for extremely rapid and sensitive parallel reaction monitoring (PRM) and MS3 targeting. The Stellar MS allowed targeting thousands of peptides originally measured on Orbitrap Astral MS, achieving high reproducibility and low coefficients of variation (CV) as well as sensitivity and specificity sufficient for more than the top 1000 plasma proteins. Furthermore, we developed targeted assays for alcohol-related liver disease (ALD) biomarkers, showcasing the potential of Stellar MS in clinical applications. Absolute quantification is typically a requirement for clinical assays and we explore the use of 15 N labeled protein standards in a rapid, streamlined and generic manner. Our results indicate that Stellar MS can bridge the gap between proteomics discovery and routine clinical testing, enhancing the diagnostic and prognostic utility of protein biomarkers.

biochemistry↗

bioRxiv2024

Deep Visual Proteomics advances human colon organoid models by revealing a switch to an in vivo-like phenotype upon xenotransplantation

AbstractIntestinal epithelial damage predisposes to chronic disorders like inflammatory bowel disease. The organoid model allows cultivation, expansion and analysis of primary intestinal epithelial cells and has been instrumental in studying epithelial behavior in homeostasis and disease. Recent advances in organoid transplantation allow studying human epithelial cell behavior within the intestinal tissue context. However, it remained unclear how organoid transplantation into the colon affects epithelial phenotypes, which is key to assessing the models suitability to study human epithelial cells. We employed Deep Visual Proteomics, integrating AI-guided cell classification, laser microdissection, and an improved proteomics pipeline to study the human colon. This created an in-depth cell type-resolved proteomics resource of human intestinal epithelial cells within human tissue, in vitro organoids, and the murine colon post-xenotransplantation. Our findings reveal that in vitro conditions induce a proliferative organoid phenotype, which was reversible upon transplantation and adjustment of organoid culturing conditions.

cell biology↗

bioRxiv2021

Spatial-proteomics reveal in-vivo phospho-signaling dynamics at subcellular resolution

Dynamic change in subcellular localization of signaling proteins is a general concept that eukaryotic cells evolved for eliciting a coordinated response to stimuli. Mass spectrometry (MS)-based proteomics in combination with subcellular fractionation can provide comprehensive maps of spatio-temporal regulation of cells, but involves laborious workflows that does not cover the phospho-proteome level. Here we present a high-throughput workflow based on sequential cell fractionation to profile the global proteome and phospho-proteome dynamics across six distinct subcellular fractions. We benchmarked the workflow by studying spatio-temporal EGFR phospho-signaling dynamics in-vitro in HeLa cells and in-vivo in mouse tissues. Finally, we investigated the spatio-temporal stress signaling, revealing cellular relocation of ribosomal proteins in response to hypertonicity and muscle contraction. Proteomics data generated in this study can be explored through https://SpatialProteoDynamics.github.io.

systems biology↗

bioRxiv2019

A Compact Quadrupole-Orbitrap Mass Spectrometer with FAIMS Interface Improves Proteome Coverage in Short LC Gradients

State-of-the-art proteomics-grade mass spectrometers can measure peptide precursors and their fragments with ppm mass accuracy at sequencing speeds of tens of peptides per second with attomolar sensitivity. Here we describe a compact and robust quadrupole-orbitrap mass spectrometer equipped with a front-end High Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) Interface. The performance of the Orbitrap Exploris 480 mass spectrometer is evaluated in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes in combination with FAIMS. We demonstrate that different compensation voltages (CVs) for FAIMS are optimal for DDA and DIA, respectively. Combining DIA with FAIMS using single CVs, the instrument surpasses 2500 unique peptides identified per minute. This enables quantification of >5000 proteins with short online LC gradients delivered by the Evosep One LC system allowing acquisition of 60 samples per day. The raw sensitivity of the instrument is evaluated by analyzing 5 ng of a HeLa digest from which >1000 proteins were reproducibly identified with 5 minute LC gradients using DIA-FAIMS. To demonstrate the versatility of the instrument we recorded an organ-wide map of proteome expression across 12 rat tissues quantified by tandem mass tags and label-free quantification using DIA with FAIMS to a depth of >10,000 proteins.

biochemistry↗