Biology subjects

Just, S.

Publications and source records attributed to Just, S..

6 recordsLinked to original sources

bioRxiv2026

From Peaks to Power: Systematic Evaluation of Chromatographic Sampling Reveals Determinants of Quantification and Biological Discovery in DIA Proteomics

Modern DIA proteomics increasingly emphasizes throughput and depth for large-cohort studies, but methods are often optimized using proxy metrics that can mask losses in quantifiable signal and statistical power. Here, we evaluate how datapoints per peak and other chromatographic features jointly contribute to quantification and downstream biological discovery. Using a matrix-matched calibration curve dataset, we checked how the number of datapoints per peak (DPPP) affects the limits of detection and quantification (LOD/LOQ). Reduced DPPP minimally affected LOD but substantially degraded LOQ. Feature modeling and nonparametric association analyses identified precursor peak area as the strongest feature-level predictor of LOQ, whereas DPPP showed weaker and context-dependent effects. Simulations of chromatographic peak integration recapitulated these trends, showing that increased sampling primarily improves integration precision, while quantitative accuracy is strongly governed by peak height and peak shape. Finally, when comparing 20 cancer vs 20 control plasma samples processed with Seer Proteograph, the decrease in DPPP led to a loss of statistical significance for proteins with low-abundance precursors. These findings argue that DIA optimization should prioritize LOQ and statistical power metrics - not identifications alone - by balancing sampling density with chromatographic peak height and quality to maximize useful biological signal.

bioinformatics↗

bioRxiv2026

Radiant DIA: A Fast, Sensitive, and Accurate Search Engine for Quantitative Proteomics

In mass spectrometry-based proteomics, robust and efficient search engines are essential for accurate peptide and protein identification and quantification. Advances in sample preparation and instrumentation have increased the demand for highly scalable processing tools, with datasets comprising hundreds or thousands of samples in single-cell and population studies. Here we present Radiant DIA, a novel Data-Independent Acquisition search engine which achieves 4x faster processing and 10x lower cloud compute costs for large experiments while ensuring rigorous control of false discovery rate (FDR) and maintaining similar sensitivity, precision, and quantitative accuracy to widely-used tools. The Radiant DIA search engine is paired with a modular pipeline deployable on cloud and desktop environments comprising individual modules for distributed re-scoring, FDR estimation, protein inference and quantification. Unlike traditional monolithic applications, this architecture enables high-performance, cloud-scale analysis without sacrificing local usability. Together, the Radiant DIA and Fulcrum Pipeline tools enhance computational efficiency to facilitate biological discovery in large-scale proteomics, as demonstrated by analyses of real-world experiments up to thousands of MS acquisitions.

bioinformatics↗

bioRxiv2024

Cloud-enabled Scalable Analysis of Large Proteomics Cohorts

Rapid advances in depth and throughput of untargeted mass-spectrometry-based proteomic technologies are enabling large-scale cohort proteomic and proteogenomic analyses. As such studies scale, the data infrastructure and search engines required to process data must also scale. This challenge is amplified in search engines that rely on library-free match between runs (MBR) search, which enable enhanced depth-per-sample and data completeness. However, to-date, no MBR-based search could scale to process cohorts of thousands or more individuals. Here, we present a strategy to deploy search engines in a distributed cloud environment without source code modification, thereby enhancing resource scalability and throughput. Additionally, we present an algorithm, Scalable MBR, that replicates the MBR procedure of the popular DIA-NN software for scalability to thousands of samples. We demonstrate that Scalable MBR can search thousands of MS raw files in a few hours compared to days required for the original DIA-NN MBR procedure and demonstrate that the results are almost indistinguishable to those of DIA-NN native MBR. The method has been tested to scale to over 15,000 injections and is available for use in the Proteograph Analysis Suite.

bioinformatics↗

bioRxiv2024

SMYD1-mediated Mono-Methylation of Lysine K35 of the sarcomeric Myosin Heavy Chain (MHC) is fundamental for thick filament assembly in zebrafish and human iPSC-derived cardiomyocytes

The SMYD family is a unique class of lysine methyltransferases (KMTases) known to methylate histones but also non-histone proteins. Among the five SMYD family members (1-5), SMYD1 was identified as a heart- and skeletal muscle-specific KMTase, which, together with Unc45b and Hsp90a, interacts with Myosin thereby regulating thick filament assembly. However, the process by which SMYD1 orchestrates Myosin assembly is largely unknown. Here, we found that SMYD1 physically interacts with Myosin heavy chain (Myh) at its N-terminus and that the Myh N-terminus specifically gets mono-methylated by SMYD1 at lysine 35 (K35). Accordingly, methylated Myh is properly integrated into functional sarcomeres, whereas unmethylated Myh molecules in Smyd1-deficient zebrafish are efficiently degraded by the Ubiquitin Proteasome System (UPS) leading to defective thick filament assembly. Although the inhibition of the UPS by MG132 is able to reconstitute Myosin levels in Smyd1-deficient zebrafish embryos, thick filament assembly is still blocked due to the lack of K35 Myh mono-methylation. Similar to the situation in zebrafish striated muscle cells, SMYD1-mediated MYH methylation is also critical for thick filament assembly in human cardiomyocytes, indicating cross-species conservation of this fundamental mechanism of Myosin methylation, which has been first described about 40 years ago. Further investigations will now be essential to explore the therapeutic potential of targeting this pathway in cardiomyopathies and skeletal muscle disorders.

molecular biology↗

bioRxiv2023

Protein Coronas on Functionalized Nanoparticles Enable Quantitative and Precise Large-Scale Deep Plasma Proteomics

BackgroundThe wide dynamic range of circulating proteins coupled with the diversity of proteoforms present in plasma has historically impeded comprehensive and quantitative characterization of the plasma proteome at scale. Automated nanoparticle (NP) protein corona-based proteomics workflows can efficiently compress the dynamic range of protein abundances into a mass spectrometry (MS)-accessible detection range. This enhances the depth and scalability of quantitative MS-based methods, which can elucidate the molecular mechanisms of biological processes, discover new protein biomarkers, and improve comprehensiveness of MS-based diagnostics. MethodsInvestigating multi-species spike-in experiments and a cohort, we investigated fold-change accuracy, linearity, precision, and statistical power for the using the Proteograph Product Suite, a deep plasma proteomics workflow, in conjunction with multiple MS instruments. ResultsWe show that NP-based workflows enable accurate identification (false discovery rate of 1%) of more than 6,000 proteins from plasma (Orbitrap Astral) and, compared to a gold standard neat plasma workflow that is limited to the detection of hundreds of plasma proteins, facilitate quantification of more proteins with accurate fold-changes, high linearity, and precision. Furthermore, we demonstrate high statistical power for the discovery of biomarkers in small- and large-scale cohorts. ConclusionsThe automated NP workflow enables high-throughput, deep, and quantitative plasma proteomics investigation with sufficient power to discover new biomarker signatures with a peptide level resolution.

biochemistry↗

bioRxiv2022

nf-encyclopedia: A cloud-ready pipeline for chromatogram library data-independent acquisition proteomics workflows

Data independent acquisition (DIA) mass spectrometry methods provide systematic and comprehensive quantification of the proteome; yet, relatively few open-source tools are available to analyze DIA proteomics experiments. Fewer still are tools that can leverage gas phase fractionated (GPF) chromatogram libraries to enhance the detection and quantification of peptides in these experiments. Here, we present nf-encyclopedia, an open-source NextFlow pipeline that connects three open-source tools--MSConvert, EncyclopeDIA, and MSstats--to analyze DIA proteomics experiments with or without chromatogram libraries. We demonstrate that nf-encyclopedia is reproducible both when run on a cloud platform or a local workstation and provides robust peptide and protein quantification. Additionally, we found that MSstats enhances protein-level quantitative performance over EncyclopeDIA alone. Finally, we benchmarked the ability nf-encyclopedia to scale to large experiments in the cloud by leveraging the parallelization of compute resources. The nf-encyclopedia pipeline is available under a permissive Apache 2.0 license--run it on your desktop, cluster, or in the cloud: https://github.com/TalusBio/nf-encyclopedia.

bioinformatics↗