Biology subjects

Rijal, J. B.

Publications and source records attributed to Rijal, J. B..

5 recordsLinked to original sources

bioRxiv2026

Finely tuned DDA-PASEF-based immunopeptidomics workflow enhances HLA class I coverage

Mass spectrometry-based immunopeptidomics is essential for the direct identification of HLA-presented peptides, but its application to low-input and clinical samples remains challenging. In this study, we present an optimized DDA-PASEF-based workflow to improve HLA class I peptide coverage. First, we optimized a magnetic bead-based sample preparation protocol to support analysis from reduced starting material and increase compatibility with automation. We then evaluated LC gradient length and column dimensions to define complementary configurations for high-throughput and high-coverage analysis. For MS acquisition, we fine-tuned key parameters, including ion accumulation time, number of PASEF scans, ion mobility range, and polygon filtering, to improve sensitivity while maintaining a suitable cycle time and limiting pre-CID truncations. The customized polygon filter improved the detection of singly charged HLA class I peptides, thereby broadening the identified immunopeptide repertoire. Finally, TIMS2Rescore was integrated to improve confidence in peptide identification. Overall, this optimized workflow improves HLA class I immunopeptide coverage across different sample types, even from low injected sample amounts, supporting its future applicability to clinically relevant samples.

biochemistry↗

bioRxiv2026

Polyploid Achromatium sp. expresses protein variants based on environmental cues

Achromatium spp. are giant bacteria harboring hundreds of non-clonal chromosomes per cell. The allelic divergence they exhibit could be a mechanism to enable fine-tuned responses to environmental changes, enabling this organism to broaden its ecological range. However, while ubiquitous in freshwater and marine environments, Achromatium has not been obtained in culture, leaving the role and functioning of its genomic and proteomic diversity poorly understood. Here, we incubated freshly collected freshwater Achromatium under three temperature conditions to conduct comparative mass spectrometry-based proteomics experiments. These were interpreted against an Achromatium-specific protein atlas developed from curated published and new assemblies from marine and freshwater environments. This de novo assembled atlas allowed a twenty-fold increase in the number of identified proteins compared to mass spectrometry data searches against the UniProt database. Predicted proteins from the Achromatium atlas were grouped into orthologs, 5% of which showed temperature-associated detection of distinct protein variants within the group. This provides the first indication that Achromatium uses distinct protein variants under different environmental conditions, enabling it to swiftly respond to environmental changes through differential expression from its cache of chromosomes.

microbiology↗

bioRxiv2026

A generalizable normalization framework to decouple protocol and instrument effects: Application to high-sensitivity proteomics multicentric study (PME13)

Multicenter studies are essential for benchmarking analytical workflows, yet their interpretation is often confounded by the combined effects of experimental protocols and instrumentation. To address this challenge, we introduce a simple normalization-based analytical framework, the recovery metric ({rho}), designed to decouple protocol driven effects from instrument dependent variability. We applied this framework to the 13th Proteomics Multicentric Experiment (PME13), a large multicentric proteomics dataset generated across 27 laboratories using high sensitivity workflows and varying sample preparation protocols. By leveraging a common digested reference sample, {rho} enables direct cross-comparison of all datasets on a unified scale, effectively minimizing instrument-related biases. Using this approach, we demonstrate that apparent instrument dependent trends are largely removed when evaluated through {rho}, revealing consistent protocol driven effects across laboratories. Statistical modeling identified key variables influencing {rho}, including sample input amount, reduction and alkylation, and the use of n-dodecyl-{beta}-D-maltoside (DDM). While DDM was associated with improved {rho}, reduction and alkylation and additional handling steps led to reduced performance, particularly at low input levels. We further highlight practical considerations for the application of ratio based normalization, including the occurrence of values exceeding theoretical bounds, which reflect deviations from underlying assumptions and require appropriate filtering. Overall, this work establishes a generalizable analytical strategy for disentangling confounding factors in multicentric datasets and provides practical guidelines for optimizing high sensitivity proteomics (HSP) workflows. The proposed framework is broadly applicable to other analytical fields where cross laboratory comparability is required.

bioinformatics↗

bioRxiv2026

LFQ Benchmark Dataset - Generation Beta: Assessing Modern Proteomics Instruments and Acquisition Workflows with High-Throughput LC Gradients

Recent advances in liquid chromatography-mass spectrometry (LC-MS) have accelerated the adoption of high-throughput workflows that deliver deep proteome coverage using minimal sample amounts. This trend is largely driven by clinical and single-cell proteomics, where sensitivity and reproducibility are essential. Here, we extend our previous benchmark dataset (PXD028735) using next-generation LC-MS platforms optimized for rapid proteome analysis. We generated an extensive DDA/DIA dataset using a human-yeast-E. coli hybrid proteome. The proteome sample was distributed across multiple laboratories together with standardized analytical protocols specifying two short LC gradients (5 and 15 min) and low sample input amounts. This dataset includes data acquired on four different platforms, and features new scanning quadrupole-based implementations, extending coverage across different instruments and acquisition strategies. Our comprehensive evaluation highlights how technological advances and reduced LC gradients may affect proteome depth, quantitative precision, and cross-instrument consistency. The release of this benchmark dataset via ProteomeXchange (PXD070049 and PXD071205), allows for the acceleration of cross-platform algorithm development, enhance data mining strategies, and supports standardization of short-gradient, high-throughput LC-MS-based proteomics.

bioinformatics↗

bioRxiv2024

TIMS2Rescore: A DDA-PASEF optimized data-driven rescoring pipeline based on MS2Rescore

The high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields - such as plasma proteomics, immunopeptidomics, and metaproteomics - must tackle specific analytical challenges, such as an increased identification ambiguity compared to routine proteomics experiments. Technical advancements in MS instrumentation can counter these issues by acquiring more discerning information at higher sensitivity levels, as is exemplified by the incorporation of ion mobility and parallel accumulation - serial fragmentation (PASEF) technologies in timsTOF instruments. In addition, AI-based bioinformatics solutions can help overcome ambiguity issues by integrating more data into the identification workflow. Here, we introduce TIMS2Rescore, a data-driven rescoring workflow optimized for DDA-PASEF data from timsTOF instruments. This platform includes new timsTOF MS2PIP spectrum prediction models and IM2Deep, a new deep learning-based peptide ion mobility predictor. Furthermore, to fully streamline data throughput, TIMS2Rescore directly accepts Bruker raw mass spectrometry data, and search results from ProteoScape and many other search engines, including MS Amanda and PEAKS. We showcase TIMS2Rescore performance on plasma proteomics, immunopeptidomics (HLA class I and II), and metaproteomics data sets. TIMS2Rescore is open-source and freely available at https://github.com/compomics/tims2rescore.

bioinformatics↗