Biology subjects

Gomez-Zepeda, D.

Publications and source records attributed to Gomez-Zepeda, D..

6 recordsLinked to original sources

bioRxiv2025

Benchmarking Software for DDA-PASEF Immunopeptidomics

Mass spectrometry (MS) is the method of choice for high-throughput identification of immunopeptides, which are generated by intracellular proteases, unlike proteomics peptides that are typically derived from trypsin-digested proteins. Therefore, the searching space for immunopeptides is not limited by proteolytic specificity, requiring more sophisticated software algorithms to handle the increased complexity. Despite the widespread use of MS in immunopeptidomics, there is a lack of systematic evaluation of data processing software, making it challenging to identify the optimal solution. In this study, we provide a comprehensive benchmarking of the most widespread/used data-dependent acquisition (DDA)-based software platforms for immunopeptidomics: MaxQuant, FragPipe, PEAKS and MHCquant. The evaluation was conducted using data obtained from the JY cell line using the Thunder-DDA-PASEF method. We assessed each softwares ability to identify immunopeptides and compared their identification confidence. Additionally, we examined potential biases in the results and tested the impact of database size on immunopeptide identification efficiency. Our findings demonstrate that all software platforms successfully identify the most prominent subset of immunopeptides with 1% false discovery rate (FDR) control, achieving medium to high identification confidence correlations. The largest number of immunopeptides were identified using the commercial PEAKS software, which is closely followed by FragPipe, making it a viable non-commercial alternative. However, we observed that larger database sizes negatively impacted the performance of some software platforms more than others. These results provide valuable insights into the strengths and limitations of current MS data processing tools for immunopeptidomics, supporting the immunopeptidomics/MS community in determining the right choice of software.

bioinformatics↗

bioRxiv2024

MAETi: Mild acid elution in a tip enables quantitative immunopeptidome profiling from 25,000 cells

Mass spectrometry (MS)-based immunopeptidomics is essential for characterizing MHC class I (MHC-I) immunopeptides and defining targets for tumor immunotherapies. Conventional workflows require large sample amounts, limiting their use in low-input scenarios, high-throughput screens, and kinetic studies. We developed Mild Acid Elution in a Tip (MAETi), an antibody-free, low-input method for MHC-I immunopeptidome profiling. An optimized {beta}-alanine MAE buffer reduces background, enhances peptide coverage, and improves reproducibility. Compared with bulk immunoprecipitation, {beta}-alanine MAE achieves similar or complementary peptide depth. Using two protocol formats we profiled 1E4-1E6 JY cells, detecting on average over 3,000 and 6,000 predicted binders, respectively. A personalized database with single amino acid variants (SAVs) enabled robust identification and quantitation of SAV epitopes from only 1E4 JY cells. Additionally, MAETi supported differential ligandome analysis of 1.5E4-5E4 FACS-sorted immune cells, revealing cell type-specific ligandome repertoires. MAETi provides a simple, fast, and scalable workflow for MS-based immunopeptidomics from minimal sample input.

immunology↗

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↗

bioRxiv2023

Unveiling IRF4-steered regulation of context-dependent effector programs in Th17 and Treg cells

The transcription factor interferon regulatory factor 4 (IRF4) is crucial for the differentiation and fate determination of pro-inflammatory T helper (Th)17 and the functionally opposing group of immunomodulatory regulatory T (Treg) cells. However, molecular mechanisms of how IRF4 steers diverse transcriptional programs in Th17 and Treg cells are far from being definitive. To unveil IRF4-driven lineage determination in Th17 and Treg cells, we integrated data derived from affinity-purification and full mass spectrometry-based proteome analysis with chromatin immune precipitation sequencing (ChIP-Seq). This allowed the characterization of subtype-specific molecular programs and the identification of novel, previously unknown IRF4 interactors in the Th17/Treg context, such as ROR{gamma}t, AHR, IRF8, BACH2, SATB1, and FLI1. Moreover, our data reveal that most of these transcription factors are recruited to IRF composite elements for the regulation of cell type-specific transcriptional programs providing a valuable resource for studying IRF4-mediated gene regulatory programs in pro- and anti-inflammatory immune responses.

immunology↗

bioRxiv2021

Locality-sensitive hashing enables signal classification in high-throughput mass spectrometry raw data at scale

Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: First, even a single experiment produces a large amount of multi-dimensional raw data and, second, signals of interest are not single peaks but patterns of peaks that span along the different dimensions. The rapidly growing amount of mass spectrometry data increases the demand for scalable solutions. Existing approaches for signal detection are usually not well suited for processing large amounts of data in parallel or rely on strong assumptions concerning the signals properties. In this study, it is shown that locality-sensitive hashing enables signal classification in mass spectrometry raw data at scale. Through appropriate choice of algorithm parameters it is possible to balance false-positive and false-negative rates. On synthetic data, a superior performance compared to an intensity thresholding approach was achieved. The implementation scaled out up to 88 threads on real data. Locality-sensitive hashing is a desirable approach for signal classification in mass spectrometry raw data. Generated data and code are available at https://github.com/hildebrandtlab/mzBucket. Raw data is available at https://zenodo.org/record/5036526.

bioinformatics↗

bioRxiv2020

Functional Characterization of the Lin28/let-7 Circuit during Forelimb Regeneration in Ambystoma mexicanum and its Influence on Metabolic Reprogramming

The axolotl (Ambystoma mexicanum) is a caudate amphibian, which has an extraordinary ability to restore a wide variety of damaged structures by a process denominated epimorphosis. While the origin and potentiality of progenitor cells that take part during epimorphic regeneration are known to some extent, the metabolic changes experienced and their associated implications, remain unexplored. However, a circuit with a potential role as a modulator of cellular metabolism along regeneration is that formed by Lin28/let-7. In this study, we report two Lin28 paralogs and eight mature let-7 microRNAs encoded in the axolotl genome. Particularly, in the proliferative blastema stage amxLin28B is more abundant in the nuclei of blastemal cells, while the microRNAs amx-let-7c and amx-let-7a are most downregulated. Functional inhibition of Lin28 factors increase the levels of most mature let-7 microRNAs, consistent with an increment of intermediary metabolites of the Krebs cycle, and phenotypic alterations in the outgrowth of the blastema. In summary, we describe the primary components of the Lin28/let-7 circuit and their function during axolotl regeneration, acting upstream of metabolic reprogramming events.

developmental biology↗