CBIO’s EuroBioc2024 posters and talks

The lab is preparing for EuroBioc2025 in Barcelona, and I realise that I forgot to post our contributions to EuroBioc2024 in Oxford. So here they are (with papers that were published in the meantime).

Differential Correlation Analysis and Biological Function Inference on Single Cell Proteomics

Author(s): Enes Sefa Ayar, Laurent Gatto

Proteins are the key molecules in executing biological functions within cells. They operate in cooperation with other proteins to carry out these functions as part of protein complexes, or biological pathways. Thus, the correlation among these proteins implies a functional interdependence, offering insights into both biological functions and mechanisms. Differential correlation analysis promises to infer these biological functions and even underlying mechanisms by identifying similar or different correlation patterns in groups of proteins across conditions (ex. cell types, treatments). However, current approaches, particularly those developed for bulk measurements, may not be suitable for single-cell proteomics (SCP) datasets as they may overlook false positives and false negatives emerging due to batch effects or missing values.

We aim to investigate the most suitable approach for uncovering functional correlation in SCP datasets. We compared two approaches used in SCP [1, 2] and two other network-based methods [3, 4], commonly used in RNAseq studies. This benchmark involves comparing these methods across various SCP datasets from scpdata package, each with different properties including sample size, protein coverage, and missing values. Thus far, our observations indicate the importance of addressing batch effect-driven correlations. Our benchmark assesses the methods based on biological relevance, statistical significance, and data simulations.

References

[1] Hu, M., Zhang, Y., Yuan, Y., Ma, W., Zheng, Y., Gu, Q., & Xie, X. S. (2023). Correlated protein modules revealing functional coordination of interacting proteins are detected by single-cell proteomics. The Journal of Physical Chemistry B, 127(27), 6006–6014.

[2] Khan, S., Conover, R., Asthagiri, A. R., & Slavov, N. (2023). Dynamics of Single-Cell Protein Covariation during Epithelial–Mesenchymal Transition.

[3] Langfelder, P., & Horvath, S. (2008). WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 9(1).

[4] Song, W.-M., & Zhang, B. (2015). Multiscale embedded gene co-expression network analysis. PLOS Computational Biology, 11(11).

Bulk vs single-cell proteomics: is there a need for identification optimization?

Author(s): Guillaume Deflandre, Samuel Grégoire, Laurent Gatto

Single-cell proteomics (SCP) has emerged as a powerful tool for elucidating cellular heterogeneity, offering opportunities beyond traditional bulk sample analysis. However, the application of current peptide identifications crafted for bulk samples may lead to false discoveries in SCP. Challenges such as reduced peak counts, lower peak intensities, and degraded signal-to-noise ratios (as identified by Boekweg et al. [1]) raise the question: do current peptide scoring methods in search engines adequately perform in the context of SCP? To address these limitations, we explore the effectiveness of search engines and rescoring tools with the use of Bioconductor packages PSMatch and Spectra. Rescoring tools take profit of as many mass spectrometry-based features as possible, such as spectral characteristics and retention time models, which can be particularly relevant to mitigate the poor quality of SCP spectra. We used MS²Rescore to generate new features, Mokapot to rescore the SCP peptides as well as the above-mentioned packages to assess the efficiency of rescoring tools and potentially improve current scoring methods in the context of SCP. Our findings demonstrate a significant increase in confidently identified peptides upon rescoring. In addition, we suggest a 4-step methodology to evaluate the usefulness of current and new potential features. Finally, our results shed light on the differences between bulk and single-cell samples whilst providing insights that can inform more accurate and reliable data interpretation in the context of SCP.

References

[1] Hannah Boekweg and Samuel H. Payne. Challenges and Opportunities for Single-cell Computational Proteomics. Molecular & Cellular Proteomics, 22(4):100518, April 2023. ISSN 15359476. doi: 10.1016/j.mcpro.2023.100518. URL https://linkinghub.elsevier.com/retrieve/pii/S1535947623000282.

From Cancer-Testis genes to Cancer-Testis enhancers

Author(s): Julie Devis, Axelle Loriot, Charles De Smet and Laurent Gatto

Cancer-Testis (CT) genes are normally expressed only in germ cells and not in healthy somatic tissues. However, they are aberrantly activated in many tumours. Many CT genes are regulated by methylation. Their promoters are highly methylated in all healthy somatic tissues and demethylated in germ cells. They are also demethylated in tumours in which they are activated. This is a consequence of the global demethylation process often observed in cancer. These characteristics give them clinical potential, as they produce cancer-specific antigens and can thus be used as target for cancer immunotherapy. We have recently developed the CTexploreR Bioconductor package, an updated database for CT genes.

Promoters are not the only regulatory regions that can be affected by DNA methylation. It has been shown that many enhancers, that are activating distal regulatory regions, can be methylated. Their methylation can be altered in tumours, affecting the expression of their target genes. We hence wondered if we could find CT enhancers that would behave like CT genes promoters. We compared ENCODE cis-regulatory elements and whole genome bisulfite-seq data in somatic and germinal healthy tissues and in cancer to find enhancers that are active and demethylated exclusively in germ cells and tumours. We identified CT-like enhancer candidates that will be further defined.

An Open Software Development-based Ecosystem of R Packages for Proteomics Data Analysis

Author(s): Laurent Gatto and RforMassSpectrometry contributors

A frequent problem with scientific research software is the lack of support, maintenance and further development. In particular, development by a single researcher can easily result in orphaned and dysfunctional software packages, especially if combined with poor documentation, missing unit tests or lack of adherence to open software development standards.

The RforMassSpectrometry (https://www.rformassspectrometry.org/) initiative aims to develop an efficient, scalable, and stable infrastructure for mass spectrometry (MS) based proteomics (Gatto et al. poster) and metabolomics (Rainer et al. poster) data analysis. As part of this initiative, a growing ecosystem of R software packages is being developed covering different aspects of metabolomics and proteomics data analysis. To avoid the aforementioned problems, community contributions are fostered, and open development, documentation and long-term support emphasised.

At the heart of the package ecosystem lies the Spectra package that provides the core infrastructure to handle, process and visualise MS data. Its design allows easy expansion to support existing and new file or data formats, including data representations with minimal memory footprint or remote data access. For proteomics data analysis, two packages in particular are dedicated to the analysis or quantitative and identification data. The PSMatch package handles and manages peptide identification data. It also provides functions to model and visualise peptide-protein relations to make informed decision about shared peptide filtering. The package also provides functions to calculate and visualise MS2 fragment ions, in conjunction with the Spectra package. The QFeatures package is the working horse for quantitative proteomics data. It builds on the familiar SummarizedExperiment and MultiAssayExperiment infrastructure and provides a familiar Bioconductor user experience to manage bulk and single-cell quantitative data across different assay levels (such as peptide spectrum matches, peptides and proteins) in a coherent and tractable way. These three packages rely on MsCoreUtils for efficient implementations of commonly used algorithms, designed to be re-used by other R packages.

In contrast to a monolithic software design, the RforMassSpectrometry ecosystem enables to build customised, modular, and reproducible analysis workflows. Future proteomics-related development will focus on improved data structures and analysis methods, better support for third-party data import, and better interoperability with other open source software including a direct integration with Python MS libraries.

Publication

Loriot, Axelle, Julie Devis, Laurent Gatto, and Charles De Smet. 2025. “A Survey of Human Cancer-Germline Genes: Linking X Chromosome Localization, DNA Methylation and Sex-Biased Expression in Early Embryos.” bioRxiv. https://doi.org/10.1101/2025.05.19.654804.

Mass spectrometry-based proteomics/metabolomics and Bioconductor: from the early days to 2024

Author(s): Laurent Gatto, Sebastien Gibb, Johannes Rainer

The Bioconductor project has always been best known for its state-of-the-art infrastructure for genomics data analysis and comprehension. Starting with packages for microarrays, and later RNA Sequencing, transcriptomics has been the most visible part of the Bioconductor iceberg. Proteomics has been part of the early days of the project, with the PROcess package to process SELDI-TO-MS data, that was cited/documented in the very first Bioconductor paper (2004) and monograph (2005). Proteomics and metabolomics have grown substantially since these early days, both in terms of packages, community contributions, and user base, culminating in the R for Mass Spectrometry initiative. In this short talk, I will provide an overview of how the mass spectrometry-based proteomics and metabolomics infrastructure has evolved since the early days, and what the goals for the future are.

Comprehensive and standardised workflow for single-cell proteomics data analysis using scp and scplainer.

Author(s): Samuel Grégoire, Christophe Vanderaa and Laurent Gatto

Single cell proteomics (SCP) via mass spectrometry has become achievable thanks to technological advancements innovated by various research teams, resulting in a broad landscape of cutting-edge methodologies [1]. While this progress has enabled the measurement of thousands of proteins at the single cell resolution, it has also resulted in various complex and divergent analysis workflows. To efficiently tackle biologically relevant questions, the field of SCP must confront the challenges inherent in SCP data. SCP data are particularly prone to technical variations, batch effects, and missing values [2].

To address these challenges, our team has developed several tools packaged within the scp R/Bioconductor package. The latest addition is the scplainer approach, which offers a standardised approach grounded in linear modeling. scplainer provides key tools to extracts meaningful insights from SCP data through variance analysis, differential abundance analysis and component analysis, while streamlining the visualisation of the results. Integrated into the scp package, scplainer leverages QFeatures and SingleCellExperiment infrastructures, providing a comprehensive interface with numerous data processing functions. In addition, we also developed scpdata, a package containing standardised and annotated single-cell proteomics data, which we are still actively extending.

In this work, we provide a comprehensive overview of SCP data processing using the scp package, starting from the output table generated by the search engine software through data processing, modeling and downstream analyses.

References

[1] Petrosius et Schoof (2023), Recent advances in the field of single-cell proteomics.

[2] Vanderaa et Gatto (2021), Replication of single-cell proteomics data reveals important computational challenges.

Publication

Grégoire, Samuel, Christophe Vanderaa, Sébastien Pyr dit Ruys, Christopher Kune, Gabriel Mazzucchelli, Didier Vertommen, and Laurent Gatto. 2024. “Standardized Workflow for Mass-Spectrometry-Based Single-Cell Proteomics Data Processing and Analysis Using the Scp Package.” In Methods in Molecular Biology, 177–220. Methods in Molecular Biology (Clifton, N.J.). New York, NY: Springer US (pre-print).

scpGUI and QFeaturesGUI: Graphical Interfaces for Single-Cell and Bulk Proteomics

Author(s): Léopold Guyot, Christophe Vanderaa, Laurent Gatto

In recent years, significant advancements have been made in the field of proteomics data analysis. However, the complexity of workflows involving programming languages such as R and Python can pose challenges for practitioners without any coding backgrounds. To address this issue, we introduce two user-friendly packages: scpGUI and QFeaturesGUI.

scpGUI is tailored for downstream visual analysis in single-cell proteomics, using the outcomes of the popular package scp. Developed using Shiny and built upon the elegant and efficient iSEE suite, scpGUI offers interactive data visualisations specifically crafted for single-cell proteomics downstream analysis. The app’s interactivity helps users in comprehending their results through various visualisations.

Similarly, QFeaturesGUI is designed for single-cell and bulk proteomics analysis, capitalising on the strengths of the QFeatures package. Providing a suite of Shiny apps, QFeaturesGUI offers comprehensive tools for data import and basic processing steps, simplifying pre-treatment of quantitative proteomics data. Its modular design ensures flexibility and adaptability to specific requirements. These apps also enhance transparency and facilitate replication by generating reproducible R code.

Together, scpGUI and QFeaturesGUI offer valuable support for proteomics data analysis. By combining user-friendly graphical interfaces with powerful back-end tools, they make advanced analysis techniques more accessible to the wider proteomics community.

Batch effect detection and visual quality control with CytoMDS, a Bioconductor package for low dimensional representation of distances between cytometry samples

Author(s): Philippe Hauchamps,Dan Lin,Laurent Gatto

Quality Control (QC) of samples is an essential preliminary step in cytometry data analysis. Notably, identification of potential batch effects and sample outliers is paramount, to avoid mistaking these effects for true biological signal in downstream analyses. However, this task can prove to be delicate and tedious, especially for datasets with many samples.

Here, we present CytoMDS, a Bioconductor package implementing a dedicated method for low dimensional representation of cytometry samples composed of marker expressions for up to millions of single cells. This method combines Earth Mover’s Distance (EMD) [1] for assessing dissimilarities between multidimensional distributions, and Multi Dimensional Scaling (MDS) [2] for low dimensional projection of distances. Some additional visual tools, both for projection quality diagnosis and for user interpretation of the projection axes, are also provided in the package.

We demonstrate the strengths and advantages of CytoMDS for QC of cytometry data on real biological datasets, revealing the presence of low quality samples, batch effects and biological signal between sample groups.

References

[1] Haidong Yi and Natalie Stanley. 2022. “CytoEMD: Detecting and Visualizing between-Sample Variation in Relation to Phenotype with Earth Mover’s Distance.” In Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, 1–14. BCB ’22 28. New York, NY, USA: Association for Computing Machinery.

[2] Jan de Leeuw and Patrick Mair. 2009. “Multidimensional Scaling Using Majorization: SMACOF in R.” Journal of Statistical Software 31 (3): 1–30.

Publication

Hauchamps, Philippe, Simon Delandre, Stéphane T. Temmerman, Dan Lin, and Laurent Gatto. 2025. “Visual Quality Control with CytoMDS, a Bioconductor Package for Low Dimensional Representation of Cytometry Sample Distances.” Cytometry. Part A: The Journal of the International Society for Analytical Cytology, March. https://doi.org/10.1002/cyto.a.24921 (pre-print).