Unlocking Proteome Insights with the Human Proteome Distribution Atlas

In the dynamic world of bioinformatics, integrating multi-omics data is key to uncovering biological insights. The recent study, "Human Proteome Distribution Atlas for Tissue-Specific Plasma Proteome Dynamics" by Malmström et al. (published in Cell, 2025), provides a robust resource for researchers aiming to infer proteome expression from transcriptome data. At Helix Horizon, our bioinformatics analysis services will harness this atlas and complement existing tools to deliver precise, actionable results. In this post, we’ll explore the atlas’s potential, review current tools for proteome inference, and show how Helix Horizon can elevate your research.

The Power of the Proteome Atlas

The Malmström et al. atlas integrates mass spectrometry-based proteomics (9,827 proteins quantified at 1% FDR) with transcriptomics across 18 vascularized organs and 8 blood cell types, covering 29 tissues and cell types. Its key features include:

• Multi-Omics Integration: A global label score (GLS) assigns proteins to tissues with high confidence, capturing both RNA-protein concordance and discrepancies.

• Broad Tissue Coverage: The atlas supports systemic protein expression analysis, ideal for plasma proteome studies.

• Plasma Dynamics Focus: Validated with disease cohorts (e.g., pancreatitis, myocardial injury), it’s a prime resource for biomarker and therapeutic research.

• Quantitative Rigor: Techniques like UMAP and weighted Gaussian kernel density estimation ensure objective tissue-specificity classifications.

This atlas is a goldmine for predicting protein expression from RNA data, but the task comes with challenges like RNA-protein discrepancies (e.g., liver-secreted proteins like albumin) and incomplete tissue coverage. Sophisticated bioinformatics expertise is essential to maximize its utility.

A Quick Review of Tools for Proteome Inference

Several tools exist for inferring proteome expression from transcriptome data, each with strengths and limitations. Here’s a snapshot of popular options:

• CIBERSORTx: Originally designed for cell-type deconvolution, CIBERSORTx can estimate protein expression by leveraging reference transcriptomes. It excels in immune cell contexts but struggles with non-immune tissues and requires extensive reference data.

• ProTExA: This tool uses machine learning to predict protein abundance from RNA data, incorporating post-transcriptional regulation models. It’s versatile but may underperform without high-quality training data like the Malmström atlas.

• DeepPep: A deep learning-based tool, DeepPep predicts protein expression using neural networks trained on paired RNA-protein datasets. It’s powerful for large datasets but can be computationally intensive and less interpretable for tissue-specific applications.

Limitations: Most tools assume linear RNA-protein relationships, which the Malmström atlas shows is oversimplified (e.g., only 5% of proteins have a GLS of 4, indicating variability). Additionally, few tools are optimized for plasma-focused or disease-specific contexts, where the atlas excels.

Why Choose Helix Horizon?

Helix Horizon is your partner in navigating the complexities of proteome inference. By combining the Malmström atlas’s unparalleled depth with our bioinformatics expertise, we offer:

• Precision in predicting protein expression from RNA data.

• Solutions tailored to your research, from basic science to clinical applications.

• Scalable workflows that save time and resources.

Existing tools provide a foundation, but they often fall short without expert customization. Helix Horizon bridges this gap, ensuring your analyses are robust, reproducible, and impactful.