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Helmholtz Munich I Daniela Barreto

New Tool Enhances Single-Cell Research Accuracy

Featured Publication, ICB,

A team led by Prof. Fabian Theis developed HADGE, a new tool that improves the accuracy and efficiency of identifying individual donors in single-cell studies. The work, recently published in Genome Biology, represents a major step forward in the field of single-cell genomics. HADGE offers researchers powerful new capabilities to better understand the differences between cells and their implications for health and disease.

Single-cell studies have advanced to the point where scientists can now analyze samples at the population level, profiling thousands of individual cells from hundreds or even thousands of people. These studies provide invaluable insights into biological processes and diseases at a cellular level.

One of the challenges in large-scale single-cell studies is accurately tracking and identifying cells from mixed samples. In recent years, techniques such as hashing (a way to label and track cells) and genotype-based multiplexing (using genetic information to identify cells) have emerged, which allow researchers to pool cells from individual samples and still determine their origins. However, these methods can sometimes struggle with poor-quality initial data.

The Solution: HADGE

In their study, a team around Fabian Theis, Head of the Computational Health Center at Helmholtz Munich and Chair for Mathematical Modelling of Biological Systems at the Technical University of Munich (TUM), has addressed this challenge by developing HADGE (hashing deconvolution combined with genotype information), a pipeline that integrates state-of-the-art methods to perform both hashing and genotype-based deconvolution. HADGE also introduces a joint deconvolution strategy that optimally combines these methods, improving the accuracy and rescuing poor-quality hashing experiments. This dual approach ensures that even when initial data quality is suboptimal, the results remain reliable and accurate. Lastly, the joint deconvolution can be leveraged to minimize the costs of multi-donor multi-condition experiments.

Impact on Research

The new technique promises to enhance the way scientists conduct population-level single-cell analyses. By improving the accuracy and efficiency of deconvolution in single cell studies, HADGE provides researchers with a powerful tool to optimize their experimental setup when profiling hundreds of patients. The increased throughput of these studies enables researchers to delve deeper into understanding complex biological systems and diseases, and can lead to more precise medical research, better disease prevention strategies, and the development of new treatments.

“This tool represents a significant step forward in single-cell studies, allowing researchers to overcome previous limitations in deconvolution of complex multi-donor experiments. We believe HADGE will empower researchers performing population-level single cell studies, ultimately shedding light on the donor-specific cellular variations that can be harnessed to design new personalized therapies.”, says Fabiola Curion, the first author of the study.


Original publication

Curion et al., 2024: hadge: a comprehensive pipeline for donor deconvolution in single-cell studies. Genome Biology. DOI: 10.1186/s13059-024-03249-z

About the scientists

Prof. Fabian Theis, Head of the Computational Health Center at Helmholtz Munich and Chair for Mathematical Modelling of Biological Systems at the Technical University of Munich (TUM)

Dr. Fabiola Curion, Postdoctoral researcher in the Theis Laboratory


Funding information
Open Access funding enabled and organized by Projekt DEAL. FC acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) –SFB- TRR 338/1 2021 –452881907. HBS acknowledges support from the German Center for Lung Research (DZL), the Helmholtz Association (CoViPa—lessons to get prepared for future pandemics), the European Union’s Horizon 2020 research and innovation program (grant agreement 874656—project discovair), and the Chan Zuckerberg Initiative (CZF2019-002438, project Lung Atlas 1.0).

Fabian Theis

Prof. Dr. Fabian Theis

Director of the Computational Health Center, Director of the Institute for Computational Biology