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Algorithms Offer Insight into Cellular Development


Through RNA sequencing, researchers can measure which genes are expressed in each individual cell of a sample. A new statistical method allows researchers to infer different developmental processes from a cell mixture consisting of asynchronous stages. This finding has been published by researchers of Helmholtz Zentrum München in collaboration with colleagues from Technical University Munich in the journal ‘Nature Methods’.

Today, cell biology no longer focuses only on static states, but rather seeks to understand the dynamic development of cells. One example for this is the formation of various types of blood cells, such as red blood cells or endothelial cells from their precursors, the blood stem cells. To understand how this process is genetically controlled, scientists analyze which genes are expressed by means of transcriptome analysis.

Development of cluster of blood stem cells into different cell types, Source: HMGU

“To me, it’s still amazing that we are now even able to determine the transcriptome of single cells,” said lead author Laleh Haghverdi, “especially when one realizes that a typical cell contains only a few picograms of RNA*.” The availability of these data is now beginning to revolutionize many fields of research, but new statistical methods are required to interpret these correctly.

“For example, all cells of a sample never start their development synchronously, and their development takes different lengths of time. Therefore, we are always dealing with a dynamic mixture,” added Haghverdi, doctoral student at the Institute of Computational Biology (ICB) at Helmholtz Zentrum München. “It is immensely difficult to construct multiple steps of a process from this, especially since the cells are only available for one measurement.”

Welcome to the era of pseudotime

To decrypt developmental processes from the measurement of a single time point, quasi a snapshot measurement, the researchers led by ICB Director Prof. Dr. Dr. Fabian Theis developed an algorithm called diffusion pseudotime to interpret single cell sequencing data.

This algorithm orders cells on a virtual timeline – the pseudotime – along which they show continuous changes in the transcriptome. Thus, it can be reconstructed which genes are expressed sequentially. By means of this method, researchers can graphically display the branching lineages of the developmental paths of different cell types.

“For example, we can show how a relatively uniform cluster of blood stem cells develops into different cell types,” said study leader Theis. “While some become red blood cells, others differentiate into endothelial cells. We can trace these fates based on the transcriptome data of the single cells.” In addition, the scientists obtain information about which gene switches underlie the developments. The relatively diffuse mixture of cells which were found to be at different stages of their development can be disentangled on the computer and, after the analysis, provides a clear picture of the ongoing individual steps.

However, this is only the beginning for the researchers because the processes of blood formation are relatively well understood. They served only as a test object to determine how well the method works. “In the future we want to focus on processes that have remained elusive until now or which may not have been discovered at all,” said Theis.**

Further Information

*RNA stands for ribonucleic acids which make up the raw material for the transcriptome analyses.

** In collaboration with experimental institutes at Helmholtz Zentrum München, the scientists are focusing on the development of brain cells and the insulin-producing beta cells in the pancreas, among other research projects. They hope that by elucidating the formation of individual cell groups, they will develop approaches to intervene in these processes – for example, when they are disturbed due to disease.

Original Publication:
Haghverdi, L. et al. (2016): Diffusion pseudotime robustly 1 reconstructs lineage branching, Nature Methods, DOI: 10.1038/nmeth.3971

The Helmholtz Zentrum München, the German Research Center for Environmental Health, pursues the goal of developing personalized medical approaches for the prevention and therapy of major common diseases such as diabetes and lung diseases. To achieve this, it investigates the interaction of genetics, environmental factors and lifestyle. The Helmholtz Zentrum München is headquartered in Neuherberg in the north of Munich and has about 2,300 staff members. It is a member of the Helmholtz Association, a community of 18 scientific-technical and medical-biological research centers with a total of about 37,000 staff members.

The Institute of Computational Biology (ICB) develops and applies methods for the model-based description of biological systems, using a data-driven approach by integrating information on multiple scales ranging from single-cell time series to large-scale omics. Given the fast technological advances in molecular biology, the aim is to provide and collaboratively apply innovative tools with experimental groups in order to jointly advance the understanding and treatment of common human diseases.

Technical University of Munich (TUM) is one of Europe’s leading research universities, with more than 500 professors, around 10,000 academic and non-academic staff, and 39,000 students. Its focus areas are the engineering sciences, natural sciences, life sciences and medicine, reinforced by schools of management and education. TUM acts as an entrepreneurial university that promotes talents and creates value for society. In that it profits from having strong partners in science and industry. It is represented worldwide with a campus in Singapore as well as offices in Beijing, Brussels, Cairo, Mumbai, San Francisco, and São Paulo. Nobel Prize winners and inventors such as Rudolf Diesel, Carl von Linde, and Rudolf Mößbauer have done research at TUM. In 2006 and 2012 it won recognition as a German "Excellence University." In international rankings, TUM regularly places among the best universities in Germany.

Contact for the media:
Department of Communication, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg - Tel. +49 89 3187 2238 - Fax: +49 89 3187 3324 - E-mail:
Scientific Contact:
Prof. Dr. Dr. Fabian Theis, Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Ingolstädter Landstr. 1, 85764 Neuherberg - Tel. +49 89 3187 4030, E-mail:

Weitere Informationen:

Soja Opitz | Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt

Further reports about: Algorithms Cellular Computational Biology Helmholtz TUM blood cells

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