Prof. Dr.
Fabian Theis
Prof. Dr.
Fabian Theis
Research background
Advances in single-cell technologies have transformed the study of gene expression by enabling the measurement of thousands of transcripts in individual cells. Techniques such as single-cell RNA sequencing and spatial molecular profiling now provide high-resolution maps of RNA abundance and cellular states within tissues. However, extracting biological insight from these high-dimensional datasets requires innovative computational approaches. How cellular heterogeneity, tissue organization, and disease-associated changes can be robustly inferred from large-scale RNA and imaging data remains a central challenge in modern biology.
The research of Fabian Theis focuses on developing machine learning methods for computational biology, with particular emphasis on single-cell and spatial genomics. His work designs algorithms to analyze transcriptomic and imaging-derived molecular data, model cellular variation, and disentangle spatial components of tissue organization. By building analytical frameworks and data infrastructures for large-scale single-cell datasets, including organ- and body-scale cell atlases, he enables systematic exploration of gene expression across diverse populations and disease contexts. Through the integration of computational modeling with experimental data, this research advances quantitative understanding of RNA-based cellular phenotypes and supports data-driven medical discovery.
Research fields
Publications
Cell2location maps fine-grained cell types in spatial transcriptomics.
Nat Biotechnol. · 2022
Show abstract
Spatial transcriptomic technologies promise to resolve cellular wiring diagrams of tissues in health and disease, but comprehensive mapping of cell types in situ remains a challenge. Here we present сell2location, a Bayesian model that can resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. Cell2location accounts for technical sources of variation and borrows statistical strength across locations, thereby enabling the integration of single-cell and spatial transcriptomics with higher sensitivity and resolution than existing tools. We assessed cell2location in three different tissues and show improved mapping of fine-grained cell types. In the mouse brain, we discovered fine regional astrocyte subtypes across the thalamus and hypothalamus. In the human lymph node, we spatially mapped a rare pre-germinal center B cell population. In the human gut, we resolved fine immune cell populations in lymphoid follicles. Collectively, our results present сell2location as a versatile analysis tool for mapping tissue architectures in a comprehensive manner.
Grassroots efforts to end structural racism throughout the US National Institutes of Health.
Nat Med. · 2022
Mass spectrometry comes of age for subcellular organelles.
Nat Methods. · 2021
Generalizing RNA velocity to transient cell states through dynamical modeling.
Nat Biotechnol. · 2020
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RNA velocity has opened up new ways of studying cellular differentiation in single-cell RNA-sequencing data. It describes the rate of gene expression change for an individual gene at a given time point based on the ratio of its spliced and unspliced messenger RNA (mRNA). However, errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. Here we present scVelo, a method that overcomes these limitations by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to systems with transient cell states, which are common in development and in response to perturbations. We apply scVelo to disentangling subpopulation kinetics in neurogenesis and pancreatic endocrinogenesis. We infer gene-specific rates of transcription, splicing and degradation, recover each cell's position in the underlying differentiation processes and detect putative driver genes. scVelo will facilitate the study of lineage decisions and gene regulation.
Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments.
Nat Methods. · 2019
Show abstract
Single cell RNA-sequencing (scRNA-seq) technology has undergone rapid development in recent years, leading to an explosion in the number of tailored data analysis methods. However, the current lack of gold-standard benchmark datasets makes it difficult for researchers to systematically compare the performance of the many methods available. Here, we generated a realistic benchmark experiment that included single cells and admixtures of cells or RNA to create 'pseudo cells' from up to five distinct cancer cell lines. In total, 14 datasets were generated using both droplet and plate-based scRNA-seq protocols. We compared 3,913 combinations of data analysis methods for tasks ranging from normalization and imputation to clustering, trajectory analysis and data integration. Evaluation revealed pipelines suited to different types of data for different tasks. Our data and analysis provide a comprehensive framework for benchmarking most common scRNA-seq analysis steps.