Theislab group picture

Theis Lab

ML in Single-Cell Genomics

Our research explores a broad range of machine learning (ML) approaches in computational biology, with a particular emphasis on single-cell analysis. We develop state-of-the-art algorithms to solve challenging biological questions and to accelerate medical discovery.

Go to our GitHub!

Our research explores a broad range of machine learning (ML) approaches in computational biology, with a particular emphasis on single-cell analysis. We develop state-of-the-art algorithms to solve challenging biological questions and to accelerate medical discovery.

Go to our GitHub!

Topics

Our team

Fabian Theis
Prof. Dr. Dr. Fabian Theis

Principal Investigator

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Selfie of Lennard Halle
Lennard Halle

Postdoc

Selfie of Sara Jimenez
Sara Jiménez

Postdoc

Nikita Moshkov

Postdoc

Sergio Marco Salas

Postdoc

Niklas Schmacke

Postdoc

Disha Sharma

Postdoc

Larsen Vornholz

Postdoc

Yuyao Zhai

Postdoc

Philipp Angerer

Software engineer

Severin Dicks

Software engineer

Ilan Gold

Software engineer

Selman Ozleyen

Software engineer

Lucas Alexander Arnoldt

PhD candidate

Selfie of Mojtaba Bahrami
Mojtaba Bahrami

PhD candidate

Sören Becker

PhD candidate

Julia Beckmann

PhD candidate

Selfie of Ali Oguz Can
Ali Oğuz Can

PhD candidate

Selfie of Suhan Cho
Suhan Cho

PhD candidate

Lorenzo Consoli

PhD candidate

Michael Dammann

PhD candidate

Selfie of Francesca Drummer
Francesca Drummer

PhD candidate

Photo of Jan Engelmann
Jan Engelmann

PhD candidate

Felix Fischer

PhD candidate

Xiaotong Fu

PhD candidate

Selfie of Robert Gutgesell
Robert Gutgesell

PhD candidate

Soroor Hediyeh-Zadeh

PhD candidate

Leon Hetzel

PhD candidate

Lukas Heumos

PhD candidate

Eva Holtkamp

PhD candidate

Kemal Inecik

PhD candidate

Yuge Ji

PhD candidate

Selfie of Raphael Kfuri Rubens
Raphael Kfuri-Rubens

PhD candidate

Dominik Klein

PhD candidate

Louis Kümmerle

PhD candidate

Christopher Lance

PhD candidate

Katharina Limbeck

PhD candidate

Zaikang Lin

PhD candidate

Selfie of Nastja Litinetskaya
Anastasia Litinetskaya

PhD candidate

Jenni Liu

PhD candidate

Manuel Lubetzki

PhD candidate

Daniele Lucarelli

PhD candidate

Laura Martens

PhD candidate

Mariia Minaeva

PhD candidate

Amir Ali Moinfar

PhD candidate

Ghaith Mqawass

PhD candidate

Michaela Müller

PhD candidate

Sarah Ouologuem

PhD candidate

Alessandro Palma

PhD candidate

Selfie of Shrey Parikh
Shrey Parikh

PhD candidate

Selfie of Datta Prakki
Datta Prakki

PhD candidate

Goncalo Rei Pinto

PhD candidate

Photo of Till Richter
Till Richter

PhD candidate

Eljas Roellin

PhD candidate

Benedikt Roth

PhD candidate

Emma Ryhänen

PhD candidate

Maiia Schulman

PhD candidate

Selfie of Mostafa Shahhosseini
Mostafa Shahhosseini

PhD candidate

Selfie of Vladimir Shitov
Vladimir Shitov

PhD candidate

Photo of Lisa Sikkema
Lisa Sikkema

PhD candidate

Laurent Simons

PhD candidate

Irena Sliskovic

PhD candidate

Daniel Strobl

PhD candidate

Selfie of Artur Szalata
Artur Szalata

PhD candidate

Alejandro Tejada

PhD candidate

Tim Treis

PhD candidate

Weixu Wang

PhD candidate

Zihe Zheng

PhD candidate

Mohammed Zidane

PhD candidate

Lea Zimmerman

PhD candidate

Chelsea Bright

Research assistant

Fatemeh Hashemi

Research assistant

Vadim Nazarov

Visiting researcher

Scanpy - visual

Our projects

Single-Cell Methodologies

Single-cell technologies, such as single-cell RNA sequencing (scRNA-seq), have increased the resolution achieved in the study of cellular phenotypes, allowing measurements of thousands of different genes in thousands of individual cells. These advances provide new insights into key biological processes and drive the need for innovative computational tools. In our lab, we develop cutting-edge methods for single-cell data analysis.
 

Single-Cell Methodologies page
Spatial transcriptomics and Imaging

Spatial Transcriptomics and Imaging

Imaging and spatial molecular profiling techniques allow us to assay morphological and molecular markers in situ. These experimental techniques provide a toolkit to investigate tissue biology at an unprecedented resolution. Computational tools are needed for the analysis of such data. We're developing analytical tools and data infrastructure for imaging and spatial molecular data, as well as modeling approaches to disentangle spatial components of cellular and tissue variation.

Spatial Transcriptomics and Imaging page
Data Analysis and Benchmarking

Data Analysis and Atlassing

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics and data from both healthy and diseased individuals. In our lab, we utilize single-cell computational methods to build and analyze large single-cell tissue atlasses. Another important focus of our lab is data analysis. In collaboration with experimental biologists, we apply computational tools to answer pressing biological questions using single-cell or spatial data.

Data Analysis and Atlassing page

Publications

2025 in
In: (42nd International Conference on Machine Learning, ICML 2025, 13-19 July 2025, Vancouver). 2025. 47478-47508 ( ; 267)

Palma, A. ; Rybakov, S. ; Hetzel, L. ; Günnemann, S. ; Theis, F.J.

Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation.
Nucleic Acids Res.:gkaf1126 (2025)

Türei, D. ; Schaul, J. ; Palacio-Escat, N. ; Bohár, B. ; Bai, Y. ; Ceccarelli, F. ; Çevrim, E. ; Daley, M. ; Darcan, M. ; Dimitrov, D. ; Dogan, T. ; Domingo-Fernández, D. ; Dugourd, A. ; Gábor, A. ; Gul, L. ; Hall, B.A. ; Hoyt, C.T. ; Ivanova, O. ; Klein, M. ; Lawrence, T. ; Mañanes, D. ; Módos, D. ; Müller-Dott, S. ; Ölbei, M. ; Schmidt, C. ; Şen, B. ; Theis, F.J. ; Ünlü, A. ; Ulusoy, E. ; Valdeolivas, A. ; Korcsmáros, T. ; Saez-Rodriguez, J.

OmniPath: Integrated knowledgebase for multi-omics analysis.
Bioinformatics 41:btaf607 (2025)

Lucarelli, D. ; Kos, T. ; Shull, C. ; Jimenez, S. ; Öllinger, R. ; Rad, R. ; Saur, D. ; Theis, F.J.

QuiCAT: A scalable and flexible framework for mapping synthetic sequences.
Nat. Commun. 16:9745 (2025)

Firsova, A.B. ; Marco Salas, S. ; Kuemmerle, L. ; Abalo, X.M. ; Sountoulidis, A. ; Larsson, L. ; Mahbubani, K.T. ; Theelke, J. ; Andrusivova, Z. ; Alonso Galicia, L. ; Liontos, A. ; Balassa, T. ; Kovács, F. ; Horvath, P. ; Chen, Y. ; Gote-Schniering, J. ; Stoleriu, M.-G. ; Behr, J. ; Meyer, K.B. ; Timens, W. ; Schiller, H.B. ; Luecken, M. ; Theis, F.J. ; Lundeberg, J. ; Nilsson, M. ; Nawijn, M.C. ; Samakovlis, C.

Spatial single-cell atlas reveals regional variations in healthy and diseased human lung.
Comm. Biol. 8:1412 (2025)

Boerstler, T. ; Kachkin, D. ; Gerasimova, E. ; Zagha, N. ; Furlanetto, F. ; Nayebzade, N. ; Zappia, L. ; Boisvert, M. ; Farrell, M. ; Ploetz, S. ; Prots, I. ; Regensburger, M. ; Günther, C. ; Winkler, J. ; Gupta, P. ; Theis, F.J. ; Karow, M. ; Falk, S. ; Winner, B. ; Krach, F.

Deciphering brain organoid heterogeneity by identifying key quality determinants.
ACS Nano 19, 39139-39156 (2025)

Voss, C. ; Han, L. ; Ansari, M. ; Strunz, M. ; Haefner, V. ; Angelidis, I. ; Mayr, C.H. ; Berthing, T. ; Zhou, Q. ; Günther, E. ; Huzain, O. ; Schmid, O. ; Vogel, U. ; Schniering, J. ; Gaedcke, S. ; Theis, F.J. ; Schiller, H.B. ; Stöger, T.

Toward a ToxAtlas of carbon-based nanomaterials: Single-cell RNA sequencing reveals initiating cell circuits in pulmonary inflammation.
BMC Genomics 26:974 (2025)

Hrovatin, K. ; Moinfar, A.A. ; Zappia, L. ; Parikh, S. ; Tejada Lapuerta, A. ; Lengerich, B. ; Kellis, M. ; Theis, F.J.

Integrating single-cell RNA-seq datasets with substantial batch effects.
Nat. Methods, DOI: 10.1038/s41592-025-02814-z (2025)

Tejada Lapuerta, A. ; Schaar, A. ; Gutgesell, R.M. ; Palla, G. ; Halle, L. ; Minaeva, M. ; Vornholz, L. ; Dony, L. ; Drummer, F. ; Richter, T. ; Bahrami, M. ; Theis, F.J.

Nicheformer: A foundation model for single-cell and spatial omics.
Science 390:eadi8577 (2025)

DeMeo, B. ; Nesbitt, C. ; Miller, S.A. ; Burkhardt, D.B. ; Lipchina, I. ; Fu, D. ; Holderreith, P. ; Kim, D. ; Kolchenko, S. ; Szalata, A. ; Gupta, I. ; Kerr, C. ; Pfefer, T.J. ; Rojas-Rodriguez, R. ; Kuppassani, S. ; Kruidenier, L. ; Doshi, P.B. ; Zamanighomi, M. ; Collins, J.J. ; Shalek, A.K. ; Theis, F.J. ; Cortes, M.

Active learning framework leveraging transcriptomics identifies modulators of disease phenotypes.
Nat. Commun. 16:9355 (2025)

Yang, K. ; Spitzer, H. ; Sterr, M. ; Hrovatin, K. ; de la O, S. ; Zhang, X. ; Setyono, E.S.A. ; Ud-Dean, M. ; Walzthoeni, T. ; Flisikowski, K. ; Flisikowska, T. ; Schnieke, A. ; Scheibner, K. ; Wells, J.M. ; Sneddon, J.B. ; Kessler, B. ; Wolf, E. ; Kemter, E. ; Theis, F.J. ; Lickert, H.

A multimodal cross-species comparison of pancreas development.

Contact us

Fabian Theis
Prof. Dr. Dr. Fabian Theis

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

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