Theis Lab

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.

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Prof. Dr. Dr. Fabian Theis

Principal Investigator

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Lennard Halle

Postdoc

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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

Mojtaba Bahrami

PhD candidate

Sören Becker

PhD candidate

Julia Beckmann

PhD candidate

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Ali Oğuz Can

PhD candidate

Suhan Cho

PhD candidate

Lorenzo Consoli

PhD candidate

Michael Dammann

PhD candidate

Francesca Drummer

PhD candidate

Jan Engelmann

PhD candidate

Felix Fischer

PhD candidate

Xiaotong Fu

PhD candidate

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

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

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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

Shrey Parikh

PhD candidate

Datta Prakki

PhD candidate

Goncalo Rei Pinto

PhD candidate

Till Richter

PhD candidate

Eljas Roellin

PhD candidate

Benedikt Roth

PhD candidate

Emma Ryhänen

PhD candidate

Maiia Schulman

PhD candidate

Mostafa Shahhosseini

PhD candidate

Vladimir Shitov

PhD candidate

Lisa Sikkema

PhD candidate

Laurent Simons

PhD candidate

Irena Sliskovic

PhD candidate

Daniel Strobl

PhD candidate

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

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

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 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

We advance biological research through AI. Come work with us in an innovative and inspiring environment!

If you’re interested in joining us as a PhD student or postdoc, please upload your speculative application here

Wooden pieces with light bulbs and message Welcome to our team

Inecik, K. ; Kara, A. ; Rose, A. ; Haniffa, M. ; Theis, F.J.

TarDis: Achieving robust and structured disentanglement of multiple covariates.

Ghirotto, B. ; Gonçalves, L.E. ; Ruder, V. ; James, C. ; Gerasimova, E. ; Rizo, T. ; Wend, H. ; Farrell, M. ; Gerez, J.A. ; Prymaczok, N.C. ; Kuijs, M. ; Shulman, M. ; Hartebrodt, A. ; Prots, I. ; Gessner, A. ; Vieth, M. ; Zunke, F. ; Winkler, J. ; Blumenthal, D.B. ; Theis, F.J. ; Riek, R. ; Günther, C. ; Neurath, M. ; Gupta, P. ; Winner, B.

TNF alpha unmasks enteric malate aspartate shuttle dysfunction bridging Parkinson disease and intestinal inflammation.

Armbrust, N. ; Grosshauser, M. ; Geilenkeuser, J. ; Stroppel, L. ; Jozinovic, M. ; Levermann, H. ; Panne, T. ; Wißmann, J. ; Goelitz, L. ; Schmidt, S. ; Orschmann, T. ; Rusha, E. ; Steinmaßl, E. ; Widenmeyer, F. ; Warsing, N. ; Sabry, M. ; Sultanbai, A. ; Santl, T. ; Geerlof, A. ; Berezin, O. ; Bodea, S.V. ; Moretti, A. ; Schneider, S. ; Theis, F.J. ; Gagneur, J. ; Truong, D.J.J. ; Westmeyer, G.G.

Non-destructive transcriptomics via vesicular export.

Koutrouli, M. ; Finotello, F. ; Schoof, E.M. ; Roussos, P. ; scverse Proteomics Consortium (Theis, F.J.) ; scverse Proteomics Consortium (Salas, S.M.)

Unpaired data as a first-order challenge in single-cell and spatial proteomics.

Bader, J.M. ; Makarov, C. ; Richter, S. ; Strauss, M.T. ; Held, F. ; Wahle, M. ; Lorenz, M.B. ; Pöschl, L. ; Skowronek, P. ; Thielert, M. ; Berthele, A. ; Zeng, W.F. ; Ammar, C. ; Bludau, I. ; Schubert, B. ; Theis, F.J. ; Gasperi, C. ; Hemmer, B. ; Mann, M.

Large-scale proteomics across neurological disorders uncovers biomarker panel and targets in multiple sclerosis.

Richter, T. ; Wang, W. ; Palma, A. ; Theis, F.J.

Generative models of cell dynamics: From Neural ODEs to flow matching.

Li, J. ; Wang, J. ; Ibarra Del Rio, I.A. ; Cheng, X. ; Luecken, M. ; Lu, J. ; Monavarfeshani, A. ; Yan, W. ; Zheng, Y. ; Zuo, Z. ; Colborn, S.L.Z. ; Cortez, B.S. ; Owen, L.A. ; Wick, B. ; Bao, X. ; Choi, J. ; Haeussler, M. ; Tran, N.M. ; Shekhar, K. ; Sanes, J.R. ; Stout, J.T. ; Chen, S. ; Li, Y. ; DeAngelis, M.M. ; Theis, F.J. ; Chen, R.

Author Correction: Single-cell atlas of the transcriptome and chromatin accessibility in the human retina.

Bahrami, M. ; Richter, T. ; Schmacke, N.A. ; Egea Lavandera, A. ; Theis, F.J.

From modality-specific to compositional foundation models for cell biology.

Tiesmeyer, S. ; Müller-Bötticher, N. ; Malt, A. ; Ma, L. ; Marco-Salas, S. ; Kiessling, P. ; Horn, P. ; Guillot, A. ; Kuemmerle, L. ; Tacke, F. ; Theis, F.J. ; Kuppe, C. ; Nilsson, M. ; Eils, R. ; Long, B. ; Ishaque, N.

Identifying 3D signal overlaps in spatial transcriptomics data with ovrlpy.

Weiler, P. ; Theis, F.J.

CellRank: Consistent and data view agnostic fate mapping for single-cell genomics.

ML in Single-Cell Genomics

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Topics

Biological discovery through novel ML methods

Reaching a wide community through open source projects

Our team

Our projects

Single-Cell Methodologies

Spatial Transcriptomics and Imaging

Data Analysis and Atlassing

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We advance biological research through AI. Come work with us in an innovative and inspiring environment!

Publications

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