How we approach science
The Gerlich lab brings together scientists with diverse backgrounds in cell biology, biochemistry, physics, and computer science to address questions on fundamental biological mechanisms.
We like to ask unconventional questions and therefore often first establish new assays to visualize the respective cell biological processes. We exploit a broad panel of advanced microscopy techniques and combine them into correlative workflows to bridge resolution gaps between molecular and cell biological scales. This has lead us to discover new biological phenomena for which molecular regulators have not yet been described.
To elucidate the molecular foundation of previously undescribed cell biological phenomena, we perform image-based loss-of-function screening. Computer scientists in our laboratory have implemented a fully automated live-cell microscopy platform and cell phenotype analysis by machine learning (CellCognition, see below). New molecular regulators identified by image-based screening are then systematically characterized in cells using state-of-the-art techniques, as for example CRISPR/Cas9-based genome engineering, functional readouts using biosensors, chemically-induced protein dimerization, and micro-manipulation using intracellular laser surgery.
Once we have gained insights into the cellular functions of novel regulators, we aim to dissect the underlying molecular mechanisms. We characterize molecular properties using a variety of biochemical technologies, including systematic analysis of molecular interactions and posttranslational modifications by mass spectrometry.
We then test lead hypotheses about molecular mechanisms by in vitro reconstitution using purified components. For example, biochemists in our team have established a variety of assays using purified chromatin, DNA-coated beads, and recombinant proteins to study the mechanical properties of mitotic chromosomes. We manipulate the components in this system using microfluidic chambers and magnetic force control, and analyze biophysical properties using light- and atomic-force microscopy. Ultimately, we aim to reconstitute the respective molecular functions by synthetic components that mimic the molecular function of the native cellular proteins.
Pursuing our ideas from a general description of cell biological processes up to the mechanistic details about the molecular components requires a broad range of technologies. This is made possible because of the outstanding research infrastructure provided at campus facilities and our strong network of international collaboration partners. Researchers in our laboratory take the lead in developing hypotheses and establish the experimental assays and approaches, but they are supported in many experiments by our facility staff. This teamwork approach leverages our research beyond the boundaries of individual biological disciplines.
Technologies and model systems
The cutting-edge technologies provided by 18 scientific facilities at our campus offer unmatched opportunities to focus entirely on the biological questions rather than troubleshooting technical details.
At IMBA, we have free access to a variety of cutting-edge microscopes through the BioOptics, VBCF Advanced Microscopy, and Electron Microscopy facilities. Their large collection of scanning confocal and spinning disc confocal microscopes, automated screening microscopes, laser microsurgery, super-resolution fluorescence, and electron microscopes enable us to approach scientific questions from multiple angles. We are particularly excited about newly emerging imaging techniques such as lattice light-sheet microscopy, which we have implemented at our campus together with the VBCF Advanced Microscopy facility.
Our biochemical experiments are supported by the Protein Chemistry, Protein Technology, and Metabolomics facilities, and chromosome conformation capture approaches are carried out in collaboration with the Next Generation Sequencing facility. Genome engineering and molecular biology services are further supported by Protein Technology and Molecular Biology facilities.
We use human tissue culture cells as a main model system and have developed a large library of cell lines stably expressing various combinations of fluorescent marker proteins for systematic phenotype profiling. Our experiments are typically automated to a very high degree, involving high-throughput transfection and automated microscopy for high-content screening. To extract quantitative phenotype traits from large-scale microscopy experiments, we have developed computer vision and machine learning methods that are disseminated as the open-source software CellCognition.
We have developed the open source software CellCognition and CellCognition Explorer for the analysis of cellular phenotypes. Both software packages combine object detection, single-cell tracking, feature extraction, and classification of morphologies (Figure 1) for automated annotation of different cell morphologies or kinetic readouts of cell features. CellCognition has been optimized for large-scale screening data and runs on Windows, Mac OS X, and Linux desktop computers and provides an interface to computing clusters for ultra-high-throughput data analysis (see Held et al., Nature Methods 2010). CellCognition Explorer provides an advanced user interface for interactive classifier training of medium-scale data, and further provides novelty detection methodology (see Sommer, Hoefler et al., MBoC 2017).
References for computer vision and machine learning methodology
C. Sommer, R. Hoefler, M. Samwer, D.W. Gerlich. A deep learning and novelty detection framework for rapid phenotyping in high-content screening. Mol Biol Cell. (2017): 28(23): 3428-3436. doi: 10.1091/mbc.E17-05-0333
Sommer C, Held M, Fischer B, Huber W, Gerlich DW. (2013). CellH5: a format for data exchange in high-content screening. Bioinformatics. 29(12):1580-2
Zhong Q, Busetto AG, Fededa JP, Buhmann JM, Gerlich DW. (2012). Unsupervised modeling of cell morphology dynamics for time-lapse microscopy. Nat Methods. 9(7):711-3
Held M, Schmitz MH, Fischer B, Walter T, Neumann B, Olma MH, Peter M, Ellenberg J, Gerlich DW. (2010). CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging. Nat Methods. 7(9):747-54
Sommer C, Gerlich DW. (2013). Machine learning in cell biology - teaching computers to recognize phenotypes. J Cell Sci. 126(Pt 24):5529-39
Conrad C, Gerlich DW. (2010). Automated microscopy for high-content RNAi screening. J Cell Biol. 188(4):453-61.
References for screening applications
Samwer, M., Schneider, MWG., Hoefler, R., Schmalhorst, PS., Jude, JG., Zuber, J., Gerlich, DW. (2017). DNA cross-bridging shapes a single nucleus from a set of mitotic chromosomes. Cell 170(5):956-972.e23
Cuylen, S., Blaukopf, C., Politi, AZ., Müller-Reichert, T., Neumann, B., Poser, I., Ellenberg, J., Hyman, AA., Gerlich, DW. (2016). Ki-67 acts as a biological surfactant to disperse mitotic chromosomes. Nature. 535(7611):308-12
Wurzenberger C, Held M, Lampson MA, Poser I, Hyman AA, Gerlich DW. (2012). Sds22 and Repo-Man stabilize chromosome segregation by counteracting Aurora B on anaphase kinetochores. J Cell Biol. 198(2):173-83
Piwko W, Olma MH, Held M, Bianco JN, Pedrioli PG, Hofmann K, Pasero P, Gerlich DW, Peter M. (2010). RNAi-based screening identifies the Mms22L-Nfkbil2 complex as a novel regulator of DNA replication in human cells. EMBO J. 29(24):4210-22.
Schmitz MH, Held M, Janssens V, Hutchins JR, Hudecz O, Ivanova E, Goris J, Trinkle-Mulcahy L, Lamond AI, Poser I, Hyman AA, Mechtler K, Peters JM, Gerlich DW. (2010). Live-cell imaging RNAi screen identifies PP2A-B55alpha and importin-beta1 as key mitotic exit regulators in human cells. Nat Cell Biol. 12(9):886-93.