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Research focus

Automated light sheet microscopy

Advanced automated light sheet microscopy is the mode of choice for extremely fast fluorescence 3D imaging of a cellular context. Dual top geometries and oblique light sheet microscopy are developed for the acquisition of spatial genetics in micro tissues and organoids. Especially the massive imaging over space or time requires for intelligent classification or deep learning algorithms, to selectively save and identify relevant genetic information in volumetric heterogenous patient biopsies.

Human single cell sequencing

Our group engineers single cell and nuclei RNA/ATAC omics sequencing libraries from patient tissues or derived organoids. Currently, we concentrate on single nuclei protocols for lung and pancreas samples preventing high autolytic capacity after tissue resection. Novel cell types and transcriptomics data are embedded in the Human Cell Atlas initiative. In general, we are employing single cell sequencing to understand different disease entities on a mechanistic cellular level, including COVID-19 since 2020.

Deep tissue learning

Artificial deep neural networks can learn to recognize and reconstruct patterns in single cell data. This allows a de novo identification of functional gene sets, master and housekeeping genes. Ultimately, tissue images should and will contain all features learnt in deep neural networks to enable seamless disease prediction and subsequent therapy management.

Selected publications

Trump, S., et al. (2020). Hypertension delays viral clearance and exacerbates airway hyperinflammation in patients with COVID-19. Nature Biotechnology, 38, 970-979. doi: 10.1038/s41587-020-00796-1

Chua, R.L., et al. (2020). COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis. Nature Biotechnology, 38, 970-979. doi: 10.1038/s41587-020-0602-4

Lukassen, S., Ten, F.W., Adam, L., Eils, R., & Conrad, C. (2020). Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders. Nature MachineIntelligence, 2, 800-809. doi: 10.1038/s42256-020-00269-9

Eismann, B., Krieger, T.G.,  Beneke, B.,  Bulkescher, R., Adam, L.,  Erfle, H., Herrmann, C., Eils, R., & Conrad, C. (2020). Automated 3D light-sheet screening with high spatiotemporal resolution reveals mitotic phenotypes. Journal ofCell Science 133: 245043, doi: 10.1242/jcs.245043 

Lukassen, S., Chua, R. L., Trefzer, T., Kahn, N.C., Schneider, M.A., Muley, T., Winter, H., Meister, M., Veith, C., Boots, A.W., Hennig, B.P., Kreuter, M., Conrad, C., & Eils, R. (2020). SARS-CoV-2 receptor ACE2 and TMPRSS2 are primarily expressed in bronchial transient secretory cells. EMBO Journal, 39(10), doi: 10.15252/embj.20105114

Tirier, S. M., Park, J., Preusser, F., Amrhein, L., Gu, Z., Steiger, S., Mallm, J. P., Krieger, T., Waschow, M., Eismann, B., Gut, M., Gut, I. G., Rippe, K., Schlesner, M., Theis, F., Fuchs, C., Ball, C. R., Glimm, H., Eils, R. & Conrad, C. (2019). Pheno-seq - linking visual features and gene expression in 3D cell culture systems. Scientific Reports9(1), 12367. doi: 10.1038/s41598-019-48771-4

Jabs, J., Zickgraf, F. M., Park, J., Wagner, S., Jiang, X., Jechow, K., Kleinheinz, K., Toprak, U. H., Schneider, M. A., Meister, M., Spaich, S., Sütterlin, M., Schlesner, M., Trumpp, A., Sprick, M., Eils, R. & Conrad, C. (2017). Screening drug effects in patient-derived cancer cells links organoid responses to genome alterations. Molecular Systems Biology13(11): 955. doi: 10.15252/msb.20177697

Wachsmuth, M., Conrad, C., Bulkescher, J., Koch, B., Mahen, R., Isokane R., Pepperkok, R. & Ellenberg, J. (2015). High-throughput fluorescence correlation spectroscopy enables analysis of proteome dynamics in living cells. Nature Biotechnology, 33, 384-389, doi: 10.1038/nbt.3146

Conrad, C., Wünsche, A., Tan, T. H., Bulkescher, J., Sieckmann, F., Verissimo, F., Edelstein, A., Walter, T., Liebel, U., Pepperkok, R. & Ellenberg, J. (2011) Micropilot: automation of fluorescence microscopy-based imaging for systems biology. Nature Methods, 8(3), 246–249, doi: 10.1038/nmeth.1558

Prof. Dr. Christian Conrad

BIH-Professor Intelligent Imaging

Anschrift:Charité – Universitätsmedizin Berlin
BIH - Digital Health Center
Campus Virchow-Klinikum
Augustenburger Platz 1