Like from the latin word intellegere = recognize, we would like to recognize from imaging how molecular biology in cells is unfolded. To interrogate genomic expression, single cell sequencing is currently the action of choice, while for 3D fluorescence, novel light sheet microscopy offers unprecedent imaging speed at lowest phototoxicity. We use these technologies in conjunction to explore the phenotype-genotype domain space and ultimately model different gene expression of patient organoids by imaging only. To correlate these big data matrices, deep learning classification became indispensable and finally drive our understanding in different aspects of therapy research and precision medicine at the Charité/ BIH.
MCF10A breast spheroids, actin (red) and nuclei (green) staining
We engineered single cell as well as nuclei RNA/ATAC sequencing libraries from different tissues derived from patients material directly or from their derived organoids. Here, we specialized on protocols for lung and pancreas biopsies adapted to high autolytic properties after tissue resection. Beside these wet lab challenges, the intelligent imaging group studies mophological-cellular features correlating with the different gene expression profiles. Ideally, assuming a sufficient collection size that a phenotypic picture of tissues or organoids holistically inform us about the genetic makeup. In essence, we are employing single cell sequencing to understand different disease entities (cancer) on cellular scale.
UMAP clustering example of high-dimensional single cell sequencing data
Christian Conrad graduated in Biology at the University Freiburg and received PhD in Bioinformatics at the University Heidelberg. In 2018, he moved to the Charité/BIH where he was appointed as Professor for Intelligent Imaging.
Prof. Dr. Christian Conrad
Group leader at BIH Center for Digital Health
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 Reports, 9(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 Biology, 13(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(4), 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
Conrad, C.*, Erfle, H.*, Warnat, P., Daigle, N., Lorch, T., Ellenberg, J., Pepperkok, R. & Eils, R.§ (2004). Automatic identification of subcellular phenotypes on human cell arrays. Genome Research, 14(6), 1130-1136. doi: 10.1101/gr.2383804
*these authors contributed equally
Robert Lorenz Chua
Dr. Sören Lukassen
Foo Wei Ten
Dr. Christian Conrad
Dr. Luca Tosti
Dr. Teresa Gabriela Krieger
Dr. Agata Rakszewska