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Research

Intelligent Imaging

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Intelligent Imaging Overview

Berlin

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.

Topics

Automated light sheet microscopy

Advanced automated light sheet microscopy and single cell sequencing are primed in the intelligent imaging lab to compare morphologies with different expression. With dual top objective geometries we apply stage-scanning modes for fast 3D image screening or acquisition of organoids in hydrogel droplets while further subsequent confocal imaging adds resolution. We are determined to automated the whole process of 3D spotting of organoids to droplet respiration, followed by single cell acid nucleic library generations. Especially, massive imaging over time demands for intelligent solution, namely deep learning algorithms, to selectively save and identify relevant information from those volumetric heterogenous data.

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Light sheet microscope objetives, top geometry
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MCF10A breast spheroids, actin (red) and nuclei (green) staining

Sample analytics by single cell genomics

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.

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UMAP clustering example of high-dimensional single cell sequencing data

Deep tissue learning

We do utilize the way artificial deep neural networks learn to recognize and reconstruct patterns in input data. This approach to single cell genomics datasets allows the de novo identification of functional gene sets, master regulator genes and housekeeping genes from any kind of tissue origin. Precise partitioning into cell types or sub clones in cancer tissues can be improve by introducing class-specific filters of measured modalities (images or clinical diagnostic parameters). Ultimately, tissue or organoid images should contain all features learnt in deep neural networks to enable seamless disease prediction and therapy management.

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Schematic concept of multiomics deep learning using different inputs (chromatin, images, gene expression)
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Christian ConradI

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

Charité Campus Virchow Klinikum (CVK)
Augustenburger Platz 1
13353 Berlin

Research Group

IntelligentImaging_Group_200205

Katharina Jechow
Lab Manager
Charité Campus Virchow Klinikum
Augustenburger Platz 1
13353 Berlin
katharina.jechow@charite.de
Robert Lorenz Chua
Doctoral Student
Charité Campus Virchow Klinikum
Augustenburger Platz 1
13353 Berlin
robert-lorenz.chua@charite.de
Timo Trefzer
Doctoral Student
Charité Campus Virchow Klinikum
Augustenburger Platz 1
13353 Berlin
timo.trefzer@charite.de
Dr. Sören Lukassen
PostDoc
Charité Campus Virchow Klinikum
Augustenburger Platz 1
13353 Berlin
soeren.lukassen@charite.de
Foo Wei Ten
Doctoral Student
Charité Campus Virchow Klinikum
Augustenburger Platz 1
13353 Berlin
foo-wei.ten@charite.de
Dr. Luca Tosti
PostDoc
Charité Campus Virchow Klinikum
Augustenburger Platz 1
13353 Berlin
luca.tosti@charite.de
Dr. Teresa Gabriela Krieger
PostDoc
Charité Campus Virchow Klinikum
Augustenburger Platz 1
13353 Berlin
teresa.krieger@charite.de
 Johannes Liebig
Doctoral Student
Charité Campus Virchow Klinikum
Augustenburger Platz 1
13353 Berlin
johannes.liebig@charite.de
 Alexander Sudy
Doctoral Student
Charité Campus Virchow Klinikum
Augustenburger Platz 1
13353 Berlin
alexander.sudy@charite.de
 Dr. Agata Rakszewska
PostDoc
Charité Campus Virchow Klinikum
Augustenburger Platz 1
13353 Berlin
agata.rakszweska@charite.de

Publications

 

Chua, R.L.*, Lukassen, S.*,Trump, S.*, Hennig, B.P.*, Wendisch, D.*, Pott, F., Debnath, O., Thürmann, L., Kurth, F., Völker, M.T., Kazmierski, J., Timmermann, B., Twardziok, S., Schneider, S., Machleidt, F., Müller-Redetzky, H., Maier, M., Krannich, A., Schmidt, S., Balzer, F., Liebig, J., Loske, J., Suttorp, N., Eils, J., Ishaque, N., Liebert, U.G., von Kalle, C., Hocke, A., Witzenrath, M., Goffinet, C., Drosten, C., Laudi, S.§,Lehmann, I., Conrad, C.§, Sander, L.-E.§ & Eils, R.§ (2020). COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis. Nature Biotechnology doi: 10.1038/s41587-020-0602-4

 

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 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

§corresponding author

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