Intelligent Imaging


Intelligent Imaging Overview


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. For details see



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.

Light sheet microscope objetives, top geometry

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 morphological-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.

Bildschirmfoto 2019-12-04 um 12.38.59

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 improved 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.

Bildschirmfoto 2019-12-04 um 12.39.06
Schematic concept of multiomics deep learning using different inputs (chromatin, images, gene expression)

Christian Conrad graduated in Biology at the University Freiburg and received his 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 Intelligent Imaging

Post Address:

Charité - Campus Charité Mitte | Charitéplatz 1 | 10117 Berlin | Germany


Visiting Address:

Rahel Hirsch Center | Luisenstraße 65 | 10117 Berlin

Level 02, Part B, room 217

Research Group Intelligent Imaging




Said Alkidani
Student Assistant
Robert Lorenz Chua
Postdoctoral Researcher
Katharina Jechow
Lab Manager
Kristin Köhler
PhD Student
Johannes Liebig
PhD Student
Lorna Morris
Postdoctoral Researcher
Agata Rakszewska
Postdoctoral Researcher
Alexander Sudy
PhD Student



Single-Nucleus and In Situ RNA-Sequencing Reveal Cell Topographies in the Human Pancreas.
Tosti L, Hang Y, Debnath O, Tiesmeyer S, Trefzer T, Steiger K, Ten FW, Lukassen S, Ballke S, Kühl AA, Spieckermann S, Bottino R, Ishaque N, Weichert W, Kim SK, Eils R, Conrad C.
Gastroenterology. 2021 Mar;160(4):1330-1344.e11.
Original publication      PMID: 33212097 

COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis.
Chua RL, Lukassen S, Trump S, Hennig BP, Wendisch D, Pott F, Debnath O, Thürmann L et al.
Nat Biotechnol. 2020 Aug;38(8):970-979.
Original publication      PMID: 32591762

Single-cell analysis of patient-derived PDAC organoids reveals cell state heterogeneity and a conserved developmental hierarchy.
Krieger TG, Le Blanc S, Jabs J, Ten FW, Ishaque N, Jechow K, Debnath O, Leonhardt CS, Giri A, Eils R, Strobel O, Conrad C.
Nat Commun. 2021 Oct 5;12(1):5826.
Original publication     PMID: 34611171

Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders.
Lukassen S, Ten FW, Lukas A, Eils R, Conrad C.
Nat Mac Intell. 2020 Dec 7;2(12):800-809.
Original publication  doi: 10.1038/s42256-020-00269-9



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