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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. For details see https://iimaging.org/

 

Topics
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Spatial Transcriptomics

Recent technological advances have driven a rapid expansion of molecular profiling techniques that preserve the spatial relationships between cells within tissues. Depending on the sample and question at hand, we employ NGS-based or imaging-based spatial transcriptomic approaches, enabling us to study gene expression in its native tissue context.

Long-Read Sequencing

Improvements in nanopore chemistry have made it possible to sequence DNA stretches spanning several megabases with greater accuracy and throughput. This brings previously inaccessible structural variants within reach, complementing short-read approaches and expanding the scope of genomic variation that can be studied in the context of disease.

Single-Cell Sequencing

The field of functional genomics was transformed in the last decade by the advent of single-cell sequencing. The technology and its many modalities allow the investigation of the transcriptome and epigenome at cellular resolution, revealing the heterogeneity within patient-derived tissues that bulk measurements obscure. Our group routinely applies single-cell sequencing to characterize the molecular and cellular underpinnings of a range of diseases.

Machine Learning

Machine learning has become an indispensable component of modern bioinformatics, particularly for the analysis of the rich and complex datasets generated by single-cell sequencing and spatial transcriptomics. By training algorithms to learn from data and identify relationships within high-dimensional feature spaces, we are able to enhance our analyses across multiple levels: from cell type identification and gene regulatory network inference to the integration of imaging and molecular data for disease characterization and biomarker discovery.
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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

 

 

 
Robert Lorenz Chua
Postdoctoral Researcher
robert-lorenz.chua@bih-charite.de
Katharina Jechow
Lab Manager
katharina.jechow@bih-charite.de
Kristin Köhler
PhD Student
kristin.koehler@bih-charite.de
Johannes Liebig
PhD Student
johannes.liebig@bih-charite.de
Philipp von Mengersen
MSc Student
philipp.von-mengersen@bih-charite.de
Alexander Sudy
PhD Student
alexander.sudy@bih-charite.de
Leonard Tilling
PhD Student
leonard.tiling@bih-charite.de
Marius Zschiesche
MSc Student
marius.zschiesche@bih-charite.de

Projects

EASI-Genomics

ESPACE

 

Publications

 

 

Transcriptomic signatures of IPF in ALI-cultured airway cells and their therapeutic implications.
Chua RL, Veith C, Schneider MA, Jechow K, Kaufhold G, et al.
Thorax. 2026 February 18; 0:1-12.
Original publication PMID:41713905

resVAE ensemble: Unsupervised identification of gene sets in multi-modal single-cell sequencing data using deep ensembles.
Ten F, Yuan D, Jabareen N, Phua Y, Eils R, Lukassen S, and Conrad C.
Front. Cell Dev. Biol. 2023 February 15; 11, 2023.
Original publication PMID: 36875765

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, and 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

 

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