Title: Mapping Single-Cell Data between Species by Combined Deep Learning and Explainable AI

Project duration: 01.01.2022 – 30.06.2025

Research Area: Life Science and Medicine, Medical AI

In the project “Mapping single-cell data between species by combined deep learning and explainable AI”, we develop a principled approach to compare the cell-type- and disease-state specific molecular response between human disease conditions and corresponding animal models at the single-cell RNA sequencing (scRNAseq) transcriptome level. We apply our approach in two contexts: First, we map molecular states of whole blood samples between human COVID-19 patients and two hamster species developing moderate (Mesocricetus auratus) or severe disease course (Phodopus roborovskii) following SARS-CoV-2 infection. Second, we focus on side-effects following immunomodulatory therapies. We identify corresponding states of immune-related toxicities between cynomolgus monkeys (Macaca fascicularis) and humans in peripheral blood mononuclear cell subpopulations (PBMCs).

Aims

• transcriptome-based quantification of disease state similarities across species
• integrating deep learning with interpretable approaches
• establishing a respective robust, general methodologic framework
• improving the transferability of pre-clinical animal models towards human disease conditions

Problems

• technological and biological batch effects to be considered
• sparse, noisy, and high-dimensional data
• challenging biological interpretation

Technology

• single-cell RNA-sequencing analysis
• deep learning, variational autoencoder
• empirical Bayes moderation

Outlook

• Extension to other diseases and conditions for preclinical research and drug development
• Method developments to improve explainability of findings

Publications

• preprint: DOI: 10.1101/2024.01.11.574849v1

Team

Lead

• Markus Scholz (IMISE)
• Geraldine Nouailles (Charité)
• Kristin Reiche (Fraunhofer IZI),

Team Members

• Holger Kirsten (IMISE)
• Martin Witzenrath (Charité)
• Vincent David Friedrich
• Peter Pennitz (Charité)
  

Partners

• Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE)
• Charité – Universitätsmedizin Berlin
• Boehringer Ingelheim International GmbH
• Fraunhofer-Institut für Zelltherapie und Immunologie (IZI)