INDUZELL

Coordinated ERDF Innovation Network: Industry-compatible bioprocesses for stem cell cultivation and biometric analyses based on principles of nature

Scaling up innovative bioprocesses for stem cell cultivation through process modeling, machine learning, AI, and multimodal microscopy

 

Project partners:

  • Hannover Medical School (MHH), Leibniz Research Laboratory for Biotechnology and Artificial Organs, PD Dr. Robert Zweigerdt (Coordinator of the overall consortium)
  • Leibniz University Hannover (LUH), Institute for Quantum Optics, PD Dr. Stefan Kalies
  • Hannover University of Applied Sciences and Arts (HsH), Data|H – Institute for Applied Data Science Hannover, Prof. Dr. Volker Ahlers
  • Emden/Leer University of Applied Sciences (HsEL), Department of Engineering/Natural Sciences and Engineering, Prof. Dr.-Ing. Jens Hüppmeier

 

Cooperation partners:

  • Cultimate Foods GmbH
  • Histomography GmbH
  • Sartorius Stedim Biotech GmbH

 

The industrial cultivation of animal cells is a cornerstone of modern biotechnology for the production of recombinant active substances and antibodies for biomedicine, pharmacology, and specific analytical methods. However, established large-scale bioprocesses on a hundred- or thousand-liter scale use transformed animal cell lines. Contrary to the principles of nature, such “production cell lines” are adapted to grow as “single-cell biofactories” that must be separated from the desired product as cellular waste. In contrast, in stem cell cultivation, the cells themselves constitute the valuable product, opening up a multitude of new applications in research and industry. These include innovative applications of stem cells and their derivatives in basic research, regenerative medicine, toxicology, drug development, as well as in cellular agriculture and food production. However, these applications can only be realized at the planned, industry-relevant scales if the stem cells and their functional derivatives are produced via efficient, cost-effective, industry-compatible bioprocesses and can be characterized and further processed using innovative methods. However, such bioprocesses and analytical methods have not yet been sufficiently developed, standardized, or scaled up. This is the starting point for the INDUZELL innovation network and, in particular, for the INDUZELL Stem Cells subproject described here.

 

MHH Subproject: “INDUZELL Stem Cells”

MHH Grant Amount: 572,146.84 euros.

Funding is provided by the European Regional Development Fund (ERDF) and the State of Lower Saxony within the program area “More Developed Region (SER)” for the funding period 2021–2027 in accordance with the “Guideline on the Granting of Subsidies to Promote Innovation by Universities and Research Institutions – 2.2.3 Innovation Networks.”

 

MHH subproject: Upscaling innovative bioprocesses for stem cell cultivation through process modeling, machine learning, AI, and multimodal microscopy.

In the “INDUZELL Stem Cells” subproject, bioreactor-based, industry-relevant processes for the cultivation of human pluripotent stem cells (PSCs) in suspension culture (3D) are to be optimized and scaled up to a process volume of up to 10 L.

The goal is to establish inoculation in 3D culture directly from frozen cell stocks to enable an innovative closed-system process for industrial and clinically relevant cell production. A central aspect is the characterization of stem cell aggregates via multimodal microscopy in the INDUZELL Image subproject. This is complemented by state-of-the-art spatially and temporally resolved spatial transcriptomics gene expression analyses and process data. The integration of cell and process data, as well as the subsequent data interpretation, is carried out using machine learning and AI-based algorithms in the subprojects “INDUZELL AI” and “INDUZELL Model” to develop an integrative model for iterative process optimization. Using continuous process samples, a minimal dataset will be identified that can be used to evaluate stem cell aggregates from ongoing processes via high-throughput microscopic analysis. A key objective of the subproject is the development of “smart” processes that respond to process deviations through feedback control and AI optimization, thereby sustainably improving cell yield and process reproducibility while reducing costs and material consumption.