IFC is currently primarily used in research rather than clinical practice. We see the data analysis as the primary hurdle: it is often prone to variation, manual tuning, and interpretation. These issues might be overcome with machine learning approaches [1]. As well, there is a need for standardization of IFC, which should include standard operating procedures and standardized quality control of hardware performance. Although a common practice for conventional flow cytometry, this has not yet been implemented as such in IFC.

User-friendly, robust, and standardized workflows that can facilitate machine learning, especially deep learning, will accelerate the paradigm shift from low- to high-content analysis in IFC. Furthermore, cloud computing can overcome the computational infrastructure hurdles. These developments are key for practical IFC applications to reach the clinic, fueling the applicability of IFC as a diagnostic, prognostic, and therapeutic tool.

Project Publications

[1] Doan M, …, Wolkenhauer O, …, Hennig H. Diagnostic potential of imaging flow cytometry. Trends in Biotechnology (2018). DOI: https://doi.org/10.1016/j.tibtech.2017.12.008

Identifying White Blood Cells using Machine Learning

Identifying the number of different white blood cells (WBC) in human blood is an established clinical routine, where WBC are labeled with fluorescent markers. WBC counts provide important diagnostic information for clinicians and can indicate various diseases. Here we develop a novel approach based on machine learning where WBC are identified label-free, i.e., without any markers. We will build an open-source workflow that seamlessly connects the recorded images from the instruments with machine learning. FlowML will enable fast, cheap and highly accurate identification of WBC, without destroying the cells and leaves marker channels free to answer other biological questions. A label-free WBC count has the potential for translation into clinical applications.