Flow Cytometry

Engineering and Physical Sciences Research Council (EPSRC)

Project Aims and Objectives

Aim 1: To perform a market analysis to determine the commercial potential of new flow cytometry analysis methods
Dr Housden collaborated with IP Pragmatics (IPP) to perform an IP and market analysis to determine whether there is commercial potential for the algorithms currently under development by performing the following tasks:

• Assess the global market for flow cytometry
• Identify industry players who may be potential partners for the software
• Advise on IP strategy roadmap to best protect the IP associated with the new software.
• Conduct primary research into the need, usefulness, application and acceptance of the technology with users of flow cytometry

Overall conclusions from this investigation are that the software is likely to be valuable and there is a need for such solutions in the flow cytometry market. However, the route to market is likely to be very challenging because of the multiple competing software packages and domination of the market by large companies.

Based on these outcomes the academic team decided against setting up a spin-out company at this point. Instead, the team will focus future activities on developing strong proof-of-concept case studies for the use of technologies in various flow cytometry applications before pursuing commercialisation.

Dr Housden’s team have submitted a funding application in collaboration with Prof. Nic Harmer and Dr. Stefano Pagliara. If successful, this £850k award would allow further development of the technology and its application to screening for novel antibiotic compounds.

Secondly, they are testing the software on data generated by the Exeter Cytomics Facility using the Aurora spectral cytometer. By applying the software to this instrument, the aim is to improve sensitivity of fluorophore detection, an area that was highlighted by IPP as a priority need. If successful, the team will then pursue a collaboration with Cytek, the manufacturer of the Aurora, towards further development and licensing of the software.

Aim 2: To extend current algorithms to allow analysis of multiple fluorescent signals from single cells
During the project period, Dr Housden extended the software to firstly optimise its performance on single fluorophores and secondly to allow multiple fluorophores to be assessed simultaneously. Optimisation of the existing algorithms improved the ability to detect weak fluorophores by 7-fold compared to standard gating methods.

Outcomes

During this project, Dr Matt Anderson and Dr Ben Housden both gained skills and experience in the application of machine learning to cytometry datasets. Based on this, a separate project was initiated to improve the analysis of cell viability data using flow cytometry. This project was highly successful and is now being prepared for publication.

In addition, these new methods are relevant to an ongoing collaboration between Dr Housden and SPT Labtech. Results from this will be presented in an invited webinar by Dr. Housden, hosted by SPT Labtech. Finally, all members of the team have gained experience and knowledge of the processes involved in software licensing and patentability as well as the cytometry marketplace.

The major non-academic output from this work has been to define the optimal route to commercialisation for the technology. Using data from this project, we have initiated a new collaboration with the Exeter Cytomics facility towards this defined route to market. In addition, the skills in machine learning gained by Drs. Housden and Anderson have led to a strengthened relationship with SPT Labtech.