Title: Self-guided Microrobotics for Automated Brain Dissection
Funder: Economic and Social Research Council

Abstract: The proposed research project aims to exploit state-of-the-art microrobotics and artificial intelligence (AI) techniques to develop a microsurgery platform which combines microdissection and multi-agent optoelectronic tweezer (OET) based cell collection. By exploiting the capacity of trained neural networks to identify features and objects in complex tissues and cell suspensions and guide and coordinate multiple microrobots, we aim to create a powerful new tool for efficient, automated collection of precious biological targets. We will apply this new tool to solve a limiting problem in neurobiology, namely the collection of neural precursor cells (NPCs). NPCs have great potential in regenerative therapies to treat Alzheimer's disease, dementia, and traumatic brain injury, however our ability to study, understand and ultimately develop new treatments using NPCs is limited by the difficulties in effectively harvesting these rare cells. The specific technical objectives are: - Develop software modules using machine learning to automatically identify the subventricular zone (SVZ, the region of the brain in which NPCs are found) within a brain tissue section and detect NPCs within a cell suspension - Develop a multi-agent OET system for efficient collection of NPCs - Integrate software and hardware modules to create a unique AI-driven microsurgery platform at the University of Toronto. The work will add to fundamental knowledge through developing ways to: - Identify features in complex biological samples such as tissue sections and cell cultures - Perform complex microsurgical operations such as microdissection and cell collection - Control and coordinate arrays of microrobotic actuators - Collect and study NPCs and other precious biological targets. The end point application is to demonstrate semi-autonomous robotics and AI for microdissection and cell collection in a highly complex neurobiology application. Specifically, we will: - Benchmark the resection and cell collection performance of the AI-driven microsurgery platform against current state of the art methods - Collect NPC's from male and female transgenic murine models - Analyse sex-dependent difference in NPC phenotypes The results from the project will feed in a programme of workshops and community building activities to help foster a new community focused on the challenges exposed by the work (across technical, economic, policy and societal issues). In the long term, we believe this will introduce new tools, investment and clinical pathways for regenerative medicine with potential for lasting societal change.

Lay abstract: Neural precursor cells (NPCs) are neural stem cells and their progeny, which are multipotent, self-renewing cells that are responsible for building the fundamental components of the vertebrate central nervous system during embryonic development. Notably, NPCs persist into the mature brain as very rare cells that continue to generate new neurons throughout the lifetime of the organism. Intensive study of these precious cells could lead to a watershed moment in the field of neurobiology and the development of regenerative therapies to treat Alzheimer's disease, dementia, and traumatic brain injury. However, our ability to harness the therapeutic potential of NPCs is limited by the practical difficulty of identifying and collecting them for analysis. Current methods require painstaking manual microdissection of neural tissue followed by cell sorting methods which, in addition to requiring large samples sizes (and thus large quantities of tissue) also often fail to distinguish between NPCs and other cells expressing the same biomarkers. This highly interdisciplinary project brings together researchers with expertise in artificial intelligence (AI), robotics, engineering and neuroscience to create a new automated microsurgical platform which will be applied to identify and collect NPCs from brain tissues. By exploiting the capacity of deep learning approaches to detect features in complex data sets we will develop two new image-guided microsurgery tools: a microrobotic resector capable of carefully excising the small region of brain tissue (the SVZ) within which NPCs are found and microrobotic cell collector, capable of efficiently harvesting individual cells for further study and analysis using a coordinated array of optoelectronic microrobots. When then aim to integrate these two systems into a single, unique robotic microsurgery platform. The new system will be applied to collect and analyse NPCs from different brain tissues. In particular, we propose to analyse the sex-dependent differences in NPCs phenotypes, which have been reported in previous research. As well as transforming our capacity for collection of NPCs and likewise supporting the development of new regenerative therapies, we believe this approach will provide a powerful new method for a wide range of microsurgery applications. Altogether, we will use these results to simulate debate among the related engineering, scientific and wider public communities to shape a new international multi-disciplinary network focusing on challenges with regenerative medicine i.e. providing new case studies for govt. policy and further investment for AI-driven healthcare applications.

Research activity codes: 5.4 SURGERY
Health categories: NEUROLOGICAL