Developing an ultra-fast and unbiased histopathology toolkit

Background: Histopathological examination of soft tissue biopsies is at the core of modern pathology and diagnostics. Despite being a manual process involving low-power microscopy and high error-rates, this remains the gold-standard for determining types of many cancers, muscle disease and connective tissue disorders. The utility and speed of these approaches are limited due to (a) the lack of sensitivity to use sub-cellular or molecular-scale features of the pathology in the diagnosis process and (b) human biases in recognising the pathological phenotypes. Whilst nanoscale imaging methods, commonly known as super-resolution microscopies, have existed for over 15 years now, they remain slow and difficult to adapt.

Overview: In this project, we will develop the protocol for rapid labelling of human biopsy samples with fluorescent nanodiamonds which will allow histopathological imaging of sub-cellular structures at nanometre-scale precision. Our recent work has demonstrated how the use of nanodiamonds can speed up super-resolution imaging by nearly 5-fold (DOI: 10.1101/2020.05.20.106716). We also recognise that the use of artificial intelligence (AI)-based image analysis and reconstruction algorithm is an opportunity to aid the recognition of cellular patterns associated with the disease phenotypes.

Objectives: This project will: (1) further speed-up nanodiamond-based super-resolution imaging of biopsies from scales of minutes to seconds, (2) characterise the spectral properties of spontaneous photoluminescence which underpins their ability to be rapidly localised, and (3) develop an AI-based real-time analysis to enhance the speed of sample scanning and assistive recognition of nanoscale pathology.

Approach: We will use antibody-conjugated nanodiamonds as molecular probes which target specific nanoscale structures of mitochondria. As a starting point, we will specifically image the cristae of mitochondria in muscle biopsy tissues taken from either diabetic or heart failure patients. A number of strategies will be used to further enhance this speed of imaging. These include adapting new camera technologies, enhanced sample illumination, extrinsic stimulation of the nanodiamonds with microwave and optimising the microenvironments of nanodiamonds and the tissue samples. A number of spectral analytical tools will be used to determine the spectral properties of spontaneous nanodiamond photoluminescence and match them to tissue types based on their intrinsic autofluorescence. Finally, we will adapt the framework of an existing AI-based image reconstruction platform to further accelerate the speed of imaging and to help identify shifts in mitochondrial structures that can indicate disease phenotypes. 

The project will primarily be supervised by Dr Izzy Jayasinghe (https://appliedbiophotonics.org/). The student will have an opportunity to contribute to both existing and new collaborations with academic and industrial partners of the group. The second supervisor of the project will be Prof Ashley Cadby (https://ashleycadby.staff.shef.ac.uk/) who will advise on the adaptation of single molecule imaging technologies and software tools. The student will be a member of the Sheffield-based Imagine student cohort (http://www.imagine-imaginglife.com/) and will have the opportunity to present novel findings at conferences and public science communication platforms. The PhD training will focus on a broad range of cutting-edge research skills including (and not limited to) histology, super-resolution microscopy, advanced protein chemistry, advanced image analysis and computer programming. 

Science Graduate School

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