In this section
We use light to develop advanced diagnostic tools, wearable sensors, and microscale robots for studying diseases and enabling minimally invasive treatments.
We use photonics to develop new technologies for medicine and to study the pathophysiology of disease. This includes new and improved diagnostic tools as well as microscale robotic devices for therapeutic applications. We use a variety of optical techniques for this purpose such as fluorescence, Raman and diffuse reflectance spectroscopy, as well as microscopy and interferometry. We develop devices ranging from wearable sensors and fibre-optic probes for minimally invasive diagnostics through to microscale robots for cellular-scale manipulation and therapy.
Our research has a number of potential clinical applications including improved monitoring of clinical therapies and interventions (e.g. in inflammatory bowel disease and malnutrition), early diagnosis of infection, and even margin mapping in tumour resection surgery.
The devices we are developing can potentially provide less invasive and lower cost diagnostics. In turn, this may facilitate patient benefits including earlier diagnosis, earlier identification of relapse (e.g. in therapy response monitoring applications), more widespread deployment and more comfortable patient experiences (e.g. through use of less invasive probes and sensors).
Dr Belal Ahmad
Imperial College Research Fellow
Dr Qian Chen
Clinical Research Fellow
Dr Ahmed Ezzat
Research Postgraduate
Dr Nilanjan Mandal
Research Associate in Optical Sensing for LMICs
Dr Alexander J Thompson
Senior Lecturer in Biomedical Sensing
Mr Zeyu Wang
Research Postgraduate
@inproceedings{Watson:2024:10.1117/12.3021679,
author = {Watson, AF and Haanaes, N and Chambers, R and Roddan, A and Sanchez, EM and Runciman, M and Thompson, AJ},
doi = {10.1117/12.3021679},
publisher = {SPIE},
title = {Towards label-free flow cytometry for automated cell identification using diffuse reflectance spectroscopy},
url = {http://dx.doi.org/10.1117/12.3021679},
year = {2024}
}
TY - CPAPER
AB - Flow cytometry is widely used for cell identification and characterization and involves labelling biological and clinical samples with fluorochrome-conjugated antibodies specific to cell markers. This requires use of expensive exogenous reagents and necessitates complex pre-processing of samples. Additionally, extensive challenges arise in clinical samples consisting of highly plastic and heterogenous cell types observed in diseases such as cancer. As such, it is challenging to apply flow cytometry to point-of-care diagnostic applications. To address this issue, we investigated the combination of diffuse reflectance spectroscopy (DRS), microfluidics and machine learning to offer rapid, low-cost, label-free cell identification for potential deployment at the point of care. To achieve this, we utilized a compact fibre-optic diffuse reflectance spectrometer with multi-depth sensing capability. This system was applied to a proof-of-concept cell identification study where we were able to discriminate triple negative breast cancer cells from healthy fibroblasts using commercially available flow channel slides (Ibidi GmbH, channel dimensions: 5 mm width, 0.4 mm height). However, we observed high interexperimental variability, which was partially attributed to the relatively large fluidic channels. Thus, we investigated in-house fabrication of microfluidics of varying channel widths (0.6-2mm). To this end, we used a Mars ELEGOO 3D printer and commercially available printing materials to batch fabricate optically and mechanically viable microfluidic chips that were both cheap and customizable. Using these in-house microfluidic devices, we demonstrated DRS-based discrimination of cancer cells of different origins, further indicating the potential of this approach for point-of-care cell identification/characterization. Ultimately, we hope this work will lead to the development of cheap, deployable, and accurate point-of-care tools for rapid, label-free cell identification.
AU - Watson,AF
AU - Haanaes,N
AU - Chambers,R
AU - Roddan,A
AU - Sanchez,EM
AU - Runciman,M
AU - Thompson,AJ
DO - 10.1117/12.3021679
PB - SPIE
PY - 2024///
TI - Towards label-free flow cytometry for automated cell identification using diffuse reflectance spectroscopy
UR - http://dx.doi.org/10.1117/12.3021679
UR - http://hdl.handle.net/10044/1/113249
ER -
The Hamlyn Centre
Bessemer Building
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Imperial College
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