Publications

@article{Owen2024-gn,

title = {Computer Vision for substrate detection in high‐throughput  biomaterial screens using bright‐field microscopy},

author = {Robert Owen and Aishah Nasir and Mahetab H Amer and Chenxue Nie and Xuan Xue and Laurence Burroughs and Chris Denning and Ricky D Wildman and Faraz A Khan and Morgan R Alexander and Felicity R A J Rose},

doi = {10.1002/aisy.202400573},

year  = {2024},

date = {2024-08-01},

urldate = {2024-08-01},

journal = {Adv. Intell. Syst.},

publisher = {Wiley},

abstract = {High‐throughput screening (HTS) can be used when ab initio 

 information is unavailable for rational design of new materials, 

 generating data on properties such as chemistry and topography 

 that control cell behavior. Biomaterial screens are typically 

 fabricated as microarrays or ``chips,\'\' seeded with the cell 

 type of interest, then phenotyped using immunocytochemistry and 

 high‐content imaging, generating vast quantities of image data. 

 Typically, analysis is only performed on fluorescent cell images 

 as it is relatively simple to automate through intensity 

 thresholding of cellular features. Automated analysis of 

 bright‐field images is rarely performed as it presents an 

 automation challenge as segmentation thresholds that work in all 

 images cannot be defined. This limits the biological insight as 

 cell response cannot be correlated to specifics of the 

 biomaterial feature (e.g., shape, size) as these features are 

 not visible on fluorescence images. Computer Vision aims to 

 digitize tasks humans do by sight, such as identify objects by 

 their shape. Herein, two case studies demonstrate how 

 open‐source approaches, (region‐based convolutional neural 

 network and algorithmic [OpenCV]), can be integrated into 

 cell‐biomaterial HTS analysis to automate bright‐field 

 segmentation across thousands of images, allowing rapid, spatial 

 definition of biomaterial features during cell analysis for the 

 first time.},

keywords = {computer vision, microscopy},

pubstate = {published},

tppubtype = {article}

}

@article{La_Cavera2024-oi,

title = {Label-free Brillouin endo-microscopy for the quantitative 3D  imaging of sub-micrometre biology},

author = {Salvatore La Cavera and Veeren M Chauhan and William Hardiman and Mengting Yao and Rafael Fuentes-Dom\'{i}nguez and Kerry Setchfield and Sidahmed A Abayzeed and Fernando P\'{e}rez-Cota and Richard J Smith and Matt Clark},

doi = {10.1038/s42003-024-06126-4},

year  = {2024},

date = {2024-04-01},

urldate = {2024-04-01},

journal = {Commun. Biol.},

volume = {7},

number = {1},

pages = {451},

abstract = {This report presents an optical fibre-based endo-microscopic 

 imaging tool that simultaneously measures the topographic profile 

 and 3D viscoelastic properties of biological specimens through 

 the phenomenon of time-resolved Brillouin scattering. This uses 

 the intrinsic viscoelasticity of the specimen as a contrast 

 mechanism without fluorescent tags or photoacoustic contrast 

 mechanisms. We demonstrate 2 μm lateral resolution and 320 nm 

 axial resolution for the 3D imaging of biological cells and 

 Caenorhabditis elegans larvae. This has enabled the first ever 3D 

 stiffness imaging and characterisation of the C. elegans larva 

 cuticle in-situ. A label-free, subcellular resolution, and 

 endoscopic compatible technique that reveals structural 

 biologically-relevant material properties of tissue could pave 

 the way toward in-vivo elasticity-based diagnostics down to the 

 single cell level.},

keywords = {Brillouin scattering, label free, microscopy},

pubstate = {published},

tppubtype = {article}

}

@article{Perez-Cota2023-nu,

title = {Classification of cancer cells at the sub-cellular level by  phonon microscopy using deep learning},

author = {Fernando P\'{e}rez-Cota and Giovanna Mart\'{i}nez-Arellano and Salvatore 3rd La Cavera and William Hardiman and Luke Thornton and Rafael Fuentes-Dom\'{i}nguez and Richard J Smith and Alan McIntyre and Matt Clark},

year  = {2023},

date = {2023-09-01},

urldate = {2023-09-01},

journal = {Sci. Rep.},

volume = {13},

number = {1},

pages = {16228},

publisher = {Springer Science and Business Media LLC},

abstract = {There is a consensus about the strong correlation between the 

 elasticity of cells and tissue and their normal, dysplastic, and 

 cancerous states. However, developments in cell mechanics have 

 not seen significant progress in clinical applications. In this 

 work, we explore the possibility of using phonon acoustics for 

 this purpose. We used phonon microscopy to obtain a measure of 

 the elastic properties between cancerous and normal breast 

 cells. Utilising the raw time-resolved phonon-derived data (300 

 k individual inputs), we employed a deep learning technique to 

 differentiate between MDA-MB-231 and MCF10a cell lines. We 

 achieved a 93% accuracy using a single phonon measurement in a 

 volume of approximately 2.5 μm3. We also investigated means 

 for classification based on a physical model that suggest the 

 presence of unidentified mechanical markers. We have 

 successfully created a compact sensor design as a proof of 

 principle, demonstrating its compatibility for use with needles 

 and endoscopes, opening up exciting possibilities for future 

 applications.},

keywords = {Deep learning, Machine learning, microscopy, Phonon microscopy},

pubstate = {published},

tppubtype = {article}

}

@article{Woodhouse2023-wp,

title = {Quantification of drugs in brain and liver mimetic tissue models 

 using Raman spectroscopy},

author = {Nathan Woodhouse and Jan Majer and Peter Marshall and Steve Hood and Ioan Notingher},

year  = {2023},

date = {2023-03-01},

journal = {Appl. Spectrosc.},

volume = {77},

number = {3},

pages = {246\textendash260},

publisher = {SAGE Publications},

abstract = {Quantitative analysis of drug delivery with in biological 

 systems is an integral challenge in drug development. Analytical 

 techniques are important for assessing both drug target 

 delivery, target action, and drug toxicology. Using mimetic 

 tissue models, we have investigated the efficacy of Raman 

 spectroscopy in quantitative detection of alkyne group and 

 deuterated drugs in rat brain and rat liver tissue models. 

 Lasers with 671 nm and 785 nm wavelengths were assessed for 

 their feasibility in this application due to opposing relative 

 benefits and disadvantages. Thin tissue sections have been 

 tested as a practical means of reducing autofluorescent 

 background by minimizing out-of-focus tissue and therefore 

 maximizing photobleaching rates. Alkyne-tagged drugs were 

 quantitatively measured at 18 $±$ 5 μg/g drug/tissue mass 

 ratio in rat brain and at 34 $±$ 6 μg/g in rat liver. 

 Quantification calibration curves were generated for a range of 

 concentrations from 0-500 μg/g. These results show the 

 potential of Raman spectroscopy as a diffraction-limited 

 spatially resolved imaging technique for assessing drug delivery 

 in tissue applications.},

keywords = {Drug delivery, drug quantification, microscopy, Raman spectroscopy},

pubstate = {published},

tppubtype = {article}

}

@article{Smith2021-jd,

title = {3D phonon microscopy with sub-micron axial-resolution},

author = {Richard J Smith and Fernando P\'{e}rez-Cota and Leonel Marques and Matt Clark},

year  = {2021},

date = {2021-02-01},

urldate = {2021-02-01},

journal = {Sci. Rep.},

volume = {11},

number = {1},

pages = {3301},

publisher = {Springer Science and Business Media LLC},

abstract = {Brillouin light scattering (BLS) is an emerging method for cell 

 imaging and characterisation. It allows elasticity-related 

 contrast, optical resolution and label-free operation. Phonon 

 microscopy detects BLS from laser generated coherent phonon 

 fields to offer an attractive route for imaging since, at GHz 

 frequencies, the phonon wavelength is sub-optical. Using phonon 

 fields to image single cells is challenging as the signal to 

 noise ratio and acquisition time are often poor. However, recent 

 advances in the instrumentation have enabled imaging of fixed 

 and living cells. This work presents the first experimental 

 characterisation of phonon-based axial resolution provided by 

 the response to a sharp edge. The obtained axial resolution is 

 up to 10 times higher than that of the optical system used to 

 take the measurements. Validation of the results are obtained 

 with various polymer objects, which are in good agreement with 

 those obtained using atomic force microscopy. Edge localisation, 

 and hence profilometry, of a phantom boundary is measured with 

 accuracy and precision of approximately 60 nm and 100 nm 

 respectively. Finally, 3D imaging of fixed cells in culture 

 medium is demonstrated.},

keywords = {3D imaging, microscopy, Phonon microscopy},

pubstate = {published},

tppubtype = {article}

}