Significance: Tumor detection and margin delineation are essential for successful tumor resection. However, postsurgical positive margin rates remain high for many cancers. Raman spectroscopy has shown promise as a highly accurate clinical spectroscopic diagnostic modality, but its margin delineation capabilities are severely limited by the need for pointwise application.
Aim: We aim to extend Raman spectroscopic diagnostics and develop a multimodal computer vision-based diagnostic system capable of both the detection and identification of suspicious lesions and the precise delineation of disease margins.
Approach: We first apply visual tracking of a Raman spectroscopic probe to achieve real-time tumor margin delineation. We then combine this system with protoporphyrin IX fluorescence imaging to achieve fluorescence-guided Raman spectroscopic margin delineation.
Results: Our system enables real-time Raman spectroscopic tumor margin delineation for both ex vivo human tumor biopsies and an in vivo tumor xenograft mouse model. We then further demonstrate that the addition of protoporphyrin IX fluorescence imaging enables fluorescence-guided Raman spectroscopic margin delineation in a tissue phantom model.
Conclusions: Our image-guided Raman spectroscopic probe-tracking system enables tumor margin delineation and is compatible with both white light and fluorescence image guidance, demonstrating the potential for our system to be developed toward clinical tumor resection surgeries.
Spontaneous Raman spectroscopy enables non-ionising, non-destructive, and label-free acquisition of a biochemical fingerprint for a given sample. However, the long integration times required largely prohibit high-throughput applications. Here, we present a comprehensive deep learning framework for extreme speed-up of spontaneous Raman imaging. Our deep learning framework enhances Raman imaging two-fold, effectively reconstructing both spectral and spatial information from low spatial resolution, low signal-to-noise ratio images to achieve extreme Raman imaging time speed-ups of 40-90x while mainting high reconstruction fidelity. As such, our framework could enable a host of higher-throughput spontaneous Raman spectroscopy applications across a diverse range of fields.
Extracellular vesicles (EVs) are biologically derived nanovectors important for intercellular communication and trafficking, yet understanding of their underlying biological mechanisms remains poor. Advances have been hampered by both the complex biological origins of EVs and a lack of suitable imaging techniques. Here, we present a strategy for simultaneous in vitro imaging and molecular characterisation of EVs in 2D and 3D based on Raman spectroscopy and minimally-obstructive metabolic deuterium labelling. Metabolically-incorporated deuterium acts as a bio-orthogonal Raman-active tag for direct Raman identification of EVs and provides insights into their biocomposition and trafficking, with implications for their development as therapeutic delivery vectors.
Comprehensive single nanoparticle analysis of synthetic drug delivery systems, as well as natural occurring particles such as Extracellular Vesicles (EVs), is still a major challenge in the field, and is necessary to enhance their successful design, screening and study towards translational application. Investigating population heterogeneity is essential for nanoparticles, as their behaviour, characteristics and thus applicability are strongly affected by this, and cannot be resolved with conventional bulk analysis techniques. Here, we present a dedicated platform for comprehensive Single Particle Automated Raman Trapping Analysis (SPARTA). Nanoparticles ranging from synthetic polymer particles to liposomes or EVs can be integrally analysed by SPARTA without any modification, to obtain their size, determine functionalisation and composition, and monitor dynamic reactions occurring on their surface. The single nanoparticle nature of this approach allows highly detailed investigation in particle heterogeneity, resolving particle mixtures and tracking sequential functionalisations and dynamics on the particle surface. By using a Raman solution marker we demonstrated for the first time the capability to size single nanoparticles in a trap solely by Raman scattering, while simultaneously obtaining their compositional information, allowing novel insights in size-composition relationships. In addition, SPARTA can be applied to study in great detail the biochemical profiles of single EVs from cancerous and non-cancerous origin, towards the use of EVs as cancerous biomarkers for diagnosis, disease progression and evaluating therapeutic efficacy. SPARTA has great potential to critically impact fields from nano drug delivery system design to cancer biomarker identification and profiling.
Accurate tumour margin detection is a crucial step in tumour resection surgeries as progression-free survival is linked to rates of complete resection. Despite this, post-surgical positive margin rates remain high for a host of cancers. While spectroscopic techniques have shown promise as highly accurate diagnostic systems, they are inherently limited by their point-based application. Current spectroscopic diagnostic implementations fail to adequately capture spatial diagnostic information, resulting in these systems operating as one-dimensional tools suboptimal for tumour margin delineation. Here we demonstrate a computer vision-based technique that captures spatial information, enabling the transformation of spectroscopic systems from one-dimensional tools to clinically-relevant two-dimensional diagnostic platforms. We show that through visual tracking of a spectroscopic probe’s location relative to the tissue, we can display spatially co-registered spectroscopic diagnoses over clinical tumour imaging data to enhance tumour margin visualisation and aid tumour resection. Our visual, marker-based tracking approach enables real-time spectroscopic diagnostics and is designed for rapid application to different spectroscopic probe modalities and geometries with robust performance under different lighting conditions and with patient movement during procedures. We demonstrate the utility of this spatial diagnostic platform using a Raman spectroscopy probe for ex vivo margin delineation, with ongoing in vivo investigations for subcutaneous xenograft tumour models in nude mice. The associated software developed for this system permits clinical-user interaction for diagnostic threshold adjustment and tumour boundary delineation, enabling clinical diagnostic control for complex tumour geometries. Our system captures essential spatial diagnostic information, transforming point-based spectroscopic systems into effective platforms for tumour delineation.
Theranostic approaches to cancer management offer the possibility of tailoring treatments to individual patients or tumours. However, the development of theranostic nanoparticles optimally suitable for both diagnostic and therapeutic modalities to achieve this is challenging. Here we demonstrate an alternative nanoparticle-free theranostic approach that circumvents many of the difficulties currently hindering clinical translation. Through the use of a multimodal optical probe, we translate the theranostic burden from complex nanoparticles within the body to an external spectroscopic device, thus alleviating many of the constraints imposed on existing theranostic systems. Using this platform, we demonstrate the combination of Raman spectroscopic diagnosis with photodynamic therapy for optical cancer theranostics. Through selection of photosensitisers with suitable optical properties for combination with Raman spectroscopy, we achieve optical theranostics without the need for complex material systems. Sequential delivery of light for real-time Raman spectroscopic diagnosis followed by photosensitiser-specific illumination, enables cancer diagnosis and treatment during a single procedure. We demonstrate the feasibility of this theranostic approach using a panel of clinically-approved photosensitisers using in vitro cell assays across three cancerous cell lines, with in vivo demonstration using subcutaneous xenograft tumour models ongoing. Our results indicate that through careful instrument design, Raman spectroscopic diagnosis can be effectively performed on photosensitiser-containing cells and tissues without impaired diagnostic accuracy or undesired premature photosensitiser activation. Together, these results show that combination of Raman spectroscopy and photodynamic therapy for optical theranostics enables nanoparticle-free cancer detection, diagnosis, and treatment in real-time using a single optical system.
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