Non linear photonics: developments & applications in biomedical imaging

Nonlinear polarization is explored in a biological and a technological contexts. Experimental set-ups are developed and built for interrogating nonlinear polarization in biological environment. Most notably, a Coherent Anti-Stokes Raman Scattering (CARS) and Second Harmonic Generation (SHG) microscopes are implemented in the Institute for Life Sciences (IfLS) at Southampton University. CARS and SHG are nonlinear effects based on different contrasts but both are label-free−and as a consequence truly in vivo; without perturbation of the biological mechanisms in opposition to fluorescence techniques (gold standard)− and enable fast imaging of living tissues, organisms and cells at 450 nm lateral spatial resolution. In collaboration with the mass-spectroscopy group at the General Hospital at Southampton and MedImmune, the capabilities of CARS & SHG are assessed for characterization of Pulmonary Alveoli Proteinosis (PAP) disease and drug impact on this phenotype and compared to its healthy version by tracking lipid droplets and collagen fibres. In an other collaboration with the clinical neuroanatomy and experimental neuropathology group at the University of Southampton, age related cerebrovascular and neurodegenerative diseases are linked to maternal obesity thanks to CARS thanks to its ability to track lipid droplets. In a second whole new project, multiplex CARS & SHG modalities are implemented and adapted to large area 4 mm2. Its methodology is developed. This last implementation allows microscopic and label-free characterization of large section of tissues which are compared to H&E (gold standard) valued by histological studies and proposed as a promising alternative. This ability leads to the development of a novel feature: texture analysis. The results obtained display novel insights and ability to characterize and localized healthy, pre-malignant and cancerous areas in tissues by a robust and unsupervised manner. Moreover, cancerous types could be further identified by this method. These results open up and bring the use of CARS & SHG for endoscopy/operative intervention for cancer/dysplasic localization at μm scale without prior labeling to an unprecedented level of specificity. To finish, a novel spectral CARS architecture is theoriticalized displaying unprecedented breadth and sensitivity; and enables the detection of many−usually too weak−biological Raman features

Hepatic steatosis accompanies pulmonary alveolar proteinosis

Maintenance of tissue-specific organ lipid compositions characterises mammalian lipid homeostasis. Lung and liver synthesise mixed phosphatidylcholine (PC) molecular species subsequently “tailored” for function. Lungs progressively enrich disaturated PC (DSPC) directed to lamellar body (LB) surfactant stores prior to secretion. Liver accumulates polyunsaturated PC directed to VLDL assembly and secretion, or triglyceride stores. In each tissue, selective PC species enrichment mechanisms lie at the heart of effective homeostasis. We tested potential coordination between these spatially separated, but possibly complementary phenomena under a major derangement of lung PC metabolism, Pulmonary Alveolar Proteinosis (PAP), which overwhelms homeostasis leading to excessive surfactant accumulation. Using static and dynamic lipidomics techniques we compared (i) tissue PC compositions and contents and (ii) in lungs, the absolute rates of synthesis from both control mice and the GM-CSF knockout model of PAP. Significant DSPC accumulation in BALF, Alveolar Macrophage (AM) and lavaged lung tissue occurred alongside increased PC synthesis consistent with reported defects in AM surfactant turnover. However, microscopy using oil red O staining, CARS, SHG and TEM also revealed neutral lipid droplet accumulations in alveolar lipofibroblasts of GM-CSF KO animals suggesting lipid homeostasis deficits extend beyond AMs. PAP plasma PC composition was significantly PUFA-enriched but content was unchanged and hepatic PUFA-enriched PC content increased by 50% with an accompanying micro/macrovesicular steatosis and a fibrotic damage pattern consistent with NAFLD. These data suggest a hepato-pulmonary axis of PC metabolism coordination with wider implications for understanding and managing lipid pathologies where compromise of one organ has unexpected consequences for another

Dynamic full-field optical coherence tomography module adapted to commercial microscopes allows longitudinal in vitro cell culture study

Abstract Dynamic full-field optical coherence tomography (D-FFOCT) has recently emerged as a label-free imaging tool, capable of resolving cell types and organelles within 3D live samples, whilst monitoring their activity at tens of milliseconds resolution. Here, a D-FFOCT module design is presented which can be coupled to a commercial microscope with a stage top incubator, allowing non-invasive label-free longitudinal imaging over periods of minutes to weeks on the same sample. Long term volumetric imaging on human induced pluripotent stem cell-derived retinal organoids is demonstrated, highlighting tissue and cell organization processes such as rosette formation and mitosis as well as cell shape and motility. Imaging on retinal explants highlights single 3D cone and rod structures. An optimal workflow for data acquisition, postprocessing and saving is demonstrated, resulting in a time gain factor of 10 compared to prior state of the art. Finally, a method to increase D-FFOCT signal-to-noise ratio is demonstrated, allowing rapid organoid screening

Dynamic Full-Field Optical Coherence Tomography module adapted to commercial microscopes for longitudinal in vitro cell culture study

Dynamic full-field optical coherence tomography (D-FFOCT) has recently emerged as a label-free imaging tool, capable of resolving cell types and organelles within 3D live samples, whilst monitoring their activity at tens of milliseconds resolution. Here, a D-FFOCT module design is presented which can be coupled to a commercial microscope with a stage top incubator, allowing non-invasive label-free longitudinal imaging over periods of minutes to weeks on the same sample. Long term volumetric imaging on human induced pluripotent stem cell-derived retinal organoids is demonstrated, highlighting tissue and cell organisation as well as cell shape, motility and division. Imaging on retinal explants highlights single 3D cone and rod structures. An optimal workflow for data acquisition, postprocessing and saving is demonstrated, resulting in a time gain factor of 10 compared to prior state of the art. Finally, a method to increase D-FFOCT signal-to-noise ratio is demonstrated, allowing rapid organoid screening

Automatic diagnosis and biopsy classification with dynamic Full-Field OCT and machine learning

Abstract The adoption of emerging imaging technologies in the medical community is often hampered if they provide a new unfamiliar contrast that requires experience to be interpreted. Here, in order to facilitate such integration, we developed two complementary machine learning approaches, respectively based on feature engineering and on convolutional neural networks (CNN), to perform automatic diagnosis of breast biopsies using dynamic full field optical coherence tomography (D-FF-OCT) microscopy. This new technique provides fast, high resolution images of biopsies with a contrast similar to H&E histology, but without any tissue preparation and alteration. We conducted a pilot study on 51 breast biopsies, and more than 1,000 individual images, and performed standard histology to obtain each biopsy diagnosis. Using our automatic diagnosis algorithms, we obtained an accuracy above 88% at the image level, and above 96% at the biopsy level. Finally, we proposed different strategies to narrow down the spatial scale of the automatic segmentation in order to be able to draw the tumor margins by drawing attention maps with the CNN approach, or by performing high resolution precise annotation of the datasets. Altogether, these results demonstrate the high potential of D-FF-OCT coupled to machine learning to provide a rapid, automatic, and accurate histopathology diagnosis