I will present a photonic sensor that can be used for remote sensing of many biomedical parameters simultaneously and continuously. The technology is based upon illuminating a surface with a laser and then using an imaging camera to perform temporal and spatial tracking of secondary speckle patterns in order to have nano metric accurate estimation of the movement of the back reflecting surface. The capability of sensing those movements in nano-metric precision allows connecting the movement with remote bio-sensing and with medical diagnosis capabilities. The proposed technology was already applied for remote and continuous estimation of vital bio-signs (such as heart beats, respiration, blood pulse pressure and intra ocular pressure), for molecular sensing of chemicals in the blood stream (such as for estimation of alcohol, glucose and lactate concentrations in blood stream, blood coagulation and oximetry) as well as for sensing of hemodynamic characteristics such as blood flow related to brain activity. The sensor can be used for early diagnosis of diseases such as otitis, melanoma and breast cancer and lately it was tested in large scale clinical trials and provided highly efficient medical diagnosis capabilities for cardiopulmonary diseases. The capability of the sensor was also tested and verified in providing remote high-quality characterization of brain activity.

This conference presentation was prepared for SPIE Optical Metrology, 2023.

We have developed different classes of quantitative phase microscope for applications ranges from material inspection to biomedical assays in cells and tissues. Here we focus on quantifying the conformations of many driven and active nematic fluids exhibit complex microstructures. We explore the structures emerging in a pressure-driven nematic lyotropic chromonic liquid crystal in a microfluidic channel. We show that twist-type topological defects spontaneously emerge under flow. Our single-shot quantitative polarization imaging method allows us to quantify the fluctuations of these defects, which we show to reflect the tumbling character of the liquid crystal. We report how the defect size is governed by the balance between nucleation and annihilation forces, a balance that can be tuned by the flow rate. Such control over the microstructure opens pathways for using these nematic materials in optical devices and to control assembly of biological systems. If time permits, we will further present a novel nanofabrication technology for creating novel meta-optical components with high refractive index contrast based on Implosion Fabrication and will describe the applications of quantitative phase imaging for manufacturing process control.

Quantitative phase imaging (QPI) is a powerful label-free imaging technique that enables high-resolution, three-dimensional imaging of unlabeled samples by exploiting refractive index (RI) distributions as intrinsic imaging contrast. In this talk, we present the latest developments in 3D QPI techniques for investigations of live cells, tissues, and organoids over a long period of time. We will introduce several reference-free QPI approaches with incoherent light sources and discuss the considerations of multiple light scattering into a tomographic reconstruction algorithm1-3. Additionally, we will present a method of measuring 3D dielectric tensor tomograms, which expands the applicability of QPI to 3D birefringent materials. These technological developments hold great promise for advancing 3D label-free high-resolution imaging and its application in various fields, particularly in photonics.

Fiber-optic dual-beam trap is an emerging method for studying biophysical properties of biological cells and organoids. Nevertheless, current optical manipulation is often limited to single-axis cell rotation. We introduce an innovative dual beam trap, utilizes multicore fibers to enable precise control of cell rotation about all three axes. This is achieved through the development of a physics-informed neural network that generates tailored light fields in the trapping region via the multicore fiber, allowing real-time holographic control of the optical force. By leveraging the capability of 3D cell rotation, our system enables 3D optical diffraction tomography with full spatial frequency coverage, eliminating the missing cone problem for cancer cells.

A normal bright-field microscope may be updated with coherent sensing capabilities easily, inexpensively, and compactly using phase imaging microscopy in the Gabor regime. The digital sensor records an in-line Gabor hologram of the target sample by incorporating coherent illumination into the regular microscope embodiment, generated by a small defocus distance of the sample at the input plane. This hologram is then numerically post-processed to obtain the quantitative phase information of the sample. However, when dealing with Gabor's regime, coherent noise and twin-image disturbances affect the recovered phase distribution. In this contribution, we describe a single-shot method for reducing these two error sources based on wavelength multiplexing. The sample is illuminated by a multi-wavelength laser source that utilized three diode lasers, and the wavelength-multiplexed Gabor hologram is to be captured on a digital color sensor using a traditional RGB color camera in a single exposure. The presented phase imaging method is completed by the implementation of a new algorithm built on a modified Gerchberg-Saxton kernel to obtain an enhanced quantitative phase image of a sample that has been improved in terms of coherent noise removal and twin-image reduction. Complex field filtering in terms of hologram's imaginary and real part numerical alteration is in place in an iterative fashion. Experimental validations are carried out in a off-the-shelf Olympus BX-60 upright microscope with a 20X 0.46NA objective lens employing static (resolution test targets) and dynamic (live spermatozoa) phase sample quantitative imaging.

Deep learning techniques create new opportunities to revolutionize tissue staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, accurate and environmentally friendly alternatives to standard chemical staining methods. These deep learning-based virtual staining techniques can successfully generate different types of histological stains, including immunohistochemical stains, from label-free microscopic images of unstained samples by using, e.g., autofluorescence microscopy, quantitative phase imaging (QPI) and reflectance confocal microscopy. Similar approaches were also demonstrated for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this presentation, I will provide an overview of our recent work on the use of deep neural networks for label-free tissue staining, also covering their biomedical applications.

Despite their key-role during the histopathological diagnosis, staining procedures are expensive and time-consuming. Label-free microscopy provides an alternative since it allows the visualization of endogenous proteins without the need of extrinsic dyes. SuperµMAPPS, a novel AI-based method, analyzes the Polarized Second Harmonic Generation signal from collagen to characterize its micro-architecture in terms of fibrils mean orientation θF and anisotropy γ, related to tumor development. After a proper validation on synthetic images, human breast cancer samples at different growth stages have been analyzed through SuperµMAPPS, highlighting its capability to detect tumorous tissue at early stages in a real clinical context.

Meta-optical devices have emerged as promising candidates for all-optical image processing. These devices are of subwavelength size and have the potential to address limitations of current image processing methods including processing speed, energy requirements as well as form factor. We present experimental results demonstrating the use of thin-film absorbers and optical metasurfaces to real-time detection of edges in images and the visualisation of phase objects including human cancer cells. Furthermore, we discuss progress towards the use of meta-optics for ultra-compact wavefront recovery. The findings to be presented have potential for applications in biological live-cell imaging, ultra-compact medical diagnostic tools, and wavefront correction methods.

The precise and non-invasive control over single particles is key for an array of physical and bio-medical applications, such as microfluidics and biophysics. In particular, the three-dimensional manipulation of single rare-earth-doped luminescent particles is of great interest due to their biocompatibility and the sensitivity of their luminescent properties to environmental conditions, which stand out among other dielectric luminescent particles. The analysis of the damped rotation dynamics of an optically trapped microparticle is a novel and powerful tool that allows not only the controlled and remote manipulation of the sensor, but also an improved characterization of the medium and fast recording of its content. Here, an optically trapped and rotated rare-earth-doped β-NaYF 4 :RE 3+ microparticle is presented as a novel sensor to characterize the properties of a liquid medium at the microscale (temperature, viscosity and detection of bio-objects).

Holographic cytometry is introduced as an ultra-high throughput implementation of quantitative phase image based on off-axis interferometry capable of extracting information from millions of cells flowing through parallel microfluidic channels. The approach allows high quality phase imaging of a large number of cells greatly extending our ability to study cellular phenotypes using individual cell images. The large volume of individual cell imaging data provides suitable input for training sets to develop machine learning and deep learning algorithms. Here we present our findings on application of this technique to examining red blood cells and to distinguishing carcinogen-exposed cells from normal cells and cancer cells. A study of storage lesion, the degradation of red blood cells due to aging, is presented. By using logistic regression to analyze morphological cell features, high accuracy for discriminating cells by storage time is obtained. Further study of red blood cells shows the ability to detect sickle cell disease by implementing deep learning algorithms with careful selection of classifier training features, suggesting potential avenues for diagnosis and monitoring of this disease. Finally, studies of carcinogen exposed cells compared to cancer cell lines show distinct traits between cell populations. Use of deep learning algorithms enables high accuracy in detecting cell phenotype. This has potential application for environmental monitoring and cancer detection by analysis of cytology samples acquired via brushing or fine needle aspiration. The results of these studies demonstrate the potential of holographic cytometry as a diagnostic tool based on high throughput single cell imaging.

I will review our latest results in the field of quantitative imaging flow cytometry using off-axis holography. Flow cytometry has a great diagnosis potential, due to its ability to analyze a large number of cells during flow for samples obtained from body fluids. Since cellular morphology analysis plays an important role in various clinical diagnoses, such as screening cancer and various chronic diseases, flow cytometry is much anticipated to incorporate imaging capabilities, providing a more comprehensive analysis by presenting a detailed morphological structure image of individual cells. In addition, some erroneous analysis results, yielded in conventional flow cytometry, can be eliminated by acquiring and analyzing such cell images by clearly distinguishing between cells, debris, and clusters of cells. While conventional flow cytometry measures the integral intensity of fluorescent emission, fluorescence microscopy is able to yield the exact morphology of the cell and its organelles. Recent advances in imaging technologies, as well as the exponentially evolving computational capacity, have enabled imaging flow cytometry (IFC) by integrating fluorescence microscopy and conventional flow cytometry. However, the current-generation IFC remains highly inaccessible technology, due to its cost, requirement for operator expertise, lack of accuracy, and lack of objectiveness of data produced. We developed new approach for IFC, which is based on stain-free interferometric phase microscopy, a digital holographic microscopy technique. Using an external interferometric module and cutting-edge deep-learning analysis methods, we generate virtually stained cell images of a volume of cells, with a clear morphological discrimination between various cellular organelles. The module acquires, in a single camera frame, the digital hologram of the cell during flow, and our rapid reconstruction algorithms retrieve the complex wavefront of the cell, from which the optical path delay (OPD) topological map is calculated. This map represents, on each spatial point, the product of the cell physical thickness and its refractive index, accounting for both the cell morphology and contents. Such map is subsequently used as the input to a deep convolutional neural network that is pre-trained to transform the cell OPD into a 2-D image with stained-like organelles. The same IFC setup is also used for automatic cell classification. We demonstrate using the system for cancer detection in liquid biopsies.

We will describe MaxwellNet, a machine learning method for analyzing and designing optical systems using Maxwell's equations as the loss function in the optimisation process. We will describe results of the application of MaxwellNet to nanophotonics, nonlinear optics and optical diffraction tomography

H&E stained sections are the gold standard for disease diagnosis but, unfortunately, the staining process is time-consuming and expensive. In an effort to overcome these problems, here, we propose a virtual staining algorithm, able to predict an Hematoxylin/Eosin (H&E) image, usually exploited during clinical evaluations, starting from the autofluorescence signal of entire liver tissue sections acquired by a confocal microscope. The color and texture contents of the generated virtually stained images have been analyzed through the phasor-based approach to detect tumorous tissue and to segment relevant biological structures (accuracy>90% compared to the expert manual analysis).

We present a new AI-based method for the quantification of liver fibrosis in tissue sections stained with Picro Sirius Red which highlights collagen. The method segments and quantifies collagen, a marker of the fibrotic response, through a deep learning model trained on 20 whole-slide images. The results show a Dice score > 90% compared to manual annotations, demonstrating its potential aid during diagnosis. Furthermore, our approach can be extended to other staining protocols.

We propose a new label-free method for noninvasive and automated cell processing for classification of WBCs. This is done by acquiring off-axis holograms of each cell during flow and achieving its optical path delay (OPD) profile. Based on this map, we extract highly discriminative features used to detect, classify, and differentiate between distinctive cells using a deep convolutional neural network. This label-free method might bring to new analysis tools for blood test processing.

Optical microscopy is an indispensable tool that is driving progress in biology and is still the only practical means of obtaining spatial and temporal resolution within living cells and tissues. In this context, staining samples with fluorescent labels provides a highly sensitive and specific method of visualizing biomolecules. However, fluorescence microscopy has various limitations including sample manipulation and staining artifacts, fluorophore photobleaching and associated phototoxicity. Therefore, much effort has been devoted to developing label-free optical microscopy techniques which are non-perturbing, photostable, and in turn offer quantitative capabilities unavailable with fluorescent methods. Our laboratory has been developing quantitative label-free optical microscopy set-ups featuring innovative excitation/detection schemes, with application ranging from synthetic lipid membranes [1] and nanoparticle materials [2-4] to living cells [5]. Specifically, we have demonstrated quantitative differential interference contrast microscopy [1], extinction microscopy [2], four-wave mixing imaging [3,4], and chemically-specific coherent Raman scattering (CRS) microscopy [5-7], including an interferometric CRS set-up which offers background-free image contrast, shot-noise limited detection, and phase sensitivity, enabling topographic imaging of interfaces [8]. We are also developing a new wide-field interferometric reflectometry method, aimed at monitoring single protein-lipid membrane interactions with unprecedented sensitivity. I will present our latest progress with these techniques and their applications to bioimaging. [1] Anal. Chem. 92, 14657 (2020). [2] Nanoscale 12, 16215 (2020). [3] Phys. Rev. X 7, 41022 (2017). [4] Nanoscale 12, 4622 (2020). [5] Analyst 146, 2277 (2021). [6] Nat. Nanotechnol. 9, 940 (2014). [7] Anal. Chem. 91, 2813 (2019). [8] APL Photonics 3, 092402 (2018).

Lysosomal storage diseases (LSDs) are a group of genetic disorders caused by defects in lysosomal function, which lead to the accumulation of undigested substrates and subsequent cell and tissue damage. Batten disease and Mucopolysaccharidosis are two neurodegenerative LSDs that have devastating consequences for affected individuals and their families. Despite significant research efforts, there is currently no cure for these diseases. Here, we present an innovative strategy to tackle LSDs that combines high-content imaging techniques with repurposing approved drugs to identify the correctors of these diseases. Our results show that several drugs that are already approved for other indications can effectively reduce lysosomal storage in different LSDs, including batten disease and Mucopolysaccharidosis. We further validated the most promising drug candidates in mouse models of these diseases and found that they can improve several disease phenotypes, including pathological storage, motor function, and neuroinflammation. In conclusion, our findings provide a promising starting point for the development of new treatments for these devastating diseases.

We have developed SHG microscope tools to probe all levels of collagen architecture organization in human high grade serous ovarian cancer (HSOC). We have found pronounced differences using machine learning classification of the fiber morphology as well as alterations in macro/supramolecular structural aspects through polarization analysis. We have used multiphoton excited fabrication to create SHG image-based orthogonal models that represent both the collagen morphology and stiffness of normal ovarian stroma and HGSOC. We found the fiber morphology of HGSOC promotes motility through a contact guidance mechanism and that stiffer matrix further promotes these same processes through a mechanosensitive mechanism. We have also developed a machine learning approach using generative adversarial networks (GANs) to optimize the scaffold design. Collectively, this data provides insight into disease etiology and suggests future diagnostic approaches.

We propose the combination of polyN-isopropylacrylamide (PNIPAM) particles and optical coherence tomography (OCT) to overcome the main limitations of current nanothermometry for medical purposes. We demonstrate that PNIPAM particles can behave as temperature-sensitive contrast agents in OCT thanks to their structural phase transition at 32 °C, resulting in changes in the refractive index that make their OCT contrast temperature-dependent. Simple experiments have been conducted to demonstrate the feasibility of this approach for three-dimensional imaging of phantom tissues subjected to photothermal processes. The results included in this work constitute an alternative route towards facile incorporation of nanothermometry into the clinical world.

This conference presentation was prepared for SPIE Optical Metrology, 2023.

The nanostructuring on titanium surfaces is studied by low-energy argon ion irradiation. The surfaces are analysed by EBSD for grain orientation mapping, SEM for surface imaging, WLI and AFM for topography characterization, XPS and ToF-SIMS for chemical surface analysis. Under normal incidence specific nanoripple structures are formed, whereas the morphology is defined by the crystallographic conditions only. A characteristic relation between grain orientation and ripple size is elaborated. Experiments on co-deposition with Al, C, Cu, Fe, and Si indicate that Fe impurities influence nanostructuring. The effect of inclined ion incidence shows an overlay between orientation-dependent and process geometry-related structure formation.

The concentration dependent attenuation, group refractive index and the Group Velocity Dispersion (GVD) of dense turbid media were determined with a Mach-Zehnder interferometer in the spectral domain for wavelengths between 400 nm and 930 nm. After calibration, all optical properties could be retrieved from a single measurement. Dependent scattering has only a small effect on the real part of the effective refractive index of a suspension. The higher sensitivity of the GVD compared to the group index allows us to test the particle concentration dependence of the real part of the effective refractive index of the medium. It was found that the interparticle correlations have a measurable effect on the GVD. The phase refractive index can be fitted to the concentration dependence of the group index. The combined measurement of the attenuation and refractive properties of the particle suspension allowed the estimation of the particle size distribution of the turbid medium through forward Mie calculations.

Precise manufacturing of volume scattering phantoms with direct control of the scattering and absorption coefficients opens up the possibility to examine the behavior of focus generation and the resulting enhancement behind these phantoms by applying wavefront shaping. Volume scattering phantoms allow measurements on samples with selected optical thicknesses. Specific combinations of scattering and absorption properties can mimic the interaction of coherent monochromatic light with biological tissue at a predetermined wavelength. Phantoms similar to muscle and fat tissue and solely scattering samples are being designed to characterize the achievable enhancement of the optimized focus intensity versus the mean intensity before optimization.

To measure optical properties of turbid fluorescent samples, an advancement of an inhouse developed integration sphere system was build up. This system based on a 3D printed integrating sphere uses photodiodes with lock-in technics for detection. The tuneable monochromatic illumination is realized by a laser pumped xenon light source with an integrated monochromator. A second monochromator on the detection side allows to discriminate between inelastically and elastically scattered light from the fluorescent media. By measuring the reflection and transmission from the investigated turbid sample, the wavelength dependent optical properties (absorption coefficient µa, effective scattering coefficient µs’) as well as the quantum efficiency (or concentration) of the fluorophore can be determinate using lookup tables generated from Monte Carlo simulations. Validation measurements are performed on turbid rhodamine phantoms.

3D cell culture resembles tissues better than traditional monolayer cultures, which differ greatly from in vivo models. We use this technique to develop multicellular spheroids from two cell lines: MCF-7 (adenocarcinoma) and U87mg (glioblastoma astrocytoma), by forced-floating method. In this work, we research the spheroid behaviour through two optical techniques: photovoltaic tweezers and laser irradiation. We use photovoltaic tweezers to manipulate spheroids and to explore their electric charge. We also investigate their biological response to laser irradiation depending on wavelength and laser power. Finally, cell viability of the spheroids after undergoing each of these optical/optoelectric treatments has been quantified.