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Nanoscale carriers such as liposomes and extracellular vesicles (EVs) are readily being explored for personalized medicine or disease prediction and diagnostics, respectively. Owing to their small size, such nanocarriers can undergo endocytosis or exocytosis, providing means to either transport cargo to the cells (liposomes) or to serve as a biomarker (EVs). When looking at current analysis methods, there is a growing need for detailed characterization of the content and composition of such nanocarriers in their natural state in aqueous media. This can be achieved through surface-enhanced Raman spectroscopy (SERS), which provides a molecular fingerprint of the analytes while reducing the detection limit. In this paper, we utilize a nano-structured SERS substrate to study different bio-nanoparticles such as liposomes, EVs and DNA nanogel in suspension. A silver-coated polydimethylsiloxane (PDMS) film-based honeycomb shaped nano-bowl surface passively traps and reduces the mobility of the nanosized bio-particles, improving the intensity and the reproducibility of the SERS signal. FDTD simulations are used for substrate geometry optimization, and a detection limit of 10−15M is demonstrated for Rhodamine 6G (R6G). The potential of the proposed SERS nano-bowl is shown through distinct spectral features following surface-(polyethylene glycol) and bilayer-(cholesterol) modification of empty liposomes. For DNA nanogels, the characterization of highly crosslinked DNA specimens exhibits enhanced peaks for nitrogenous bases, sugar, and phosphate groups. EVs isolated from various cells provided spectral signatures of specific protein content, lipid components, and nucleic acids. Concluding, the findings of the spectral signatures of a wide range of molecular complexes and chemical morphology of bio-membranes in their natural state highlight the possibilities of using SERS as a sensitive and instantaneous characterization alternative.

The photo-kinetics of fluorescent molecules has enabled the circumvention of far-field optical diffraction-limit. Despite its enormous potential, the necessity to label the sample may adversely influence the delicate biology under investigation. Thus, continued development efforts are needed to surpass the far-field label-free diffraction barrier. The coherence of the detected light in label-free mode hinders the application of existing super-resolution methods based on incoherent fluorescence imaging. In this article, we present the physics and propose a methodology to circumvent this challenge by exploiting the photoluminescence of silicon nitride waveguides for near-field illumination of unlabeled samples. The technique is abbreviated EPSLON, Evanescently decaying Photoluminescence Scattering enables Label-free Optical Nanoscopy. We demonstrate that such an illumination has properties that mimics the photo-kinetics of nano-sized fluorescent molecules. This allows to develop a label-free incoherent system that is linear in intensity, and stable with time thereby permitting the application of techniques like structured illumination microscopy (SIM) and intensity-fluctuation based optical nanoscopy (IFON) in label-free mode to circumvent the diffraction limit. We experimentally demonstrate label-free super-resolution imaging of nanobeads (polystyrene and gold), extra-cellular vesicles and human placenta tissue. We believe EPSLON is a step forward within the nascent field of label-free super-resolution microscopy that holds the key to investigate delicate biological systems in its natural state without the need for exogenous labels., Comment: 22 pages including supplementary, 5 figures in main text and 5 figures in supplementary

The world health organization considers antimicrobial resistance (AMR) to be a critical global public health problem. Conventional culture-based methods that are used to detect and identify bacterial infection are slow. Thus, there is a growing need for the development of robust, cost-effective, and fast diagnostic solutions for the identification of pathogens. Surface-enhanced Raman spectroscopy (SERS) can be used to identify target analytes with sensitivity down to the single-molecule level. Here, we developed a SERS chip by optimizing the entire fabrication pipeline of the metal-assisted chemical etching (MACE) method. The MACE approach offers a large-scale, densely packed silver (Ag) nanostructure on top of silicon nanowires (Si-NWs) with a large aspect ratio that significantly enhances the Raman signal due to localised surface plasmonic enhancement. The optimised SERS chips exhibited sensitivity down to 10-12M concentration of R6G molecule and detected reproducible Raman spectra of bacteria down to a concentration of 100 colony forming units (CFU)/ml, which is a thousand times lower than the clinical threshold of bacterial infections like UTI (105CFU/ml). A Siamese neural network model was used to classify SERS Raman spectra from bacteria specimens. The trained model identified 12 different bacterial species, including those which are causative agents for tuberculosis and urinary tract infection (UTI). Next, the SERS chips and another Siamese neural network model were used to differentiate antibiotic-resistant strains from susceptible strains ofE. coli. The enhancement offered by SERS chip enabled acquisitions of Raman spectra of bacteria directly in the synthetic urine by spiking the sample with only 103CFU/mlE. coli. Thus, the present study lays the ground for the identification and quantification of bacteria on SERS chips, thereby offering a potential future use for rapid, reproducible, label-free, and low limit detection of clinical pathogens.

The current state-of-the-art infection and antimicrobial resistance diagnostics (AMR) is based mainly on culture-based methods with a detection time of 48-96 hours. Slow diagnoses lead to adverse patient outcomes that directly correlate with the time taken to administer optimal antimicrobials. Mortality risk doubles with a 24-hour delay in providing appropriate antibiotics in cases of bacteremia. Therefore, it is essential to develop novel methods that can promptly and accurately diagnose microbial infections at both species and strain levels in clinical settings. Here, we demonstrate that the complimentary use of label-free optical assay with whole-genome sequencing (WGS) can enable high-speed culture-free diagnosis of infection and AMR. Our assay is based on microscopy methods exploiting label-free, highly sensitive quantitative phase microscopy (QPM) followed by deep convolutional neural networks (DCNNs) based classification. We benchmarked our proposed workflow on 21 clinical isolates from four WHO priority pathogens (Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, andAcinetobacter baumannii) that were antibiotic susceptibility testing (AST) phenotyped, and their antimicrobial resistance (AMR) profile was determined by WGS. The proposed optical assay was in good agreement with the WGS characterization. Highly accurate classification based on the gram staining (100% for gram-negative and 83.4% for gram-positive), species (98.6%), and resistant/susceptible type (96.4%), as well as at the individual strain level (100% accurate in predicting 19 out of the 21 strains). These results demonstrate the potential of the QPM assay as a rapid and first-stage tool for species, presence, and absence of AMR, and strain-level classification, which WGS can follow up for confirmation of the pathogen ID and the characterization of the AMR profile and susceptibility antibiotic. Taken together, all this information is of high clinical importance. Such a workflow could potentially facilitate efficient antimicrobial stewardship and prevent the spread of AMR.

Duplikat av https://hdl.handle.net/10037/26158. 17.02.2023 NielsC In scanning acoustic microscopy (SAM), the image quality depends on several factors such as noise level, resolution, and interaction of the waves with sample boundaries. The theoretical equations for the reflection coefficient and transmission coefficient are suitable for plane boundaries but fail for curved/rough boundaries. We presented a finite element method-based modeling for the loss coefficients in SAM. A focused and unfocused lens with a flat object, furthermore a focused lens with a curved object was selected for loss coefficients calculation. The loss calculation in terms of energy for defining the acoustic reflection and transmission losses and its dependence on the radius of curvature of the test object has also been presented.

We present a highly spatially sensitive quantitative phase microscopy system integrated with on-chip nanoscopy to visualize 3D morphology of liver sinusoidal endothelial cells (LSECs). We used the system to obtain 3D morphology of LSEC by using chip-based nanoscopy for lateral super-resolution, and QPM for mapping nanoscale thickness.

Our ability to generate new distributions of light has been remarkably enhanced in recent years. At the most fundamental level, these light patterns are obtained by ingeniously combining different electromagnetic modes. Interestingly, the modal superposition occurs in the spatial, temporal as well as spatio-temporal domain. This generalized concept of structured light is being applied across the entire spectrum of optics: generating classical and quantum states of light, harnessing linear and nonlinear light-matter interactions, and advancing applications in microscopy, spectroscopy, holography, communication, and synchronization. This Roadmap highlights the common roots of these different techniques and thus establishes links between research areas that complement each other seamlessly. We provide an overview of all these areas, their backgrounds, current research, and future developments. We highlight the power of multimodal light manipulation and want to inspire new eclectic approaches in this vibrant research community., Under review in J. Opt

Significance We propose the integration of chip-based optical nanoscopy with high spatially sensitive quantitative phase microscopy to obtain three-dimensional (3D) morphology of liver sinusoidal endothelial cells (LSEC). LSEC contain large numbers of transcellular nanopores —“fenestrations”—in the plasma membrane, typically clustered in groups of 10 to 50 within areas called sieve plates. Determining the diameter and the height of fenestrated regions is an important indicator of a cell’s functionality, and these dimensions can be influenced by agents such as drugs. Our proposed multimodal microscope offers a solution for 3D nanoscale characterization of fenestration diameter and measurement of the optical thickness of the sieve plates., Visualization of three-dimensional (3D) morphological changes in the subcellular structures of a biological specimen is a major challenge in life science. Here, we present an integrated chip-based optical nanoscopy combined with quantitative phase microscopy (QPM) to obtain 3D morphology of liver sinusoidal endothelial cells (LSEC). LSEC have unique morphology with small nanopores (50-300 nm in diameter) in the plasma membrane, called fenestrations. The fenestrations are grouped in discrete clusters, which are around 100 to 200 nm thick. Thus, imaging and quantification of fenestrations and sieve plate thickness require resolution and sensitivity of sub-100 nm along both the lateral and the axial directions, respectively. In chip-based nanoscopy, the optical waveguides are used both for hosting and illuminating the sample. The fluorescence signal is captured by an upright microscope, which is converted into a Linnik-type interferometer to sequentially acquire both superresolved images and phase information of the sample. The multimodal microscope provided an estimate of the fenestration diameter of 119 ± 53 nm and average thickness of the sieve plates of 136.6 ± 42.4 nm, assuming the constant refractive index of cell membrane to be 1.38. Further, LSEC were treated with cytochalasin B to demonstrate the possibility of precise detection in the cell height. The mean phase value of the fenestrated area in normal and treated cells was found to be 161 ± 50 mrad and 109 ± 49 mrad, respectively. The proposed multimodal technique offers nanoscale visualization of both the lateral size and the thickness map, which would be of broader interest in the fields of cell biology and bioimaging.

Fringe pattern analysis is the central aspect of numerous optical measurement methods, e.g., interferometry, fringe projection, digital holography, quantitative phase microscopy. Experimental fringe patterns always contain significant features originating from fluctuating environment, optical system and illumination quality, and the sample itself that severely affect analysis outcome. Before the stage of phase retrieval (information decoding) interferogram needs proper filtering, which minimizes the impact of mentioned issues. In this paper we propose fully automatic and adaptive fringe pattern pre-processing technique - improved period guided bidimensional empirical mode decomposition algorithm (iPGBEMD). It is based on our previous work about PGBEMD which eliminated the mode-mixing phenomenon and made the empirical mode decomposition fully adaptive. In present work we overcame key problems of original PGBEMD – we have considerably increased algorithm’s application range and shortened computation time several-fold. We proposed three solutions to the problem of erroneous decomposition for very low fringe amplitude images, which limited original PGBEMD significantly and we have chosen the best one among them after comprehensive analysis. Several acceleration methods were also proposed and merged to ensure the best results. We combined our improved pre-processing algorithm with the Hilbert Spiral Transform to receive complete, consistent, and versatile fringe pattern analysis path. Quality and effectiveness evaluation, in comparison with selected reference methods, is provided using numerical simulations and experimental fringe data.

Photonic chip-based total internal reflection fluorescence microscopy (c-TIRFM) is an emerging technology enabling a large TIRF excitation area decoupled from the detection objective. Additionally, due to the inherent multimodal nature of wide waveguides, it is a convenient platform for introducing temporal fluctuations in the illumination pattern. The fluorescence fluctuation-based nanoscopy technique multiple signal classification algorithm (MUSICAL) does not assume stochastic independence of the emitter emission and can therefore exploit fluctuations arising from other sources, as such multimodal illumination patterns. In this work, we demonstrate and verify the utilization of fluctuations in the illumination for super-resolution imaging using MUSICAL on actin in salmon keratocytes. The resolution improvement was measured to be 2.2–3.6-fold compared to the corresponding conventional images.

Total internal reflection fluorescence (TIRF) microscopy is an imaging technique that, in comparison to confocal microscopy, does not require a trade-off between resolution, speed, and photodamage. Here, we introduce a waveguide platform for chip-based TIRF imaging based on a transparent substrate, which is fully compatible with sample handling and imaging procedures commonly used with a standard #1.5 glass coverslip. The platform is fabricated using standard complementary metal-oxide-semiconductor techniques which can easily be scaled up for mass production. We demonstrate its performance on synthetic and biological samples using both upright and inverted microscopes, and show how it can be extended to super-resolution applications, achieving a resolution of 116 nm using super resolution radial fluctuations. These transparent chips retain the scalable field of view of opaque chip-based TIRF and the high axial resolution of TIRF, and have the versatility to be used with many different objective lenses, microscopy methods, and handling techniques. We see this as a technology primed for widespread adoption, increasing both TIRF’s accessibility to users and the range of applications that can benefit from it. Total internal reflection fluorescence microscopy typically relies on opaque optical waveguides, compatible only with upright microscopes. Here, a versatile approach is reported that uses CMOS-compatible transparent chips, demonstrated for the imaging of synthetic and biological samples, including super-resolution applications.

High space-bandwidth product with high spatial phase sensitivity is indispensable for a single-shot quantitative phase microscopy (QPM) system. It opens avenue for widespread applications of QPM in the field of biomedical imaging. Temporally low coherence light sources are implemented to achieve high spatial phase sensitivity in QPM at the cost of either reduced temporal resolution or smaller field of view (FOV). In addition, such light sources have low photon degeneracy. On the contrary, high temporal coherence light sources like lasers are capable of exploiting the full FOV of the QPM systems at the expense of less spatial phase sensitivity. In the present work, we demonstrated that use of narrowband partially spatially coherent light source also called pseudo-thermal light source (PTLS) in QPM overcomes the limitations of conventional light sources. The performance of PTLS is compared with conventional light sources in terms of space bandwidth product, phase sensitivity and optical imaging quality. The capabilities of PTLS are demonstrated on both amplitude (USAF resolution chart) and phase (thin optical waveguide, height ~ 8 nm) objects. The spatial phase sensitivity of QPM using PTLS is measured to be equivalent to that for white light source and supports the FOV (18 times more) equivalent to that of laser light source. The high-speed capabilities of PTLS based QPM is demonstrated by imaging live sperm cells that is limited by the camera speed and large FOV is demonstrated by imaging histopathology human placenta tissue samples. Minimal invasive, high-throughput, spatially sensitive and single-shot QPM based on PTLS will enable wider penetration of QPM in life sciences and clinical applications.

Mitochondria play a crucial role in cellular metabolism. This paper presents a novel method to visualize mitochondria in living cells without the use of fluorescent markers. We propose a physics-guided deep learning approach for obtaining virtually labeled micrographs of mitochondria from bright-field images. We integrate a microscope’s point spread function in the learning of an adversarial neural network for improving virtual labeling. We show results (average Pearson correlation 0.86) significantly better than what was achieved by state-of-the-art (0.71) for virtual labeling of mitochondria. We also provide new insights into the virtual labeling problem and suggest additional metrics for quality assessment. The results show that our virtual labeling approach is a powerful way of segmenting and tracking individual mitochondria in bright-field images, results previously achievable only for fluorescently labeled mitochondria.

Histopathological assessment involves the identification of anatomical variations in tissues that are associated with diseases. While diffraction-limited optical microscopes assist in the diagnosis of a wide variety of pathologies, their resolving capabilities are insufficient to visualize some anomalies at subcellular level. Although a novel set of super-resolution optical microscopy techniques can fulfill the resolution demands in such cases, the system complexity, high operating cost, lack of multimodality, and low-throughput imaging of these methods limit their wide adoption in clinical settings. In this study, we interrogate the photonic chip as an attractive high-throughput super-resolution microscopy platform for histopathology. Using cryopreserved ultrathin tissue sections of human placenta, mouse kidney, and zebrafish eye retina prepared by the Tokuyasu method, we validate the photonic chip as a multi-modal imaging tool for histo-anatomical analysis. We demonstrate that photonic-chip platform can deliver multi-modal imaging capabilities such as total internal reflection fluorescence microscopy, intensity fluctuation-based optical nanoscopy, single-molecule localization microscopy, and correlative light-electron microscopy. Our results demonstrate that the photonic chip-based super-resolution microscopy platform has the potential to deliver high-throughput multimodal histopathological analysis of cryopreserved tissue samples.

On-chip super-resolution optical microscopy is an emerging field relying on waveguide excitation with visible light. Here, we investigate two commonly used high-refractive index waveguide platforms, tantalum pentoxide (Ta2O5) and silicon nitride (Si3N4), with respect to their background with excitation in the range 488–640 nm. The background strength from these waveguides were estimated by imaging fluorescent beads. The spectral dependence of the background from these waveguide platforms was also measured. For 640 nm wavelength excitation both the materials had a weak background, but the background increases progressively for shorter wavelengths for Si3N4. We further explored the effect of the waveguide background on localization precision of single molecule localization for direct stochastic optical reconstruction microscopy (dSTORM). An increase in background for Si3N4 at 488 nm is shown to reduce the localization precision and thus the resolution of the reconstructed images. The localization precision at 640nm was very similar for both the materials. Thus, for shorter wavelength applications Ta2O5 is preferable. Reducing the background from Si3N4 at shorter wavelengths via improved fabrication will be worth pursuing.

Examining specific sub-cellular structures while minimizing cell perturbation is important in the life sciences. Fluorescence labeling and imaging is widely used. With the advancement of deep learning, digital staining routines for label-free analysis have emerged to replace fluorescence imaging. Nonetheless, digital staining of sub-cellular structures such as mitochondria is sub-optimal. This is because the models designed for computer vision are directly applied instead of optimizing them for microscopy data. We propose a new loss function with multiple thresholding steps to promote more effective learning for microscopy data. We demonstrate a deep learning approach to translate the labelfree brightfield images of living cells into equivalent fluorescence images of mitochondria with an average structural similarity of 0.77, thus surpassing the state-of-the-art of 0.7 with L1. We provide insightful examples of unique opportunities by data-driven deep learning-enabled image translations.

Contrast in fluorescence microscopy images allows for the differentiation between different structures by their difference in intensities. However, factors such as point-spread function and noise may reduce it, affecting its interpretability. We identified that fluctuation of emitters in a stack of images can be exploited to achieve increased contrast when compared to the average and Richardson-Lucy deconvolution. We tested our methods on four increasingly challenging samples including tissue, in which case results were comparable to the ones obtained by structured illumination microscopy in terms of contrast.

Mitochondria are essential energy-providing organelles of particular importance in energy-demanding tissue such as the heart. The production of mitochondria-derived vesicles (MDVs) is a cellular mechanism by which cells ensure a healthy pool of mitochondria. These vesicles are small and fast-moving objects not easily captured by imaging. In this work, we have tested the ability of the optical super-resolution technique 3DSIM to capture high-resolution images of MDVs. We optimized the imaging conditions both for high-speed video microscopy and fixed-cell imaging and analysis. From the 3DSIM videos, we observed an abundance of MDVs and many dynamic mitochondrial tubules. The density of MDVs in cells was compared for cells under normal growth conditions and cells during metabolic perturbation. Our results indicate a higher abundance of MDVs in H9c2 cells during glucose deprivation compared to cells under normal growth conditions. Furthermore, the results reveal a large untapped potential of 3DSIM in MDV research. This article is protected by copyright. All rights reserved.

Quantifying small, rapidly evolving forces generated by cells is a major challenge for the understanding of biomechanics and mechanobiology in health and disease. Traction force microscopy remains one of the most broadly applied force probing technologies but typically restricts itself to slow events over seconds and micron-scale displacements. Here, we improve >2-fold spatially and >10-fold temporally the resolution of planar cellular force probing compared to its related conventional modalities by combining fast two-dimensional total internal reflection fluorescence super-resolution structured illumination microscopy and traction force microscopy. This live-cell 2D TIRF-SIM-TFM methodology offers a combination of spatio-temporal resolution enhancement relevant to forces on the nano- and sub-second scales, opening up new aspects of mechanobiology to analysis., Quantifying rapid and small cellular forces is a major challenge in mechanobiology. Here, the authors show a >2-fold spatially and >10-fold temporally force sampling improvement combining traction force microscopy with total internal reflection fluorescence super-resolution structured illumination microscopy.

We propose the photonic-chip as a multimodal imaging platform for histopathological assessment, allowing large fields-of-view across diverse microscopy methods including total internal reflection fluorescence and single-molecule localization.

Fibrosis is an extracellular matrix disease affecting several vital organs' functions and can lead to life-threatening pathologies like cancer. The standard diagnostic protocol involves an immuno-histochemical examination of the fibrosis-linked protein's distribution in the tissues. Of specific interest are the primarily affected dense matrix-proteins like collagen. But labeling such dense proteins is prone to subjectivity. Besides distribution, the collagen nano-fibril characteristics, usually revealed by ultra-structural imaging, also have diagnostic relevance. Super-resolution microscopy can aid high-resolution clinical decisions by imaging protein nano-structures but is challenging for dense proteins. Here we exploit the natural fluctuations of tissue autofluorescence (tAF) signal from proteins in dense tissue matrix for super-resolving them. We achieved super-resolution over a clinically relevant large area using a simple, low-cost, low numerical aperture (NA) optical microscope and a recent fluctuation-based nanoscopy technique MUSICAL. We could quantify protein distribution and nano-fibril dimensions as low as 43 nm in human oral mucosa with pre-cancer fibrosis and mouse skin samples to assess disease progression. Thus tAF-MUSICAL enables early, label-free, and high-resolution diagnosis of matrix-associated diseases like fibrosis.

Photonic-chip based TIRF illumination has been used to demonstrate several on-chip optical nanoscopy methods. The sample is illuminated by the evanescent field generated by the electromagnetic wave modes guided inside the optical waveguide. In addition to the photokinetics of the fluorophores, the waveguide modes can be further exploited for introducing controlled intensity fluctuations for exploitation by techniques such as super-resolution optical fluctuation imaging (SOFI). However, the problem of non-uniform illumination pattern generated by the modes contribute to artifacts in the reconstructed image. To alleviate this problem, we propose to perform Haar wavelet kernel (HAWK) analysis on the original image stack prior to the application of (SOFI). HAWK produces a computational image stack with higher spatio-temporal sparsity than the original stack. In the case of multimoded non-uniform illumination patterns, HAWK processing breaks the mode pattern while introducing spatio-temporal sparsity, thereby differentially affecting the non-uniformity of the illumination. Consequently, this assists nanoscopy methods such as SOFI to better support super-resolution, which is otherwise compromised due to spatial correlation of the mode patterns in the raw image. Furthermore, applying HAWK prior to SOFI alleviates the problem of artifacts due to non-uniform illumination without degrading temporal resolution. Our experimental results demonstrate resolution enhancement as well as reduction in artifacts through the combination of HAWK and SOFI.