Peter T. C. So

Three-dimensional metastructures with nanoscale feature sizes exhibit unique properties compared with structures with larger feature sizes, but are difficult to fabricate. Here we introduce implosion carving (ImpCarv), a method for photopatterning vacancies of complex geometry throughout materials, followed by isotropic shrinkage (>10-fold). ImpCarv works by photoactivating sensitizers to generate reactive oxygen species that cleave a swollen hydrogel at defined points, followed by controlled shrinkage via dehydration. ImpCarv creates three-dimensional metastructures where the refractive index of each point throughout a material can be specified with nanoscale precision via material presence or absence. By leveraging refractive index programmability for precise phase control, we demonstrate an all-optical machine learning device with nanoscale neuron sizes operating at visible wavelengths. ImpCarv may thus support diverse applications in nanophotonics and nanotechnology.

This paper presents a novel framework for cooperative robotics competitions (coopetitions) that promote the transferability and composability of robotics modules, including software, hardware, and data, across heterogeneous robotic systems. The framework is designed to incentivize collaboration between teams through structured task design, shared infrastructure, and a royalty-based scoring system. As a case study, the paper details the implementation and outcomes of the first euROBIN Coopetition, held under the European Robotics and AI Network (euROBIN), which featured fifteen robotic platforms competing across Industrial, Service, and Outdoor domains. The study highlights the practical challenges of achieving module reuse in real-world scenarios, particularly in terms of integration complexity and system compatibility. It also examines participant performance, integration behavior, and team feedback to assess the effectiveness of the framework. The paper concludes with lessons learned and recommendations for future coopetitions, including improveme

This paper presents a novel framework for cooperative robotics competitions (coopetitions) that promote the transferability and composability of robotics modules, including software, hardware, and data, across heterogeneous robotic systems. The framework is designed to incentivize collaboration between teams through structured task design, shared infrastructure, and a royalty-based scoring system. As a case study, the paper details the implementation and outcomes of the first euROBIN Coopetition, held under the European Robotics and AI Network (euROBIN), which featured fifteen robotic platforms competing across Industrial, Service, and Outdoor domains. The study highlights the practical challenges of achieving module reuse in real-world scenarios, particularly in terms of integration complexity and system compatibility. It also examines participant performance, integration behavior, and team feedback to assess the effectiveness of the framework. The paper concludes with lessons learned and recommendations for future coopetitions, including improveme

Aging induces physical changes in organisms, many of which are at the cellular level, but the mechanisms underlying these changes are poorly understood. While the cytoplasm provides a crucial physical environment to host essential cellular processes, how its properties change in aging remains largely unknown. Here, using cells from well-established aging mice models, we first investigate the morphological and dynamic changes of aging cells and how they relate to the physical state of the cytoplasm. We find that aged cells spread larger and rounder and migrate slower than young cells. Using particle fluctuation, optical tweezers, and force spectrum microscopy, we demonstrate that aging increases cytoplasmic stiffness and reduces intracellular movement, even while active intracellular forces increase. In addition, using tomographic phase microscopy, we observe a higher refractive index in aged cells which indicates a denser cytoplasm, hinting that aging causes a more crowded cell interior. This crowding behavior underlines the increased cytoplasmic stiffness and the decreased intracellular movement, thereby influencing the altered cell behavior. Our results imply a crucial physical mechanism behind cellular-level changes due to aging. Though mechanisms behind these observations remain unclear, this understanding of cells' physical nature may support fundamental biological functions explored in aging research.

As robots are deployed into new domains, teleoperation with a human-in-the-loop remains an important method for training new skills. Direct control of a remote robot over a network demands high concentration of the human teleoperator and remains challenging due to unavoidable network delays and congestion that can degrade performance. In this work, we demonstrate a shared control solution using well established virtual fixtures (VFs) as a teleoperator assistance tool and introduce a novel VF generation pipeline leveraging mate constraints in available CAD data of the manipulated objects. This approach simplifies the definition of VFs by taking parameters from relationships already defined within the CAD data. Using an industry-sponsored task board and a bilateral leader-follower hand-guided robot scenario, we demonstrate how a set of VFs can be constructed and activated in a series to support teleoperators with a multi-step manipulation task including the pressing of buttons, peg-in-hole with the picking and inserting of a Multimeter Probe Plug, and a novel VF task of opening of a hinged door. We present data from a pilot user study with eight teleoperators and 67 trial attempts with two scenarios (with and without VFs enabled) across three test conditions of round-trip network delays of 0 ms, 100 ms, and 250 ms. We found teleoperators had an increased task success rate, lowered the total travel distance, and overall reduced task execution time, in the best case of 0 ms delay by 26 seconds, or 21%, when VFs were enabled versus when they were not. Performance was maintained or improved at higher network delays.

Optical processors, built with "optical neurons," can perform large-scale high-dimensional linear operations at the speed of light. With the current advances in micro-fabrication, such optical processors can now be 3D-fabricated, but at limited precision, eventually leading to a model mismatch due to quantized optical weights. To address this issue, we propose a quantization-aware training framework. Our approach accounts for physical constraints during the training process, leading to robust designs. We numerically demonstrate that our approach can design state-of-the-art optical processors using diffractive networks for multiple tasks despite quantized learnable parameters. We thus lay the foundation upon which improved optical processors may be 3D-fabricated in the future.

High Resolution Image Download MS PowerPoint Slide Noninvasive blood glucose monitoring with precision comparable to standard invasive or minimally invasive methods has been a long-sought goal, especially as diabetes rates soar, with 592 million cases worldwide expected by 2035. Various optical and spectroscopic technologies have challenged noninvasive continuous glucose monitoring (CGM), but most methods fail to detect physiological levels or lack miniaturization for practical use. Based on our previous success in direct observation of glucose signals from in vivo skin, we developed a band-pass Raman spectroscopy method that enables noninvasive, physiological-level CGM in a compact device. Using off-axis 830 nm near-infrared illumination and intraspectrum reference, we eliminate most elastically scattered photons, revealing the glucose Raman signal through an amplified photodetector, while compensating for background variations. Our approach, validated on both tissue phantoms and in vivo human skin, overcomes bulky spectrometers and makes portable Raman-based CGM devices a reality.

Cellular senescence is a complex biological process that plays a pathophysiological role in aging and age-related diseases. The biological understanding of senescence at the cellular and tissue levels remains incomplete due to the lack of specific biomarkers as well as the relative rarity of senescent cells, their phenotypic heterogeneity and dynamic features. This Review provides a comprehensive overview of multiomic approaches for the characterization and biological understanding of cellular senescence. The technical capability and challenges of each approach are discussed, and practical guidelines are provided for selecting tools for identifying, characterizing and spatially mapping senescent cells. The importance of computational analyses in multiomics research, including senescent cell identification, signature detection and interactions of senescent cells with microenvironments, is highlighted. Moreover, tissue-specific case studies and experimental design considerations for individual organs are presented. Finally, future directions and the potential impact of multiomic approaches on the biological understanding of cellular senescence are discussed. This Review discusses multiomic approaches for the characterization and biological understanding of cellular senescence, including detailed case studies on skeletal muscle and adipose tissue that highlight current outstanding issues in the field.

, which uniquely characterize a wide range of molecular bonds and subcellular structures), as well as morphological data from three-dimensional refractive index tomograms (providing measurements of cell volume, surface area, footprint, and sphericity at nanometer resolution, alongside dry mass and density). By systematically breaking down the rich single-cell details that RS and TPM deliver, we demonstrate, in a quantitative manner, the advantage of such a multimodal microscopy method for phenotyping tumoral breast cancer cells. Our tools also provide further insight into the subcellular information without the use of any labels. Finally, we study and discuss any unique or correlated information that RS- and TPM-derived datasets feature. We believe our tools and quantitative data analysis pipelines can revolutionize phenotyping tasks in biomedical research when in need of a rapid and non-perturbative method on living cells in culture, with the potential for future translation into clinical and diagnostic applications.

Label-free detection of biological events at single-cell resolution in the brain can non-invasively capture brain status for medical diagnosis and basic neuroscience research. NADH is an universal coenzyme that not only plays a central role in cellular metabolism but may also be used as a biomarker to capture metabolic processes in brain cells and structures. We have developed a new label-free, multiphoton photoacoustic microscope (LF-MP-PAM) with a near-infrared femtosecond laser to observe endogenous NAD(P)H in living cells. The imaging depth of NAD(P)H in tissues with all-optical methods is limited to ~100 μm in brain tissue by the strong absorption of the near-ultraviolet fluorescence. Here, acoustic detection of the thermal signature of multi-photon (three-photon) excitation of NAD(P)H, a low quantum yield fluorophore, allows detection at an unprecedented depth while the focused excitation ensures high spatial resolution. We validated the photoacoustic detection of NAD(P)H by monitoring an increase in intracellular NAD(P)H in HEK293T cells and HepG2 cells incubated in NADH solution. We also demonstrated the detection of endogenous NAD(P)H photoacoustic signals in brain slices to 700 μm depth and in cerebral organoids to 1100 μm depth. Finally, we developed and demonstrated simultaneous photoacoustic and optical imaging of NAD(P)H in brain cells with a real-time image acquisition and processing pipeline. This approach could open a new door to monitor brain metabolic changes during development and disease, and changes due to neuronal activity, at single-cell level deep in the brains of both humans and animals.

Introduction and Objective: MIT Laser Biomedical Research Center has been developing noninvasive glucose monitoring sensor for the past decades. Based on Raman spectroscopy, we have recently demonstrated the first direct observation of glucose signal from in-vivo skin. Here, we compare its performance with commercially available methods using swine glucose clamping experiments. Methods: One Female Yorkshire swine was selected and used for the three glucose clamping experiments with two-week interval. The blood glucose level was modulated within the range from 75mg/dL to 350 mg/dL. Raman-based noninvasive glucose monitoring sensor was installed to the ear and Dexcom G6 were installed to the belly. YSI and Accuchek measurements were performed on IV blood draw samples. All four measurements were performed every five minutes. Results: Raman-based noninvasive glucose monitoring sensor demonstrated successful prospective prediction with no laser-induced damages to the skin. MARD from Raman-based sensor was 8.8% in comparison with 9.0% from Dexcom G6 and 5.0% from Accuchek. The Clarke Error Grid Analysis confirms the high correlation with the YSI measurement with 96.6% (144/149) in Zone A and all remaining measurements in Zone B. Conclusion: After decades of research, Raman-based noninvasive glucose monitoring may finally become a viable long-term solution for diabetic patients. Disclosure J. Kang: None. H. Kim: None. H. Yoon: None. P.T.C. So: Research Support; Apollon. Bio. Funding National Institutes of Health (NIBIB P41EB015871), Samsung Advanced Institute of Technology, Apollon

This second workshop on noninvasive glucose monitoring was held at the Massachusetts Institute of Technology (MIT) on November 5, 2025 (https://sites.mit.edu/nigm-workshop). Eleven invited speakers, representing industry, academia, clinical practice, and regulatory affairs, gave presentations that covered (1) an overview of the noninvasive glucose monitoring technologies; (2) the state of the art in noninvasive glucose monitoring technologies, such as Near-Infrared (NIR), photoacoustic, photothermal, and Raman spectroscopies; (3) a clinician's perspective on the impact of the current continuous glucose monitoring devices for patient care; and (4) regulatory considerations. Four posters were also presented by junior researchers in the field.

High-throughput 3D imaging of biological structures and their dynamics has become increasingly important for biological applications including immune response to infection, cancer growth, and cellular metabolism. Recent computational imaging modalities coupling novel acquisition strategies with advanced computation techniques have successfully captured such dynamics, but their use of intensive computation can incur multi-day or weeklong reconstruction timescales preventing biologists from directly analyzing their sample’s behavior. Here, we developed differential structured illumination microscopy (dSIM) for capturing 3D biological sample dynamics at near-isotropic 1μm 3D resolution over a 1mm2 field-of-view at 10Hz volume rates. This modality applies plane waves and grating-based illuminations in a differential scheme to encode high-angle, traditionally nonlinear “darkfield” illuminations into linear intensity measurements for efficient 3D object reconstruction with linear inverse scattering models. Capturing this darkfield information enables 4.5X better lateral resolution over the coherent imaging bandwidth. We illustrate this technique on cell cultures and thin tissue slices.

Single-cell RNA-seq and other profiling assays have provided unprecedented insight into cellular properties, regulation, dynamics, and function. However, because these assays are inherently destructive, they do not allow us to track temporal changes in living cells. To overcome this limitation, we previously developed Raman2RNA (R2R), an experimental and computational framework that employs Raman microscopy and machine learning to infer single-cell expression profiles in live cells. Although this method works well in cell culture, however, the intrinsically weak Raman signal made it challenging to apply to tissue-scale imaging. To this end, we introduce total internal reflection Raman microscopy (TIRR). TIRR simultaneously captures Raman photons along a single line focus without requiring high-power lasers, while still delivering sufficient energy per illumination volume. We demonstrate its advantages across various cell types and tissues, showing that TIRR offers a marked speed improvement over confocal or line-illuminated Raman microscopes. This enhanced, “high-speed Raman2RNA” approach opens the door to broad applications for studying expression dynamics at scale, in vitro and in vivo.

Sickle cell disease (SCD) is a widespread genetic disorder where afflicted patients experience painful vaso-occlusive crisis events and significantly increased morbidity due to the presence of malformed hemoglobin in the patient. Sickle red blood cells (sRBCs) containing this hemoglobin exhibit unique morphological, biophysical, and mechanical properties that could relate to the disease’s clinical outcomes, but specific connections between these micro-scale cellular changes and macro-scale events are still elusive. To bridge this gap, we have developed interferometric phase and amplitude microscopy (iPAM), a high-throughput diffraction phase microscope coupled with efficient linear computational models, that extracts single-cell morphological and biophysical information from snapshot sRBC images. Across 67 patients, we show iPAM’s unique recovery of morphological and morphometric sRBC metrics at single-cell resolution matches clinically recovered global cellular volume and hemoglobin information and correlates well in predicting the patient’s hemoglobin chemical composition just from the sRBC morphological information.

2D angular scattering data can be inverted to produce useful estimates of organelle size distribution. Scattering theory models for the inversion, however, typically include simplifying assumptions. Using 3D optical diffraction tomography (ODT), these simplifications can be explored one at a time to quantify increasing discrepancy with the experimental angular scattering values. We find that the strongest discrepancies are due to cell/medium refractive-index mismatch and to cytosol heterogeneity. This talk will offer suggestions for mitigating artifacts in the ODT reconstruction. It will also discuss whether 3D datasets—–that have knowledge of individual organelle shape and location—–can provide guidance for how to obtain ensemble averages from 2D datasets, which are more convenient to obtain experimentally.

Human cerebral organoids have become valuable tools in neurodevelopment research, holding promise for investigating neurological diseases and reducing drug development costs. However, clinical translation and large-scale production of brain organoids face challenges due to invasive methodologies such as immunohistochemistry and omics that are traditionally used for their investigation. These hinder real-time monitoring of organoids and highlight the need for a nondestructive approach to promote resource-efficient production and standardization and enable dynamic studies for drug testing and developmental monitoring. Here, we propose a label-free methodology utilizing Raman spectroscopy (RS) and machine learning to discern cortical organoid maturation stages and to observe their biochemical variations. We validated the method's robustness by analyzing both pluripotent stem cell-derived organoids and embryonic stem cell-derived organoids, revealing also significant biochemical variability between the two. This finding paves the way for the use of RS for longitudinal studies to observe dynamic changes in brain organoids, offering a promising tool for advancing our understanding of brain development and accelerating drug discovery.

Quantitative phase imaging (QPI), propelled by advancements in digital holography and computational imaging, has revolutionized the ability to retrieve phase delays with high precision. Over the past two decades, the field has seen tremendous growth, contributing to numerous applications in biomedicine and material metrology, including live cell monitoring, material structure profiling, and defect inspection, among others. This special issue commemorates Prof. Gabriel "Gabi" Popescu, a former faculty member at the University of Illinois at Urbana-Champaign and a pioneer in QPI and label-free biological imaging, who passed away on June 16, 2022, in his hometown of Prundu, Romania. The issue honors Gabi's legacy with a collection of articles exploring QPI methodologies and their diverse applications.

Established methods for imaging the living mammalian brain have, to date, taken optical properties of the tissue as fixed; we here demonstrate that it is possible to modify the optical properties of the brain itself to significantly enhance at-depth imaging while preserving native physiology. Using a small amount of any of several biocompatible materials to raise the refractive index of solutions superfusing the brain prior to imaging, we could increase several-fold the signals from the deepest cells normally visible and, under both one-photon and two-photon imaging, visualize cells previously too dim to see. The enhancement was observed for both anatomical and functional fluorescent reporters across a broad range of emission wavelengths. Importantly, visual tuning properties of cortical neurons in awake mice, and electrophysiological properties of neurons assessed ex vivo, were not altered by this procedure.

High Resolution Image Download MS PowerPoint Slide Optical fiber probe-based Raman spectroscopy systems are widely used for in situ measurements ranging from material characterization to biomedical applications. However, small Raman cross sections necessitate the use of high-power lasers or long exposure times that limit Raman’s larger application to multiple research fields. This limitation can be overcome by collecting more Raman photons through additional collection fibers with taller detectors. This system configuration requires replacement of the detector and modification of the spectrograph to incorporate larger optical components, making it a costly and cumbersome option. In probe-based Raman systems, a typical detector image shows stacked collection fibers on the vertical axis and Raman spectra on the horizontal axis. While the vertical pixels are fully packed with multiple collection fibers, horizontal pixels have broad silent regions due to the narrow bandwidth of Raman peaks, potentially wasting valuable detector pixels. Here, we propose a new approach utilizing horizontally shifted collection fibers rather than vertically stacked ones. We designed and fabricated a novel collection fiber bundle that has horizontally shifted optical fibers in two vertical lines at the spectrograph entrance. This custom-made fiber bundle was incorporated into the imaging spectrograph to provide multiple horizontally shifted spectra on the detector. Through deconvolution, the original spectra can be recovered with an improved detection limit from greater photon collection. We demonstrate an enhanced limit of detection on various bioanalytes, such as glucose, urea, and lactate. Further, we applied the probe to measure tissue Raman spectra and successfully decomposed them into basis spectra, demonstrating the potential application of high-throughput in vivo tissue diagnosis. Our approach provides a simple, cost-effective, and universal method to increase the throughput without modifying existing Raman spectrometers.

Accurate, rapid and non-invasive cancer cell phenotyping is a pressing concern across the life sciences, as standard immuno-chemical imaging and omics require extended sample manipulation. Here we combine Raman micro-spectroscopy and phase tomography to achieve label-free morpho-molecular profiling of human colon cancer cells, following the adenoma, carcinoma, and metastasis disease progression, in living and unperturbed conditions. We describe how to decode and interpret quantitative chemical and co-registered morphological cell traits from Raman fingerprint spectra and refractive index tomograms. Our multimodal imaging strategy rapidly distinguishes cancer phenotypes, limiting observations to a low number of pristine cells in culture. This synergistic dataset allows us to study independent or correlated information in spectral and tomographic maps, and how it benefits cell type inference. This method is a valuable asset in biomedical research, particularly when biological material is in short supply, and it holds the potential for non-invasive monitoring of cancer progression in living organisms.

During the development of butterfly wing scales, ordered periodic cell membrane modulations occur at the upper surface of scale-forming cells, priming the formation of ridges. Ridges are critical for wing scale functionality, including structural color, wetting characteristics, and thermal performance. Here, we combine a morphoelastic model based on Föppl-von-Kármán plate theory with experimental observations to shed light on the biomechanical processes governing early-stage ridge formation in Painted Lady butterflies. By comparing the model predictions with time-resolved phase imaging data from live butterflies, we provide evidence that the onset of ridge formation is governed by a mechanical buckling transition induced by the interplay of membrane growth and confinement through association with regularly spaced actin bundles. Beyond ridge formation in Painted Lady scales, our theory offers a rationale for the absence of scale ridges in the lower lamina of many lepidopterans and for the alternating ridge pattern of other butterfly species.

This repository contains the raw data for: JF Totz, AD McDougal, L Wagner, S Kang, PTC So, J Dunkel, BD Wilts, and M Kolle, Cell membrane buckling governs early-stage ridge formation in butterfly wing scales, (forthcoming). The raw data is of a volumetric time series of scales growing on the wing of an individual Vanessa cardui pupa, collected with quantitative phase imaging. Additional details may be found in the Materials and Methods, as well as the SI, of the above publication. The companion code repository may be found on Zenodo: JF Totz, AD McDougal, L Wagner, S Kang, PTC So, J Dunkel, BD Wilts, and M Kolle. (Forthcoming). "Cell membrane buckling governs early-stage ridge formation in butterfly wing scales:code" (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8369163 Note that file A-40-01_11_04_34_set_115.mat was previously released in: AD McDougal, S Kang, Z Yaqoob, PTC So, and M Kolle, Data and analysis codes for “In vivo visualization of butterfly scale cell morphogenesis in Vanessa cardui.” Zenodo. https://doi.org/10.5281/zenodo.5532941. We include it here for completeness of this time series.

This repository contains the raw data for: JF Totz, AD McDougal, L Wagner, S Kang, PTC So, J Dunkel, BD Wilts, and M Kolle, Cell membrane buckling governs early-stage ridge formation in butterfly wing scales, (forthcoming). The raw data is of a volumetric time series of scales growing on the wing of an individual Vanessa cardui pupa, collected with quantitative phase imaging. Additional details may be found in the Materials and Methods, as well as the SI, of the above publication. The companion code repository may be found on Zenodo: JF Totz, AD McDougal, L Wagner, S Kang, PTC So, J Dunkel, BD Wilts, and M Kolle. (Forthcoming). "Cell membrane buckling governs early-stage ridge formation in butterfly wing scales:code" (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8369163 Note that file A-40-01_11_04_34_set_115.mat was previously released in: AD McDougal, S Kang, Z Yaqoob, PTC So, and M Kolle, Data and analysis codes for “In vivo visualization of butterfly scale cell morphogenesis in Vanessa cardui.” Zenodo. https://doi.org/10.5281/zenodo.5532941. We include it here for completeness of this time series.

Systems that can image in three dimensions at cellular resolution and across different locations within an organism may enable insights into complex biological processes, such as immune responses, for which a single location measurement may be insufficient. In this Letter, we describe an in vivo two-site imaging probe (TIP) that can simultaneously image two anatomic sites with a maximum separation of a few centimeters. The TIP consists of two identical bendable graded index (GRIN) lenses and is demonstrated by a two-photon two-color fluorescence imaging system. Each GRIN lens has a field of view of 162 × 162 × 170 µm 3 , a nominal numerical aperture of 0.5, a magnification of 0.7, and working distances of 0.2 mm in air for both ends. A blind linear unmixing algorithm is applied to suppress bleedthrough between channels. We use this system to successfully demonstrate two-site two-photon two-color imaging of two biomedically relevant samples, i.e., (1) a mixture of two autofluorescent anti-cancer drugs and (2) a live hybrid tumor consisting of two spectrally distinct fluorescent cell lines.

Temporal focusing two-photon microscopy has been utilized for high-resolution imaging of neuronal and synaptic structures across volumes spanning hundreds of microns in vivo. However, a limitation of temporal focusing is the rapid degradation of the signal-to-background ratio and resolution with increasing imaging depth. This degradation is due to scattered emission photons being widely distributed, resulting in a strong background. To overcome this challenge, we have developed multiline orthogonal scanning temporal focusing (mosTF) microscopy. mosTF captures a sequence of images at each scan location of the excitation line. A reconstruction algorithm then reassigns scattered photons back to their correct scan positions. We demonstrate the effectiveness of mosTF by acquiring neuronal images of mice in vivo. Our results show remarkable improvements in in vivo brain imaging with mosTF, while maintaining its speed advantage.

SignificanceRaman spectroscopy has been used as a powerful tool for chemical analysis, enabling the noninvasive acquisition of molecular fingerprints from various samples. Raman spectroscopy has proven to be valuable in numerous fields, including pharmaceutical, materials science, and biomedicine. Active research and development efforts are currently underway to bring this analytical instrument into the field, enabling in situ Raman measurements for a wider range of applications. Dispersive Raman spectroscopy using a fixed, narrowband source is a common method for acquiring Raman spectra. However, dispersive Raman spectroscopy requires a bulky spectrometer, which limits its field applicability. Therefore, there has been a tremendous need to develop a portable and sensitive Raman system.AimWe developed a compact swept-source Raman (SS-Raman) spectroscopy system and proposed a signal processing method to mitigate hardware limitations. We demonstrated the capabilities of the SS-Raman spectroscopy by acquiring Raman spectra from both chemical and biological samples. These spectra were then compared with Raman spectra obtained using a conventional dispersive Raman spectroscopy system.ApproachThe SS-Raman spectroscopy system used a wavelength-swept source laser (822 to 842 nm), a bandpass filter with a bandwidth of 1.5 nm, and a low-noise silicon photoreceiver. Raman spectra were acquired from various chemical samples, including phenylalanine, hydroxyapatite, glucose, and acetaminophen. A comparative analysis with the conventional dispersive Raman spectroscopy was conducted by calculating the correlation coefficients between the spectra from the SS-Raman spectroscopy and those from the conventional system. Furthermore, Raman mapping was obtained from cross-sections of swine tissue, demonstrating the applicability of the SS-Raman spectroscopy in biological samples.ResultsWe developed a compact SS-Raman system and validated its performance by acquiring Raman spectra from both chemical and biological materials. Our straightforward signal processing method enhanced the quality of the Raman spectra without incurring high costs. Raman spectra in the range of 900 to 1200 cm−1 were observed for phenylalanine, hydroxyapatite, glucose, and acetaminophen. The results were validated with correlation coefficients of 0.88, 0.84, 0.87, and 0.73, respectively, compared with those obtained from dispersive Raman spectroscopy. Furthermore, we performed scans across the cross-section of swine tissue to generate a biological tissue mapping plot, providing information about the composition of swine tissue.ConclusionsWe demonstrate the capabilities of the proposed compact SS-Raman spectroscopy system by obtaining Raman spectra of chemical and biological materials, utilizing straightforward signal processing. We anticipate that the SS-Raman spectroscopy will be utilized in various fields, including biomedical and chemical applications.

Optical processors, built with “optical neurons”, can efficiently perform high-dimensional linear operations at the speed of light. Thus they are a promising avenue to accelerate large-scale linear computations. With the current advances in micro-fabrication, such optical processors can now be 3D fabricated, but with a limited precision. This limitation translates to quantization of learnable parameters in optical neurons, and should be handled during the design of the optical processor in order to avoid a model mismatch. Specifically, optical neurons should be trained or designed within the physical-constraints at a predefined quantized precision level. To address this critical issues we propose a physics-informed quantization-aware training framework. Our approach accounts for physical constraints during the training process, leading to robust designs. We demonstrate that our approach can design state of the art optical processors using diffractive networks for multiple physics based tasks despite quantized learnable parameters. We thus lay the foundation upon which improved optical processors may be 3D fabricated in the future.

We developed differential structured illumination microscopy (dSIM) for efficiently imaging and reconstructing 3D biological samples at high-resolution over a large field-of-view. Using plane wave and grating-based structured illumination pairs in a differential illumination scheme, dSIM encodes scattering information from high-angle, traditionally nonlinear "darkfield" illuminations into linear intensity measurements enabling efficient 3D object reconstruction with linear inverse scattering models. This illumination scheme exceeds the 2X resolution limit enhancement of traditional phase-based SIM techniques. We reconstruct 3D objects with 4.5X better resolution than the coherent imaging bandwidth while maintaining an almost 1mm2 field-of-view. We illustrate this technique in simulation and experimentally on cell cultures and other living biological specimens.

We present a novel approach to achieve high-speed depth-resolved two-photon imaging through the development of a deep-learning-based temporal-focusing two-photon microscope utilizing the De-scattering with Excitation Patterning (DEEP) method, referred to as DEEP-Line. DEEP-Line incorporates a line-scanning scheme, widefield detection utilizing a high-speed Silicon Photomultiplier array, and employs deep-learning-based image reconstruction. The performance of our system is validated using diverse biological samples. Our imaging method achieves orders of magnitude improvement in speed by reducing excitation patterns to several tens and employing MHz parallel detections. Furthermore, our approach can enable fluorescence lifetime imaging and enhances axial resolution.

We present label-free morpho-chemical cancer cell phenotyping by combining confocal Raman micro-spectroscopy and three-dimensional holotomography. Observing colon cancer cell types with different progression stages from adenoma to metastasis, we demonstrate the advantage of a multimodal approach for rapid and accurate cancer cell phenotyping. We introduce data processing pipelines to decode and comprehensively interpret molecular and structural information from hyperspectral Raman images and co-registered refractive index tomograms. Finally, we investigate and discuss any unique or shared information that combined Raman spectroscopy and holotomography can provide when characterizing the same sample, and how this synergy advances cell type differentiation.

Brain organoids offer immense potential for studying brain development, neurological disorders, and evaluating drug responses. However, their exploitation is hindered by several challenges, including the lack of a label-free and non-destructive monitoring technique for studying their development and response to the external perturbations. To address this, we propose a non-invasive Raman Spectroscopy approach, enabling label-free discrimination of maturation stages without disruption. Using a custom high-throughput multi-modal Raman microscope equipped with a 785 nm laser source, we collected Raman Spectroscopy (RS) data from cortical organoids at various developmental stages and employing machine learning techniques, we extracted crucial features linked to each stage. This label-free methodology facilitates observing dynamic changes in organoids without compromising their growth, enabling longitudinal studies for deeper insights into their development and drug responses.

At the present, the two clinical biomarkers used to monitor diabetic progression are blood glucose and HbA1c. However, advanced glycation end products (AGEs) have been shown to contribute to diabetic pathogenesis, and there is interest in the use of AGEs in tissues as long-term glycemic markers. In this study, we investigate the in vitro rate of fluorescent AGEs (fAGEs) formation with multiphoton microscopy in different porcine tissues (aorta, cornea, kidney, dermis, and tendon) from glucose, galactose, and fructose, three primary monosaccharides found in human diets. These results may be of value in developing long-term glycemic biomarkers for diabetes.

We demonstrate a single-shot full-field reflection-mode off-axis quantitative phase microscopy with temporal focusing. The new imaging system is capable of achieving diffraction-limited optical sectioning quantitative phase imaging without any spatial or angular multiplexing. It has high potential to achieve ultrafast imaging with camera-limited frame rate. We show that the QPM can measure the depth-dependent intracellular scattering dynamics of cells and reveals 3D structure of tissues with different phenotypes at sub-cellular resolution.

Single-cell RNA sequencing and other profiling assays have helped interrogate cells at unprecedented resolution and scale, but are inherently destructive. Raman microscopy reports on the vibrational energy levels of proteins and metabolites in a label-free and nondestructive manner at subcellular spatial resolution, but it lacks genetic and molecular interpretability. Here we present Raman2RNA (R2R), a method to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and domain translation. We predict single-cell RNA sequencing profiles nondestructively from Raman images using either anchor-based integration with single molecule fluorescence in situ hybridization, or anchor-free generation with adversarial autoencoders. R2R outperformed inference from brightfield images (cosine similarities: R2R >0.85 and brightfield <0.15). In reprogramming of mouse fibroblasts into induced pluripotent stem cells, R2R inferred the expression profiles of various cell states. With live-cell tracking of mouse embryonic stem cell differentiation, R2R traced the early emergence of lineage divergence and differentiation trajectories, overcoming discontinuities in expression space. R2R lays a foundation for future exploration of live genomic dynamics. Transcriptional profiles of single living cells are generated from Raman imaging data.

Angularly resolved light scattering (ALS) has become a useful tool for assessing the size and refractive index of biological scatterers at cellular and organelle length scales. Sizing organelle populations with ALS relies on Mie scattering theory models, which require significant assumptions about the object, including spherical scatterers and a homogeneous medium. These assumptions may incur greater error at the single cell level, where there are fewer scatterers to be averaged over. We investigate the validity of these assumptions using 3D refractive index (RI) tomograms measured via optical diffraction tomography (ODT). We compute the angular scattering on digitally manipulated tomograms with increasingly strong model assumptions, including RI-matched immersion media, homogeneous cytosol, and spherical organelles. We also compare the tomogram-computed angular scattering to experimental measurements of angular scattering from the same cells to ensure that the ODT-based approach accurately models angular scattering. We show that enforced RI-matching with the immersion medium and a homogeneous cytosol significantly affects the angular scattering intensity shape, suggesting that these assumptions can reduce the accuracy of size distribution estimates.