The industrial realization of 3D metastructures is currently hindered by the inability of existing manufacturing techniques to produce complex, arbitrary geometries with nanoscale feature sizes. We here present Implosion carving (ImpCarv), a nanofabrication platform that overcomes these limitations by enabling the precise photopatterning of 3D vacancies throughout a material volume, followed by isotropic shrinkage (>10-fold) to achieve subwavelength resolution. ImpCarv employs a hydrogel process where photopatterned sites are cleaved by reactive oxygen species generated by photosensitizers, allowing for the creation of intricate internal voids that are densified through controlled dehydration. This approach provides a unique capability to program the refractive index of a material with nanoscale precision in 3D, a degree of design freedom essential for next-generation optical components. To demonstrate the functional utility of this platform, we report the fabrication of the first all-optical machine learning device featuring nanoscale neuron sizes and operating at visible wavelengths. By enabling the production of highly complex, functional nanophotonic devices without reliance on extreme-UV or electron-beam lithography, ImpCarv offers a practical pathway for developing advanced optical processors and sensors for the broader nanotechnology industry.

Century-old histopathological imaging of hematoxylin and eosin (H&E)-stained biopsies remains the gold standard for many biomedical applications but suffers from delayed, single-time-point analysis. To address this, we developed the first bendable graded index (bGRIN) lens microendoscopy, which is compatible with interventional needles and catheters, for real-time, cellular resolution, and minimally invasive in-situ imaging. In this work, incorporating two-photon excitation and second-harmonic generation imaging, we demonstrate label-free histopathological imaging of human tissues, with correlation to conventional H& E-stained histology.

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.

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.

Background: Accurate and painless glucose monitoring remains a major challenge in diabetes management. To address this need, we developed a compact, portable, and non-invasive continuous glucose monitoring (CGM) device based on transdermal band-pass Raman (BPR) spectroscopy intended for benchtop point-of-care use. Methods: The BPR-CGM optically probes the molecular vibrations of glucose in the interstitial fluid targeting only three ultra-narrow Raman bands to extract glucose concentration trends while compensating for background variations through intra-spectrum referencing. By refraining from collecting redundant full-spectrum information as in standard Raman spectroscopy, our technology overcomes the need for bulky and expensive components while maintaining high sensitivity, making point-of-care non-invasive CGM a reality. Results: A six-participant clinical study compared the non-invasive BPR-CGM with a standard blood glucometer and two commercial needle-based CGMs during a standard oral glucose tolerance test, inducing dynamic postprandial glucose variations. Glucose trends inferred by the BPR-CGM closely matched reference blood glucose values without algorithmic lag compensation. Across participants, the device achieved a mean absolute relative difference of 11.34 ± 1.96%, statistically indistinguishable from needle-based CGM sensors performance (10.42 ± 2.46% and 12.93 ± 4.69%). All BPR-CGM readings fell within clinically acceptable zones A and B of the Parkes (consensus) error grid. No adverse skin reactions were observed following the trial. Conclusions: These results demonstrate that the BPR-CGM technology can achieve clinical-grade accuracy comparable to invasive systems, paving the way for wearable, needle-free, and miniaturized glucose monitoring solutions.

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.

Raman spectroscopy is attractive for noninvasive glucose monitoring due to its molecular specificity and potential for continuous measurement. Direct observation of glucose-specific Raman peaks in interstitial fluid has been demonstrated in vivo, but signal quality suffers from interference with strong, time-varying skin autofluorescence and shot noise. Stimulated Raman scattering (SRS), a coherent Raman scattering technique, is promising for this application in that it offers narrowband contrast, avoids non-resonant signal background, and can achieve a several-order-of-magnitude increase in signal strength over spontaneous Raman, therefore enabling faster acquisition times. We present a feasibility study of SRS glucose detection in a reflectance collection system using a tissue phantom. Preliminary investigations in high glucose concentration tissue phantoms show qualitative agreement with aqueous glucose standards acquired by spontaneous Raman scattering in the signature region and spectral differentiation from the tissue base solution. This work builds toward in vivo application of SRS technology in transdermal continuous glucose monitoring.

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.

Label-free morpho-molecular imaging circumvents the invasiveness of conventional omics while rapidly delivering quantitative phenotypes of live cells and three-dimensional cell aggregates. By integrating phase tomography with spontaneous and coherent Raman spectroscopy, we reveal and track elusive yet critical cell states in developmental and cancer biology.

Quantitative phase microscopy (QPM) enables label-free imaging of structure and dynamics in biological and physical systems, yet achieving high-speed three-dimensional (3D) QPM with strong optical sectioning remains a central challenge. Here, we introduce a single-shot reflection-mode temporal focusing QPM (TF-QPM) that provides sub-micron optical sectioning without needs of any mechanical scanning or multiplexed acquisitions. By extending temporal focusing beyond its conventional use in multiphoton fluorescence microscopy, TF-QPM enables diffraction-limited label-free phase-sensitive volumetric imaging with 402 nm lateral and 920 nm axial resolution, markedly reduced speckle noise, and depth-resolved imaging at 3,709 Hz frame rate -an order of magnitude faster than most existing techniques and currently only limited by the camera speed. The resulting spatiotemporal phase sensitivity enables precise 3D tracking of particle motion and quantitative characterization of fast dynamics in complex and anisotropic media. For tissue imaging applications, TF-QPM achieves histology-level resolution in intact samples and supports pixel-level virtual staining, providing a rapid, label-free alternative to conventional sectioning-based workflows. Together, these results establish TF-QPM as a scanless, high-speed platform for rapid, label-free volumetric imaging across both basic research and translational applications.

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.

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.

This first workshop on noninvasive glucose monitoring (NIGM) was held at the Massachusetts Institute of Technology (MIT) on October 30, 2024. Six invited speakers, representing industry, academia, and clinics, gave presentations that covered (1) an overview of the NIGM technologies, (2) the state of the art in NIGM technologies, such as near-infrared (NIR), mid-infrared (IR), photoacoustic, and Raman spectroscopies, (3) minimally invasive implantable continuous glucose monitoring (CGM) sensors, and (4) a clinician’s perspective on the impact of the current CGM devices for patient care.

Aging and tissue repair involve multilayered and spatially heterogeneous remodeling across transcriptional, biochemical, and cellular dimensions, yet prevailing definitions rely on isolated molecular markers that obscure how biochemical and transcriptional states co-evolve in tissues. Here we present RamanOmics, a multimodal framework that integrates single-nucleus RNA sequencing (snRNA-seq), spatial transcriptomics, and label-free Raman imaging to map the spatial vibrational-biochemical and molecular architecture of aging and senescence directly in intact tissues. Applied to mouse lung and skin, RamanOmics generates spatially resolved biochemical-molecular maps revealing tissue-specific programs: lung senescent cells are enriched for extracellular matrix (ECM) remodeling and TGF-β signaling (Serpine1, Dab2, Igfbp7), whereas skin senescence is dominated by keratinization and barrier homeostasis modules (Krt10, Lor, Sbsn). Across tissues, we identify a conserved branched-chain fatty-acid-linked biochemical profile and Raman signature (1131-1135 cm-1) that robustly marks p21 + senescent cells. To unify these layers, we develop a machine learning derived "multimodal barcode" that quantitatively integrates biochemical and transcriptional features, enabling non-destructive identification of senescence in situ. In a wound-healing model, RamanOmics further reveals coordinated reactivation of barrier-repair programs in senescent cells, marked by upregulation of Krt10, Lor, Sbsn, Sfn, and Dmkn together with matching increases in lipid-associated Raman signatures, confirming biological generalizability beyond steady-state aging. By directly integrating gene programs to spatial vibrational-biochemical states, RamanOmics provides a general framework and resource for scalable, multimodal profiling of cellular states.

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.