Quantitative oblique back-illumination microscopy (qOBM) has emerged as a powerful technique for label-free, 3D quantitative phase imaging of arbitrarily thick biological specimens. However, in its initial embodiment, qOBM requires multiple captures for phase recovery, which reduces imaging speed and increases system complexity. In this work, we present a novel advancement in qOBM: single-capture qOBM (SCqOBM) which utilizes a deep learning model to accurately reconstruct phase information from a single oblique back-illumination capture. We demonstrate that SCqOBM achieves remarkable phase imaging accuracy, closely matching the results of traditional four-capture qOBM in diverse biological samples. We first highlight the unique potential of SCqOBM for non-invasive, in-vivo imaging applications by visualizing blood flow in mouse brain and human arm. Additionally, we demonstrate single-slice (en-face) quantitative phase imaging at 2 kHz and volumetric refractive index tomography at speeds up to 10 volumes per second. SCqOBM offers transformative advantages in speed, simplicity, and system accessibility, making it highly suitable for dynamic and real-time imaging applications. Its ability to produce high-resolution, quantitative phase and refractive index images with minimal hardware complexity opens new frontiers in biomedical research and clinical diagnostics, including non-invasive hematological assessments and in-vivo tissue imaging.

Deep-UV microscopy enables high-resolution, label-free molecular imaging by leveraging biomolecular absorption properties in the UV spectrum. Recent advances in UV-imaging hardware have renewed interest in this technique for quantitative live cell imaging applications. However, UV-induced photodamage remains a concern for longitudinal dynamic imaging studies. Here, we quantify UV phototoxicity with several cell types at notable UV wavelengths. We find that the fluence required for cell death via UV phototoxicity with continuous UV exposure varies with cell type and wavelength from ∼0.5µJ/µm2 to 2µJ/µm2, but is independent of typical illumination power/radiant flux of UV microscopy (e.g., 0.1-20 nW/µm2). We also show results from fractionation studies that reveal cell repair following UV exposure, which increases the tolerance to UV radiation by a factor of 2 or more, depending on the fractionation paradigm. Results further show that UV tolerance exceeds ANSI guidelines for maximum permissible exposure. Finally, we calculate imaging limits for a typical application of UV microscopy, such as hematology analysis. Together, this work provides UV fluence thresholds that can serve as guidelines for nondestructive, longitudinal, and dynamic deep-UV microscopy experiments.

Characterization of T-cell phenotypes, including their activation state and subtype, is critical for monitoring physiological immune responses to diseases and treatments (e.g., chemotherapy), and assessing manufacturing pipelines for emerging CAR- and adoptive-T cell therapies. The current gold standard for T-cell phenotyping is flow cytometry, which uses expensive commercial analyzers and class- specific fluorophores requiring trained laboratory personnel and biochemical reagents. Due to the use of exogenous fluorophores, conventional flow cytometry assays are also destructive (i.e., end point measurements). Recent advances in optical techniques, including fluorescence lifetime imaging microscopy and quantitative phase imaging, have enabled label-free, non-destructive analysis of lymphocyte populations with varying degrees of specificity. However, these techniques still have inherent drawbacks such as the requirement for expensive instrumentation, limited lateral resolution, and/or low throughput, limiting the analysis that can be performed and capability for translation to the clinic. To address these limitations, we apply deep-UV microscopy, a high-resolution, labelfree molecular imaging technique previously demonstrated for complete blood count analysis and bone marrow hematopathology, among other applications. We show that static deep-UV images of T-cells can be used to phenotype cell viability and activation state using both feature-based algorithms and supervised machine learning models. Furthermore, by quantifying dynamic intracellular activity from a single-wavelength deep-UV time series, we demonstrate subtyping of CD4/8+ T-cells. Cells remain viable after the brief UV light exposure. Ultimately, we highlight the potential of deep-UV microscopy as a simple, label-free alternative for fast and accurate T-cell characterization.

The five-year survival rates for glial brain tumors are extremely variable, ranging from 95% for low-grade astrocytomas to 5% for high-grade glioblastomas. For most brain tumors, accurate diagnosis and resection in the operating room is one of the most important factors in prolonging survival. Currently, the gold standard for brain tumor diagnosis is histopathological analysis, during which tumor samples undergo time-consuming processing outside the operating room. However, there is a lack of intraoperative tools that can successfully identify cancerous tissue in the operating room in-vivo and in real time. Quantitative oblique back-illumination microscopy (qOBM) is a label-free, noninvasive, and real-time imaging modality that has been applied to image several clinical samples at subcellular resolution. This technology has been able to identify cancerous brain tissue in animal models and has been used to image the brain of animal models in-vivo and in real time. Here, we propose to use qOBM as a diagnostic tool in distinguishing glioma tumor types in the human brain. We have imaged ex-vivo human brain samples spanning three tumor types - astrocytoma, glioblastoma, and oligodendroglioma. From these images, we identify visual differences in nuclear morphology between these tumor types. Simultaneously, we compare two machine learning approaches to classify qOBM images by tumor type. First, we train a network to classify by image feature extraction; second, we train a network to classify images by the image data alone. We aim to leverage the superior classification algorithm as we image human brain samples intraoperatively, allowing for in-vivo, real-time tumor diagnosis.

In this work we develop and demonstrate the utility of a compact, handheld quantitative phase imaging microscope that enables label-free, in vivo optical imaging of bulk tissues with clear cellular and subcellular histological detail in real-time. The proposed device overcomes significant challenges in optical imaging for in vivo applications, particularly for clinical human use. The approach uses quantitative oblique back illumination microscopy (qOBM) to obtain quantitative phase information of opaque samples using epi-illumination. The compact handheld probe achieves 0.8 µm lateral resolution, 5 µm axial resolution, 300 µm X 300 µm field of view, and operates at 25Hz in a wide-field (non-scanning) configuration, enabling real-time imaging. The probe is also inexpensive and has no moving components, making it robust. The utility of the probe is demonstrated in (1) human skin in vivo, (2) brain tumor tissue ex vivo from a murine tumor model and from discarded human tissue from neurosurgery, and (3) in vivo using healthy brain tissue from a large animal model (swine), simulating neurosurgical conditions. Given the clear cellular and subcellular histological detail (i.e., "optical biopsy") obtained in real-time, combined with the ease-of-use and low-cost of the system, the proposed device has significant implications for a broad range of clinical applications.

Cell therapies, such as T cell immunotherapies, hold significant promise for treating complex diseases; however, their widespread adoption has been hindered by challenges related to monitoring cells during culture, which has affected their consistency, potency, and cost. Here, we present a compact, low-cost, label-free quantitative phase imaging (QPI) platform to enable continuous, non-destructive, in-line monitoring of T cell cultures within bioreactors. We further develop quantitative, image-based assays that accurately characterizes T cell culture viability and activation from over 50 independent donors-including therapeutically relevant CAR-T cells - while also preserving culture sterility and eliminating the need for disruptive sampling and endpoint assays. Our findings establish a QPI-pipeline for label-free, in-line cell monitoring and characterization which can significantly improve cell manufacturing processes.

T cell characterization is critical for understanding immune function, monitoring disease progression, and optimizing cell-based therapies. Current technologies to characterize T cells, such as flow cytometry, require fluorescent labeling and typically are destructive endpoint measurements. Non-destructive, label-free imaging methods have been proposed, but face limitations with throughput, specificity, and system complexity. Here we demonstrate deep-ultraviolet (UV) microscopy as a label-free, non-destructive, fast and simple imaging approach for assessing T cell viability, activation state, and subtype with high accuracy. Using static deep-UV images, we characterize T cell viability and activation state, demonstrating excellent agreement with flow cytometry measurements. We further apply dynamic deep-UV imaging to quantify intracellular activity, enabling fast and accurate subtyping of CD4 + and CD8 + T cells. These results corroborate recent studies on metabolic activity differences between these subtypes, but now with deep-UV microscopy they are enabled by a non-destructive, fast, low-cost and simple approach. Together, our results demonstrate deep-UV microscopy as a powerful tool for high-throughput immune cell characterization, with broad applications in immunology re-search, immune monitoring, and development of emerging cell-based therapies.

Despite the disadvantages of labeling cells, fluorescence imaging remains a cornerstone of biological and biomedical imaging. However, quantitative phase imaging (QPI) is increasingly being recognized in the biomedical field as an equally indispensable tool given its label-free, non-destructive, highly sensitive and long-term quantitative imaging capabilities. Recently, deep neural network-based methods have been developed to generate multi-spectral virtual fluorescent images from QPI, providing the best of both worlds. Yet, such methods have been limited to thin transparent samples, given the inherent inability of traditional QPI methods to image thick scattering samples. In this study, we develop a modified quantitative back-illumination microscopy (qOBM) system to obtain 3D quantitative phase information from fresh, thick samples and fluorescence data in tandem. The system can provide perfectly co-registered multi-modal images which can also be used to train a deep neural network to translate phase information into fluorescent information. Here we present the theoretical foundation of this method, describe the simple multi-modal system, and showcase results from both imaging modalities (quantitative phase and fluorescence in 3D) as well as the virtually stained images. This innovative virtual staining technique has significant implications for the investigation of cells in complex scattering media (e.g., tissue), as it holds the potential to significantly reduce time, labor, and costs, while providing new capabilities for high throughput, label-free, high-contrast imaging without compromising cell health. Additionally, for clinical applications, this method holds great promise to enable high-fidelity label-free virtual histological imaging in-situ/in-vivo with known ground truth for nuclear contrast.

Bone marrow aspirations are pivotal for diagnosing and monitoring various hematological conditions, including cancers. However, a significant portion (10%-50%) of aspirations yield suboptimal or inadequate diagnostic material. The difficulty and scarcity of bedside adequacy assessment strategies further exacerbate the challenges in this procedure, which can consequently lead to delays in diagnosis and treatment, among other complications. To address this unmet clinical need, we apply deep UV microscopy, a real-time, low-cost, label-free molecular imaging technology that recapitulates the appearance of Giemsa stains. We present results from a prospective clinical study comprising 51 pediatric oncology patients, where the deep UV images of unstained bone marrow aspirate smears are evaluated and compared with the clinical standard of care (a hematopathologist inspection of the same slides after Giemsa staining). Results show that both real-time visual UV inspection and an automated classification algorithm applied to the unstained deep UV images achieve accurate adequacy assessment, with accuracies of 94.1% and 95.7%, respectively. Additionally, we demonstrate whole-slide imaging of bone marrow aspirate smears using a compact and low-cost deep UV microscope that is well suited for point-of-care use. Together, this work has significant implications for improving bone marrow aspirations and the clinical management of many hematological patients. (c) 2025 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Significance:The acetowhitening effect of acetic acid (AA) enhances light scattering of cell nuclei, an effect that has been widely leveraged to facilitate tissue inspection for (pre)cancerous lesions. Here, we show that a concomitant effect of acetowhitening-changes in refractive index composition-yields nuclear contrast enhancement in quantitative phase imaging (QPI) of thick tissue samples. Aim:We aim to explore how changes in refractive index composition during acetowhitening can be captured through a novel epi-mode 3D QPI technique called quantitative oblique back-illumination microscopy (qOBM). We also aim to demonstrate the potential of using a machine learning-based approach to convert qOBM images of fresh tissues into virtually AA-stained images. Approach:We implemented qOBM, an imaging technique that allows for epi-mode 3D QPI to observe phase changes induced by AA in thick tissue samples. We focus on detecting nuclear contrast changes caused by AA in mouse brain samples. As a proof of concept, we also applied a Cycle-GAN algorithm to convert the acquired qOBM images into virtually AA-stained images, simulating the effect of AA staining. Results:Our findings demonstrate that AA-induced acetowhitening leads to significant nuclear contrast enhancement in qOBM images of thick tissue samples. In addition, the Cycle-GAN algorithm successfully converted qOBM images into virtually AA-stained images, further facilitating the nuclear enhancement process without any physical stains. Conclusions:We show that the acetowhitening effect of acetic acid induces changes in refractive index composition that significantly enhance nuclear contrast in QPI. The application of qOBM with AA, along with the use of a Cycle-GAN algorithm to virtually stain tissues, highlights the potential of this approach for advancing label-free and slide-free, ex vivo, and in vivo histology.

Quantitative oblique back illumination microscopy (qOBM) is a recently developed imaging technique that enables 3D quantitative phase imaging (QPI) and refractive index (RI) tomography of thick scattering samples. To quantify the phase and RI information with qOBM, the optical transfer function (OTF) of the system must be known or estimated, which requires knowledge of the angular distribution of light at an imaging plane inside a highly scattering medium. To date, this information has been estimated using a Monte Carlo photon transport method which relies on documented tissue scattering properties. While this numerical approach has shown high-fidelity quantitative results, it is limited by its dependence on published scattering parameters and simulated conditions. Here we propose a novel approach that allows experimental measurement of the angular distribution of the multiple-scattered light at the imaging plane inside a highly scattering medium. Experimental results using samples with known and unknown scattering properties are presented, including excised brain tissue, in-vivo skin, and formalin-fixed and paraffin-embedded (FFPE) tissues. Results further support qOBM’s quantitative fidelity across different tissue types, and show how directly measuring the angular distribution of light can widen qOBM’s utility to more complex samples with unknown or highly variable scattering properties.

Background aims Biomanufacturing of cell therapies involves highly complex and labor-intensive processes, where process parameters and biological variabilities can significantly influence product quality, reproducibility and therapeutic efficacy. Here, we developed a vertical wheel-based bioreactor platform with automated controls and in-line process analytical technologies (PAT) to demonstrate successful closed-system T cell biomanufacturing. Methods By identifying the critical process parameters (CPP), a process development strategy was optimized for expanding primary human unmodified and chimeric antigen receptor (CAR) T cells using multiple activation systems, including degradable microscaffolds. Results Spent media analysis combined with symbolic regression identified CPPs, which were validated through small-scale experiments and large-scale expansions in the bioreactor platform. Closed-loop automation with analytics such as real-time imaging also was integrated into the bioreactor platform for continuous monitoring and process control. Conclusions This integrated bioreactor platform provides a proof-of-concept design for multiplexed PAT integration, process optimization and feedback-controlled intelligent automation to enable discovery, monitoring and control of critical quality attributes and critical process parameters for cell therapy manufacturing.

Ultraviolet (UV) microscopy of live cells has been challenging due to phototoxicity, with UV radiation affecting cellular components leading to irreversible cell death. Despite this challenge, recent advances in UV light sources and detectors have renewed interest in UV microscopy due to its high resolution and label-free molecular imaging capabilities. Indeed, UV microscopy has been recently demonstrated for a wide variety of cellular imaging applications, including multispectral imaging of cancer tissue sections, cells at varying time scales, and hematological analysis of whole blood cells. While these studies have leveraged UV microscopy to image static samples and cellular dynamics over short periods of time, UV phototoxicity remains a problem during live cell imaging sessions lasting over several hours and longitudinal imaging of a single sample. In this work, we characterize UV-induced photodamage by quantifying the flux required for cell death at notable wavelengths in the deep-UV region. We demonstrate how this flux can vary with cell adherence type using adherent and non-adherent cell lines. We then present fractionation studies conducted over time scales ranging from several hours to days and discuss the ability of cell populations to recover in each case. Finally, we provide viable live-cell imaging frameworks for UV microscopy applications ranging from single multispectral imaging sessions to long-term observation of samples.

Spheroids offer a unique opportunity to study personalized disease treatment; however, monitoring of these spheroids relies on time-consuming, end-point analyses. Here we apply 3D QPI using quantitative oblique back illumination microscopy (qOBM) to continuously monitor glioblastoma spheroids treated with radiation, immunotherapies, and chemotherapies.

Phase imaging and fluorescence microscopy provide valuable complementary information, and individually form the basis for a significant portion of the routing biological and biomedical optical imaging performed today. While multimodal phase and fluorescence microscopy has been explored for thin transparent samples to obtain structural information based on the refractive index distribution (with phase contrast) and molecular content (with fluorescence), combining these complementary technologies to study thick samples has been challenging and remains largely unexplored. This work presents the results of a study that combines quantitative phase imaging (QPI) and refractive index (RI) tomography in thick samples—using quantitative oblique back illumination—and bright field fluorescence deconvolution microscopy. The two technologies use a simple bright field microscope configuration with epi-illumination and through-focus z-stack acquisition, along with a deconvolution algorithm, to achieve 3D imaging. Phase and RI information is acquired nearly simultaneously with the fluorescence information with inherent co-registration of the two modalities. In this work, we will present the theoretical underpinning of this multimodal approach, describe the simple multimodal system, and show imaging results of thick tissues, such as labeled mice brains. This multimodal imaging approach could help biologists and clinicians gain a more comprehensive understanding of the tissue's morphology and molecular composition, and can be widely applied across a number of biological and biomedical disciplines, including neuroscience, pathology, and oncology.

Quantitative phase imaging (QPI) offers label-free access to refractive index information of biological samples, which can achieve nanometer-level optical-path-length sensitivity with cellular/sub-cellular biophysical and histological details. Recently we introduced quantitative oblique back-illumination microscopy (qOBM) which works in epi-mode and uses multiply scattered photons within thick samples to yield quantitative phase in thick scattering tissues, thus overcoming QPI's long-standing limitation to thin transparent samples. qOBM provides real-time quantitative phase in 3D, and can be configured in a compact form factor. Here we describe a handheld qOBM probe, suitable for in-vivo diagnostic applications such as brain tumor assessment, dermatology, and more.

Bone marrow aspiration procedures play an important role in the assessment of patients with blood and marrow diseases, including cancers. Evaluating the adequacy of aspirates, indicated by the presence of bony spicules, is crucial to ensure the procedural success and collection of relevant diagnostic material. Unfortunately, inadequate samples occur in approximately 50 % of cases, requiring patients to undergo repeat procedures. This is particularly problematic for pediatric patients who need to be anesthetized before each procedure. The current gold standard is hematopathologist examination of Giemsa-stained slides, which is time consuming and requires expensive biochemical reagents and trained technicians. Recently, Here we present a portable, LED-based UV microscope designed for real-time inspection of bone marrow aspirates. We discuss results from a clinical trial with pediatric oncology patients demonstrating excellent agreement between UV examination of unstained slides and ground truth pathologist examination of stained slides. Furthermore, we demonstrate whole slide imaging using a previously developed, compact UV microscopy system and automated spicule detection with deep neural networks to work towards point-of-care applications.