Enumerating and sizing subvisible particles (SbVP) is an important aspect of ensuring pharmaceutical drug product (DP) quality. Existing SbVP characterization is limited by the destructive nature of testing methods and the need to withdraw solution from DP primary containers. Mie-Scattering light sheet (MSLS) SbVP analysis, a technology that measures scattering from a sheet of light projected directly through a DP primary container, has the potential to address these shortcomings. MSLS testing has previously been demonstrated on solutions of homogeneous polystyrene (PS) beads in standard DP vials. Proteinaceous SbVP inherent to DP biologics have heterogeneous size distribution, aspherical morphology, and lower optical contrast than bead standards. In the work described herein, the MSLS analyzer's ability to enumerate and size proteinaceous SbVP was evaluated. Use cases were developed to assess how proteinaceous SbVP suspended in solutions of monoclonal antibody (mAb) can be analyzed after exposure to agitation and storage with varied formulation pH. Measurement variability was assessed on scan-to-scan and container-to-container basis across the full functional range of the system (10 to 300,000 particles/mL). Results show the MSLS analyzer successfully elucidated SbVP growth dynamics while preserving the DP sample, enabling direct assessment of product stability. The sensitivity, working range, and accuracy of the MSLS analyzer for characterizing inherent particles were also presented, and future development direction discussed.

Bacteria often coordinate collective behaviors such as biofilm formation and secondary metabolite production through quorum sensing (QS), a regulatory system traditionally linked to high cell density. However, in environments such as soil, where microbial populations are spatially fragmented, sparse, nutrient-limited, and subject to mass transport, the mechanisms that enable QS-dependent processes remain incompletely understood. Here, we investigate the regulation of a secreted redox-active metabolite, phenazine-1-carboxylic acid (PCA), in Pseudomonas synxantha 2-79, a model rhizobacterium, under phosphorus (P) limitation, a persistent stress in many soils. Using a combination of microscopy and molecular genetic approaches, we show that P limitation sensitizes the QS activation threshold by an order of magnitude, enabling phenazine induction at relatively low population densities compared with P-replete conditions. This induction is abolished in QS-deficient mutants and restored by the addition of exogenous acyl-homoserine lactone (AHL), demonstrating that QS remains essential, but its threshold becomes environmentally tuned. Under P limitation, spatial confinement and pore saturation levels further shape the timing and location of induction, illustrating how physical structure and nutrient stress can modulate bacterial activities. Moreover, P stress confers both collaborative and competitive advantages, enabling P. synxantha to undergo low-cell-density AHL cross-induction with related Pseudomonas spp. and to suppress other rhizobacteria. Lastly, on plant roots, phenazine biosynthetic genes are more strongly induced under P limitation. These findings illustrate how the nutrient status of an environment can modulate the onset of QS, enabling quorum-regulated behaviors to activate at lower thresholds.

Cell detection is one of the most significant tasks in circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) analysis, as these cells are emerging as potential biomarkers for cancer prognosis and diagnosis. Traditional approaches to cell detection are often manual, leading to long turnaround times and significant variability between experts. In this chapter, we first provide an overview of deep learning techniques for general cell detection, with a specific focus on CTC and CAF detection. We then discuss developments in optical imaging systems that offer high-resolution, high-quality, and all-in-focus imaging capabilities. By integrating advanced optical hardware with deep learning algorithms, we demonstrate high-accuracy and high-fidelity detection of CTCs and CAFs in microfiltered-based samples. We also emphasize the need for refined technologies and models to enhance the clinical utility of CTC characterization and deepen the understanding of metastasis. The integrated approach introduced in this chapter has the potential to establish a new automated paradigm for CTC and CAF analysis.

Automation in optical microscopy is critical for enabling high-throughput imaging across a wide range of biomedical applications. Among the essential components of automated systems, robust autofocusing plays a pivotal role in maintaining image quality for both single-plane and volumetric imaging. However, conventional autofocusing methods often struggle with implementation complexity, limited generalizability across sample types, incompatibility with thick specimens, and slow feedback. We observed that the digitally summed Fourier spectrum of two images acquired from two-angle illumination exhibits interference-like fringe modulation when the sample is defocused. These digital fringes correlate directly with defocus through a physics-based relation. Based on this principle, we developed an automatic, efficient, and generalizable defocus detection method termed digital defocus aberration interference (DAbI). Implemented with a simple two-LED setup, DAbI can quantify the defocus distance over a range of 443 times the depth-of-field for thin samples and 296 times for thick specimens. It can additionally extend the natural depth-of-field of the imaging system by 20-fold when integrated with complex-field imaging. We demonstrated the versatile applications of DAbI on brightfield, complex-field, refractive index, confocal, and widefield fluorescence imaging, establishing it as a promising solution for automated, high-throughput optical microscopy.

The ability to image blood flow in early-stage avian embryos has significant applications in developmental biology, drug and vaccine testing, as well as determining sex differentiation. In this project, a recently developed laser speckle contrast imaging (LSCI) system was used to non-invasively image extraembryonic blood vessels and used these images to attempt early sex identification of chick embryos. Specifically, blood vessels images were captured from 1,251 living chicken embryos between day three and day four of incubation. Then, deep neural network (DNN) models were applied to evaluate whether it is possible to differentiate sex based on vascular patterns. Using ResNetBiT and YOLOv5s-cls models, our results indicate that sex differentiation from extraembryonic blood vessel images was not achievable with sufficiently high accuracy or statistical significance for practical use. Specifically, ResNetBiT had a five-fold cross-validated average accuracy of 56% ± 4% (p-value of 0.28 across cross-validation folds) at day 3 and 57% ± 3% (p-value of 0.07 across cross-validation folds) at day 4. YOLOv5s-cls had a five-fold cross-validated average accuracy of 55% ± 2% (p-value of 0.13 across folds) at day 3 and 57% ± 3% (p-value of 0.10 across folds) at day 4. Our findings suggest that under the current experimental conditions and modeling approaches, per-egg evaluation did not produce sufficiently accurate or statistically robust results for early sex classification.

Fourier ptychographic microscopy (FPM) is a powerful computational imaging modality that achieves high space–bandwidth product imaging for biomedical samples. However, its adoption is limited by slow data acquisition due to the need for sequential measurements. Multiplexed FPM strategies have been proposed to accelerate imaging by activating multiple light-emitting diodes simultaneously, but they typically require careful parameter tuning, and their lack of effective aberration correction makes them prone to image degradation. To address these limitations, we introduce hybrid-illumination multiplexed Fourier ptychographic microscopy (HMFPM), which integrates analytic aberration retrieval capability with the efficiency of multiplexed illumination. Specifically, HMFPM employs a hybrid illumination strategy and a customized reconstruction algorithm with analytic and optimization methods. This hybrid strategy substantially reduces the number of required measurements while ensuring robust aberration correction and stable convergence. We demonstrate that HMFPM achieves 1.08 µ m resolution, representing a four-fold enhancement over the system’s coherent diffraction limit, across a 1.77 × 1.77 mm ^2 field of view using 20–28 measurements. HMFPM remains robust under diverse aberrations, providing up to 78 µ m digital refocusing capability, and effectively corrects both field-dependent and scanning-induced aberrations in whole-slide pathology imaging. These results establish HMFPM as a practical, high-throughput, and aberration-free solution for biological and biomedical imaging.

Three-dimensional (3D) refractive index (RI) tomography offers label-free, quantitative volumetric imaging but faces limitations due to optical aberrations, limited resolution, and the computational complexity inherent to existing approaches. To overcome these barriers, we propose Analytic Fourier Ptychotomography (AFP), a new computational microscopy technique that analytically reconstructs aberration-free, complex-valued 3D RI distributions without iterative optimization or axial scanning. AFP incorporates a new concept called the finite sample thickness (FST) prior, and analytically solves the inverse scattering problem through three sequential steps: complex-field reconstruction via the Kramers-Kronig relation, linear aberration correction using overlapping spectra, and analytic spectrum extension into the darkfield region. Unlike iterative reconstruction methods, AFP does not require parameter tuning or computationally intensive optimizations, which are often error-prone and non-generalizable. We experimentally demonstrate that AFP significantly enhances image quality and resolution under various aberration conditions across a range of applications. AFP corrected aberrations associated with 25 Zernike modes (with a maximal phase difference of 2.3π and maximal Zernike coefficient value of 4), extended the synthetic numerical aperture from 0.41 to 0.99, and provided a two-fold resolution enhancement in all directions. AFP's simplicity and robustness make it an attractive imaging technology for quantitative 3D analysis in biological, microbial ecological, and medical studies.

Cerebral blood flow is a critical metric for cerebrovascular monitoring, with applications in stroke detection, brain injury evaluation, aging, and neurological disorders. Noninvasively measuring cerebral blood dynamics is challenging due to the presence of scalp and skull, which obstruct direct brain access and contain their own blood dynamics that must be isolated. We developed an aggregated seven-channel speckle contrast optical spectroscopy (SCOS) system to measure blood flow and blood volume noninvasively. Each channel, with a distinct source-to-detector distance, targeted different depths to detect scalp and brain blood dynamics separately. By briefly occluding the superficial temporal artery, which supplies blood only to the scalp, we isolated surface blood dynamics from brain signals. Results on 20 subjects show that scalp-sensitive channels experienced significant reductions in blood dynamics during occlusion, while brain-sensitive channels experienced minimal changes. This provides experimental evidence of scalp blood flow sensitivity in diffuse optical measurements such as SCOS, highlighting optimal configuration for preferentially probing brain signals noninvasively.

Spatial transcriptomics (ST) enables simultaneous mapping of tissue morphology and spatially resolved gene expression, offering unique opportunities to study tumor microenvironment heterogeneity. Here, we introduce a computational framework that predicts spatial pathway activity directly from hematoxylin-and-eosin-stained histology images at microscale resolution 55 and 100 um. Using image features derived from a computational pathology foundation model, we found that TGFb signaling was the most accurately predicted pathway across three independent breast and lung cancer ST datasets. In 87-88

Recent advances in stem cell-derived embryo models have transformed developmental biology, offering insights into embryogenesis without the constraints of natural embryos. However, variability in their development challenges research standardization. To address this, we use deep learning to enhance the reproducibility of selecting stem cell-derived embryo models. Through live imaging and AI-based models, we classify 900 mouse post-implantation stem cell-derived embryo-like structures (ETiX-embryos) into normal and abnormal categories. Our best-performing model achieves 88% accuracy at 90 h post-cell seeding and 65% accuracy at the initial cell-seeding stage, forecasting developmental trajectories. Our analysis reveals that normally developed ETiX-embryos have higher cell counts and distinct morphological features such as larger size and more compact shape. Perturbation experiments increasing initial cell numbers further supported this finding by improving normal development outcomes. This study demonstrates deep learning’s utility in improving embryo model selection and reveals critical features of ETiX-embryo self-organization, advancing consistency in this evolving field. Stem cell-derived embryo models offer insights into early development but suffer from variability. Here, authors used AI to classify and predict outcomes for generation of mouse stem cell-derived embryo-like structures, improving selection accuracy and understanding of self-organization.

The ability to image blood flow in early-stage avian embryos has significant applications in developmental biology, drug and vaccine testing, as well as determining sex differentiation. In this project, we used our recently developed laser speckle contrast imaging (LSCI) system to non-invasively image extraembryonic blood vessels and used these images to attempt early sex identification of chick embryos. Specifically, we captured images of blood vessels from 1,251 living chicken embryos between day three and day four of incubation. We then applied deep neural network (DNN) models to evaluate whether it is possible to differentiate sex based on vascular patterns. Using ResNetBiT and YOLOv5 models, our results indicate that sex differentiation from extraembryonic blood vessel images was not achievable with sufficiently high accuracy or statistical significance for practical use. Specifically, ResNetBiT had a five-fold cross-validated average accuracy of 59%±5% (fold-wise p-value, p ≤ 0.3) at day 3 and 61%±3% (fold-wise, p ≤ 0.04) at day 4. YOLOv5 had a five-fold cross-validated average accuracy of 55%±3% (fold-wise, p ≤ 0.3) at day 3 and 53%±3% (fold-wise, p ≤ 0.5) at day 4. Our findings suggest that using vascular pattern imaging alone is inconclusive for reliable early sex identification in chicken embryos. ### Competing Interest Statement The authors have declared no competing interest.

Single-shot fluorescence imaging techniques have gained increasing interest in recent years due to their ability to rapidly capture complex biological data without the need for extensive scanning. In this letter, we introduce polarized Fourier light field microscopy (pFLFM), a novel fluorescence imaging technique that captures five-dimensional information (3D intensity and 2D polarization) in a single snapshot. This technique combines a polarization camera with an FLFM setup, significantly improving data acquisition efficiency. We experimentally validated the pFLFM system using a fluorescent Siemens star, demonstrating consistent resolution and an extended depth of field across various polarizations. Using the 5D imaging capabilities of pFLFM, we imaged plant roots and uncovered unique heterogeneities in cellulose fibril configurations across various root sections. These results not only highlight the potential of pFLFM in biological and environmental sciences, but also represent a significant advancement in the design of single-shot fluorescence imaging systems.

Significance:Cerebral blood flow (CBF) and cerebral blood volume (CBV) are key metrics for regional cerebrovascular monitoring. Simultaneous, non-invasive measurement of CBF and CBV at different brain locations would advance cerebrovascular monitoring and pave the way for brain injury detection as current brain injury diagnostic methods are often constrained by high costs, limited sensitivity, and reliance on subjective symptom reporting. Aim:We aim to develop a multi-channel non-invasive optical system for measuring CBF and CBV at different regions of the brain simultaneously with a cost-effective, reliable, and scalable system capable of detecting potential differences in CBF and CBV across different regions of the brain. Approach:The system is based on speckle contrast optical spectroscopy and consists of laser diodes and board cameras, which have been both tested and investigated for safe use on the human head. Apart from the universal serial bus connection for the camera, the entire system, including its battery power source, is integrated into a wearable headband and is powered by 9-V batteries. Results:The temporal dynamics of both CBF and CBV in a cohort of five healthy subjects were synchronized and exhibited similar cardiac period waveforms across all six channels. The potential use of our six-channel system for detecting the physiological sequelae of brain injury was explored in two subjects, one with moderate and one with significant structural brain damage, where the six-point CBF and CBV measurements were referenced to structural magnetic resonance imaging (MRI) scans. Conclusions:We pave the way for a viable multi-point optical instrument for measuring CBF and CBV. Its cost-effectiveness allows for baseline metrics to be established prior to injury in populations at risk for brain injury.

Biological tissues are highly turbid in the optical regime - the mean optical scattering length is on the order of 100 microns. This extreme turbidity prevents scientists and clinicians from performing deeply penetrating high resolution optical imaging through humans and animal models alike. The challenge associated with deep-tissue optical imaging is akin to the challenge of focusing light through a dense fog - scattering prevents meaningful solutions to this problem by traditional means. This is why high-resolution optical imaging and optical excitation methods cannot reach more than 1 mm into biological tissues. Deep penetrating imaging modalities, such as x-ray imaging, MRI, and ultrasound imaging, are excellent at revealing structures within biological entities, but provide no biochemical detection capability. On the other hand, there is a wide variety of optical domain methods capable of revealing biochemical content, including fluorescence, harmonic generation, Raman spectroscopy and absorption spectral signatures. If we can overcome optical tissue turbidity, optical imaging can dramatically change the way we do bioscience research and practice medicine.

Single-shot volumetric fluorescence（SVF） imaging offers a significant advantage over traditional imaging methods that require scanning across multiple axial planes, as it can capture biological processes with high temporal resolution. The key challenges in SVF imaging include requiring sparsity constraints, eliminating depth ambiguity in the reconstruction, and maintaining high resolution across a large field of view. We introduce the QuadraPol point spread function（PSF） combined with neural fields, an approach for SVF imaging. This method utilizes a custom polarizer at the back focal plane and a polarization camera to detect fluorescence, effectively encoding the three-dimensional scene within a compact PSF without depth ambiguity. In addition, we propose a reconstruction algorithm based on the neural field technique that provides improved reconstruction quality compared with classical deconvolution methods. QuadraPol PSF, combined with neural fields, significantly reduces the acquisition time of a conventional fluorescence microscope by ～20 times and captures a 100-mm 3 cubic volume in one shot. We validate the effectiveness of both our hardware and algorithm through all-in-focus imaging of bacterial colonies on sand surfaces and visualization of plant root morphology. Our approach offers a powerful tool for advancing biological research and ecological studies.

Three-dimensional (3D) refractive index tomography offers label-free quantitative volumetric imaging. However, existing tomography approaches are limited by optical aberrations, limited resolution, and computational complexity. To overcome these issues, we propose Analytic Fourier Ptychotomography (AFP), a computational microscopy technique that analytically reconstructs aberration-free, complex-valued 3D refractive index distributions without iterative optimization or axial scanning. AFP employs a unique prior based on the finite sample thickness to recast the inverse scattering problem into analytically solvable linear equations. Unlike iterative methods, AFP does not require parameter tuning and computationally intensive optimizations, and can achieve efficient, robust, and generalizable image reconstructions across diverse samples and systems. We experimentally demonstrated that AFP greatly enhanced image quality and resolution under various aberration conditions across a range of applications. AFP corrected aberrations associated with 25 Zernike modes (with maximal phase difference of 2.3π and maximal Zernike coefficient value of 4), extended the synthetic numerical aperture from 0.41 to 0.99, and provided a two-fold resolution enhancement in all directions. With its simplicity, robustness, and broad applicability, AFP offers a user-friendly imaging platform for quantitative 3D analysis in biology, microbial ecology, and clinical science.

Cerebral blood flow is a critical metric for cerebrovascular monitoring, with applications in stroke detection, brain injury evaluation, aging, and neurological disorders. Non-invasively measuring cerebral blood dynamics is challenging due to the scalp and skull, which obstruct direct brain access and contain their own blood dynamics that must be isolated. We developed an aggregated seven-channel speckle contrast optical spectroscopy system to measure blood flow and blood volume non-invasively. Each channel, with distinct source-to-detector distance, targeted different depths to detect scalp and brain blood dynamics separately. By briefly occluding the superficial temporal artery, which supplies blood only to the scalp, we isolated surface blood dynamics from brain signals. Results on 20 subjects show that scalp-sensitive channels experienced significant reductions in blood dynamics during occlusion, while brain-sensitive channels experienced minimal changes. This provides experimental evidence of brain-to-scalp sensitivity in optical measurements, highlighting optimal configuration for preferentially probing brain signals non-invasively.