Objective To identify the ability of optical coherence tomography (OCT) to assist in the diagnosis and treatment of patients presenting to a primary care provider with signs and symptoms suggesting acute otitis media (AOM). Design-Setting-Participants An NIH-funded clinical trial was conducted and enrolled 75 children between 1 and under 7 years of age. Children presented with complaints of ear pain or suspected ear infection from their caregiver history to primary care clinics associated with an academic children’s hospital. These patients upon presentation were eligible to enroll in the trial. Following a complete history and physical exam, including pneumatic otoscopy (PO), patients were provided with a diagnosis and treatment plan. Diagnoses included: AOM, middle ear effusion without AOM, or no evidence of otitis media with a clear middle ear space. Following this, OCT was performed, and the diagnosis and treatment plan was reassessed using this data. Results The diagnosis and/or treatment plan changed for 15.3% of patients following the use of OCT compared with the initial use of PO. The greatest influence was seen in the patient group with MEE without AOM, with 36% (5/14) of these patients having their diagnosis and/or treatment plan changed after OCT was employed. Conclusions OCT provides an additional adjunct for primary care providers to enhance visualization of the tympanic membrane and middle ear space. This data, in our clinical trial, resulted in enhanced clinical decision making, in particular for patients with MEE who were not diagnosed with AOM on PO. Trial Registration Information Coherent Optical Detection of Middle Ear Disease (OCTII). Clinicaltrials.gov ID number: NCT05353569

Drug-induced nephrotoxicity (DIN) is a major cause of drug development failure, yet many cases are detected only late in clinical trials or after approval. Conventional methods to detect DIN often lack sensitivity and specificity, particularly for early or region-specific kidney injury. Here, we evaluate Simultaneous Label-free Autofluorescence Multiharmonic (SLAM) microscopy as a rapid, label-free imaging approach to quantify kidney microstructural and functional metabolic changes associated with DIN. SLAM simultaneously captures endogenous NAD(P)H and FAD autofluorescence, collagen-derived second harmonic generation, and third-harmonic generation from structural interfaces. SLAM images were acquired from the cortex and outer medulla (OM) of rat kidneys after cisplatin dosing at days 1, 6, and 29 post-administration. Imaging revealed region-specific injury, with the OM showing greater sensitivity to DIN than the cortex. The most prominent changes-tubular degeneration and hyaline casts-peaked at day 6, whereas tubular dilation and fibrosis-related features persisted to day 29. Feature-based classification captured these spatial and temporal patterns, achieving higher balanced accuracy in the OM (0.944) than in the cortex (0.860). Key model drivers included granularity and inter-channel correlations, underscoring the value of multi-channel SLAM data. Overall, these results demonstrate the potential of SLAM microscopy for sensitive, region-specific detection and characterization of DIN in preclinical safety studies. SLAM microscopy provides a rapid, label-free way to detect, localize, and classify drug-induced nephrotoxicity with improved sensitivity and regional specificity, potentially strengthening preclinical kidney safety screening and reducing late-stage development failures.

The selection of high-performing cell lines is crucial for biopharmaceutical production but is often time-consuming and labor-intensive. We investigated label-free multimodal nonlinear optical microscopy for non-perturbative profiling of biopharmaceutical cell lines based on their intrinsic molecular contrast. Employing simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy with fluorescence lifetime imaging microscopy (FLIM), we characterized Chinese hamster ovary (CHO) cell lines at early passages (0-2). A machine learning (ML)-assisted analysis pipeline leveraged high-dimensional information to classify single cells into their respective lines. Remarkably, the monoclonal cell line classifiers achieved balanced accuracies exceeding 96.8% as early as passage 2. Correlation features and FLIM modality played pivotal roles in early classification. This integrated optical bioimaging and machine learning approach presents a promising solution to expedite cell line selection process while ensuring identification of high-performing biopharmaceutical cell lines. The techniques have potential for broader single-cell characterization applications in stem cell research, immunology, cancer biology and beyond.

The applications of ultrafast optics to biomedical microscopy have expanded rapidly in recent years, including interferometric techniques like optical coherence tomography and microscopy (OCT/OCM). The advances of ultra-high resolution OCT and the inclusion of OCT/OCM in multimodal systems combined with multiphoton microscopy have marked a transition from using pseudo-continuous broadband sources, such as superluminescent diodes, to ultrafast supercontinuum optical sources. We report anomalies in the dispersion profiles of low-coherence ultrafast pulses through long and non-identical arms of a Michelson interferometer that are well beyond group delay or third-order dispersions. This chromatic anomaly worsens the observed axial resolution and causes fringe artifacts in the reconstructed tomograms in OCT/OCM using traditional algorithms. We present DISpersion COmpensation Techniques for Evident Chromatic Anomalies (DISCOTECA) as a universal solution to address the problem of chromatic dispersion mismatch in interferometry, especially with ultrafast sources. First, we demonstrate the origin of these artifacts through the self-phase modulation of ultrafast pulses due to focusing elements in the beam path. Next, we present three solution paradigms for DISCOTECA: optical, optoelectronic, and computational, along with quantitative comparisons to traditional methods to highlight the improvements to the dynamic range and axial profile. We explain the piecewise reconstruction of the phase mismatch between the arms of the spectral-domain interferometer using a modified short-term Fourier transform algorithm inspired by spectroscopic OCT. Finally, we present a decision-making guide for evaluating the utility of DISCOTECA in interferometry and for the artifact-free reconstruction of OCT images using an ultrafast supercontinuum source for biomedical applications.

Fluorescence lifetime imaging microscopy (FLIM) provides valuable insights into molecular interactions and states in complex cellular environments. Conventional FLIM analysis methods struggle with accurate lifetime estimation with low photons-per-pixel (PPP). We propose DeepFLR, a self-supervised deep learning framework for robust FLIM signal restoration with limited photons. By exploiting the spatiotemporal dependencies of FLIM signals, DeepFLR reconstructs the fluorescence decay curves, leading to accurate lifetime estimations using existing lifetime estimation methods. The results demonstrate that DeepFLR enables reliable lifetime estimation with less than 10 PPP for a diverse set of biological samples. The proposed approach significantly reduces the photon budget of FLIM and opens up numerous low-light FLIM applications.

The COVID-19 pandemic triggered the resurgence of synthetic RNA vaccine platforms allowing rapid, scalable, low-cost manufacturing, and safe administration of therapeutic vaccines. Self-amplifying mRNA (SAM), which self-replicates upon delivery into the cellular cytoplasm, leads to a strong and sustained immune response. Such mRNAs are encapsulated within lipid nanoparticles (LNPs) that act as a vehicle for delivery to the cell cytoplasm. A better understanding of LNP-mediated SAM uptake and release mechanisms in different types of cells is critical for designing effective vaccines. Here, we investigated the cellular uptake of a SAM-LNP formulation and subsequent intracellular expression of SAM in baby hamster kidney (BHK-21) cells using hyperspectral coherent anti-Stokes Raman scattering (HS-CARS) microscopy and multiphoton-excited fluorescence lifetime imaging microscopy (FLIM). Cell classification pipelines based on HS-CARS and FLIM features were developed to obtain insights on spectral and metabolic changes associated with SAM-LNPs uptake. We observed elevated lipid intensities with the HS-CARS modality in cells treated with LNPs versus PBS-treated cells, and simultaneous fluorescence images revealed SAM expression inside BHK-21 cell nuclei and cytoplasm within 5 h of treatment. In a separate experiment, we observed a strong correlation between the SAM expression and mean fluorescence lifetime of the bound NAD(P)H population. This work demonstrates the ability and significance of multimodal optical imaging techniques to assess the cellular uptake of SAM-LNPs and the subsequent changes occurring in the cellular microenvironment following the vaccine expression.

We mapped and quantified sub-cellular distribution of antisense oligonucleotides in 3D and established spectroscopic components enabling precise detection of cuboidal hepatocytes in a liver-on-a-chip platform with advanced light microscopy methods.

Efficient cell line development is crucial for optimizing biopharmaceutical production. We demonstrate the potential of SLAM and FLIM microscopy to optimize this process by correlating metabolism-related features with measured productivity in early CHO cell passages. Eight CHO cell lines were imaged using SLAM and FLIM microscopy, and a pipeline was developed to classify the cells. A linear SVM achieved 95% accuracy in predicting productivity. Important features and their channel affiliations were identified, revealing optical metabolic characteristics from NAD(P)H and FAD associated with productivity. SLAM features correlated with growth and viability, while FLIM features correlated with protein production, highlighting the importance of multimodal label-free imaging.

The biopharmaceutical industry relies on selecting high-performing cell lines to meet quality and manufacturability criteria. However, this process is time- and labor-intensive. To address this, label-free multimodal multiphoton microscopy techniques were employed to characterize biopharmaceutical cell lines in early passages. Using a machine learning-assisted single-cell analysis pipeline, over 95% accuracy for monoclonal cell line classification was achieved in all passages. Additionally, Open Set Recognition allowed the differentiation of desired cell lines in polyclonal pools. The study offers a promising solution to expedite the cell line selection process, reducing time and resources while ensuring the identification of high-performance biopharmaceutical cell lines.

In the production of biotherapeutics, Chinese hamster ovary (CHO) cells are known as the gold standard. One challenge in the development of these cell lines is the identification of high expressing, yet stable CHO cells. Here we apply simultaneous label-free autofluorescence multi-harmonic (SLAM) microscopy to four CHO cell lines of varying levels of productivity and stability. With the assistance of machine learning, we were able to classify the CHO cell lines into their respective categories with an accuracy of 85%. Application of this CHO cell characterization technology to upstream bioprocessing can potentially improve workflows such as high-throughput screening and monitoring.