BMC Methods

Fluorescent reporters are powerful tools to reveal intercellular heterogeneity among proliferating cells. However, there are few tools to analyze differences among quiescent (G0) cells, though such differences are relevant for development, tissue maintenance, and cancer cell behavior. Quiescence heterogeneity, also known as quiescence depth, typically correlates with time after cell cycle arrest, yet directly measuring cell age is not feasible for all cell types or most tissues. Here, we describe ELDR-Glo, a genetically-encoded fluorescent biosensor that estimates relative cell age, i.e., time since the last cell cycle. The biosensor integrates replication-coupled degradation in S phase with a slow-maturing mCherry and a normalization module. We demonstrate that ELDR-Glo signal correlates with true cell age by both live-cell imaging and in fixed cells. ELDR-Glo distinguishes early and late G0 cells and functions as a relative quiescence depth reporter in situ. The biosensor is compatible with multiplexed immunofluorescence and flow cytometry. ELDR-Glo provides a unique and scalable tool to investigate cell proliferation control.

Imaging Mass Cytometry (IMC) combines metal-tagged antibody labelling with laser ablation mass spectrometry to generate highly multiplexed spatial images of tissue sections. However, the area that can be acquired within a single region of interest (ROI) is limited by hardware and software constraints, requiring large tissues to be imaged as multiple tiled ROIs. Reconstructing these ROIs into whole-slide images requires additional processing, while the proprietary .mcd file format can hinder integration with standard bioimage analysis workflows. Here, we present MCD Stitcher, an open-source Python package for converting .mcd files into OME-TIFF images with automated whole-slide stitching. The tool supports rectangular and polygonal ROIs, accommodates variable pixel sizes between ROIs, and uses memory-aware chunked reading during data ingestion to process large datasets on standard workstations. The generated OME-TIFF outputs preserve spatial, channel, and acquisition metadata for downstream analysis in tools such as QuPath, napari, and ImageJ/Fiji. MCD Stitcher provides a reproducible workflow for converting raw IMC data into interoperable image formats, enabling whole-slide spatial analysis without reliance on vendor-specific software.

High content imaging is being applied to achieve quantitative fluorescence readouts in increasingly complex 3-dimensional (3D) cell culture models such as spheroids and organoids. Compared to conventional 2D assays, 3D assays better represent biological heterogeneity but require more complex sample preparation, 3D imaging and 3D image analysis that can affect the accuracy and precision of such assays. We used spheroids formed from the NRAS-activated melanoma cell line 19161 modified to express an ERK kinase translocation reporter (KTR) as an exemplar 3D phenotypic assay carried out in 96-well plates. The spheroids were treated with the ERK activator TPA and a range of concentrations of the MEK inhibitor Binimetinib. 3D live-cell imaging with sub-cellular spatial resolution was performed using a dual-view oblique plane microscope (dOPM) - a form of single-objective light-sheet microscope - and the experiment was performed separately at 4 different institutes. The results were analysed using an identical 3D analysis pipeline and parameters. We assessed the variation in assay readout using a linear mixed effects model. Random variance at the well level was negligible (SD = 0.0048 relative to range of KTR biosensor readout at reference site of 0.17), indicating low technical noise. Treatment effects were dose-dependent and highly statistically significant compared to DMSO control across all sites (Dunnett-corrected p < 0.001). The range in KTR readout between the minimum (3.5 M Binimetinib) and maximum (100 nM TPA) treatments varied between 59 to 96% relative to the reference site. Measured bias in KTR readout between sites was between 6 and 12% of the range of the reference site. This study quantifies the reproducibility of a 3D live spheroid-based assay employing a fluorescence biosensor requiring readout out at the per-cell level using the dOPM platform and discusses areas where experimental protocol could be improved in the future to further improve reproducibility.

Longitudinal monitoring of circulating tumor DNA (ctDNA) has emerged as a promising framework for characterizing treatment response dynamics in cancer. Scalable tumor-naive approaches for quantifying ctDNA often involve whole-genome sequencing (WGS) or DNA methylation profiling, but their comparative performance and capacity for complementary integration remain poorly understood. Here we systematically benchmarked tumor-naive WGS- and methylation-based ctDNA quantification methods using plasma from 150 patients with colorectal, lung and breast cancer. Using paired high-depth WGS and EM-seq data, we generated 40,000 in silico samples and evaluated detection accuracy, limits of detection (LoD) and quantification (LoQ) across cancer types and sequencing depths (0.1x-30x). We further assessed single- and multimodal method combinations, identifying conditions under which integrated approaches enhance analytical performance for detection and quantification relative to single modalities. This benchmark delineates key performance trade-offs and provides a practical framework to support method development and guide future research applications in ctDNA-based biomarker studies.

Isothermal nucleic acid amplification tests enable rapid and decentralized molecular diagnostics but often lack robust quantitative readouts compared to quantitative PCR. Here, we present a semi-quantitative nucleic acid measurement approach using machine learning to extract spatiotemporal features from real-time fluorescence imaging of rapid isothermal amplification reactions in microfluidic chips. A convolutional neural network was trained on multiple images sampled throughout a chip-based recombinase polymerase amplification reaction to classify samples into clinically relevant or logarithmically spaced concentration ranges spanning five orders of magnitude. The clinical classification model achieved 94.6% accuracy, and the logarithmic model achieved 92.7% accuracy, with most errors occurring between adjacent concentration categories. By learning spatiotemporal patterns of fluorescence development rather than relying on explicit feature extraction, the model remained accurate at both high and low nucleic acid concentration regimes where other quantitative isothermal molecular tests struggle. This approach enables automated interpretation of amplification reactions and extends the usable dynamic range of the assay. These results demonstrate that integrating machine learning with image-based amplification methods can support rapid semi-quantitative molecular testing and may facilitate broader deployment of nucleic acid diagnostics outside centralized laboratory settings. Author summaryMany rapid nucleic acid testing methods for infectious diseases are simple to run but struggle to measure how much genetic material is present, which limits their usefulness in clinical decision-making. In our work, we study a technique that produces visible fluorescent patterns during nucleic acid amplification reactions. Traditionally, the amount of nucleic acids present are measured by counting individual bright spots, but this becomes difficult when the target nucleic acid concentration is high and the spots merge together. We developed a machine learning approach that models how the fluorescence pattern changes over time. By analyzing a sequence of images from each reaction, our model can assign samples to concentration ranges across a wide span. This allows us to extract meaningful information even when traditional analysis methods break down. Because this approach works with simple imaging systems and does not require complex equipment, it could help support more informative and accessible diagnostic testing in point-of-care and low-resource settings.

Here we introduce OpenEvo, a fully open-source, low-cost turbidostat platform for automated continuous culture and directed evolution experiments. Existing tools are expensive, complex, or lack open-source hardware; OpenEvo addresses this gap. OpenEvo is a complete, fully automated evolution platform with detailed, illustrated construction instructions for beginners, open-source software and firmware, and a single device priced around $300. An optional PC-based version offers enhanced functionality, including remote access, programmable evolution cycles, programmable LED stimulation, and a data visualization tool. OpenEvo can cycle through three types of media for positive, negative, and neutral selection conditions, supporting a wide range of experimental designs. We validate the use of OpenEvo by evolving H. volcanii to grow from 15% to 12% salt over ~150 cycles, ~1,000 hours. Evolved cells grew 36% faster than wild-type at 12% salt. Whole-genome sequencing of adapted cells found SNPs and large deletions. We also demonstrate positive and negative selection using the OpenEvo LEDs to drive optogenetics via a Phytochrome B-based optogenetic tool, with light as the selection stimulus during over 4000 hours of growth. OpenEvo lowers the technical and cost barriers for continuous evolution experiments, serves as a teaching tool, and is designed to grow an open community of users who share modifications.

Targeted long read sequencing (LRS) of native genomic DNA (gDNA) using Oxford Nanopore Technologies (ONT) is an economically and computationally accessible method for sequencing selected genomic regions without the limitations associated with amplification-based approaches. At present, efficiency, multiplexing, and scalability remain key challenges for existing targeted LRS. We have developed Cas12a-Targeted Multiplexed Nanopore Sequencing (CTM-nSeq), which combines Cas12a-targeting, DNA fragment enrichment, and optimized adapter ligation using T7 DNA ligase. Unlike previously established protocols, CTM-nSeq is compatible with the latest ONT flow cell chemistry. Performing CTM-nSeq on a single sample with an R10.4 MinION flow cell routinely yields hundreds of on-target reads. Furthermore, CTM-nSeq enables targeting of multiple loci and is the first targeted ONT sequencing method, allowing reliable, barcode-assisted multiplexing. CTM-nSeq is an efficient and accessible method for sequencing native gDNA and analysing DNA methylation, repeat expansions, and sequence integrity. As such, CTM-nSeq has a wide range of analytical and diagnostic applications.

The combination of spatial transcriptomics with long-read sequencing enables spatial characterization of full-length transcripts within solid tissue sections. However, standardized computational analysis frameworks are lacking, and it remains unclear whether available long-read sequencing platforms from Oxford Nanopore Technologies and Pacific Biosciences yield comparable results. Here, we present a computational strategy for spatial full-length transcript analysis, focusing on the spatial profiling of adaptive immune receptor repertoires (AIRR). Our approach introduces an adaptive filtering strategy that dynamically refines read selection and significantly improves consensus accuracy, enabling high-confidence sequence reconstruction independent of platform-specific sequencing error profiles. We further derive evidence-based guidelines tailored to the consistent and robust analysis of spatial AIRR data. The resulting software LongAIRR is modular and interoperable with existing spatial transcriptomics and AIRR analysis frameworks. This work establishes a methodological foundation for spatial immunology, enabling precise mapping of immune repertoires within their native tissue microenvironments.

Quantifying blood-brain barrier (BBB) integrity from fluorescence microscopy remains limited by subjective scoring and categorical classification methods that lack reproducibility. For objective and consistent BBB phenotyping, we present two semi-automated image-analysis pipelines that replace manual scoring with quantitative, continuous-variable measurements. Our in vitro pipeline, implemented in Python, quantifies the connectivity of tight junction structures by measuring discrete ZO-1 fragment objects within manually traced junction regions. It outputs continuous metrics including average fragment area, total junctional area, and a junctional fragmentation ratio that captures degree of ZO-1 continuity versus discontinuity. In human brain microvascular endothelial cells subjected to glycocalyx component knockdown, the pipeline detected significantly reduced fragment area (37% decrease for both CD44 and syndecan-1 (SDC1) knockdown, p = 0.0148 and 0.0084) and junctional fragmentation ratio (p = 0.0061 and 0.0137). Our in vivo pipeline integrates ilastik-based pixel classification with FIJI macro automation to quantify vascular marker colocalization and to separate vessel signal from microglial contamination within a single fluorescence channel, eliminating the need for dedicated counterstains. Applied across four mouse cohorts [young, aged, Alzheimer's, traumatic brain injury (TBI)] and three brain regions [prefrontal cortex (PFC), hippocampus, midbrain], the pipeline revealed concurrent ZO-1 loss and ICAM-1 elevation in the PFC and hippocampus of aged and Alzheimer's mice, with Alzheimer's-specific doubling of eNOS occurring in the PFC (p = 0.0013). TBI mice showed persistent ZO-1 loss with transient ICAM-1 and eNOS changes. Both deterministic pipelines are available on GitHub and designed for adoption beyond the specific markers and systems analyzed here.

Mesenchymal Stromal Cells (MSC) face several heterogeneity challenges hindering clinical and commercial success. Employing a multiple response model, interplay between donor heterogeneity, and critical processing parameters (CPPs), effects on MSC potency and cell expansion attributes were investigated through computed composite attribute scores. Twelve unique CPP combinations were tested in thirteen marrow-derived MSC(M) and five adipose-tissue MSC(AT) training and test datasets, respectively. Donor heterogeneity and select CPP conditions affected a curated gene panel (surrogate for MSC potency); while MSC expansion was primarily influenced by CPPs. Model performances were evaluated against clinical effectiveness data from a previously deployed clinical trial; top-performing model predicted donor rankings coincided with clinical effectiveness data, validating the modeling approach used. Our model predicted that only 8% of tested donors were agnostic to CPPs; a majority (62%) of donors showed CPP-dependent optimal composite quality attributes, with MSC seeding density as a key driver; medium supplementation and oxygen preferences were highly donor dependent. Approximately 30% of donors performed poorly at all conditions tested and may be prospectively identified using a subset of genes (TGFB, VEGF, PDCD1LG1, PDCD1LG2, IDO). Model predicted optimal parameters worked for 69% of tested donors, while sub-optimal parameters worked for only 23% of donors and were confirmed in an independent CD14+ macrophage assay. Our integrated computational and experimental framework predictably identified interactive effects of donor heterogeneity and CPP conditions to optimize MSC potency attributes.

Background. Scanning electron microscopy (SEM) is widely used to determine fibrin fiber structural properties such as fiber diameter and fiber length. However, conventional SEM preparation protocols are time-consuming and typically require conductive sputter coating. The coating process introduces an additional layer onto the sample surface and may influence measurements of nanoscale fiber structure. Furthermore, preparation of purified fibrinogen clots often follows protocols originally developed for plasma clots, resulting in unnecessary processing steps. Objective. To evaluate indium tin oxide (ITO) as a flat, conductive substrate for SEM imaging of fibrin fibers, investigate the effects of sputter coating on measured fiber diameter, and develop a simplified SEM preparation protocol for purified fibrinogen clots. Methods. Platelet-poor plasma clots and purified fibrinogen clots were formed on ITO substrates and imaged by SEM following 0 s, 45 s, or 90 s sputter coating. Fibrin fiber diameters were quantified and compared across coating conditions. For purified fibrinogen clots, an ITO-based simplified preparation protocol, in which clots were formed and imaged directly on the conductive ITO surface, was compared with a previously developed, standardized SEM protocol, in which clots were formed in microtube lids and subsequently transferred onto carbon tape for imaging. Results. Fiber diameter measurements were affected by sputter coating duration, with increasing coating time resulting in larger apparent fiber diameters. Plasma and purified fibrinogen clots exhibited distinct fiber diameter distributions and coating responses. For purified fibrinogen clots, the simplified ITO-based protocol produced fiber diameter measurements that were not significantly different from those obtained using the standardized lid-to-carbon-tape workflow when identical coating times were applied. Conclusions. ITO provides a practical conductive substrate for SEM imaging of fibrin fibers and enables substantial simplification of purified fibrinogen clot preparation. When coating conditions are matched, the simplified ITO-based protocol yields fiber diameter measurements comparable to those obtained using the previously standardized lid-to-carbon-tape workflow. These findings support the use of ITO as an alternative conductive imaging substrate and provide a simplified workflow for SEM analysis of purified fibrinogen clots. By reducing washing and transfer steps, this workflow may also provide a useful platform for future controlled studies of fibrin interactions with added proteins or other associated components.

Flow imaging microscopy (FIM) is an important technology for high-throughput characterization of microscopic particles and microorganisms. However, conventional FIM relies on single-plane imaging (SPI), resulting in out-of-focus particles, reduced measurement precision, and incomplete characterization of irregularly shaped objects extending along the z-axis. To address these limitations, a volumetric flow imaging (VFI) framework was developed and implemented on the portable ARTiMiS platform. This approach captures multiple frames along the z-axis and extracts the highest fidelity image for each particle, which can also be used for single image generation with all particles in focus (i.e., all in focus image) and for three-dimensional reconstruction of irregularly shaped objects. Benchmarking VFI with microspheres, live cells (Chlorella vulgaris), and filamentous cyanobacteria demonstrated increased fraction of particles in focus, reduced variability in particle size measurement, and increased resolvability of elongated particles in comparison to conventional SPI on commercially available FIM technologies. For C. vulgaris, VFI-derived size distributions closely matched curated FlowCam measurements without requiring post-processing to exclude out-of-focus particles. All-in-focus image reconstruction enabled simultaneous visualization of particles distributed across multiple depths and consistently resolved a greater proportion of filamentous structures as compared to SPI. For Aphanizomenon sp., Dolichospermum sp., and Planktothrix agardhii, the SPI approach captured only 84%, 61%, and 58%, respectively, of the total filament length resolved by AIF reconstruction. Beyond image-based characterization, VFI enabled estimation of dynamic particle properties such as sinking velocity and mass density. Application of this framework to C. vulgaris cultures revealed distinct mass-density trajectories under nitrogen-replete and nitrogen-deplete conditions, with cell mass density increasing over time under nitrogen-replete conditions and decreasing under nitrogen deprivation. Collectively, these results establish VFI as a next-generation framework for FIM that expands its analytical capabilities beyond conventional morphometric characterization and provides new opportunities for single-cell-enabled environmental monitoring and biomanufacturing.

Alterations of fluorescence properties in retinal pigment epithelium (RPE) cells caused by diseases such as age-related macular degeneration (AMD) highlight the need for detailed analysis of the fluorescent RPE granules at the individual level. Precise segmentation and classification of these granules remain challenging due to their limited visual separability. In this study, we present Classi4RPE, a computational algorithm designed to accurately segment RPE granules and classify them into three categories -- lipofuscin (L), melanolipofuscin (ML), and melanin (M) -- based on fluorescence lifetime imaging data, which provide distinctive contrast. The method is implemented in a custom Python framework and employs seeded watershed segmentation to isolate individual granules. Lipofuscin granules are identified as hyperfluorescent structures with longer lifetimes, while granules with shorter lifetimes are further analyzed based on their spatial lifetime distribution from the center to edge, enabling discrimination of ML from other melanin-rich granules. Our approach achieves high performance, with mean sensitivities of 0.99 for L granules and 0.90 for ML granules, and corresponding specificities of 0.93 and 0.98, respectively, compared to manually annotated ground truth. These results demonstrate the potential of Classi4RPE to surpass human visual limitations and provide a robust tool for quantitative RPE analysis.

BackgroundScratch assays are widely used to study wound closure in vitro, but quantitative image analysis remains constrained by manual variability, proprietary workflows, and tools requiring programming expertise. We developed InVitroGap, a Python-based application with a browser-accessible interface for automated quantification of scratch assay closure from sequential microscopy images. MethodsRCC-ER and Renca cells were seeded in 96-well ImageLock plates and scratched using a WoundMaker device for uniform linear wounds or a 200 {micro}L pipette tip for crisscross wounds. Phase-contrast time-lapse images acquired at 0, 24, and 48 h with an IncuCyte SX5 system were independently analyzed using IncuCyte 2023A Rev2 and InVitroGap. The InVitroGap pipeline combines Gaussian smoothing, gradient-based texture mapping, adaptive percentile thresholding, and morphological post-processing to quantify wound confluence and relative wound density (RWD). Agreement was evaluated using paired comparisons, Pearson and Spearman correlations, Bland-Altman analysis, and mean absolute error (MAE). ResultsInVitroGap measurements closely tracked IncuCyte outputs across both cell lines, with no significant between-method differences (p > 0.05), strong pooled correlations (R{superscript 2} = 0.964 for RWD; R{superscript 2} = 0.983 for wound confluence), and small mean biases (absolute bias [&le;] 1.64%). The tool successfully processed crisscross wounds from brightfield image series, and a complete four-timepoint series was analyzed in approximately 10 seconds, with robust performance across distinct cell morphologies and wound geometries. ConclusionsInVitroGap provides a transparent, computationally efficient, and platform-independent alternative for scratch assay analysis, delivering performance comparable to commercial systems while remaining freely accessible at https://invitrogap.vercel.app/. HighlightsO_LIOpen-source Python tool for automated, platform-independent in vitro scratch assay analysis C_LIO_LITexture-based adaptive pipelines enable robust wound segmentation across cell types C_LIO_LIQuantifies wound confluence and relative wound density from time-lapse images C_LIO_LIStrong agreement with IncuCyte measurements in the tested datasets C_LI

Background: Single cell RNA sequencing provides a wealth of information to explore the complexities of the tumour microenvironment, but crucially the spatial topology of the tumour is lost and studying cellular interactions is limited. Spatial transcriptomics aims to address this however the technique remains cost prohibitive for the generation of data from meaningfully-sized clinical cohorts. In contrast, spatial proteomic profiling with multiplex immunofluorescence, preserves spatial interactions, is relatively cost accessible, and is scalable for large clinical cohorts to address powerful translational questions. Whilst multiplex approaches have advanced in recent years, we note that cancer-associated fibroblasts (CAFs) have been explored in less detail, potentially due to difficulties associated with CAF heterogeneity and the diversity of markers used to define them. Methods: We designed, optimised, and validated a multiplex immunofluorescence panel that combines four frequently used CAF markers; alpha smooth muscle actin (aSMA), fibroblast activation protein (FAP), podoplanin (PDPN) and platelet-derived growth factor receptor alpha (PDGFRa) with CD8 and pan-cytokeratin. Here we share our methodology and the practical considerations taken to inform the final panel design. We also highlight the benefits of robust optimisation experiments.

Flow cytometry produces high-dimensional single-cell protein measurements central to immunophenotyping and clinical monitoring. Yet analysis still relies largely on manual gating, which is labour-intensive, poorly reproducible, and ill-suited to large marker panels. Existing computational approaches address classification or discovery in isolation, treating cell-type identity as a post-hoc annotation rather than as part of the generative model itself. We present MARVIN, a semi-supervised variational autoencoder that encodes the assumption that cells organise into discrete populations with continuous intra-population variability through a Gaussian mixture prior in the latent space. Because each component represents a distinct cell population, classification, discovery, and density estimation emerge as complementary views of the same representation. On public benchmarks, MARVIN matches or exceeds existing methods using as few as 10% labelled cells. Trained exclusively on healthy samples, it identifies leukaemic cells through elevated reconstruction error, providing an unsupervised anomaly detection signal. On paired stimulation data, it maintains stable population assignments while capturing condition-specific shifts in abundance and marker expression at patient-level resolution. MARVIN is open-source and designed for local deployment, adapting to institution-specific panels and instruments

Diagnostic assessment of breast cancer biopsies remains reliant on resource-intensive histopathology and molecular profiling, which often lack real-time physiological readouts. Magnetic levitation (MagLev) enables label-free density profiling of single cells, yet its application to intact tissue biopsies has been precluded by size-dependent geometric artifacts and the absence of analytical frameworks for biopsy-scale samples. Here, we report the first application of MagLev to intact invasive breast carcinoma biopsies (200-600 m) for biophysical profiling, generating multivariate biophysical signatures from 203 samples across 17 patients. We developed a physics-based size-correction algorithm (xmc) that isolates biological density from geometric artifact, and demonstrate that tissue viability is predicted not by average levitation height, but by spatial heterogeneity across replicate samples, reflecting the microenvironmental complexity of metabolically active tumors. Multivariate integration using Partial Least Squares (PLS) regression and Factor Analysis of Mixed Data (FAMD) identified nodal status (N) as the strongest biophysical predictor, suggesting that lymphatic dissemination capacity leaves a measurable signature in the primary tumor density profile. Unsupervised patient clustering in PLS-derived latent space recovered three clinically coherent subgroups aligned with molecular subtypes. This 30-minute, low-cost assay provides exploratory biophysical stratification complementary to existing diagnostics, particularly in resource-limited settings.

Optogenetics enables experiments in out-of-equilibrium conditions to clarify biological mechanisms and quantify biophysical parameters. However, modelling and control techniques to study mammalian cell biology under optogenetic perturbation remain underutilised. Here, we benchmark these methods within mammalian cells by steering nucleocytoplasmic transport via the optogenetic LEXY protein in outcome-driven microscopy. First, we employ system identification to obtain models that predict transport dynamics by minimising the prediction error. We quantify this prediction accuracy for one biophysical model and two black-box models. Second, we evaluate closed-loop control efficacy by steering transport along a predefined trajectory using model-free Proportional Integral (PI) control, model-based Linear Quadratic Regulation (LQR) and Model Predictive Control (MPC). Both the predictive models and the applied control techniques demonstrate robust performance against cell-to-cell variation. This biological variation is quantified by the parameter distributions obtained from model identification with single-cell trajectories. While we show that model-free techniques such as PI and gain-scheduled PI achieve steering without explict model knowledge, predictive architectures offer better performance under this cell-to-cell variation and time-varying setpoints. Moreover, black-box predictive accuracy suggests that this model-based control is possible, even when explicit mechanistic understanding is missing. Ultimately, we demonstrate that predictive modelling and optogenetics enable quantitative characterisation and precise manipulation of mammalian cells, while offering practical guidelines for the implementation of these techniques.

Generation of otic progenitors from pluripotent stem cells requires precise timed regulation of signalling pathways, including bone morphogenetic protein 4 (BMP4). Because endogenous levels of BMP4 varie between cell lines, the optimal concentration of exogenous BMP4 must be determined individually to achieve efficient otic differentiation. Three different human induced pluripotent stem cell lines (hiPSCs) underwent ectodermal differentiation to early otic induction stages in the presence of various concentrations of BMP4 (0-5 ng/ml). Differentiation outcomes were assessed by immunofluorescence staining, and quantitative gene expression analysis. Raman microscopy was used to characterize biochemical differences between hiPSC differentiated cultures exposed to different BMP4 concentration. We observed distinct ectodermal fate were after 8 days of in vitro differentiation depending on BMP4 concentration, including neural, non-neural/otic ectoderm and surface epidermal fates. The proportion of PAX2-otic progenitors varied substantially between cell lines and culture conditions, ranging from approximately 9% to 77%. Raman spectroscopy revealed concentration dependent spectral differences and enabled discrimination between differentiating condition within individual hiPSC lines. Analysis of Raman spectral features indicated differences in nucleic acid, lipid, protein, and collagen associated signatures across culture conditions and cell lines. These findings demonstrate that Raman microscopy provides a non-destructive, label-free method for monitoring molecular changes associated with early otic differentiation. By complementing conventional molecular and immunocytochemical analyses, Raman spectroscopy offers a valuable tool for optimizing BMP4-mediated otic induction protocols and improving the reproducibility of stem cell-based strategies for inner ear research and regenerative medicine.

MicroRNAs (miRNAs) act as key regulators of gene expression across diverse cellular processes, and their precise quantification can provide unique insight into disease pathogenesis. High-throughput sequencing allows for comprehensive small RNA profiling; however, standard commercial library preparation workflows are challenged by issues of low sensitivity and representational bias, limiting reliable profiling, especially in scenarios where samples are scarce. Several structural studies have shown that this bias primarily arises due to sequence and secondary structure variations between miRNAs and adapters during enzyme-catalyzed biochemical reactions. In this work, we propose a new approach to ligation adapter engineering using a bioinformatic analysis of the human miRNome to rationally design structure-forcing 5 adapters, that physically override localized, unpredictable structural variations during the intermediate ligation state. We show that this approach combined with a practical fluorescence-guided workflow, utilizing a fluorescently-labeled 3 adapter and novel Fluorescent Ligation Rulers (FLRs) to guide precise band excision, can minimize representational bias and increase the sensitivity of small RNA sequencing from low-input biological matrices. In comprehensive benchmarks using a synthetic panel, this method significantly reduced bias and outperformed alternative commercial protocols. Finally, we demonstrate that this workflow enhances biomarker detection and library quality in challenging clinical matrices, especially in cerebrospinal fluid. Overall, this protocol enables highly accurate miRNome characterization and is well-suited for biomarker discovery in challenging sample types.