BMC Methods

SignificanceStandard methods to characterize peripheral blood mononuclear cells (PBMCs) are often destructive, lack metabolic information, or do not provide single-cell resolution. Label-free tools that non-destructively measure single-cell metabolism within PBMCs can provide new layers of information to characterize disease state and cell therapy potential. AimDetermine whether non-destructive fluorescence lifetime imaging microscopy (FLIM) of endogenous metabolic co-factors NAD(P)H and FAD, or optical metabolic imaging (OMI), can identify immune cell subsets and activation state within heterogeneous PBMC cultures. ApproachOMI measured single-cell metabolism of PBMCs from 3 different human donors in the quiescent or activated (phorbol 12-myristate 13-acetate and ionomycin) state. Fluorescent antibodies were used as ground truth labels for single-cell classifiers of immune cell subtypes. ResultsOMI identified quiescent vs. activated PBMCs with 93% accuracy at only 2 hours post-stimulation, identified monocytes within quiescent and activated PBMCs with 96% and 88% accuracy, respectively, and identified NK cells within quiescent and activated PBMCs with 74% accuracy. ConclusionOMI identifies activation state and immune cell subpopulations within PBMCs, enabling single-cell and label-free measurements of metabolic heterogeneity within complex PBMC samples. Therefore, OMI could enhance PBMC immunophenotyping for diagnostic and therapeutic applications. Statement of DiscoveryWe demonstrate that autofluorescence lifetime imaging can resolve functional and phenotypic metabolic subpopulations within a mixed culture of immune cells from human blood. This provides a new technique to characterize metabolic activity within immune cells from the peripheral blood of patients, which could improve disease diagnostics and the production of cell therapies.

Longitudinal live cell imaging is valuable for characterizing dynamic morphological and phenotypic changes in biological systems. However, conventional approaches rely on manual microscope operation, which is labor-intensive, limits imaging frequency, and disrupts the cellular environment. These constraints reduce scalability, increase experimental variability, and restrict both the duration and temporal resolution of continuous imaging. Although automated imaging platforms partially address these limitations, existing solutions are often constrained by the cost, footprint, and inflexibility of in-incubator microscopes or stage-top incubators. Here, we present an automated in-incubator epifluorescence microscope designed for long-term operation. The system features a modular architecture with optional multi-fluorescence imaging, automated plate scanning, configurable light sources, and compatibility with multiple plate formats, including integration with fluidic automation devices. By positioning the light sources and control electronics outside the incubator, the platform improves thermal stability and long-term operational reliability. This approach enables continuous, high-frequency imaging over extended durations, providing a source of rich data for quantifying time-dependent tissue phenotypes, morphological remodeling, and transient biological processes.

Electron Microscopy (EM) is widely used in many scientific fields, particularly in life sciences, offering high-resolution information on the ultrastructure of biological organisms. Accurate characterization of EM image quality is important for assessing the EM tool performance, in addition to sample preparation protocol, imaging conditions, etc. This paper provides an overview of tools we developed as plugins for the popular image processing package Fiji (ImageJ) (1). These tools include signal-to-noise ratio analysis, contrast evaluation, and resolution analysis, as well as the capability to import images acquired on custom FIB-SEM instruments (2). We have also made these tools available in Python, with both versions available on GitHub.

Cellular organelles are not just static structures; they are highly dynamic and directly linked to cellular functions. Changes in their morphology can be early indicators of diseases. Recent advancements in light microscopy techniques have transformed organelle research from qualitative descriptions to precise, quantitative measurements, enabling nanoscale resolution, high-throughput image analysis, and live-cell compatibility. This enables accurate measurement of organelle morphology, dynamics, and spatial organization using modern imaging and analysis techniques. By quantifying organelles, we go beyond simply visualizing to measuring and statistically comparing cellular features across different samples. This protocol addresses a wide range of cellular organelles across all major experimental systems, specifically mentioning mitochondria, myofibers, actin filaments, endoplasmic reticulum, and Golgi apparatus, by integrating experimental design, optimized sample preparation, high-resolution imaging, and validated Fiji/ImageJ-based analysis workflows. For each organelle, step-by-step methods specify reagents, equipment, acquisition parameters, and expected results. While recent advances, such as expansion microscopy, correlative light-electron microscopy, and AI-powered segmentation, offer gains in throughput and resolution, this workflow demonstrates that Fiji-based analysis remains fully capable of delivering high-precision organelle quantification. The entire workflow can be completed within 2-4 weeks, from initial design through validation and the production of measurements suitable for cross-study comparisons. Overall, this protocol establishes a flexible approach to standardize organelle quantification to understand multiple organelles simultaneously in their cellular contexts. Basic Protocol 1: Mitochondrial Quantification Basic Protocol 2: Myofibril Quantification Basic Protocol 3: Golgi Apparatus Morphometry Basic Protocol 4: Endoplasmic Reticulum Network Analysis Alternate Protocol 1: Super-Resolution Imaging Protocol

BackgroundLive-cell fluorescence microscopy enables the study of dynamic cellular processes. However, fluorescence microscopy can damage cells and disrupt these dynamic processes through photobleaching and phototoxicity. Reducing light exposure mitigates the effects of photobleaching and phototoxicity but results in low signal-to-noise ratio (SNR) images. Deep learning provides a solution for restoring these low-SNR images. However, these deep learning methods require large, representative datasets for training, testing, and benchmarking, as well as substantial GPU memory, particularly for denoising large images. ResultsWe present a new fluorescence microscopy dataset designed to expand the range of imaging conditions and specimens currently available for evaluating denoising methods. The dataset contains 324 paired high/low-SNR images ranging from four to 282 megapixels across 12 sub-datasets that vary in specimen, objective used, staining type, excitation wavelength, and exposure time. The dataset also includes spinning disk confocal microscopy examples and extreme-noise cases. We evaluated three state-of-the-art deep learning denoising models on the dataset: a supervised transformer-based model, a supervised CNN model, and an unsupervised single image model. We also developed an image stitching method that enables large images to be processed in smaller crops and reconstructed. ConclusionsOur dataset provides a diverse benchmark for evaluating deep learning denoising methods, and our stitching method provides a solution to GPU memory constraints encountered when processing large images. Among the evaluated deep learning models, the supervised transformer-based model had the highest denoising performance but required the longest training time.

Winnie mice are a widely used in vivo model of inflammatory bowel disease carrying a missense mutation in the Muc2 gene. Here, we present a protocol for genotyping Winnie mice using TaqMan allelic discrimination quantitative PCR. We describe tissue collection, rapid crude DNA extraction, probe-based amplification with dual-labeled fluorophores, and fluorescence-based genotype calling in a single reaction. This protocol enables qualitative SNP genotyping without post-amplification processing and can be readily adapted to other defined point mutations. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=165 SRC="FIGDIR/small/704640v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1f5d985org.highwire.dtl.DTLVardef@19bbd34org.highwire.dtl.DTLVardef@1a2d2fcorg.highwire.dtl.DTLVardef@c9baed_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIAllelic discrimination qPCR protocol for genotyping the Muc2 p.Cys52Tyr mutation using dual-labeled hydrolysis probes C_LIO_LIEnables rapid discrimination of wild-type, heterozygous, and mutant alleles in a single reaction C_LIO_LICompatible with standard real-time PCR instruments and requires no post-PCR processing C_LIO_LISupports high-throughput genotyping from crude DNA with minimal hands-on time C_LI

Fluorescent in situ hybridization (FISH) enables highly sensitive, high-resolution detection of gene transcripts. Moreover, by employing multiple probes, this technique allows for multiplexed, simultaneous detection of distinct gene expression patterns spatiotemporally, making it a valuable spatial transcriptomics approach. Owing to these advantages, FISH techniques are rapidly being adopted across diverse areas of basic biology. However, conventional protocols often rely on volatile, toxic reagents such as formalin or methanol, posing potential health risks to researchers. Here, we present a safer protocol that replaces these chemicals with low-toxicity alternatives, without compromising the high detection sensitivity of FISH. We validated this protocol using both in situ hybridization chain reaction (HCR) and signal amplification by exchange reaction (SABER)-FISH in frozen sections of various model organisms, including mouse (Mus musculus), amphibians (Xenopus laevis and Pleurodeles waltl), and medaka (Oryzias latipes). Our results demonstrate successful multiplexed detection of morphogenetic and cell-type marker genes in these model animals using this safer protocol. The protocol has the additional advantage of requiring no proteolytic enzyme treatment, thus preserving tissue integrity. Furthermore, we show that this protocol is fully compatible with EGFP immunostaining, allowing for the simultaneous detection of mRNAs and reporter proteins in transgenic animals. This protocol retains the benefits of highly sensitive, multiplexed, and multimodal detection afforded by integrating in situ HCR and SABER-FISH with immunohistochemistry, while providing a safer option for researchers, thereby offering a valuable tool for basic biology.

Traditional nucleic acid extraction methods are costly, lengthy, and highly variable depending on the complexity of the sample matrix or the organism of interest. Workflows may exceed twenty steps, require separate kits for RNA and DNA, and demand expensive instrumentation, creating barriers to both speed and scalability. The AutolabTM HBH system addresses these limitations by using hyperbaric heating (HBH) to achieve temperatures above 100 {degrees}C in a sealed, pressurized environment through induction heating, enabling rapid lysis of diverse organisms and neutralization of macromolecular PCR inhibitors within minutes. The combination of extreme heat and HBH-optimized lyophilized reagents rapidly inactivates nucleases while preserving free nucleic acids. The workflow is streamlined to two steps: heating up to 1 mL of sample in the proprietary HBH bullet, followed by a brief centrifugation to pellet additives. The resulting supernatant is immediately compatible with real-time reverse transcription polymerase chain reaction (RT-PCR) and other downstream molecular assays. Here, we evaluate the systems broad compatibility with diverse sample buffers, matrices, and organisms. Comparative testing was conducted alongside Qiagen extraction methods to benchmark performance.

Extracellular vesicles (EVs) are lipid spheres released from cells. Research utilizing EVs has met several hurdles owing to the small size of the majority of EVs and other nanoparticles (<150 nm) and the lack of detection technologies capable of providing high-throughput single particle measurements at this scale. The use of high-throughput single particle measurements is critical for the assessment of EV heterogeneity and abundance which are features often used to assess the development of isolation protocols or particle characterization. The Coulter principle, known in the field as resistive pulse sensing (RPS), has been used for several decades to size and count cells. More recently, this technology has evolved to accommodate nanoparticle analysis. In the last decade a platform utilizing microfluidic resistive pulse sensing (MRPS) has been demonstrated for nanoparticles, offering ergonomic characterization of nanoparticles along with utilizing open format data. To date, assessment of MRPS accuracy and reporting standards have not been assessed. With the aim of increasing data accuracy, ergonomics, and reporting transparency, we developed a microfluidic resistive pulse sensing post-acquisition analysis software (RPSPASS) application for automated cohort calibration, population gating, statistical output, QC plot generation, alternative data file outputs, and standardized reporting templates.

Motion is an essential component of any living system. It is rich with information, but it is often challenging to quantitatively extract biologically informative results from the motion apparent in microscopy images. This challenge is exacerbated by the wide variety in biological movement, which often takes the form of difficult-to-segment amorphous structures undergoing complex motion. An image processing technique known as optical flow can capture motion at each pixel in an image, thus bypassing the need for object segmentation or a priori definition of motion types. This makes it a powerful tool for quantitative assessment of biological systems from the protein to organism scale. However, despite its flexibility and strengths for analyzing fluorescence microscopy images, its adoption in the bioimaging community has been limited by the availability of easy-to-use tools and guidance in results interpretation. Here we describe an optical flow tool, OpticalFlow3D, that can be run in Python or MATLAB and is compatible with three-dimensional microscopy images. Using biological examples across length scales, we illustrate how OpticalFlow3D can enable new biological insight.

BackgroundAntimicrobial resistance in Neisseria gonorrhoeae is an urgent public health threat. Resistance-guided therapy can assure appropriate treatment and reintroduce alternative therapeutic options by identifying genetic predictors of resistance. Mosaicism at codons 375-377 of the penA gene are associated with cefixime resistance. Rapid, field-deployable assays for predicting cefixime susceptibility are lacking. MethodsWe used a machine-learning algorithm to develop a CRISPR Cas13a-based assay to detect the absence of mosaicism at codons 375-377 of the penA gene combined with isothermal amplification in a single reaction. We integrated the assay onto a portable fluorescence-based platform. We evaluated performance using cultured isolates and compared results with PCR genotyping and phenotypic antimicrobial susceptibility testing. We also assessed feasibility of reagent lyophilization for cold-chain-independent deployment. ResultsAmong 40 N. gonorrhoeae isolates, the Cas13a penA assay demonstrated 100% concordance with PCR genotyping and 92{middle dot}5% (95% CI 79.6-98.4%) concordance with phenotypic cefixime susceptibility. Median time to detection was 12 minutes (IQR 5 minutes). The lyophilized detection system detected all 12 isolates with a median time to detection of 45{middle dot}0 minutes (IQR 40-45) compared to 45{middle dot}0 minutes (IQR 35-50) for the positive aqueous control, although peak fluorescence was higher for the aqueous control (p<0.01). ConclusionThe Cas13a assay was rapid and demonstrated strong correlation with genotypic and phenotypic cefixime susceptibility in N. gonorrhoeae, while a lyophilized assay retained functionality.

Advances in the generation of proteins in silico has enabled the efficient design of such that can bind to a specified target. Here, we demonstrate the use of a fluorescently-labelled de novo-designed protein to bind its target in situ and be imaged using fluorescence microscopy, a widely used experimental technique that typically relies on antibodies or similar evolutionary derived binders to identify the presence and location of targets in their native environment. Our de novo-designed protein binds the C-terminal domain M10 (Ig-169) of the giant muscle protein titin, which spans half a sarcomere, the basic contractile unit of striated muscle. M10 antibodies suitable for fluorescence microscopy are unavailable. Confocal microscopy of muscle sections shows the binder localises to the M-band of the sarcomere - where M10 is found - and fails to label muscle in competition experiments and in mutant muscle where M10 is absent. These results demonstrate the utility of de novo-designed proteins in immunostaining-like experiments and suggest a future where targets can be routinely identified in complex biological samples by in silico-generated binders. Such an approach avoids the need to generate antibodies or similar binders either in vivo or in vitro, which can have technical, financial and ethical challenges.

Collecting cells from zebrafish embryos for genotyping is critical to rapid research with these model organisms. The standard collection process is manual, labor-intensive, time-consuming, and requires a skilled person to perform it. To overcome this challenge, researchers are exploring the development of automated genotyping tools for live animals, which would significantly enhance the efficiency and accuracy of genetic screening in zebrafish and other species. The focus of this research was to optimize the Zebrafish Embryo Genotyper (ZEG), an automated system used for the rapid extraction of cellular material from zebrafish embryos. This system rapidly vibrates a roughened chip containing a zebrafish embryo to collect genetic material safely and efficiently. The aim was to improve the efficiency of DNA collection from the chips used with the ZEG by identifying the key factors that contribute to the process. First, the chips were modified to resolve issues associated with loss of sample volume from the chip wells due to evaporation during processing. Second, we experimented with three critical parameters - sample volume in the wells, the vibrational frequency of the system, and the operation time - on the quantity of DNA collected. The performance was evaluated by measuring embryo survival and quantifying the DNA collected. The sensitivity (previously 90%) of the DNA collection and embryo survival (previously 95%) of the were both found to be greater than 95% after optimization. The optimized design parameters (15 {micro}L solution volume, 2.4 V, and a 5-minute run with 5 s alternating on/off) provided a >50% increase in DNA collection compared to the previous designs and parameters. The proposed chip design and operation do not appear to cause any apparent adverse effects on the development or survival of the embryos.

X-ray fluorescence microscopy (XFM) continues to develop as a powerful quantitative technique for high resolution, label-free, elemental mapping of biological, environmental, and material samples. Methods for rigorously fitting spectra, increasing throughput, accounting for background signals, and deconvoluting overlapping emission lines continue to evolve. We show here that quantitative fits of XFM data obtained after removing a baseline, calculated by connecting peak edges, can be unexpectedly dependent upon acquisition dwell-time and spectral aggregation leading to differences in apparent elemental content. Using mouse preimplantation embryos and ovarian follicles as model samples, we demonstrate how these variables influence quantitative comparisons between samples. We find that subtracting an empirically measured blank spectrum instead of a baseline provides quantitative XFM elemental mapping results that are independent of dwell time and spectral aggregation dependencies.

Rapid and sensitive detection of plant pathogens, such as the Avocado Sunblotch Viroid (ASBVd), is essential for early disease management and agricultural biosecurity. Yet, most current diagnostic methods not only require relatively large sample inputs but also often lack the ultrasensitivity required for reliable detection with scarce or minimally collected plant material. Here, we report a novel low-input but ultrasensitive diagnostic platform that integrates isothermal recombinase polymerase amplification (RPA), CRISPR-Cas12a detection, and a solid-state nanopore array for the detection of ASBVd. The system leverages CRISPR-Cas12a collateral cleavage activity to generate single-bead fluorescent signals, which are captured by a nanopore array through pressure-driven blockage. Our platform achieves a detection limit down to 1.68 copies/L while using only 40 nL of bead-fluorophore mixture per readout, which is over 100-fold less than conventional assays based on fluorescent readout using an imaging reader, enabling detection from minimal avocado sample collection. We demonstrate robust binary classification of ASBVd-positive and -negative samples from multiple avocado tissue types and orchards in California. The assay requires just 60 minutes and operates entirely under isothermal conditions, avoiding the need for bulky PCR instruments and supporting on-site deployment with minimal equipment. This method provides a promising platform for field-deployable, ultrasensitive, and low-input diagnostics of viroids and other low-titer pathogens in plant or clinical settings.

Accurate and transparent characterization of extracellular vesicle (EV) preparations is essential to ensure reproducibility, comparability, and adherence to MISEV reporting standards. However, data outputs from commonly used instruments for assessing EV size, concentration, and surface charge (zeta potential) vary widely in format and structure, complicating standardized analysis and integration across platforms. We present PHoNUPS (Plotting the Histogram of Non-Uniform Particles Sizes), free and open-source software (FOSS) developed in R, that enables unified processing, analysis, and visualization of EV characterization data. PHoNUPS computes statistics and generates standardized histograms and contour plots (for size against zeta potential) suitable for transparent reporting and cross-study comparison. The software produces high-quality, publication-ready figures. Third-party graphical editing tools allow users to refine and annotate visualizations for presentation or manuscript preparation. PHoNUPS supports multiple measurement file formats, thereby facilitating dataset integration from different instruments. PHoNUPS was developed with extensibility at its core, providing a basis for user-driven growth. We invite the EV community--researchers, analysts, and tool developers--to use PHoNUPS, share feedback on their experience and needs, and contribute to the platform by integrating additional input data formats, analytical routines, and visualization functionalities. Graphical abstractThe free software PHoNUPS processes the outputs of several different EV characterization instruments and it is extensible with further ones. It computes statistics of particle size and zeta potential distributions and it plots the corresponding histograms or contour plots. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/702479v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@b2b3a1org.highwire.dtl.DTLVardef@2f2907org.highwire.dtl.DTLVardef@2ec521org.highwire.dtl.DTLVardef@903624_HPS_FORMAT_FIGEXP M_FIG C_FIG

Small extracellular vesicles (sEV) are membrane-enclosed nanoparticles found in body fluids that carry molecular cargo from their cells of origin. Their stability and disease-associated molecular signatures make them promising targets for the development of non-, or minimally invasive liquid biopsies, yet scalable approaches enabling single-vesicle quantification of sEV while resolving their heterogeneity remain limited. Here, we present PICO (Protein Interaction Coupling), a reference-free quantitative assay adapted for sensitive multiplex profiling of individual intact vesicles. PICO detects vesicle markers by requiring colocalization of two or more copies of the same protein or of distinct proteins (e.g., CD9 or CD9/CD63) on individual vesicles, using DNA-barcoded antibodies and digital PCR (dPCR) for quantitative readout. We demonstrate that this unique architecture of the assay provides high specificity by distinguishing EV-bound proteins from soluble counterparts, and can be adapted to target either surface-exposed or intravesicular biomarkers. PICO requires minimal sample input (1 {micro}l) and no specialized instrumentation beyond standard digital PCR. In a head-to-head comparison with nano-flow cytometry, PICO achieved a comparable limit of detection for sEV subpopulations. Profiling sEV isolates from blood for canonical markers (CD9, CD63 and CD81) and HER2 demonstrates precise, high-resolution quantification of sEV subpopulations in complex clinical samples and supports integration of scalable single-EV analysis into research and diagnostic workflows.

Quantitative organelle analysis is highly sensitive to image-processing choices, limiting reproducibility across microscopy studies. Here, we systematically compare automated, interactive machine learning, and deep learning-based pipelines for lipid droplet and mitochondrial quantification in live human osteosarcoma cells imaged by fluorescence microscopy and label-free holotomography. Using standardized downstream feature extraction, we evaluated script-based workflows (Fiji, Python), a modular platform (CellProfiler), interactive machine learning (ilastik), and pretrained deep learning models. Lipid droplet segmentation was qualitatively consistent across approaches; however, droplet counts, and size distributions varied substantially between pipelines and imaging modalities, with ilastik reducing background-driven detections and improving cross-modality agreement. In contrast, mitochondrial quantification proved highly sensitive to segmentation and skeletonization choices, particularly in holotomography where global intensity-threshold-based methods failed to capture network structure. Based on these cross-pipeline comparisons, we demonstrate how organelle- and modality-specific benchmarking can guide pipeline selection, illustrated by the analysis of metabolic perturbations affecting lipid droplets and mitochondria. Together, these results highlight modality- and morphology-dependent limitations in common analysis pipelines and provide practical guidance for selecting robust, reproducible strategies for quantitative organelle imaging.

Filamentous fungi have complex, three-dimensional growth patterns and a non-adherent nature, which can present challenges for live-cell imaging for quantitative assessment of dynamic cellular processes. To address these challenges, a live-cell imaging system has been modified to constrain the model fungus Aspergillus nidulans to growth in a single focal plane. This enables high-resolution time-lapse imaging of actin dynamics throughout development using a Lifeact actin marker. This system was used to perform kymographic analysis to quantify actin velocity and hyphal extension rates during early hyphal development. Results show two distinct growth phases: germ tube extension (0.58 m/min) and hyphal extension (1.52 m/min). Actin exhibited bi-directional transport along hyphae with biased movement toward the spore body. Actin was also observed re-localizing from hyphal tips to sites of septum formation indicating active redistribution of cytoskeletal resources based on cellular demands. This technological advancement overcomes longstanding limitations in fungal live-cell imaging and provides a new platform for quantitative systems-level analysis of mycelial development, offering new insights into the spatiotemporal coordination of cytoskeletal dynamics during filamentous growth.

Microscopy-based analysis of red blood cell (RBC) morphology is widely used to study phenotypes in sickle cell disease (SCD). Although AI models have been developed to automate classification, most are trained on pre-cropped single-cell images and thus struggle with full-scope microscopic images containing densely packed cells and diverse morphologies, which require both accurate detection and fine-grained classification. We propose an end-to-end computational framework to identify individual RBCs in full-scope microscopy images and classify them into five morphological categories: discocytes (DO), echinocytes (E), elongated and sickle-shaped cells (ES), granular cells (G), and reticulocytes (R). We first evaluate advanced detection-classification models, including You Only Look Once (YOLO) and Detection Transformers (DETR), and demonstrate that while these models effectively detect cells, their classification performance falls short of specialized classifiers trained on single-cell images, particularly for minority phenotypes. To address this limitation, we introduce a two-step framework in which a YOLO-based detector localizes and crops individual cells from full-scope images, followed by a fine-tuned DenseNet121 ensemble classifier that assigns each cell to one of the five morphological categories. The proposed framework achieves a detection-level F1-score of 0.9661 and a weighted-average classification F1-score of 0.9708, with an overall classification accuracy of 97.06%. Compared with the single-step YOLO26n baseline, the two-step pipeline yields a macro-average F1-score improvement of +0.1675, with particularly substantial gains for minority classes (E: +0.1623; G: +0.2774; R: +0.2603). Overall, this hybrid framework demonstrates a practical strategy for adapting fast, general-purpose detection models to domain-specific biomedical tasks by combining them with specialized classifiers, delivering both efficiency and high accuracy for scientific and clinical image analysis.