Current Protocols

Background: Transcardial perfusion fixation is widely used to preserve rodent brains for histological and ultrastructural analysis, but protocols can vary in the use of pre-fixation washout, fixative formulation, and osmotic additives. Relatively few studies have directly compared how the chemical composition of the perfusate affects tissue preservation. Methods: Male Sprague-Dawley rats underwent transcardial perfusion using a series of aldehyde-based fixative formulations that varied in fixative composition, use of a phosphate buffered saline washout, and the addition of mannitol or polyethylene glycol 35 kDa (PEG35). Perfusion outcomes were assessed using gross brain morphology, semi-quantitative grading of vascular blood clearance, quantitative detection of residual erythrocytes in whole-slide histological images, and light microscopic measures of cellular visualization and morphology. Perfusate osmolality was measured, and selected specimens were examined by electron microscopy. Results: Substantial vascular blood clearance was frequently achieved with fixative-only perfusion, indicating that a pre-fixation washout was not required to achieve high levels of blood clearance under the conditions tested. Routine light microscopy-based measures of cellular visibility and morphology were broadly similar across treatment groups. Adding mannitol and PEG35 to the fixative solution both led to concentration-dependent gross tissue shrinkage, particularly with PEG35, without correspondingly large or consistent changes on light microscopy. A substantial degree of case-to-case variation in perfusion quality was also observed among animals undergoing nominally similar procedures. Conclusions: Substantial variation in perfusate composition produced relatively modest differences in the light microscopy outcomes examined, despite pronounced effects of osmotic additives on gross brain morphology. There were some cases where perfusion with fixative alone led to near-complete blood vessel clearance, and the use of a preceding phosphate buffered saline washout was not associated with higher vascular clearance scores. The degree of case-to-case variability observed within treatment conditions suggests that procedural factors can play an important role on perfusion quality in addition to perfusate composition.

1. For many animals, vision represents the primary sensory modality governing a vast diversity of behaviours. Creating 'virtual twins', or visually realistic digital reconstructions of real individuals interacting within their environments, offers a promising approach for understanding how visual information guides an animal's decisions, behaviours, and interactions. Such systems enable the creation of video scenes from any view, including the viewpoint of the animals themselves. This allows receiver information to then be analysed from the perspective of species-specific visual systems, or for the accurate reconstruction of a real animal's movement to provide social stimuli for behavioural experiments. 2. Despite advances in 3D modelling, photorealistic scene rendering, and animal tracking, efficiently creating realistic digital reconstructions of movement remains challenging for many species. In fishes, a continuously-deforming body due to the absence of discrete joints make it particularly difficult to map tracked body keypoints onto articulated digital models, often resulting in unnatural or erratic animations. 3. Here, we introduce DigiFish, an add-on for the open-source animating software Blender, that generates realistic fish animations from tracking data. By integrating anatomical keypoint tracking with spline-based body curvatures, DigiFish reconstructs biologically-realistic swimming postures and movements of fishes within customisable virtual aquatic environments. The system also supports virtual 'point-of-view' cameras that can be placed arbitrarily within a scene, or dynamically driven by eye-tracking data, enabling unlimited perspectives on how individuals, conspecifics, or landmarks appear against complex backgrounds and how these views change with realistic swimming and eye movements. 4. We provide the Blender add-on and user guide and use DigiFish to reconstruct two different contexts: staged predator-prey interactions in the laboratory and natural schooling of wild fish in the field. Using a further reconstruction of fish swimming in an arena, we also use an Elementary Motion Detector model to show that our virtual reconstructions successfully capture perceptually-realistic movements and postures of individual fish within a digitally reconstructed scene. 5. By integrating photo-realistic scenes with biologically-realistic, simulated motion, DigiFish offers a flexible and accessible platform for investigating visually guided behaviour, perception, and cognition in complex and dynamic environments.

The chick embryo is a widely used vertebrate model for developmental biology, cardiovascular, and bioengineering research. We present an automated laser speckle contrast imaging (LSCI) platform for non-invasive, longitudinal, label-free imaging of the developing heart and extraembryonic vasculature of the chick embryo through the intact eggshell. The platform can detect blood vessels as small as 70 +/- 17 um in diameter and enable repeated imaging from day 0 to day 8 of incubation without disturbing normal embryonic development. It provides both structural (a map of growing extraembryonic blood vessels) and functional (blood flow dynamics and heart rate) information. Using this system, we generated the first continuous, non-invasive, time-lapse recordings of the early functioning chick heart and its extraembryonic vessels. Longitudinal imaging was performed on 15 embryos from different chicken breeds with eggshell colors white, brown and green. Analysis revealed slight variability in early cardiac and vascular morphogenesis across breeds and shell types and shows its potential for automated developmental staging. Finally, for the first time, the growing vasculature of the yolk sac and the chorioallantoic (CAM) membrane was imaged from day 4 to 8. The heart rate and growth of the extraembryonic blood vessels were quantified automatically during imaging.

The modeling of aroma release in food science is a complex, multidisciplinary field integrating mass transfer theory and principles of physical chemistry. While mechanistic models have long been used to understand volatile release, their practical adoption has been hindered because existing implementations are often private, "in-house" tools that lack reproducibility and extensibility. This paper introduces digimouth, an open-source Python framework designed to provide a unified, modular, and reproducible environment for simulating and fitting aroma release kinetics. A key innovation of digimouth is its handling of events and stages. Events represent discrete, instantaneous changes in the system (such as opening a bottle or swallowing), while stages allow for multi-phase simulations where experimental conditions or physical dynamics change over time. To demonstrate the tool's utility, the authors implemented established in vitro convection models and performed sensitivity analyses on parameters such as air-water partition coefficients and mass transfer coefficients. The package also includes an optimization module based on the least-squares method to fit parameters to experimental data. Validation against real-time data from Proton Transfer Reaction-Time of Flight- Mass Spectrometry (PTR-ToF-MS) shows a satisfactory fit, with an average error of approximately 2%. Ultimately, digimouth facilitates robust model development and hypothesis testing, offering the food science community a collaborative tool to better understand the mechanisms of flavor release.

Maximizing the morphological and molecular fidelity of preserved mammalian brain tissue is essential for basic neuroscience and brain banking, where tissue quality determines the reliability of downstream analyses. Aldehyde fixation and cryogenic storage are the most powerful preservation techniques available. However, how to best combine them is incompletely characterized. Here, we tested twelve different approaches to preserve the murine brain, including standard aldehyde perfusion fixation, variants of aldehyde-stabilized cryopreservation (ASC) using ethylene glycol (EG), aldehyde fixation followed by unprotected freezing, and several cryopreservation protocols without preceding aldehyde fixation, such as interleaved equilibration with vitrification solution. Preservation quality was assessed using light microscopy, transmission electron microscopy, and patch-level feature analysis with the DINOv2 vision foundation model. We found that ASC with sodium dodecyl sulfate (SDS)-mediated blood-brain barrier permeabilization preserved nuclear morphology, myelin periodicity, and neuropil texture comparable to standard aldehyde perfusion fixation, providing the first independent replication of ASC. Omitting SDS caused severe parenchymal dehydration, due to a mismatch between water and cryoprotectant transport. Aldehyde fixation followed by unprotected slow freezing confined ice damage primarily to perivascular zones, while fast freezing led to intranuclear clefts and cavities. Among non-fixation protocols, interleaved equilibration with vitrification solution preserved myelinated axon profiles and chromatin patterns, but induced perivascular edema. High concentration glycerol perfusion caused significant osmotic dehydration. Straight freezing of unfixed tissue without cryoprotectant was followed by membrane disruption, but resulted in the visualization of more structure than expected, likely as a result of structural restitution upon thawing. Taken together, our results provide a framework for matching preservation strategies to the needs of different types of brain banking and neuroscience research.

Biofilms are widely distributed in both natural and engineered systems and play a fundamental role in microbial ecology and biotechnological applications. When grown on agar substrates, biofilms often exhibit macroscopic structural features due to mechanical instabilities driven by matrix production. These wrinkles encode relevant information about biofilm growth and structural development and have even been shown to possess some functional roles. Despite their relevance, currently available approaches for wrinkle detection from images rely on manual annotation, which is time-consuming, not scalable, and strongly dependent on the annotator. In the present study, we developed an end-to-end machine learning pipeline, named AutoWrinkleID, that starts from bright field images of biofilms and performs wrinkle identification, characterisation, and quantification across multiple bacterial species and strains. The name reflects the automated identification and subsequent characterisation and quantification of biofilm wrinkles. The annotations used to generate the masks, namely images containing only the wrinkle structures, were obtained both through manual labelling and using constitutive fluorescence and motility fluorescence. Interestingly, we found that models trained using only manual annotations perform worse than those trained using motility fluorescence markers. A comparison between motility and constitutive fluorescence masks further indicates that motility based annotations consistently outperform constitutive fluorescence across all strains, strongly suggesting that motile phenotypes are tightly associated with wrinkle structures. Additional computational analysis is conducted on the same dataset to assess the robustness and limitations of the results as a function of training dataset. We also observed that Sholl analysis, originally developed for the study of neuronal dendrites and later applied to wrinkle analysis, can effectively characterize wrinkle patterns, although its applicability is strongly limited by wrinkle shape and by the quality of the masks.

Animal pose estimation typically requires manual annotation of body parts to train neural networks for each new recording system or environmental condition. Here we use a general-purpose vision-language model (VLM) to generate annotations in multi-animal recordings of fruit flies (Drosophila melanogaster). Initial annotations follow written anatomical descriptions and are reviewed by an independent VLM session using neighboring video frames. On 120 held-out fly images, AI annotations had a median distance of 2.28 pixels from human labels, with 84.0% agreement within eight pixels, averaged across landmark types. The comparison included 10 landmarks, with head and eyes analyzed separately because of systematic differences in placement. Review improved agreement primarily at the feet, with the largest remaining discrepancies at positions with limited visual evidence. Visual inspection revealed cases in which the AI annotations were more anatomically accurate than those of an experienced human annotator, particularly at the feet. A convolutional network trained using SLEAP on 300 AI-annotated fly images showed similar agreement with human labels and closely reproduced the AI annotations on held-out images, with a median distance of 1.56 pixels. We also applied the procedure to recordings from another laboratory, using new AI annotations to train a separate pose network. Although VLM annotation required minutes per fly, the trained networks enabled pose estimation throughout more than 700,000 frames. These results demonstrate that VLM annotations can support fast animal pose estimation without manually labeled training images.

Nuclear segmentation in 3D microscopy remains challenging, particularly for low signal-to-noise ratio (SNR) data from timelapse imaging, in vivo experiments, or dense tissue samples. Yet accurate 3D nuclear segmentation is a prerequisite for quantitative single-cell analysis, making this a key bottleneck for high-throughput study of challenging datasets. We present NucLogic, a fast and robust 3D nuclear segmentation algorithm that combines the maturity of 2D segmentation with geometric stitching rules based on nuclear shape, orientation, and intensity, to reconstruct accurate 3D volumes. NucLogic reliably segments challenging datasets including long-term timelapse imaging of organoids, and large dense structures such as the zebrafish brain. Benchmarked against other state-of-the-art segmentation tools, NucLogic matches F1 scores at high SNR, with increasing outperformance as SNR decreases. Importantly, segmentation completes at 20-50% of the computation time, with greater speed gains on larger images. Beyond segmentation, NucLogic includes an intuitive graphical user interface allowing non-experts to segment, quantify, and visualize 3D and 4D data. This enables easy quantification of cell numbers and integration of a variety of both cell-intrinsic properties, such as phenotype, morphology, and local environment metrics, including density and spatial interactions. Together, NucLogic makes quantitative 3D nuclear analysis of previously unmanageable datasets accessible to a broad research community.

Motion-activated camera traps are an effective tool for monitoring animals, but a single study can produce millions of images, which must be analyzed before they yield data. The burden of analyzing them has led many teams to automate the work with computer vision. These models lose accuracy on images unlike those they were trained on, however, and a general model may not use the categories a project needs. Adapting a model to a project usually requires at least one of the following: many labeled images per species, programming expertise, a graphics processing unit (GPU), or uploading images to a remote server. I describe WildObserve, a desktop application that adapts to each project without any of these requirements. SpeciesNet suggests a species for each animal that MegaDetector finds, and WildObserve keeps SpeciesNet's feature vector for each one. A project model, a logistic regression on those features, then retrains in about a second as the reviewer labels, learning the project's own species and names. From the reviewed records, and optionally the suggestions, WildObserve also fits occupancy and abundance models. Images stay on the user's computer, and every record says which model suggested its species and who confirmed it. WildObserve processed 8.1 images/s on an Apple M1 Pro laptop. On Oregon Critters, a dataset released after SpeciesNet was trained, the project model's accuracy passed that of SpeciesNet with ten labels per species (balanced accuracy 0.783 against 0.751). I also tested occupancy on 63 cameras in Malawi that SpeciesNet had not seen. With 5% of the images reviewed and the project model's suggestions counted for the rest, species' occupancy estimates differed from those of a full review by 0.11 on average. From the reviewed images alone, they differed by 0.41. WildObserve takes a project from images to species records in its own categories and on to occupancy estimates, without writing code, needing a GPU, or sending images elsewhere.

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

Synthetic biologists aim for standardization and simplicity in order to ensure reproducibility and reliability. Toolkits offer both, making it easier to share DNA sequences between labs while minimizing context dependent effects from sequences surrounding the construct of interest. The Yeast Toolkit (YTK) for the model organism Saccharomyces cerevisiae has found wide adoption within the scientific community. While the Toolkit and its existing extensions offer a large variety of different parts, there is a lack of characterized logic gates and insulator containing backbones for the assembly of genetic circuits with the yeast toolkit syntax. In this work, we present the Yeast Logic Toolkit (YLT). YLT contains 40 pre-assembled backbones with dropouts, 109 parts for NOT/NOR gate assembly, and a subdivided promoter Type 2 part that allows more flexible 5'UTR design. For insulation, the backbones contain spacer sequences and self-cleaving ribozymes which buffer against transcriptional read-through from upstream sequences. Due to our subdivision of Type 2 parts, we are able to manipulate the 5' untranslated region without a cloning scar upstream of the start codon. We also characterized the NOT and NOR gates from the Cello library for yeast in the sequence context of the yeast toolkit cloning scheme. This toolkit extension will make it easier and faster for scientists to assemble genetic circuits in yeast while providing a minimum of context dependent effects such as genetic read-trough due to the standardization of up- and downstream sequences surrounding the transcriptional units.

Roseoloviruses, notably human herpesviruses 6A and 6B (HHV-6A and HHV-6B), are neurotropic viruses implicated as agents in neurological disorders, including: epilepsy, multiple sclerosis, and chronic fatigue syndrome. However, the effects of roseolovirus infection on neuronal signaling and network activity are not characterized. This is, in part, due to the complexities of monitoring electrical activity in individual neurons and across neuronal connections during viral infection. This protocol describes a human induced pluripotent stem cell (iPSC)-derived neuronal culture system that employs multi-electrode array (MEA) recordings to study neurophysiological changes during viral infection. Two culture platforms are described: (a) NGN2-induced forebrain neuronal cultures; and, (b) progenitor cell-derived neuron-astrocyte mixed cultures. Cell composition in cultures is validated by immunofluorescence staining using neuronal, glial, and viral markers. Functional activity is assessed using extracellular recordings detected via the MEA2100 system. Mean firing rate, inter-spike interval, single-electrode bursting, and network burst activity are characterized between roseolovirus-infected versus uninfected (control) states. Pharmacological treatment with bicuculline, gabazine, and nicotine in conjunction with immunofluorescence is used to confirm functional responsiveness of defined neuronal neurotransmitter chemotypes. Here, we show that HHV-6A infection changes neuronal firing compared to uninfected controls. The methods/workflows described provide a strategy to study how virus infection alters neuronal excitability and may be adapted to compare the effects of different viruses on nerve cell function, infection time courses, different multiplicities of infection (MOI), and therapeutic interventions.

To reduce health risks for human space exploration, it is important to model the effects of deep-space radiation on biological systems. The budding yeast Saccharomyces cerevisiae is a common model organism in space radiobiology, but little is known about how radiation damage to individual cells translates into the population-level effects that experiments measure. The Agent-Based Model for Microbial Populations Exposed to Radiation (AMMPER) is a spatially explicit agent-based model that simulates the effects of ionizing radiation on S. cerevisiae cells and produces population-level results. Its goal is to improve experiment design and data interpretation. AMMPER-1 demonstrated that agent-based simulations can qualitatively reproduce the effects of proton radiation on the growth rate of wild-type and DNA-repair mutant yeast. Here, we present AMMPER-2, which addresses limitations of the first model and expands its capabilities. AMMPER-2 incorporates the dynamics of the redox dye alamarBlue, a data type commonly generated in spaceflight microbiology experiments, and models the fate of reactive oxygen species in more detail. It also adds an improved graphical interface and runs faster. AMMPER-2 remains easy to execute in Python on a standard personal laptop, providing space biologists a tool to explore experimental conditions, generate hypotheses, and interpret data from microbial space radiation experiments.

The field of microglial research has evolved throughout the years. Microglia, the immune cells of the central nervous system, are recognized as highly heterogeneous and dynamic cells, known for modifying their structure and function based on the local context. Investigating microglial density, spatial distribution, and morphological states is important for uncovering their distinct functional states in health and pathology. However, quantifying these features accurately is often a methodological challenge. Fully automated computational approaches often fail to capture subtle biological nuances and complex structural variations. Also, the high diversity of image sets makes it difficult to maintain consistent reliability. In contrast, entirely manual quantification is labor-intensive and prone to observer bias. To bridge this gap, we propose a semi-automated framework using the open-source software CellProfiler. Our workflow is divided into two distinct pipelines designed to combine automation of batch analysis with targeted user oversight, allowing for manual intervention when necessary to ensure maximum accuracy. Both pipelines are capable of recognizing microglial soma and tracing their processes. In the density workflow, it automatically calculates cell density and provides spatial distribution measurements, such as closest-neighbor distance and spacing index. For morphological profiling, it yields extensive structural data, including area, perimeter, convex area, form factor, and various shape descriptors. Furthermore, we demonstrate how researchers can optimize the pipeline settings to accommodate varied image datasets and experimental conditions. We hope this open-source framework can standardize microglial density, distribution, and morphological analysis and reduce systematic bias, providing researchers with a robust tool to better characterize their heterogeneity.

Broad-range RT-qPCR assays are essential for detecting highly diverse viruses within virus families, but designing suitable primers remains challenging because of extensive sequence diversity and biases in public sequence databases. Here, we present a semi-automated workflow that combines sequence clustering and varVAMP to streamline pan-RT-qPCR primer design. The workflow reduces sequence redundancy, improves the representation of viral diversity, and facilitates the identification of broadly reactive primer candidates. We demonstrate the workflow using the arboviral genera Orthoflavivirus and Alphavirus as proof-of-concept datasets, showing its applicability to large and genetically diverse viral sequence collections. The workflow provides a practical, scalable, and reproducible framework for developing broad-range primer candidates and may support surveillance, diagnostics, and preparedness for emerging or divergent viruses.

The Siamese fighting fish (Betta splendens) is well-known for its high levels of aggression and complex, stereotyped displays, making it an ideal model for testing deep learning-based tools for high-throughput, unbiased quantification of aggressive behaviour. A deep-learning object recognition model (BettaAI) was developed to detect the species' aggressive displays, including threat (opercula opening and fin distension), attack (bites) behaviours, as well as surface air breathing, a behaviour correlated with the frequency and intensity of aggression. Videos of males confronting their mirror image were acquired simultaneously with synchronised top and side cameras. Two separate models were trained, with some behaviours identified from top-view frames and others from side-view frames; outputs from both models were combined and post-processed for final quantification. BettaAI performed with high accuracy: F1 scores were 0.99 for the top-view model and 0.93 for the side-view model. Average Precision (AP) at 0.5 IoU exceeded 0.98 for all categories except bites, a rare behaviour in the dataset, which reached 0.865. Comparisons with a manually curated "ground-truth" dataset from videos of 10 males showed strong concordance between automated quantification and manual annotations by expert observers across all behaviours. A case study on 461 males reacting either to a mirror or to a video playback of a conspecific demonstrated the high-throughput and unbiased advantages of BettaAI, with threat displays being more frequent toward the mirror image and attack rates not differing between treatments. This work underscores the potential of BettaAI for automated, bias-free quantification of aggressive displays in B. splendens.

Understanding how neural populations support natural behaviour requires knowing what a population does while an animal performs a specific, self-initiated action. For responses evoked by an experimenter-delivered stimulus, the signal can be aligned by the trigger itself, and no behavioural scoring is needed to define the event. Complex behaviours such as parental care and social interaction provide no such trigger. They are defined from the video record, usually offline and post hoc, and are conventionally scored in one program and exported to another for analysis. In this study we developed EthoTrace, a single self-contained HTML file that runs in a desktop browser without installation, server or network access and carries a session from behaviour and calcium signal through to the statistical comparison. Behaviour is annotated on a timeline shared with the {Delta}F/F trace, the signal is corrected and scaled under a stated baseline convention, single bouts stay inspectable in their behavioural context, and repeated bouts are aligned without filling absent data. We demonstrate the complete workflow on a parenting behaviour experiment, in which behaviour was scored and the photometry signal analysed in a single session folder. We expect EthoTrace to facilitate integrative analysis in neuroscience, where behavioural observation and fiber photometry signals are interpreted together.

High-throughput enzymology enables quantitative characterization of enzyme function across hundreds to thousands of sequence variants and experimental conditions. Nevertheless, the scale and complexity of these datasets create substantial challenges for analysis and quality control. High-Throughput Microfluidic Enzyme Kinetics (HT-MEK), for example, generates large microscopy datasets that must pass through multiple analytical stages, including image processing, initial-rate fitting, and kinetic modeling. Choices or errors made at any stage can propagate into the final kinetic parameters without being evident from fit statistics alone. Here, we provide a broadly applicable guide for analyzing high-throughput enzymology data from the HT-MEK platform, using Michaelis-Menten kinetics as a representative example. We first outline the conceptual workflow from raw fluorescence measurements to estimates of kcat, KM, and kcat/KM. We then discuss common experimental and analytical failure modes and present a framework for deciding when data should be refitted, filtered, qualified, or repeated. Finally, we provide a step-by-step workflow using Mercury, an open-source Python framework that integrates scalable HT-MEK data processing with traceable quality control and diagnostic visualization. This workflow preserves the connection between reported parameters and their underlying measurements and can be adapted to other kinetic models and biochemical assays.

Structural model building and refinement need many decisions between different programs. These decisions often depend on an experienced researcher who knows how the programs work together and how to read experimental data. We used Claude Code with Claude Opus 5.5 to supervise this work for 24 cryo-EM and X-ray cases. The work finished in about 36 hours on one workstation. The agent prepared the inputs, ran established programs, read the validation results, compared candidate models and chose the next step. For 15 deposited cryo-EM models with weak starting geometry, the median MolProbity score improved from 2.62 to 1.76. For three crystal structures, the agent models came close to the published Rfree values. Chemical information such as ligands, ions and modifications must be provided by the researcher, in the same way as the sequence. With this information, the agent can place and check these components. These results show that agent supervision is acceptable when every decision can be inspected. The researcher makes the final scientific judgment. We suggest practical requirements for this kind of use. Future AI agents may help researchers obtain high quality and reliable structures of biological macromolecules.

Double-stranded RNAs are inflammatory byproducts of in vitro transcription of single-stranded RNA. Here we investigate removal of dsRNA byproducts using dsRNA affinity chromatography (dsRNA-AC) and two common mRNA molecules: 1) GFP, and 2) high dsRNA wild-type firefly luciferase (fLucWT). For GFP mRNA, dsRNA levels decreased by 2,000-fold, and mRNA recovery was high. In contrast, dsRNA levels decreased only two-fold for fLucWT, and mRNA yields were lower. Biophysical characterization revealed that fLucWT contains polydisperse higher-order structures that interfered with dsRNA-AC. Sedimentation-velocity analytical ultracentrifugation experiments demonstrated the higher-order structures were driven by mRNA concentration. Decreasing fLucWT concentrations increased the effectiveness of dsRNA-AC by reducing polydispersity. dsRNA levels were measured with ELISA, dot-blots, immuno-northern blots, and a cell-based interferon release assay, enabling comparison and cross-validation of these analytical methods. dsRNA-AC is a new chromatography modality, and this study identifies sample polydispersity as a key determinant of efficient dsRNA removal.