Methods

Microscopes are essential tools for discoveries on a scale invisible to the unaided human eye. The development of immuno-fluorescence followed by molecular biology techniques and fluorescent fusion proteins have revolutionised the use of optical microscopy in bioscience. The quality of the data produced is dependent upon the sample, its preparation and the instrument used. However, instruments can degrade over time without easily visible changes to the produced images and, in turn, negatively impacts results. By testing instruments and doing comparisons between results over time and between different instruments, problems can be highlighted and corrective action can be taken. Using small fluorescent beads the point spread function (PSF) of the microscope can be recorded and the image resolution measured. Beads were prepared in a concentration matched to the field of view size and dried onto coverslips and mounted on slides. The beads were then imaged as 3D Z-stacks of sufficient size to fully enclose the PSF of the system. This data was uploaded to OMERO and processed using OMERO-metrics, an OMERO plugin developed for this purpose. This paper summarizes the development of workflows and protocols to enable this process, presents the results obtained and demonstrates the detection of significant instrument issues.

Mechanotransduction via the actin cytoskeleton is linked to fundamental cellular processes such as morphogenesis, cell division, and motility, requiring the control of tensile forces mediated by the motor protein non-muscle myosin 2 (NM2). Formins such as mDia1 have been shown to elongate actin structures that are under mechanical tension; conversely, mDia1s elongation rates are modulated by the applied force. Despite their relevance at the membrane/cortex interface, reported values for tension in formin-elongated actin filaments stem from theoretical estimates and simulations, but have not been amenable experimentally so far. Thus, we developed a Forster resonance energy transfer (FRET)-based, tension-sensitive probe (mDia1TS) and quantified the measured tension in live U2OS cells using fluorescence lifetime imaging microscopy (FLIM). Through whole-cell ROI analysis we show a short and long lifetime component, reporting an intensity-weighted, averaged lifetime corresponding to [~]3.5 pN. Upon mitogen stimulation of cells using EGF, we show that the tension homeostasis changed significantly, with a measurable increase in tension in the cells periphery and relaxation in its center. Furthermore, the reported average tension relaxed by 2 pN after adding the NM2 inhibitor para-nitroblebbistatin. We utilized siRNA knockdowns of individual NM2 paralogs (NM2-A, NM2-B, or NM2-C) to measure their individual contribution, revealing NM2-A as the main paralog to produce tensile force in this system. Taken together, we demonstrate that mDia1TS is able to directly determine that active mDia1 in cells is under tension, and that subcellular quantification with pN precision is possible. SignificanceDespite the fundamental importance of formins in regulating actin-based processes, reported values for tension in formin-mediated actin structures stem from simulations and theoretical estimates. In this study we developed a FRET-based, tension-sensitive reporter probe for formin mDia1, which we termed mDia1TS. Given the expanding clinical spectrum of DIAPH1/mDia1 mutations, our tool mDia1TS provides a quantitative tool for elucidation of changes in cytoskeletal assemblies.

SignificanceDigital Holographic Microscopy (DHM) provides label-free quantitative phase images (QPIs) of living cells and has become a powerful tool for studying cellular morphology and dynamics. While most DHM studies have focused on cell-level analysis, the quantitative characterization of neuronal network organization and maturation from DHM images remains largely unexplored, highlighting the need for dedicated computational approaches. AimWe aimed to develop an automated framework combining deep-learning-based image analysis and graph theory to quantitatively characterize the organization, connectivity, and maturation of neuronal networks in primary rat cortical cultures imaged by DHM. ApproachTwo U-Net convolutional neural networks were trained on manually annotated DHM phase images to segment neuronal cell bodies and neurites. The resulting segmentation maps were used to infer putative morphological connections between neurons and generate graph representations of neuronal networks, referred to as graph fingerprints. A panel of 18 connectomics-inspired graph features was then computed to characterize local and global properties of network organization across four stages of culture maturation. ResultsThe mean area under the receiver operating characteristic curves was 0.98 for cell-body and 0.91 for neurite segmentation, indicating near-perfect identification. Graph-theoretical analysis revealed reproducible topological changes during network maturation in vitro, including increased density, reduced modularity, and progressive network integration. Correlation analysis showed that the 18 graph features grouped into two highly correlated families. A Random Forest classifier identified density and modularity as the most informative descriptors, achieving an accuracy of 87% in classifying maturation stages of neuronal cultures. ConclusionsOur results demonstrate that combining DHM, deep-learning-based segmentation, and graphtheoretical analysis enables quantitative characterization of neuronal network organization and maturation from label-free phase images. This framework provides a foundation for future studies of pharmacological experiments, neuronal network phenotyping, and human induced pluripotent stem cell (hiPSC)-derived neuronal cultures, where quantitative assessment of network organization remains a major challenge.

Fluorescent dyes are critical to visualizing nucleic acids in many applications. SYTOX Orange and SYTOX Green are cyanine dyes, used in dead cell staining and increasingly in single-molecule assays to probe DNA supercoiling and processing. However, their structures and effects on DNA mechanics are not or only partially known. We determine the structure of SYTOX Orange to be (E)-2-((2-(4 ((diethyl(methyl)ammonio)methyl)phenyl)-6-methoxy-1-methylquinolin-4(1H)-ylidene)methyl)-4-methyloxazolo[4,5-b]pyridin-4-ium, identical to SYBR Gold except for an aza-benzoxazol core that is fundamentally different from other dyes in the SYTOX and SYBR families. We report SYTOX Green to be (Z)-2-(bis(3-(trimethylammonio)propyl)amino)-4-((3-methylbenzo[d]thiazol-2(3H)-ylidene)methyl)-1-phenylquinolin-1-ium, similar to PicoGreen. Using magnetic tweezers, we characterize the effect of SYTOX Orange and SYTOX Green on DNA mechanics. They lengthen and unwind DNA consistent with intercalation and the DNA unwinding angles per dye are 21.1(1) degree and 20.5(1) degree for SYTOX Orange and Green, respectively. Both dyes leave the DNA bending persistence length and plectoneme size almost unaltered (<10% change up to 1 uM), which is advantageous in assays probing DNA supercoiling. Their photophysical properties reveal close agreement between single-molecule manipulation and optical absorbance and fluorescence spectroscopy. Our comprehensive set of complementary measurements relates mechanical and optical properties to the molecular structures and provides recommendations for their use in applications.

For structural-biology and protein-production pipelines, the hardest part of a difficult protein is not the biology -- it is obtaining a well-behaved sample for functional studies. Programs routinely stall at construct design, expression, and purification: deciding where to truncate, which tags to use, how to express, and how to purify so the protein survives concentration and handling. These decisions are still made largely by literature precedent and experimental experience, and they require trial-and-error before arriving at a functional construct for hard targets. We present a prospective, single-pair wet-lab case study testing whether an integrated computational platform can improve these decisions. For human fibroblast growth factor 21 (FGF21) -- a clinically important and stability-challenged metabolic hormone -- we compared two expression constructs produced side by side under the same experimental workflow, using two different design strategies: one designed by a scientist from the literature (reproducing the published core-domain construct, PDB 6M6E), and one designed by the Orbion platform -- an AI, prediction-guided protein-design system (orbion.life) -- which additionally generated the expression and purification protocols (executed scientist-in-the-loop). The platforms construct used an unconventional, longer C-terminal boundary not found in public sequence databases. Since the two constructs differ in more than one feature, we treat them as workflow-level designs throughout. The scientist construct gave a higher initial yield ([~]2.4 xmore protein recovered at affinity capture). The platform-designed construct, however, showed a more favourable downstream developability profile: it concentrated higher (1.4 vs 0.7 mg/mL) while remaining more monodisperse by dynamic light scattering (DLS). The scientist construct, in contrast, aggregated on concentration, so its initial-yield advantage did not survive: in the final concentrated sample the Orbion construct provided the more usable material for downstream studies. Computed for the mammalian host used, the platform had prospectively scored its own design higher (composite 68.7 vs 59.0 for the scientist-designed construct), and its predictions of yield, solubility, and disorder matched the wet-lab outcome. This is a single, deliberately scoped case study, not a population-level benchmark; the two constructs differ in more than one feature, and biological activity was not assayed. Alongside the bottlenecks of this approach discussed here, used as a decision aid, prediction-guided construct and protocol design has the potential to remove costly iteration cycles of protein production campaigns.

We describe microfluidic-free, droplet-based methods for single-nucleus epigenomic measurements: Particle-templated Instant Partition single-nucleus assay for transposase-accessible chromatin using sequencing (PIP-ATAC-seq) and its multiomic version (PIP-Multiome-seq). We benchmarked these assays by generating data sets containing thousands of nuclei using cell lines and mouse brains and compared to other established methods. PIP-Multiome and PIP-ATAC are straightforward to implement, affordable, and produce high-quality data, providing useful additions to the single-cell molecular measurement armamentarium.

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.

Whole-genome sequencing identifies focal DNA amplifications with base-pair resolution but cannot determine whether amplified sequences reside on extrachromosomal DNA (ecDNA, also known as double minutes) or within chromosomally integrated homogeneously staining regions (HSRs). DNA fluorescence in situ hybridization (DNA-FISH) metaphase spreads remain the gold standard for distinguishing these amplification states at single-cell resolution. Here, we present a detailed protocol for DNA-FISH metaphase spreads using human cancer cell lines, encompassing cell culture, metaphase arrest, hypotonic treatment, fixation, chromosome spreading, fluorescent probe hybridization, and fluorescence imaging. The protocol incorporates intermediate quality-control steps to verify successful chromosome dispersion and optimize metaphase spread quality, making the workflow accessible to laboratories without specialized cytogenetics expertise. Results demonstrate clear visualization of ecDNA and HSR amplification states using locus-specific probes and illustrate common technical artifacts that can affect interpretation. This protocol provides a robust and reproducible approach for studying the structural organization of oncogene amplification in cancer cells.

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.

Plasma membrane sheets generated by controlled mechanical disruption provide direct access to the cytosolic face of the plasma membrane while preserving the native organization of membrane-associated proteins and lipids. Here, we present a protocol for generating and validating sonication-derived plasma membrane sheets from cultured cells, primary neurons, and isolated synaptosomes. We further describe their application for live and fixed imaging of membrane protein localization, organization, conformational dynamics, and protein-protein interactions, as well as quantitative membrane-associated synaptic vesicle recruitment assays. This versatile platform preserves the native membrane environment while enabling direct visualization and quantitative analysis of membrane-associated processes at high spatial resolution. The protocol can be readily adapted to investigate diverse membrane proteins, lipid-dependent mechanisms, and vesicle tethering events across a wide range of cellular systems.

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.

Thermodynamic characterization of biomolecular interactions is essential for understanding the enthalpic and entropic driving forces of molecular recognition, but established label-free techniques are limited either by bulk refractive-index sensitivity or by the lengthy thermal equilibration required to suppress it. Here, we used focal molography to investigate the temperature-dependent binding of the protein kinase A regulatory subunit (PKA-R) to cyclic AMP (cAMP) derivatives and to derive apparent thermodynamic signatures from kinetic measurements. We first validated the diffractometric readout under conditions that challenge refractometric sensors: the coherent mass density channel strongly suppressed temperature-induced bulk refractive-index effects and resolved binding in 50% human serum despite measurable non-specific adsorption, reducing the need for lengthy equilibration and buffer matching. We then combined focal molography with DNA-directed immobilization (DDI), allowing five cAMP derivatives to be presented in parallel on the same multiplexed chip and followed across five temperatures. This format yielded distinct, internally consistent apparent thermodynamic fingerprints for each derivative, separating ligands with similar affinities by their enthalpic and entropic contributions. Together, these results establish focal molography with DDI as a multiplexed workflow for comparative thermodynamic fingerprinting of biomolecular interactions at higher throughput.

Cancer phenotypic plasticity drives invasion, treatment resistance, and relapse. Quantifying how cells dynamically couple morphology and migration in real time, without molecular labels, remains unsolved. Static molecular markers report on protein expression state rather than functional migratory behavior. Existing image-based metrics treat shape and migration as independent features, missing the coordinated coupling that defines plastic migratory states. We introduce Directional Shape Coupling (DSC), a quantitative metric purpose-built for live label-free imaging. DSC integrates movement direction consistency, shape deformation, and directional-shape alignment into a single interpretable score. Component weights are derived from PCA, adapting automatically to any dataset without manual tuning. Applied to differential interference contrast imaging of pancreatic cancer cells on a tissue-mimicking substrate recapitulating desmoplastic tumor stroma, DSC exhibited a large phenotype-associated effect size,{varepsilon} 2 = 0.65, across five distinct migratory phenotypes within a genetically homogeneous population, demonstrating that behavioral heterogeneity is structured and non-genetic. DSC encodes information orthogonal to classical shape and motion descriptors. Critically, DSC reveals that dynamic shape adaptation to mechanical cues rather than directional commitment drives phenotypic identity in this system. DSC provides the label-free imaging community a transparent, generalizable framework for quantifying dynamic non-genetic plasticity directly from live imaging data.

Protein oligomerization equilibria are central to many biological processes and are often highly sensitive to environmental conditions such as ionic strength, pH, and ligand binding. Quantitative characterization of these equilibria remains experimentally challenging because stable protein complexes frequently dissociate only at concentrations that are difficult to access with conventional biophysical methods. Fluorescence correlation spectroscopy (FCS) is uniquely suited to this problem, as it provides direct access to diffusion coefficients of fluorescently labeled proteins at nanomolar concentrations. However, the quantitative interpretation of FCS data from oligomeric systems requires a rigorous mathematical framework and careful experimental practice that have not previously been described in sufficient detail to guide implementation. Here, we provide a comprehensive description of the experimental workflow and analytical framework for determining dissociation equilibrium constants by FCS, covering instrument calibration, sample preparation, data quality control, after-pulse correction, and nonlinear least-squares fitting. We discuss common sources of error and provide practical guidance on critical experimental considerations including surface passivation, buffer preparation, equilibration time, and the role of labeling efficiency. Using the homotrimeric sliding clamp PCNA as a model system, we demonstrate the complete workflow under a range of KCl concentrations and show that moderate ionic strength stabilizes the PCNA trimer while very high salt partially destabilizes the complex. The approach is general and applicable to any reversible protein self-association reaction accessible by fluorescence detection at low protein concentrations.

Translation elongation and protein folding have long been proposed to coordinate during co-translational folding, yet the lack of technologies capable of simultaneously tracking both processes in live cells has hindered mechanistic understanding of this relationship. Here, we developed co-translational folding tracking (coTFT), a live-cell imaging platform that directly and simultaneously tracks translation and folding from individual mRNAs. Using reporters with distinct folding kinetics, we found that differences in folding kinetics were accompanied by corresponding changes in translation elongation rates. Conversely, altering translation elongation markedly affected protein folding outcomes. Combining coTFT with mathematical modeling enabled estimation of reporter folding times on translating ribosomes in live cells, confirming their distinct folding kinetics. Together, our results reveal that translation elongation and folding are bidirectionally coupled during co-translational folding.

O_LIThe effect of some bioactive compounds on living organisms is dependent on their concentration and gradients, as is the case of hormones and signalling peptides, determining cell identity, activity and organism development. C_LIO_LIThere are a handful of methods that allow to produce spatially confined peaks of concentration local application of biochemicals on plants, such as agar blocks and microinjection, but they lack in precision, throughput and/or simplicity. C_LIO_LIWe developed the MicroTron, a microfluidics-based method specifically for filamentous organisms or life cycle stages, like the moss plant Physcomitrium patens protonemata, that serves as a platform for the application of chemicals on single cells and study the cell response. C_LIO_LIWe show how chemical applications could be performed on cells, either on the side or apically with dyes and hormones, targeting the cell wall, cell membrane, cytosol and nucleus. C_LIO_LITreatments could be applied on single filaments and with a precision of up to single cells in optimal conditions. C_LIO_LIThis method could be used to study live responses to chemicals with high spatiotemporal resolution. C_LI

Spectral bleed-through remains a persistent practical problem in multichannel fluorescence microscopy. Signal from one fluorophore can be recorded in the detection channel of another, thereby biasing intensity measurements, inflating apparent colocalization, and complicating the interpretation of dynamic microscopy data. Although many correction strategies exist, routine workflows often remain fragmented across ad hoc scripts, manually tuned graphical procedures, or method-specific blind-unmixing implementations with limited provenance. Here we present spectral-unmixing, an open-source Python package for reproducible linear spectral unmixing in multidimensional microscopy stacks. The package unifies directed two-channel correction with multiple alpha-estimation strategies, optional bidirectional two-channel correction through explicit inversion of a 2 x 2 mixing model, and PICASSO-family blind unmixing for multichannel data. Microscopy inputs are normalized at the API boundary to canonical TZCY X stacks, allowing the same unmixing code to be applied across file formats without manual axis handling. Machine-readable sidecar reports preserve the effective processing configuration and estimated coefficients for every output, so that workflows can be audited and reproduced. Synthetic and real-data-derived benchmarks show that the implemented workflows accurately estimate and correct bleed-through when their model assumptions are satisfied. In fixed-alpha two-channel simulations, the mean-ratio and linear-fit estimators recovered {approx} 0.283 for a ground-truth value of 0.28 and reduced target-channel normalized root mean squared error from approximately 0.029 to 0.003. In time-varying simulations, per-time-point estimation tracked coefficient drift substantially better than reference-time-point estimation. Bidirectional inversion recovered reciprocally mixed channels accurately when coefficients were known or well estimated. PICASSO-family benchmarks further showed a practical trade-off between reducing residual inter-channel dependence and preserving fluorophore identity, with MATLAB-style workflows behaving more conservatively and source-sink formulations providing stronger dependence suppression when meaningful directional priors are available. Together, these elements make spectral-unmixing a practical, transparent, and extensible platform for reproducible spectral unmixing of fluorescence microscopy data in neuroscience and other quantitative bioimage-analysis settings.

The growing number of approved nucleic acid therapeutics illustrates the potential to treat diseases by targeting their genetic blueprints in vivo. The 10-23 DNAzyme is capable of cleaving a wide range of target RNA with high selectivity. However, its poor performance in vivo restricts its therapeutic application as gene silencing agent. Studies on ribozymes have shown that the crowded environment in cells and associated effects can impact ribozyme folding and thermostability, resulting in a change in activity. This opens up the question whether DNAzymes are also affected by molecular crowding. Here, we investigate the functional and structural influence of molecular crowding conditions on the 10-23 DNAzyme. The stability and activity of a PrP-specific 10-23 DNAzyme were examined in presence of PEG, dextran, and osmolytes. Our results indicate that osmolytes decrease DNAzyme activity in a concentration-dependent manner, while certain PEG and dextran concentrations promote activity. To rationalize our observations, we studied the cosolutes effect on physicochemical solution properties and the structure of the DNAzyme:RNA complex using FCS and SAXS. The data reveal that enhanced activity is observed under conditions where a combination of physiochemical properties matches an optimum that seems to be dependent on the metal ion cofactor. Structural influence under such conditions is indicated less. We propose that a certain degree of molecular crowding is required to favor a state, which allows for higher catalytic turnover. In addition, we show that the requirement for magnesium and manganese as a cofactor remains unchanged under the conditions applied. Our work contributes to a better understanding of how the cellular environment affects DNAzyme structure and function.

Temperature is a fundamental parameter governing all molecular processes, including those that define life. Fluorescence microscopy is a powerful tool to observe molecular processes in living systems in real time. Precise control and measurement of temperature during fluorescence microscopy is therefore essential. We present here a robust temperature measurement based on the excited-state lifetime of the widely available and relatively inexpensive fluorescent dye pentamethine cyanine (Cy5). The excited-state lifetime of Cy5 shows a monotonic decline in the measurement range of 0 {degrees}C - 80 {degrees}C. The measured dependency is linear until 39 {degrees}C and monoexponential above. The dependance of excited-state lifetime upon temperature is used to measure temperature up to a precision of 0.5 {degrees}C or less, a temporal resolution down to <1 millisecond and to resolve temperature gradients with spatial resolutions that are only diffraction-limited. The far-red excitation and emission of Cy5 leaves bandwidth to simultaneously measure at least 3 additional spectral channels in standard fluorescent microscopes simultaneously. We demonstrate determination of temperature during 4-color live-cell fluorescence microscopy for a temperature-controlled experiment. We also show its applicability in measuring temperature gradients and laser-induced sample heating such as during STED nanoscopy.

Chemical imaging is a powerful branch of modern microscopy encumbered by a lack of flexible, high-throughput analysis tools. Bespoke analytical pipelines typically perform ratiometric analysis on two layers in a multispectral image to describe the relative composition of molecules in a sample. This strategy has been implemented across fields, spanning histopathology, cell biology, environmental science, and materials science. The commercialization of chemical imaging microscopes has facilitated the collection of large multispectral datasets, necessitating accessible ways to process them. This paper describes Multispectral Analysis Graphical User Interface (msaGUI), a desktop graphical user interface to analyze individual and batch datasets of multispectral images. Data is loaded as CSV, TSV, or TIFFs and processed through a user-defined sequence of modular image operations that can be flexibly combined, e.g. to reduce spectral crosstalk or background noise. After analysis, data is visualized as exportable images, histograms, and statistics. To yield publication-quality figures, outputted images are fully customizable. Written in Python with open-source libraries, the msaGUI program is packaged into an executable for Windows and Mac for a fully no-code application. Other operating systems are supported via the Python source code. In summary, msaGUI provides a rapid and user-friendly solution for analyzing and visualizing multispectral data.