Methods

Micrococcal nuclease (MNase) digestion is widely used to profile chromatin accessibility and nucleosome footprinting. However, its application is often limited by sensitivity to reaction conditions, high cell input requirements, and the lack of standardized protocols across cell types. Here we developed a robust MNase workflow encompassing buffer composition, DNA purification chemistry, fixation and decrosslinking parameters, cell input scalability, and an in-house yeast spike-in for quantitative normalization. We validated this unified framework across human induced pluripotent stem cells (hiPSCs), hiPSC-derived cardiomyocytes at multiple differentiation stages, primary murine embryonic cardiac cells, and adult mouse cardiomyocytes, and demonstrated comparable digestion efficiencies and kinetics despite marked differences in cellular architecture and chromatin organization. Genome-wide MNase-seq in hiPSCs, combined with the nucMACC bioinformatic pipeline, resolved concentration-dependent nucleosomal occupancy and precise nucleosome positioning at pluripotency-related regulatory elements. This modular, end-to-end, and scalable workflow provides a standardized platform for reproducible MNase-based chromatin profiling across diverse in vitro and in vivo models. TEASERA unified, rapid, and scalable MNase-seq workflow for reproducible mononucleosomal and subnucleosomal profiling from stem cells to adult cardiomyocytes. HIGHLIGHTSO_LISystematic MNase optimization across buffer, DNA purification, and cell input variables C_LIO_LIUnified workflow validated in hiPSCs, hiPSC-CMs, embryonic, and adult cardiomyocytes C_LIO_LIFixed-cell protocol enables weeks of storage without loss of DNA quality C_LIO_LICost-effective yeast spike-in ensures quantitative normalization for MNase-seq C_LIO_LIGenome-wide analyses confirm robust and precise nucleosome positioning at regulatory elements C_LI

Transcription factors (TFs) are master regulators of gene expression and control a wide range of cellular functions including embryonic development, signaling pathways, immune response, and differentiation. A tight regulation of gene expression is crucial during all stages of life and changes in the TF function can lead to developmental abnormalities, diseases such as cancer, or resistance to treatment. Therefore, TFs are a promising class of drug targets, and the techniques that would contribute to the development of TF modulators are critical. Electrophoretic Mobility Shift Assay (EMSA) has been a primary tool to verify protein-DNA interactions, where a fluorescent, biotin, or isotope end-labelled DNA probe is used to quantify binding. Such labeling techniques, however, can be costly, time consuming, possess safety hazard risks and require capital equipment for imaging. Here, we optimized a label-free, staining-based EMSA to characterize a potential drug target, homeodomain (HD) TF Pre-B-cell leukemia homeobox-1 (Pbx1) and its binding partner Homeobox A9 (HoxA9). Staining the polyacrylamide EMSA gel with a DNA intercalating green cyanine dye - SYBR safe - allowed the visualization of Pbx1 homeodomain interactions with DNA at nanomolar concentrations and enabled quantitative determination of protein-DNA apparent binding affinity in the sub-micromolar range. Furthermore, a ternary complex of homeodomains of Pbx1 with HoxA9 and the DNA was also visible in the assay. We have shown that the staining-based EMSA can be used to evaluate inhibitors of Pbx1 that block the interaction with DNA. We have further validated the data from our assay with fluorophore labeling-based EMSA. Overall, using HD transcription factors Pbx1 and HoxA9 as a model, we have optimized a reliable and cost-effective staining-based EMSA that enables the high-sensitivity visualization and quantitative evaluation of transcription factor-DNA complexes without the need for end-labeled DNA probes. The streamlined workflow could be readily adapted to other DNA-binding proteins to study their interactions with the DNA, inhibitors, and other proteins.

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.

Fluorescence-based CRISPR diagnostic assays have become a popular platform for nucleic acid detection due to their programmability, configurability, specificity, and compatibility with standard laboratory equipment. However, reported enzymatic kinetic rates and limits of detection for CRISPR trans-cleavage assays vary by several orders of magnitude across the literature. This variation in performance parameters is coupled with and exacerbated by inconsistent calibration, incomplete correction of measurement biases, and nonstandardized or incomplete data-analysis procedures. We present an experimental protocol and quantitative analysis framework for fluorescence-based enzyme assays using routine laboratory instrumentation, including thermocyclers and fluorescence microplate readers. Building on previous studies of CRISPR enzyme kinetics and fluorescence calibration, we describe procedures for flat-field and background correction; comprehensive fluorescence calibration including correction for inner-filter-effect; quantification and implications of reporter degradation; extraction of Michaelis-Menten kinetic parameters; and determination of assay limits of detection. We provide step-by-step experimental guidelines and open-source Python implementations for each stage of the workflow. Using representative Cas12 trans-cleavage datasets, we demonstrate that explicit fluorescence calibration and correction procedures substantially reduce systematic bias in measured kinetic rates and improve consistency between experiments. Our framework aims to establish standardized practices for quantitative fluorescence-based CRISPR assays and provides researchers with practical tools for reproducible kinetic characterization and rational assay design.

Polysome profiling is a powerful technique used to analyze the association of mRNA with ribosomes, providing insights into the translational status of a cell. It relies on the separation of ribosome-bound mRNAs through sucrose density gradient centrifugation, where the number of ribosomes on an mRNA correlates with its sedimentation rate. While numerous studies have successfully applied this method to cell line and mouse tissue, application to the human postmortem brain remains scarce due to challenges related to sample quality and low concentration of recoverable material. To overcome these challenges, we: O_LIImplemented a protocol specifically optimized for low-concentration human post-mortem brain tissue. C_LIO_LIImplemented a gradient-maker-free method to manually prepare sucrose gradients with tunable sensitivity for low-input samples. C_LIO_LIAdapted the human brain tissue protocol for neuronal cell lines and mouse brain with minimal modification. C_LI

Single-molecule tracking measures the stochastic motion of individual biomolecules in the cellular environment. Statistical analysis of trajectory ensembles is required to gain insight into the biophysical nature of mobility states and molecular interactions that they reflect. Mobility states can be parameterized by a generalized diffusion coefficient and anomalous exponent. Experimental constraints such as finite track length and localization precision limit how accurately these parameters can be determined. We compare the performance of analysis methods to recover the input parameters from ensembles of simulated single molecule tracks from different states spanning the range of anomalous diffusive behaviors observed in the cell nucleus. We further develop a framework to quantify error rates in the assignment of mobility states to individual molecules based on recall rates and precision. Our analysis shows that single-track analysis methods are superior to bulk methods in their ability to recover parametric descriptors from mixed populations. The most complete description is obtained by combining outputs from different tools. Our work provides a guide to assess the accuracy of analyses and obtain the most accurate parametric description of experimental single particle tracking data. Statement of significanceExperimental single particle tracking data provides rich insight into molecular interactions directly in living cells. But data analysis depends critically on choosing the correct diffusion model and appropriate tools to extract accurate information. Importantly, it is usually not obvious from the output of a method whether the results are accurate or not. In this work, we use ensembles of tracks simulated with fractional Brownian motion methods to characterize the impact of track length and localization precision on analysis outcomes. We elaborate on specific strengths and weaknesses of commonly used and newly developed analysis tools to provide a template for thorough assessment and quantification of error rates in experimental data analysis.

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.

Fluorescence imaging of dense cellular regions of interest (ROIs) in cells using fluorescence microscopy provides detailed images with pixels that report ensemble- and time-averaged biomolecular data, due to the diffraction limit when super-resolution modalities are not used and acquisition times are slower than typical biomolecular mobilities. The fluorescently-tagged biomolecules that are undergoing imaging can be more heterogeneous and dynamic, all within the dimensions of a single acquired image pixel. The ability to acquire data one biomolecule at a time within a given ROI can help recover some of the underlying biomolecular subpopulations that are otherwise averaged out. In this work, we present a relatively simple approach to achieving single-biomolecule photon bursts, BLeaching In-cell Single-molecule burstS (BLISS). We reveal millisecond photon bursts arising from clusters of mCherry-tagged heterochromatin protein 1 (mCherry-HP1) within heterochromatin biomolecular condensates in undifferentiated mouse embryonic stem cells (ESCs). Fluorescence lifetimes of these bursts are substantially lower than the averaged-out values observed per pixel in fluorescence lifetime imaging microscopy (FLIM), attributed to higher density in mCherry-HP1 clusters. These higher density clusters are observed primarily in undifferentiated ESCs. Two days after retinoic acid (RA) induction of differentiation, these bursts are rarely observed. In summary, using BLISS, we revealed a rare subpopulation of dense mCherry-HP1 clusters characterized by rapid, millisecond dynamics. These clusters are part of heterochromatin biomolecular condensates in ESCs at the pluripotent state, which would be otherwise averaged out in diffraction-limited fluorescence microscopy.

We demonstrate the use of fluorescence correlation spectroscopy (FCS) to characterize fluorescently-labeled protein production. We use cell-free protein synthesis to express the protein YFP-CopB, a fusion of Chlamydia Outer Protein (Cop) B and Yellow Fluorescent Protein (YFP). CopB is a [~]50 kDa protein believed to have a critical role in chlamydial infection.1 After adding a plasmid encoding YFP-CopB to an E. coli cell-free lysate, protein expression begins. We track the cell-free reaction over several hours using the EI-FLEX, a commercial instrument with FCS capability. As protein is expressed over time, YFP-CopB increases in concentration, and the EI-FLEX detects an increase in fluorescent signal above the background of the cell-free lysate. The FCS data collected gives information about the size, aggregation tendencies, rates of production and fluorescent protein maturation, and concentration of the YFP-CopB produced. The use of FCS concurrent with cell-free synthesis presents a simple method to characterize proteins of interest as they are produced without the need for purification.

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.

Background and ObjectivesThe linear relation between human metaphase chromosome length and DNA content has never been rigorously reconciled with modern Hi-C models of mitotic chromatin folding. We tested whether the relation implies a quantitative unit of mitotic chromosome organization. MethodsWe pooled metaphase lengths for 24 human chromosomes from five cytogenetic studies of cultured peripheral lymphocytes and regressed length against base-pair content from GRCh38 and T2T-CHM13 v2.0. Pre-specified analyses comprised ordinary least-squares and power-law fits, arm-level decomposition, and reconciliation with the Gibcus 2018 helical loop-array model. Orthogonal validation used Rao 2014 Hi-C boundary counts and Pope 2014 Repli-Seq. ResultsLength scales linearly with DNA content as L (m) = 0.0329 x Mb + 0.043 (R-squared = 0.998, power-law exponent 0.98 +/-0.01), with a cross-karyotype compaction density of 33.4 +/-1.0 nm/Mb. T2T-CHM13 reanalysis identifies satellite over-condensation; the arm-level residual correlates with p-arm fraction (r = 0.65, p = 5.6e-4). We propose an operational Metaphase Chromatin Unit (MCU) as a quantitative scaling unit (not a discrete structural quantum, as quantization tests are negative): 1 MCU = 7.6 Mb DNA = 0.25 m axial length, numerically corresponding to one Gibcus late-metaphase helical turn; the human haploid genome scales to 406 MCUs. The pairwise megabase-shift slope of 32.75 nm/Mb (R-squared = 0.996) and the approximately 6 Mb Abbe optical detection threshold correctly classify 9 of 9 microdeletion syndromes as karyotype-visible versus FISH-required. ConclusionsThe MCU provides a unified quantitative unit for human mitotic chromosome organization, integrating cytogenetic, Hi-C, polymer-biophysical, and clinical scales.

A comprehensive understanding of protein-nucleic acid interactions in the crowded nuclear milieu is essential for elucidating genome function. State-of-the art methods provide the finest details of genome organization, but lack integration of imaging and sequencing modalities. We introduce OptiFoot, a fluorescence-based platform for recording protein-nucleic acid interactions in cells. OptiFoot employs an engineered sequence-unspecific N6-adenine methyltransferase that attaches diverse functional groups to DNA and RNA. When targeted to protein of interest by genetic fusion or antibody, it generates covalent high contrast fluorescent footprints that can be visualized by super-resolution microscopy and analyzed by optical mapping of single native DNA molecules, allowing complementary spatial and genomic analyses. To illustrate OptiFoot versatility, we imaged lamina-associated domains, DNA replication sites, CTCF binding sites and histone modifications in human cells and produced corresponding genome profiles. We use OptiFoot to demonstrate that majority of nucleoporin NUP153-DNA interactions occur in nucleoplasm, outside nuclear pore.

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.

How gene expression patterns change spatially as the embryo transitions from simple to complex structures remains a major developmental biology question. Recently developed imaging-based spatial transcriptomics (ST) enable mapping expression of multiple gene at a single-cell resolution. Although Xenopus is a key model in embryology there is no established ST pipeline, and commercially available techniques face many challenges (sample preparation, probe design, cell segmentation). Furthermore, the highly diverse cell shapes and sizes across developmental stages and between different tissues represent major hurdles to accurately defining cells. Here, we describe an optimized workflow for ST in blastula-to-tailbud-stage frog embryos using Merscope, commercial MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization) originally designed for standard mammalian tissues. With stringent quality control and tailored computational pipelines, we optimize this technology for robust, semi-quantitative profiling of spatial transcriptomic landscapes in non-mammalian embryos. Reliable tissue preservation and cell-segmentation enable high-resolution mapping of gene expression during the development of a complex multi-tissue organization. This versatile strategy applies broadly to various dynamic systems, from embryos of various model organisms to complex and heterogeneous organs in mammals. Summary statementThis Single-cell Spatial Transcriptomics pipeline and reference atlas in Xenopus - a model organism in embryology - overcome technical challenges and resolve dynamic changes in patterning during development.

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.

Many genomic regions exhibit allele-specific expression. This effect is most pronounced in imprinted genes, where one copy of a gene is epigenetically silenced, and the inactive X chromosome of female cells, where almost the entire chromosome is silenced. Allele specific gene expression can have significant effects on human health and is implicated in a wide array of diseases. Research into allele specific expression is most often carried out in mouse models where cross breeding of mouse strains can yield progeny with well characterised haplotypes where parent of origin is known for a huge number of SNPs. The same approach cannot be taken with human data and haplotypes must be assembled using expensive and labour intensive long read sequencing and Hi-C based approaches. Although resolved haplotypes are available for a number of cell lines, allowing accurate measurement of allele-specific gene expression, this type of analysis is inaccessible for non-specialist labs. We demonstrate how to use previously published haplotypes to investigate X linked gene silencing and epigenetic changes. Additionally, in this paper we present a method to exploit the profound difference in expression levels between the two human X chromosomes to assign SNPs in expressed RNA to the active or inactive X chromosome using only short read DNA and RNA sequencing. We demonstrate this technique using sequencing libraries generated in house and sequencing data from publicly available databases including for a cell line with a complex karyotype. In each instance we identified genes that were silenced in each cell line opening them up to further research avenues. This X chromosome haplotyping technique can be applied to any clonally derived human cell line with 2 or more X chromosomes allowing researchers to investigate X linked gene silencing in cell lines already present in their lab rather than in the limited number of cell lines for which a haplotype is available.

To properly respond to their environment, cells adjust the activity of key regulatory proteins and rates of gene expression. Methods to detect and quantify these forms of regulatory dynamics in living cells are of central importance for understanding cellular signaling events in both physiological and pathological conditions. Current technologies in this field make use of fluorescent probes to track cell signaling dynamics. Although these technologies have been used for decades, challenges remain. In particular, the segmentation, tracking, and interpretation of single cell dynamic data are time-consuming, prone to subjective errors, and often lacking in standardization across experiments. Here, we present SPIFEE, a data pipeline that uses experiment-dependent parameters to smooth noise and quantify key features of fluorescence data from time-lapse imaging studies. Processing data in this manner enhances and accelerates quantification of live-cell gene and protein expression, simplifies data analysis, and facilitates hypothesis generation. Author SummaryCells adjust protein activity and gene expression levels over time to respond to changes in their environment, a process referred to as cell signaling dynamics. Quantifying cell signaling dynamics in living cells often uses fluorescent probes, such as green fluorescent protein (GFP) and its spectral variants, to track changes in gene expression or protein activity over time. Challenges inherent in analyzing fluorescence data from single cells stem from biological and experimental noise, time-consuming quantification, and subjective errors. To address these challenges, we developed a computational tool called Signal Processing and Integrated Feature Extraction (SPIFEE). The pipeline improves the quality of fluorescence data analysis by reducing noise and extracting signal features in a way that is both intuitive and objective. The pipeline provides more accurate, rapid, and unbiased quantification of time-lapse microscopy data.

The rapid rise of stem cell-based human embryo models has reignited interest in studying early human development while offering a promising platform to de-risk drugs. Among these, three-dimensional human gastruloids provide a tractable system to model symmetry breaking, germ layer specification and axial organization. However, existing gastruloid protocols remain expensive, specialized, variable and evaluated in a limited number of human pluripotent stem cell (hPSC) lines, restricting broader adoption. Here, we present a simple, robust, standardized gastruloid protocol achieving greater than 90% elongation efficiency with low inter- and intra-experimental variability, developed primarily in BJNhem20, a well-characterized Indian-origin human embryonic stem cell line. Further, we show that the protocol is applicable in a diverse set of hPSC lines. Using a TBXT (Brachyury)-GFP reporter in BJNhem20, we optimized cell seeding density, induction medium and Wnt activation strength, guided by real-time, quantitative assessment of mesoderm induction and symmetry breaking, allowing precise titration of CHIR99021. Comparative testing identified an in-house "Essential 6" medium formulation as the most consistent condition for robust TBXT induction. Optimization of aggregation density produced reproducible gastruloids with polarized TBXT expression and consistent axial elongation, within 72 hours. Single-cell RNA sequencing of individual gastruloids confirmed high transcriptional reproducibility and conserved lineage clusters, aligned with developmental trajectories. Cell line-specific CHIR99021 titration was sufficient to successfully transfer the optimized protocol to two additional lines, BJNhem19 and RUES2-GLR. This simplified and robust protocol reduces costs and improves accessibility, enabling broader application of stem cell-based human embryo models.

Plasmids are widely used for gene expression, yet their coding potential beyond the intended coding sequence (CDS) is often poorly characterized. Here, we explored putative "hidden open reading frames" (hidden ORFs) embedded within non-canonical reading frames of plasmid sequences through a computational workflow for their identification. Using enhanced green fluorescent protein (eGFP) as a target gene, we observed unexpectedly uninterrupted ORFs in both the +2 coding frame and the reverse frame. Immunoblotting detected stable expression of the +2 frame-derived protein, but not the reverse-frame ORF. Motivated by these observations, we developed a computational pipeline and analyzed 6,308 eGFP-containing plasmids, identifying putative hidden ORFs in approximately 21% of constructs. Approximately 25% of hidden ORFs occurred in the +2 frame, with the remainder occurring in the reverse frame. The same analytical pipeline, if utilized for plasmids beyond eGFP plasmids, can contribute to avoiding unintended outcomes, in applications such as gene replacement therapy.

Understanding skeletal muscle metabolism involves real-time monitoring of key cellular parameters, such as calcium ions (Ca2+), adenosine triphosphate (ATP), cyclic adenosine monophosphate (cAMP), and intracellular temperature. Fluorescent protein (FP)-based biosensors are used for live-cell imaging of these signals with high spatiotemporal resolution. Differentiated myotubes are in vitro models used for physiological muscle metabolism research. However, efficient transfection of FP-based biosensors into these cells is challenging. Here, we developed an electroporation-based strategy for delivering recombinant protein biosensors into fully differentiated myotubes. Biosensors for Ca2+, ATP, cAMP, and temperature were recombinantly produced using Escherichia coli and introduced into myotubes using electroporation. Electroporation conditions were optimised to maximise delivery efficiency, preserve cell viability, and minimise cellular damage. We established a robust intracellular delivery system that effectively demonstrated Ca2+, ATP, and temperature dynamics. Furthermore, we achieved the successful co-delivery of two biosensors that enabled dual imaging of Ca2+ and cAMP in response to stimulation.