Nature Protocols

Three-dimensional (3D) spheroid models have become essential in cancer biology, drug screening, and tissue engineering. However, their small size, fragile structure, and tendency to disintegrate during routine histoprocessing present persistent technical challenges. Conventional paraffin embedding often results in tissue fragmentation, loss of spatial orientation, and poor section quality, whereas cryosectioning often compromises cellular morphology. Here, we present a robust, cost-effective protocol for preserving and sectioning fragile 3D spheroids, resulting in high-quality histological sections with intact architecture and excellent cellular detail. The method involves optimized handling and embedding procedures that stabilize spheroids during standard formalin fixation, paraffin infiltration, and microtomy, eliminating mechanical distortion and preserving spherical integrity for consistent sectioning. We demonstrate successful application across different cell line spheroids, with subsequent compatibility with hematoxylin and eosin (H&E) staining protocols. Compared to conventional methods, our approach significantly reduces sample loss, improves inter-section reproducibility, and preserves fine structural features such as necrotic cores, proliferative zones, and extracellular matrix components. This protocol provides a reliable, accessible solution for routine histological analysis of fragile 3D spheroids, facilitating more accurate morphological and molecular assessment in translational research settings. Key featuresO_LIMaintains spheroid integrity: Prevents mechanical distortion, fragmentation, and loss of spatial orientation during processing. C_LIO_LISignificantly reduces sample loss: Decreases failure rate compared to traditional methods, conserving valuable samples. C_LIO_LIBroad spheroid compatibility: Works effectively with primary tumor-derived, stem cell-derived, and co-culture spheroid models. C_LIO_LIEnables high-quality sectioning and staining: Delivers consistent, reproducible sections that are fully compatible with H&E, IHC, and IF. C_LI Graphical overview O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/743094v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1670c4org.highwire.dtl.DTLVardef@145810aorg.highwire.dtl.DTLVardef@1accb1org.highwire.dtl.DTLVardef@17481c0_HPS_FORMAT_FIGEXP M_FIG C_FIG

Very short RNAs play critical roles in modern biology, and are thought to have been crucial for genome replication during the origin of life. Next-generation sequencing is an essential tool for characterizing pools of small RNAs, but current library preparation methods suffer from strong size and sequence biases. Here we present tinyRNA-seq, an optimized library preparation method designed to minimize length- and sequence-dependent capture bias enabling the sequencing of RNA fragments as short as 2 nucleotides. We use degenerate adaptor regions to reduce ligation sequence bias and facilitate unique molecular identifier (UMI) installation. We benchmarked tinyRNA-seq against commercial kits using a model primordial RNA genome consisting of hundreds of defined oligonucleotides ranging from 2 to 12 nucleotides. tinyRNA-seq reproduced the input RNA distribution without the size and sequence bias of the commercial kits. tinyRNA-seq also enables the detection of de novo oligonucleotide generation, an important process for the origins of life. Applied to biologically derived small RNAs including miRNAs, piRNAs, and cityRNAs, tinyRNA-seq showed significantly lower capture bias and recovered a wider range of sequences than commercial kits. tinyRNA-seq may thus provide a more complete and quantitatively accurate representation of small RNAs from both biological and chemical sources. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/743385v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@95ee64org.highwire.dtl.DTLVardef@155fb06org.highwire.dtl.DTLVardef@1d3665forg.highwire.dtl.DTLVardef@1e61404_HPS_FORMAT_FIGEXP M_FIG C_FIG

This document describes the Statistical Analysis Plan (SAP) for PETRA, a randomised controlled trial in people with generalised anxiety disorder comparing pregabalin plus an antidepressant and standard care, with placebo plus an antidepressant and standard care, with respect to the primary outcome of the GAD-7 score at week 12.

Click-qPCR is a browser-based application for relative qPCR analysis that requires a tidy-format CSV file containing four columns: sample, group, gene, and Cq. Preparing this input from qPCR instrument output typically requires manual reformatting and calculation of mean Cq values for technical replicates. To simplify this process, we developed Click-Prep (https://kubo-azu.shinyapps.io/Click-Prep/), an interactive web-based application designed specifically to create Click-qPCR input files. Click-Prep imports CSV, TXT, TSV, and XLS/XLSX files and supports skipping of instrument-generated metadata rows, interactive column mapping, and manual assignment of experimental groups. Users can review technical-replicate measurements, exclude selected rows according to predefined quality-control criteria, and calculate mean Cq values for each sample-group-target combination. Missing or nonnumeric Cq values are flagged for review and must be resolved before the mean is calculated. Click-Prep can also combine compatible formatted CSV files, such as datasets obtained from separate qPCR plates. The resulting dataset is exported as a standardized CSV file containing the four fields required by Click-qPCR. By integrating these operations into a guided browser-based workflow, Click-Prep enables users to prepare Click-qPCR input files rapidly and consistently without programming.

Protein-ligand interaction diagrams are a routine part of structural and medicinal chemistry, but the tools that produce them tend to force a choice: comprehensive detection with tabular output, publication-quality figures behind a licence, or a scripting environment that assumes expertise. PandaMap (Protein AND ligAnd interaction MAPper) is an open-source Python package that produces a 2D interaction diagram, an interactive 3D viewer, a text report, a machine-readable CSV, and a four-panel graphical summary from a single command. It reads PDB, mmCIF and PDBQT files, detects 15 interaction classes using crystallographically validated distance thresholds, and depends only on NumPy, Matplotlib, BioPython and Requests; RDKit improves the 2D ligand layout when present but is not required. Hydrogen bonds are filtered on the true D-H{middle dot} {middle dot} {middle dot} A angle when the structure contains explicit hydrogens, matching PLIPs 100{whitebullet} criterion on the same evidence, and on distance alone otherwise, with the provenance of each measurement recorded. We benchmarked the package on three complexes chosen for different chemistry: enolase with a phosphonate transition-state analogue (PDB 1ELS), the EGFR kinase with erlotinib (1M17), and aldose reductase with IDD594 (1US0). PandaMap recovers the contacts these structures are known for, including the EGFR hinge hydrogen bond to MET769 and the IDD594 bromine{middle dot} {middle dot} {middle dot} THR113 halogen bond, both at distances identical to PLIPs. All detection thresholds, scoring weights and the exact commands used are given in the Supplementary Information, and the release carries a regression suite covering each interaction class. PandaMap 4.3.0 is available on PyPI under the MIT licence.

Quantification of microbial growth inhibition is central to assays ranging from antibiotic susceptibility of bacteria to sensitivity of yeasts to antifungal therapeutics. Classical analysis approaches derive from zone-of-inhibition (termed halo) formats using filter paper discs, spanning methods from laser detection to machine learning. However, these tools struggle with non-uniform halos, fail to account for lawn density variability despite its experimental influence, and lack accessible, reproducible code. Here, we present Halo Unbiased Measurement of growth Inhibition (HaloUMI); an open-source Python graphical user interface for automated, high-throughput analysis of lawn-based microbial assays. HaloUMI integrates robust image processing with physics-informed models to quantify inhibition zones irrespective of shape, enabling accurate segmentation of uniform and irregular halo phenotypes. This analysis pipeline incorporates the critical correction for spatial heterogeneity in lawn density, improving reproducibility across experimental conditions. The software enhances usability without sacrificing precision, allowing rapid batch processing and intuitive parameter control. HaloUMI can be applied to multiple assay types, including yeast toxin halo, microbial mating, and conventional filter paper disc assays. It yields high-precision measurement of halo size and morphology, with improved consistency compared to standard thresholding and circular fitting. By combining accessibility, flexibility, and biophysical modelling, HaloUMI provides a quantitative framework for irregularly shaped halos of lawns of varying growth potential, enabling generalisable analysis of broad microbial interactions. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/743694v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1bd7c88org.highwire.dtl.DTLVardef@13ac9b6org.highwire.dtl.DTLVardef@9108e1org.highwire.dtl.DTLVardef@1de06bd_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background: Individuals receiving buprenorphine treatment for opioid use disorder (OUD) remain at high risk for treatment discontinuation and return to opioid use. Transcranial magnetic stimulation (TMS) has shown efficacy in reducing craving and substance use in other substance use disorders, but its application in OUD remains limited and the neural mechanism underlying its therapeutic effects is poorly understood. Determining the feasibility and generalizability of TMS in patients receiving buprenorphine - the most commonly prescribed medication for OUD - is therefore critical. This protocol aims to address these issues in a clinical trial of weekly TMS sessions for OUD. Methods: We will enroll up to 120 individuals with OUD taking buprenorphine in a randomized, single-blind, sham-controlled trial of left dorsolateral prefrontal cortex (DLPFC)-targeted intermittent theta burst stimulation (iTBS). Participants will receive active or sham iTBS weekly (2 sessions of 1800 pulses each applied once per week x 8 weeks, 16 sessions total) with pre- and post-iTBS assessments (10, 12, 20 weeks) of craving, opioid use, and treatment retention. A subset of individuals will undergo optional pre- and post-iTBS neuroimaging. The study will be conducted at an academic medical center and a private outpatient TMS clinic. Aims: Our primary aim is to determine whether 16 sessions of active iTBS applied to the left DLPFC results in reduced craving and opioid use, and higher treatment retention, relative to sham. In a secondary aim, we will also examine whether iTBS-related changes in craving are associated with changes in functional connectivity between the left DLPFC and both the dorsal striatum and anterior cingulate cortex. Discussion: By evaluating the feasibility and efficacy of a weekly TMS protocol that aligns with routine care and focuses on patients maintained on buprenorphine, this study addresses key limitations of prior TMS research in OUD. Furthermore, the inclusion of neuroimaging will help characterize the neural mechanisms underlying TMS-related changes in craving. Trial registration: This clinical trial is registered at ClinicalTrials.Gov; ID NCT07457489; date of registration: 03/02/2026.

Genome-wide coexpression analysis of intact tissue samples is a powerful approach for identifying reproducible signatures of cell types and states, since it can survey vast numbers of individuals, cells, and transcripts. However, it can be difficult to optimize gene coexpression network construction and compare results from independent analyses. To address these challenges, we developed OMICON (theomicon.ucsf.edu) for research on human brain gene coexpression networks. OMICON contains gene expression data from >17K normal and neoplastic human brain samples with standardized metadata. Systematic analysis of independent datasets identified >250K gene coexpression modules, which were characterized and compared via enrichment analysis with >40K gene sets. All modules are discoverable via an advanced search engine that can filter by genes, metadata, and enrichment results. Analyses can also be browsed with an interactive workflow visualization tool, and users can communicate within OMICON using @mention functionality to support communal research on human brain gene coexpression networks.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) enables the spatial mapping of endogenous biomolecules within native biological specimens; however, it remains limited in achieving single-cell resolution. While advances in instrument modifications, computational processing methods, and tissue-based sample preparation have facilitated high lateral resolutions and cellular level imaging, resolving metabolic heterogeneity at the single-cell level remains challenging for users without specific expertise or custom instrumentation. Here, we present tensile expansion mass spectrometry (TExMS), a cost-effective approach for single-cell MALDI-MSI that is compatible with commercial MSI instrumentation. TExMS utilizes highly stretchable hydrogels as a substrate for live-cell seeding, attachment, and desiccation, avoiding the need for chemical fixation and enabling the retention of both intracellular and extracellular metabolites, including media-derived components that are lost during fixation and washing. We used TExMS to expand individual cells of a human high-grade serous ovarian cancer (HGSOC) cell line and spatially map their small molecule (<800 Da) production. TExMS enabled [~]4-fold linear expansion of the hydrogel, translating to a [~]1.7-fold increase in average cell area and [~]1.3-fold increase in nuclear area and resulting in improved lateral resolution of metabolite distributions. Benchmarking against other platforms for high resolution MALDI-MSI, TExMS offered comparable spatial resolution to microgrid-enabled MALDI-MSI with 15 to 20-fold shorter acquisition times. We then used TExMS to map numerous intermediates from glycolysis, the tricarboxylic acid (TCA) cycle, and amino acid biosynthesis and probe the effects of serum starvation conditions on metabolic flux through these pathways, demonstrating a powerful use case for single-cell MALDI-MSI through TExMS. Table of Contents (TOC) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/745024v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@18f8c09org.highwire.dtl.DTLVardef@132bc4aorg.highwire.dtl.DTLVardef@1e7c2caorg.highwire.dtl.DTLVardef@a586cf_HPS_FORMAT_FIGEXP M_FIG C_FIG

The development of a new integrated operant system was driven by two challenges in behavioral neuroscience: the high cost and technical complexity of commercial rigs, and their limited adaptability across experiments. We developed the NeuroHab, an integrated behavioral arena for high-fidelity operant conditioning and automated data collection in a single unified system. Food and water reward, conditioned-stimulus presentation, and event recording are tied together programmatically with easy-to-install open-source code to facilitate throughput and reproducibility. All behavioral events are processed by internal microcontrollers and logged with <1 ms latency (typical range 56-728 s). This precise timing is critical for integrating the system with two-photon imaging and electrophysiology, enabling real-time alignment of behavior with brain activity. The NeuroHab uses solenoid-actuated, capacitive-sensing Lickports that let an untethered mouse drink from an automated port, and delivers food via the Kravitz Lab FED3. Conditioned stimuli are presented by dedicated buzzer/LED modules. A central controller (the Core) coordinates all modules and logs event timestamps using TTL-low signaling between two microcontrollers, at a maximum recording rate of 16.67 Hz for single-pulse events. We have deployed the NeuroHab in over 50 behavior trials and over 20 sessions alongside a Mini two-photon microscope. At approximately $1,400, easily modified, and compatible with existing analysis tools, the NeuroHab lowers barriers to multimodal behavioral neuroscience. Significance StatementThe study of how neural activity gives rise to behavior depends on operant systems that are both temporally precise and affordable, yet commercial rigs are costly and difficult to adapt across experiments. We introduce the NeuroHab, an integrated, open-source operant platform that unifies reward delivery, conditioned-stimulus presentation, and event logging with sub-millisecond timing (typical latency 56-728 s). Built for approximately $1,400, the system forwards all behavioral timestamps to external acquisition hardware, enabling millisecond-scale alignment of behavior with two-photon imaging and electrophysiology. By lowering the cost and technical barriers to synchronized behavioral and neural recording, the NeuroHab makes multimodal, reproducible operant neuroscience accessible to a broad range of laboratories and adaptable to diverse experimental paradigms.

Databases of post-translational modifications (PTMs) catalogue modified sites and increasingly add static structural context, but trajectory-derived descriptors remain scattered across specialised tools and general molecular dynamics archives. Dyna-MO PTM brings together 1,079 AlphaFold 3-seeded systems covering lysine acetylation, lysine and arginine monomethylation, and serine, threonine and tyrosine phosphorylation. Each system is linked to three completed 10 ns replicas generated with CHARMM36m and TIP3P, for 32.37 s of aggregate sampling. A 118-column table joins simulation and quality-control provenance with global relaxation measures, site solvent exposure, rotamers, secondary structure and ionic-contact proxies. Versioned identifiers connect the records to starting structures, trajectories, manifests and analysis scripts. Researchers can use the resource to filter PTM contexts, reproduce descriptors, prioritise longer simulations and evaluate trajectory-analysis or generative methods. The trajectories describe finite-window relaxation rather than equilibrium free energies, kinetics or matched PTM effects.

Scientific agents can produce plausible answers while remaining unable to establish whether the computation behind an answer is executable, recoverable, or reproducible. We present BloClaw, an AI4S workstation built around a simple principle: a scientific agent should know what it can do, show how it did it, and state what remains unvalidated. Each capability declares an execution state, input constraints, dependencies, expected outputs, and scientific limitations. Natural-language requests are translated into structured tasks, validated against this registry, executed through scientific tools, and recorded in a provenance-aware Living Lab Notebook. The system is designed to detect invalid inputs, failed tool calls, missing dependencies, and remote timeouts, and to route them to repair, retry, or escalation. The implemented and tested scope comprises RDKit-based molecular property and rule screening, protein structure analysis, docking-pose inspection, 3D visualization, and structured reporting. We demonstrate the workflow on a PubChem-retrieved osimertinib structure and a supplied 6LU7 docking artifact: the former yields deterministic descriptors (molecular weight 499.619 Da, cLogP 4.5098, TPSA 87.55 A^2), while the latter contains 2,387 protein ATOM records, 309 residues, and nine pose records. These examples are workflow demonstrations, not efficacy or affinity studies. Beyond retrospective prediction, the manuscript specifies a prior-minimized constructive mode in which a desired function is compiled into explicit physical, chemical, and systems constraints, candidate mechanisms are simulated, and observations are reintroduced for calibration and falsification; this is a proposed extension rather than a result of the present case studies. We describe an evaluation protocol that compares BloClaw with a standard single-agent workflow and fixed-script execution using task completion, scientific correctness, recovery success, provenance completeness, reproducibility, human review time, latency, and cost. This manuscript reports the system design, verified capability boundary, deterministic software artifacts, and a reproducible evaluation protocol; it does not claim benchmark improvements before those experiments are run. BloClaw is an execution and accountability layer for AI-assisted research, complementing expert review and experimental validation rather than replacing them.

Motivation: Interactive tools that let non-programmers explore an analyzed single-cell dataset, its embeddings, gene expression, cell metadata, and marker genes, have become standard laboratory infrastructure. Every actively maintained tool in this space (ShinyCell, ScRDAVis, sCIRCLE, scViewer) is built on R Shiny and requires a Seurat object as input. Laboratories whose primary analysis pipeline is Python/scanpy, the dominant framework for single-cell RNA-seq, spatial, and multi-omic analysis, therefore have no lightweight, language-native option that pairs a shareable web-based viewer with a scriptable Python API: sharing a scanpy result means either exporting to Seurat first or handing over a notebook that only a programmer can run. Results: We present scPyviewer, a web-based viewer that ingests AnnData objects directly and reproduces the core interaction patterns of the incumbent R Shiny tools without leaving the Python stack. In a feature-parity audit against three actively maintained R Shiny incumbents, scPyviewer matches or exceeds every baseline capability (7/7); among these, it uniquely offers native AnnData ingestion with no Seurat conversion, and cross-dataset comparison over shared genes and matched cell-type composition. Benchmarked head-to-head against the R/Seurat rendering substrate the incumbents are built on, identical operations, identical data, across three datasets spanning 22,315 to roughly 313,000 cells, scPyviewer renders every core view faster at every scale tested (up to 3.6x on a single view) and at a fraction of the memory (5.2x lower on the smallest dataset). At the largest scale tested, the gap becomes categorical rather than incremental: scPyviewer completes every view on a 313,000-cell dataset while the Seurat substrate exhausts an 8 GB memory budget and fails outright. Beyond the interactive app, scPyviewer installs via pip or conda and exposes a public Python API that returns Matplotlib figures and pandas tables for scripted, publication-ready output. Availability and implementation: scPyviewer is implemented in Python 3.11 (scanpy 1.11.5, anndata 0.12.19, streamlit 1.59.2, plotly 6.9.0) and distributed with a one-command reproduction interface that installs pinned dependencies, regenerates the benchmark and all figures, and launches the interactive app. Source code is available at https://github.com/xuan13hao/scPyviewer.git.

Background: The October 7, 2023 attack in southern Israel was one of the deadliest terror attacks in modern history, with 1,182 fatalities, more than 4,000 wounded individuals, and 251 hostages. The Nova music festival, an all-night outdoor rave near the Gaza border, suffered the highest number of civilian casualties, with more than 370 festival attendees killed. Survivors were exposed to prolonged life-threatening trauma with similar characteristics and within a narrow time window. Many survivors also reported being under the acute influence of psychoactive substances during the attack and the following hours. This tragic combination of civilian mass trauma and naturalistic pharmacological exposure created a rare opportunity to study trauma processing prospectively. Objective: This paper describes the rationale, design, and methodology of the Nova Protocol, a multimodal longitudinal observational study of survivors of the October 7, 2023 Nova festival attack and a sociocultural comparison group. Methods: The protocol spans from the first weeks to approximately 24 months post-trauma and includes three major assessment time points. It integrates repeated online clinical assessments, prolonged wearable-sensor monitoring, ecological assessments, saliva-based endocrine and inflammatory markers, structural and functional MRI, cardiac interoception paradigms, online and in-scanner reinforcement-learning tasks, and semi-structured qualitative interviews. Primary outcomes are PTSD symptom severity (PCL-5) and general psychological distress (K6), supplemented by a rich battery of secondary measures. Conclusion: The Nova Protocol provides an unusually rich longitudinal framework for characterizing psychological, behavioral, physiological, inflammatory, neural, interoceptive, and subjective mechanisms that shape clinical trajectories after civilian mass trauma. Because psychoactive substance exposure was naturalistic and self-selected, findings will be interpreted as mechanistic and prognostic associations rather than causal effects. The protocol is expected to inform early risk detection and scalable post-disaster monitoring and intervention strategies, as well as unique insights into how psychoactive substances impact trauma processing.

Single-cell differential expression (DE) reports changes in mature mRNA abundance, but the same fold-change can reflect faster synthesis or slower decay. Metabolic labeling resolves this ambiguity but is costly and cannot be applied retrospectively to the vast majority of published scRNA-seq. scATrans closes this gap using layers every standard pipeline already generates: from DE-selected genes, a reference-corrected unspliced residual annotates each change as transcription- or stabilization-weighted, with no additional experiment. Benchmarked against metabolic-labeling systems with independent kinetic ground truth, the residual separates the two mechanisms at matched mature abundance (ROC-AUC 0.68-0.74, full-length NASC-seq2 K562; 0.59-0.63, 3' scEU-seq RPE1); effect size scales with intron capture, not model complexity, and explicit kinetic fitting adds nothing over the static contrast. Per-gene, the residual recovers the classical bulk exon-intron contrast (EISA); what scATrans adds is the inference layer single-cell reanalysis actually needs--DE-defined membership, gene-structure correction, a capture-regime reliability pre-flight, induction-matched testing, and a permutation-calibrated program score--so that confident calls are reserved for where the data support them: gene programs, not single genes. Applied to standard 10x data with no labeling, scATrans recovers textbook post-transcriptional biology: a curated AU-rich-element program is called stabilization-weighted in LPS-stimulated PBMCs (confirmed by per-donor pseudobulk DE in an independent four-donor cohort), while a glucocorticoid-response program is called transcription-weighted in dexamethasone-treated A549 cells-- opposite mechanisms recovered from unlabeled counts. scATrans turns any spliced/unspliced-resolved DE table into a mechanism-typed one, retrospectively and at scale. Availability and implementationscATrans requires Python [&ge;]3.9, interoperates with the scverse ecosystem (AnnData, Scanpy), and is released under the Apache-2.0 license. Install with pip install scatrans or from Bioconda. Documentation and tutorials: https://scatrans.readthedocs.io. Analyses in this manuscript use software version 0.10.9.

Circular single-stranded DNA (cssDNA) is a versatile biomolecule with applications spanning genome editing, DNA nanotechnology, synthetic biology, molecular diagnostics, and aptamer development. Compared with linear single-stranded DNA, cssDNA offers enhanced structural stability, resistance to in-cellulo degradation by exonucleases and enables the generation of long, sequence-defined DNA molecules that are difficult to obtain through conventional chemical synthesis methods. Despite its growing utility, widespread adoption of cssDNA has been limited by the lack of accessible, scalable, and cost-effective production methods, with many existing workflows relying on specialised reagents, extensive optimisation, or commercially synthesised DNA. Here, we present a streamlined, end-to-end protocol for the laboratory-scale production of high-purity cssDNA using an M13 phagemid-based system and standard molecular biology laboratory infrastructure. The workflow encompasses bacterial culture, phage amplification, nuclease treatment, phage precipitation, anion-exchange purification, and quality control, with practical optimisations to improve yield, reproducibility, and scalability. Using this approach, yields range from 120-195 {micro}g of purified cssDNA from 300 mL of culture supernatant. The protocol provides detailed guidance on critical process parameters, troubleshooting, and quality assessment, enabling reliable production of cssDNA suitable for a wide range of downstream molecular biology and genome engineering applications.

The blood-brain barrier (BBB) is a highly selective cell layer that restricts the diffusion of diverse chemical entities into the central nervous system (CNS) from systemic circulation. Macromolecular therapeutics including oligonucleotides, peptides, and monoclonal antibodies exhibit only minimal brain distribution after systemic dosing due to exclusion by the BBB. Receptor-mediated transcytosis (RMT) has evolved to transport vital cargo across the BBB through a specialized vesicular transport pathway. Transferrin receptor 1 (TfR1) shuttles transferrin, its natural ligand, across the BBB, as well as TfR1-binding IgG antibodies and conjugates. Here, we describe a novel monovalent TfR1-binding VHH-Fc for the delivery of oligonucleotide cargo, including antisense oligonucleotides (ASOs) and small interfering RNAs (siRNAs) across the BBB in rodents and non-human primates (NHPs), supporting the translational potential of the VHH-antisense RMT platform for the treatment of neurological disorders. We explore the role of binding affinity, conjugation site, drug-antibody ratio (DAR), and conjugation chemistry, and determine that binding affinity, DAR and conjugation site are major determinants of RMT capacity and brain activity of siRNAs delivered across the BBB. Graphical Abstract / Highlights O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/744307v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@d1d648org.highwire.dtl.DTLVardef@4b22d3org.highwire.dtl.DTLVardef@db8b6borg.highwire.dtl.DTLVardef@19e5ac3_HPS_FORMAT_FIGEXP M_FIG C_FIG - Anti-TfR1 (-TfR1) VHH ligands formatted as heterodimeric, 2-chain monovalent VHH-Fc were engineered for conjugation to siRNA and ASO. - Systematic in vivo evaluation of VHH clones spanning a range of TfR1 binding affinities revealed a relationship between TfR1 binding affinity and the CNS activity of intravenously dosed VHH-Fc-siRNA conjugates. - By optimizing TfR1 binding affinity, conjugation site, and conjugation chemistry, we identified VHH-Fc-siRNA molecules that efficiently cross the BBB via receptor-mediated transcytosis and reduce target mRNA across CNS tissues, including deeper brain regions, after intravenous (IV) or subcutaneous (SC) dosing in mice and non-human primates (NHPs).

BackgroundHighly variable gene (HVG) selection begins almost every single-cell RNA-seq analysis. While ranking formulas have been compared extensively, the integer gene budget at which any ranking must be truncated is typically left to the user and habitually fixed near 2,000. Relying on such a convention carries hidden costs--lists that are too short erase subtle structure, whereas lists that are too long add noise and computational overhead. Moreover, because global rankings measure variance across all cells, markers for rare populations often lose the "variance vote count" to dominant bulk variation, leading to an unfair feature allocation at the hard cutoff. Whether this convention is defensible, and whether the budget and tail can be set from data without disturbing the ranking, has not been examined systematically. ResultsUnder a frozen seurat_v3 ranking, k-sweeps across 18 labeled datasets show that n = 2,000 is ARI-optimal on 1 of 18 datasets and that the best available budget is worth a mean ARI gain of +0.033 over it, establishing cardinality as a real and largely unexploited design axis. We present scFair, a Scanpy-compatible HVG layer that automates list length alone: geometry-aware auto_n sets a base size k from multi-seed density and stability features of an intermediate embedding (trading a modest, intentional compute increase for a safer data-driven default), and a same-rank append step acts as a conservative safeguard against cutoff unfairness by adding a short near-miss tail. The ranking is never recomputed or reweighted. On the 18-dataset panel, the default path improved Leiden-label agreement over HVG@2000 (median {Delta}ARI = +0.016; 13/5; Wilcoxon P = 0.0077) and outperformed the neighborhood-based selector triku at author defaults on 15/18 datasets (median +0.024; P = 0.004), while triku did not improve on HVG@2000. Controls locate the effect: cell-number-only rules do not beat HVG@2000, an FDR-chosen length imposed on the frozen ranking is flat, and a fixed HVG@2200 default is not a general substitute because it cannot produce the short lists that compact matrices call for. ConclusionsA fixed budget near 2,000 HVGs is frequently suboptimal, and list cardinality is a separable design axis that can be automated without changing the ranking formula. Effect sizes are modest, the short-list branch rests on four datasets, and rule thresholds were developed with partial overlap to the evaluation panel.

Here, we present MC-Bayes, a Python-based script for processing cryo-electron microscopy EER movies on one or more GPUs using MotionCor3 in a user-friendly manner. Further, it generates the .star files necessary for RELION to perform Bayesian polishing (a.k.a.: reference-based motion correction) with EER movies. Until now, Bayesian polishing of EER data was only possible if the CPU-based "RELIONCor" implementation of MotionCor2 was used, which is sub-optimal on GPU-heavy cryo-EM processing systems. This wrapper was created for those facilities and/or users who may have (many) powerful GPUs, but for whatever reason have few CPU cores or less system RAM. Leveraging MotionCor3, MC-Bayes allows motion correction of EER data 2 or more times faster (depending on system) than the RELION CPU implementation, except in circumstances where dozens or hundreds of CPU cores with high quantities of system RAM can be utilised.

PanGBank (https://pangbank.genoscope.cns.fr) is a comprehensive open-access database providing precomputed prokaryotic pangenomes at a broad taxonomic scale. Built upon PPanGGOLiN partitioned pangenome graphs, PanGBank addresses the growing need for large-scale comparative genomics through a standardized, regularly updated, and fully accessible resource. The initial release comprises two complementary collections covering more than 4,600 prokaryotic species from the Genome Taxonomy Database (GTDB), encompassing over 393,000 genomes: GTDB all, maximizing taxonomic and environmental diversity through the inclusion of MAGs and SAGs, and GTDB refseq, focusing on high-quality, annotation-rich genomes. Each species-level pangenome integrates graph-based statistical partitions into persistent, shell, and cloud gene families, together with regions of genomic plasticity (panRGP) and co-localized functional modules (panModule). PanGBank offers multiple access modes, including a REST API, a command-line interface (PanGBank-cli), and an interactive web interface. By combining large-scale pangenome resources with advanced graph-based analyses, PanGBank provides a scalable framework for exploring microbial diversity, genome evolution, functional variation, and the dissemination of adaptive traits across prokaryotic populations, as illustrated by a use case on Acinetobacter baumannii pangenome investigating the distribution and evolution of antimicrobial resistance determinants. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/742796v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@186b88dorg.highwire.dtl.DTLVardef@1be33d0org.highwire.dtl.DTLVardef@3bc596org.highwire.dtl.DTLVardef@292401_HPS_FORMAT_FIGEXP M_FIG C_FIG