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

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

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.

Biopharmaceutical companies routinely use forward screening to identify potential ligands for targets of biological consequence. Increasingly, they are also using reverse screening to proactively identify downstream off-target liabilities and new repurposing opportunities. Exhaustive reverse docking of one or a multitude of molecules across every characterized site is one approach, but the computational cost is often prohibitive. One molecule against 28,579 receptor sites takes 40.5 hours on twenty CPU cores. We present a method that puts a retrieval step in front of the docking. Every co-crystallized ligand in the Protein Data Bank is indexed by the 3-dimensional pharmacophore fingerprint it presents, together with the UniProt accessions it was solved against, and a query retrieves the sites belonging to its nearest neighbors. Fingerprints are predicted from two-dimensional structures with PharmCast, so a query is fingerprinted in 4 ms and searched against 27,797 indexed ligands in 40 ms. For a query molecule we take its 5 most similar indexed ligands and pool every protein those 5 were crystallized with, which averages 21.1 distinct proteins. Across 3,000 held-out molecules that pool contained the molecule's own known target 48.8 percent of the time. Docking those 21.1 proteins takes 108 s, against 40.5 hours for the full panel. Pooling the twenty-five most similar ligands instead gives 104.5 proteins and finds the target 60.8 percent of the time. To be indexed, a structure only has to establish which ligand sat in which protein. Holding the index to pocket-grade coordinates had been excluding whole receptor classes. Admitting X-ray at 2.5 [A] and cryo-EM at 4.0 [A] takes coverage from 3,670 target sites to 28,579. Two 2025 clinical molecules were cross-validated. Orforglipron returned the glucagon-like peptide 1 (GLP-1) receptor first of 27,797 ligands, through a non-identical analog at 0.901; daraxonrasib returned the KRAS and cyclophilin A tri-complex fourth, at 0.836. When run in batches the pipeline reverse screens about 96,000 molecules per hour on 1 core, roughly 2.3 million per day, so the retrieval step is practical even with a massive virtual library. The method is available at reversescreen.ai, which allows users the opportunity to screen molecules against the current index, returns the retrieved sites, and docks them individually. The index itself can be downloaded for confidential screening in a local environment. Keywords: reverse screening, target identification, pharmacophore fingerprint, molecular docking, polypharmacology, off-target prediction, virtual screening, repurposing

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

The ModelSEED Biochemistry Database (https://modelseed.org) supplies foundational mass- and charge-balanced reaction networks for metabolic reconstructions. Here we present an update on the biochemistry where the database has been expanded to [~] 46,000 compounds, and [~] 56,000 reactions, featuring [~] 37,000 metabolic structures. We have expanded our approach for handling thermodynamic data, enabling multiple sources of data to be derived, integrated, and presented to the wider research community. We now publish predictions of pKa, reaction energy (and respective uncertainties), and estimates of reaction direction from multiple independent sources. Each reaction is graded gold, silver or bronze according to the strength of the evidence behind it, so that users can weigh its reliability directly. We also release reaction directions predicted by an ensemble of large language models. This multi-source approach exposes agreements and discrepancies between sources for [~] 33,000 reactions. Finally, to ensure data integrity, a new conflict-resolution pipeline reconciles structures across sources, documenting input from curators. Our work is publicly available at https://github.com/ModelSEED/ModelSEEDDatabase.

Availability of commercial antibodies for immunochemistry is typically limited to major model species. Researchers working on non-model organisms therefore often need either to generate novel species-specific antibodies or venture into expensive empirical screening from available antibody catalogues, hoping to find cross-reactive reagents. SixPack-AbScan is a free web server aiding researchers in transferring antibodies across species: using the available epitope-mapping information, the software performs a simple computational pre-screening of potential cross-reactivity. The workflow is species-agnostic and designed to help non-model-species researchers prioritize antibodies for experimental validation. A hit indicates sequence-level conservation of the epitopes and a high probability of cross-reactivity; the server does not model substitutions, structure, accessibility, expression or binding affinity. The web server is available at https://sixpack-abscan.serve.scilifelab.se.

Choosing which region of a protein to express remains poorly standardized in antibody discovery, recombinant reagent generation, structural biology and computational binder design. For human cell-surface and secreted proteins, this requires reconciling topology, processing, predicted and experimental structure, modifications, interaction partners, orthologs, paralogs and cross-reactivity risk before ordering DNA. OpenAntigens is a free, no-login database of construct-design reports for 5328 human secreted, GPI-anchored, single-pass and multipass proteins. It integrates UniProt topology, AlphaFold pLDDT and PAE, PDB precedent, InterPro and Pfam domains, mouse and cynomolgus orthologs, paralog and family context, Open Targets disease associations, partner and assembly context, and BLAST searches. It provides 55 305 construct suggestions spanning full design regions, PDB-backed boundaries, annotated domains, pLDDT/PAE-derived regions and membrane-expression options, plus 148 722 sequence-similarity hits to help choose constructs and assess cross-reactivity. For targets with compatible AlphaFold models, the interactive designer links sequence, structure, pLDDT and PAE, allowing users to revise boundaries and export species-equivalent sequences with real-time cysteine and modification warnings. OpenAntigens places reproducible construct suggestions, comparative context and browser editing in one workflow, reducing manual reconciliation across resources. OpenAntigens is available at openantigens.org. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/741735v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@11171e4org.highwire.dtl.DTLVardef@4c41b8org.highwire.dtl.DTLVardef@6ebf30org.highwire.dtl.DTLVardef@ca21fa_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.

A compound in a cell meets a spectrum of proteins drawn from many families at once, while screening most often interrogates one target at a time. Two questions asked many times in a rank ordering workflow ultimately guide decisions on what gets made and what gets counter-screened, and both are comparative: which of two targets does a compound prefer, and which of two compounds does a target prefer. We built one model for each: the target comparison over a roster of 2,279 human proteins in 34 protein families, the compound comparison over 2,079 in 32. Each model is given two chemical structures and a sequence, or two sequences and a chemical structure, and returns which member of the pair is preferred together with how firmly it holds that view. No conformational analysis, protein structure, binding site or docked pose is used. Asked which of two targets a compound prefers, the model is correct 0.75 of the time over 8,689 held-out comparisons setting two families against each other, and 0.78 of the time over 32,738 comparisons between two targets of one family, rising to 0.94 and 0.95 on the most confident third of each. Asked which of two compounds a single target prefers, it is correct 0.71 of the time over 65,725 held-out comparisons, rising to 0.96 on the most confidently held. Neither compound in any of those comparisons appeared anywhere in training. Accuracy follows the gap between the two measurements. Where they sit within half a log unit the models are right 0.57 to 0.64 of the time, and where they differ by more than two logs, 0.90 to 0.92. The compound result holds across 31 protein families, not only the best-measured one. Within the chemistry and the targets they were built on, these models rank compounds and rank targets well. Both models can be explored and downloaded at familyfoundationmodel.com. Keywords: target preference; compound preference; pairwise comparison; polypharmacology; off-target triage; ESM2; random forest; structure-free prediction; ChEMBL

Laboratory automation is increasingly being adopted in academic research to improve throughput, reproducibility, and integration with computational workflows. A common bottleneck in molecular biology workflows is the isolation of DNA, typically performed with spin-column kits relying on centrifugation or vacuum. Existing automation solutions depend on magnetic separation systems requiring costly additional modules and specialist kits, making them impractical for laboratories that perform large numbers of extractions only occasionally. Here we present an open-source, 3D-printed vacuum manifold compatible with the Opentrons family of liquid handling robots that enables automated spin-column DNA isolation for up to 24 samples per run (96 with four manifolds in series). The manifold can be manufactured for approximately $2 using standard ABS filament and is compatible with widely available spin-columns, requiring no additional specialist reagents. Using this system, hands-on time for 24 plasmid minipreps was reduced by 80% compared to manual centrifugation and vacuum protocols. The automated workflow demonstrated comparable or greater plasmid yields and eliminated failed recoveries. All design files, labware definitions, and Opentrons protocols are released under the CERN Open Hardware Licence or MIT License. This device lowers the cost barrier to automated DNA purification for academic laboratories worldwide.

Whole plate detection of calcium flux signals using fluorescence is a powerful technology for monitoring oscillatory activity of excitable cells in a high throughput fashion that sees broad applications in neurotoxicity and cardiotoxicity research. However, analysis of calcium oscillation profiles can output more than 60 peak kinetic parameters, which makes manual data analysis challenging. We have created the CalFluxTools R package, an automated pipeline for parsing plate data for 384 well plates, generating quality control metrics and plots, and performing data analysis, including T-tests, Z-score analysis, and machine learning prediction of compound toxicity values. CalFluxTools requires a plate map and experimental manifest that includes parameter filtration specifications, as well as data in the ".statall" format. CalFluxTools runs in less than a minute on a Macbook Pro with a 2.6 GHz 6-Core Intel Core i7 processor.

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.

Deep learning models such as Chemprop have advanced quantitative molecular property prediction, but their reliance on large training sets limits use in data-scarce domains. We propose a framework that fine-tunes a general baseline model trained on publicly available data on small, class-specific datasets. The resulting models retain the baseline's generalization ability while gaining class-specific accuracy and produce probabilistic outputs that capture uncertainty in the training data. We demonstrate the approach on three toxicity classes defined by a common core structure, target, or mode of action: (i) organophosphates, (ii) androgen receptor antagonists, and (iii) estrogen receptor beta antagonists. Each fine-tuned model outperforms classical machine-learning methods and the EPA TEST tool. The probabilistic nature of the predictions enables prioritization of compounds for experimental validation and seamless integration with data streams of varying quality, supporting iterative decision-making in chemical safety and drug discovery.

Artificial intelligence methods now span the drug-discovery pipeline --structure prediction, de novo design, docking, affinity estimation, and ADMET -- yet each tool ships with its own, often incompatible, GPU software stack, so chaining several into a workflow requires reproducing conflicting software environments and access to datacenter-class hardware that most labs do not have. bioq is a dependency-light command-line client backed by bioq-services (accontrol-plane gateway plus a growing fleet of 38+ containerized drug-discovery tools spanning 7 discovery stages and 6 molecular modalities). The bioq CLI is self-describing and consistent across the fleet of computational tools, giving researchers and coding agents uniform access to any tool from a laptop, with no local model code, CUDA setup, or cloud configuration, running on serverless GPUs billed per job. The interface-gateway-services architecture makes bioq an execution substrate for automated, agent-driven discovery. bioq and bioq-services are open source under the MIT License and available at https://github.com/wolfsonliu/bioq and https://github.com/wolfsonliu/bioq-services. bioq runs on Python > 3.10 with httpx as its only runtime dependency; services run as Linux containers and are self-hostable via the provided local deployment scripts (alongside scripts for Alibaba Cloud Function Compute). Install, quickstart, test data, and self-hosting instructions are in the repository. The project is actively maintained and will continue to receive updates to both features and services.

ABSTRACT Background: Low intensity focused ultrasound (FUS) offers a noninvasive method for directly modulating deep brain structures with millimeter-scale precision, potentially addressing a major limitation of existing noninvasive neuromodulation approaches. The amygdala is a key node in affective neurocircuitry and a compelling target for psychiatric intervention. We conducted a first in human active controlled trial (NCT05147142) of amygdala-targeted FUS in patients with major depressive disorder, examining safety and target engagement. Methods: Ten participants with major depressive disorder completed a single-blind randomized crossover trial with blinded symptom ratings, comparing imaging guided FUS targeting the right amygdala versus left primary somatosensory cortex as an active control. FUS comprised two 10 minute applications within diagnostic ultrasound safety limits. Primary outcomes were safety and target engagement assessed with BOLD fMRI during sonication, post-sonication arterial spin labeling (ASL) and resting state functional connectivity. Secondary and exploratory outcomes included symptom change and imaging x symptom relationships. Results: Nine participants completed both sessions. No serious adverse events occurred; neurological, neuropsychological, and MRI safety assessments were unremarkable. Adverse events were more frequent after amygdala than control sonication (p=0.028); one participant experienced clinical worsening following amygdala sonication and required monitoring. Amygdala sonication produced greater perfusion change in the targeted amygdala compared to the control region (p<0.001), and ipsilateral hippocampus (p<0.001). BOLD fMRI during sonication demonstrated engagement of ventromedial prefrontal and rostral anterior cingulate regions, while post sonication resting state connectivity decreased between basolateral amygdala and sensorimotor regions (corrected ps<0.05). Symptom improvement did not differ between conditions, but exploratory imaging x symptom relationships were significant. Spontaneous reports of calm, clarity, or lightness occurred after amygdala but not control sonication (p<0.001). Conclusions: Amygdala targeted FUS produced anatomically specific, multimodal evidence of neuromodulation relative to active control, with a manageable safety profile. Convergent target and circuit level signals support further development of FUS as a precision approach for modulating deep brain targets in psychiatric disorders. Key words: low-intensity focused ultrasound, amygdala, neuroimaging, depression, anxiety

Stereotaxic intracranial microinjection in mice is a critical step in neuroscience workflows, enabled by robust hardware design, precise operation, and neuroanatomical expertise. Manual injections are prone to error and stereotax-mountable commercial automated injectors are extremely expensive. Further, the knowledge needed for performing accurate intracranial injections and surgeries is heterogeneous and disparate. Here we demonstrate the design and characterization of the UD Neuroinjector software-hardware-knowledge architecture, which serves to both reduce the cost of performing precisely controlled stereotaxic microinjections and provide a web platform for planning accurate targeting of specific mouse brain regions. We designed the UD Neuroinjector using low-cost hardware and 3D-printed parts, with a total build-cost of $300 --- less than 0.1x the cost of a commercial injector. Arduino-based firmware enables programmable flow rates, automated injection and extraction, and manual joystick control. Capillary-fluid measurements show stable, highly linear displacement (R2 > 0.93) at commonly used injection rates. In a head-to-head comparison with a commercial injector, in vivo DAPI injections into the mouse thalamus showed comparable spread and DAPI+ cell counts. For effective experimental design of intracranial injections, the web-based components of our architecture provide AI-enabled assistance and atlas-guided navigation. We used a hybrid retrieval-augmented generation (RAG) approach to ground AI-assisted protocol guidance for mouse intracranial surgeries using a corpus of 3,738 papers. Using the Common Coordinate Framework version 3 (CCFv3), we designed an interactive tool for identifying target coordinates. Together, the UD Neuroinjector architecture integrates and enables efficient knowledge discovery, experimental design, planning, and implementation of mouse intracranial surgeries.