Cell Reports Methods

The role of antigen-specific T cells responding to antigen is a topic of intense studies, and critical for mechanistic insight of diseases and development of therapeutic strategies. Methods for broad-scale detection of antigen-specific CD4 T cells are lacking, while such methods have demonstrated great value in exploring CD8 T cell response in health and disease. Furthermore, major histocompatibility complex II (MHCII) assays are technically challenging due to high HLA diversity, lower binding affinities, low frequencies of ex vivo antigen-specific CD4 T cells and several bottlenecks in production and peptide exchange of MHCII monomers. Here we use peptide-loaded MHCII (pMHCII) proteins multimerized on a barcode- and fluorophore-labelled dextran backbone to provide a method for the detection of peptide-specific CD4 T cells by using a large display of MHCII-associated peptides. We have established a protocol for MHCII production and peptide-exchange suitable for the generation of large libraries of peptide-MHCII complexes. We validate the use of such pMHCII complexes in the form of barcode-labelled MHCII multimers to detect antigen-specific CD4 T cells. We demonstrate that we can identify antigen specific CD4 T cells, using these DNA barcoded peptide-MHCII multimer. The multimer bound CD4 T cells were selected based on the fluorochrome signal, and the co-attached DNA barcodes were hereafter amplified and used to identify the peptide-MHCII response/binding. In cases where the peptide-specific CD4 T cells frequencies are very low, we expanded the cell population with peptide-pools and in the presence of IL2. The given CD4 T cell populations hereby reach a cell number allowing for the DNA-barcoded pMHCII multimers to detect responses otherwise missed out. Applying this technology, we utilized a panel of 150 peptides derived from human cytomegalo virus (CMV), Epstein barr virus (EBV), Influenza (Flu), SARS CoV 2 and SARS CoV1, Hepatitis B virus (HBV), and Hepatitis C virus (HCV) loaded onto HLA-DRB1*01:01 and DRB1*04:01 to screen peripheral blood mononuclear cells (PBMC). We assessed ex vivo responses in 16 participants with HCV infection, and successfully detected naturally occurring viral-specific CD4 T cells at frequencies as low as 0.004% of total CD4 T cells. The low-frequency responses, identified via the barcode screen, were rigorously validated using individual fluorophore-labelled tetramer staining after a peptide-driven expansion in 15 participants. Furthermore, we assessed the recognition of novel HCV epitopes in 11 additional participants. Through this, we identified a total of 12 distinct HCV epitopes, including 9 that have not been previously utilized in assays to detect CD4 T cells. Overall, this barcoded-multimer platform provides a powerful tool for the large-scale discovery of class II epitopes and the broad profiling of CD4 T cell specificities. This method will allow for in-depth analyses of immune interactions, provide a better understanding of the antigen-driven associations between CD4 and CD8 T cell responses, and help dissect the complexities of CD4 T cell protection in HCV infection.

Downstream analysis in single-cell and spatial transcriptomics is highly dependent on a sequence of upstream modeling choices. The non-canonicity of these choices presents challenges for reproducibility. In particular, measures of cellular heterogeneity and diversity do not solely reflect biological variation, but are also sensitive to parameter settings. A diversity measure that is robust to modeling choices, such as clustering resolution, is therefore desirable to improve reproducibility and interpretability. Here, we introduce scDIV, a similarity-sensitive measure of cellular diversity inspired by mathematical ideas in ecological science, which is robust to graph-based clustering parameters and remains applicable even in the absence of cell-type clusters. We use scDIV to quantitatively track the progress of tissue differentiation in both single-cell and spatial mouse development datasets and to evaluate different engineered stem-cell-based embryo models. In contrast to traditional entropy-based methods, such as the Hill number, used to quantify biodiversity, scDIV remains robust to clustering.

This step-by-step protocol provides a cross-species, non-surgical approach that enables prenatal gene delivery to the developing nervous system in rats and marmosets. Under transabdominal ultrasound guidance, intracerebroventricular injection of recombinant adeno-associated virus vectors into the fetal brain achieves robust and long-term transduction from prenatal stages into adulthood. This approach can be adapted to other species and target sites outside nervous system, enabling safe and selective intrauterine manipulation and the generation of diverse experimental models for basic and preclinical research. For complete details on the use and execution of this protocol, please refer to Ribeiro Gomes et al (2026)1. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/737050v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@696364org.highwire.dtl.DTLVardef@fc3c7forg.highwire.dtl.DTLVardef@1e7c7caorg.highwire.dtl.DTLVardef@1edcef0_HPS_FORMAT_FIGEXP M_FIG C_FIG Before you beginExperimental procedures during gestation allow researchers to study developmental processes, including how manipulations of the fetus and its intrauterine environment influence biological outcomes. Ultrasound imaging guidance greatly facilitates such interventions by providing safe and targeted access to fetal compartments, including for prenatal gene delivery to developing neural cell populations. Critically, delivery of recombinant adeno-associated viruses (rAAVs) into the cerebrospinal fluid (CSF) of developing animals enables widespread gene transfer across the brain. The efficiency and distribution of transduction are strongly influenced by developmental stage, making the timing of delivery an important experimental variable. In altricial species such as mice, major developmental processes, including cortical lamination and the establishment of long-range connections, begin prenatally but continue throughout early postnatal life. In primates, however, development is more advanced at birth, and many equivalent developmental events are shifted to the prenatal period. Consequently, developmental stages that can be targeted postnatally in mice require prenatal access in primates. Here, we present a step-by-step protocol for ultrasound-guided fetal intracerebroventricular viral injection (FIVI) of rAAV in marmosets (Callithrix jacchus) and rats (Rattus norvegicus). The procedure was initially developed and optimized in rats before being translated to marmosets, small New World primates that share key developmental, anatomical, and functional characteristics with humans. Together, these models illustrate the cross-species applicability of the approach, while providing gene delivery strategies for both a genetically tractable rodent model and a translationally relevant nonhuman primate. FIVI enables broad gene transfer and stable, long-term transgene expression in wild type animals, facilitating the generation of complementary quasi-transgenic models for research and translational applications from prenatal development through adulthood.

Antigen specificity of T cells defines the adaptive immune response, yet the vast majority of known T cell receptors (TCRs) lack annotated antigen targets. Single-cell peptide-MHC (pMHC) multimer assays offer a scalable approach to map TCR-antigen interactions. Still, their utility is limited by pervasive non-specific binding and severe overlap between signal and noise, which confound the accurate identification of antigen-specific cells. To address these limitations, we present DextraDemixer, a Bayesian hierarchical mixture model that disentangles antigen-specific T cells from background noise in pMHC multimer data. The model integrates information from negative controls and clonotype structure while providing calibrated uncertainty estimates for classification. We further introduce a dynamic thresholding scheme that enables credible interval-bounded control of the false discovery rate. Extensive benchmarking on simulated datasets and antigen-specific spike-in experiments demonstrated the model's robustness and improved accuracy over established methods. In a longitudinal SARS-CoV-2 vaccine study, DextraDemixer identified antigen-specific TCRs characterized by high sequence similarity, elevated antigen-specificity prediction scores, and strong clonal purity. Annotations showed high concordance with external validation data and supported the identification of antigen-specific motifs. Overall, DextraDemixer provides a principled probabilistic framework for reliable identification of antigen-specific TCRs from single-cell pMHC-multimer assays.

Self-assembling protein fibers enable to record events in single cells, bypassing the need for long-term time-lapse imaging. Fluorescent marks introduced within the growing fiber at user-defined times provide timestamps, giving access to the temporal dynamics of the recorded event. Here, we introduce CATCHFiber, a single-color timestamping strategy for tracing cellular events with high temporal resolution into self-assembling protein fibers. Relying on chemically-induced dimerization to precisely and rapidly control the incorporation of fluorescent proteins into the fiber, CATCHFiber allows the introduction of short 30-min spaced timestamps, significantly increasing the precision of event timings compared to existing methods. This increase in temporal resolution expands the use of fiber-based recorders beyond transcriptional activity, allowing to trace the kinetics of faster processes such as protein degradation, protein neosynthesis and kinase activity, and to determine the timing of cell cycle steps.

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

Selection of regions of interest (ROIs) is often a crucial step in spatial molecular profiling and many pathology tasks, with substantial implications for research reproducibility and biological interpretability. To provide a reproducible and adaptive framework for AI-guided ROI selection, we developed a modular generalist-specialist solution across spatial profiling platforms. In a cohort comprising 55 tumor types from 160 tissue donors profiled using NanoString Digital Spatial Profiling and multiplex immunofluorescence, we first established a protein-profiling reference atlas capturing compartment-specific immune, checkpoint, stromal, and proliferation patterns. We then developed an AI Specialist Task-Oriented Model for ROI Selection (ASTROS) and tested comprehensive benchmarks considering specialist-only (ASTROS), generalist-only (PLIP/GFM), and hybrid generalist-specialist strategies, showing that the latter provides a balanced tradeoff across slide-level signal preservation, pathologist-reference concordance, within-slide placement consistency, and large-slide computational efficiency. We further demonstrated the feasibility of virtual staining for ROI preview and modular ROI placement for other spatial omics technologies, Visium and Visium HD workflows. Together, these results support our proposed framework to enable ROI selection responding to unmet needs for reducing inter-rater variability, reproducibility, and versatility in spatial profiling experiments.

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

Candida species are fungal pathogens whose rapidly increasing antifungal resistance poses a substantial public health challenge. High-throughput CRISPR-based screening could accelerate antifungal target discovery, yet its application in Candida has been limited by low DNA transformation efficiency. Chemical transformation exposes cells to environmental stresses to permit DNA uptake, but the physiological constraints on transformability remain poorly defined. Here, we show that genetic transformability in C. albicans is governed by the cellular capacity to withstand and recover from transformation-induced stress. Nutrient limitation markedly enhances transformation efficiency, while extracellular pH and lithium acetate chemistry strongly modulate this response. Systems-level proteomic analyses reveal that nutrient limitation and transformation chemistry prime oxidative stress tolerance, and transformation efficiency correlates with the expression of oxidative stress response proteins. Guided by these insights, we developed a generalizable fungal advanced chemical transformation (FACT) method that increases transformation efficiency across diverse Candida species and enables robust pooled CRISPR screening.

MicroRNAs (miRNAs) act as key regulators of gene expression across diverse cellular processes, and their precise quantification can provide unique insight into disease pathogenesis. High-throughput sequencing allows for comprehensive small RNA profiling; however, standard commercial library preparation workflows are challenged by issues of low sensitivity and representational bias, limiting reliable profiling, especially in scenarios where samples are scarce. Several structural studies have shown that this bias primarily arises due to sequence and secondary structure variations between miRNAs and adapters during enzyme-catalyzed biochemical reactions. In this work, we propose a new approach to ligation adapter engineering using a bioinformatic analysis of the human miRNome to rationally design structure-forcing 5 adapters, that physically override localized, unpredictable structural variations during the intermediate ligation state. We show that this approach combined with a practical fluorescence-guided workflow, utilizing a fluorescently-labeled 3 adapter and novel Fluorescent Ligation Rulers (FLRs) to guide precise band excision, can minimize representational bias and increase the sensitivity of small RNA sequencing from low-input biological matrices. In comprehensive benchmarks using a synthetic panel, this method significantly reduced bias and outperformed alternative commercial protocols. Finally, we demonstrate that this workflow enhances biomarker detection and library quality in challenging clinical matrices, especially in cerebrospinal fluid. Overall, this protocol enables highly accurate miRNome characterization and is well-suited for biomarker discovery in challenging sample types.

Cryo-correlative light and electron microscopy methods enable targeted structural analysis of fluorescently labelled features in vitrified specimens. However, correlative workflows on high-pressure frozen samples often remain challenging due to the lack of persistent landmarks for reliable sample tracking and image registration between different microscopes. Standard high-pressure freezing carriers provide little intrinsic reference information, as the exposed sample surface is often smooth and rotationally ambiguous, complicating localisation of regions of interest across imaging platforms. Here, we introduce PinCorr, a 3-mm high-pressure freezing carrier with an integrated coordinate system formed by four asymmetrically arranged pillars with distinct geometries. These built-in landmarks remain visible after freezing and provide a stable, sample-independent reference frame for orientation and correlation between cryo-fluorescence microscopy and electron microscopy. We show that PinCorr supports fluorescence-guided cryo-volume imaging, serial lift-out for cryo-electron tomography and freeze-substitution workflows followed by room-temperature on-section correlation. PinCorr thus provides a hardware-based approach to establishing a persistent spatial reference frame in HPF-based correlative imaging workflows for thick and multicellular specimens.

Serial-section immunohistochemistry (IHC) is the largest available source of paired hematoxylin and eosin (HE) and IHC whole slide images, yet it remains underexploited for cell-level supervision: adjacent sections sample non-identical cells, and residual registration error prevents direct assignment of IHC labels to individual HE cells. We present CellDF (Cell Displacement Field), which turns registered serial-section data into pairs of HE cells and their IHC labels by solving cell matching at whole-slide scale and assessing its reliability without ground-truth correspondences. CellDF estimates a locally adaptive residual displacement field through iterated kernel regression over each HE cells K nearest IHC candidates; a sparse-kernel variant keeps it tractable at the cell counts of a whole slide, where pairwise matchers are not. The within-tile distribution of the estimated displacements yields two ground-truth-free statistics, the directional scatter{sigma}{theta} and the between-tile angular deviation |{Delta}{theta}|, that localize matching quality more finely than landmark-based target registration error and drive a two-stage outlier filter that withholds labels where matching is unreliable. On 54 same-section HyReCo pairs,{sigma}{theta} correlates only moderately with landmark error and flags localized restaining damage that global error misses; on 30 four-marker Acrobat serial-section cases, the same statistic flags which IHC marker, if any, lies physically close enough to HE to support cell-level transfer. As a proof of concept, IHC labels transferred through CellDF trained a cell classifier on HE embeddings that generalized to held-out cells within the sample (F1 0.85, AUROC 0.88), establishing serial-section IHC as a usable cell-level labeling resource. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/733058v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@a9b3dcorg.highwire.dtl.DTLVardef@15f652corg.highwire.dtl.DTLVardef@1eb3396org.highwire.dtl.DTLVardef@87dda2_HPS_FORMAT_FIGEXP M_FIG C_FIG

Motivation: Exploring the fungal circular RNA (circRNA) landscape is bottlenecked by both experimental and computational limits. While standard mRNA-seq systematically discards circRNAs due to their lack of poly(A) tails, high-cost total RNA-seq remains prohibitive for large-scale screening. Consequently, discovery relies heavily on computational prediction. However, existing models trained exclusively on human or plant sequences fail in fungi because of the extreme genomic divergence across fungal lineages, which span from intron-poor Candida to intron-rich filamentous fungi. As a result, no computational framework currently exists for de novo fungal circRNA prediction, leaving the vast majority of non-model fungi entirely inaccessible. Results: We present mycoCirc, the first end-to-end pan-fungal multi-modal pretrained model for fungal circRNA prediction, integrating three mandatory modalities with bidirectional cross-attention for donor-acceptor site interaction. Pre-trained on 22 strains with 16,483 positive gene-circRNA associations spanning Ascomycete yeast, Basidiomycete yeast, and Filamentous fungi groups and fine-tuned per group using 5-fold cross-validation, mycoCirc achieved AUROC 0.69-0.70 on held-out test species under Mode A (Genome+GTF, no RNA-seq), substantially outperforming JEDI (0.51-0.57) and CircPCBL (0.49-0.53). Cross-species evaluation on four independent fungi datasets demonstrated robust generalization across all fine-tuned variants (AUROC 0.63-0.72). Beyond gene-level classification, the JunctionEncoder module enabled backsplicing junction identification for detailed circRNA validation. We further build mycoCircAtlas, a companion database providing 319,860 high-confidence gene-circRNA predictions across 768 fungal species from Ensembl Fungi Release 113, enabling researchers to query precomputed predictions and design validation primers without local model deployment.

Preclinical evaluation of chimeric antigen receptor (CAR)-T therapies for acute myeloid leukemia (AML) is limited by the lack of models that faithfully recapitulate the human bone marrow (BM) niche. Here, we implemented a humanized ossicle-based AML model that enables simultaneous engraftment of leukemic blasts and longitudinal assessment of responses to CAR-based immunotherapies. Intravenous or intra-ossicle injection of AML blasts produced robust, reproducible disease mimicking features of human AML within its microenvironment. To monitor tumor burden and immune effector cells in real-time, we developed a dual bioluminescence system using distinct luciferases in AML and CAR-T cells. This approach allowed non-invasive longitudinal tracking of CAR-T cell localization, expansion, persistence, and leukemic clearance within the ossicle. Overall, our model provides a powerful platform to study CAR-T cell behavior within a human BM niche and, for the first time, allows simultaneous longitudinal visualization of leukemic burden and CAR-T cell dynamics in a physiologically relevant ossicle-based AML model. TeaserHumanized ossicles combined with dual BLI enable tracking of AML progression and CAR-T cell dynamics in a human stromal niche.

Targeted knock-in technologies have enabled precise insertion of reporters, affinity tags, degrons, and other functional payloads into endogenous genomic loci. Over the past decade, a diverse collection of genome engineering strategies has emerged, including approaches based on homology-directed repair (HDR), microhomology-mediated end joining (MMEJ), homology-mediated end joining (HMEJ), and related methodologies. While these advances have greatly expanded the capabilities of endogenous genome engineering, they have also increased the complexity of donor design, assembly, and validation. Here, we describe FORGE-KI (Functional Oncology Research Genetic Engineering - Knock in), a pathway-matched design workflow for endogenous knock-in engineering that aligns the assembly strategy with the underlying repair mechanism. For large-cargo insertions, we use a modular five-component framework that separates gene-specific targeting arms from reusable functional modules, allowing rapid assembly of HDR donor constructs targeting AHR, IRF1, and FOSL1 from a shared reagent collection. For MMEJ/PITCh applications, where short targeting elements permit rapid fabrication, we developed a streamlined one-step pipeline in which the entire donor and selection payload is synthesized as a single continuous fragment for direct cloning, compressing the design-to-reagent cycle time. This MMEJ workflow is paired with a dual-promoter nuclease vector (pForge-KI-MMEJ-Cas9-DualGuide) that drives the PITCh-release and locus-specific guides from distinct promoters, a design intended to reduce the repeated-promoter instability associated with some dual-guide vectors. We also established a standardized workflow for donor assembly, generation of knock-in cell populations, molecular validation, and selectable-cassette removal, and we demonstrate it by generating a functional, selection-marker-free, cytokine-inducible IRF1 HDR reporter line and an inducible IRF1 PITCh/MMEJ reporter pool with confirmed junction enrichment. In parallel, we developed forgeKI, an R package that automates C-terminal reporter knock-in design across both HDR and PITCh/MMEJ repair pathways, including guide selection, target-biology validation, targeting-arm design, domestication, donor-assembly planning, and generation of synthesis-ready constructs. Together, the reagents and software provide a practical system for endogenous knock-in engineering that supports multiple payloads, selection strategies, and repair pathways within a shared donor organization. Rather than replacing existing knock-in technologies, this framework provides a modular foundation for incorporating, extending, and automating the published knock-in methods.

Coherent Raman spectroscopy enables label-free biochemical fingerprinting of live cells with subcellular resolution. We previously developed a machine learning framework capable of classifying glioma FFPE tissues using Raman spectral signatures. To accelerate live cell acquisition, we previously developed RADAR (Raman Spectral Analysis Using Deep Learning for Artifact Removal), a method that increases imaging speed by an order of magnitude while preserving spectral integrity. By integrating high-speed Raman imaging with supervised machine learning, we aimed to define unique biochemical fingerprints specific to cell type. We hypothesized that intrinsic biochemical composition alone is sufficient to distinguish cellular identity and tumor subtype. To test this, we generated metabolic maps of diverse brain-derived cell types--including astrocytoma, oligodendroglioma, and glioblastoma cells--using coherent Raman spectroscopy at single-cell resolution. Patient-derived brain tumor cell lines representing genetically heterogeneous backgrounds were analyzed. Samples were stratified by IDH1 mutation status (IDH1-mutant and IDH1-wild-type) and histologically classified as oligodendroglioma or astrocytoma. Raman spectral data were acquired from 286 live single cells across the two principal molecular classes, with further subdivision into two histologic subtypes within the IDH1-mutant group. Classification was performed using an XGBoost model with shallow tree depth (1-3), a 20% held-out test set, and grouped, stratified 5-fold cross-validation to control for sample-level bias. The machine learning framework distinguished IDH1-mutant from IDH1-wild-type cells with a ROC-AUC of 0.78 and further discriminated IDH1-mutant astrocytoma from oligodendroglioma cells with a ROC-AUC of 0.81. Feature importance analysis demonstrated that separation between IDH1-mutant and IDH1-wild-type cells was driven primarily by Raman peaks associated with protein amide bands, total NADH, unsaturated fatty acids, and heme-related vibrational modes. Within the IDH1-mutant class, discrimination between oligodendroglioma and astrocytoma was driven by lipid-rich vesicle signatures, protein/polyamide amide bands, and lipid-associated spectral features. Together, these findings support the feasibility of label-free, machine learning-assisted Raman profiling to resolve clinically relevant glioma subtypes at single-cell resolution. This scalable analytical framework provides a translational platform for investigating metabolic heterogeneity, therapeutic response, co-culture systems, and patient-derived organoid models.

T cells are a key part of the adaptive immune system. Using their surface-bound T cell antigen receptors (TCRs), these cells scan peptides and other antigens presented to them by major histocompatibility complex molecules (MHCs) on the surface of cells, searching for abnormalities. Although determining the map between TCRs and their target antigens is of vital importance for the design of safe and effective T cell-based vaccines and therapeutics, decoding these interactions is challenging. Experimental methods are not scalable, and sequence-based computational methods have issues generalising to new antigens. The IMMREP25 benchmark of methods for predicting T cell antigen specificity showed that AlphaFold-based methods promise improved generalisation to novel antigens. However, the ability of structure prediction models to predict T cell antigen specificity has not been robustly evaluated previously. In this work, we characterise AlphaFolds ability to predict T cell antigen specificity. We created a pipeline for high-throughput prediction of TCR:peptide-MHC (pMHC) structures using AlphaFold that is > 100 fold faster than the default implementation and used it to benchmark AlphaFold 3 (AF3) and similar models at predicting T cell antigen specificity. We investigated the underlying correlates of AlphaFold-derived binding scores and found that the models predictive power is related to the positioning of TCRs over the pMHC and not chemical interactions. Furthermore, we refine the AlphaFold-derived binding scores by training a machine learning model we call the PAE Aggregator. We then investigate AF3s ability to uncover the clustering rules of TCR repertoires and recapitulate mutational scanning experiments. These analyses show that AlphaFold3 clusters sequence-similar TCRs according to their binding mode and detects disrupting point mutations accurately. Our results highlight both the promise and the current limitations of structure-based approaches for predicting TCR specificity, guiding the development of more reliable immunological prediction methods.

Acoustic droplet ejection (ADE) enables nanoliter-scale liquid handling for complex microplate assays, yet translating experimental designs into validated, instrument-ready instructions remains a bottleneck. We present PickliPy, an open-source framework that converts spreadsheet-based assay designs into validated ADE picklists. PickliPy.Assay supports combinatorial, dose-response, and multi-addition time-course dispensing, while PickliPy.Screen extends to high-throughput workflows, including library reformatting and shortlisting. Across diverse biological contexts, the framework generated reproducible, assay-ready plates and standardized execution in human cohort studies. Acoustic pre-dispensing deepened bioenergetic phenotyping of isolated human skeletal muscle mitochondria, capturing substrate switching, and sharpened dose-response precision in human pancreatic {beta}-cells, revealing an age-associated change in succinate dehydrogenase kinetics. We benchmarked a wash-free, live-cell screen of mitochondrial function and morphology, in which deep-learning image analysis widened the assay window and ADE enabled integrative dose-response co-response analysis. Together, these tools make complex ADE experiments easier to design, reproduce, and scale from single benches to screening campaigns.

High-resolution 3D imaging is an important strategy for visualizing and analysing complex skeletal tissue architecture and the bone marrow microenvironment. However, multicolor immunolabeling and imaging of intact skeletal tissues are technologically challenging. The current immunolabeling and clearing methods for intact skeletal elements are very limited, time-consuming and generate low-resolution data or depend on the use of reporter mice. Here, we describe a protocol for efficient clearing and immunolabeling of intact calcified tissues that enables superfast, single-cell resolution, and quantitative 3D light-sheet imaging of intact skeletal elements and teeth. A key aspect of our protocol is the addition of a collagenase digestion step after fixation and decalcification. This step enhances antibody penetration, resulting in deep, comprehensive staining throughout immunostained bones and other calcified tissues. The protocol includes soft tissue removal, fixation, decalcification, bone dehydration, and bleaching, followed by antigen retrieval and permeabilization before the collagenase digestion step. This procedure is performed to prepare the samples for the tissue clearing process that improves bone tissue transparency prior to light-sheet imaging. The entire protocol, from bone collection to image analysis and quantification, takes about 4 days to complete, thus offering significant improvements over previous methods. This protocol is broadly applicable to the visualization of bone microstructure, bone marrow analysis, vascular and neural network mapping, and the study of signaling molecules in bone development and growth. The protocol requires experience with standard tissue processing and immunostaining techniques, and prior experience in tissue clearing and light-sheet imaging is beneficial but not essential. Key pointsO_LIA protocol for efficient clearing and immunolabeling of intact calcified tissues that enables superfast, high-resolution, and quantitative 3D imaging of various intact bones and teeth. C_LIO_LIThe entire protocol takes only 4 days to complete the comprehensive staining and perfect transparency throughout the intact bones, offering significant improvements over previous methods. C_LI Key referencesBiswas, L. et al. Cell 186, 382-397.e24 (2023): https://doi.org/10.1016/j.cell.2022.12.031

Accurate measurement of cellular oxygen levels is essential for understanding the balance between oxygen demand and supply in tissues. However, conventional methods only yield compromised results. We harnessed the oxygen dependence of bioluminescence to develop OxyBLI--a noninvasive optical method that directly monitors oxygen levels in specific cell populations of intact experimental animals. We characterized OxyBLI signals across various critical situations associated with common interventions. Hypoxic breathing and subsequent systemic tissue hypoxia caused blood to be redistributed in a way that prioritized brain oxygenation. In contrast, hyperoxic breathing sharply increased tissue oxygenation, which promptly returned to the target level owing to a vasoconstrictor response. These findings are expected to help resolve the long-standing clinical issue regarding the risks and benefits of administering supplemental oxygen to acutely ill patients. Our multifaceted approach, which presents multiple challenges to individual animals over time, will advance our understanding of the delicate interaction between hypoxia and hyperoxia.