Cell Reports Methods

We describe here an efficient method for gene editing in mouse T cells, based on well-established, high-efficiency retroviral transduction protocols. Our platform allows analysis of mutant phenotypes in primary murine T cells in vitro and in vivo. This approach uses a single retroviral vector to simultaneously knockout an endogenous gene and ectopically express its mutant version. This knockout/re-expression vector can be used as the only plasmid to transduce Cas9-expressing T cells, or used together with a Cas9 retroviral vector to transduce T cells from any mouse strain. We validated the system for analysis of murine T cells by targeting key molecules in proximal T cell signaling, i.e. CD3{gamma} and Zap70. We obtain high knockout and re-expression efficiencies in both Cas9-expressing and non-Cas9 T cells. Knockout efficiencies can be further improved by gRNA multiplexing. Endogenous proteins compete with their ectopically expressed mutants or tagged versions for cellular location, protein interactions and cellular functions. Here, we quantified the incorporation of CD3{gamma}-GFP into surface T cell receptor (TCR) complexes. Our data shows that the knockout and re-expression platform improves integration of CD3{gamma}-GFP into the TCR. Therefore, eliminating competition between endogenous and ectopic proteins benefits analyses of protein assemblies and signaling pathways in primary T cells. Furthermore, we validated advantages of our system for mutant analysis using wild-type and mutant Zap70s. Zap70 mutants deficient in TCR binding or kinase activity show their phenotypes only in the absence of endogenous protein, further validating our knockout/re-expression approach. Most importantly, this system can be used to generate gene-edited primary T cells for in vivo studies, such as the quantification of anti-tumor responses. Our knockout and re-expression platform provides a useful gene editing tool for primary T cells in fundamental research and immunotherapy development.

Calcium transients encode cellular and neuronal activity across timescales ranging from milliseconds to hours, yet linking these transient signals to downstream molecular states remains a major challenge. We recently introduced Caprola, a calcium-dependent protein labeling tool that converts calcium transients into permanent fluorescent marks for later analysis. In this way, Caprola enables tracking of neuronal activities in animal models as well as retrospective identification of labeled cells for isolation and transcriptomic analysis. However, the relatively slow labeling kinetics of Caprola required high concentrations of fluorophore probe and relatively long labeling times, which limits its sensitivity and applicability, in particular in vivo. To address this limitation, we generated Caprola variants with up to 29-fold faster labeling rates than their predecessor. We demonstrate that our new Caprola variants record calcium transients in cells and in zebrafish larval brains under conditions where previous Caprola variants did not show labeling. We further expand the applicability of Caprola to activity-dependent marking of postsynaptic compartments, opening new avenues for coupling functional activity histories with downstream molecular and transcriptomic analyses.

Investigating the consequences of astrocyte loss in the intact brain is both important and challenging. As integral components of the neuro-glia-vascular unit, astrocytes are involved in a variety of brain processes including water homeostasis, metabolic supply, regulation of cerebral blood flow, and coordination of neuronal circuit activity. Astrocyte impairment has been associated with numerous neurological disorders. However, experimental models combining focal astrocyte ablation with longitudinal in vivo imaging in the intact adult brain have been lacking, limiting efforts to define the causal contribution of astrocyte loss to central nervous system (CNS) pathology and repair. Here, we present an in vivo model of antibody-mediated astrocyte ablation that enables longitudinal imaging and detailed investigation of ensuing cellular responses. It integrates focal induction of aquaporin-4 antibody-mediated astrocyte loss, chronic in vivo two-photon imaging, genetically encoded sensors, and reporter mouse lines. This advancement allows visualization and quantification of cellular and subcellular events in living organisms during lesion progression and recovery. It overcomes many longstanding limitations of previous models that are either constrained by non-specific hypoxic or mechanical tissue damage or require sacrificing animals at discrete time points, hindering the ability to monitor dynamic biological processes over time. In contrast, the selective targeting of astrocytes prevents the formation of the glial border, enabling the investigation of CNS response in a scar-free environment. Overall, this new approach represents a significant technical advancement, enabling comprehensive longitudinal studies of CNS responses to astrocyte loss, thus opening new avenues for understanding astrocytopathy-driven pathology, evaluating therapeutic interventions, and promoting translational research.

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.

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

Third-generation sequencing technologies, such as nanopore sequencing, enable long-read sequencing and direct characterization of nucleic acid modifications at low cost. However, nanopore sequencing is limited by low throughput, necessitating targeted sequencing for interrogation of specific genomic elements. The current standard is nanopore Cas9-targeted sequencing (nCATS), which utilizes blunt-end cleavage of dephosphorylated DNA to render targeted DNA sites as the only ligation-capable ends for sequencing adapter addition. nCATS significantly improves on-target sequencing yield but suffers from lower total sequencing output and faster flow cell degradation, resulting in an increased cost per sequencing due to inert DNA. Here, we present a modified approach, based on creating predictable base overhangs with Cas12a/Cpf1 as ligation substrates for biotinylated oligos followed by bead enrichment, termed nanopore Cas-12a Targeted Ligation-Enrichment Sequencing, or nCasTLES. nCasTLES removes off-target DNA via bead washes rather than rendering it inert. Removal of the inert off-target DNA allows nCasTLES libraries to be pooled with other sequencing libraries in a single sequencing run to achieve equivalent on-target DNA sequencing as nCATs while improving overall yield of useful data and decreasing the speed of flow cell degradation. We demonstrate the power of nCasTLES to characterize methylation dynamics at a frequently-methylated gene promoter. We also directed the Cas12a cleavage to an integrated lentiviral vector, allowing us to assess clonality of a transfected population and interrogate the integration state and transgene effects in selected clones. Finally, we demonstrate the utility of nCasTLES increased flow cell throughput by spike-in of nCasTLES libraries to WGS libraries to also characterize genetic and modified base information, such as clonal copy number variation analysis or BrdU incorporation, alongside the targeted sequencing. This approach will enable highly focused genomic interrogation in combination with full throughput of off-target reads.

Fundamental cellular decisions of life and death are governed by intricate and tightly regulated intracellular signalling pathways that determine whether cells proliferate, enter quiescence, or undergo programmed cell death (apoptosis). Live-cell fluorescence imaging enables these processes to be observed in real time at single-cell resolution, but two problems limit their study. First, existing biosensors do not allow apoptotic status and cell cycle progression to be resolved in tandem within the same cell. Second, interpreting live-cell imaging data is challenging even where multiplex reporters exist, as the biological meaning of fluorescent signals depends on their temporal ordering, and large-scale imaging experiments generate complex, multidimensional data that are difficult to analyse systematically and at scale. Here we address both problems. We present FluoroFate, a generalisable and user-friendly graphical interface-driven tool for time-resolved single-cell analysis of multiplex live-cell imaging datasets, which integrates existing, robust deep learning-based segmentation, cell tracking, and temporal classification methods to quantify fluorescent reporter dynamics in individual cells across time without the need for specialist computational expertise. Alongside FluoroFate, we develop tricistronic Fluorescent Ubiquitination-based Cell Cycle Indicator (Fucci) and apoptosis biosensors, enabling simultaneous monitoring of cell cycle progression and caspase activation within the same cell. Applying FluoroFate, we resolve apoptotic and non-apoptotic cell death at the single-cell level based on the temporal ordering of Annexin V and propidium iodide signals, identifying distinct kinetic and phenotypic cell death profiles in response to pharmacological perturbation. We highlight divergent temporal dynamics and modes of cell death between birinapant and cycloheximide treatment, reflecting differences in how TNF/TNFR1 signalling is disrupted by these agents. At the single-cell level, we uncover parallel, independently regulated death programmes, demonstrating that loss of RIPK1 selectively impairs apoptotic cell death whilst leaving non-apoptotic death largely unaffected. We then use FluoroFate to analyse timelapse images of our combined Fucci-apoptosis reporters, resolving cell cycle progression and caspase activation within the same cell over time. Together, FluoroFate and our new cell cycle and apoptosis biosensors represent a broadly applicable platform for extracting mechanistic insight from live-cell imaging data.

Dominance hierarchies are a fundamental aspect of social organization, enabling animals to minimize aggression and optimize access to resources. Previous studies have highlighted the energetic and physiological demands of dominant status, as well as the behavioral flexibility required of subordinates to navigate these hierarchies. Despite advancements in automated behavior tracking, limitations persist in tracking fine-scale, real-time interactions within complex social environments. Here, we developed and validated a novel RFID-based system to continuously monitor dominance hierarchies in group-housed male mice over 10 days. This system enabled unbiased behavioral inference across light phases and revealed spatial and temporal patterns of dominance behavior undetectable through traditional live-scored methods. Automated tracking accurately identified alpha individuals and consistently inferred linear hierarchies across cohorts, with greater precision for higher-ranked individuals. Behavioral metrics, such as transition frequencies and proximity to food zones, were consistent with dominance driven activity. Hormonal analyses revealed that higher-ranked mice exhibited increased leptin and peptide YY, consistent with heightened activity and satiety signaling, while lower C-peptide levels reflected greater metabolic demands of dominance. Furthermore, dominance rank was associated with differences in light-dark activity, which were in turn related to circulating hormone profiles. This study demonstrates the utility of automated RFID tracking in capturing dominance hierarchies with temporal and spatial granularity, while revealing links between social rank, metabolic regulation, and activity patterns advancing our understanding of social behavior dynamics.

A central challenge in systems neuroscience research is to elucidate how distinct neuronal cell types coordinate to produce complex behaviors: an endeavor that requires linking their molecular identity, connectivity and activity patterns in behaving animals. Recent advances in single-cell RNA sequencing and spatial transcriptomics have revealed remarkable molecular diversity among neurons. However, most functional recording techniques, including in vivo two-photon calcium imaging, do not reveal the molecular cell identities of recorded cells. Although manual one-to-one matching between functional imaging and post hoc molecular profiling has been achieved for dozens to a few hundred neurons, these approaches are typically labor-intensive, difficult to scale, and capture only a limited fraction of cell types. Integrating these two modalities at large scale remains challenging, limiting our ability to fully understand the general logic of neural computation. Here we present a cross-modal workflow that integrates in vivo two-photon calcium imaging in behaving mice with Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH), a thick-tissue spatial transcriptomic approach. As a proof of concept, we apply this workflow to the mouse dorsal hippocampus, a brain region with high neuronal density and small cell size where manual cell matching is impractical, making it a stringent test for the accuracy and scalability of this method. Using our approach, we achieved highly accurate alignment between in vivo neural activity and ex vivo molecular cell-type identity across hundreds to thousands of neurons, enabling direct mapping of neural dynamics to molecularly defined neuronal populations. We showed that longitudinal neural recordings followed by molecular mapping reveal distinct neural activity patterns that emerge as animals learn a spatial navigation task. By linking activity patterns to molecularly defined cell types, this method provides a framework for testing whether neural representations are organized through cell-type-specific coding or distributed population activity, offering insight into the computational logic by which neural circuits generate complex behaviors.

Cryo-electron tomography (cryo-ET) enables near-native visualization of subcellular architectures, yet applying it to moderately thick, multilayered tissues such as the retina is hampered by inadequate vitrification and inaccurate depth-targeting. Here, we developed PLCT, an integrated approach combining modified high-pressure freezing, cryo-ultramicrotome trimming, and plasma-based cryo-FIB milling to overcome these barriers. PLCT reliably vitrified <100 m retinal strips with minimal ice artifacts, navigates precisely to the outer plexiform layer using morphological landmarks, and produces high-quality lamellae suitable for high-resolution cryo-ET. Subtomogram averaging (STA) analysis identified microtubules at 16.33 [A] within retinal horizontal cell processes. Importantly, STA also resolved a 10-nm-diameter filamentous structure at 24.81 [A] in the same processes, featuring six peripheral strands surrounding an elongated central density with continuous intervening cavities, an architecture consistent with intermediate filaments. Together with its native localization and immunoreactivity, these features collectively identify the filaments as neurofilaments. Separately, 3D reconstruction of synaptic ribbons uncovered a previously unrecognized "mahjong tile"-like fine ultrastructure. These results demonstrate that PLCT-produced lamellae are of sufficient quality to support structural analysis in native tissue. Although demonstrated on retinal photoreceptor synapses as a proof-of-principle, PLCT is inherently generalizable, with its depth-navigation and vitrification strategies directly applicable to any multilayered tissues. This work establishes PLCT as a robust, reproducible platform for depth-resolved in situ cryo-ET of multilayered tissues.

Precise characterization of genome editing outcomes remains a major challenge because edited cell populations contain diverse alleles generated by homology-directed repair, non-homologous end joining (NHEJ), or base editing. While next-generation sequencing enables comprehensive analysis, its routine use is constrained by cost and turnaround time. Here, we developed a multi-color droplet digital PCR (ddPCR) assay that exploits six-color fluorescence detection to quantitatively distinguish multiple edited alleles within a single reaction. Using CRISPR-Cas9 and base editing model systems, we designed sequence-specific probe sets that distinguished recurrent NHEJ alleles generated by CRISPR-Cas9 editing as well as target and bystander alleles generated by base editing. The assay quantitatively resolved individual editing outcomes that could not be distinguished by conventional Sanger sequencing. Together, these results establish multi-color ddPCR as a rapid, scalable, and sequence-specific approach for quantification of genome editing outcomes across multiple editing modalities.

BackgroundPathway-activity analysis summarizes gene-level single-cell measurements into interpretable functional modules, but widely used methods lack an integrated significance framework, do not account for the batch effects that pervade multi-sample studies, and are not natively interoperable with Python-based workflows. The field also lacks simulation resources with ground-truth pathway activity for quantitative benchmarking. ResultsWe present scROMA, a singular-value-decomposition-based method that quantifies pathway activity as coordinated variation, with per-cell scores, per-gene contributions, and permutation-based significance, natively integrated with the Scanpy/AnnData ecosystem. Its batch-aware extension is, to our knowledge, the first to correct batch effects within the gene-set subspace rather than across the full transcriptome, isolating technical variation at the pathway level while preserving signal in other genes. We also release a generative simulation framework producing synthetic data with fully specified ground-truth activities. On simulated benchmarks scROMA is competitive across tasks, and under batch effects its batch-aware mode recovers ordinal pathway structure that full-transcriptome integration misses. Across cystic fibrosis airway, intestinal-organoid, breast cancer, and lung cancer datasets it recovers established biology while separating it from technical and inter-donor variation; in the intestinal-organoid atlas it reproducibly recovers an inflammatory program across donors, separates its sustained from transient components, and resolves cell-type-specific niche-factor targets. ConclusionsscROMA is open-source and released with the simulation framework and pre-generated benchmark datasets as a community resource, providing a scalable, statistically grounded, and batch-aware approach to pathway-level analysis in single-cell transcriptomics.

Recording and manipulating brain activity during behavior is critical to understanding the underlying mechanisms of decision-making. Linking neural activity to behavior requires behavioral hardware and software tightly integrated with recording and perturbation systems on a shared clock. We built SPOUT (State-machine Platform for Operant Uni/dual-spout Tasks), an open-source, Teensy-driven state-machine platform with a MATLAB interface that runs 10 unique decision-making tasks (with dozens of variations available through user-friendly settings) to study behavior in head-restrained mice. The platform is built on several custom hardware devices: a dual-lick detector, headplate designs for optogenetics and two-photon calcium imaging, a three-axis motorized spout manipulator, and an optogenetics power modulator. The firmware differentiates between one and two lick spout tasks and can be controlled by a user-friendly interface. Task settings can be selected through the interface or by loading predefined settings files. We validated the clock speed and lick detection against an independent, external acquisition system and identified highly precise, sub-millisecond detection of single licks. Using a pseudo-random synchronization pulse generated by SPOUT, we corrected for missing data due to glitches in the acquisition system and clock drift. We showcase the versatility of SPOUT by training mice on an uninstructed lick-left/lick-right task in which the rewarded side switches unexpectedly and found that mice use history-dependent action-outcome associations to guide future behavior. Transiently inhibiting the anterior lateral motor cortex (ALM) during cue presentation induced contralateral deficits, without affecting ipsilateral trials. Finally, two-photon imaging of ALM neurons revealed stronger population responses during contralateral choice licks compared to ipsilateral ones. Together, SPOUT offers an open-source, affordable platform to study decision-making in head-restrained mice while combining neural recordings and manipulations.

Tyrosine kinase signaling for cell development and homeostasis in multicelluar organisms and a major biochemical contribution is by driving interactions between phosphorylated tyrosines (pY) and SH2 domain containing proteins. This assembly is so important to driving cell outcomes that a wide variety of experimental and computational approaches have been used to understand which SH2-pY interactions occur, which still remains a challenge given the immensity (more than 45,000 pY and 120 SH2 domains in the human proteome). Based on biophysical constraints suggested by comprehensive contact mapping, here, we ask whether an approach might consider first asking if pY sequences conform to the shared rules of SH2 domain recognition by developing a classification approach that combines diverse training data. A wide range of validation suggests this approach, SpY-C, can classify pY sites as having the potential, or not, to be involved in SH2 domain interactions. We find that a relatively small set of representative SH2 binders, integrated from different experimental techniques, provides good classification. We use this classifier to annotate the human phosphoproteome and individual experiments, to explore the consequences of using super-SH2 domain reagents for pY enrichment, and to analyze the effects of mutations in altering pY site function. SpY-C provides a helpful step to more rapidly annotating pY function and for possibly improving machine learning approaches focused on specific SH2-pY interactions downstream of a first pass classification approach.

BackgroundTraditional immunohistochemistry (IHC) with chromogen detection has limited multiplex capacity, detecting at most 4 protein markers per tissue section simultaneously, thereby restricting comprehensive spatial analysis of valuable human biopsies. We developed and validated a robust serial IHC (sIHC) staining method to detect multiple antigens on a single kidney biopsy slide, maximizing data yield for diagnosing and studying complex kidney diseases. MethodsFormalin-fixed, paraffin-embedded kidney biopsy sections were subjected to repeated IHC/imaging cycles with antibody removal using an optimized sodium dodecyl sulfate-glycerol buffer stripping protocol. Images were then co-registered, and analysis was performed using a variety of methodologies, including color deconvolution, cell segmentation, and spatial clustering. ResultsThis optimized sIHC method successfully detected up to 20 antigens on a single slide. Combining image analysis and artificial intelligence software, for example with HALO (Indica Labs), the assay assembles high-dimensional images and enables quantitative histology and single-cell spatial analysis. Using this advanced method, we were able to identify rare cell populations, such as double-negative T cells, that are challenging to detect conventionally. ConclusionWe have developed a validated, high-capacity sIHC protocol that uses standard IHC procedures with commercially available, clinically validated off-the-shelf antibodies. This method is a valuable, cost-effective tool for obtaining extensive, high-dimensional single-cell-resolved spatial data from limited pathology samples, such as a human kidney biopsy.

Probe-based genomics technologies are extending molecular analysis into intact tissues and fixed cells, yet strategies to decode complex experimental conditions encoded in cellular RNA remain limited. Here we present a modular framework that integrates custom software tools with purpose-built cloning reagents to design, assemble, validate, and deploy combinatorial DNA barcodes. Combinatorial barcodes comprise spatially adjacent collections of known sequences, enabling millions of unique molecules to be efficiently distinguished using a limited set of probes. Our software tools integrate with optimized assembly plasmids and whole plasmid long-read sequencing for high-fidelity construction and structural validation of diverse combinatorial barcode architectures. Assembled barcode libraries are flexibly transferred into user-modified expression vectors to support diverse downstream experimental applications. We showcase the versatility of this framework by assembling two structurally distinct combinatorial barcode libraries, each containing millions of unique sequences. Following rabies virus-based delivery to the mouse brain, we validate in vivo decoding of a combinatorial barcode architecture capable of distinguishing ~16.3 million expressed RNAs through probe-based in situ sequencing. Our framework for flexible and accurate combinatorial barcode construction fills a technically demanding niche delivering cost-effective molecular reagents for multiplexed experimentation on current and evolving probe-based genomics platforms.

CD8 T cells play a central role in immune responses to infection and cancer. However, the diversity of T cell receptor (TCR) specificities makes it challenging to study the mechanisms that regulate T cell activation, differentiation, and effector function. Beyond TCR transgenic mouse models, various complex genome-editing approaches have been employed to overcome this challenge. However, these strategies are often technically demanding, time-intensive, and difficult to adapt. Investigators who are interested in testing de novo TCRs under their chosen experimental conditions would benefit from a standardized and accessible method. Here, we describe a protocol that combines ribonucleoprotein (RNP)-based CRISPR-Cas9 editing with retroviral transduction to enable efficient genetic manipulation of murine CD8 T cells. We show that T cells engineered via this protocol can be generated at sufficient scale for downstream in vitro assays and in vivo adoptive transfer experiments. We expect this method will be useful for investigators who require a standardized and accessible way to study how TCR specificity impacts CD8 T cell responses.

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

Current approaches for imaging fibrotic remodeling have sensitivity, specificity and cost drawbacks that limit both preclinical research and clinical diagnosis. Here, we show that fast green FCF, a small molecule that binds to fibrillar collagen, enables highly sensitive and specific imaging of fibrosis in lung samples from mice and humans using fluorescence microscopy. We report strategies for using fast green FCF staining to assess fibrotic remodeling using precision-cut lung slice and whole-biopsy preparations. Our findings demonstrate that fluorescence imaging of fast green FCF-stained collagen will be useful for fibrosis research and may help to improve detection of fibrosis in clinical pathology.

Selecting stochastic gene-expression models from single-cell counts requires accurate parameter inference and efficient model selection. Likelihood methods in count space can be costly when full stationary count distributions are unavailable, whereas approximate methods may lose accuracy. Probability generating functions (PGFs) offer a compact analytical alternative, but existing PGF workflows are generally not likelihood based and therefore rely on computationally intensive cross-validation. We develop a likelihood-based PGF framework for both tasks. Correlated empirical PGF values are used to construct a Gaussian quasi-likelihood for parameter inference and PGF-based Bayesian information criterion (BIC) for model selection. We show that the empirical PGF is exactly unbiased and that the parameter estimator is consistent, converges at the inverse-square-root sample-size rate, and is first-order asymptotically unbiased. For large samples and a uniquely preferred model, PGF-BIC selects the same model as leave-one-out cross-validation in PGF space.