Cell Reports Methods

Automated home-cage systems measure individual behavior in social groups for days to months. Among these systems, the IntelliCage has become a widely used platform for longitudinal and socially embedded behavioral phenotyping. Yet the analysis layer often remains less standardized than the experiment itself: raw exports, phase definitions, exclusion rules, time alignment, and derived behavioral metrics are transformed by lab-specific scripts that are difficult to audit, compare, or reuse. We present ic-analysis, an open-source Python toolkit for standardized, scriptable, and shareable IntelliCage workflows. The toolkit separates user-defined experiment metadata and workflow scripts from a reusable analysis core with modular analysis and plotting functions, allowing users to flexibly assemble experiment-specific pipelines without editing package internals. Due to its modular design, the analysis core can be applied to a broad range of experimental paradigms, including general activity, exploratory, motivational, cognitive, and social readouts, rather than being limited to a single fixed protocol. It aligns biological phase windows across staggered cage runs and exports plots together with quantitative result tables, applied settings, and audit files that support reproducible and FAIR reporting. Here, we demonstrate this flexible design in a realistic synthetic place-learning/place-reversal experiment with two mouse groups and deliberately offset cage starts. The workflow recovered the implanted behavioral differences while preserving the required experimental-time alignment. Group A showed stronger endpoint saccharin preference (81.7 +/- 1.7% vs. 27.4 +/- 3.4%, p = 1.9e-), higher liquid uptake, faster place-learning onset (56.6 +/- 8.1 vs. 278.3 +/- 33.2 visits), and better reversal performance (64.1 +/- 1.3% vs. 25.6 +/- 1.7% rewarded correct-corner visits) compared to Group B. This demonstration shows how standardized, explicitly defined readouts can turn complex IntelliCage exports into interpretable behavioral profiles while preserving the analysis history needed for inspection and reuse. ic-analysis therefore provides both a working analysis scaffold and an extensible, community-friendly route toward IntelliCage workflows that are easier to reproduce, compare, extend, and share.

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.

The diverse functions of the enteric nervous system (ENS) arise from communication between molecularly distinct neuronal populations organized into complete circuits. While recent single-cell transcriptomic studies have resolved the molecular identity of enteric neuron classes, methods for defining their synaptic connectivity remain limited. Here, we describe the implementation of mWmC, an anterograde monosynaptic tracer based on a fusion of wheat germ agglutinin (WGA) and mCherry, as a non-toxic, single-component viral tool for mapping neuronal circuits within and beyond the ENS. Following adeno-associated virus (AAV)-mediated expression in enteric neurons, mWmC was efficiently expressed and transmitted selectively to postsynaptic neurons, with no detectable transfer to enteric glia, interstitial cells of Cajal, blood vessels or other mesenchymal cell types. The method also identified postsynaptic neurons in the celiac-superior mesenteric ganglia following tracing of intestinofugal enteric neurons, demonstrating its utility for mapping inter-organ circuits. As proof of principle, we applied mWmC to two genetically defined myenteric interneuron populations and identified preferential postsynaptic targets, revealing selective connectivity with distinct enteric neuron classes. Time-course experiments showed that transsynaptic labeling occurred between 4 and 10 days and reached a plateau thereafter, consistent with monosynaptic transfer. Finally, we developed a dual-reporter version of the system that simultaneously distinguishes input and target neurons within the same tissue. Together, mWmC provides a robust approach for defining circuit architecture in the ENS, linking molecular cell atlases with neuronal connectivity and paving the way for deeper insights into the circuit mechanisms underlying gut physiology. Graphical AbstractSchematics illustrating the implementation and applications of the anterograde monosynaptic tracer mWmC for mapping enteric neuronal circuits. Parts of schematics are generated with Biorender. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=36 SRC="FIGDIR/small/741165v2_ufig1.gif" ALT="Figure 1"> View larger version (12K): org.highwire.dtl.DTLVardef@19635bborg.highwire.dtl.DTLVardef@a18ffborg.highwire.dtl.DTLVardef@f3d011org.highwire.dtl.DTLVardef@e1359c_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

Quantifying social behavior requires accurate assignment of posture and actions to individual animals, which is often hindered by close contact, occlusion, and identity switches. Meanwhile, current behavioral recognition pipelines still lack stable predictive accuracy. Here we developed SABER, a locally deployable framework that couples multianimal pose estimation with identity preserving tracking, interpretable behavioral factor mining, and multiscale temporal behavior prediction from a single overhead video stream. Across spontaneous two mice social interaction, mating, aggression, and four mice recordings, SABER improved pose estimation accuracy and tracking continuity relative to comparator pipelines. Its behavioral factor-mining procedure identified interpretable kinematic, postural, and social descriptors, and temporal integration improved classification of behavioral categories. SABER offers an intuitive, open-source interface to facilitate use. Applied to social defeat stress mice, SABER detected reduced approach behavior and a multivariate behavioral profile that distinguished depression susceptible from control animals. SABER outputs could also be synchronized with miniscope calcium recordings, enabling joint analysis of behavioral states and neuronal population activity. SABER therefore provides an accessible, identity resolved route from single view social interaction video to behavioral phenotyping and brain behavior analysis.

Genetic perturbations can reshape both the state of a targeted cell and the behavior of neighboring cells. Measuring these effects systematically in intact tissues requires pooled screening methods that jointly identify perturbations, molecular phenotypes, and spatial context of individual cells. Here, we report an imaging-based pooled screening method that detects perturbation identities and endogenous RNAs without enzymatic amplification. Relative to our previous enzymatic-amplification-based workflow, amplification-free MERFISH detection with direct hybridization increased recovery of perturbation barcodes by ~10-fold and endogenous transcripts by 2.5-fold, improving perturbation assignment as well as molecular profiling of both perturbed and neighboring cells. Applying this method to a genetic mosaic mouse liver screen targeting ~200 genes in hepatocytes, we profiled 1.5 million cells in intact tissues and mapped both cell-autonomous responses of perturbed hepatocytes and cell non-autonomous changes in neighboring immune and vascular populations. These included localized accumulation of pDC-like cells near hepatocytes with impaired V-ATPase function and angiogenic responses in vascular cells near Vhl-perturbed hepatocytes with hypoxia activation. Our method provides a simplified, scalable approach to linking genetic perturbations with cell state, multicellular response, and tissue organization.

The advent of optogenetics and the rapid development of genetically encoded actuators and reporters for calcium, voltage, neurotransmitters, and other molecules have revolutionized neuroscience research by enabling precise, non-invasive optical interrogation of neural circuits. Successful implementation, however, critically depends on efficient reporter expression in defined neuronal populations. We present a Gateway(R)-based cloning platform providing a robust pipeline for rapid, efficient construction of recombinant adeno-associated virus (rAAV) vectors optimized for neuroscience applications. This platform addresses promoter selection, indicator engineering, and capsid-type optimization, and enables systematic optimization of vector components across a range of optical tools. The benefit of this modular approach is illustrated in the case of ULoVE, a two-photon microscopy method based on acousto-optic deflectors (AODs). This method provides serial light-targeting with kHz sampling rates and high signal-to-noise ratio in vivo, which imposes stringent requirements on indicator expression -- including cell-type specificity, sparse labelling, and precise control of expression levels-- efficiently met through the combinatorial flexibility of the Gateway pipeline. As specific examples, Gateway-constructed Cre-driver viruses combined with Cre-dependent reporters enabled cell-type-specific labelling for two complementary applications. In cerebellar Purkinje cells, an L7::Cre driver virus paired with a Gateway-constructed voltage indicator (JEDI2P-Kv) and ULoVE two-photon excitation at ~5 kHz enables resolution of sub-millisecond dendritic voltage dynamics in awake, behaving mice, including optical detection of dendritic spikelets previously accessible only via intracellular electrophysiology. The same driver virus paired with calcium indicators (GCaMP6f, jRGECO1a) resolves climbing fiber-evoked calcium kinetics under the same conditions. In acute cerebellar slices, a kit::Cre driver virus enabled cell-type-specific expression of the optogenetic actuator ChR2(H134R) in molecular layer interneurons, where ULoVE doughnut-pattern photostimulation achieved single-cell, sub-millisecond optogenetic activation with micron-scale spatial resolution. Beyond ULoVE applications, the same sparse, strong labelling also proved suitable for anatomical tracing of axonal projections in cleared cerebellar tissue, resolving individual DCN axon terminals at the granule cell layer --molecular layer boundary. Taken together, our dual-front approach --combining a modular AAV expression pipeline with AOD-based optical acquisition --provides an integrated platform for developing sophisticated optical tools, including but not limited to AOD-based microscopy, for neural circuit interrogation.

Image-based transcriptomic approaches can define, discover, and chart cell types and states within an array of tissues. However, measurement quality depends on RNA integrity, and the modern tissue preservation toolbox was not designed to protect this highly labile molecule. Here we leverage MERFISH to show that tissue-dependent differences in endogenous RNase activity can shape spatial transcriptomics data quality for different preservation methods and that RNase-activity-guided protocol optimization can improve data quality. In parallel, we introduce an RNA-aware pan-tissue preservation approach, Rapid Inhibition and Permanent Inactivation of Nucleases (RIPIN), that rapidly stabilizes samples with a broad-spectrum RNase inhibitor while permitting slow, chemical inactivation. RIPIN produces high-quality MERFISH measurements in all profiled human and mouse tissues, is compatible with clinical workflows, and is easily integrated with frozen or paraffin sectioning. By highlighting how RNA integrity can be lost during tissue processing, our work may inspire the next generation of RNA-aware histology methods.

DNA methylation-based classification is increasingly a cornerstone of CNS tumor diagnostics. Recently,nanopore sequencing enables very rapid methylation profiling to support surgical decision making. Time-constrained sequencing yields sparse methylation profiles, necessitating specialized classifiers for accurate predictions. All publicly available sparse-profile classifiers rely on a single reference set of only 2,801 samples published in 2018 and its accompanying classification system. Here we present Sturgeon V2, trained on nearly 10,000 methylation profiles assembled from a range of public repositories and collaborations. To harmonize differences in tumor class labels, we developed an iterative semi-supervised relabeling strategy with a two-tiered concordance filter. We demonstrate that this filtering is essential for optimal performance. Compared to Sturgeon V1, we further redesigned the classifier architecture to provide more stable, and consistent confidence estimates while accumulating sequencing data and we introduce an updated class system including new classes, such as meningioma subtypes, PATZ- fusion neuroepithelial tumors and PLAG1/2-amplified embryonal tumors. In a retrospective intraoperative cohort of 94 pediatric cases, Sturgeon V2 was confidently correct in 85 cases (Sturgeon V1: 78) with one confident misclassification, and in simulated sequencing runs (n=1880) reduced the error rate among confident calls from 7.1% to 2.8%, outperforming publicly available alternatives. Sturgeon V2 is the first openly available, locally executable classifier for sparse methylation profiles trained beyond the 2018 reference era.

Protein labelling using genetically encoded fluorescent protein fusions has revolutionized the study of protein localization and function. However, visualizing endogenous proteins in specific cell types remains challenging, as existing approaches often require overexpression or laborious genetic engineering. To address these limitations, we developed a library of fluorescent protein binders using existing nanobodies and single-chain variable fragments that recognize fluorescent proteins and short peptide tags, allowing for modular, cell type-specific labelling of genetically tagged endogenous proteins. We found that fluorescent protein binders generate background fluorescence in the absence of the target protein, reducing signal specificity. To overcome this limitation, we generated destabilized fluorescent protein binders directed against GFP, ALFA, GCN4 peptide and V5. These binders display increased antigen-dependent stability and reduced background fluorescence, thereby improving the signal-to-noise ratio of endogenous protein visualization. We demonstrate the detection of endogenous proteins in drosophila larvae and embryos, zebrafish embryos, as well as in specific neuronal populations of the adult drosophila brain. In the brain, tissue-specific labelling revealed previously unknown protein expression and subcellular localization in sleep-regulatory neurons. Together, our panel of tag-stabilized fluorescent protein binders provides a versatile, ready-to-use toolkit for background-free, cell type-specific visualization of endogenous proteins.

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 rapidly increasing number of video tracking-based behavioral summary tools and methods raises the question as to the most suitable approaches for pharmacological fingerprinting in pre-clinical research. We have recently shown that social context has a strong effect on behavioral syntax in mice, further suggesting that treatment effects can be context dependent. Here, we aim to answer the question whether there is an optimal combination of context and behavioral summary method for effect detection and discrimination of different psychoactive substances in a controlled environment. To this end, we applied eight different treatment-dosage pairs (amphetamine 1.5,3,6 mg/kg; modafinil 5,10,50 mg/kg; seltorexant 3,10 mg/kg) and evaluated five different approaches to behavioral summary: parametric aggregation, unsupervised segmentation in Keypoint-MoSeq (KPMS) & Variational Animal Motion Encoding (VAME), and supervised segmentation in Simple Behavioral Analysis (SimBA) & A-SOiD, across two different contexts (Solitary & Social) in 314 recordings of freely moving mice in an open-field arena. Surprisingly, our results show no significant differences in performance across models and context. Across treatment effect detection to treatment-dose discrimination, all models showed performance significantly above chance that was insensitive to various data limitations and extensions. Overall, our study shows that under the tested conditions and treatments, the choice of a behavioral summary model does not meaningfully affect the description of treatment effects. Simple aggregate measures from tracking data and machine learning based behavioral summary approaches that are expensive, in terms of training data and computational resources, performed equally well. Our findings taken together with literature suggest that the fuller decomposition of complex behavior through unsupervised machine learning might be necessary for the description of large-scale datasets but does not necessarily align with the goals present in smaller-scale treatment discrimination tasks common in pre-clinical 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.

Induced pluripotent stem cell (iPSC) workflows typically rely on monoclonal cultures, requiring multiple independent cell lines to compensate for clonal variability. This increases workload, cost, and limits scalability for translational applications. Here, we establish polyclonal reprogramming strategies using Sendai virus to generate clonally diverse iPSC cultures already during reprogramming through FACS, MACS, manual selection, or prolonged culture. Both monoclonal and FACS-derived polyclonal cultures reached pluripotency. Reprogramming transgenes were silenced, whereas Sendai virus (SeV) mRNA persisted across passages in both culture types; heat treatment at 38.5C markedly reduced SeV levels. Monoclonal and polyclonal cultures differentiated efficiently into neural crest-like cells and sensory neurons. As a second polyclonal strategy, monoclonal cultures were pooled at day5 of sensory neuron differentiation to generate percentage-controlled progenitor mixtures. TRPA1 protein expression has not been shown reliably in iPS-derived sensory neurons. We hypothesized that increased clonal diversity might facilitate detection of TRPA1 protein expression. Despite rigorous antibody validation, TRPA1 protein expression was detectable only diffusely across the whole cell area. Together, our results show that polyclonal iPSC strategies enable clonally inclusive generation of patient-specific sensory neurons. These workflows reduce early workload and provide a robust foundation for scalable drug-screening and patient-in-a-dish applications.

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 cerebellar cortex regulates motor and cognitive function through its sole output neurons, the Purkinje cells (PCs), which inhibit downstream targets, yet precise manipulation of PC activity in vivo remains technically challenging. Here, we describe two viral strategies with differing cell-type specificities for in vivo optogenetic control of PCs and functional assessment of neural activity in downstream target regions. Specifically, we detail strategies for expression of the fast channelrhodopsin ChETA in PCs that enable light-evoked activation of PC axonal terminals in medial deep cerebellar nuclei (mDCN), and demonstrate light-evoked modulation of mDCN neural activity using in vivo extracellular electrophysiology. This protocol provides a reproducible and adaptable framework for cell-type-specific manipulation and functional characterization of cerebellar circuits, with broad applicability to studies of cerebellar physiology, motor and nonmotor behavior, learning and disease.

In silico perturbation (ISP) enables virtual genetic screens, prioritizing candidate regulators before costly wet-lab experiments. However, applying foundation models requires programming expertise. Here, we present the ISP Platform, that automates ISP powered by Geneformer without code execution. The platform enables cross-species analyses by independently selecting input species and human or mouse Geneformer models. The platform also implements sequential multi-gene ISP, in which each perturbation chains from the cell state produced by the preceding step. Using induced pluripotent stem cell (iPSC) datasets, the platform successfully performed cross-species analysis of human and mouse single cell-RNA (scRNA)-seq data. Screening all 24 permutations of the Yamanaka factors identified an optimal overexpression sequence that produced the highest shift toward the pluripotent state. Genome-wide knockdown screens prioritized key pluripotency regulators but revealed an asymmetric cross-species concordance. The human model effectively captured mouse pluripotency programs, whereas applying the mouse model to human cells yielded predominantly translation-related terms. Furthermore, a targeted pairwise overexpression screen nominated candidate modulators of the primed-to-naive transition beyond the conventional NANOG and KLF2 benchmark. Together, the ISP Platform provides a unified, accessible framework for three dimensions of in silico perturbation (cross-species, sequential, and multi-gene) to guide focused experimental validation and reduce exploratory animal use.

Cellular diversity is required for the proper function of many tissues, including the retina, with its ~130 cell types.1-3 Previous lineage studies suggested that retinal progenitor cells (RPCs) generate cell diversity through biased cell divisions.4-9 However, the extent to which each progenitor cell contributes to diversity, and the overarching rules governing the lineages of the collection of progenitor cells, are unknown. While classic lineage tracing approaches including retroviral labeling4,6,10, dye injection11,12, and time-lapse microscopy13,14 were used to make the foundational insights regarding retinal lineages, they cannot be used to interrogate multigenerational lineages at scale. Here we describe the creation of an evolvable lineage tracing tool, "SCRIBBLE" for Sequential Combinatorial Recorder for Iterative Barcode-Based Lineage Evolution. SCRIBBLE uses prime editor to iteratively edit a barcode sequence while preserving the order of genesis of clonally related progeny. Through a series of rationally designed high-throughput screens and design optimizations, we identified novel sequences that support a high level of barcode editing and information content. Evaluation of SCRIBBLE in embryonic mouse retina recapitulated known lineages in retinal development, demonstrating that SCRIBBLE is capable of high-resolution lineage tracing in a complex tissue.

Formalin-fixed paraffin-embedded (FFPE) tissue is the dominant clinical pathology resource yet whether it faithfully preserves organ signalling biology and supports directional regulatory analysis remains unquantified. We generated a phosphoproteome map from eight healthy rat organs, separating preservation effects from biological variation. Using mass spectrometry, we quantified 54,710 phosphosites on 5,994 proteins across receptors, kinase cascades and nuclear regulators. Organ-specific phosphosite signatures matched known physiological and proliferative states. Paired antagonistic phosphosites converted into "activating-minus-inhibitory" indices that quantified net tissue-specific pathway activity, while a "kinase-by-organ activity" matrix resolved functional hierarchies. Joint analysis with an external fresh-frozen phosphoproteome dataset yielded 58,631 phosphosites total, recovering 86% of the 28,888 sites detected in the frozen dataset. Organ identity explained over 92% of the total variance after batch correction, versus under 0.5% for preservation method. Per-organ phosphosite intensities agreed closely between preservation modes except in brain. This establishes that archived pathology tissue supports biologically faithful phosphoproteome analysis at organ, pathway, and site resolution, providing a framework for retrospective signalling studies in clinical archives. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/741173v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@7ec1forg.highwire.dtl.DTLVardef@1f22bcorg.highwire.dtl.DTLVardef@217f5forg.highwire.dtl.DTLVardef@1316b61_HPS_FORMAT_FIGEXP M_FIG C_FIG

Barcode-based multiplexing strategies can significantly increase sample throughput and decrease costs, while mitigating batch effects of single-cell RNA sequencing experiments. However, these approaches can be limited by inaccurate or inefficient demultiplexing, resulting in cell loss and reduced statistical power. Here, we present BEACON, a novel sample demultiplexing method that efficiently learns the background count distribution from data and removes it from individual cells, thereby improving classification accuracy. BEACON outperforms other state-of-the-art methods on multiple human data sets. We apply it to cancer cell line time course experiments in vitro, enabling the identification of genes associated with aggressive tumors in vivo. Finally, we adapt BEACON to multimodal protein-transcriptome profiling, enhancing protein signal recovery to identify a CD161-positive effector memory CD4 T-cell population with a Th17-like phenotype, which we prospectively validate. BEACON can therefore be applied to other droplet-based single-cell sequencing methodologies.