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.

Tissue-clearing and light-sheet microscopy have transformed volumetric imaging of intact organs, yet limited mechanistic understanding of dehydration-based clearing continues to constrain rational protocol design and broader applicability. Here, we define the cardinal chemical and physical principles underlying dehydration-based tissue-clearing and establish a new pipeline for large-volume imaging. To maximize imaging performance, we developed the mesoSPIM-ultra, an upgraded mesoSPIM platform with a temperature-controlled sample chamber, a large field-of-view (FoV) camera and specialized optics to achieve long-working-distance, high-resolution imaging of cleared samples. We applied this approach to investigate the projectome of Chx10+ neurons, a cell population with complex axonal morphologies along the entire mouse spinal-cord and brain, and implicated in ipsilateral orienting behaviors. By combining behavioral analysis with post-hoc single-neuron reconstructions, we revealed previously inaccessible branching architectures and long-range projections extending from the brainstem to the spinal cord. Together, our work establishes a mechanistic foundation for tissue-clearing and scalable imaging.

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.

Large-scale brain imaging has relied heavily on fluorescent reporters; however, photobleaching and signal variability limit quantitative analysis in intact tissues. Here, we intro-duce MelaCAST (melanin-based scattering CAST imaging), a genetically encoded scattering strategy for whole-brain imaging. AAV-mediated delivery of tyrosinase enables cell-type-specific melanin production, generating stable intracellular scattering contrast throughout the mouse brain. By integrating tissue clearing with scattering tomography, MelaCAST enables non-photobleaching, high-throughput volumetric imaging of genetically defined cell populations in in-tact brains. This approach establishes melanin as a genetically encoded scattering reporter and expands whole-organ imaging beyond fluorescence-based modalities.

Abstract/SummaryWe present a streamlined protocol that enables the characterization of the metabolic state of immune cell populations through their distinct NADH/FAD autofluorescence fingerprints using a FACSymphony A5 spectral cytometer. We demonstrate the utility of this approach by profiling the metabolic status of diverse splenic B-cell subsets and assessing metabolic changes associated with their activation state.

The success of Chimeric Antigen Receptor (CAR) T cell therapy is heavily dependent on the quality of the final cellular product. Current expansion protocols often rely on reagents that require removal from cell culture media, posing logistical challenges in manufacturing, and can also lead to terminal differentiation. Here, we evaluate the use of a soluble, bead-free T cell activator, T cell expansion protein (T-CEP), as a streamlined alternative for generating potent CAR-T cells. Human T cells were activated with T-CEP or known T cell activators (Dynabeads and TransAct) and transduced with either CD19 or interleukin-13 (IL-13) mutein (tetravariant-13; TV-13)-based CAR lentiviral vectors. Our results demonstrate that T-CEP supports robust CAR-T cell expansion and achieves transduction efficiencies comparable to commercial reagents for both types of CAR-T cells. Notably, T-CEP significantly favored the expansion of CD8+ T cells, yielding an enhanced CD27+ phenotype and a lower CD4:CD8 ratio compared to TransAct. Cytotoxicity assays confirmed that T-CEP-expanded CAR-T cells possess cytolytic function equivalent to commercial reagents for both CARs, while exhibiting lower levels of inflammatory cytokine secretion. In summary, T-CEP represents a competitive alternative to existing expansion agents, as it does not require its removal during CAR-T manufacturing and generates a CD8+ dominant, less-differentiated phenotype without compromising efficacy.

Intestinal organoids are powerful in vitro models, but their use in large-scale analyses remains constrained by the low throughput, labor-intensive handling, and high reagent consumption of conventional Matrigel dome culture. Here, we present Organoid-in-Bead (OrB), a vortex-based compartmentalization workflow that partitions organoid fragments into thousands of discrete Matrigel microbeads, enabling scalable, high-density culture from a single batch preparation. OrB maintains dome-comparable organoid growth and epithelial polarity, supports passaging-based culture expansion, yields more than 5,000 organoids in the final 10 cm dish format, and reduces Matrigel and medium consumption by approximately 70% on a per-organoid basis. OrB therefore provides a practical and scalable upstream workflow for generating screening-scale intestinal organoids. HighlightsO_LIOrB generates Matrigel microcompartments by vortexing without microfluidics C_LIO_LIOrB enables scalable, high-density intestinal organoid culture in one batch C_LIO_LIOrB maintains dome-comparable growth and epithelial polarity and supports passaging C_LIO_LIOrB yields >5,000 organoids per batch with [~]70% less Matrigel/medium per organoid C_LI

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.

Understanding cell biology in native environments requires imaging of subcellular organization in three dimensions. In the intestinal epithelium, multiple cell types organize along the crypt-villus axis, where cell-cell interfaces and subcellular architecture control cell differentiation, tissue organization and epithelial function. Resolving these features volumetrically remains challenging: light microscopy offers molecular specificity but has limited resolution, whereas electron microscopy provides ultrastructural detail but is poorly suited to volumetric acquisition combined with specific protein labeling. Here, we show that expansion microscopy enables the multiscale volumetric study of epithelial ultrastructure in tissue sections and organoid models. Using an optimized workflow, we resolve epithelial tissue architecture, cell types and subcellular features within volumes across scales. Application to a microvillus inclusion disease (MVID) organoid model revealed disease-associated ultrastructural phenotypes that were only observed using electron microscopy. Our results establish expansion microscopy as key technology for studying three-dimensional cell biology within intestinal tissue and tissue mimics.

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

Long-term memory (LTM) formation typically requires extensive training. While operant conditioning is expected to produce stronger LTM than classical conditioning due to active learning, laser-based social conditioning in Drosophila yielded an unexpected discrepancy: operant paradigms produced higher short-term memory (STM) but rapid LTM decay, whereas classical paradigms maintained higher LTM. To resolve this, we applied the AI Complex Systems Response (AI-CSR) framework, which reconstructs high-dimensional learning landscapes to predict globally optimal training conditions. AI-CSR optimization doubled operant LTM scores, yielding the strongest 24-hour social memory reported in flies and confirming the superiority of active learning previously obscured by standard protocols. Conversely, AI-CSR halved classical conditioning training time without altering LTM performance. Finally, single-cell RNA sequencing revealed expanded neuronal recruitment marked by distinct gene activation and inhibition profiles. Together, these findings link circuit-level reorganization with the molecular programs underlying efficient LTM, demonstrating how AI-guided optimization can uncover latent learning capacities in biological systems.

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.

Detailed spatial analysis of the tumor micro-environment (TME) through multiplexed fluorescence imaging requires quantitative image-processing and data-analysis methods. While data-preprocessing down to segmentation of individual cells is captured by available methods, statistical analysis of single-cell features is compromised by the uneven noise distribution especially in complex tissues such as the TME, as well as by labor-intensive manual cell-type annotation and region segmentation. Here, we present SPARQ-MI (Spatial Phenotyping, Architecture Reconstruction and Quantification from Multiplexed Imaging) for streamlined spatial single-cell analysis, along with a tissue microarray PhenoCycler data-set with 37 fluorescent channels from melanoma patients under immunotherapy. We demonstrate that SPARQ-MI enables robust reconstruction of the cellular and spatial composition in this and other tissue types. Our analysis reveals associations of the cell-state and spatial location of CD8 T cells with response to immunotherapy. Overall, SPARQ-MI allows for quantitative analysis of complex fluorescence histology samples under minimal user input, and accounting for spatially uneven coverage of antibody signals, setting the stage for quantitative analysis of clinical samples.

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

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.

Mass spectrometry imaging (MSI) records rich molecular spectra at each pixel, but pathology-oriented interpretation requires visualizations analogous to complementary histopathological stains. We present an expert-aligned framework for constructing multi-view MSI panels. Soft Landmark Contrast Edges (SoLaCE) extracts molecular boundaries directly from high-dimensional spectra. Because standard visualization metrics correlated poorly with rankings from a single expert pathologist, we combine luminance contrast and chromatic diversity with SpecEdge-Dice, a boundary-aware measure of agreement between visualization edges and SoLaCE boundaries. Parametric MiCS+LMC (pMiCS) uses a neural network trained on subsampled data to distill multiple MSI segmentations into a reusable spectral-to-RGB mapping, enabling rapid full-image inference, out-of-sample projection, and more consistent color semantics across aligned images. A concept-based interpretation procedure explains pMiCS outputs through sparse mixtures of spectral concepts. In a blinded benchmark, pMiCS ranked highest among the compared methods. We integrate these components into Virtual Pathology Panels, which use hyperparameter optimization to select high-performing or spatially complementary views. This framework supports future workflows that combine morphology-oriented tissue assessment and molecular analysis within a single MSI acquisition. TeaserVirtual pathology panels transform MSI spectra into complementary views for scalable, interpretable tissue analysis.

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.