Cell Reports Methods

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.

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.

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.

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.

Assessing metabolic variations and flux quantities enable systematic understandings of metabolic shifts, reprogramming, adaptation and interactions in human diseases. However, omics-based estimation of metabolic flux and its variation remains challenging due to several fundamental limitations: the need for disease and tissue context specific metabolic model; nonlinear enzyme kinetic model that links enzyme and substrate changes to reaction flux; partial, unpaired, and snap-shot measurements across omics modalities; and uncertainty in computational prediction. Here, we present Michaelis-Menten model-based Flux Estimation Analysis (mmFEA), a Monte Carlo framework for estimating condition-specific flux changes by integrating paired or unpaired metabolomics and transcriptomics (or proteomics) data. mmFEA separates each reaction-rate change into enzyme- and substrate-associated components and assess reaction rate using Michaelis-Menten kinetics equation. A baseline metabolite saturation rate is introduced by integrating protein language model predicted kinetic parameters and human baseline level metabolic concentration to enable kinetic-aware integration of unpaired substrate and enzyme level measurements. Distribution of metabolic flux and variations between conditions are further computed using MCMC sampling by treating Michaelis-Menten-derived marginal flux distribution as prior and coherency in flux balance as likelihood. To benchmark mmFEA, we generated an in-house multi-omics data set including transcriptomics, metabolomics, metabolic activity functional assay, and CRISPR screening data using pancreatic cancer cell line system treated by APEX1 inhibitors. We demonstrated that mmFEA could accurately capture experimentally observed metabolic changes and achieved a better performance than all baseline methods. Our analysis revealed the necessity in using both substrate and enzyme modality and kinetic aware model in metabolic flux assessment. Further analysis using independent pancreatic cancer cohorts further validated the robustness of mmFEA, supporting integration of condition-linked unpaired data. Pan-cancer and spatial multi-omic applications demonstrated the use of mmFEA for resolving context-dependent metabolic variation when direct flux measurements are unavailable. Together, mmFEA provides a mechanistically grounded framework for estimating relative metabolic flux changes and their uncertainty from heterogeneous omics data.

Ambient RNA contamination confounds expression analysis in droplet-based single-cell and single-nucleus RNA sequencing. Here we present AmbiDose, which combines an ambient profile estimated from empty droplets with cell-specific nominal removal budgets and expression-based protection of lineage-enriched genes. Its ambient dose guides count removal rather than estimating a calibrated contamination fraction. In a held-out human-mouse GEM-X mixture, AmbiDose removed 92.0% of cross-species UMIs while retaining 99.6% of same-species UMIs. It reduced off-target marker expression in kidney-tumor and fetal-liver cohorts and yielded the lowest proximal-tubule marker leakage among the tested methods across five strain-mixed kidney libraries. Simulations supported the contribution of cell-specific dose estimates but identified reduced endogenous retention or incomplete removal when ambient and endogenous expression overlapped. AmbiDose is available as a Python package compatible with Scanpy.

Cytokines are critical mediators of intercellular communication, and a comprehensive characterization of their activity is essential for understanding health and disease. Existing tools to infer cytokine activity rely on experimental measurements. However, such measurements are available only for a small minority (43) of cytokines, and moreover, cytokine activity and response are highly context-specific, making a comprehensive experimental profiling across tissues, disease states, and biological contexts impractical. To address this gap, we developed TxCyto - a deep learning-based framework that infers the activity of cytokines, and more broadly of the tumor secretome, directly from the whole transcriptome profile of a sample. Trained on pan-cancer TCGA tumor transcriptomes, TxCyto was extensively validated in multiple independent datasets, including cytokine perturbation experiments. Across multiple cancer immunotherapy cohorts, TxCyto identified cytokines whose predicted activity was associated with therapeutic response. Furthermore, in spatial transcriptomic data for Liver cancer, TxCyto discovered spatial niches associated with response to immunotherapy. Overall, we develop a machine learning tool -TxCyto, for predicting the activity of 645 cytokines and tumor secretome from readily available whole transcriptomes. The TxCyto framework is generally applicable to other classes of regulatory molecules and TxCyto code base, and the tools are provided at https://github.com/Rahulncbs/TxCyto.

LINE-1 (L1) is transcribed by RNA polymerase (Pol) II, whereas the non-coding Short INterspersed Element (SINE) Alu is transcribed by Pol III and exploits L1 ORF2p for mobilization. Conventional L1 retrotransposition reporters rely on spliceosome-dependent introns and cannot directly monitor Alu retrotransposition. Here, we developed EGFPTet, a fluorescent SINE reporter in which EGFP is interrupted by a Tetrahymena thermophila group I self-splicing intron. EGFPTet enabled flow cytometry, fluorescence microscopy, cell sorting, and time-course analysis of Alu mobilization without colony formation. Reporter activation required ORF2p reverse transcriptase activity, and recovered Alu-EGFPTet insertions displayed retrotransposition hallmarks. EGFPTet quantified Alu retrotransposition across cell lines and host-factor perturbations and was repurposed to detect mobilization of the phylogenetically distinct mouse B2 SINE. Defined amino acid substitutions generated an mTurquoise2 derivative without redesigning the self-splicing junctions, allowing dual-color analysis of Alu trans-mobilization and L1 cis retrotransposition in individual cells. In this assay, wild-type L1 supported a higher Alu retrotransposition frequency than the ORF1p-mutant L1. Despite their shared dependence on ORF2p, Alu co-expression did not significantly reduce L1 retrotransposition, and Alu/L1 dual-positive counts exceeded independence-based expectations, indicating preferential co-occurrence of the two reporter signals. EGFPTet provides a fluorescent platform for investigating the shared and distinct cellular conditions that support SINE and L1 mobilization.

Signed effects of target-state programmes are unidentifiable in balanced optimal transport because fixed endpoint distributions make their contributions constant across feasible plans. We introduce UOT-IOT, a semi-relaxed inverse transport framework that restores measurable targetmass responses and provides a rank condition for local identifiability. It recovers coefficients inground-truth benchmarks (correlation 0.997 versus 0.081 for balanced transport), transfers frozen directions to six unseen samples with specificity controls, and achieves the lowest transition error against seven methods under matched evaluation conditions. GH-IOT separates shared directions from context and lineage-history offsets, revealing opposing history-associated effects. IFN/HLA is associated with patient treatment response, whereas EMT relates to drug sensitivity and perturbation-induced state changes. PERSIST-IOT improves future population-composition prediction in an independent experiment. Together, these modules distinguish current state,transition propensity and future population outcomes while supporting testable biological interpretations.

Cell-level analysis of high-resolution spatial transcriptomics depends on accurate segmentation and transcript assignment, yet current workflows often trade transcript capture for boundary purity and can require substantial image-analysis expertise. We developed MCseg, a downloadable no-code platform whose fixed segmentation engine was derived by an AI-agent-guided search in which an AI agent iteratively proposed and evaluated combinations of image-processing and segmentation operations against Xenium-derived cell boundaries. In a lung adenocarcinoma development set, fixed-parameter MCseg increased mean panoptic quality from 0.432 to 0.472 relative to an Optuna-tuned two-diameter Cellpose baseline, while a reference-guided calibration analysis reached 0.554. In an independent expert-annotated colorectal cancer region, MCseg showed higher lineage recall and micro-F1 than the StarDist-based ENACT workflow among cells covered by both methods. Across 15 colorectal cancer regions, MCseg increased neighborhood expression discordance and reduced lineage-exclusive co-expression relative to Space Ranger at similar UMI density. The fixed workflow also transferred to fresh-frozen breast cancer without tissue-specific architecture search, illustrating an agent-guided route to reproducible, locally deployable cell-level spatial transcriptomic analysis.

Histological analysis of 3D tissue models is limited by geometric sampling bias arising from inconsistent positioning during embedding. We present Coplanar Organoid Reproducible Embedding (CORE), which aligns heterogeneous specimens on a shared sectioning plane to enable reproducible, bias-free histology. By eliminating depth-dependent sampling artifacts, CORE reveals biological differences obscured by sectioning depth and integrates with automated liquid- handling workflows.

Pooled gain-of-function screening is a powerful method for screening for transcription factors whose overexpression leads to changes in cellular identity. However, variable transgene copy number, integration site and genomic context from random viral integration can confound quantitative comparisons between perturbations. We present STEER-seq (Single-cell Transgene Expression via Exchange Recombination sequencing), a virus-free platform for pooled ORF gain-of-function screening in human induced pluripotent stem cells (hiPSCs). STEER-seq combines irreversible Bxb1-mediated integration at a defined genomic safe-harbour locus with promoterless donor libraries and barcode-enabled perturbation tracking, enabling single-copy expression from a common promoter and genomic context and direct recovery of perturbation identity by single-cell sequencing. In a focused haematopoietic screen, SPI1 expression alone was sufficient to drive differentiation towards HSC/MPP-like progenitors in the absence of exogenous cytokines, with transient induction generating progenitors that retained multilineage haematopoietic differentiation potential. Joint RNA-chromatin accessibility profiling linked transcription factor-induced transcriptional responses to chromatin remodelling and enabled reconstruction of perturbation-induced gene regulatory networks, including a RUNX2-driven osteoblast programme. Scaling STEER-seq to around 2,000 human coding sequences enabled systematic discovery of transcription factors capable of inducing distinct cell-state programmes, including neural progenitor-, melanocyte- and vascular smooth muscle-like states, as well as maintenance of pluripotency. STEER-seq provides a scalable, quantitative framework for single-cell and multimodal gain-of-function screening, enabling functional discovery and engineering of human cell identity.

While different antigen-presenting cell (APC) populations can drive divergent T-cell fates even when presenting identical antigens (Ags), our ability to trace these specific in vivo interaction histories remains constrained by a lack of compatible molecular tools. Here, we describe an enhanced Labeling Immune Partnerships by SorTagging Intercellular Contacts (LIPSTIC) platform utilizing the immunologically inert human CD58/CD2 receptor-ligand pair (SrtA-hCD58/G5-hCD2). This system provides a modular alternative to previous LIPSTIC animal models and enables the detection and recovery of T cells following Ag-dependent interactions with distinct APC populations in vitro and in vivo. We applied this approach to interrogate the role of type 3 innate lymphoid cells (ILC3s), an unconventional ROR{gamma}t Ag-presenting population whose role in shaping Ag-specific T cells in vivo remain poorly defined. Modified LIPSTIC successfully recorded cognate interactions between ILC3s and naive CD4 T cells in the spleen and enabled direct comparison with CD4+ T cells activated by conventional dendritic cells (cDCs). Although interactions with ILC3s resulted in lower frequency of in vivo-labeled T cells than cDC2s, CD4+ T cells showed comparable cell division and expression of activation markers irrespectively of an APC type. Together, these findings establish SrtA-hCD58/G5-hCD2 as a versatile LIPSTIC receptor-ligand pair for tracking cell-cell interactions in vivo and provide a robust tool for studying how APC identity influences Ag-specific T-cell responses.

High-throughput tracking yields rich time-series data of animal behaviour, yet extracting biological insight remains a bottleneck, even for simple control--treatment comparisons. Existing analyses struggle to pinpoint the multiple, nonlinearly intertwined differences hidden in the data and fail to generalise across diverse recording set-ups. Here we present DiverScan, a deep-learning framework for attention-guided exploratory analysis of behavioural time series. DiverScan disentangles condition-dependent behavioural differences by pairing a loss function tailored to data exploration with an attention-branch architecture, and highlights condition-specific behavioural features visually and with explanatory text generated by a large language model. DiverScan is validated on a synthetic benchmark and on datasets from seven laboratories spanning multiple species and both single- and multi-animal behaviour. Beyond recovering known phenotypes, we demonstrate that DiverScan helps researchers generate new hypotheses across these diverse datasets. Available as Jupyter notebooks with a graphical user interface and flexible input formats, DiverScan can integrate into existing behavioural-analysis pipelines.

Single-molecule enzyme activity analysis enables proteoform-resolved quantification of catalytically active enzyme molecules but remains limited by the need for enzyme-specific fluorogenic substrates, particularly for enzymes involved in central metabolism. Here we establish a biosensor-based framework that integrates genetically encoded fluorescent metabolite biosensors with single-molecule enzyme activity profiling (SEAP), enabling native enzymatic reactions to be monitored without enzyme-specific fluorogenic substrates. By employing different biosensors, the framework was applied to distinct metabolic enzymes, including lactate dehydrogenase and pyruvate kinase. Biosensor-based SEAP enables absolute quantification of active enzyme molecules, transforming conventional activity measurements (U/mL) into molecular abundance (molecules/mL). Applying this approach to single-cell functional proteoform analysis revealed functional heterogeneity in lactate dehydrogenase activity during T-cell activation. In addition, analysis of circulating enzymes showed that much of the plasma lactate dehydrogenase pool could be quantitatively accounted for by physiological erythrocyte turnover. These results establish genetically encoded fluorescent protein-based metabolite biosensors as modular optical transducers for single-molecule functional enzyme analysis across cellular and circulating samples.

Behavioral scoring is central to preclinical pain research but is typically performed manually, which is inherently low-throughput and subject to variability between observers. Automated alternatives exist, but cost and programming expertise limit adoption beyond the laboratories that build them. Here we describe PixelPaws, an open-source platform combining a near-infrared imaging enclosure built from consumer parts for under $300, multi-camera acquisition software, a bundled pose-estimation network, and eight classifiers in a graphical user interface. We evaluated the platform across standard assays of pain, itch, and opioid withdrawal. Licking, scratching, and jumping classifiers were validated against trained human observers by leave-one-session-out evaluation. In the formalin assay, classifier-scored licking followed the expected biphasic time course and was reduced dose-dependently by oxycodone, with a phase 2 potency estimate comparable to blinded manual scoring. Chloroquine roughly tripled scratching, and naloxone precipitated a burst of jumping in oxycodone-treated mice. From the same recordings we derived a set of paw contour metrics. Contour intensity fell after formalin and tracked the oxycodone dose series, and contour area shifted toward symmetry after oxycodone in nerve-injured mice. Behavioral sequencing revealed distinct changes in the transi