Biology subjects

Li, B.

Publications and source records attributed to Li, B..

At least 73 records · Page 4Linked to original sources

bioRxiv2026

SCALLOPS: a scalable, integrated computational framework for Optical Pooled Screens

Optical pooled screens (OPS) link pooled genetic perturbations to high-dimensional image-based phenotypes at scale, but their widespread adoption is hindered by computational bottlenecks in processing terabyte-scale, multimodal image data. We present SCALLOPS, a unified, modular, and cloud-native computational framework that overcomes these bottlenecks. SCALLOPS implements a "well-centric" processing strategy that integrates robust stitching with a non-linear two-stage registration strategy, enabling accurate alignment of multi-magnification images, reliable single-cell genotype-phenotype linkage, and efficient morphological feature extraction. Benchmarking with public and newly-generated datasets demonstrated SCALLOPS superior performance over existing solutions. Crucially, SCALLOPS uniquely enables robust processing of 4x magnification in situ sequencing data, accelerating image acquisition by around six-fold. We applied SCALLOPS to an optical pooled screen investigating the estrogen receptor (ER) degrader vepdegestrant in a breast cancer cell line, successfully recovering its known mechanism of action, highlighting the value of OPS in translational research. SCALLOPS provides a scalable end-to-end solution, making large-scale OPS routine.

bioinformatics↗

bioRxiv2026

Combinatorial transcription factor interactions drive modular gene regulatory networks

Transcription factors (TFs) cooperatively drive gene regulatory networks (GRNs) to establish transcriptional states. Forced induction of TFs in combination can reprogram cell state by supplanting existing GRNs. Thus, TFs and GRNs are the building blocks to engineering transcriptional state. However, one key challenge is that the relationship between TF combinations and GRNs remains largely uncharacterized and difficult to accurately predict. Here, we apply single-cell overexpression screens to map the combinatorial activities of [~]100 TFs to gene expression states. Our analysis identifies diverse TF combinations driving cell-type specific regulatory programs. Notably, different TF combinations induce shared gene sets with cell-type specific functions, suggesting a modular regulatory architecture of the transcriptome. Furthermore, we define pairwise TF interactions and show that cooperative interactions improve transcriptional reprogramming. Finally, we developed tools to predict combinatorial TF phenotypes. These findings improve our understanding of cell state and how to manipulate it for biomedical applications. HIGHLIGHTSO_LICombinatorial over-expression screens for [~]100 transcription factors (TFs). C_LIO_LIDiverse TF combinations drive cell-type specific regulatory programs. C_LIO_LITF regulatory networks reveal a modular regulatory architecture of the transcriptome. C_LIO_LITF-TF interactions and predictive models enhance reprogramming cocktails. C_LI

genomics↗

bioRxiv2026

A User-Friendly Ear-EEG-Based Brain-Computer Interface Using Text Sequence Stimulation

BackgroundEar-EEG-based brain-computer interfaces (BCIs) provide improved wearability and comfort compared to traditional scalp-EEG systems. However, their performance is constrained by low signal-to-noise ratios (SNRs) and high rates of BCI illiteracy under conventional luminance-modulated steady-state visual evoked potential (SSVEP) paradigms. MethodsThis study introduces a text-sequence stimulation paradigm to address these limitations by leveraging ventral visual pathway responses that are more accessible to electrodes near the ear. Using offline frequency-sweeping experiments across 4-8 Hz, we identified optimal stimulus parameters (4.6-6.8 Hz with 0.25{pi} phase shifts) and integrated them into a 12-target BCI system. We further conducted online experiments to compare the response characteristics and real-time spelling performance between the proposed text-sequence paradigm and conventional luminance stimulation. ResultsComparative experiments with 14 participants demonstrate that text sequence stimuli achieve an average information transfer rate (ITR) of 44.59 {+/-} 10.50 bits/min, outperforming luminance modulation by 76.18% in ITR. Notably, text sequence stimulation effectively mitigated BCI illiteracy, with all participants achieving near or above 70% accuracy (mean: 86.37 {+/-} 9.61%). This represents a significant improvement over luminance modulation, where 50% of users fell below 70% accuracy. ConclusionsBy reducing the flicker area by 14% and mimicking the natural luminance variations that occur during reading, the proposed method enhanced visual comfort. The online results further validate text-sequence stimulation as a high-performance and user-friendly paradigm for ear-EEG BCIs, supporting their practicality for assistive applications.

neuroscience↗

bioRxiv2026

Substitution rate variation, not hidden paralogy, drives false hybridization signal in phylogenetic network inference

Phylogenetic network inference methods are increasingly used to detect hybridization and gene flow from genomic data, but their robustness to common sources of model violation remains poorly characterized. We conducted a simulation study to evaluate the effects of hidden paralogy and substitution rate variation on two widely used network inference methods: find_graphs from ADMIXTOOLS 2 and SNaQ. Using an eight-taxon species tree calibrated from an empirical reptile phylogeny, we simulated data under various levels of hidden paralogy (from none to strong) and three levels of rate variation (none, gene-specific, and lineage-specific). We found that hidden paralogy had limited impact on network inference under the conditions examined: both network methods correctly favored a tree without reticulation, and ASTRAL recovered the correct species tree every time. In contrast, lineage-specific rates severely biased find_graphs, inflating worst f-statistic residuals well beyond the standard acceptance threshold. SNaQ correctly selected a tree model almost always across all conditions, though its network with h = 1 reticulation displayed the true species tree with a lower probability under lineage-specific rates. We also show that the standard worst residuals threshold of 3 for find_graphs produces inflated type I error even without rate variation, and we recommend empirical calibration of this threshold within each study system.

evolutionary biology↗

bioRxiv2026

AI-enabled virtual immunopeptidomics links quantitative neoantigen presentation to immunogenicity

Effective anti-tumor T cell response depends on both neoantigen quality (non-selfness) and quantity (abundance). However, existing methods for neoantigen prioritization largely overlook peptide abundance because it is difficult to measure and model. To bridge this gap, we developed epiVIP, a deep learning framework that predicts the abundance of individual HLA-I peptides using widely available (sc)RNA-seq data. Trained on 1.7 million immune peptides paired with gene expression profiles, epiVIP demonstrated strong generalizability across unseen samples. Analyzing 33,711 neoantigens from clinical datasets revealed a compensatory relationship between abundance and non-selfness in determining antigenicity, providing quantitative support for the TCR avidity theory. Importantly, abundance independently predicted tumor reactivity and patient survival across multiple neoantigen vaccine cohorts and immune checkpoint blockade cohorts. Mechanistic interpretation of epiVIP further identified directional regulation of MAGEA3 epitope presentation by PSME4, which was validated experimentally using T cell functional assays. Together, these findings established AI-enabled virtual immunopeptidomics as a powerful strategy to improve cancer immunotherapy.

bioinformatics↗

bioRxiv2026

Linking live-cell behavior to transcriptional responses across perturbations using dynamic caging

Single-cell technologies, encompassing molecular, morphological, and functional assays, have emerged as cornerstones of modern biological research and discovery. However, current experimental methods often fail to explicitly link these omic modalities, especially in live cells or longitudinally through time, impeding the study of multi-scale interactions and mechanisms of regulation. CellCage Enclosure (CCE) technology overcomes these limitations by dynamically compartmentalizing cells, allowing for scalable, live-cell, longitudinal exploration and simultaneous analysis of transcriptomic, proteomic, and morphological profiles. Using this novel technology, we generate previously inaccessible insights across various in vitro cellular systems under a diverse set of perturbations, including the discovery of morphological and proteomic features linked to immune suppressive gene set expression in human primary regulatory T cells (Tregs), as well as direct association of morphological and proteomic features with inflammatory gene modules in human colonic fibroblasts. We then develop a novel pooled CRISPR genetic screening technology using CCEs, PERTURB-LINK (PERTURBational LINKage of transcriptomics and imaging in single cells via enclosure-based screening) and apply this approach in murine bone marrow derived macrophages (BMDMs), enabling multiomic dissection of NF-{kappa}B pathway regulation in response to lipopolysaccharide (LPS) stimulation. Together, these findings demonstrate the broad impact that advancements in live-cell, paired multimodal technologies, especially upon perturbation, may offer in deepening our understanding of cellular biology.

cell biology↗

bioRxiv2026

Analysis of Confounding Factors in Reactive Cysteine Profiling Reveals Enhanced Chromatin-Protein Association via CDK7 Inhibition by THZ1

Recent advances in activity-based proteome profiling (ABPP) have enabled global mapping of cysteine ligandability, uncovering novel biological insights and opportunities for identifying disease vulnerabilities. While both live cell-based and native lysate-based ABPP have been applied, how cysteine ligandability differs between these systems and what factors influence these measurements remain unclear. Building on our previous development of a high-throughput TMT-ABPP workflow for native lysates, here we adapt the protocol for live cells and systematically compare cysteine ligandability across both platforms. Our analysis reveals three major contributors to the discrepancies: in-cellulo cysteine accessibility, protein abundance changes, and protein relocalization. Notably, we highlight that CDK7 inhibitor THZ1 induces substantial protein relocalization and promotes chromatin binding. Together, these results provide a practical framework for ABPP experimental design and data interpretation, supporting more accurate application of ABPP in functional proteomics and drug discovery.

cell biology↗

bioRxiv2026

Microglial pruning of extinction-ensemble synapses preserves fear memory

Fear extinction suppresses learned fear without erasing the original memory, yet how competing fear and extinction ensembles are selectively updated remains unknown. Here, we show that microglia preserve fear memory by selectively editing extinction-ensemble synapses during retrieval. In medial prefrontal cortex, microglial processes, but not somata, expand, ramify, and tighten their engagement with extinction-ensemble dendrites and spines, where they preferentially engulf excitatory postsynaptic material and bias spine remodeling toward elimination. This selectivity is instructed by local find-me, eat-me, and dont-eat-me cues: purinergic signaling recruits microglial processes, phosphatidylserine exposure licenses engulfment of extinction-ensemble synapses, and CD47-SIRP protects fear ensembles from removal. Weakening microglial recruitment or engulfment, or removing this protection, accelerates extinction without impairing fear acquisition. These findings identify microglial processes as active gatekeepers of ensemble competition and reveal a neuroimmune mechanism that preserves fear memory by limiting extinction. In BriefDuring memory updating, microglial processes selectively engage extinction-ensemble synapses rather than globally pruning active circuits. Local recruitment, engulfment, and protection cues determine this choice, allowing microglia to preserve fear memory by limiting extinction. HighlightsO_LIExtinction retrieval selectively recruits microglial processes to extinction ensembles. C_LIO_LIMicroglia preferentially engulf excitatory postsynaptic material from extinction ensembles. C_LIO_LILocal "find-me," "eat-me," and "dont-eat-me" cues determine synapse selection. C_LIO_LIDisrupting microglial pruning facilitates extinction without impairing fear learning. C_LI

neuroscience↗

bioRxiv2026

PEPR-GNN: Perturbation-Enhancer-Promoter-RNA Graph Neural Networks for Multiome Perturb-Seq modeling of regulomes

Cellular reprogramming is a complex interplay between perturbations and regulatory elements, culminating in gene expression changes. Current computational approaches do not explicitly model these regulatory interactions. Here, we performed combinatorial reprogramming with cardiac transcription factors, followed by Multiome Perturb-Seq to measure perturbations, open chromatin, and gene expression in individual cells. We then developed PEPR-GNN (Perturbation-Enhancer-Promoter-RNA Graph Neural Network), a theoretical and computational framework to model regulome responses during complex genetic perturbations. By statistically associating gene regulatory relationships, PEPR-GNN organizes genes into regulomes with shared gene regulatory responses to reprogramming, including easy-to-reprogram cardiac genes, difficult-to-reprogram fibroblast genes, and context-specific genes where the impact of a reprogramming factor depends on the presence of others. Finally, we use PEPR-GNN for in silico modeling of how genetic modifications of enhancers can be used to tune gene responses to reprogramming. Overall, through the use of causal perturbation information and an enhancer-aware regulome model of gene regulation, PEPR-GNN can effectively model complex cellular responses to perturbation. HighlightsO_LIMultiome Perturb-Seq of GHMT reprogramming in MEFs with RNA/ATAC-Seq readout. C_LIO_LIPEPR-GNN: a computational framework to model perturbation-induced regulomes. C_LIO_LIPEPR-GNN aids the interpretation of regulomes by diverse reprogramming responses. C_LIO_LIPEPR-GNN enables in silico perturbation to tune gene responses to reprogramming. C_LI

genomics↗

bioRxiv2026

MVCBench: A Multimodal Benchmark for Drug-induced Virtual Cell Phenotypes

Drugs induce coordinated phenotypic changes across multiple modalities, including transcriptional reprogramming and cellular morphological remodeling. Predicting these drug-induced modality changes is central to drug discovery, mechanism-of-action studies and precision therapeutics, however, prediction performance depends critically on how both drug compounds and cellular states are represented. Despite rapid advances in drug molecular and gene representation methods, a systematic evaluation of these methods remains lacking. Herein, we introduce MVCBench, a comprehensive benchmarking framework for evaluating drug molecular and gene representation methods in predicting drug-induced multimodal virtual cell (MVC) phenotypes. MVCBench leverages large-scale transcriptomic and high-content imaging data and systematically evaluates 24 representation methods (12 drug molecular and 12 gene representation methods) across nearly 1.1 million drug-induced profiles, under both in-distribution and out-of-distribution settings spanning unseen compounds, cell lines, assay plates and datasets. Our benchmarking reveals a pronounced modality-dependent asymmetry: advanced drug molecular representations substantially improve the prediction of drug-induced morphological phenotypes but provide only limited gains for gene expression prediction relative to classical fingerprints, whereas task-specific gene representations outperform general-purpose foundation models in predicting drug-induced transcriptomic responses. Predictive performance also deteriorates sharply under distribution shift, highlighting persistent challenges in cross-dataset and cross-platform generalization. We further show that integrating transcriptomic and morphological modalities consistently improves prediction accuracy, and derive practical design principles for MVC architectures, including modality-aware loss calibration and fusion strategies. Together, MVCBench provides a systematic foundation for evaluating representation methods and offers guidance for developing robust MVC models of drug-induced cellular responses.

bioinformatics↗

bioRxiv2026

Structure-guided, physics-aware reconstruction expands scan-limited multiphoton imaging

Multiphoton microscopy is increasingly pushed toward deep, large-scale and fast functional imaging, where acquisition speed has become a bottleneck. Here we present SPARC, a structure-guided, physics-aware reconstruction framework for scan-limited multiphoton imaging. Rather than treating undersampled recovery as generic super-resolution, SPARC formulates it as a constrained reconstruction problem tailored to point-scanning microscopy by incorporating a sample-matched structural reference from the same field of view and the anisotropic acquisition physics of scan-limited imaging. SPARC jointly integrates denoising and deep upsampling, enabling stable recovery from noisy sparse measurements. In simulated calcium imaging with ground truth, SPARC improved spatial reconstruction fidelity and temporal signal recovery relative to existing methods. In vivo, SPARC improved temporal readout in three-photon and mesoscale two-photon calcium imaging, and enabled a more temporally informative 400-Hz voltage readout on a standard resonant-galvo two-photon microscope. These results suggest SPARC expands the functional operating range of existing multiphoton microscopes under scan-limited conditions.

neuroscience↗

bioRxiv2026

Autoluminescence characteristics of 25 Phalaenopsis orchids following transient transformation with eFBP2

Engineering autoluminescent plants, especially horticultural crops, has recently emerged as a promising research area, with one current approach involving the transgenic introduction of fungal bioluminescence pathway genes. Although autoluminescent plants such as tobacco and petunia have been created, it remains unclear whether all horticultural plants have the potential to be autoluminescent after genetic modification, especially those having flowers with dark colors and thick cuticles. To understand whether, and what if any, floral traits affect autoluminescence potential, we assess the autoluminescence characteristics of 25 representative Phalaenopsis cultivars after transient transformation of fungal bioluminescence pathway genes, alongside several key morphological and biochemical traits. Our results demonstrate that autoluminescence characteristics are correlated with floral color lightness, organ textures and epidermal cell types. In contrast, the content of the substrates of luciferin--caffeic acid and tyrosine, and the infiltration ease of inoculation solution into floral organs after injection, have limited effects on autoluminescence characteristics. Autoluminescence intensity can be reasonably predicted using five floral traits investigated, as 80.4% of variation can be explained by these traits. Our study not only identifies specific Phalaenopsis cultivars with high potential for developing autoluminescent lines but also provides a selection framework applicable to other horticultural crops.

plant biology↗

bioRxiv2026

FABP4 Couples Lipid Metabolism to PD-L1 Stabilization in Immunosuppressive Macrophages

Metabolic dysregulation in obesity reshapes immune function, but how lipid signals drive immune suppression remains unclear. Here, we identify a FABP4-PD-L1 axis that links lipid metabolism to immune checkpoint regulation in monocytes and macrophages. Single-cell transcriptomics revealed a distinct FABP4high immunosuppressive macrophage subset enriched under high-fat diet (HFD) conditions, characterized by impaired antigen presentation and elevated PD-L1 expression. Mechanistically, palmitic acid (PA) induces FABP4 and promotes PD-L1 palmitoylation, leading to its stabilization on the cell surface independent of transcriptional regulation. FABP4 is essential for this process, which enables PD-L1 surface stabilization, immunosuppression and mammary tumor progression. In humans, a conserved CD14intCD16 monocyte population exhibits elevated FABP4-PD-L1 signaling and correlates with obesity and invasive breast cancer. These findings establish PD-L1 as a metabolically regulated protein and reveal a mechanism by which lipid excess drives immune evasion, suggesting that targeting FABP4 may enhance responses to immune checkpoint blockade. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/717546v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1fe3f2corg.highwire.dtl.DTLVardef@e587a7org.highwire.dtl.DTLVardef@12d0f75org.highwire.dtl.DTLVardef@151f916_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIFABP4 defines a lipid-responsive, immunosuppressive monocyte/macrophage subset C_LIO_LIFABP4 links lipid sensing to PD-L1 expression in macrophages C_LIO_LIFABP4 enables palmitic acid-dependent PD-L1 palmitoylation and stabilization C_LIO_LIFABP4-PD-L1 signaling correlates with obesity and invasive breast cancer in humans C_LI

immunology↗

bioRxiv2026

Low-intensity vibration (LIV) cellular mechanotherapy reprograms tumor transcriptomes to suppress cancer-promoting inflammation

LIV is a non-pharmacological intervention for multiple pathologies, most notably pain, yet its potential in oncology remains unexplored due to poorly understood mechanotransduction pathways. Its therapeutic efficacy depends on precise dosing parameters - frequency, amplitude, and duration; suboptimal application, whether excessive or insufficient, risks exacerbating symptoms or rendering treatment ineffective. In a breast cancer-immune cell co-culture model, a 15-min regimen of 90-Hz, 0.43-g LIV attenuates protumorigenic crosstalk by suppressing proinflammatory cytokines and reprogramming malignant cells into a transient metastasis-incompetent "benignant" state that retains oncogenic markers but loses invasive capacity. Our results reveal LIV as a non-drug inhibitor of IFN{gamma} and CXCL12{beta} - silencing IFN{gamma} impairs inflammatory bursts, while silencing CXCL12{beta} starves the CXCR4/CXCR7 axis, depriving tumor cells of motility cues. LIV enforces a genome-wide transcriptional brake, upregulating tumor-suppressive miRNAs (hsa-miR-141, hsa-miR-122) while downregulating protumorigenic miRNAs (hsa-miR-105-5p, hsa-miR-181a-5p). This work repositions LIV as a non-drug modality for coordinately targeting multiple cancer hallmarks. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/716739v1_ufig1.gif" ALT="Figure 1"> View larger version (80K): org.highwire.dtl.DTLVardef@1100863org.highwire.dtl.DTLVardef@196bcf4org.highwire.dtl.DTLVardef@14f3903org.highwire.dtl.DTLVardef@2802fd_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_ST_ABSDosing PrecisionC_ST_ABSLIV mechanotherapy has an optimal therapeutic window. Too little is ineffective, while too much can exacerbate symptoms. This mirrors the pharmacokinetic language of classical pharmacology. The "Benignant" ConceptA 15-min regimen of 90-Hz, 0.43-g LIV attenuates protumorigenic crosstalk by suppressing proinflammatory cytokines and reprogramming malignant cells into a transient, metastasis-incompetent benignant state defined by mechanical constraint rather than pharmacological intervention. Dual Cytokine SilencingLIV acts as a non-pharmaco-logical inhibitor of IFN{gamma} and CXCL12{beta}. IFN{gamma} is a double-edged sword in cancer (pro-immunity vs. exhaustion), and CXCL12{beta} is the specific isoform implicated in metastasis. Targeting both simultaneously addresses immune crosstalk and chemotaxis. In briefMeena et al. demonstrate that the efficacy of LIV hinges on optimal dosing parameters. When applied correctly, it functions as a non-drug modality to target multiple oncogenic and inflammatory pathways. At the molecular level, LIV drives global transcriptional repression via miRNA regulation, increasing tumor-suppressive miRNAs (hsa-miR-141, hsa-miR-122) and decreasing protumorigenic miRNAs (hsa-miR-105-5p, hsa-miR-181a-5p).

cancer biology↗

bioRxiv2026

Sequence-Independent In Situ Imaging of GlycoRNA in Living Cells and Tissues based on COMPASS

Current methods for the imaging of glycosylated RNAs (glycoRNAs) focus on limited types with identified RNA sequences, making the distribution of global glycoRNAs at single-cell and tissue levels largely unexplored. To address this gap, we developed a co-metabolic proximity labeling assay (COMPASS) for sequence-independent imaging of glycoRNAs. COMPASS employs metabolic chemical reporters (MCRs) to label glycans and RNAs, followed by click-chemistry-based dye conjugation to generate Forster resonance energy transfer (FRET) signals. The MCR-enabled RNA labeling allows for sequence-independent imaging, and FRET ensures accurate localization of glycoRNA distributions. COMPASS facilitates glycoRNA imaging across multiple cell lines, cerebral organoids, and mouse tissues. We observed the downregulation of glycoRNAs in metastatic lung nodules and upregulation in original regions of brain development in cerebral organoids, suggesting their potential association with carcinogenesis and neurogenesis. COMPASS illuminates previously invisible glycoRNAs and offers a comprehensive profile of glycoRNA distribution for studying their functions.

bioengineering↗

bioRxiv2026

Modeling gene regulatory perturbations via deep learning from high-throughput reporter assays

Assessing likely variant effects on phenotypes is of critical importance in diagnostic settings, and while much progress has been made in interpreting genic mutations based on our understanding of coding sequence, noncoding variants can be much more challenging to reliably interpret based on DNA sequence alone. High-throughput reporter assays such as STARR-seq and MPRA have shown utility in experimentally measuring regulatory effects of noncoding variants present in samples but provide no readout for variants not present in the assay inputs. However, whole-genome reporter assays provide copious data that can be used to train predictive models for prioritizing variants not directly observed in the experiment. We describe a retrainable predictive modeling framework, BlueSTARR, for this task, and present results of training several models with this framework on whole-genome STARR-seq data from two cell lines and one drug treatment. Using these models, we uncover a global signature across the human genome consistent with purifying selection against both loss-of-function and gain-of-function regulatory variants, with the latter showing a significant bias consistent with selection against gains of cis regulatory function in closed chromatin proximal to genes. By testing the model on synthetic enhancers with binding motifs for transcription factors GR and AP-1, we find that when trained on drug perturbation data, the model is able to learn distance-dependent and treatment-dependent binding patterns and their resulting reporter gene activation. These results demonstrate that lightweight, easily retrainable models such as ours have utility in probing latent signals present in novel experimental data. Finally, we find only modest differences in performance between different deep-learning architectures when trained on this single data modality, and while somewhat greater predictive accuracy can be achieved with much larger models trained at great expense on many terabytes of data, there is still copious room for improvement even for industrial strength, state-of-the-art models.

bioinformatics↗

bioRxiv2026

Channel Capacity for Time-Resolved Effective Connectivity in Functional Neuroimaging

Understanding how brain regions influence one another over time is a central goal of neuroscience. However, existing approaches to effective connectivity often involve tradeoffs among mechanistic interpretability, computational scalability, and time-resolved estimation. Here, we introduce information channel capacity, a model-based measure of directed information transfer between brain regions, and combine it with a sliding-window framework to estimate time-varying directional interactions. We validate channel capacity across multimodal neuroimaging datasets in humans and rodents because this breadth is needed to test three complementary properties that no single dataset can establish alone: sensitivity to evoked information transfer, specificity against false-positive directional effects, and the ability to capture meaningful temporal variability in directed brain-network interactions. Human motor-task fMRI tests sensitivity, showing that channel capacity detects task-related increases in directed interactions and stronger directional effects during task than during rest in motor-related regions. Concurrent rat local field potential (LFP)-fMRI tests specificity, showing minimal spurious directional asymmetry relative to scan-to-scan variability and consistent temporal dynamics across neural and BOLD signals. Mouse Ca2+-fMRI tests temporal variability, showing that channel-capacity patterns identify reproducible connectivity states and transitions over time. Together, these results establish channel capacity as a physiologically grounded framework for measuring dynamic directional interactions across species and neuroimaging modalities.

neuroscience↗

bioRxiv2026

Single-cell full-length transcriptome of human lung reveals genetic effects on isoform regulation beyond gene-level expression

Genetic regulation of splicing uniquely contributes to trait-associated genome-wide association studies (GWAS) signals. However, quantitative trait loci (QTL) analysis using short-read sequencing of bulk tissues fails to capture full-length and cell-type-specific isoforms. Here, we present an isoform-level lung cell atlas from 129 never-smoking Korean women using single-cell long-read RNA-sequencing, identifying abundant unannotated and cell-type-specific isoforms. Isoform-level signatures of 37 lung cell types display a larger difference and therefore improve cell-type classification compared to gene-level expression. Notably, isoform-QTLs (isoQTLs) detect unannotated and/or cell-type-specific isoforms with independent genetic regulation from expression-QTL (eQTL), supported by enriched splicing functional elements. IsoQTLs nominate susceptibility isoforms from previously unexplained lung function and cancer GWAS loci, via eQTL-independent signals. We highlight a potentially functional novel variant of PPIL6 in multiciliated cells underlying lung cancer risk through alternative splicing. This isoform-level resource advances our understanding of cell-type-specific isoform regulation and its contribution to lung traits and diseases.

genetics↗