Wnt Family Member 2B (WNT2B) mutations result in Diarrhea-9, a congenital diarrhea syndrome with an extreme phenotype and unique histological defects. Attempts to model Diarrhea-9 in rodents and study patient epithelial tissue have not been able to fully reproduce the human phenotype, making understanding this condition challenging. Here, we aimed to interrogate the mechanisms and the specific cellular compartment contributing to Diarrhea-9 using a human intestinal organoid model. Live and histological imaging revealed partial epithelial delamination in human intestinal organoids with WNT2B loss, which was absent in controls. A significant number of crypts in human intestinal organoids with WNT2B loss lacked olfactomedin 4 (OLFM4), a surrogate marker of stem cell activity. Key transcriptomic pathways altered between groups included trafficking of apical digestion proteins, which was confirmed via immunofluorescence. Patient-derived enteroid proteomic analysis revealed similar results. Recombination experiments in human intestinal organoids suggested compartment-specific effects of WNT2B loss, with WNT2B-deficient mesenchyme producing a more pronounced disruption of epithelial architecture and organization. These findings suggest compartment-specific roles for WNT2B in human intestinal development and function, with WNT2B-deficient mesenchyme having a particularly strong influence on epithelial architecture and organization. This study further highlights human intestinal organoids as a useful model for investigating human-specific intestinal disorders that are not fully recapitulated in murine models.
The development of personalised cancer vaccines relies on accurately identifying neoepitopes capable of eliciting strong immune responses. T cell receptor (TCR)-epitope interactions are fundamental to cancer immunotherapy. Traditional computational approaches focus primarily on epitope-major histocompatibility complex (MHC) binding, often overlooking the critical contribution of TCR binding. Furthermore, the clinical applicability of existing methods is constrained by fragmented pipelines that require separate workflows for variant calling, HLA typing, and independent peptide-MHC (pMHC) or peptide-TCR (pTCR) evaluation stages. We present DeepPROTECTNeo, a unified deep learning framework that integrates genomic variant detection, HLA typing, high-affinity pMHC binding prediction, variant-driven TCR repertoire mining, followed by a hybrid transformer-convolutional neural network dual-branch feature extractor with an explicit cross-attention-based deep learning model for TCR-epitope binding prediction. Our reverse vaccinology-inspired biologically informed architecture integrates bidirectional long short-term memory (Bi-LSTM) sequence features, convolutional-attention physicochemical/evolutionary descriptors via gated fusion, and TCR numbered contextual embeddings to enable residue-level interpretable modelling. Under a strict TCR-split strategy, it achieved a mean AUROC of 0.7856 and AUPRC of 0.7932 outperforming six state-of-the-art predictors by 4–5
Methane is a potent greenhouse gas with profound implications for climate change, and its global emissions are largely driven by biological methanogenesis—a process long thought to be restricted to Euryarchaeota. However, emerging evidence indicates that Thermoproteota (formerly the TACK superphylum) also carry this capability. Through metagenomic sequencing of 157 terrestrial geothermal springs in Tengchong, China, we here reconstructed 104 methyl-coenzyme M reductase (Mcr)-containing metagenome-assembled genomes (MAGs). Among these, 90 were identified as MAGs encoding Group II Mcr proteins, which predominated in relative abundance over euryarchaeotal MAGs (Group I Mcr lineages) in most geothermal spring samples. Methanosuratincolia was the most numerous lineage among the recovered MAGs, and a typical operon structure associated with methyl catabolism was delineated across this group. Physicochemical factors, particularly pH and temperature, were associated with the distribution of Mcr-containing archaea, with metatranscriptomics revealing high transcriptional activity of genes associated with methane metabolism in thermal (> 70 °C) and alkaline (pH > 9) environments. We identified three Bathyarchaeia MAGs encoding pathways potentially linked to hydrogenotrophic methanogenesis and heterotrophic metabolism. Evolutionary analyses suggest a possible horizontal gene transfer event between Mcr-containing Bathyarchaeia and Methanonezhaarchaeia, although the direction of transfer cannot be resolved. Phylogenetic reconstruction of methanogenesis-associated proteins is consistent with methylotrophic methanogenesis being ancestral among currently sampled Mcr-containing Thermoproteota, followed by lineage-specific acquisition, loss and modification of hydrogenotrophic components. Our study expands the known diversity of Mcr-containing archaea, offering new insights into their biogeography, ecological functions and evolutionary origins of underdescribed lineages.
Genomic analysis of infected herbarium specimens provides the opportunity to investigate the evolutionary history of plant pathogens, leading to discoveries such as the identification of FAM-1/HERB-1 as the Phytophthora infestans lineage that caused the nineteenth-century Irish Potato Famine. However, the subsequent evolutionary history of P. infestans in Europe remains unclear. Using herbarium specimens to study genetic changes in plant pathogens such as P. infestans with high spatial and temporal resolution requires an efficient sampling strategy to specifically target the pathogen DNA from herbarium specimens. We sampled plant tissue infected with P. infestans from four different locations from each of 23 herbarium specimens collected in Europe between 1850 and 1982. We detected highest pathogen DNA yields in tissue from lesions and tissue adjacent to lesions. The obtained genomic sequencing data allowed the reconstruction of ten mitochondrial and five nuclear P. infestans genomes, which we analyzed together with previously published historical and modern genomes. Our phylogenetic analysis confirmed the persistence of the FAM-1/HERB-1 lineage in Europe until at least 1946, after which FAM-1/HERB-1 appears to have been replaced by the lineage US-1/Ib. In addition, metagenomic analysis revealed historical co-infections between P. infestans and Alternaria solani, supporting the feasibility of our sampling strategy to recover plant pathogens. We established an efficient tissue-sampling strategy for plant-pathogen DNA recovery from herbarium samples. Our findings provide important insights into the evolutionary history of P. infestans in Europe including the replacement of FAM-1/HERB-1 by US-1/Ib in the mid-twentieth century as well as historical co-infections with two other plant pathogens.
Aging is a progressive and irreversible biological process that typically increases susceptibility to infectious diseases. However, unexpectedly, the opposite outcome was observed in oysters: older oysters exhibit increased tolerance to Pacific oyster mortality syndrome (POMS), a panzootic disease responsible for severe losses worldwide. We investigated this pattern by challenging four biparental families of oysters aged 4, 16, and 28 months. We conducted an integrative multiomics analysis, which included epigenomics, transcriptomics, and metabolomics, on the two families that exhibited the greatest age-related increase in survival. Our results reveal that aging is characterized by coordinated epigenetic, transcriptional, and metabolic reprogramming that reduces host permissiveness to POMS. We show that the epigenetic remodeling of immune regulators (e.g., toll-like receptors and myeloid differentiation primary response 88; MyD88) aligns with the transcriptional rewiring of the nuclear factor-kappa B (NF-κB) and ubiquitin pathways, producing a tuned state with enhanced antiviral activity. We also identify age-related repression of mechanistic target of rapamycin (mTOR) signaling, which likely promotes autophagy and enhances viral control. These changes are tightly linked to metabolic adjustments, including reduced activity of the tricarboxylic acid cycle (TCA), altered nitrogen metabolism, and altered glutathione dynamics, supporting a stress-tolerant, energy-conserving phenotype. Together, our findings reveal juveniles prioritize growth at the cost of viral susceptibility, whereas adults invest in cellular maintenance and antiviral preparedness.
Changes in gene expression regulation are central to the acquisition of novel phenotypes during mammalian evolution. However, transcriptomes substantially accumulate neutral divergence over evolutionary time. As a result, selection-driven gene expression shifts are difficult to distinguish from neutral changes, thereby blurring adaptive signatures during genome evolution. Here, we integrate comparative multiomics data within a phylogeny-aware framework of quantitative trait evolution to jointly investigate how gene expression and regulation have evolved in African mole-rats. This group of rodents displays striking physiological adaptations to subterranean life; how those adaptations are encoded in their genomes remains poorly characterised. Using RNA-seq from liver and heart in two mole-rat species and two rodent outgroups, we reliably identify hundreds of genes whose expression levels have experienced lineage-specific accelerated evolution consistent with selection rather than drift. To connect expression evolution with regulatory mechanisms, we integrate this transcriptome data with epigenomic profiles of cis-regulatory landscapes, which experience high turnover in mammalian evolution. Our results demonstrate that genes with lineage-specific expression shifts are surrounded by epigenomic landscapes that also display signals of accelerated evolution, providing support for selection over genomic processes diverging at different rates. Our approach illuminates how gene regulation and expression evolve in concert in this mammalian model and highlights genomic loci with concordant evidence for phenotypic adaptation. The molecular signatures we observe reveal selective pressures acting on key pathways for mole-rat physiology, including metabolism and stress responses.
Cerebral ischemia–reperfusion injury (CIRI) is characterized by complex and overlapping cell death processes, including autophagy, apoptosis, and necrosis. Accurate discrimination among these cell death modalities is essential for pathological investigation, yet conventional pathological methods remain time-consuming and limited in precision. The present study aimed to develop a deep learning-based approach for the automated identification of autophagy, apoptosis, and necrosis following CIRI, thereby improving the efficiency and accuracy of pathological classification. A convolutional neural network (CNN)-based deep learning model was established to classify major cell death modalities after CIRI. On the independent test set, the model achieved an accuracy exceeding 93
Tandem repeats (satellite DNA) are prevalent in eukaryotic genomes and contribute to chromosome organization and genome evolution. Amphibians possess unusually large, repeat-rich genomes and therefore provide a valuable system for investigating the origin, diversity, and genomic dynamics of tandem repeats. Despite this, comparative analyses of tandem repeat repertoires in amphibians remain limited. A genome-wide analysis of tandem repeats in nine species of true frogs from the family Ranidae showed that tandem repeat content is exceptionally high and positively correlated with genome size. Comparative analyses revealed a hierarchical organization of tandem repeat families, that range from widely conserved families to lineage-restricted and species-specific repeats. A prominent feature of these genomes is the high proportion of tandem repeats with sequence homology to transposable elements; such repeats account for 43–91
Large-scale connectomics requires the nanometer-scale resolution of electron microscopy to resolve ultrastructural details, but its acquisition is time- and labor-intensive. In contrast, high-throughput light microscopy offers high throughput but lacks the spatial resolution required for fine-grained analysis. To bridge this gap, we propose a computational framework for cross-modal ultrastructural inference, which maps high-throughput light microscopy data to an electron microscopy-like structural representation to accelerate downstream workflows. Rather than performing de novo synthesis, our framework recovers ultrastructural representation from diffraction-limited optical signals by enhancing latent morphological cues within the light microscopy data. This workflow is powered by a novel deep learning framework, the Content-Decoupled Schrödinger Bridge, which disentangles modality-invariant physical content from imaging-specific attributes and incorporates a physics-informed perceptual loss to ensure structural plausibility. Our approach accelerates the connectomics pipeline, demonstrated in three key applications. First, the enhanced clarity of the generated images reduced expert miss rates for region-of-interest selection. Second, the generated images are inherently aligned with the source light microscopy data while matching the appearance of target electron microscopy data, streamlining multi-modal registration. Third, they improve segmentation accuracy by allowing pre-trained electron microscopy models to be applied directly to light microscopy data. In summary, this work provides a computational solution for bridging the gap between imaging speed and resolution. By enhancing the analytical value of light microscopy data, our workflow accelerates key stages of large-scale connectomics mapping, from targeted acquisition to quantitative analysis.
Light, acting via the circadian system or other brain networks, can exert both immediate and longer-term impacts on memory performance. Despite the clear behavioural evidence for such effects, information about how light and time-of-day directly shape the activity of integrated brain memory circuits remains scarce. To this end, we employed large-scale in vivo electrophysiological recordings in anaesthetised mice, to assess synaptic excitability and plasticity across the hippocampus, medial prefrontal cortex (mPFC) and nucleus reuniens (NRe) circuit that underpins key aspects of learning and memory. Notably, we found an enhancement of CA1 synaptic excitability during the day vs. night but antiphase nocturnal increases in short-term and long-term synaptic plasticity, specifically localised to the CA1 molecular layer. Moreover, we found a time-of-day-dependent impact of acute light exposure on mPFC responses to NRe (but not hippocampal) input, with light driving increased synaptic facilitation during the day and suppressing it at night. In sum, our data provide new insight into the neural mechanisms that underpin daily variations in memory performance and their modulation by environmental light.
Proinflammatory peptides (PIPs) play a significant role in regulating inflammatory responses and are intricately linked to the progression of several inflammatory disorders. Although current experimental and machine learning-based methods for predicting PIPs have achieved significant results, they are labor-intensive, expensive, and unable to provide deeper contextual and structural representations of peptide samples. In this study, we present a novel computational predictor, iPIPs-sABiTCN, for the accurate prediction of PIPs. The input peptides were represented in the form of a 2D matrix using a position-specific scoring matrix (PSSM) and substitution matrix representation (SMR). Subsequently, the generated 2D matrix is passed through local phase quantization (LPQ) to produce the intrinsic local and evolutionary structure-based descriptors, namely, SMR-LPQ and LPQ-PSSM. Multiple variants of the evolutionary scaling matrix (ESM-2) were investigated for contextual representation. Additionally, a BTGA + KNN-based genetic algorithm is utilized to select the high-ranked features from the integrated hybrid vector. Among several classifiers, the proposed self-attention-based Bidirectional Temporal Convolutional Network (sABiTCN) model demonstrated superior predictive performance. The proposed iPIPs-sABiTCN predictor achieved the highest training accuracy of 87.30
Drought is expected to become more frequent and severe under climate change, affecting forest structure and carbon sequestration. Tree responses involve multiple physiological and morphological adjustments, but the integrated mechanisms governing drought resistance and post-drought recovery under future climate scenarios remain insufficiently understood. We investigated drought resistance and recovery responses of Dendropanax trifidus seedlings, a warm-temperate species, under three shared socio-economic pathway (SSP) scenarios reflecting elevated CO2 and temperature by the late twenty-first century. During drought exposure, increased absorption flux (ABS/RC) and dissipated energy flux (DIo/RC) per reaction center indicated enhanced photoprotective energy dissipation. Concurrently, decreases in net photosynthetic rate (Pn) and stomatal conductance (gs) showed restricted gas exchange and carbon assimilation, while biomass allocation shifted toward roots to prioritize water acquisition. After rewatering, increased maximum quantum yield of photosystem II (Fv/Fm) and decreased ABS/RC and DIo/RC indicated alleviation of the drought-induced photoprotective state. These changes were accompanied by increases in Pn, gs, and aboveground biomass reallocation. Notably, Pn failed to return to pre-drought levels under SSP1 but exceeded them under both SSP3 and SSP5, suggesting that scenario-specific environmental conditions influenced photosynthetic recovery. These findings suggest that D. trifidus drought resistance depends primarily on photosystem protection, reduced water loss, and root-centered resource allocation, rather than maintaining photosynthetic carbon gain. Post-rewatering recovery involves alleviating photoprotective regulation, restoring gas exchange and carbon assimilation, and shifting biomass allocation toward aboveground organs.
Therapeutic peptides exert pivotal effects in diverse biological processes, and have attracted significant interest in the field of biomedicine in recent years. However, most existing methods often fail to adequately capture the intricate interactions among amino acid residues and the contextual dependencies within peptide sequences, which hampers the extraction of deep semantic representations and ultimately restricts predictive performance. Moreover, the task of multi-functional therapeutic peptide prediction is inherently constrained by the challenge of imbalanced multi-label classification resulting from long-tailed distribution patterns. In this study, we propose a two-stage hierarchical deep learning framework, named TPpred-PepPA, for the prediction of multi-functional therapeutic peptides based on pragmatic analysis. Specifically, ProtT5 is employed to extract deep semantic representations that capture residue-level contextual information. In the first stage, a transformer-based network is utilized to perform shared representation learning, wherein the encoder model captures the intricate inter-residue interaction to characterize the contextual semantics of peptide sequences. In the second stage, the framework is fine-tuned by incorporating task-specific classifiers and optimizing the classification decision with Asymmetric Loss. Then the dynamic thresholding strategy is utilized to address the long-tail distribution problem, enabling more accurate prediction performance of multi-functional therapeutic peptide. Moreover, we adopted the SHAP analysis and motif identification to interpret feature contributions and identify key functional peptide fragments, respectively. Our experimental results indicate that TPpred-PepPA significantly outperforms all current baseline methods in identifying multi-functional therapeutic peptides and exhibits robust performance in recognizing rare functional categories. We developed TPpred-PepPA, a two-stage hierarchical deep learning framework based on the ProtT5 pre-trained large language model. Compared with existing methods, TPpred-PepPA achieves state-of-the-art predictive performance and provides valuable interpretability for the discovery of multi-functional therapeutic peptides. Finally, a web server has been established and is accessible at http://bliulab.net/TPpred-PepPA .
Recent phylogenetic evidence placing ctenophores as a sister lineage to the rest of the animals has challenged existing views as to the origins of muscles, neurons, and nervous systems. Most of the electrophysiological and morphological data available for ctenophores predate the genomic age. An integrated cellular, genomic, and transcriptomic approach has yet to be employed in ctenophores; accordingly, little is known about how ctenophore cells detect, process, and coordinate internal and external signals. Here we show that excitable cells of the ctenophore Mnemiopsis neuromuscular system express a basic set of voltage-gated channels capable of generating and transmitting action potentials. Activation of those cells and generation of action potentials are accompanied by recurrent transient elevations in cytoplasmic Ca2+, allowing calcium imaging and evaluation of the activity of populations of sensory cells, neurons, and muscles. Despite the complex evolutionary histories of many of the channels, receptors, and specification factors involved in the operation and specification of these cell types, ctenophore excitable cells share core physiological and transcriptomic properties with those of cnidarians and bilaterians. Together, these data support an evolutionary scenario in which the muscle and neural cells of ctenophores, cnidarians, and bilaterians descended from an excitable cell present in the last common ancestor of animals.
Aging is a key risk factor for breast cancer; however, the independent effects of the aged extracellular matrix (ECM) remain understudied. To address this, we developed a novel hybrid in vivo model that enables the independent investigation of age-related ECM influences on breast cancer development and progression. To examine the effects of genes known to be enriched in a cancerous and aged microenvironment, we first seeded normal or stable knockdown cells onto decellularized ECM (dECM) from aged murine mammary glands and implanted them into young Rag1−/− mice. We identified LOX as a principal driver of tumor progression, with knockdown reducing invasion, in vitro and in vivo, and stress-related pathways. To further isolate the independent influence of ECM aging on tumor growth, normal MCF10A cells were seeded atop young or aged matrices and implanted. Aged tumors exhibited significantly greater volume and a larger tumorigenic region when compared to young tumors, with single-cell RNA sequencing revealing enrichment of inflammatory and invasive genes. Together, these findings identify LOX as a driver of tumor progression and a potential therapeutic target and demonstrate that the aged ECM alone is sufficient to promote breast cancer progression.
The oviduct smooth muscle layer is traditionally regarded as a contractile unit. Limited understanding exists regarding the molecular heterogeneity and intercellular communication during development timeline, which is critical for female reproductive function. To address this, we performed single-cell RNA sequencing (scRNA-seq) to profile the cellular composition of mice oviducts at four developmental stages: neonatal (7 days), pre-pubertal (3 weeks), young adult (8 weeks) and middle-aged (9 months). We aim to provide a framework for further investigation of oviduct biology and associated reproductive disorders. We delineate three distinct (SMC) subtypes, with their relative proportions shifting dynamically across the developmental timeline. SMC1 represented a transient progenitor population exclusive to neonatal oviducts. SMC2 displayed a mature contractile phenotype, while SMC3 uniquely expressed hormone-responsive genes (Esr1, Pgr) and showed enrichment of autophagy-related pathways. Functional studies demonstrated that SMC-specific deletion of Vps34 disrupts oviduct coiling and muscular integrity, leading to vacuolar degeneration and a significant reduction in litter size in mice. Additionally, we map five ciliated and six secretory epithelial subtypes and suggest that FN1 signaling may serve as a potential mediator of crosstalk between epithelial and SMCs during development, with the FN1-SDC4 axis emerging as the dominant interaction in mature oviducts. Thus, while the contractile and hormone-responsive functions of the oviductal muscle layer have long been recognized, our work provides its underlying cellular complexity during development. Our study constructs a developmental atlas of murine oviduct SMCs, revealing that the myosalpinx is composed of functionally distinct SMC subtypes with dynamic developmental trajectories. The identification of Vps34 as a critical guardian of myosalpinx integrity offers a new cellular framework for understanding human tubal pathologies. These findings highlight the importance of SMC subtype homeostasis and autophagy-mediated maintenance of myosalpinx structure, providing a basis for future mechanistic studies on oviduct function and dysfunction.
Rhodopsins are photoreceptive membrane proteins widely used as optogenetic tools in basic research and medical applications, and extensive mutational studies have been performed to improve or modify their functional properties. Recently, in the broader field of protein engineering, data-driven strategies based on machine learning have attracted increasing attention, as they enable efficient exploration of vast mutational spaces with a reduced number of experiments. Such approaches require large, consistent datasets that link predefined mutations to quantitative functional properties, which necessitates systematic construction and characterization of targeted variants rather than random mutagenesis. For rhodopsins, however, generating these datasets remains challenging due to operator-dependent, non-integrated workflows that are difficult to scale and standardize. To address this limitation, we developed an automated screening platform termed Rhobot-Screen, based on a robotic liquid-handling workstation, which integrates multiple experimental steps into a standardized workflow with reduced dependence on operator-specific expertise. This platform performs site-directed mutagenesis, plasmid preparation, protein expression in bacterial and mammalian cultured cells, and functional characterization in a 96-well format through automated liquid-handling operations. For spectroscopic characterization, we established a 96-well plate-based hydroxylamine bleaching assay that determines absorption maximum wavelengths without protein purification. As a demonstration of the platform, we comprehensively mutated three established color-tuning residues in Gloeobacter rhodopsin, generating 57 single-point variants. Using Rhobot-Screen, the absorption maxima of 46 variants were successfully determined. The resulting dataset revealed position-dependent relationships between spectral shifts and amino acid physicochemical properties, with clear correlations between absorption wavelength and side-chain volume at positions 129 and 256, but not at position 226. The platform was further extended to mammalian cell-based assays for functional characterization of animal rhodopsins. Rhobot-Screen provides an integrated workflow in which all liquid-handling steps for systematic construction and spectroscopic characterization of rhodopsin variants are automated in a 96-well plate format under standardized conditions. By automating and standardizing multiple operator-dependent steps, the platform provides a reproducible framework for acquiring quantitative sequence–function data from predefined rhodopsin variants. This framework should support both mechanistic studies of rhodopsins and future data-driven engineering of rhodopsin functions.
The success of mRNA-based COVID-19 vaccines has accelerated interest in applying mRNA technology to oncology. mRNA cancer vaccines offer the potential for precise, patient-specific treatment with reduced off-target effects and may become an important component of future cancer care. This review outlines the mechanisms underlying mRNA cancer vaccines, their current limitations, and recent advances in RNA engineering and delivery systems. Although early clinical trial results are encouraging, benefits over existing therapies remain modest, whilst current high costs and manufacturing challenges hinder widespread implementation. We argue that combining scalable off-the-shelf vaccine platforms with selected personalised features may provide the most practical pathway towards wider clinical use.
The evolution of castes in social insects is one of the most spectacular examples of phenotypic plasticity. However, this adaptive plasticity could instead be exploited by parasites to develop an extended phenotype. We characterize the timing and molecular changes induced by enigmatic twisted-wing insects to manipulate developing social wasps. These parasites reprogram short-lived female workers into phenotypes that are long-lived like queens, while hindering ovary development and cooperative behavior. Using differential gene expression analyses across developmental stages of naturally infected and uninfected brood coupled with experimental infections, we uncovered that parasites manipulate the developmental plasticity that determines caste bias. Parasite larvae infected most host larval instars. Across development, we identified 2341 differentially expressed genes (DEGs) between uninfected workers and gynes (future queens), 529 DEGs between infected and uninfected workers, and 2672 DEGs between infected workers and uninfected gynes. However, robust differences in the number of DEGs were unique to each host stage. Specifically, candidate genes linked caste bias to insulin and juvenile hormone pathways, immune responses, and neural development. Controlling for the effect of nutrition and social interactions in development through our infection experiment revealed that the parasites consistently targeted caste bias and neural genes. Finally, infecting long-lived gyne brood that is not targeted in nature showed minimal manipulation. Our findings reveal how stage-specific targeting of caste plasticity initiates the reprogramming of hosts towards the long-lived extended phenotype. We propose leveraging emerging host-parasite systems to unravel the molecular mechanisms underlying adaptive developmental plasticity.
Slow change blindness, when attentive observers fail to notice large changes that happen gradually, raises questions about how visual information is combined across time. One plausible integration strategy is serial dependence: blending information from the recent past into current perception. We investigate serial dependencies in perception of a cartoon object that slowly changes hue. In a one-shot experiment, observers each viewed a single trial with a random degree of hue change and provided one hue judgment response. Across participants, the entire morph was probed. Observers’ hue reports revealed an overall bias toward the past that increased in magnitude as more of the morph was experienced. In three follow-up experiments, we verified that observers experienced slow change blindness, confirmed that the bias was serial dependence, and replicated the results with a repeated-trials design. Overall, we provide evidence that serial dependence actively biases perception during gradual changes, sometimes producing slow change blindness.
