Biology subjects

Qin, P.

Publications and source records attributed to Qin, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Sex specificity of inhibitory gating deficits in individuals with high autistic traits

Empirical evidence from traditional inhibitory control tasks regarding inhibitory deficits in high autistic traits has been mixed, indicating that this issue remains controversial. Although sex differences are widely documented in autistic cognitive profiles, their role in inhibitory gating mechanisms remains underexplored. Given that the expression of inhibitory gating deficits may be modulated by the social versus non-social nature of stimuli, and no prior study has investigated this topic by integrating both sex differences and stimulus domain, we addressed these two questions with the attribute amnesia paradigm. We manipulated stimulus type (non-social vs. social). In Experiment 1, participants performed a location task with animal drawings as targets and were unexpectedly asked to report animal identity on a surprise trial. High autistic trait females showed significantly higher accuracy on the surprise trial than all other groups, reflecting a failure to actively filter out task-irrelevant non-social information, that is, a reduced inhibitory gating efficiency. In Experiment 2, using face stimuli and a self-vs. other-face design, this gating deficit was no longer expressed: all groups performed at chance levels on the identity judgment, regardless of autistic trait level, sex, or face type. This dissociation aligns with a dual-mechanism framework: the inhibitory gating deficit in high autistic trait females is specific to non-social stimuli and masked by camouflaging for social ones. This study demonstrates that the inhibitory gating deficit in high autistic trait females is not a global impairment but rather a stimulus-dependent one, highlighting the need to consider sex and stimulus type.

neuroscience↗

bioRxiv2026

Sleep reactivation of positive self-evaluation reveals neural-evaluative alignment that varies with depressive symptoms

Positive self-evaluation underpins resilience and mental health, yet how to enhance positive self-evaluation and the underlying neurocognitive mechanisms remain poorly understood. Existing data mostly examined self-evaluative processing of the wakeful minds, with the role of sleep remain unexplored. Here, we demonstrated that reactivation of positive self-evaluation during sleep revealed fine-grained neural-evaluative alignment that varies with depressive symptoms. We found that targeted reactivation of positive personality traits during non-rapid eye movement (NREM) sleep enhanced positive self-evaluation, but this benefit diminished with elevated depressive symptoms. Moreover, cueing positive traits during NREM sleep elicited trait-specific, 11-16 Hz EEG sigma power-based neural representations that aligned with post-sleep positive self-evaluative representational structures, as revealed by representational similarity analyses. Notably, individuals with elevated depressive symptoms exhibited weaker neural-evaluative representational alignments, identifying a maladaptive self-evaluative processing that bridges across sleep and wakefulness states. These findings provide mechanistic insights on how fine-grained neural-evaluative alignment can substantiate positive self-evaluation, and why it was disrupted as depressive symptoms increased. HighlightsO_LIPositive-trait reactivation during NREM sleep enhances positive self-evaluation C_LIO_LIThis behavioral benefit decreases as depressive symptoms increase C_LIO_LISleep cueing evokes trait-specific sigma-band neural representations C_LIO_LINeural-evaluative alignment weakens as depressive symptoms increase C_LI

neuroscience↗

bioRxiv2026

Selective encoding failure of self-face identity in subthreshold depression

Subthreshold depression is associated with significant functional impairment and elevated risk of major depressive disorder. A negative self-concept may disrupt the implicit positive association evoked by ones own face, impairing incidental encoding of self-relevant information. Whether subthreshold depression involves a selective deficit in encoding self-face identity remains unclear. The attribute amnesia paradigm is well suited to address this question because it can dissociate attentional selection from working memory encoding. Using this paradigm, we examined the issue across two experiments. Experiment 1 employed nonsocial stimuli (animal drawings) and confirmed an intact attribute amnesia effect in subthreshold depression (n = 30) comparable to healthy controls (n = 30), ruling out a generalized encoding deficit. Experiment 2 replaced targets with faces (self or other) and revealed a selective enhancement of the attribute amnesia effect for self-face identity in subthreshold depression. Specifically, on the surprise trial, accuracy for self-face identity dropped to near-chance levels in the subthreshold depression group, whereas no such deficit emerged for other-faces or in controls. Encoding recovered rapidly once explicit memory expectations were introduced, indicating intact basic encoding capacity. These findings suggest that subthreshold depression is associated with a specific impairment in incidentally encoding self-face identity. This impairment likely stems from a negative self-concept that weakens self-face salience under incidental encoding conditions. By capturing this selective encoding failure, the present study reveals that the self-processing deficit in subthreshold depression can arise at the gating stage between attention and working memory consolidation.

neuroscience↗

bioRxiv2026

Accurate, scalable, and unified single-cell atlas integration with scBIOT

Single-cell omics technologies have revolutionized the study of cellular diversity, yet integrating datasets across experiments remains challenging due to technical artifacts that obscure biology and confound clustering. We introduce scBIOT (Single-cell Biological Insights via Optimal Transport and Omics Transformers), a self-supervised framework that combines optimal transport alignment with Transformer-based variational autoencoders (VAEs) to learn a shared latent space across batches and modalities. This approach mitigates technical variation while preserving lineage boundaries and continuous trajectories, producing unsupervised clusters consistent with expert annotations. A semi-supervised variant, supBIOT, leverages partial labels to enhance cell-type resolution and cross-dataset consistency. Across multi-batch single-cell RNA sequencing benchmarks, scBIOT matches or outperforms leading integration methods without collapsing related subtypes, and the architecture generalizes to single-nucleus ATAC-seq and multimodal data with minimal adaptation. By integrating geometry-aware alignment with long-range feature modeling, scBIOT provides a scalable, modality-agnostic framework for high-resolution single-cell integration and analysis.

bioinformatics↗

bioRxiv2025

Dual apical methyltransferases orchestrate motility initiation in apicomplexan parasites

Apicomplexan parasites such as Toxoplasma gondii initiate motility through rapid, spatially confined cytoskeletal activation at their apical end. While calcium-, lipid-, and kinase-based signalling pathways have been partially elucidated, how these cues are translated into mechanical force remains unclear. Here, we uncover a dual methyltransferase mechanism that orchestrates this process. We identify PCKMT, a preconoidal ring-associated lysine methyltransferase, as an essential upstream regulator of motility. PCKMT anchors the actin nucleator Formin-1 (FRM1) at the conoid, enabling conoid protrusion and F-actin assembly. Loss of PCKMT abolishes FRM1 recruitment, blocks conoid extrusion, and arrests invasion and egress despite preserved conoid structure. In contrast, the apical methyltransferase AKMT, previously linked to motility through recruitment of the glideosome-associated connector (GAC), acts downstream, disengaging from the conoid upon activation and likely promoting GAC-dependent force transmission. PCKMT depletion prevents AKMT translocation, revealing that actin assembly and lysine methylation are mechanistically coupled. Together, these findings define a two-step methylation checkpoint that coordinates actin nucleation with force propagation, uncovering methylation as a central regulatory axis for motility initiation in apicomplexan parasites.

microbiology↗

bioRxiv2025

Bodily Self in Macaque Monkeys and Human Infants

Whether monkeys possess the concept of bodily self remains controversial. We found that macaque monkeys trained for mirror self-recognition (MSR) could spontaneously recognize themselves when viewing video images of their back from a third-person perspective (3PP), but this generalization required a few minutes of "exploration". In contrast, through visual-tactile synchronization training similar to that used in human out-of-body studies, naive monkeys could recognize themselves from 3PP back images and demonstrated MSR within seconds. Interestingly, in human infants, we found that 3PP back self-recognition developed spontaneously several months after MSR. These results demonstrate the existence of hierarchical bodily self in macaques and similar stepwise development of bodily self in human infants, offering insights into the emergence and manifestation of bodily self in natural and artificial intelligence.

neuroscience↗

bioRxiv2025

Distinguishing cortical indexes of arousal and awareness in sleeping patients with unresponsive wakefulness syndrome

Consciousness is assumed to be defined by two components: awareness and arousal. However, compared with awareness, the cortical index for arousal remains lacking in clues. Since patients with unresponsive wakefulness syndrome (UWS) are supposed to have no awareness but arousal, sleep-wake cycle in patients with UWS may reflect pure arousal fluctuations, and then could highlight the cortical indexes for arousal. This study recorded nighttime polysomnography in patients with UWS, patients with minimally conscious state and healthy controls. The EEG signal showed that spectral slope could index arousal, fluctuating among sleep stages in all three groups. Both spectral entropy and Lempel-Ziv complexity indexed awareness, showing a significant difference between conscious and unconscious states. These findings provide fundamental evidence for the two-component hypothesis of consciousness.

neuroscience↗

bioRxiv2025

Breaking the Link: Neural Dissociation of Attention and Working Memory through Inhibitory Control

Attention and working memory (WM) encoding have traditionally been considered inseparable processes with shared neural mechanisms. Here, using an innovative experimental design and a multimodal approach, we provide the first direct neural evidence that attention and WM encoding are dissociable. Functional MRI identifies the supramarginal gyrus (SMG) as the key region enabling this dissociation, while dynamic causal modeling reveals the neural circuitry through which the SMG exerts inhibitory control over attentional representations, regulating their integration into WM. Furthermore, neuromodulation via transcranial direct current stimulation (tDCS) demonstrates that enhancing SMG activity strengthens this inhibitory control, providing causal evidence for the dissociation mechanism. A second tDCS experiment with varied stimuli confirms the generalizability of this mechanism and reinforces the robustness of our results. These findings challenge the long-standing view that attention and WM encoding form a continuous process, demonstrating instead that they constitute two dissociable neural processes of information selection.

neuroscience↗

bioRxiv2025

Distal Gene Expression Governed by Lamins and Nesprins via Chromatin Conformation Change

The nuclear lamina is a vital structural component of eukaryotic cells, playing a pivotal role in both physiological processes, such as cell differentiation, and pathological conditions, including laminopathies and cancer metastasis. Lamina associated proteins, particularly lamins and nesprins, are integral to mechanosensing, chromatin organization, and gene regulation. However, their precise contributions to gene regulation remain incompletely understood. This study explores the functions of lamin A, LMNA, and SYNE2 in gene expression, with a particular focus on their influence on distal chromatin interactions and conformational changes. Using inducible shRNA knockdown, RNA-seq analysis, and dCas9-mediated live imaging of chromosomes, we demonstrate that lamin A affects RNA synthesis, LMNA governs chromatin spatial organization, and SYNE2 regulates chromatin modifications. Furthermore, both lamins and nesprins enhance telomere dynamics. These findings elucidate nuclear envelope-associated mechanisms in gene regulation, offering valuable insights into chromatin dynamics under both physiological and pathological contexts.

cell biology↗

bioRxiv2025

FT-Kernel: An innovative kernel for decoding cellular secrets related time

The cell fate participants characterization based on single-cell RNA sequencing (scRNA-seq) data greatly facilitates the mechanism understandings of cellular differentiation. However, inferring these fate factors dynamics along the pseudotime is challenging. Based on cell-state density and pseudotime regression weights, we present an algorithm TimeFactorKernel (FT-Kernel), to predict the key cell fate factors, not only the minimum lineage transition genes, but also the related genesets/pathways, and cellular interaction dynamics along the pseudotime. By extrapolating the pseudotime-related key genes from spectral data as a pseudotime-kernel, FT-Kernel outperformed previous methods. Beyond time-related genes, FT-Kernel offered a comprehensive analysis of the inferred cellular interactions dynamics and lineage pathways dynamics along the pseudotime, which were limited in other methods. Additionally, it facilitated time-related fate factors prediction across various data modalities. Our work developed an important pseudotime-kernel in predicting the fate factors, and provided insights into the cellular hierarchies during development. SignificanceUnderstanding cellular changes over time is key to deciphering lifes processes from development to disease. Current single-cell methods miss crucial information by overlooking cell-state density dynamics and thus neglecting rare, low-density transitional states vital for cell fate decisions. TimeFactorKernel, a novel computational tool, overcomes this limitation. By prioritizing genes active in these low-density transitional states and employing machine learning for precise gene identification, TimeFactorKernel provides a powerful new approach to decode cell fate. Validated and widely adopted, TimeFactorKernel also uniquely models cell-cell interactions, opening new avenues for understanding complex biological systems in health and disease. KeypointsO_LIFT-Kernel is a novel kernel algorithm designed to predict time-related fate factors dynamics from the cell state density and pseudotime regression weights. C_LIO_LIFT-Kernel can be applied to identify lineage transition key genes, cellular interactions, and the multimodal fate kernel inference of genesets/pathways. C_LIO_LICompared to other temporal-related kernels, FT-Kernel minimizes the skewness of gene distribution and more accurately captures the state of fate transitions. C_LIO_LIFT-Kernel is an extensible multimodal framework facilitating to understand the mechanisms of cell differentiation and is available on GitHub. C_LI

bioinformatics↗

bioRxiv2024

Task-induced internal bodily rather than brain states regulate human self-perception

A fundamental ability for us is to identify and distinguish with others, known as the self, whose neural substrate is hard to identify by external self-related stimuli because of possible interference from internal self-related states of both body and brain. Using the same stimuli, we ruled out this dilemma by manipulating human subjects performing own- and celebrity-face discrimination tasks to induce self-related and non-self-related states, respectively. Results showed that stimulus-driven sensory sensory differences among own-, celebrity-, and stranger-faces were independent of task-induced internal states, whereas their perceptual differences were strongly modulated by these states. Intriguingly, we further found that subjects bodily (indexed by heartbeat-evoked potentials) rather than brain (indexed by pre-stimulus -powers) states not only predicated their self-perception but also moderated the relationship between external stimuli and self-perception. Our results reveal for the first time an adaptive self-perception, shaped by not only external stimuli but also internal states, especially the interoception.

neuroscience↗

bioRxiv2024

Hypergraph Cortical Cytoarchitectonic Parcellation with Multimodal Canine Brain Atlas

Brain atlases are vital tools in exploring the brain structure-function relationship. The burgeoning cross-species atlases have significantly accelerated our understanding of human brain development, evolution, function, and diseases. However, the existing coarse-grained macroscopic canine brain atlases greatly constrain their utility as an animal model for neurocognition research. Finer-grained brain atlas and partitions are crucial for decoding brain spatial heterogeneity and topology at different scales. Therefore, we conduct macroscopic and microscopic brain imaging to construct an interactive online dataset of multimodal canine brain atlas. Additionally, we develop a pioneering method for cortical cytoarchitectonic partitioning based on hypergraph learning. By integrating high-dimensional cytoarchitectonic features and spatial connections between cortical columns, the method leads to fine-grained partitioning patterns. This innovative approach aims to decode the biological heterogeneity of cortical microstructures, contributing to the structural annotation of canine atlas as well as public human brain atlases. The study not only offers valuable resources but also presents a novel zonation approach to investigate the cellular organization pattern and topology of the cortex.

neuroscience↗

bioRxiv2023

Universal protection against SARS-CoV-2 viruses by multivalent mRNA vaccine in mice

The continual emergence of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants challenges available SARS-CoV-2 vaccines for adequate control of outbreaks. Currently, universal vaccines capable of obviating the need for exact strain matching between mRNA vaccines and circulating viruses are absent. In this study, we designed, manufactured, and evaluated a nucleoside-modified lipid nanoparticle mRNA vaccine, aimed for offering broad-spectrum protection against recent SARS-CoV-2 variants. Additionally, the protection efficiency of monovalent, bivalent, quadrivalent, and XBB.1.5 mRNA vaccines was compared with the proposed universal vaccine. The neutralizing antibody activity against wuhan-1, BA.4/5, XBB.1.5, B.1.1.529, BQ.1.1, EG.5.1 and JN.1 was assessed using enzyme-linked immunosorbent assay, rapid fiber-optic biolayer interferometry-based biosensor, and pseudovirus neutralization test. Our results reveal that the proposed multivalent vaccine affords comprehensive protection against previously circulating, current and previously unidentified SARS-CoV-2 strains.

immunology↗

bioRxiv2023

Inferring cell trajectories of spatial transcriptomics via optimal transport analysis

The integration of cell transcriptomics and spatial coordinates to organize differentiation trajectories remains a challenge. Here we introduce spaTrack, a trajectory inference method using optimal transport to incorporate both transcriptomics and distance of spatial transcriptomics sequencing data into transition costs. spaTrack could construct fine spatial trajectories reflecting the true differentiation topology, as well as trace cell dynamics across multiple samples with temporal intervals. To capture the dynamic drivers, spaTrack models the cell fate as a function of expression profile along temporal intervals driven by transcription factors. Applying spaTrack, we successfully disentangle spatiotemporal trajectories of axolotl telencephalon regeneration and mouse midbrain development. Furthermore, we uncover diverse malignant lineages expanding in a primary tumor. One of the lineages with upregulated extracellular matrix organization implants to the metastatic site and subsequently colonizes to a secondary tumor. Overall, spaTrack greatly facilitates trajectory inference from spatial transcriptomics, providing insights in cell differentiation of broad areas.

bioinformatics↗

bioRxiv2023

AutoUnmix: an autoencoder-based spectral unmixing method for multi-color fluorescence microscopy imaging

Multiplexed fluorescence microscopy imaging is widely used in biomedical applications. However, simultaneous imaging of multiple fluorophores can result in spectral leaks and overlapping, which greatly degrades image quality and subsequent analysis. Existing popular spectral unmixing methods are mainly based on computational intensive linear models and the performance is heavily dependent on the reference spectra, which may greatly preclude its further applications. In this paper, we propose a deep learning-based blindly spectral unmixing method, termed AutoUnmix, to imitate the physical spectral mixing process. A tranfer learning framework is further devised to allow our AutoUnmix adapting to a variety of imaging systems without retraining the network. Our proposed method has demonstrated real-time unmixing capabilities, surpassing existing methods by up to 100-fold in terms of unmixing speed. We further validate the reconstruction performance on both synthetic datasets and biological samples. The unmixing results of AutoUnmix achieve a highest SSIM of 0.99 in both three- and four-color imaging, with nearly up to 20% higher than other popular unmixing methods. Due to the desirable property of data independency and superior blind unmixing performance, we believe AutoUnmix is a powerful tool to study the interaction process of different organelles labeled by multiple fluorophores.

cell biology↗

bioRxiv2022

Reactivating Positive Personality Traits During Sleep Impacts Self-Evaluative Memories

Positive self-view is evident by a bias in favor of positive self-referential processing, as individuals tend to endorse positive characteristics over negative ones when making self-judgments. While research suggests that a positivity bias can contribute to psychological well-being, it remains unclear how to enhance positive self-referential processing. Here, we reported an integrated training procedure that aimed at enhancing individuals positive self-referential processing. Specifically, participants engaged in a cue-approach training task (CAT) during wakefulness where they gave speeded motor responses to positive personality traits. In a subsequent nap, we unobtrusively re-played half of the trained positive traits during participants slow-wave sleep to reactivate memories associated with these positive traits (targeted memory reactivation, TMR). Upon awakening, we found that CAT+TMR enhanced participants positive self-referential processing, as evidenced by faster endorsement of positive traits. Further analysis revealed that this enhancement was associated with specific brainwave patterns during sleep: delta (1-4 Hz) traveling waves moving from posterior to anterior brain regions. These findings demonstrate the potential benefits of integrated wakeful cue-approach training and sleep-based memory reactivation in strengthening positive self-referential processing.

neuroscience↗

bioRxiv2022

The Neural Correlates of Arousal: The Ventral Posterolateral Nucleus-Global Transient Co-Activation

Arousal and awareness are two components of consciousness whose the neural mechanisms remain unclear. Spontaneous increases of global (brain-wide) blood-oxygenation-level-dependent (BOLD) signal has been found to be sensitive to changes in arousal. By contrasting BOLD datasets with altered arousal levels, we found that the activation of ventral posterolateral nucleus (VPL) decreased during transient increase in the global signal (top 17% data) in low arousal and awareness states (non-rapid eye movement sleep and anesthesia) as compared to wakefulness, and even in eye-closed (compared with eyes-open) in healthy awake-states, while this activation remained unchanged in patients with unresponsive wakefulness syndrome characterized by high arousal without awareness. These results demonstrate that co-activation of the VPL and global activity is critical to arousal, but not to awareness. One-Sentence SummaryThe VPL nucleus-global brain transient co-activation is related to physiological arousal but not to perceptual awareness.

neuroscience↗

bioRxiv2022

Identifying Patients with Cognitive Motor Dissociation Using Resting-state Temporal Stability

BackgroundUsing task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness, which was defined as cognitive motor dissociation (CMD). Although many efforts were made to identify the CMD, existing task-dependent approaches might fail when patients had multiple cognitive function (e.g., attention, memory) impairments, and thus lead to false-negative findings. However, recent advances in resting-state fMRI (rs-fMRI) analysis allow investigation of the dynamic change of spontaneous brain activity, which might be a powerful tool to test the patients cognitive functions, while its capacity in identifying CMD was unclear. MethodsThe rs-fMRI study included 119 participants from three independent research sites. A sliding-window approach was used to investigate the dynamic functional connectivity of the brain in two aspects: the global and regional temporal stability, which measures how stable the brain functional architecture is across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC patients from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset where patients underwent an rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. ResultsOur results showed that the global and regional temporal stability were impaired in DOC patients, especially in regions from the cingulo-opercular task control, default mode, fronto-parietal task control, and salience network. Using the temporal stability as features, the SVM model not only showed a good performance in the first dataset (accuracy = 90 %), but a good generalizability in the second dataset (accuracy = 82 %). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91 %). ConclusionThe current findings suggested that rs-fMRI could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of the consciousness.

neuroscience↗