Biology subjects

Jiang, T.

Publications and source records attributed to Jiang, T..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Energy inefficiency underpinning brain state dysregulation in individuals with major depressive disorder

Disruptions in brain state dynamics are a hallmark of major depressive disorder (MDD), yet their underlying mechanisms remain unclear. This study, building on network control theory, revealed that decreased state stability and increased state-switching frequency in MDD are driven by elevated energy costs and reduced control stability, indicating energy inefficiency. Key brain regions, including the left dorsolateral prefrontal cortex, exhibited impaired energy regulation capacity, and these region-specific energy patterns were correlated with depressive symptom severity. Neurotransmitter and gene expression association analyses linked these energy deficits to intrinsic biological factors, notably the 5-HT2a receptor and excitatory-inhibitory balance. These findings shed light on the energetic mechanism underlying brain state dysregulation in MDD and its associated biological underpinnings, highlighting brain energy dynamics as a potential biomarker by which to explore therapeutic targets and advance precise interventions for restoring healthy brain dynamics in depression.

neuroscience↗

bioRxiv2025

Hydroxychloroquine-functionalized Ionizable Lipids Mitigate Inflammatory Responses in mRNA Therapeutics

Lipid nanoparticle (LNP)-based mRNA therapeutics, highlighted by the success of SARS-CoV-2 vaccines, face challenges due to inflammation caused by ionizable lipids. These ionizable lipids can activate the immune system, particularly when co-delivered with nucleic acids, leading to undesirable inflammatory responses. We introduce a novel class of anti-inflammatory ionizable lipids functionalized with hydroxychloroquine (HCQ), which suppresses both lipid-induced and nucleic acid-induced immune activation. These HCQ-functionalized LNPs (HL LNPs) exhibit reduced proinflammatory responses while maintaining efficient mRNA delivery. Structural and physicochemical analyses revealed that HCQ-functionalization results in a distinct particle structure with significantly improved stability. The efficacy of HL LNPs was demonstrated across various therapeutic contexts, including a prophylactic vaccination model against varicella-zoster virus (VZV) and CRISPR-Cas9 gene editing targeting PCSK9. Notably, HL LNPs showed robust mRNA expression after repeated administration, addressing concerns of inflammation and ensuring sustained therapeutic effects. These findings highlight the potential of HCQ-functionalized LNPs in expanding the safe use of mRNA therapeutics, particularly for applications requiring repeated dosing and in scenarios where inflammation-induced side effects must be minimized.

bioengineering↗

bioRxiv2025

Physical Activity and Depressive Mood Shared the Structural Connectivity between Motor and Reward Network: a population-based study from the UK Biobank

BackgroundExercise has been revealed to leave a positive effect on alleviating depressive symptoms by various existing studies. However, the neural basis behind this phenomenon remains unknown, as well as its underlying biological mechanism.AimsUsing a large neuroimaging cohort from UK Biobank, this study aimed to identify structural connectivity (SC) patterns simultaneously linked with physical activity and depression, as well as its biological interpretation.Method492 participants with major depression disorder (MDD) and 535 healthy controls (HC) were extracted from the UK Biobank dataset. Partial least squares regression was first utilized to explore a replicable SC pattern simultaneously associated with physical activity and depressive mood. The neuromaps toolbox was applied to interpret the biological ontologies of the identified SC pattern. Neuroimaging-transcriptome association and enrichment analyses were conducted to explore its underlying genetic basis, pathways and cell types. The reproducibility and generalizability were tested on another independent MDD dataset (N=3,496) and bipolar disorder (BD) dataset (N=81).ResultsA SC pattern linked with exercise was identified to be both significantly correlated with depressive mood and group discriminative between MDDs and HCs, which primarily located between the motor-related regions and reward-related regions. This pattern was associated with multiple neurotransmitter receptors, such as serotonin and GABA receptors, and enriched in pathways like synaptic signaling and astrocytes cell type. The SC pattern and genetic results were also replicated in another independent MDD dataset and present commonalities with BD.ConclusionsThis study not only initially identified a reproducible shared SC pattern between physical activity and depressive mood, but also elucidated the underlying biological mechanisms, which enhanced our understanding of how exercise helps alleviate depression and may inform the development of novel neuromodulation targets.

neuroscience↗

bioRxiv2025

GFHunter enables accurate and efficient gene fusion detection in long-read cancer transcriptomes

The precise identification of gene fusions is crucial for cancer diagnosis and therapeutic decision-making. Long-read transcriptome sequencing provides distinct advantages over short-read technologies by capturing full-length fusion gene structures. However, fully harnessing long-read data for cancer research necessitates advanced computational approaches. In this study, we present GFHunter, a novel computational framework designed for efficient and accurate gene fusion detection. Benchmarking on both simulated and real long-read transcriptome datasets from non-tumor and cancer cell lines demonstrates that GFHunter accurately detects gene fusions with high sensitivity and significantly reduces false positives. Additionally, GFHunter runs 2-3 times faster and requires only 16%-50% of the memory compared to state-of-the-art tools. Notably, GFHunter uniquely identifies two known cancer-related fusions in HCT-116 and SKBR-3 cancer cell lines. These results highlight GFHunters potential as a powerful tool for advancing precision oncology and molecular diagnostics.

bioinformatics↗

bioRxiv2024

GlioME: A Novel Organoid Model Retaining the Glioma Mi-croenvironment for Personalized Drug Screening and Thera-peutic Evaluation

Glioma is an aggressive brain tumor with a poor prognosis. Establishing an in vitro culture model that closely replicates the cellular composition and microenvironment of the original tumor has been challenging, limiting its clinical applications. Here, we present a novel approach to generate glioma organoids with a microenvironment (GlioME) from patient-derived glioma tissue. These organoids maintain the genetic and epigenetic characteristics of the primary tumor and preserve cell-to-cell interactions within the tumor microenvironment, including resident immune cells. Bulk RNA sequencing, whole exome sequencing, and DNA methylation analysis were used to confirm the molecular similarities between the organoids and primary glioma tissues. Immunofluorescence and flow cytometry were used to assess immune cell viability, comparing GlioME with floating glioma organoids. GlioME exhibited high responsiveness to chemotherapy and targeted therapy, demonstrating its potential for therapeutic screening applications. Notably, GlioME accurately predicted patient response to the recently approved MET inhibitor, vebreltinib. Thus, this organoid model provides a reliable in vitro platform for glioma microenvironment-related research and clinical drug screening.

cancer biology↗

bioRxiv2024

Intracellular amphiregulin is critical for heterochromatin maintenance and genomic stability in response to replication stress in BRCA2mut/+mammary epithelial cells

The EGFR ligand, amphiregulin (AREG) is a key mammary ductal cell differentiation and growth factor. AREG has also been detected in the nucleus of some epithelial cancers although the physiological stimulus and nuclear role are not known. Using immortalized mammary epithelial cells (MECs), we have discovered that AREG undergoes retrograde trafficking to the nuclear membrane (nAREG) in close proximity with lamin A where it is required to both maintain constitutive heterochromatin and transiently increases H3K9me3 in response to replication stress (RS). RS resulted in an increase in AREG protein, enhanced nuclear membrane prelamin A and increased heterochromatin protein, HP1. In contrast, siRNA-mediated depletion of endogenous AREG reduced HP1 and SUV39h1 proteins accompanied by decompaction and reduction in H3K9me3 heterochromatin despite the presence of soluble AREG. The nuclear membrane (NM) was also impacted resulting in dissipation of the Ran-GTPase gradient, reduced matrix lamin A with increased invaginations. Moreover, AREG knockdown slowed replication fork speed, increased new replication origins and enhanced global transcription while promoting and exacerbating DNA damage in response to RS. DNA damage was most pronounced in AREG-depleted BRCA2mut/+ MECs which entered senescence following RS, indicating an important nAREG-dependent role in genomic stabilization in these cells. Overall, this study reveals a novel and fundamental role for nAREG in heterochromatin maintenance and the response to RS, that is most critical in BRCA2mut/+ MECs deficient in replication fork protection.

cancer biology↗

bioRxiv2024

Ligand-Receptor Interactions for Cell-Cell Communication Analysis in Rat, Chicken, Pig, and Monkey Single-Cell and Spatial Transcriptomics

Cell-cell communication is a frequently used analysis approach in single cell RNA and spatial transcriptomics, and many tools like CellPhoneDB, CellChat and stLearn have been developed. Ligand-receptor interactions are the core of cell-cell communication analysis. Since receptor-ligand and even protein-protein interactions were focus on humans and mice research, curated human and mouse receptor-ligand databases have been established, cell-cell interactions for these two species single-cell RNA sequencing data can be directly analyzed. However, for rats, chickens, pigs, monkeys, and other species, cell-cell interaction analysis is often implemented through orthologous gene mapping, due to the lack of curated ligand-receptor interaction databases for these species. We collected cell-cell interaction data mainly from KEGG for rats, chickens, pigs, and monkeys, and extended the data from Reactome and IntAct. Then, by using CellChatV2 with our collected rat ligand-receptor interactions and CellChatV2s own mouse data, respectively, we analyzed 10x Genomics rat public scRNA data, and found that 70 significantly ligand-receptor interactions from the mouse analysis result were also significantly in rats. We also obtained some chicken, pig, and monkey scRNA data from published literature, and analyzed cell-cell interactions using our collected ligand-receptor interactions for these species, and it was proved that our data is reliable and useful. Lastly, we have transformed the ligand-receptor interactions for rat, chicken, pig, and monkey species into the CellChatDB format, which enables swift and straightforward analysis of cell-cell communication in single-cell and spatial data of these four species. All the ligand-receptor interaction datasets for rats, chickens, pigs, and monkeys, as well as the program codes, are available at https://github.com/qingchen36/ligand-receptor. Using our program, one can rapidly obtain receptor-ligand interaction data for other species.

bioinformatics↗

bioRxiv2024

Hand Position Fields of Neurons in the Premotor Cortex of Macaques during Natural Reaching

Planning and execution for goal-directed movements requires the integration of the current position of body or body parts with various kinematic parameters of the movement itself. In the hippocampus, the field-based representation of spatial information plays an essential role in this process during navigation. However, if a similar, field-based encoding framework is also utilized by the motor areas during hand reaching remains unclear. In this study, we investigated the hand position tuning in the dorsal premotor cortex (PMd) neurons (n = 601) when four monkeys performing a naturalistic reach-and-grasp task. We show that 132/601 (22%) of PMd neurons increased their firing rates when the monkeys hand occupied specific positions in the space, forming the "position fields" in their spatial firing maps that can be well described by 2D Gaussian functions/kernels. We further analyzed how the field-tuning of hand position is co-represented with other task-related parameters including the hand moving direction, speed, and reward location in the same population of PMd neurons, revealing a mixed-selective framework also similar to that discovered in the hippocampus. These position-tuned cells demonstrated high efficiency in encoding hand position, with ~10% of overall recorded neurons contributing >80% of accuracy in decoding instantaneous hand moving trajectories. These results suggest that a field-based encoding framework of position may be a common component to representing spatial information and integrating it with kinematic parameters for guiding goal-directed movements of the body or body parts.

neuroscience↗

bioRxiv2024

A new stegosaur (Dinosauria: Ornithischia) from the Upper Jurassic Qigu Formation of Xinjiang, China and a revision on Chinese stegosaurs phylogeny

Stegosaurs are a small but iconic clade of ornithischian dinosaurs. They and their sister taxa, the ankylosaurs, formed the clade Eurypoda which means broad-footed. Here, we describe a stegosaur from the Upper Jurassic Qigu Formation of Xinjiang, China, based on an associated partial skeleton that includes axial, pectoral girdle, pelvic girdle, limb and armor elements. It can be diagnosed as a new taxon, Angustungui, based on numerous autapomorphies. Some morphologies of Angustungui are more similar to the taxa from Europe, Africa and North America than to those from Asia. Our phylogenetic analysis recovers it as the sister taxon of Loricatosaurus. More importantly, the narrow and claw-shaped ungual of Angustungui proves that Eurypoda, at least stegosaur, has claw-shaped unguals. Besides, we revised the character scores for Chinese stegosaurs based on observations of the specimens.

paleontology↗

bioRxiv2024

Modeling the antigenic evolution of seasonal influenza viruses with PREDAC for vaccine strain selection in the season of 2025

IntroductionThe antigenic evolution of seasonal influenza viruses have been reported to be discontinuous as clusters. Timely identification of antigenic clusters could help for influenza virus vaccine strain selections. MethodsHere, we revealed the antigenic evolution of seasonal influenza viruses, including A(H3N2), A(H1N1pdm) and B-Victoria, using our previously developed PREDAC methods, and identified antigenic clusters of seasonal influenza viruses based on existing HA protein sequences in GISAID database. ResultsDuring the last six months (2024.2[~]2024.7), for the influenza A(H3N2) virus, the antigenic clusters DA21 (A/Darwin/6/2021-like and A/Massachusetts/18/2022-like) continuously dominated and nearly all viruses are antigenic similar to the current vaccine strain A/Massachusetts/18/2022; for the influenza A(H1N1pdm) virus, the antigenic cluster SY21 (A/Sydney/5/2021-like and A/Victoria/4897/2022-like) dominated and nearly all viruses are antigenic similar to the current vaccine strain A/Victoria/4897/2022; for the influenza B-Victoria, antigenic clusters AU21 (B/Austria/1359417/2021-like) dominated and nearly all viruses are antigenic similar to the current vaccine strain B/Austria/1359417/2021. ConclusionThe analysis suggests that there is no need to update the vaccine strains in the flu season of 2025 on the aspect of antigenicity.

microbiology↗

bioRxiv2024

CaMutQC: An R Package for Integrative Quality Control of Cancer Somatic Mutations

The quality control and filtration of cancer somatic mutations (CAMs), including the elimination of false positives resulting from technical bias and the selection of key mutation candidates, is crucial for downstream analysis in cancer genomics. Due to diverse needs and the absence of standardized filtering criteria, the filtering strategies employed vary from study to study, often leading to reduced efficiency, accuracy, and comparability across similar analyses. Here we present CaMutQC, a heuristic quality control and soft-filtering R/Bioconductor package for CAMs. With CaMutQC, the removal of false positives, selection of potential mutation candidates, and Tumor Mutation Burden estimation can be executed in a single line of code, using default or customized parameters. A filter report and a code log generated after the filtration process assist with recording and comparison. The application of CaMutQC on a Whole-exome Sequencing (WES) benchmark dataset demonstrated its impressive capability by eliminating 85.55% of false positive mutations while retaining 90.72% of true positive mutations. Additionally, an extra 11.56% of true positive mutations were recused by the union strategy embedded in CaMutQC. CaMutQC is now available through Bioconductor at https://bioconductor.org/packages/CaMutQC/ under the GPL v3 license, and it will be updated regularly to incorporate top filtration strategy and parameter sets shared within the community.

bioinformatics↗

bioRxiv2024

Sex-specific GABAergic microcircuits that switch vulnerability into resilience to stress and reverse the effects of chronic stress exposure

Clinical and preclinical studies have identified somatostatin (SST)-positive interneurons as key elements that regulate the vulnerability to stress-related psychiatric disorders. Conversely, disinhibition of SST neurons in mice results in resilience to the behavioral effects of chronic stress. Here we established a low-dose chronic chemogenetic protocol to map these changes in positively and negatively motivated behaviors to specific brain regions. AAV-hM3Dq mediated chronic activation of SST neurons in the prelimbic cortex (PLC) had antidepressant drug-like effects on anxiety- and anhedonia-like motivated behaviors in male but not female mice. Analogous manipulation of the ventral hippocampus (vHPC) had such effects in female but not male mice. Moreover, activation of SST neurons in the PLC of male and the vHPC of female mice resulted in stress resilience. Activation of SST neurons in the PLC reversed prior chronic stress-induced defects in motivated behavior in males but was ineffective in females. Conversely, activation of SST neurons in the vHPC reversed chronic stress-induced behavioral alterations in females but not males. Quantitation of c-Fos+ and FosB+ neurons in chronic stress-exposed mice revealed that chronic activation of SST neurons leads to a paradoxical increase in pyramidal cell activity. Collectively, these data demonstrate that GABAergic microcircuits driven by dendrite targeting interneurons enable sex- and brain-region-specific neural plasticity that promotes stress resilience and reverses stress-induced anxiety- and anhedonia-like motivated behavior. Our studies provide a mechanistic rationale for antidepressant efficacy of dendrite-targeting, low-potency GABAA receptor agonists, independent of sex and despite striking sex differences in the relevant brain substrates.

neuroscience↗

bioRxiv2024

MetaGXplore: Integrating Multi-Omics Data with Graph Convolutional Networks for Pan-cancer Patient Metastasis Identification

The spread of cancer cells from the primary tumor to distant anatomical sites, known as tumor metastasis, poses a significant challenge in clinical prognosis, impairing treatment efficacy and reducing patient survival time. Current methods for predicting and diagnosing tumor metastasis rely heavily on extensive examinations and subjective clinical judgments. Accurate and rapid prediction of tumor metastasis likelihood remains an unresolved challenge, crucial for guiding effective clinical interventions and extending patient survival. Additionally, identifying key genes highly associated with metastasis probability is a pressing issue, essential for providing valuable insights into the potential identification of tumor metastasis-specific biomarkers. We developed MetaGXplore, a pioneering Graph Convolutional Neural Network (GCN)-based framework designed to predict metastasis probability by integrating pan-cancer multi-omic datasets with a protein-protein interaction network, while also identifying key genes involved in the metastatic process. Multi-omics datasets offer a comprehensive view of cancer biology, enhancing accuracy in metastasis forecasting through superior deep learning algorithms. Our classification model results were interpreted with GNNExplainer. The efficacy of MetaGXplore was validated via model evaluations, graph structure analysis, and multi-omics data assessment. Enrichment analysis of key genes further explored their biological roles.

bioinformatics↗

bioRxiv2024

The Role of ADGRA3 in Adipose Thermogenesis: A Potential Therapeutic Target for Obesity

The induction of adipose thermogenesis plays a critical role in maintaining body temperature and improving metabolic homeostasis to combat obesity. {beta}3-adrenoceptor ({beta}3-AR) is widely recognized as a canonical {beta}-adrenergic G protein-coupled receptor (GPCR) that plays a crucial role in mediating adipose thermogenesis in mice. Nonetheless, the limited expression of {beta}3-AR in human adipocytes restricts its clinical application. The objective of this study was to identify a GPCR that is highly expressed in human adipocytes and to explore its potential involvement in adipose thermogenesis. Our research findings have demonstrated that the adhesion G protein-coupled receptor A3 (ADGRA3), an orphan GPCR, plays a significant role in adipose thermogenesis through its constitutively active effects. ADGRA3 exhibited high expression levels in human adipocytes and mouse brown fat. Furthermore, the knockdown of Adgra3 resulted in an exacerbated obese phenotype and a reduction in the expression of thermogenic markers. Conversely, Adgra3 overexpression activated the adipose thermogenic program and improved metabolic homeostasis without an exogenous ligand supplementation. We found that ADGRA3 facilitates the biogenesis of beige adipocytes through the Gs-PKA-CREB axis. Moreover, hesperetin was identified as a potential agonist of ADGRA3, capable of inducing adipocyte browning and ameliorating insulin resistance. In conclusion, our study demonstrated that the overexpression of constitutively active ADGRA3 or the activation of ADGRA3 by hesperetin can induce adipocyte browning by Gs-PKA-CREB axis. These findings indicate that the utilization of hesperetin and the selectively overexpression of ADGRA3 in adipose tissue could serve as promising therapeutic strategies in the fight against obesity.

cell biology↗

bioRxiv2024

Comparative Analysis of Human-Chimpanzee Divergence in Brain Connectivity and its Genetic Underpinnings

Chimpanzees (Pan troglodytes) are humans closest living relatives, making them the most directly relevant comparison point for understanding human brain evolution. Zeroing in on the differences in brain connectivity between humans and chimpanzees can provide key insights into the specific evolutionary changes that might have occured along the human lineage. However, conducting comparisons of brain connectivity between humans and chimpanzees remains challenging, as cross-species brain atlases established within the same framework are currently lacking. Without the availability of cross-species brain atlases, the region-wise connectivity patterns between humans and chimpanzees cannot be directly compared. To address this gap, we built the first Chimpanzee Brainnetome Atlas (ChimpBNA) by following a well-established connectivity-based parcellation framework. Leveraging this new resource, we found substantial divergence in connectivity patterns across most association cortices, notably in the lateral temporal and dorsolateral prefrontal cortex between the two species. Intriguingly, these patterns significantly deviate from the patterns of cortical expansion observed in humans compared to chimpanzees. Additionally, we identified regions displaying connectional asymmetries that differed between species, likely resulting from evolutionary divergence. Genes associated with these divergent connectivities were found to be enriched in cell types crucial for cortical projection circuits and synapse formation. These genes exhibited more pronounced differences in expression patterns in regions with higher connectivity divergence, suggesting a potential foundation for brain connectivity evolution. Therefore, our study not only provides a fine-scale brain atlas of chimpanzees but also highlights the connectivity divergence between humans and chimpanzees in a more rigorous and comparative manner and suggests potential genetic correlates for the observed divergence in brain connectivity patterns between the two species. This can help us better understand the origins and development of uniquely human cognitive capabilities.

neuroscience↗

bioRxiv2024

Fast adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging

Lattice light-sheet microscopy (LLSM) provides a crucial observation window into intra- and inter-cellular physiology of living specimens with high speed and low phototoxicity, however, at the diffraction-limited resolution or anisotropic super-resolution with structured illumination. Here we present the meta-learning-empowered reflective lattice light-sheet virtual structured illumination microscopy (Meta-rLLS-VSIM), which instantly upgrades LLSM to a near-isotropic super resolution of [~]120-nm laterally and [~]160-nm axially, more than twofold improvement in each dimension, without any modification of the optical system or sacrifice of other imaging metrics. Moreover, to alleviate the tremendous demands on training data and time necessitated by existing deep-learning (DL) methods, we devised an adaptive online training approach by synergizing the front-end imaging system and back-end meta-learning framework, which reduced the total time for data acquisition and model training down to tens of seconds. With this method, a new model can be well-trained with tenfold less data and three orders of magnitude less time than current standard supervised learning. We demonstrate the versatile functionalities of Meta-rLLS-VSIM by imaging a variety of bioprocesses with ultrahigh spatiotemporal resolution for long duration of hundreds of multi-color volumes, characterizing the dynamic regulation of contractile ring filaments during mitosis and the growth of pollen tubes, and delineating the nanoscale distributions, dispersion, and interaction pattern of multiple organelles in embryos and eukaryotic cells.

biophysics↗

bioRxiv2024

HisCl1 regulates gustatory habituation in sweet taste neurons and mediates sugar ingestion in Drosophila

Similar to other animals, the fly, Drosophila melanogaster, reduces its responsiveness to tastants with repeated exposure, a phenomenon called gustatory habituation. Previous studies have focused on the circuit basis of gustatory habituation in the fly chemosensory system 1,2. However, gustatory neurons reduce their firing rate during repeated stimulation 3, suggesting that cell-autonomous mechanisms also contribute to habituation. Here, we used deep learning-based pose estimation and optogenetic stimulation to demonstrate that continuous activation of sweet taste neurons causes gustatory habituation in flies. We conducted a transgenic RNAi screen to identify genes involved in this process and found that knocking down Histamine-gated chloride channel subunit 1 (HisCl1) in the sweet taste neurons significantly reduced gustatory habituation. Anatomical analysis showed that HisCl1 is expressed in the sweet taste neurons of various chemosensory organs. Using single sensilla electrophysiology, we showed that sweet taste neurons reduced their firing rate with prolonged exposure to sucrose. Knocking down HisCl1 in sweet taste neurons suppressed gustatory habituation by reducing the spike frequency adaptation observed in these neurons during high-concentration sucrose stimulation. Finally, we showed that flies lacking HisCl1 in sweet taste neurons increased their consumption of high-concentration sucrose solution at their first meal bout compared to control flies. Together, our results demonstrate that HisCl1 tunes spike frequency adaptation in sweet taste neurons and contributes to gustatory habituation and food intake regulation in flies. Since HisCl1 is highly conserved across many dipteran and hymenopteran species, our findings open a new direction in studying insect gustatory habituation.

neuroscience↗

bioRxiv2024

Time-lapse Image Super-resolution Neural Network with Reliable Confidence Evaluation for Optical Microscopy

Single image super-resolution (SISR) neural networks for optical microscopy have shown great capability to directly transform a low-resolution (LR) image into its super-resolution (SR) counterpart, enabling low-cost long-term live-cell SR imaging. However, when processing time-lapse data, current SISR models failed to exploit the important temporal dependencies between neighbor frames, often resulting in temporally inconsistent outputs. Besides, SISR models are subject to inference uncertainty that is hard to accurately quantify, therefore it is difficult to determine to what extend can we trust the inferred SR images. Here, we first build a large-scale, high-quality fluorescence microscopy dataset for the time-lapse image super-resolution (TISR) task, and conducted a comprehensive evaluation on two essential components of TISR neural networks, i.e., propagation and alignment. Second, we devised a deformable phase-space alignment (DPA) based TISR neural network (DPA-TISR), which adaptively enhances the cross-frame alignment in the phase domain and outperforms existing state-of-the-art SISR and TISR models. Third, we combined the Bayesian training scheme and Monte Carlo dropout with DPA-TISR, developing Bayesian DPA-TISR, and designed an expected calibration error (ECE)minimization framework to obtain a well-calibrated confidence map along with each output SR image, which reliably implicates potential inference errors. We demonstrate the unique characteristics of Bayesian DPA-TISR underlie the ultralong-term live-cell SR imaging capability with high spatial fidelity, superb temporal consistency, and accurate confidence quantification on a wide variety of bioprocesses.

biophysics↗