Biology subjects

Zhang, S.

Publications and source records attributed to Zhang, S..

At least 523 records · Page 29Linked to original sources

bioRxiv2024

Brain-derived exosomal hemoglobin transfer contributes to neuronal mitochondrial homeostasis under hypoxia

Hypoxia is an important physiological stress causing nerve injuries and several brain diseases. However, the mechanism of brain response to hypoxia remains unclear, thus limiting the development of interventional strategies. This study conducted combined analyses of single-nucleus transcriptome sequencing and extracellular vesicle transcriptome sequencing on hypoxic mouse brains, described cell-cell communication in the brain under hypoxia from intercellular and extracellular dimensions, confirmed that hemoglobin mRNA was transferred from non-neuronal cells to neurons, and eventually expressed. Then we further explored the role of exosomal hemoglobin transfer in vitro, clarified that hypoxia promoted the transfer and expression of exosomal hemoglobin between endothelial cells and neurons. And we found the vital function of exosomal hemoglobin to protect against neurological injury by maintaining mitochondrial homeostasis in neurons. In conclusion, this study identified a novel mechanism of mutual aid in hypoxia responses in the brain, involving exosomal hemoglobin transfer, clarified the important role of exosomal communication in the process of brain stress response, and provided a novel interventional perspective for hypoxia-related brain diseases.

cell biology↗

bioRxiv2024

Interactively Integrating Reach and Grasp Information in Macaque Premotor Cortex

Successful reach-to-grasp movements necessitate the integration of both object location and grip type information. However, how these two types of information are encoded in a single brain region and to what extend they interact with each other, remain largely unknown. We designed a novel experimental paradigm that sequentially prompted reach and grasp cues to monkeys and recorded neural activity in the dorsal premotor cortex (PMd) to investigate how the encoding structures change and interact during arm reaching and hand grasping movements. This paradigm required monkeys to retain the first prompted cue when the second one arrived, and integrate both to accomplish a final goal movement. PMd neurons represented both reach and grasp to similar extend, yet the encodings were not independent. Upon the arrival of second cue, PMd continued to encode the first cue, albeit with a significantly altered structure, as evidenced by more than half of the neurons displaying incongruent modulation. At a population level, the encoding structure formed a distinct subspace that differed from, but was not entirely orthogonal to, the original one. Employing canonical correlation analysis, we identified a subspace that consistently preserved the encoding of the initial cue, potentially serving as a mechanism for downstream brain regions to extract coherent information. Furthermore, this shared subspace comprised a diverse population of neurons, including both congruent and incongruent units. these findings support the argument that reach and grasp information are interactively integrated within PMd, with a shared subspace likely underpinning a consistent encoding framework.

neuroscience↗

bioRxiv2024

Identification of a gene expression signature of vascular invasion and recurrence in stage I lung adenocarcinoma via bulk and spatial transcriptomics

Microscopic vascular invasion (VI) is predictive of recurrence and benefit from lobectomy in stage I lung adenocarcinoma (LUAD) but is difficult to assess in resection specimens and cannot be accurately predicted prior to surgery. Thus, new biomarkers are needed to identify this aggressive subset of stage I LUAD tumors. To assess molecular and microenvironment features associated with angioinvasive LUAD we profiled 162 resected stage I tumors with and without VI by RNA-seq and explored spatial patterns of gene expression in a subset of 15 samples by high-resolution spatial transcriptomics (stRNA-seq). Despite the small size of invaded blood vessels, we identified a gene expression signature of VI from the bulk RNA-seq discovery cohort (n=103) and found that it was associated with VI foci, desmoplastic stroma, and high-grade patterns in our stRNA-seq data. We observed a stronger association with high-grade patterns from VI+ compared with VI- tumors. Using the discovery cohort, we developed a transcriptomic predictor of VI, that in an independent validation cohort (n=60) was associated with VI (AUROC=0.86; p=5.42x10-6) and predictive of recurrence-free survival (HR=1.98; p=0.024), even in VI- LUAD (HR=2.76; p=0.003). To determine our VI predictors robustness to intra-tumor heterogeneity we used RNA-seq data from multi-region sampling of stage I LUAD cases in TRACERx, where the predictor scores showed high correlation (R=0.87, p<2.2x10-16) between two randomly sampled regions of the same tumor. Our study suggests that VI-associated gene expression changes are detectable beyond the site of intravasation and can be used to predict the presence of VI. This may enable the prediction of angioinvasive LUAD from biopsy specimens, allowing for more tailored medical and surgical management of stage I LUAD.

cancer biology↗

bioRxiv2024

Statistical batch-aware embedded integration, dimension reduction and alignment for spatial transcriptomics

Spatial transcriptomics (ST) technologies provide richer insights into the molecular characteristics of cells by simultaneously measuring gene expression profiles and their relative locations. However, each slice can only contain limited biological variation, and since there are almost always non-negligible batch effects across different slices, integrating numerous slices to account for batch effects and locations is not straightforward. Here, we propose a hierar-chical hidden Markov random field model STADIA to reduce batch effects, extract common biological patterns across multiple ST slices, and simultaneously identify spatial domains. We demonstrate the effectiveness of STADIA using five datasets from different species (human and mouse), various organs (brain, skin, and liver), and diverse platforms (10x Visium, ST, and Slice-seqV2). STADIA can capture common tissue structures across multiple slices and preserve slice-specific biological signals. In addition, STADIA outperforms the other three competing methods (PRECAST, fastMNN and Harmony) in terms of the balance between batch mixing and spatial domain identification.

bioinformatics↗

bioRxiv2024

Preeclampsia-specific immune cell network in placenta revealed by Cytometry by time of flight and single-cell RNA-seq

Preeclampsia (PE), a major cause of maternal and perinatal mortality with highly heterogeneous causes and symptoms, is usually complicated by gestational diabetes mellitus (GDM). However, a comprehensive understanding of the immune microenvironment in the placenta of PE and the differences between PE and GDM is still lacking. In this study, Cytometry by time of flight (CyTOF) indicated that the frequencies of memory-like Th17 cells (CD45RA-CCR7+IL-17A+CD4+), memory-like CD8+ T cells (CD38+CXCR3-CCR7+Helios-CD127-CD8+) and pro-inflam Macs (CD206-CD163-CD38midCD107alowCD86midHLA-DRmidCD14+) were increased, while the frequencies of anti-inflam Macs (CD206+CD163-CD86midCD33+HLA-DR+CD14+) and granulocyte myeloid-derived suppressor cells (gMDSCs, CD11b+CD15hiHLA-DRlow) were decreased in the placenta of PE compared with that of NP, but not in that of GDM or GDM&PE. The pro-inflam Macs were positively correlated with memory-like Th17 cells and memory-like CD8+ T cells but negatively correlated with gMDSCs. Single-cell RNA sequencing revealed that transferring the F4/80+CD206- pro-inflam Macs with a Folr2+Ccl7+Ccl8+C1qa+C1qb+C1qc+ phenotype from the uterus of PE mice to normal pregnant mice induced the production of memory-like IL-17a+Rora+Il1r1+TNF+Cxcr6+S100a4+CD44+ Th17 cells via IGF1-IGF1R, which contributed to the development and recurrence of PE. Pro-inflam Macs also induced the production of memory-like CD8+ T cells but inhibited the production of Ly6g+S100a8+S100a9+Retnlg+Wfdc21+ gMDSCs at the maternal-fetal interface, leading to PE-like symptoms in mice. In conclusion, this study revealed the PE-specific immune cell network, which was regulated by pro-inflam Macs, providing new ideas about the pathogenesis of PE.

immunology↗

bioRxiv2024

Spatial Transcriptomics-Aided Localization for Single-Cell Transcriptomics with STALocator

Single-cell RNA-sequencing (scRNA-seq) techniques can measure gene expression at the single-cell resolution but lack spatial information. The spatial transcriptomics (ST) techniques simultaneously provide gene expression data and spatial information. However, the data quality on the spatial resolution or gene coverage is still much lower than the single-cell transcriptomics data. To this end, we develop a Spatial Transcriptomics-Aided Locator for single-cell transcriptomics (STALocator) to localize single cells to corresponding ST data. Applications on simulated data showed that STALocator performed better than other localization methods from different angles. When applied to human brain scRNA-seq data and dorsolateral prefrontal cortex 10x Visium data, STALocator could robustly reconstruct the laminar organization of layer-associated cell types. Applications on scRNA-seq data and Spatial Transcriptomics data of human squamous cell carcinoma illustrated that STALocator could robustly reconstruct the relative spatial relationship between tumor-specific keratinocytes, microenvironment-associated cell populations, and immune cells. Moreover, STALocator could enhance gene expression patterns for Slide-seqV2 data and predict genome-wide gene expression data for FISH data, leading to the identification of more spatially variable genes and more biologically relevant GO terms compared to raw data.

bioinformatics↗

bioRxiv2024

Short heat shock factor A2 confers heat sensitivity in Arabidopsis: Insights into heat resistance and growth balance

Cells prevent heat damage through the highly conserved canonical heat stress response (HSR), where heat shock factors (HSFs) bind heat shock elements (HSEs) to activate heat shock proteins (HSPs). Plants generate short HSFs (S-HSFs) derived from HSF splicing variants, yet their functions remain poorly understood. While an enhanced canonical HSR confers thermotolerance, its hyperactivation inhibits plant growth. How plants prevent this hyperactivation to ensure proper growth remains unknown. Here, we report that Arabidopsis S-HsfA2, S-HsfA4c, and S-HsfB1 confer sensitivity to extreme heat (45 {degrees}C) and constitute new HSF types featuring a unique truncated DNA-binding domain (tDBD). This tDBD binds a new heat-regulated element (HRE), which confers minimal promoter heat-responsiveness and exhibits heat stress sensing and transmission patterns. Using S-HsfA2, we investigated whether and how S-HSFs prevent canonical HSR hyperactivation. HSP17.6B, a common direct target of HsfA2 and S-HsfA2, confers thermotolerance; however, its overexpression partially causes HSR hyperactivation. Moreover, HRE-HRE-like and HSE elements mediate the HSP17.6B promoters heat response. We further show S-HsfA2 alleviates hyperactivation via two mechanisms: 1) S-HsfA2 negatively regulates HSP17.6B via the HRE-HRE-like element, establishing a noncanonical HSR (S-HsfA2-HRE-HSP17.6B) that antagonistically represses HsfA2-activated HSP17.6B expression. 2) S-HsfA2 binds the HsfA2 DBD, preventing HsfA2 from binding HSEs and thereby attenuating HsfA2-activated HSP17.6B promoter activity. Overall, our findings highlight the essential role of S-HsfA2 in preventing hyperactivation of plant heat tolerance to maintain proper growth.

plant biology↗

bioRxiv2024

Frontotemporal dementia-like disease progression elicited by seeded aggregation and spread of FUS

RNA binding proteins have emerged as central players in the mechanisms of many neurodegenerative diseases. In particular, a proteinopathy of fused in sarcoma (FUS) is present in some instances of familial Amyotrophic lateral sclerosis (ALS) and about 10% of sporadic FTLD. Here we establish that focal injection of sonicated human FUS fibrils into brains of mice in which ALS-linked mutant or wild-type human FUS replaces endogenous mouse FUS is sufficient to induce focal cytoplasmic mislocalization and aggregation of mutant and wild-type FUS which with time spreads to distal regions of the brain. Human FUS fibril-induced FUS aggregation in the mouse brain of humanized FUS mice is accelerated by an ALS-causing FUS mutant relative to wild-type human FUS. Injection of sonicated human FUS fibrils does not induce FUS aggregation and subsequent spreading after injection into naive mouse brains containing only mouse FUS, indicating a species barrier to human FUS aggregation and its prion-like spread. Fibril-induced human FUS aggregates recapitulate pathological features of FTLD including increased detergent insolubility of FUS and TAF15 and amyloid-like, cytoplasmic deposits of FUS that accumulate ubiquitin and p62, but not TDP-43. Finally, injection of sonicated FUS fibrils is shown to exacerbate age-dependent cognitive and behavioral deficits from mutant human FUS expression. Thus, focal seeded aggregation of FUS and further propagation through prion-like spread elicits FUS-proteinopathy and FTLD-like disease progression.

neuroscience↗

bioRxiv2024

Structure-specific variation in per- and polyfluoroalkyl substances toxicity among genetically diverse Caenorhabditis elegans strains

BackgroundThere are >14,500 structurally diverse per- and polyfluoroalkyl substances (PFAS). Despite knowledge that these "forever chemicals" are in 99% of humans, mechanisms of toxicity and adverse health effects are incompletely known. Furthermore, the contribution of genetic variation to PFAS susceptibility and health consequences is unknown. ObjectivesWe determined the toxicity of a structurally distinct set of PFAS in twelve genetically diverse strains of the genetic model system Caenorhabditis elegans. MethodsDose-response curves for four perfluoroalkyl carboxylic acids (PFNA, PFOA, PFPeA, and PFBA), two perfluoroalkyl sulfonic acids (PFOS and PFBS), two perfluoroalkyl sulfonamides (PFOSA and PFBSA), two fluoroether carboxylic acids (GenX and PFMOAA), one fluoroether sulfonic acid (PFEESA), and two fluorotelomers (6:2 FCA and 6:2 FTS) were determined in the C. elegans laboratory reference strain, N2, and eleven genetically diverse wild strains. Body length was quantified by image analysis at each dose after 48 hr of developmental exposure of L1 arrest-synchronized larvae to estimate effective concentration values (EC50). ResultsThere was a significant range in toxicity among PFAS: PFOSA > PFBSA {approx} PFOS {approx} PFNA > PFOA > GenX {approx} PFEESA > PFBS {approx} PFPeA {approx} PFBA. Long-chain PFAS had greater toxicity than short-chain, and fluorosulfonamides were more toxic than carboxylic and sulfonic acids. Genetic variation explained variation in susceptibility to PFBSA, PFOS, PFBA, PFOA, GenX, PFEESA, PFPeA, and PFBA. There was significant variation in toxicity among C. elegans strains due to chain length, functional group, and between legacy and emerging PFAS. ConclusionC. elegans respond to legacy and emerging PFAS of diverse structures, and this depends on specific structures and genetic variation. Harnessing the natural genetic diversity of C. elegans and the structural complexity of PFAS is a powerful New Approach Methodology (NAM) to investigate structure-activity relationships and mechanisms of toxicity which may inform regulation of other PFAS to improve human and environmental health.

pharmacology and toxicology↗

bioRxiv2024

Dimensionality Reduction and Denoising of Spatial Transcriptomics Data Using Dual-Channel Masked Graph Autoencoder

Recent advances in spatial transcriptomics (ST) technology allow researchers to comprehensively measure gene expression patterns at the level of individual cells or even subcellular compartments while preserving the spatial context of their tissue. Spatial domain identification is a critical task in analyzing the ST data. However, effectively capturing distinctive gene expression features and relationships between genes poses a significant challenge. We develop a graph self-supervised learning method STMask for the analysis and exploration of the ST data. STMask combines the masking mechanism with a graph autoencoder, compelling the gene representation learning channel to acquire more expressive representations. Simultaneously, it combines the masking mechanism with graph self-supervised contrastive learning methods, pulling together the embedding distances between spatially adjacent points and pushing apart the representations of different clusters, allowing the gene relationship learning channel to learn more comprehensive relationships. The applications of STMask to four ST datasets demonstrate that STMask outperforms state-of-the-art methods in various tasks, including spatial clustering and trajectory inference. Source code is available at https://github.com/donghaifang/STMask. Author summarySpatial Transcriptomics (ST) is an emerging transcriptomic sequencing technology aimed at revealing the spatial distribution of gene expression and cell types within tissues. This method enables the acquisition of gene expression profiles at the level of individual cells or spots within the tissue, uncovering the spatial expression patterns of genes. However, accurately identifying spatial domains in ST data remains challenging. In our study, we introduce STMask, a self-supervised learning method that combines a dual-channel masked graph autoencoder with masking and contrastive learning. Our work contributes primarily in two aspects: (1) We propose a novel graph self-supervised learning method (STMask) specifically tailored for the analysis and research of ST data, which enhances the ability to capture the unique features of gene expression and spatial relationships within tissues. (2) Through comprehensive experiments, STMask provides valuable insights into biological processes, particularly in the context of breast cancer. It identifies enrichment of various differentially expressed genes in tumor regions, such as IGHG1, which can serve as effective targets for cancer therapy.

genomics↗

bioRxiv2024

Amygdala engages non-emotional multi-item working memory maintenance through amygdala-hippocampus interaction

Retaining information in working memory (WM) is an active process that requires neural activity within and between regions. The human amygdala (AMY) and hippocampus (HPC) are known to play crucial roles in WM processing. Although electrophysiological studies have revealed that the HPC supports multi-item maintenance in a load-dependent manner, the characteristics of the AMY and the circuit-level mechanisms underlying AMY-HPC interactions remain largely unexplored. To address this knowledge gap, intracranial EEG recordings from the AMY and HPC in nine epileptic patients were employed to evaluate intraregional neural representations and interregional communications during maintenance under different non-emotional WM loads. High load enhanced low-frequency power and intraregional theta-gamma phase-amplitude coupling (PAC) in the AMY and HPC. At the network level, a high load elicited an increase in the strength of the HPC theta phase modulation, which entrains the AMY gamma amplitude. Furthermore, a high load increased AMY-anterior HPC (aHPC) theta phase synchrony and directional connectivity strength from the aHPC to the AMY. Conversely, posterior HPC (pHPC)-AMY synchrony was not affected by load variations. Overall, these findings highlight the importance of the AMY in non-emotional WM tasks and provide new insights into the neurophysiological basis of AMY-HPC interactions during WM maintenance.

neuroscience↗

bioRxiv2024

Single-nucleus transcriptomics reveal the cytological mechanism of conjugated linoleic acids in regulating intramuscular fat deposition

Conjugated linoleic acids (CLAs) can serve as a nutritional intervention to regulate quality, function and fat infiltration in skeletal muscles but the specific cytological mechanisms are still unknown. Here, we applied single-nucleus RNA-sequencing (snRNA-seq) to characterize the cytological mechanism of CLAs regulates fat infiltration in skeletal muscles based on pig models. We investigated the regulatory effects of CLAs on cell populations and molecular characteristics in pig muscles and found CLAs could promote the transformation of fast glycolytic myofibers into slow oxidative myofibers. We also observed three subpopulations including SCD+/DGAT2+, FABP5+/SIAH1+, and PDE4D+/PDE7B+ subclusters in adipocytes and CLAs could increase the percentage of SCD+/DGAT2+ adipocytes. RNA velocity analysis showed FABP5+/SIAH1+ and PDE4D+/PDE7B+ adipocytes could differentiate into SCD+/DGAT2+ adipocytes. We further verified the differentiated trajectory of mature adipocytes and identified PDE4D+/PDE7B+ adipocytes could differentiate into SCD+/DGAT2+ and FABP5+/SIAH1+ adipocytes by using high IMF content Laiwu pig models. The cell-cell communication analysis identified the interaction network between adipocytes and other subclusters such as fibro/adipogenic progenitors (FAPs). Pseudotemporal trajectory analysis and RNA velocity analysis also showed FAPs could differentiate into PDE4D+/PDE7B+ preadipocytes and we discovered the differentiated trajectory of preadipocytes into mature adipocytes. Besides, we found CLAs could promote FAPs differentiate into SCD+/DGAT2+ adipocytes via inhibiting c-Jun N-terminal kinase (JNK) signalling pathway in vitro. This study provides a foundation for regulating fat infiltration in skeletal muscles by using nutritional strategies and provides potential opportunities to serve pig as an animal model to study human fat infiltrated diseases.

cell biology↗

bioRxiv2024

Cell-free synthesis and purification of recombinant nucleocapsid (N), membrane (M), and envelope (E) proteins

Rapid production of soluble recombinant antigens is important for developing in vitro diagnostics, vaccines, and drugs against virus such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this research, hard-to-express nucleocapsid, membrane, and envelope proteins were successfully expressed by an Escherichia coli-based cell-free protein synthesis system. The solubility of the proteins was optimized using various amphipathic molecules. Most of the impurities were easily removed by a one-step Ni-NTA affinity chromatography. This study provides an easy and quick alternative for viruss trans-membrane and nucleotides associated recombinant protein expression, which has potential downstream application for early screening of newly emerging viruses.

synthetic biology↗

bioRxiv2024

Massively parallel free energy calculations for in silico affinity maturation of designed miniproteins

Computational protein design efforts continue to make remarkable advances, yet the discovery of high-affinity binders typically requires large-scale experimental screening of site-saturated mutant (SSM) libraries. Here, we explore how massively parallel free energy methods can be used for in silico affinity maturation of de novo designed binding proteins. Using an expanded ensemble (EE) approach, we perform exhaustive relative binding free energy calculations for SSM variants of three miniproteins designed to bind influenza A H1 hemagglutinin by Chevalier et al. (2017). We compare our predictions to experimental{Delta}{Delta} G values inferred from a Bayesian analysis of the high-throughput sequencing data, and to state-of-the-art predictions made using the Flex ddG Rosetta protocol. A systematic comparison reveals prediction accuracies around 2 kcal/mol, and identifies net charge changes, large numbers of alchemical atoms, and slow side chain conformational dynamics as key contributors to the uncertainty of the EE predictions. Flex ddG predictions are more accurate on average, but highly conservative. In contrast, EE predictions can better classify stabilizing and destabilizing mutations. We also explored the ability of SSM scans to rationalize known affinity-matured variants containing multiple mutations, which are non-additive due to epistatic effects. Simple electrostatic models fail to explain non-additivity, but observed mutations are found at positions with higher Shannon entropies. Overall, this work suggests that simulation-based free energy methods can provide predictive information for in silico affinity maturation of designed miniproteins, with many feasible improvements to the efficiency and accuracy within reach.

biophysics↗

bioRxiv2024

Immunopipe: A comprehensive and flexible scRNA-seq and scTCR-seq data analysis pipeline

Single-cell sequencing technologies provide us with information at the level of individual cells. The combination of single-cell RNA-seq and single-cell TCR-seq profiling enables the exploration of cell heterogeneity and T-cell receptor repertoires simultaneously. The integration of both types of data can play a crucial role in enhancing our understanding of T-cell-mediated immunity and, in turn, facilitate the advancement of immunotherapy. Here, we present immunopipe, a comprehensive and flexible pipeline to perform integrated analysis of scRNA-seq and scTCR-seq data. In addition to the command line tool, we provide a user-friendly web interface for pipeline configuration and execution monitoring, benefiting researchers without extensive programming experience. With its comprehensive functionality and ease of use, immunopipe empowers researchers to uncover valuable insights from scRNA-seq and scTCR-seq data, ultimately advancing the understanding of immune responses and immunotherapy development.

bioinformatics↗

bioRxiv2024

Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases

Polygenic risk scores (PRSs) are commonly used for predicting an individuals genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimers disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.

genetics↗

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

Highly accurate metagenome-assembled genomes from human gut microbiota using long-read assembly, binning, and consolidation methods

Long-read metagenomic sequencing is a powerful approach for cataloging the microbial diversity present in complex microbiomes, including the human gut microbiome. We performed a deep-sequencing experiment using PacBio HiFi reads to obtain metagenome-assembled genomes (MAGs) from a pooled human gut microbiome. We performed long-read metagenome assembly using two methods (hifiasm-meta, metMDBG), used improved bioinformatic and proximity ligation binning strategies to cluster contigs and identify MAGs, and developed a novel framework to compare and consolidate MAGs (pb-MAG-mirror). We found proximity ligation binning yielded more MAGs than bioinformatic binning, but our novel comparison framework resulted in higher MAG yields than either binning strategy individually. In total, from 255 Gbp of total HiFi data we produced 595 total MAGs (including 175 high-quality MAGs) using hifiasm-meta, and 547 total MAGs (including 277 high-quality MAGs) with metaMDBG. Hifiasm-meta assembled almost twice as many strain-level MAGs as metaMDBG (246 vs. 156), but both assembly methods produced up to five strains for a species. Approximately 85% of the MAGs were assigned to known species, but we recovered >35 high-quality MAGs that represent uncultured diversity. Based on strict similarity scores, we found 125 MAGs were unequivocally shared across the assembly methods at the strain level, representing [~]22% of the total MAGs recovered per method. Finally, we detected more total viral sequences in the metaMDBG assembly versus the hifiasm-meta assembly ([~]6,700 vs. [~]4,500). Overall, we find the use of HiFi sequencing, improved metagenome assembly methods, and complementary binning strategies is highly effective for rapidly cataloging microbial genomes in complex microbiomes.

microbiology↗