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Biology subjects

Lin, Z.

Publications and source records attributed to Lin, Z..

At least 217 records · Page 12Linked to original sources

A connectomic study of a petascale fragment of human cerebral cortex

We acquired a rapidly preserved human surgical sample from the temporal lobe of the cerebral cortex. We stained a 1 mm3 volume with heavy metals, embedded it in resin, cut more than 5000 slices at [~]30 nm and imaged these sections using a high-speed multibeam scanning electron microscope. We used computational methods to render the three-dimensional structure containing 57,216 cells, hundreds of millions of neurites and 133.7 million synaptic connections. The 1.4 petabyte electron microscopy volume, the segmented cells, cell parts, blood vessels, myelin, inhibitory and excitatory synapses, and 104 manually proofread cells are available to peruse online. Many interesting and unusual features were evident in this dataset. Glia outnumbered neurons 2:1 and oligodendrocytes were the most common cell type in the volume. Excitatory spiny neurons comprised 69% of the neuronal population, and excitatory synapses also were in the majority (76%). The synaptic drive onto spiny neurons was biased more strongly toward excitation (70%) than was the case for inhibitory interneurons (48%). Despite incompleteness of the automated segmentation caused by split and merge errors, we could automatically generate (and then validate) connections between most of the excitatory and inhibitory neuron types both within and between layers. In studying these neurons we found that deep layer excitatory cell types can be classified into new subsets, based on structural and connectivity differences, and that chandelier interneurons not only innervate excitatory neuron initial segments as previously described, but also each others initial segments. Furthermore, among the thousands of weak connections established on each neuron, there exist rarer highly powerful axonal inputs that establish multi-synaptic contacts (up to [~]20 synapses) with target neurons. Our analysis indicates that these strong inputs are specific, and allow small numbers of axons to have an outsized role in the activity of some of their postsynaptic partners.

neuroscience↗

Combination of a Sindbis-SARS-CoV-2 spike vaccine and αOX40 antibody elicits protective immunity against SARS-CoV-2 induced disease and potentiates long-term SARS-CoV-2-specific humoral and T-cell immunity

The COVID-19 pandemic caused by the coronavirus SARS-CoV-2 is a major global public threat. Currently, a worldwide effort has been mounted to generate billions of effective SARS-CoV-2 vaccine doses to immunize the worlds population at record speeds. However, there is still demand for alternative effective vaccines that rapidly confer long-term protection and rely upon cost-effective, easily scaled-up manufacturing. Here, we present a Sindbis alphavirus vector (SV), transiently expressing the SARS-CoV-2 spike protein (SV.Spike), combined with the OX40 immunostimulatory antibody (OX40) as a novel, highly effective vaccine approach. We show that SV.Spike plus OX40 elicits long-lasting neutralizing antibodies and a vigorous T-cell response in mice. Protein binding, immunohistochemical and cellular infection assays all show that vaccinated mice sera inhibits spike functions. Immunophenotyping, RNA Seq transcriptome profiles and metabolic analysis indicate a reprogramming of T-cells in vaccinated mice. Activated T-cells were found to mobilize to lung tissue. Most importantly, SV.Spike plus OX40 provided robust immune protection against infection with authentic coronavirus in transgenic mice expressing the human ACE2 receptor (hACE2-Tg). Finally, our immunization strategy induced strong effector memory response, potentiating protective immunity against re-exposure to SARS-CoV-2 spike protein. Our results show the potential of a new Sindbis virus-based vaccine platform to counteract waning immune response that can be used as a new candidate to combat SARS-CoV-2. Given the strong T-cell responses elicited, our vaccine is likely to be effective against variants that are proving challenging, as well as, serve as a platform to develop a broader spectrum pancoronavirus vaccine. Similarly, the vaccine approach is likely to be applicable to other pathogens.

immunology↗

GPI anchor remodeling by the plant PGAP1 ortholog HLD1 is essential for Papaver self-incompatibility

In eukaryotes, glycosylphosphatidylinositol anchored proteins (GPI-APs) are tethered to the outer leaflet of the plasma membrane where they function as key regulators of a plethora of biological processes. Self-incompatibility (SI) plays a pivotal role regulating fertilization in higher plants through recognition and rejection of self pollen. Here we used Arabidopsis thaliana lines engineered to be self-incompatible by expression of Papaver SI determinants for an SI suppressor screen. We identify HLD1, an ortholog of human GPI-inositol deacylase PGAP1, whose mutation completely abolishes the SI response. We show that HLD1 functions as a GPI-inositol deacylase and that this GPI-remodeling activity is essential for SI. Using GFP-SKU5 as a representative GPI-AP, we show that HLD1 mutation does not affect GPI-AP production and targeting, but alters the configuration of mature GPI-APs. This prevents GPI-AP release from the plasma membrane, suggesting that this process plays a critical role in the regulation of SI. Our data not only identify GPI anchoring as a new pathway of SI providing new directions to investigate SI mechanisms, but identifies for the first time a function for GPI-AP remodeling by inositol deacylation in plants. One sentence summaryThe Papaver self-incompatibility response requires GPI-anchor modification by HLD1, an ortholog of the mammalian PGAP1.

plant biology↗

Transcriptome analysis provides genome annotation and expression profiles in the central nervous system of Lymnaea stagnalis at different ages

Molecular studies of the freshwater snail Lymnaea stagnalis, a unique model organism for neurobiology research, has been severely hindered by the lack of sufficient genomic information. As part of our ongoing effort studying L. stagnalis neuronal growth and connectivity at various developmental stages, we provide the first age-specific transcriptome analysis and gene annotation of young, adult, and old L. stagnalis central nervous system (CNS). RNA sequencing using Illumina NovaSeq 6000 platform produced 56-69 millions of 150 bp paired-end reads, and 74% of these reads were mapped to the draft genome of L. stagnalis. We provide gene annotations for 32,288 coding sequences with a minimum of 100 codons, contributing to the largest number of annotated genes for the L. stagnalis genome to date. Lastly, transcriptomic analyses reveal age-specific differentially expressed genes and enriched pathways in young, adult, and old CNS. These datasets represent the largest and most updated L. stagnalis CNS transcriptomes.

neuroscience↗

Nicotinic acetylcholine receptor partial antagonist polyamides from tunicates and their predatory sea slugs

In our efforts to discover new drugs to treat pain, we identified molleamines A-E (1-5) as major neuroactive components of the sea slug, Pleurobranchus forskalii and their prey, Didemnum molle tunicates. The chemical structures of molleamines were elucidated by spectroscopy and confirmed by the total synthesis of molleamines A (1) and C (3). Synthetic 3 completely blocked acetylcholine-induced calcium flux in peptidergic nociceptors (PNs) in the somatosensory nervous system. Compound 3 affected neither the 7 nAChR nor the muscarinic acetylcholine receptors in calcium flux assays. In addition to nociceptors, 3 partially blocked the acetylcholine-induced calcium flux in the sympathetic nervous system, including neurons from the superior cervical ganglion. Electrophysiology revealed a block of 3{beta}4 (mouse) and 6/3{beta}4 (rat) nicotinic acetylcholine receptors (nAChRs), with IC50 values of 1.4 and 3.1 {micro}M, respectively. Molleamine C (3) is a partial antagonist, reaching a maximum block of 76-82% of the acetylcholine signal and showing no partial agonist response. Molleamine C (3) may thus provide a lead compound for the development of neuroactive compounds with unique biological properties.

neuroscience↗

The tunicate metabolite 2-(3,5-diiodo-4-methoxyphenyl)ethan-1-amine targets ion channels of vertebrate sensory neurons

Marine tunicates produce defensive amino-acid derived metabolites, including 2-(3,5-diiodo-4-methoxyphenyl)ethan-1-amine (DIMTA), but their mechanisms of action are rarely known. Using an assay-guided approach, we found that out of the many different sensory cells in the mouse dorsal root ganglion (DRG), DIMTA selectively affected low-threshold cold thermosensors. Whole-cell electrophysiology experiments using DRG cells, channels expressed in Xenopus oocytes and human cell lines revealed that DIMTA blocks several potassium channels, reducing the magnitude of the afterhyperpolarization and increasing the baseline [Ca2+]i of low-threshold cold thermosensors. When injected into mice, DIMTA increased the threshold of cold sensation by >3 oC. DIMTA may thus serve as a lead in the further design of compounds that inhibit problems in the cold-sensory system, such as cold allodynia and other neuropathic pain conditions.

neuroscience↗

Evidence for the equilibrium between monomers and dimers of the death domain of the p75 neurotrophin receptor

The p75 neurotrophin receptor (p75NTR) is an important mediator of synaptic depression and neuronal cell death, and its expression increases upon nerve injury and in neurodegenerative diseases. However, the molecular mechanisms leading to the activation of this receptor are still a matter of debate. The oligomerization properties of the death domain (DD) of p75NTR are critical for our understanding of the activation mechanisms of the receptor. In this paper, we present additional evidence supporting the existence of an equilibrium between monomeric and dimeric forms of the p75NTR DD in solution and in the absence of any other protein. Dynamic light scattering (DLS) measurements of native, untagged human p75NTR DD at room temperature yielded Rh=2.11 for this domain in 20mM phosphate buffer, corresponding to a molecular weight (MW) of approximately 19kDa, much closer to the theoretical MW of the homodimer (i.e. 21kDa) than the monomer. MWs deduced from the Rh of different control proteins used as standards were all congruent with their theoretical MWs. In addition, size-exclusion FPLC profiles of un-tagged human p75NTR DD in both HEPES and phosphate buffers revealed elution volumes corresponding to a MW of about 15kDa, which is intermediate between monomer and dimer, and indicative of dynamic monomer/dimer interconversion during the run. Together with our previous NMR studies, as well as biophysical data for other investigators, these results support the notion that the DD of p75NTR exists in equilibrium between monomers and dimers in solution, a notion that is in agreement with the oligomerization properties of all members of the DD superfamily.

biochemistry↗

In situ electro-sequencing in three-dimensional tissues

Pairwise mapping of single-cell gene expression and electrophysiology in intact three-dimensional (3D) tissues is crucial for studying electrogenic organs (e.g., brain and heart)1-5. Here, we introduce in situ electro-sequencing (electro-seq), combining soft bioelectronics with in situ RNA sequencing to stably map millisecond-timescale cellular electrophysiology and simultaneously profile a large number of genes at single-cell level across 3D tissues. We applied in situ electro-seq to 3D human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) patches, precisely registering the CM gene expression with electrophysiology at single-cell level, enabling multimodal in situ analysis. Such multimodal data integration substantially improved the dissection of cell types and the reconstruction of developmental trajectory from spatially heterogeneous tissues. Using machine learning (ML)-based cross-modal analysis, in situ electro-seq identified the gene-to-electrophysiology relationship over the time course of cardiac maturation. Further leveraging such a relationship to train a coupled autoencoder, we demonstrated the prediction of single-cell gene expression profile evolution using long-term electrical measurement from the same cardiac patch or 3D millimeter-scale cardiac organoids. As exemplified by cardiac tissue maturation, in situ electro-seq will be broadly applicable to create spatiotemporal multimodal maps and predictive models in electrogenic organs, allowing discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.

genomics↗

Neural Potts Model

AO_SCPLOWBSTRACTC_SCPLOWWe propose the Neural Potts Model objective as an amortized optimization problem. The objective enables training a single model with shared parameters to explicitly model energy landscapes across multiple protein families. Given a protein sequence as input, the model is trained to predict a pairwise coupling matrix for a Potts model energy function describing the local evolutionary landscape of the sequence. Couplings can be predicted for novel sequences. A controlled ablation experiment assessing unsupervised contact prediction on sets of related protein families finds a gain from amortization for low-depth multiple sequence alignments; the result is then confirmed on a database with broad coverage of protein sequences.

bioinformatics↗

scAMACE: Model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation

The advancement in technologies and the growth of available single-cell datasets motivate integrative analysis of multiple single-cell genomic datasets. Integrative analysis of multimodal single-cell datasets combines complementary information offered by single-omic datasets and can offer deeper insights on complex biological process. Clustering methods that identify the unknown cell types are among the first few steps in the analysis of single-cell datasets, and they are important for downstream analysis built upon the identified cell types. We propose scAMACE for the integrative analysis and clustering of single-cell data on chromatin accessibility, gene expression and methylation. We demonstrate that cell types are better identified and characterized through analyzing the three data types jointly. We develop an efficient expectation-maximization (EM) algorithm to perform statistical inference, and evaluate our methods on both simulation study and real data applications. We also provide the GPU implementation of scAMACE, making it scalable to large datasets. The software and datasets are available at https://github.com/cuhklinlab/scAMACE_py (python implementation) and https://github.com/cuhklinlab/scAMACE (R implementation).

bioinformatics↗

MR-APSS: a unified approach to Mendelian Randomization accounting for pleiotropy and sample structure using genome-wide summary statistics

Mendelian Randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong assumptions, resulting in many false positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy. Here we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, which (i) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; and (ii) allows to include more genetic variants with moderate effects as instrument variables (IVs) to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls, and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects.

genetics↗

Coding Recognition of the Dose-Effect Interdependence of Small Biomolecules Encrypted on Paired Chromatographic-Based Microassay Arrays

The discovery of small biomolecules suffers from the lack of a comprehensive framework to express the intrinsic correlation between bioactivity and contributing small molecules in complex samples with molecular and bioactivity diversity. Here, by mapping a samples 2D-HPTLC fingerprint to microplates, the paired chromatographic-based microassay arrays are created, which can be used as quasi-chip to characterize multiple attributes of chromatographic components; and as the array differential expression of the bioactivity and molecular attributes of irregular chromatographic spots for dose-effect interdependent encoding; and also as the automatic-collimated array mosaics of the multi-attributes of each component itself encrypted by its chromatographic fingerprint. Based on this homologous framework, we propose a correlating recognition strategy for small-biomolecules through their self-consistent chromatographic behavior characteristics. In the approach, the small-biomolecule recognition in diverse compounds is transformed into a constraint satisfaction problem, which is addressed through examining the dose-effect interdependence of the homologous 2D code pairs by array matching algorithm, instead of preparing diverse compound monomers of complex test sample for identifying item-by-item. Furtherly, considering the dose-effect interdependent 2D code pairs as links and the digital-specific quasimolecular ions as nodes, an extendable self-consistent framework that correlates mammalian cell phenotypic and target-based bioassays with small biomolecules is established. Therefore, the small molecule contributions and the correlations of bioactivities, as well as their pathway can be comprehensively revealed, so as to improve the reliability and efficiency of screening. This strategy was successfully applied to galangal, and practiced the high-throughput digital preliminary screening of small-biomolecules in a natural product. HighlightRapid self-recognition of small biomolecules in diverse samples without pre-isolation Recognized by dose-effect interdependency developed from homologous TLC fingerprint Matching of HPTLC-based molecular imprinting and bioautography on microassay arrays Microarray-based differential expression of substance attributes instead of spot scan An array framework for combining phenotype-based and target-based assays with TLC-MS ToC graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/434720v6_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@496c72org.highwire.dtl.DTLVardef@6ec60eorg.highwire.dtl.DTLVardef@8cdf1aorg.highwire.dtl.DTLVardef@8b440b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

Metabolic characteristics of taste differences under the soil and hydroponic cultures of sweet potato leaves by using non-targeted metabolomics

Sweet potato leaves are consumed as green leafy vegetables in most of the world due to their nutritional and functional values, and the taste characteristics determine their commodity value and consumer acceptance. However, the metabolic composition and formation mechanism of taste quality in its leaves are not clear. In this study, we found that sweet potato leaves under different growing patterns, soil culture and hydroponic culture, which result in different taste quality. In particular, the taste quality in leafy sweet potato was effectively improved under hydroponic culture. Meanwhile, we further profiled metabolites in leaves of sweet potatoes under different growing patterns by using GC-QToF-MS. A total of 200 metabolites were identified, covering most of the metabolic pathways in plants. A comparison of the good taste and poor taste of sweet potato leaves resulted in 71 metabolites related to taste quality formation. In addition, the leaves with poor taste had lower levels of metabolites regarding amino acids metabolism, whereas was accompanied by high levels of metabolites in carbohydrates and secondary metabolism. This study provides new insights into the improvement of taste quality in leafy sweet potato.

plant biology↗

ClusterMap: multi-scale clustering analysis of spatial gene expression

Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we present an unsupervised and annotation-free framework, termed ClusterMap, which incorporates physical proximity and gene identity of RNAs, formulates the task as a point pattern analysis problem, and thus defines biologically meaningful structures and groups. Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and consistently performs on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell-cell interactions, and tissue organization principles from high-dimensional transcriptomic images.

genomics↗

coupleCoC+: an information-theoretic co-clustering-basedtransfer learning framework for the integrative analysis of single-cell genomic data

Technological advances have enabled us to profile multiple molecular layers at unprecedented single-cell resolution and the available datasets from multiple samples or domains are growing. These datasets, including scRNA-seq data, scATAC-seq data and sc-methylation data, usually have different powers in identifying the unknown cell types through clustering. So, methods that integrate multiple datasets can potentially lead to a better clustering performance. Here we propose coupleCoC+ for the integrative analysis of single-cell genomic data. coupleCoC+ is a transfer learning method based on the information-theoretic co-clustering framework. In coupleCoC+, we utilize the information in one dataset, the source data, to facilitate the analysis of another dataset, the target data. coupleCoC+ uses the linked features in the two datasets for effective knowledge transfer, and it also uses the information of the features in the target data that are unlinked with the source data. In addition, coupleCoC+ matches similar cell types across the source data and the target data. By applying coupleCoC+ to the integrative clustering of mouse cortex scATAC-seq data and scRNA-seq data, mouse and human scRNA-seq data, mouse cortex sc-methylation and scRNA-seq data, and human blood dendritic cells scRNA-seq data from two batches, we demonstrate that coupleCoC+ improves the overall clustering performance and matches the cell subpopulations across multimodal single-cell genomic datasets. coupleCoC+ has fast convergence and it is computationally efficient. The software is available at https://github.com/cuhklinlab/coupleCoC_plus.

bioinformatics↗

The Human walking kinematic model combined with upper and lower limb movement

The purpose of this study is to build a relatively complete human walking kinematic model. This model is combined with the rolling-foot model (lower limb) and multi-rod swinging model (upper limb) connected by COM. We calculated the velocity of COM and other critical joints of the upper limb by marker point capture experiment using the high-speed camera. This research shows that the hand joint velocity measured through the experiment can achieve high coincidence with that calculated by the theoretical model given specific inputs. Moreover, the common pattern of upper limb angles is also studied for an accurate description. The proposed kinematic model is expected to forecast desired motion intention for better compliance by the rehabilitation and assistive robots.

biophysics↗

Self-Supervised Representation Learning of Protein Tertiary Structures (PtsRep): Protein Engineering as A Case Study

In recent years, deep learning has been increasingly used to decipher the relationships among protein sequence, structure, and function. Thus far these applications of deep learning have been mostly based on primary sequence information, while the vast amount of tertiary structure information remains untapped. In this study, we devised a self-supervised representation learning framework (PtsRep) to extract the fundamental features of unlabeled protein tertiary structures deposited in the PDB, a total of 35,568 structures. The learned embeddings were challenged with two commonly recognized protein engineering tasks: the prediction of protein stability and prediction of the fluorescence brightness of green fluorescent protein (GFP) variants, with training datasets of 16,431 and 26,198 proteins or variants, respectively. On both tasks, PtsRep outperformed the two benchmark methods UniRep and TAPE-BERT, which were pre-trained on two much larger sets of data of 24 and 32 million protein sequences, respectively. Protein clustering analyses demonstrated that PtsRep can capture the structural signatures of proteins. Further testing of the GFP dataset revealed two important implications for protein engineering: (1) a reduced and experimentally manageable training dataset (20%, or 5,239 variants) yielded a satisfactory prediction performance for PtsRep, achieving a recall rate of 70% for the top 26 brightest variants with 795 variants in the testing dataset retrieved; (2) counter-intuitively, when only the bright variants were used for training, the performances of PtsRep and the benchmarks not only did not worsen but they actually slightly improved. This study provides a new avenue for learning and exploring general protein structural representations for protein engineering.

bioinformatics↗

The Wood Image Analysis and Dataset (WIAD): open-access visual analysis tools to advance the ecological data revolution

O_LIEcological data is collected and shared at an increasingly rapid pace, but it is often shared in inconsistent and untraceable processed forms. Images of wood contain a wealth of information such as colours and textures but are most commonly reduced to ring-width measurements before they can be shared in various common file formats. Archiving digital images of wood samples in libraries, that have been developed for ecological analysis and are publicly available, remains the exception. C_LIO_LIWe developed the Wood Image Analysis and Dataset (WIAD) an open-source application including a web-interface to integrate basic visual analysis of wood samples, such as increment cores, thin sections or x-ray films, basic data processing, and archiving of the images and derived data to facilitate transparency and reproducibility in studies using visual characteristics of wood. C_LIO_LIWIAD provides user-friendly tools to manipulate images of wood samples, mark and measure wood characteristics such as growth increments, density fluctuations, early- and latewood widths, and fire scars, and to visualise, process, and archive images, metadata, and the derived data. C_LIO_LIWIAD constitutes a step towards the reproducible automation of tree-ring analysis, while establishing an open-source foundation to create improved community developed repositories which would enable novel ecological studies harnessing the wealth of existing visual data. C_LI

plant biology↗