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Lin, Z.

Publications and source records attributed to Lin, Z..

At least 163 records · Page 9Linked to original sources

Multiplexed Volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex

Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.

neuroscience↗

Spatiotemporal transcriptome atlas of human embryos after gastrulation

The spatial and temporal atlas of gene expression in the human embryo at early gestation is critical in understanding embryo development, organogenesis, and disease origins. We obtained the spatiotemporal transcriptome from 90 sagittal sections of 16 whole human embryos from 3 to 8 post-conception weeks by Stereo-seq with high resolution and ultra-large field, establishing the development trajectory/regulatory profiling of 49 organs. We uncovered the organ-specific regulons as potential lineage-determining factors and identified the new regulatory networks during heart and brain development. The atlas refines the key organs/cell types vulnerable to virus infection and genetic disorders, and, reveals the dynamics of allelic gene expression in specific organs at different stages. These results present the first comprehensive delineation of the spatiotemporal transcriptomic dynamics of human organogenesis. One Sentence SummaryThe spatiotemporal transcriptome atlas presents a comprehensive delineation of human embryogenesis after gastrulation.

developmental biology↗

A Comprehensive Benchmark Study on Biomedical Text Generation and Mining with ChatGPT

In recent years, the development of natural language process (NLP) technologies and deep learning hardware has led to significant improvement in large language models(LLMs). The ChatGPT, the state-of-the-art LLM built on GPT-3.5, shows excellent capabilities in general language understanding and reasoning. Researchers also tested the GPTs on a variety of NLP related tasks and benchmarks and got excellent results. To evaluate the performance of ChatGPT on biomedical related tasks, this paper presents a comprehensive benchmark study on the use of ChatGPT for biomedical corpus, including article abstracts, clinical trials description, biomedical questions and so on. Through a series of experiments, we demonstrated the effectiveness and versatility of Chat-GPT in biomedical text understanding, reasoning and generation.

bioinformatics↗

Disulfiram Exerts anti-pulmonary Fibrosis Effect by Activating PGE2 Synthesis

Idiopathic pulmonary fibrosis (IPF) is marked with the replacement of normal alveolar tissue by thicker and harder fibrous material, damaged exchange ability. Currently, nintedanib and pirfenidone, are the only FDA-approved drugs with limited efficacy for IPF, which indicated an urgent need to explore new therapies. Disulfiram (DSF), an acetaldehyde dehydrogenase inhibitor, used as anti-alcohol treatment. Despite reported with anti-hepatic fibrosis effect of DSF, the underlying mechanism remains unclear. In our study, DSF exhibited regulative impact on abnormal proliferation, EMT and ECM production in cell models of IPF including primary DHLF-IPF cells and TGF-{beta}1-stimulated A549 cells. The absence of COX-2 was restored by DSF treatment, together with elevated prostaglandin biosynthesis both in vitro and in vivo models of IPF. Furthermore, the anti-fibrotic effect of DSF was impeded with COX-2 knockdown or pharmacological inhibition in TGF-{beta}1-stimulated A549 cells, however, exogenous PGE2 reclaimed with anti-EMT function. In established animal model of IPF, DSF ameliorated declined lung function and histopathological changes, and restrained the lung hydroxyproline content. Together, these findings suggest that the anti-fibrotic effect of DSF was achieved through re-activation of COX-2 mediated PGE2 biosynthesis. The above results suggest that DSF can be applied therapeutically in fibrotic conditions.

cell biology↗

SpatialScope: A unified approach for integrating spatial and single-cell transcriptomics data using deep generative models

The rapid emergence of spatial transcriptomics (ST) technologies are revolutionizing our under-standing of tissue spatial architecture and their biology. Current ST technologies based on either next generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), while providing hugely informative insights, remain unable to provide spatial characterization at transcriptome-wide single-cell resolution, limiting their usage in resolving detailed tissue structure and detecting cellular communications. To overcome these limitations, we developed SpatialScope, a unified approach to integrating scRNA-seq reference data and ST data that leverages deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate the utility of SpatialScope through comprehensive simulation studies and then apply it to real data from both seq-based and image-based ST approaches. SpatialScope provides a spatial characterization of tissue structures at transcriptome-wide single-cell resolution, greatly facilitating the downstream analysis of ST data, such as detection of cellular communication by identifying ligand-receptor interactions from seq-based ST data, localization of cellular subtypes, and detection of spatially differently expressed genes.

bioinformatics↗

U1 AMO (antisense morpholino oligos) disrupts U1 snRNP structure to promote intronic PCPA (premature cleavage and polyadenylation)

Functional depletion of U1 snRNP with a 25 nt U1 AMO (antisense morpholino oligonucleotides) may lead to intronic premature cleavage and polyadenylation (PCPA) of thousands of genes, a phenomenon known as U1 snRNP telescripting; however, the underlying mechanism remains elusive. In this study, we demonstrated that U1 AMO could disrupt U1 snRNP structure both in vitro and in vivo, thereby affecting U1 snRNP/RNAP polymerase II (RNAPII) interaction. We further showed that U1 AMO treatment might promote RNAPII disassociation with pre-mRNA in an RNA pull-down assay. By performing ChIP-seq for phosphorylation of Ser2 (Ser2P) and Ser5 (Ser5P) of the C-terminal domain (CTD) of RNA polymerase II (RNAPII), we showed that transcription elongation was disturbed upon U1 AMO treatment, with a particular high Ser2P signal at intronic cryptic polyadenylation sites (PASs). In addition, we showed that core 3 processing factors CPSF/CstF are involved in the processing of intronic cryptic PAS. Their recruitment accumulated toward cryptic PASs upon U1 AMO treatment, as indicated by ChIP-seq and iCLIP-seq analysis. Furthermore, we showed that most of these PCPAed transcripts could be exported to cytoplasm and have the potential to be translated. Conclusively, our data provide more insight into U1 snRNP telescripting, and suggest a common theme that modulation of transcription elongation may be an important mode for the regulation of mRNA polyadenylation.

molecular biology↗

Chromosome-level genome assembly of tree sparrow reveals a burst of new genes driven by segmental duplications

The creation of new genes is a major force of evolution. Despite as an important mechanism that generated new genes, segmental duplication (SD) has yet to be accurately identified and fully characterized in birds because the repetitive complexity leads to misassignment and misassembly of sequence. In addition, SD may lead to new gene copies, which makes it possible to test the "out of testis" hypothesis which suggests genes are frequently born with testis-specific expression. Using a high-quality chromosome-level assembly, we performed a systematic analysis and presented a comprehensive landscape of SDs in tree sparrow (Passer montanus). We detected co-localization of newly expanded genes and long terminal repeat retrotransposons (LTR-RTs), both of which are derived from SDs and enriched in microchromosomes. The newly expanded genes are mostly found in eight families including C2H2ZNF, OR, PIM, PAK, MROH, HYDIN, HSF and ITPRIPL. The large majority of new members of these eight families have evolved to pseudogenes, whereas there still some new copies preserved transcriptional activity. Among the transcriptionally active new members, new genes from different families with diverse structures and functions shared a similar testis-biased expression pattern, which is consistent with the "out of testis" hypothesis. Through a case analysis of the high-quality genome assembly of tree sparrow, we reveal that the SDs contribute to the formation of new genes. Our study provides a comprehensive understanding of the emergence, expression and fate of duplicated genes and how the SDs might participate in these processes and shape genome evolution.

genomics↗

CXCR6 promotes dermal CD8+ T cell survival and transition to long-term tissue residence

Tissue resident memory T cells (TRM) provide protection against local re-infection, and yet the interstitial signals necessary for their formation and persistence remain incompletely understood. Here we show that antigen-dependent induction of the chemokine receptor, CXCR6, is a conserved adaptation to peripheral tissue infiltration that promotes TRM formation after viral infection. Deficient TRM formation in the absence of CXCR6 was not explained by canonical trafficking as CXCR6 was not required for tissue entry, was dispensable for the early accumulation of antigen-specific CD8+ T cells in skin, and did not restrain their exit. Further, single cell sequencing indicated that Cxcr6-/- CD8+ T cells were competent to acquire a transcriptional program of residence and TRM that formed were equally functional compared to their WT counterparts when reactivated greater than 100 days post primary infection. The reduced numbers observed at memory time points, where instead found to associate with impaired redox homeostasis and antioxidant capacity during the transition from effector to memory states. As such, Cxcr6-/- CD8+ T cells exhibited increased rates of apoptosis in the dermis relative to controls, which led to reduced numbers of TRM in the epidermis at memory. CXCR6 therefore promotes the metabolic adaptation of T cells as they engage antigen in tissue to increase the probability of memory differentiation and long-term residence. One Sentence SummaryCXCR6 promotes mechanisms of cellular adaptation to tissue that support local survival and the transition to tissue residence.

immunology↗

Evolution of a fatty acyl-CoA elongase underlies desert adaptation in Drosophila

To survive in extreme environments such as hot-arid deserts, desert-dwelling species have evolved physiological traits to withstand the high temperatures and low aridity beyond the physiologically tolerable ranges of most species. Such traits which include reducing water loss have independently evolved in multiple taxa. However, the genetic and evolutionary mechanisms underlying these traits have thus far not been elucidated. Here we show that Drosophila mojavensis, a fruitfly species endemic to the Sonoran and Mojave deserts, had evolved extremely high desiccation resistance, by producing very long chained methylbranched cuticular hydrocarbons (mbCHCs) that contributes to a cuticular waterproofing lipid layer reducing water loss. We show that the ability to synthesize these longer mbCHCs is due to evolutionary changes in a fatty acyl-CoA elongase (mElo). CRISPR/Cas9 knockout of mElo in D. mojavensis led to loss of longer mbCHC production and significant reduction of desiccation resistance at high temperatures but did not affect mortality at high temperatures or desiccating conditions individually, indicating that this gene is crucial for desert adaptation. Phylogenetic analysis showed that mElo is a Drosophila specific gene with no clear ortholog outside Diptera. This suggests that while the physiological mechanisms underlying desert adaptation are general, the genetic mechanisms may be lineage-specific.

evolutionary biology↗

Phenelzine-based probes reveal Secernin-3 is involved in thermal nociception

Chemical platforms that facilitate both the identification and elucidation of new areas for therapeutic development are necessary but lacking. Activity-based protein profiling (ABPP) leverages active site-directed chemical probes as target discovery tools that resolve activity from expression and immediately marry the targets identified with lead compounds for drug design. However, this approach has traditionally focused on predictable and intrinsic enzyme functionality. Here, we applied our activity-based proteomics discovery platform to map non-encoded and post-translationally acquired enzyme functionalities (e.g. cofactors) in vivo using chemical probes that exploit the nucleophilic hydrazine pharmacophores found in a classic antidepressant drug (e.g. phenelzine, Nardil (R)). We show the probes are in vivo active and can map proteome-wide tissue-specific target engagement of the drug. In addition to engaging targets (flavoenzymes monoamine oxidase A/B) that are associated with the known therapeutic mechanism as well as several other members of the flavoenzyme family, the probes captured the previously discovered N-terminal glyoxylyl (Glox) group of Secernin-3 (SCRN3) in vivo through a divergent mechanism, indicating this functional feature has biochemical activity in the brain. SCRN3 protein is ubiquitously expressed in the brain, yet gene expression is regulated by inflammatory stimuli. In an inflammatory pain mouse model, behavioral assessment of nociception showed Scrn3 male knockout mice selectively exhibited impaired thermal nociceptive sensitivity. Our study provides a guided workflow to entangle molecular (off)targets and pharmacological mechanisms for therapeutic development.

neuroscience↗

Inferring membrane properties during clathrin-mediated endocytosis using machine learning

Endocytosis is a fundamental cellular process for eukaryotic cells to transport molecules into the cell. To understand the molecular mechanisms behind the process, researchers have obtained abundant biochemical information about the protein dynamics involved in endocytosis via fluorescence microscopy and geometric information about membrane shapes via electron tomography. However, measuring the biophysical information, such as the osmotic pressure and the membrane tension, remains a problem due to the small dimension of the endocytic invagination. In this work, we combine Machine Learning and Helfrich model of the membrane, as well as the dataset of membrane shapes extracted from the electron tomography to infer biophysical information about endocytosis. Our results show that Machine Learning is able to find solutions that both match the experimental profile and fulfill the membrane shape equations. Furthermore, we show that at the early stage of endocytosis, the inferred membrane tension is negative, which implies strong compressive forces acting at the boundary of the endocytic invagination. This method provides a generic framework to extract membrane information from the super-resolution imaging. SIGNIFICANCEEndocytosis is a fundamental cellular process that has been extensively studied with the help of fluorescence microscopy and electron microscopy. A large amount of data has been accumulated about the protein dynamics and the membrane shapes. In this work, we combine the widely used Helfrich model and experimental data of membrane shapes to infer the physical information about endocytosis, including the membrane tension and the osmotic pressure. Our work not only proves Machine Learning as a power tool is able to solve the complicated membrane shape equations, but also provides novel biological insights about the initiation of endocytosis in yeast cells.

biophysics↗

Decapping factor Dcp2 controls mRNA abundance and translation to adjust metabolism and filamentation to nutrient availability

Degradation of most yeast mRNAs involves decapping by Dcp1/Dcp2. DEAD-box protein Dhh1 has been implicated as an activator of decapping, in coupling codon non-optimality to enhanced degradation, and as a translational repressor, but its functions in cells are incompletely understood. RNA-Seq analyses coupled with CAGE sequencing of all capped mRNAs revealed increased abundance of hundreds of mRNAs in dcp2{Delta} cells that appears to result directly from impaired decapping rather than elevated transcription, which was confirmed by ChIP-Seq analysis of RNA Polymerase II occupancies genome-wide. Interestingly, only a subset of mRNAs requires Dhh1 for targeting by Dcp2, and also generally requires the other decapping activators Pat1, Lsm2, Edc3 or Scd6; whereas most of the remaining transcripts utilize NMD factors for Dcp2-mediated turnover. Neither inefficient translation initiation nor stalled elongation appears to be a major driver of Dhh1-enhanced mRNA degradation. Surprisingly, ribosome profiling revealed that dcp2{Delta} confers widespread changes in relative TEs that generally favor well-translated mRNAs. Because ribosome biogenesis is reduced while capped mRNA abundance is increased by dcp2{Delta}, we propose that an increased ratio of mRNA to ribosomes increases competition among mRNAs for limiting ribosomes to favor efficiently translated mRNAs in dcp2{Delta} cells. Interestingly, genes involved in respiration or utilization of alternative carbon or nitrogen sources are derepressed, and both mitochondrial function and cell filamentation (a strategy for nutrient foraging) are elevated by dcp2{Delta}, suggesting that mRNA decapping sculpts gene expression post-transcriptionally to fine-tune metabolic pathways and morphological transitions according to nutrient availability.

molecular biology↗

A high-level programming language for generative protein design

Combining a basic set of building blocks into more complex forms is a universal design principle. Most protein designs have proceeded from a manual bottom-up approach using parts created by nature, but top-down design of proteins is fundamentally hard due to biological complexity. We demonstrate how the modularity and programmability long sought for protein design can be realized through generative artificial intelligence. Advanced protein language models demonstrate emergent learning of atomic resolution structure and protein design principles. We leverage these developments to enable the programmable design of de novo protein sequences and structures of high complexity. First, we describe a high-level programming language based on modular building blocks that allows a designer to easily compose a set of desired properties. We then develop an energy-based generative model, built on atomic resolution structure prediction with a language model, that realizes all-atom structure designs that have the programmed properties. Designing a diverse set of specifications, including constraints on atomic coordinates, secondary structure, symmetry, and multimerization, demonstrates the generality and controllability of the approach. Enumerating constraints at increasing levels of hierarchical complexity shows that the approach can access a combinatorially large design space.

synthetic biology↗

Spatial-temporal evolution characteristics of land use and its driving factor analysis during the period of 2000-2020 in Putian City, Fujian Province

Exploring the spatial and temporal evolution characteristics of land use and its driving force can provide scientific basis for the construction of regional ecological civilization. Based on the remote sensing cloud computing platform of Google Earth Engine (GEE), the study took the remote sensing images of the three periods of 2000, 2010 and 2020 as the basic data and interpreted them to obtain the basic land use data of Putian city and its five districts and counties. The land-use change characteristics of Putian city in those periods were systematically analyzed by using the methods of single land-use dynamics and Geo-informatic Tupu, so as to reveal the Tupu characteristics of land-use transfer and the spatial-temporal evolution pattern in the past 20 years. Then social and economic development indicators were selected to further explore the key driving force of land-use evolution in the study area by canonical correspondence analysis. The results showed that: 1) the land use structure of Putian city was mainly composed of cultivated land and forest land, and the other land types were embedded in them and the built-up land continued to expand outward. 2) The spatial distribution of Tupu units of land use transfer in Putian city was significantly different, change types of land use were diversified and the areas of cultivated land and grassland transferred out were the most obvious. Different degrees of cultivated land abandonment had occurred in various regions. Putian city was facing great challenges in ensuring food security and curbing the non-agricultural and non grain of cultivated land. 3) The macro-economic development in a specific period, especially the urban expansion and the development of the secondary industry caused by the merger of cities and counties, were the key factors driving the spatial and temporal evolution of land use and the differential distribution pattern in Putian city. The author suggested that land resources should be used efficiently in addition to optimizing the layout of land use and carrying out the renovation of abandoned land. Therefore, the study proposed to strengthen the scientific planning and effective regulation of land use, combine spatial and temporal resources of cultivated land with agriculture, culture and tourism, and build ecological barriers and ecological corridors to ensure the coordinated and sustainable development of regional ecology and economy.

ecology↗

Structure-Kinetic Relationship for Drug Design Revealed by PLS Model with Retrosynthesis-Based Pre-trained Molecular Representation and Molecular Dynamics Simulation

Drug design based on their molecular kinetic properties is growing in application. Pre-trained molecular representation based on retrosynthesis prediction model (PMRRP) was trained from 501 inhibitors of 55 proteins and successfully predicted the koff values of 38 inhibitors for HSP90 protein from an independent dataset. Our PMRRP molecular representation outperforms others such as GEM, MPG, and common molecular descriptors from RDKit. Furthermore, we optimized the accelerated molecular dynamics to calculate relative retention times for 128 inhibitors of HSP90. We observed high correlation between the simulated, predicted, and experimental -log(koff) scores. Combining machine learning (ML) and molecular dynamics (MD) simulation help design a drug with specific selectivity to the target of interest. Protein-ligand interaction fingerprints (IFPs) derived from accelerated MD further expedite the design of new drugs with the desired kinetic properties. To further validate our koff ML model, from the set of potential HSP90 inhibitors obtained by similarity search of commercial databases, we identified two novel molecules with better predicted koff values and longer simulated retention time than the reference molecules. The IFPs of the novel molecules with the newly discovered interacting residues along the dissociation pathways of HSP90 shed light on the nature of the selectivity of HSP90 protein. We believe the ML model described here is transferable to predict koff of other proteins and enhance the kinetics-based drug design endeavor.

biophysics↗

NR5A2 connects genome activation to the first lineage segregation in early mouse development

After fertilization, zygotic genome activation (ZGA) marks the beginning of the embryonic program for a totipotent embryo, which further gives rise to the pluripotent embryonic lineages and extraembryonic trophectoderm after the first lineage commitment. While much has been learned about pluripotency regulation, how ZGA is connected to the pluripotency commitment in early embryos remains elusive. Here, we investigated the role of nuclear receptor 1 family transcription factors (TFs) in mouse pre-implantation embryos, whose motifs are highly enriched in accessible chromatin at the 2-cell (2C) to 8-cell (8C) stages after ZGA. We found NR5A2, an NR TF highly induced upon ZGA, is required for early development, as both the knockdown and knockout of Nr5a2 from 1C embryos led to morula arrest. While the zygotic genome was largely activated at the 2C stage, 4-8C-specific gene activation (mid-preimplantation activation) was substantially impaired. Genome-wide chromatin binding and RNA-seq analyses showed NR5A2 preferentially regulates its binding targets including a subset of key pluripotency genes (i.e., Nanog, Pou5f1, and Tdgf1). Finally, NR5A2-occupied sites at the 2C and 8C stages predominantly reside in accessible B1 elements where its motif is embedded. Taken together, these data identify NR5A2 as a key regulator that connects ZGA to the first lineage segregation in early mouse development.

developmental biology↗

INSIDER: Interpretable Sparse Matrix Decomposition for Bulk RNA Expression Data Analysis

RNA-Seq is widely used to capture transcriptome dynamics across tissues from different biological entities even across biological conditions, with the aim of understanding the contribution of gene activities to phenotypes of biosamples. However, due to variation from tissues and biological entities (or other biological conditions), joint analysis of bulk RNA expression profiles across multiple tissues from a number of biological entities to achieve the aim is hindered. Moreover, it is crucial to consider interactions between biological variables. For example, different brain disorders may affect brain regions heterogeneously. Thus, modeling the disorder-region interaction can shed light on the heterogeneity. To address these key challenges, we propose a general and flexible statistical framework based on matrix factorization, named INSIDER (https://github.com/kai0511/insider). INSIDER decomposes variation from different biological variables into a shared low-rank latent space. In particular, it considers interactions between biological variables and introduces the elastic net penalty to induce sparsity, thus facilitating interpretation. In the framework, the biological variables and interaction terms can be defined based on the research questions and study design. Besides, it enables us to compute the adjusted expression profiles for biological variables that control variation from other biological variables. Lastly, it allows various downstream analyses, such as clustering donors with donor representations, revealing development trajectory in its application to the BrainSpan data, and uncovering mechanisms underlying variables like phenotype and interactions between biological variables (e.g., phenotypes and tissues).

bioinformatics↗

Cell types in the mouse amygdala and their transcriptional response to fear conditioning

The amygdala is one of the most widely studied regions in behavioral neuroscience. A plethora of classical, and new paradigms have dissected its precise involvement in emotional and social sensing, learning, and memory. Several important insights resulted from the use of genetic markers - yet, in the age of single cell transcriptomics, the amygdala remains molecularly underdescribed. Here, we present a molecular cell type taxonomy of the full mouse amygdala in fear learning and consolidation. We performed single-cell RNA-seq on naive and fear conditioned mice, inferred the 130 neuronal cell types distributions in silico using orthogonal spatial transcriptomic datasets, and describe the cell types transcriptional responses to learning and memory consolidation. Only a fraction of cells, within a subset of all neuronal types, were transcriptionally responsive to fear learning, memory and retrieval. These activated engram cells upregulated activity-response genes, and processes of synaptic signaling, plasticity, development and neurite outgrowth. Our transcriptome-wide data confirm known actors, and describe several new candidate genes. The atlas may help pinpoint the amygdalas circuits in performing emotional sensing and integration, and provide new insights to the global cellular processes involved.

neuroscience↗