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Lin, D.

Publications and source records attributed to Lin, D..

At least 91 records · Page 5Linked to original sources

Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics

Delineating the spatial multiomics landscape will pave the way to understanding the molecular basis of physiology and pathology. However, current spatial omics technology development is still in its infancy. Here, we developed a high-throughput multiomics in situ pairwise sequencing (MiP-Seq) strategy to efficiently decipher multiplexed DNAs, RNAs, proteins, and small biomolecules at subcellular resolution. We delineated dynamic spatial gene profiles in the hypothalamus using MiP-Seq. Moreover, MiP-Seq was unitized to detect tumor gene mutations and allele-specific expression of parental genes and to differentiate sites with and without the m6A RNA modification at specific sites. MiP-Seq was combined with in vivo Ca2+ imaging and Raman imaging to obtain a spatial multiomics atlas correlated to neuronal activity and cellular biochemical fingerprints. Importantly, we proposed a "signal dilution strategy" to resolve the crowded signals that challenge the applicability of in situ sequencing. Together, our method improves spatial multiomics and precision diagnostics and facilitates analyses of cell function in connection with gene profiles.

molecular biology↗

Identification of S1PR4 as an immune modulator for favorable prognosis in HNSCC through unbiased machine learning

As the largest family of membrane proteins, G protein-coupled receptors (GPCRs) are the most prominent family of pharmacological targets. However, only a few GPCRs have been well-defined in terms of their physiological and pathological functions. Thus, an efficient way to identify key GPCRs involved in tumour formation is urgently needed. In this study, patients with head and neck squamous cell carcinoma (HNSCC) were classified into two different subtypes based on the characteristics of GPCRs using an unbiased machine learning method. Notably, these two subtypes showed significant differences in prognosis, gene expression, and immune microenvironment, especially in the infiltration of CD8+ T cells. Based on these differences, we screened three potential key regulators (S1PR4, S1PR5, and GPR87) from all GPCRs and constructed a prognostic nomogram for patients with HNSCC. We identified S1PR4 as the key GPCR in determining the two subtypes for positive correlation with the proportion and cytotoxicity of CD8+ T cells in HNSCC and was mainly expressed in a subset of CX3CR1+CD8+ T cells. We also demonstrated that S1PR4 is an immune modulator for the favourable prognosis of HNSCC patients. We found that S1PR4 was highly expressed in CD8+ T cells from the tumours of HNSCC patients, which was significantly associated with better prognosis, and S1PR4 expression was accompanied by higher T cell cytotoxic marker expression (IFNG and GZMB). Notably, S1PR4 co-localised with CX3CR1, which has been identified as the most cytotoxic marker of CD8+ T cells. Furthermore, S1PR4 upregulation could significantly increase T cell function in CAR-T cell therapy, indicating its great potential in cancer immunotherapy. Therefore, these results identified S1PR4 as a key indicator of cytotoxicity and the proportion of tumour-infiltrating CD8+ T cells and confirmed the prognostic value of S1PR4 in HNSCC. Our findings contribute to the knowledge of S1PR4 in anti-tumour immunity, providing a potential GPCR-targeted therapeutic option for future HNSCC treatment.

cancer biology↗

A dedicated hypothalamic oxytocin circuit controls aversive social learning

To survive and thrive in a complex social group, it is essential to not only know who to approach but more importantly who to avoid. After a single defeat, mice learn to stay away from the winning aggressor for weeks. Here, we identify oxytocin neurons in the retrochiasmatic supraoptic nucleus (SOROXT) and oxytocin receptor expressing cells in the anterior subdivision of ventromedial hypothalamus, ventrolateral part (aVMHvlOXTR) as a key circuit motif for defeat-induced social avoidance learning. After defeat, aVMHvlOXTR cells drastically increase their responses to aggressor cues. This response change is functionally important as optogenetic activation of aVMHvlOXTR cells elicits time-locked social avoidance towards a benign social target whereas inactivating the cells suppresses defeat-induced social avoidance. Furthermore, OXTR in the aVMHvl is itself essential for the behavior change. Knocking out OXTR in the aVMHvl or antagonizing the receptor during defeat, but not during post-defeat social interaction, impairs defeat-induced social avoidance. aVMHvlOXTR receives its private source of oxytocin from SOROXT cells, which are highly activated by the noxious somatosensory inputs associated with defeat. Oxytocin released from SOROXT depolarizes aVMHvlOXTR cells and facilitates their synaptic potentiation, and hence, increases aVMHvlOXTR cell responses to aggressor cues. Ablating SOROXT cells impairs defeat-induced social avoidance learning whereas activating the cells promotes social avoidance after a subthreshold defeat experience. Altogether, our study reveals an essential role of SOROXT-aVMHvlOXTR circuit in defeat-induced social learning and highlights the importance of brain oxytocin system in social plasticity.

neuroscience↗

Robust identification of extrachromosomal DNA and genetic variants using multiple genetic abnormality sequencing (MGA-Seq)

Genomic abnormalities, including structural variation (SV), copy number variation (CNV), single-nucleotide polymorphism (SNP), homogenously staining regions (HSR) and extrachromosomal DNA (ecDNA), are strongly associated with cancer, rare diseases and infertility. A robust technology to simultaneously detect these genomic abnormalities is highly desired for clinical diagnosis and basic research. In this study, we developed a simple and cost-effective method - multiple genetic abnormality sequencing (MGA-Seq) - to simultaneously detect SNPs, CNVs, SVs, ecDNA and HSRs in a single tube. This method has been successfully applied in both cancer cell lines and clinical tumour samples and revealed that focal amplification in tumour tissue is substantially heterogeneous. Notably, we delineated the architecture of focal amplification and the ecDNA network by MGA-Seq, which facilitated the exploration of the regulation of gene expression in ecDNA. This method could be extensively applied for diagnosis and may greatly facilitate the investigation of the genomic mechanism for genetic diseases.

genomics↗

Whole-Brain fMRI Functional Connectivity Signatures Predict Sustained Emotional Experience in Naturalistic Contexts

Determining and decoding emotional brain processes under ecologically valid conditions remains a key challenge in affective neuroscience. The current functional magnetic resonance imaging (fMRI) based emotion decoding studies are mainly based on brief and isolated episodes of emotion induction, while sustained emotional experience in naturalistic environments that mirror daily life experiences are scarce. Here we use 10-minute movie clips as ecologically valid emotion-evoking procedures in n=52 individuals to explore emotion-specific fMRI functional connectivity (FC) profiles on the whole-brain level at high spatial resolution (400 atlas based parcels). Employing machine-learning based decoding and cross validation procedures allowed to develop predictive FC profiles that can accurately distinguish sustained happiness and sadness and that generalize across movies and subjects. Both functional brain network-based and subnetwork-based emotion prediction results suggest that emotion manifests as distributed representation of multiple networks, rather than a single functional network or subnetwork. Further, the results show that the Visual Network (VN) and Default Mode Network (DMN) associated functional networks, especially VN-DMN, exhibit a strong contribution to emotion prediction. To further estimate the cumulative effect of naturalistic long-term movie-based video-evoking emotions, we divide the 10-min episode into three stages: early stimulation (1 ~ 200 s), middle stimulation (201 ~ 400 s), and late stimulation (401 ~ 600 s) and examine the emotion prediction performance at different stimulation stages. We found that the late stimulation has a stronger predictive ability (accuracy=85.32%, F1-score=85.62%) compared to early and middle stimulation stages, implying that continuous exposure to emotional stimulation can lead to more intense emotions and further enhance emotion-specific distinguishable representations. The present work demonstrates that sustained sadness and happiness under naturalistic conditions are presented in emotion-specific network profiles and these expressions may play different roles in the generation and modulation of emotions. These findings elucidate the importance of network level adaptations for sustained emotional experiences during naturalistic contexts and open new venues for imaging network level contributions under naturalistic conditions.

neuroscience↗

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics.

We seek to transform how new and emergent variants of pandemiccausing viruses, specifically SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pretraining on over 110 million prokaryotic gene sequences and finetuning a SARS-CoV-2-specific model on 1.5 million genomes, we show that GenSLMs can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLMs represents one of the first whole genome scale foundation models which can generalize to other prediction tasks. We demonstrate scaling of GenSLMs on GPU-based supercomputers and AI-hardware accelerators utilizing 1.63 Zettaflops in training runs with a sustained performance of 121 PFLOPS in mixed precision and peak of 850 PFLOPS. We present initial scientific insights from examining GenSLMs in tracking evolutionary dynamics of SARS-CoV-2, paving the path to realizing this on large biological data.

bioinformatics↗

Mutations in DNA polymerase δ subunit 1 mediate CMD2-type resistance to Cassava Mosaic Geminiviruses

Cassava mosaic disease suppresses cassava yields across the tropics. The dominant CMD2 locus confers resistance to the cassava mosaic geminiviruses. It has been reported that CMD2-type landraces lose resistance after regeneration through de novo morphogenesis. As full genome bisulfite sequencing failed to uncover an epigenetic mechanism for loss of resistance, we performed whole genome sequencing and genetic variant analysis and fine-mapped the CMD2 locus to a 190 kilobase interval. Data suggest that CMD2-type resistance is caused by a nonsynonymous, single nucleotide polymorphism in DNA polymerase {delta} subunit 1 (MePOLD1) located within this region. Virus-induced gene silencing of MePOLD1 in a Cassava mosaic disease-susceptible cassava variety produced a recovery phenotype typical of CMD2-type resistance. Analysis of other CMD2-type cassava varieties identified additional resistance alleles within MePOLD1. MePOLD1 resistance alleles represent important genetic resources for resistance breeding or genome editing, and elucidating mechanisms of resistance to geminiviruses.

plant biology↗

Cell type-specific Expression Quantitative Trait Loci

Mapping cell type-specific gene expression quantitative trait loci (ct-eQTLs) is a powerful way to investigate the genetic basis of complex traits. A popular method for ct-eQTL mapping is to assess the interaction between the genotype of a genetic locus and the abundance of a specific cell type using a linear model. However, this approach requires transforming RNA-seq count data, which distorts the relation between gene expression and cell type proportions and results in reduced power and/or inflated type I error. To address this issue, we have developed a statistical method called CSeQTL that allows for ct-eQTL mapping using bulk RNA-seq count data while taking advantage of allele-specific expression. We validated the results of CSeQTL through simulations and real data analysis, comparing CSeQTL results to those obtained from purified bulk RNA-seq data or single cell RNA-seq data. Using our ct-eQTL findings, we were able to identify cell types relevant to 21 categories of human traits.

genomics↗

A toolkit of highly selective and sensitive genetically encoded neuropeptide sensors

Neuropeptides are key signaling molecules in the endocrine and nervous systems that regulate many critical physiological processes, including energy balance, sleep and circadian rhythms, stress, and social behaviors. Understanding the functions of neuropeptides in vivo requires the ability to monitor their dynamics with high specificity, sensitivity, and spatiotemporal resolution; however, this has been hindered by the lack of direct, sensitive and non-invasive tools. Here, we developed a series of GRAB (G protein-coupled receptor activation-based) sensors for detecting somatostatin (SST), cholecystokinin (CCK), corticotropin-releasing factor (CRF), neuropeptide Y (NPY), neurotensin (NTS), and vasoactive intestinal peptide (VIP). These fluorescent sensors utilize the corresponding GPCRs as the neuropeptide-sensing module with the insertion of a circular-permutated GFP as the optical reporter. This design detects the binding of specific neuropeptides at nanomolar concentration with a robust increase in fluorescence. We used these GRAB neuropeptide sensors to measure the spatiotemporal dynamics of endogenous SST release in isolated pancreatic islets and to detect the release of both CCK and CRF in acute brain slices. Moreover, we detect endogenous CRF release induced by stressful experiences in vivo using fiber photometry and 2-photon imaging in mice. Together, these new sensors establish a robust toolkit for studying the release, function, and regulation of neuropeptides under both physiological and pathophysiological conditions.

neuroscience↗

Engineered high-density lipoprotein particles that chaperone bioactive lipid mediators to combat endothelial dysfunction and thromboinflammation

High-density lipoprotein (HDL) particles suppress inflammation-induced tissue injury via vascular and myeloid cell-dependent mechanisms. As such, HDL-associated bioactive lipids such as sphingosine 1-phosphate (S1P) and prostacyclin (PGI2) signal via their respective G protein-coupled receptors on target cells to promote vascular endothelial function and suppress platelet and myeloid-dependent pathophysiology. Here we have constructed a fusion protein of apolipoprotein A1 (ApoA1) and apolipoprotein M (ApoM) (A1M) that forms HDL-like particles and chaperones S1P and Iloprost, stable PGI2 analog. The A1M/S1P complex activates S1P receptor-1 (S1PR1) as a Gi-biased agonist and attenuates the inflammation-induced NF{kappa}B pathway while A1M/Iloprost acts via IP receptor to inhibit platelet aggregation and promote endothelial barrier function. In addition to enhancing the endothelial barrier, A1M/S1P suppresses neutrophil influx, oxidative burst and inflammatory mediator secretion in a sterile inflammation model. We propose that A1M could be useful as a therapeutic to induce S1P and PGI2-dependent anti-inflammatory functions and suppress collateral tissue injury.

biochemistry↗

Compartmental Neuropeptide Release Measured Using a New Oxytocin Sensor

As a peptide hormone and neuromodulator, oxytocin (OT) plays a critical role in a variety of physiological and pathophysiological processes in both the central nervous system and the periphery. However, the processes that regulate spatial OT release in the brain remain enigmatic. Here, we developed a genetically encoded GPCR activation-based (GRAB) OT sensor called GRABOT1.0. Using this sensor, we directly visualized stimulation-induced OT release from specific compartments of OT neurons in acute brain slices, and discovered that N-type calcium channels predominantly mediate axonal OT release, while L-type calcium channels mediate somatodendritic OT release. In addition, we found that components in the fusion machinery of OT release differ between axon terminals versus somata and dendrites. Finally, we demonstrated the sensor responses to the activation of OT neurons in various brain regions in vivo and revealed region specific OT release during male courtship behavior. Taken together, these results provide key insights regarding the role of compartmental OT release in the control of physiological and behavioral functions.

neuroscience↗

Time-tagged ticker tapes for intracellular recordings

A core taken in a tree today can reveal climate events from centuries past. Here we adapt this idea to record histories of neural activation. We engineered slowly growing intracellular protein fibers which can incorporate diverse fluorescent marks during growth to store linear ticker tape-like histories. An embedded HaloTag reporter incorporated user-supplied HaloTag-ligand dyes, leading to colored stripes whose boundaries mapped fiber growth to wall-clock time. A co-expressed eGFP tag driven by the cFos immediate early gene promoter recorded the history of neural activity. High-resolution multispectral imaging on fixed samples read the cellular histories. We demonstrated recordings of cFos activation in ensembles of cultured neurons with a single-cell absolute accuracy of approximately 39 min over a 12-hour interval. Protein-based ticker tapes have the potential to achieve massively parallel single-cell recordings of multiple physiological modalities.

neuroscience↗

CellDepot: A unified repository for scRNA-seq data and visual exploration

CellDepot serves as an integrated web application to assist users in exploring single-cell RNA-seq (scRNA-seq) datasets and comparing the datasets among various studies through a user-friendly interface with advanced visualization and analytical tools. To begin with, it provides an efficient data management system that users can upload single cell datasets and query the database by multiple attributes such as species and cell types. In addition, the advanced query function incorporated in MySQL database system and its conditional filtering, allows users to quickly query and compare the expression of gene(s) across the datasets of interest. Moreover, by embedding the cellxgene VIP tool, CellDepot enables fast exploration of individual dataset in the manner of interactivity and scalability to gain more refined insights such as cell composition, gene expression profiles, and differentially expressed genes among cell types. In summary, the web portal allows large scale single cell data sharing, analysis and visualization for supporting decision-making, and encouraging scientists to contribute to the single-cell community in a tractable and collaborative way. Finally, CellDepot is released as open-source software to motivate crowd contribution, broad adoption, and local deployment for private data.

bioinformatics↗

OmicsView: omics data analysis through interactive visual analytics

With advances in NGS technologies, transcriptional profiling of human tissue across many diseases is becoming more routine, leading to the generation of petabytes of data deposited in public repositories. There is a need for bench scientists with little computational expertise to be able to access and mine this data to understand disease pathology, identify robust biomarkers of disease and the effect of interventions (in vivo or in vitro). To this end we release an open source analytics and visualization platform for expression data called OmicsView, http://omicsview.org. This platform comes preloaded with 1000s of samples across many disease areas and normal tissue, including the GTEx database, all processed with a harmonized pipeline. We demonstrate the power and ease-of-use of the platform by means of a Crohns disease data mining exercise where we can quickly uncover disease pathology and identify strong biomarkers of disease and response to treatment.

bioinformatics↗

Time cell encoding in deep reinforcement learning agents depends on mnemonic demands

The representation of "what happened when" is central to encoding episodic and working memories. Recently discovered hippocampal time cells are theorized to provide the neural substrate for such representations by forming distinct sequences that both encode time elapsed and sensory content. However, little work has directly addressed to what extent cognitive demands and temporal structure of experimental tasks affect the emergence and informativeness of these temporal representations. Here, we trained deep reinforcement learning (DRL) agents on a simulated trial-unique nonmatch-to-location (TUNL) task, and analyzed the activities of artificial recurrent units using neuroscience-based methods. We show that, after training, representations resembling both time cells and ramping cells (whose activity increases or decreases monotonically over time) simultaneously emerged in the same population of recurrent units. Furthermore, with simulated variations of the TUNL task that controlled for (1) memory demands during the delay period and (2) the temporal structure of the episodes, we show that memory demands are necessary for the time cells to encode information about the sensory stimuli, while the temporal structure of the task only affected the encoding of "what" and "when" by time cells minimally. Our findings help to reconcile current discrepancies regarding the involvement of time cells in memory-encoding by providing a normative framework. Our modelling results also provide concrete experimental predictions for future studies.

neuroscience↗

Dopamine release in nucleus accumbens core during social behaviors in mice

Social behaviors are among the most important and rewarding motivational behaviors. How dopamine, a "reward" signal, releases in the nucleus accumbens (NAc) during social behaviors has become a topic of interest for decades. However, limitations in early recording methods, such as microdialysis, prevented a complete understanding of moment-to-moment dopamine responses during social behaviors. Here, we employ a genetically encoded dopamine sensor, GRABDA2h, to record dopamine activity in the NAc core in mice and find acute changes in extracellular dopamine levels during all three phases of social behaviors: approach, investigation and consummation. Dopamine release during approach phase correlates with animals motivation towards the conspecific whereas its release during consummatory phase signals the valence of the experience. Furthermore, dopamine release during sexual and aggressive behaviors shows sex differences that correlate with the potential value of those experiences. Overall, our results reveal rich and temporally precise motivation and value information encoded by NAc dopamine during social behaviors and beyond.

neuroscience↗

Inferring relevant tissues and cell types for complex traits in genome-wide association studies

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results.

bioinformatics↗

Stromal HIF2 Regulates Immune Suppression in the Pancreatic Cancer Microenvironment

Background & AimsPancreatic ductal adenocarcinoma (PDAC) has a hypoxic, immunosuppressive stroma, which contributes to its resistance to immune checkpoint blockade therapies. The hypoxia-inducible factors (HIFs) mediate the cellular response to hypoxia, but their role within the PDAC tumor microenvironment remains unknown. MethodsWe used a dual recombinase mouse model to delete Hif1 or Hif2 in -smooth muscle actin (SMA)-expressing cancer-associated fibroblasts (CAFs) arising within spontaneous pancreatic tumors. The effects of CAF-Hif2 expression on tumor progression and composition of the tumor microenvironment were evaluated by Kaplan-Meier analysis, quantitative real-time polymerase chain reaction, histology, immunostaining, and by both bulk and single-cell RNA sequencing. CAF-macrophage crosstalk was modeled ex vivo using conditioned media from CAFs after treatment with hypoxia and PT2399, a HIF2 inhibitor currently in clinical trials. Syngeneic flank and orthotopic PDAC models were used to assess whether HIF2 inhibition improves response to immune checkpoint blockade. ResultsCAF-specific deletion of HIF2, but not HIF1, suppressed PDAC tumor progression and growth, and improved survival of mice by 50% (n = 21-23 mice/group, Log-rank P = 0.0009). Deletion of CAF-HIF2 modestly reduced tumor fibrosis and significantly decreased the intratumoral recruitment of immunosuppressive M2 macrophages and regulatory T cells. Treatment with the clinical HIF2 inhibitor PT2399 significantly reduced in vitro macrophage chemotaxis and M2 polarization, and improved tumor responses to immunotherapy in both syngeneic PDAC mouse models. ConclusionsTogether, these data suggest that stromal HIF2 is an essential component of PDAC pathobiology and is a druggable therapeutic target that could relieve tumor microenvironment immunosuppression and enhance immune responses in this disease.

cancer biology↗