Biology subjects

Xu, Y.

Publications and source records attributed to Xu, Y..

At least 397 records · Page 22Linked to original sources

bioRxiv2024

Grape extract and resveratrol mitigate sleep fragmentation, Aβ accumulation, and abnormal neuronal excitability in a Drosophila model of Alzheimer's disease

Consumption of red wine and grape extracts may offer a range of health benefits, largely attributable to the grapes rich content of vitamins, fiber, and antioxidant compounds, such as polyphenols. To determine if resveratrol (RES) present in grape extracts is responsible for these benefits, we conducted a study on the effects of red grape skin extract (GSKE), seed extract (GSEE), and RES on sleep patterns, amyloid-beta (A{beta}) deposition, neuronal excitability, and lifespan in a Drosophila model expressing A{beta}42. A{beta}42 flies experienced significant sleep fragmentation at night, yet their overall sleep duration was unaffected. Dietary GSKE significantly enhanced sleep duration and mitigated sleep fragmentation in these flies, whereas GSEE only increased the duration of sleep bouts during the day. RES demonstrated a similar effect, albeit to a lesser extent compared to GSKE. All three dietary interventions led to a reduction in A{beta}42 levels and an extension of the lifespan in A{beta}42 flies, with GSEE showing the least pronounced effects. Furthermore, GSEE and RES were able to reverse the hyperexcitability of mushroom body neurons (MBNs) caused by A{beta}42 expression. These results suggest that GSKE and RES are potent promoters of sleep and have the potential to ameliorate sleep disturbances. Additionally, the study highlights that other bioactive component in GSKE, beyond RES, may contribute to its diverse pharmacological activities, which could differ from those of GSEE or RES alone. This underscores the multifaceted nature of grape extracts and their potential therapeutic applications in addressing sleep disorders and neurodegenerative conditions associated with A{beta} deposition.

neuroscience↗

bioRxiv2024

Allosteric regulation of the tyrosine phosphatase PTP1B by a protein-protein interaction

The rapid identification of protein-protein interactions has been significantly enabled by mass spectrometry (MS) proteomics-based methods, including affinity purification-MS, crosslinking-MS, and proximity-labeling proteomics. While these methods can reveal networks of interacting proteins, they cannot reveal how specific protein-protein interactions alter protein function or cell signaling. For instance, when two proteins interact, there can be emergent signaling processes driven purely by the individual activities of those proteins being co-localized. Alternatively, protein-protein interactions can allosterically regulate function, enhancing or suppressing activity in response to binding. In this work, we investigate the interaction between the tyrosine phosphatase PTP1B and the adaptor protein Grb2, which have been annotated as binding partners in a number of proteomics studies. This interaction has been postulated to co-localize PTP1B with its substrate IRS-1 by forming a ternary complex, thereby enhancing the dephosphorylation of IRS-1 to suppress insulin signaling. Here, we report that Grb2 binding to PTP1B also allosterically enhances PTP1B catalytic activity. We show that this interaction is dependent on the proline-rich region of PTP1B, which interacts with the C-terminal SH3 domain of Grb2. Using NMR spectroscopy and hydrogen-deuterium exchange mass spectrometry (HDX-MS) we show that Grb2 binding alters PTP1B structure and/or dynamics. Finally, we use MS proteomics to identify other interactors of the PTP1B proline-rich region that may also regulate PTP1B function similarly to Grb2. This work presents one of the first examples of a protein allosterically regulating the enzymatic activity of PTP1B and lays the foundation for discovering new mechanisms of PTP1B regulation in cell signaling. Significance StatementProtein-protein interactions are critical for cell signaling. The interaction between the phosphatase PTP1B and adaptor protein Grb2 co-localizes PTP1B with its substrates, thereby enhancing their dephosphorylation. We show that Grb2 binding also directly modulates PTP1B activity through an allosteric mechanism involving the proline-rich region of PTP1B. Our study reveals a novel mode of PTP1B regulation through a protein-protein interaction that is likely to be exploited by other cellular interactors of this important signaling enzyme.

biochemistry↗

bioRxiv2024

Vcam1+ Fibro-adipogenic Progenitors Mark Fatty Infiltration in Chronic Limb Threatening Ischemia

Skeletal muscle health and function is a critical determinant of clinical outcomes in patients with peripheral arterial disease (PAD). Herein, we identify fatty infiltration, the ectopic deposition of adipocytes in skeletal muscle, as a histological hallmark of end-stage PAD, also known as chronic limb threatening ischemia (CLTI). Leveraging single cell transcriptome mapping in mouse models of PAD, we identify a pro-adipogenic mesenchymal stromal cell population marked by expression of Vcam1 (termed Vcam1+ FAPs) that expands in the ischemic limb. Mechanistically, we identify Sfrp1 and Nr3c1 as regulators of Vcam1+ FAP adipogenic differentiation. Loss of Sfrp1 and Nr3c1 impair Vcam1+ FAP differentiation into adipocytes in vitro. Finally, we show that Vcam1+ FAPs are enriched in human CLTI patients. Collectively, our results identify a pro-adipogenic FAP subpopulation in CLTI patients and provide a potential therapeutic target for muscle regeneration in PAD.

genomics↗

bioRxiv2024

Reinforcement learning guides single-cell sequencing in decoding lineage and cell fate decisions

The rapid development of single-cell sequencing offers an unparalleled opportunity to delineate the heterogeneity of individual cells. However, current methods struggle to pinpoint the states of cell fate decisions. In this study, we introduce a novel approach called Single-cell Reinforcement Learning (scRL), which integrates reinforcement learning into single-cell data analysis using an actor-critic architecture. Among existing dimensionality reduction methods, we identified one with the best interpretability. Based on this latent space and combined with our scRL algorithm, we assessed the intensity of fate decisions at the single-cell level. Extensive evaluations demonstrate that scRL outperforms existing techniques, as well as their variants and alternative approaches, in assessing cell fate decisions. Moreover, scRL offers an alternative method for evaluating the intensity of cell lineage differentiation which shows competitive interpretability as well. The superiority of scRL in assessing fate decisions is confirmed across several types of single-cell datasets.

bioinformatics↗

bioRxiv2024

Synergically enhanced anti-tumor immunity of in vivo CAR by circRNA vaccine boosting

Chimeric Antigen Receptor (CAR) T cell therapy has shown promise in treating hematologic malignancies. However, it is limited to individualized cell therapy and faces challenges, including high costs, extended preparation time, and limited efficacy against solid tumors. Here, we generated circular RNAs (circRNAs) encoding Chimeric Antigen Receptor (CAR) transmembrane proteins, referred to as circRNACAR, which mediated remarkable tumor killing in both T cells and macrophages. In addition, macrophages exhibited efficient phagocytosis of tumor cells and pro-inflammatory polarization induced by circRNACAR in vitro. We demonstrated that circRNACAR, delivered with immunocyte-tropic lipid nanoparticles (LNPs), significantly inhibited tumor growth, improved survival rates and induced a pro-inflammatory tumor microenvironment in mice. Importantly, the combination of circRNAAnti-HER2-CAR and circRNA-based cancer vaccines encoding the corresponding transmembrane HER2 antigen, termed circRNAHER2, exhibited synergistically enhanced anti-tumor activity. This proof-of-concept study demonstrated that the combination of circRNA-based in vivo CAR and vaccines, termed in vivo CAR-VAC, holds the potential to become an upgraded off-the-shelf immunotherapy.

immunology↗

bioRxiv2024

Ultra-resolution Deconvolution of Spatial Transcriptomics Unveils Spatiotemporal Cellular Dynamics in Complex Microenvironments

Immune cells infiltrate tissues in response to exogenous pathogens or spontaneous tumors, generating protective immunity to safeguard tissues. Despite advancements in spatial transcriptomics (ST), the precise spatial distribution and functional specialization of immune cells within tissue microenvironments remain elusive. Here, we introduce an ultra-precision ST deconvolution algorithm (UCASpatial) that enhances the mapping of cell subpopulations to spatial locations by leveraging the contribution of genes indicative of cell identity through entropy-based weighting. Using both in silico and real ST datasets, we demonstrate that UCASpatial improves the robustness and accuracy in identifying low-abundant cell subpopulations and distinguishing transcriptionally heterogeneous cell subpopulations. Applying UCASpatial to human colorectal cancer (CRC), we link genomic alterations in individual cancer clones to multi-cellular characteristics of the tumor immune microenvironment (TIME) and reveal the co-evolution of tumor cells and TIME at a clonal resolution. We show that the copy number gain on chromosome 20q (chr20q-gain) in tumor cells orchestrates a T cell-excluded TIME, indicative of resistance to immunotherapy in CRC, and is associated with tumor-intrinsic human endogenous retrovirus subfamily H (HERV-H) silencing and impaired type I interferon response. In murine wound healing models, we illuminate the spatiotemporal dynamics of individual cell subsets across various stages of the healing process. We discovered that the scarring-healing mice (C57BL/6) exhibited a replacement of Prg4+ chondrogenic progenitors to Igfbp5+ chondrocytes in the wound bed at the regeneration stage, a change not observed in the regenerative strain (MRL/MpJ). The Igfbp5+ chondrocyte, spatially coordinating with Cd36+ Gpnmb+ Il1b- macrophage and Fmod+ fibroblast, forms a pro-fibrotic community associated with regeneration failure. The cell-cell interactions within this three-cell cluster, mediated by the IL11-IL11RA axis, drive the pro-fibrotic community formation and limit regeneration in C57BL/6 mice. Our findings present UCASpatial as a versatile tool for deciphering fine-grained cellular landscapes in ST and exploring intercellular mechanisms in complex and dynamic microenvironments.

bioinformatics↗

bioRxiv2024

A chromosome-level genome assembly of Dracaena cambodiana and comparative genomics analysis highlights the distinct biological features of two resource species of dragon's blood

This study reports a chromosome-level genome for Dracaena cambodiana, one of two typical dracaena species in China. This work will help to deepen the understanding of the dracaena species and the conservation and development of plant resources. The final assembly consisted of 54 scaffolds, spanning 1.08 Gb, with a scaffold N50 size of 52.29 Mb, encoded 36860 predicted protein-coding genes. A total of 1,064,810,157 bp of genome sequences were located on 20 chromosomes (2n = 40), accounting for 98.46%. We investigate the possible mechanisms of the longevity of dracaena, the longest-lived plant in the world, which involve DNA repair and post-translational modification. We also performed comparative genomic analysis of the previously assembled genome of Dracaena cochinchinesis with this genome, and found that the two involved interaction of plant-pathogen interaction and ubiquitin mediated proteolysis, which may reveal why Dracaena has environmental adaptability and longevity characteristics.

genomics↗

bioRxiv2024

IsRNAcirc: 3D structure prediction of circular RNAs based on coarse-grained molecular dynamics simulation

As an emerging class of RNA molecules, circular RNAs play pivotal roles in various biological processes, thereby determining their three-dimensional (3D) structure is crucial for a deep understanding of their biological significances. Similar to linear RNAs, the development of computational methods for circular RNA 3D structure prediction is challenging, especially considering the inherent flexibility and potentially long length of circular RNAs. Here, we introduce an extension of our previous IsRNA2 model, named IsRNAcirc, to enable circular RNA 3D structure predictions through coarse-grained molecular dynamics simulations. The workflow of IsRNAcirc consists of four main steps, including input preparation, end closure, structure prediction, and model refinement. Our results demonstrate that IsRNAcirc can provide reasonable 3D structure predictions for circular RNAs, which significantly reduce the locally irrational elements contained in the initial input. Moreover, for a validation test set comprising 34 circular RNAs, our IsRNAcirc can generate 3D models with better scores than the template-based 3dRNA method. These findings demonstrate that our IsRNAcirc method is a promising tool to explore the structural details along with intricate interactions of circular RNAs. Author SummaryKnowledge of the 3D structure of circular RNA molecules can help us better understand their roles in eukaryotic cells. However, experimental determination of circular RNA 3D structure remains challenging, with currently only one complete circular RNA 3D structure available in the Protein Data Bank. Thus, extending from their linear counterparts, computational methods that can predict circular RNA 3D structure are urgently needed. In particular, physics-based de novo 3D structure prediction methods are promising due to their powerful sampling capability in the conformational space, which avoid the problem of limited available templates in template-based approaches. We have developed IsRNAcirc, a new physics-based method that employs an accurate coarse-grained force field and replica-exchange molecular dynamics simulations to predict the 3D structure of circular RNAs. IsRNAcirc is shown to significantly reduce the number of locally irrational structures in the input trials, which are caused by the lack of suitable templates and the improper assembly of fragments. Furthermore, in the absence of experimental reference structures, the predicted 3D models were validated using the popular scoring functions, such as DFIRE-RNA and rsRNASP, demonstrating that IsRNAcirc outperforms the template-based 3dRNA method in generating low-energy conformations.

biophysics↗

bioRxiv2024

Evaluation of the efficacy of the protopine total alkaloids of Macleaya cordata (Willd.) R. Br. in controlling E. coli infection in broiler chickens

ObjectiveThis study was carried out to investigate the preliminary evaluation of the effectiveness of protopine total alkaloids of Macleaya cordata (Willd.) R. Br. (MPTA) extract in the control of artificially infected avian pathogenic E. coli in the peritoneal cavity of chickens. MethodsIn this test, Lingnan yellow hybrid chickens (male, 10 days old) were attacked with E. coli O78 and then treated orally with different concentrations (25 - 1600 mg/kg) of MPTA Pulvis (MPTA-P) and 0.5% Siweichuanxinlian Powder (SWCXL-P). ResultsThe results showed that different concentrations of MPTA-P and SWCXL-P were effective in reducing the mortality of E. coli and promoting the recovery of the affected organs, with the best intervention being the supplementation of 400-1600 mg/kg of MPTA-P for 7 consecutive days. It has been concluded that the addition of 400 mg/kg MPTA-P for 7 days reduces the severity and mortality and accelerates the recovery process of E. coli disease in chickens and has a protective effect against organ lesions caused by E. coli infection. LimitationsThe study lacked comparisons of carrier populations and characterization of inflammatory markers. ConclusionsMPTA may be a potential alternative drug for the prevention or treatment of avian E. coli disease.

pharmacology and toxicology↗

bioRxiv2024

IGF-1 Acts through Kiss1-expressing Cells to Influence Metabolism and Reproduction

ObjectiveKisspeptin, encoded by the Kiss1 gene, ties puberty and fertility to energy status; however, the metabolic factors that control Kiss1-expressing cells need to be clarified. MethodsTo evaluate the impact of IGF-1 on the metabolic and reproductive functions of kisspeptin producing cells, we created mice with IGF-1 receptor deletion driven by the Kiss1 promoter (IGF1RKiss1 mice). Previous studies have shown IGF-1 and insulin can bind to each others receptor, permitting IGF-1 signaling in the absence of IGF1R. Therefore, we also generated mice with simultaneous deletion of the IGF1R and insulin receptor (IR) in Kiss1-expressing cells (IGF1R/IRKiss1 mice). ResultsLoss of IGF1R in Kiss1 cells caused stunted body length. In addition, female IGF1RKiss1 mice displayed lower body weight and food intake plus higher energy expenditure and physical activity. This phenotype was linked to higher proopiomelanocortin (POMC) expression and heightened brown adipose tissue (BAT) thermogenesis. Male IGF1RKiss1 mice had mild changes in metabolic functions. Moreover, IGF1RKiss1 mice of both sexes experienced delayed puberty. Notably, male IGF1RKiss1 mice had impaired adulthood fertility accompanied by lower gonadotropin and testosterone levels. Thus, IGF1R in Kiss1-expressing cells impacts metabolism and reproduction in a sex-specific manner. IGF1R/IRKiss1 mice had higher fat mass and glucose intolerance, suggesting IGF1R and IR in Kiss1-expressing cells together regulate body composition and glucose homeostasis. ConclusionsOverall, our study shows that IGF1R and IR in Kiss1 have cooperative roles in body length, metabolism, and reproduction.

neuroscience↗

bioRxiv2024

Atf4 protects islet beta-cell identity and function under acute glucose-induced stress but promotes beta-cell failure in the presence of free fatty acid

Glucolipotoxicity, caused by combined hyperglycemia and hyperlipidemia, results in {beta}-cell failure and type 2 diabetes (T2D) via cellular stress-related mechanisms. Activating transcription factor 4 (Atf4) is an essential effector of stress response. We show here that Atf4 expression in {beta}-cells is dispensable for glucose homeostasis in young mice, but it is required for {beta}-cell function during aging and under obesity-related metabolic stress. Henceforth, aged Atf4-deficient {beta}-cells display compromised secretory function under acute hyperglycemia. In contrast, they are resistant to acute free fatty acid-induced loss-of identity and dysfunction. At molecular level, Atf4-deficient {beta}-cells down-regulate genes involved in protein translation, reducing {beta}-cell identity gene products under high glucose. They also upregulate several genes involved in lipid metabolism or signaling, likely contributing to their resistance to free fatty acid-induced dysfunction. These results suggest that Atf4 activation is required for {beta}-cell identity and function under high glucose, but this paradoxically induces {beta}-cell failure in the presence of high levels of free fatty acids. Different branches of Atf4 activity could be manipulated for protecting {beta}-cells from metabolic stress-induced failure. HighlightsO_LIAtf4 is dispensable in {beta}-cells in young mice C_LIO_LIAtf4 protects {beta}-cells under high glucose C_LIO_LIAtf4 exacerbate fatty acid-induced {beta}-cell defects C_LIO_LIAtf4 activates translation but depresses lipid-metabolism C_LI

genetics↗

bioRxiv2024

Single-cell RNA Sequencing Reveals Methamphetamine Inhibits the Liver Immune Response with Involvement of the Dopamine D1 Receptor

Methamphetamine (METH) is a highly addictive psychostimulant that causes physical and psychological damage and immune system disorder, especially in the liver, which contains a significant number of immune cells. Dopamine, which is a key neurotransmitter in METH addiction and immune regulation, plays a crucial role in this process. In this study, we developed a chronic METH administration model and conducted single-cell RNA sequencing to investigate the effect of METH on liver immune cells and the involvement of the dopamine receptor D1 (DRD1) in this process. Our findings revealed that chronic exposure to METH induced an immune cell shift from Ifitm3+Mac and Ccl5+Mac to Cd14+Mac, and from Fyn+CD4+Teff, CD8+T, and NKT to Fos+CD4+T and Rora+ILC2, along with suppression of multiple immune functional pathways. DRD1 was implicated in the regulation of some of these pathways and the shifts of hepatic immune cells. This research provides valuable insights into the development of therapies aimed at mitigating METH-induced immune impairment.

immunology↗

bioRxiv2024

Deep learning of protein energy landscape and conformational dynamics from experimental structures in PDB

Protein structure prediction has reached revolutionary levels of accuracy on single structures, implying biophysical energy function can be learned from known protein structures. However apart from single static structure, conformational distributions and dynamics often control protein biological functions. In this work, we tested a hypothesis that protein energy landscape and conformational dynamics can be learned from experimental structures in PDB and coevolution data. Towards this goal, we develop DeepConformer, a diffusion generative model for sampling protein conformation distributions from a given amino acid sequence. Despite the lack of molecular dynamics (MD) simulation data in training process, DeepConformer captured conformational flexibility and dynamics (RMSF and covariance matrix correlation) similar to MD simulation and reproduced experimentally observed conformational variations. Our study demonstrated that DeepConformer learned energy landscape can be used to efficiently explore protein conformational distribution and dynamics.

biophysics↗

bioRxiv2024

Marseilleviruses biological properties and viral translation-associated proteins based on in silico tertiary structure

Marseilleviruses are a group of double-strand DNA viruses that infect amoeba within the Nucleocytoviricota phylum and are ubiquitous in water and soil globally. Here, we report four novel strains isolated from mangroves in Guangdong province, China, namely, futianmevirus, futianmfvirus, dashavirus str. E, and xiwanvirus. Viral particles presented about 220[~]240 nm icosahedrally shaped capsids and were wrapped by membranes to form giant vesicles. Based on stability assays, viral particles were halotolerant and acid-tolerant, but sensitive to chloroform and high temperature, while giant vesicles conferred thermal and acid/alkaline resistance to particles. Genomics and phylogenetic analyses showed that the four strains formed divergent branches within different lineages of marseillevirus. Notably, to our knowledge, futianmevirus was the first reported marseillevirus lacking translation elongation factor EF-1alpha (EF1A). Our in silico analysis of marseillevirus coded translation-associated homolgs suggested their conserved functions. Additionally, we predicted at least four novel proteins that were structurally similar to components of the protozoan ribosome. Overall, not only our data comprehensively described the diversity of marseillevirus biological properties, but also proposed a new perspective on the giant virus translation system. ImportanceThe family Marseilleviridae was the second reported family of giant viruses and distributed globally. In this work, we reported the four novel marseilleviruses isolated from saltwater samples of mangrove. Difference of biological properties between giant vesicles and viral particles revealed the environment fitness of marseillevirus. On the other hand, sensitivity to chloroform indicated the importance of lipid components for viral infection. Additionally, our comparative genomics, phylogenetic analysis, and protein structure comparison revealed the diverse translation-associated gene sets of marseilleviruses. The prediction of ribosome components expands the knowledge about the giant viral translation-associated proteins, and will be helpful in future to reveal how giant viruses hijack the amoeba translation system.

microbiology↗

bioRxiv2024

Deep generative models generate mRNA sequences with enhanced translation capacity and stability

Despite the tremendous success of messenger RNA (mRNA) COVID-19 vaccines, the extension of this modality to a broader spectrum of diseases necessitates substantial enhancements, particularly in the design of mRNAs with elevated expression levels and extended durability. Here we present GEMORNA, a deep generative model designed to generate novel mRNA coding sequences (CDSs) and untranslated regions (UTRs) with superior translation capacity, comparable to the sophisticated task of language translation and free-form poetry composition with accurate grammar and semantics. Our AI model was trained on an extensive collection of RNA sequences from diverse families, further enhanced with labeled data to refine its performance. Remarkably, we demonstrate that our AI-generated mRNAs exhibited 8.2-fold and 15.9-fold increases in firefly luciferase expression compared to benchmark mRNAs in two different cell types. Additionally, Our AI- designed COVID-19 mRNA vaccine elicited a 4-fold increase in anti-COVID antibody titer in mice relative to BNT162b2. Furthermore, GEMORNAs versatility extends to circular mRNA design, which we facilitated a 27-fold increase in human erythropoietin protein expression in vivo than a systematically optimized benchmark sequence. We also created circular mRNAs with substantial improvements in expression levels, durability and anti-tumor cell cytotoxicity in mRNA-transduced CAR-T cells compared with an experimentally validated benchmark. In summary, GEMORNA generates novel mRNA sequences with significant performance improvements and has the potential to enable a wide range of therapeutic and vaccine applications.

synthetic biology↗

bioRxiv2024

The cell cycle oscillator and spindle length set the speed of chromosome separation in Drosophila embryos

Anaphase is tightly controlled in space and time to ensure proper separation of chromosomes. The mitotic spindle, the self-organized microtubule structure driving chromosome segregation, scales in size with the available cytoplasm. Yet, the relationship between spindle size and chromosome movement remains poorly understood. Here, we address how the movement of chromosomes changes during the cleavage divisions of the Drosophila blastoderm. We show that the speed of chromosome separation gradually decreases during the 4 nuclear divisions of the blastoderm. This reduction in speed is accompanied by a similar reduction in the length of the spindle, thus ensuring that these two quantities are tightly linked. Using a combination of genetic and quantitative imaging approaches, we find that two processes contribute to controlling the speed at which chromosomes move at mitotic exit: the activity of molecular motors important for microtubule depolymerization and sliding, and the cell cycle oscillator. Specifically, we found that the levels of Klp10A, Klp67A, and Klp59C, three kinesin-like proteins important for microtubule depolymerization, and the level of microtubule sliding motor Klp61F (kinesin-5) contribute to setting the speed of chromosome separation. This observation is supported by quantification of microtubule dynamics indicating that poleward flux rate scales with the length of the spindle. Perturbations of the cell cycle oscillator using heterozygous mutants of mitotic kinases and phosphatases revealed that the duration of anaphase increases during the blastoderm cycles and is the major regulator of chromosome velocity. Thus, our work suggests a potential link between the biochemical rate of mitotic exit and the forces exerted by the spindle. Collectively, we propose that the cell cycle oscillator and spindle length set the speed of chromosome separation in anaphase.

cell biology↗

bioRxiv2024

Learning context-aware, distributed gene representations in spatial transcriptomics with SpaCEX

Distributed gene representations are pivotal in data-driven genomic research, offering a structured way to understand the complexities of genomic data and providing foundation for various data analysis tasks. Current gene representation learning methods demand costly pretraining on heterogeneous transcriptomic corpora, making them less approachable and prone to over-generalization. For spatial transcriptomics (ST), there is a plethora of methods for learning spot embeddings but serious lacking method for generating gene embeddings from spatial gene profiles. In response, we present SpaCEX, a pioneer cost-effective self-supervised learning model that generates gene embeddings from ST data through exploiting spatial genomic "context" identified as spatially co-expressed gene groups. SpaCEX-generated gene embeddings (SGE) feature in context-awareness, rich semantics, and robustness to cross-sample technical artifacts. Extensive real data analyses reveal biological relevance of SpaCEX-identified genomic contexts and validate functional and relational semantics of SGEs. We further develop a suite of SGE-based computational methods for a range of key downstream objectives: identifying disease-associated genes and gene-gene interactions, pinpointing genes with designated spatial expression patterns, enhancing transcriptomic coverage of FISH-based ST, detecting spatially variable genes, and improving spatial clustering. Extensive real data results demonstrate these methods superior performance, thereby affirming the potential of SGEs in facilitating various analytical task. Significance StatementSpatial transcriptomics enables the identification of spatial gene relationships within tissues, providing semantically rich genomic "contexts" for understanding functional interconnections among genes. SpaCEX marks the first endeavor to effectively harnesses these contexts to yield biologically relevant distributed gene representations. These representations serve as a powerful tool to greatly facilitate the exploration of the genetic mechanisms behind phenotypes and diseases, as exemplified by their utility in key downstream analytical tasks in biomedical research, including identifying disease-associated genes and gene interactions, in silico expanding the transcriptomic coverage of low-throughput, high-resolution ST technologies, pinpointing diverse spatial gene expression patterns (co-expression, spatially variable pattern, and patterns with specific expression levels across tissue domains), and enhancing tissue domain discovery.

bioinformatics↗

bioRxiv2024

Transcranial Brain Atlas Based on Photon Measurement Density Function in a Triple-Parameter Standard Channel Space

Functional near-infrared spectroscopy (fNIRS) is a widely used transcranial brain imaging technique in neuroscience research. Nevertheless, its lack of anatomical information poses challenges for designing appropriate optode montage and localizing fNIRS signals to underlying anatomical regions. The photon measurement density function (PMDF) is often employed to address these issues, as it accurately measures the sensitivity of a fNIRS signal channel to perturbations of absorption coefficients at any brain locations. However, existing PMDF-based localization methods have two limitations: a limited channel space, and PMDF estimation based on single standard head models, which differ anatomically from individual subjects. To overcome these limitations, this study proposes a continuous fNIRS standard channel space and constructs a PMDF-based transcranial brain atlas (PMDF-TBA) by calculating PMDFs based on MRI images of 48 adults. PMDF-TBA contains group-averaged sensitivities of channels to gray matter and brain regions of atlases, such as Brodmann, AAL2, and LPBA40. Through leave-one-out cross-validation, we evaluated the prediction ability of PMDF-TBA for sensitivity of unseen individuals and found that it outperformed PMDFs based on single standard head models. Thus, PMDF-TBA serves as a more generalizable fNIRS spatial localization tool. Therefore, PMDF-TBA can be utilized to optimize optode montage design, improve channel sensitivity to target brain regions, and assist in the source localization of fNIRS data, thereby promoting the application of fNIRS in neuroscience research.

neuroscience↗