Biology subjects

Feng, G.

Publications and source records attributed to Feng, G..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Brain-region-specific changes and dysregulation of activity regulated genes in Gria3 mutant mice, a genetic animal model of schizophrenia

Protein-truncating variants in GRIA3 (encoding the GluA3/GluR3 subunit of -amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)-type glutamate receptors) are associated with substantially increased risk of schizophrenia (SCZ). Here we characterized Gria3 mutant mice carrying a protein-truncating mutation that mimics a SCZ-associated variant. Transcriptomic analysis revealed that activity-regulated genes are downregulated in cortical regions, while immune and glia-related pathways exhibit brain-region-specific changes. The transcriptomic changes in Gria3 mutant mice are remarkably different from those in Grin2a mutant mice, particularly in the prefrontal cortex, even though both encode glutamate receptors and are associated with SCZ risk. Proteomic analysis further demonstrated that loss-of-function of Gria3 profoundly alters the protein composition of synapses. These findings in a genetic mouse model provide potential insights into the pathophysiological mechanisms underlying SCZ.

neuroscience↗

bioRxiv2024

scCompass: An integrated cross-species scRNA-seq database for AI-ready

Emerging single-cell sequencing technology has generated large amounts of data, allowing analysis of cellular dynamics and gene regulation at the single-cell resolution. Advances in artificial intelligence enhance life sciences research by delivering critical insights and optimizing data analysis processes. However, inconsistent data processing quality and standards remain to be a major challenge. Here we propose scCompass, which provides a data quality solution to build a large-scale, cross-species and model-friendly single-cell data collection. By applying standardized data pre-processing, scCompass integrates and curates transcriptomic data from 13 species and nearly 105 million single cells. Using this extensive dataset, we are able to archieve stable expression genes (SEGs) and organ-specific expression genes (OSGs) in human and mouse. We provide different scalable datasets that can be easily adapted for AI model training and the pretrained checkpoints with state-of-the-art (SOTA) single-cell foundataion models. In summary, the AI-readiness of scCompass, which combined with user-friendly data sharing, visualization and online analysis, greatly simplifies data access and exploitation for researchers in single cell biology(http://www.bdbe.cn/kun).

bioinformatics↗

bioRxiv2024

Astrocyte regional specialization is shaped by postnatal development

Astrocytes are an abundant class of glial cells with critical roles in neural circuit assembly and function. Though many studies have uncovered significant molecular distinctions between astrocytes from different brain regions, how this regionalization unfolds over development is not fully understood. We used single-nucleus RNA sequencing to characterize the molecular diversity of brain cells across six developmental stages and four brain regions in the mouse and marmoset brain. Our analysis of over 170,000 single astrocyte nuclei revealed striking regional heterogeneity among astrocytes, particularly between telencephalic and diencephalic regions, at all developmental time points surveyed in both species. At the stages sampled, most of the region patterning was private to astrocytes and not shared with neurons or other glial types. Though astrocytes were already regionally patterned in late embryonic stages, this region-specific astrocyte gene expression signature changed dramatically over postnatal development, and its composition suggests that regional astrocytes further specialize postnatally to support their local neuronal circuits. Across mouse and marmoset, we found hundreds of species differentially expressed genes, as well as divergence in the expression of astrocytic region- and age-differentially expressed genes and the timing of astrocyte maturation relative to birth between the species. Finally, we used expansion microscopy to show that astrocyte morphology is also regionally specialized across cortex, striatum, and thalamus in the mouse.

neuroscience↗

bioRxiv2024

High-throughput unsupervised quantification of patterns in the natural behavior of marmosets

Recent advances in genetic engineering have accelerated the production of nonhuman primate models for neuropsychiatric disorders. To use these models for preclinical drug testing, behavioral screening methods will be necessary to determine how the model animals deviate from controls, and whether treatments can restore typical patterns of behavior. In this study, we collected a multimodal dataset from a large cohort of marmoset monkeys and described typical patterns in their natural behavior. We found that these behavioral measurements varied substantially across days, and that behavioral state usage was highly correlated to the behavior of cagemates and to the vocalization rate of other animals in the colony. To elicit acute behavioral responses, we presented animals with a panel of stimuli including novel, appetitive, neutral, aversive, and social stimuli. By comparing these behavioral conditions, we demonstrate that outlier detection can be used to identify atypical responses to a range of stimuli. This data will help guide the study of marmosets as models for neuropsychiatric disorders.

animal behavior and cognition↗

bioRxiv2024

Cell-Graph Compass: Modeling Single Cells with Graph Structure Foundation Model

Inspired by the advancements in pre-trained Large Language Models, there has been a surge of studies in the Life Sciences focusing on constructing foundation models with large scale single-cell RNA-seq data. These studies typically involve pre-training a transformer model on large-scale single-cell sequencing data, followed by fine-tuning for a variety of downstream tasks, achieving notable performance. However, these models all share a common short-coming: to utilize the transformer architecture, originally designed for textual data, they artificially impose a sequential structure on genes within cells, simplifying the complex interactions between genes. Furthermore, they focus solely on transcriptomic data, neglecting other relevant biological information. To address these issues, here we introduce Cell-Graph Compass (CGC), the first foundational model that leverages graph structures to model single cells and describes cells from multiple perspectives, including transcriptional profiles, gene text summaries, transcription factor regulatory networks, gene co-expression patterns, and gene positional relationships. By incorporating self-attention mechanisms, we pretrained the model on 50 million human single-cell sequencing data, resulting in a robust digital representation of cells. Extensive downstream experiments demonstrate that our approach can capture meaningful biological knowledge and achieve superior results in various problem scenarios, achieving the state-of-the-art (SOTA).

bioinformatics↗

bioRxiv2024

How do Large Language Models understand Genes and Cells

Researching genes and their interactions is crucial for deciphering the fundamental laws of biological activity, advancing disease treatment, drug discovery and so on. Large language Models (LLMs), with their profound text comprehension and generation capabilities, have made significant strides across various natural science fields. However, their application in cell biology remains notably scarce. To alleviate this issue, in this paper, we selects seven mainstream LLMs and evaluates their performance across a range of problem scenarios. Our findings indicate that LLMs possess a certain level of understanding of genes and cells, and hold potential for solving real-world problems. Moreover, we have improved the current method of textual representation of cells, enhancing the LLMs ability to tackle cell annotation tasks. We encourage cell biology researchers to leverage LLMs for problem-solving while also being mindful of some challenges associated with their use. We release our code and data at https://github.com/epang-ucas/Evaluate_LLMs_to_Genes. CCS ConceptsApplied computing [->] Recognition of genes and regulatory elements; Bioinformatics; Computational genomics; Computational transcriptomics. ACM Reference FormatChen Fang, Yidong Wang, Yunze Song, Qingqing Long, Wang Lu, Linghui Chen, Pengfei Wang, Guihai Feng, Yuanchun Zhou, and Xin Li. 2024. How do Large Language Models understand Genes and Cells. 1, 1 (March 2024), 14 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

bioinformatics↗

bioRxiv2024

Conditional knockout of Shank3 in the ventral CA1 by quantitative in vivo genome-editing impairs social memory

Individuals with autism spectrum disorder (ASD) have a higher prevalence of social memory impairment. A series of our previous studies revealed that hippocampal ventral CA1 (vCA1) neurons possess social memory engram and that the neurophysiological representation of social memory in the vCA1 neurons is disrupted in ASD-associated Shank3 knockout mice. However, whether the dysfunction of Shank3 in vCA1 causes the social memory impairment observed in ASD remains unclear. In this study, we found that vCA1-specific Shank3 conditional knockout (cKO) by the adeno-associated virus (AAV)- or specialized extracellular vesicle (EV)-mediated in vivo gene editing was sufficient to recapitulate the social memory impairment in male mice. Furthermore, the utilization of EV-mediated Shank3-cKO allowed us to quantitatively examine the role of Shank3 in social memory. Our results suggested that there is a certain threshold for the proportion of Shank3-cKO neurons required for social memory disruption. Thus, our study provides insight into the population coding of social memory in vCA1, as well as the pathological mechanisms underlying social memory impairment in ASD.

neuroscience↗

bioRxiv2024

Enhancement of Mediodorsal Thalamus Rescues Aberrant Belief Dynamics in a Novel Mouse Model for Schizophrenia

Optimizing behavioral strategy requires belief updating based on new evidence, a process that engages higher cognition. In schizophrenia, aberrant belief dynamics may lead to psychosis, but the mechanisms underlying this process are unknown, in part, due to lack of appropriate animal models and behavior readouts. Here, we address this challenge by taking two synergistic approaches. First, we generate a mouse model bearing patient-derived point mutation in Grin2a (Grin2aY700X+/-), a gene that confers high-risk for schizophrenia and recently identified by large-scale exome sequencing. Second, we develop a computationally trackable foraging task, in which mice form and update belief-driven strategies in a dynamic environment. We found that Grin2aY700X+/- mice perform less optimally than their wild-type (WT) littermates, showing unstable behavioral states and a slower belief update rate. Using functional ultrasound imaging, we identified the mediodorsal (MD) thalamus as hypofunctional in Grin2aY700X+/- mice, and in vivo task recordings showed that MD neurons encoded dynamic values and behavioral states in WT mice. Optogenetic inhibition of MD neurons in WT mice phenocopied Grin2aY700X+/- mice, and enhancing MD activity rescued task deficits in Grin2aY700X+/- mice. Together, our study identifies the MD thalamus as a key node for schizophrenia-relevant cognitive dysfunction, and a potential target for future therapeutics.

neuroscience↗

bioRxiv2023

Evolutionary and Developmental Specialization of Foveal Cell Types in the Marmoset

In primates, high-acuity vision is mediated by the fovea, a small specialized central region of the retina. The fovea, unique to the anthropoid lineage among mammals, undergoes notable neuronal morphological changes during postnatal maturation. However, the extent of cellular similarity across anthropoid foveas and the molecular underpinnings of foveal maturation remain unclear. Here, we used high throughput single cell RNA sequencing to profile retinal cells of the common marmoset (Callithrix jacchus), an early divergent in anthropoid evolution from humans, apes, and macaques. We generated atlases of the marmoset fovea and peripheral retina for both neonates and adults. Our comparative analysis revealed that marmosets share almost all its foveal types with both humans and macaques, highlighting a conserved cellular structure among primate foveas. Furthermore, by tracing the developmental trajectory of cell types in the foveal and peripheral retina, we found distinct maturation paths for each. In-depth analysis of gene expression differences demonstrated that cone photoreceptors and Muller glia, among others, show the greatest molecular divergence between these two regions. Utilizing single-cell ATAC-seq and gene-regulatory network inference, we uncovered distinct transcriptional regulations differentiating foveal cones from their peripheral counterparts. Further analysis of predicted ligand-receptor interactions suggested a potential role for Muller glia in supporting the maturation of foveal cones. Together, these results provide valuable insights into foveal development, structure, and evolution. Significance statementThe sharpness of our eyesight hinges on a tiny retinal region known as the fovea. The fovea is pivotal for primate vision and is susceptible to diseases like age-related macular degeneration. We studied the fovea in the marmoset-a primate with ancient evolutionary ties. Our data illustrated the cellular and molecular composition of its fovea across different developmental ages. Our findings highlighted a profound cellular consistency among marmosets, humans, and macaques, emphasizing the value of marmosets in visual research and the study of visual diseases.

neuroscience↗

bioRxiv2023

Sibling chimerism among microglia in marmosets

Chimerism happens rarely among most mammals but is common in marmosets and tamarins, a result of fraternal twin or triplet birth patterns in which in utero connected circulatory systems (through which stem cells transit) lead to persistent blood chimerism (12-80%) throughout life. The presence of Y-chromosome DNA sequences in organs of female marmosets has long suggested that chimerism might also affect these organs. However, a longstanding question is whether this chimerism is driven by blood-derived cells or involves contributions from other cell types. To address this question, we analyzed single-cell RNA-seq data from blood, liver, kidney and many brain regions across a number of marmosets, using transcribed single nucleotide polymorphisms (SNPs) to identify cells with the siblings genome in various cell types within these tissues. Sibling-derived chimerism in all tissues arose entirely from cells of hematopoietic origin (i.e., myeloid and lymphoid lineages). In brain tissue this was reflected as sibling-derived chimerism among microglia (20-52%) and macrophages (18-64%) but not among other resident cell types (neurons, glia or ependymal cells). The percentage of microglia that were sibling-derived showed significant variation across brain regions, even within individual animals, likely reflecting distinct responses by genetic-sibling microglia to local recruitment or proliferation cues or, potentially, distinct clonal expansion histories in different brain areas. In the animals and tissues we analyzed, microglial gene expression profiles bore a much stronger relationship to local/host context than to sibling genetic differences. Naturally occurring marmoset chimerism will provide new ways to recognize the effects of genes, mutations and brain contexts on microglial biology and to distinguish between effects of microglia and other cell types on brain phenotypes.

neuroscience↗

bioRxiv2023

Transgenic Targeting of Fcrls Creates a Highly Efficient Constitutively Active Microglia Cre Line with Differentiated Specificity

Microglia carry out important functions as the resident macrophages of the brain. To study their role in health and disease, the research community needs tools to genetically modify them with maximum completeness in a manner that distinguishes them from closely related cell-types, such as monocytes. While currently available tamoxifen-inducible CreERT2 lines are able to achieve the differentiation from other cells, the field needs improved and publicly available constitutively active Cre lines, especially ones with favorable efficiency and specificity profiles for studies where high recombination efficiency is imperative and where tamoxifen administration is contraindicated. Here, we leverage the microglia-specific Fcrls gene to generate mice expressing Cre. Using genomic methods, we show correct positioning of the transgene and intact microglia homeostasis in Fcrls-2A-Cre mice. Crossing Fcrls-2A-Cre mice to four different reporters, we demonstrate highly efficient recombination in microglia across differentially sensitive loxP alleles in different genomic contexts, indicating robust applicability of the line. Further, we show that microglia recombine a loxP reporter during early embryonic development, supporting the use of the line for developmental studies. Finally, using immunofluorescence and flow cytometry, we reveal that most border associated macrophages (BAMs) are also targeted whereas only few liver and spleen macrophages and virtually no white blood cell subsets exhibit Cre activity, distinguishing this line from another publicly available Cre line, Cx3cr1-CreM (MMRRC). Fcrls-2A-Cre mice are immediately available (JAX Stock #036591) and serve as a valuable addition to the communitys microglia toolbox by providing highly efficient constitutive Cre activity with excellent specificity, particularly for studies where tamoxifen administration is undesirable. Significance StatementThe microglia toolbox is continuously growing with more transgenic lines and most recently even viral tools becoming available. When selecting a Cre driver line, investigators must weigh relative strengths and weaknesses of available lines and carefully make the best choice for their given application. These tradeoffs include (1) availability and ease of employment, (2) chromosomal positioning of Cre with respect to the floxed allele (should not be on the same chromosome for conditional knockout studies), (3) activity level of a given Cre line and thus completeness of recombination across the microglia population, (4) specificity with respect to acceptable off-target cell types and tissues, (5) temporal aspects including earliest onset of Cre expression or inducibility, (6) robustness in disease contexts, and (7) potential perturbation of microglia homeostasis through Cre itself or disruption of the targeting locus. When selecting a mouse line, it is evident that there may not be a one-size-fits all solution but an application-based preference and choice from the diverse repertoire of microglia tools. Fcrls-2A-Cre mice are an excellent addition to this toolbox.

neuroscience↗

bioRxiv2023

GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with Knowledge-Informed Cross-Species Foundation Model

Deciphering the universal gene regulatory mechanisms in diverse organisms holds great potential to advance our knowledge of fundamental life process and facilitate research on clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms, resulting in limited collection and integration of complex features on various cell types across species. Recent breakthroughs in single-cell sequencing and advancements in deep learning techniques present an unprecedented opportunity to tackle this challenge. In this study, we developed GeneCompass, the first knowledge-informed, cross-species foundation model pre-trained on an extensive dataset of over 120 million single-cell transcriptomes from human and mouse. During pre-training, GeneCompass effectively integrates four types of biological prior knowledge to enhance the understanding of gene regulatory mechanisms in a self-supervised manner. Fine-tuning towards multiple downstream tasks, GeneCompass outperforms competing state-of-the-art models in multiple tasks on single species and unlocks new realms of cross-species biological investigation. Overall, GeneCompass marks a milestone in advancing knowledge of universal gene regulatory mechanisms and accelerating the discovery of key cell fate regulators and candidate targets for drug development.

bioinformatics↗

bioRxiv2023

CellPolaris: Decoding Cell Fate through Generalization Transfer Learning of Gene Regulatory Networks

Cell fate changes are determined by gene regulatory network (GRN), a sophisticated system regulating gene expression in precise spatial and temporal patterns. However, existing methods for reconstructing GRNs suffer from inherent limitations, leading to compromised accuracy and application generalizability. In this study, we introduce CellPolaris, a computational system that leverages transfer learning algorithms to generate high-quality, cell-type-specific GRNs. Diverging from conventional GRN inference models, which heavily rely on integrating epigenomic data with transcriptomic information or adopt causal strategies through gene co-expression networks, CellPolaris employs high-confidence GRN sources for model training, relying exclusively on transcriptomic data to generate previously unknown cell-type-specific GRNs. Applications of CellPolaris demonstrate remarkable efficacy in predicting master regulatory factors and simulating in-silico perturbations of transcription factors during cell fate transition, attaining state-of-the-art performance in accurately predicting candidate key factors and outcomes in cell reprogramming and spermatogenesis with validated datasets. It is worth noting that, with a transfer learning framework, CellPolaris can perform GRN based predictions in all cell types even across species. Together, CellPolaris represents a significant advancement in deciphering the mechanisms of cell fate regulation, thereby enhancing the precision and efficiency of cell fate manipulation at high resolution.

bioinformatics↗

bioRxiv2023

Divide-and-conquer quantum algorithm for hybrid de novo genome assembly of short and long reads

Computational biology holds immense promise as a domain that can leverage quantum advantages due to its involvement in a wide range of challenging computational tasks. Researchers have recently explored the applications of quantum computing in genome assembly implementation. However, the issue of repetitive sequences remains unresolved. In this paper, we propose a hybrid assembly quantum algorithm using high-accuracy short reads and error-prone long reads to deal with sequencing errors and repetitive sequences. The proposed algorithm builds upon the variational quantum eigensolver and utilizes divide-and-conquer strategies to approximate the ground state of larger Hamiltonian while conserving quantum resources. Using simulations of 10-qubit quantum computers, we address problems as large as 140 qubits, yielding optimal assembly results. The convergence speed is significantly improved via the problem-inspired ansatz based on the known information about the assembly problem. Besides, entanglement within quantum circuits is qualitatively verified to notably accelerate the assembly path optimization.

bioinformatics↗

bioRxiv2023

Spatial and temporal pattern of structure-function coupling of human brain connectome with development

Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7 to 21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC-FC coupling. Our findings revealed that SC-FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC-FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC-FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC-FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC-FC coupling in typical development.

neuroscience↗

bioRxiv2023

Exploring multi-omics latent embedding spaces for characterizing tumor heterogeneity and tumoral fitness effects

The ecological and evolutionary perspectives of tumorigenesis can be characterized as a process of microevolution in tumor cells that altered the tumor microenvironment and further induced tumor cell proliferation, metastasis, and the death of tumor patients. Here, we introduced XgeneVAE, an interpretable unsupervised deep learning framework that quantified the semantic changes in multi-omics embedding space for characterizing the microevolution processes and fitness effects of heterogeneous tumor samples. We then validated that the scales of the latent embedding variances can reflect the differences in the overall survival of tumor patients, as well as their applications in uncovering the driving genomic alternations in different cancer types. These results confirmed that the XgeneVAE model can better represent the heterogeneity in distinct cancer types and as an interpretable model for understanding the fitness effects in tumorigenesis and their association with clinical outcomes.

bioinformatics↗

bioRxiv2023

Reproductive sexual dimorphisms in two willow species, Salix exigua Nutt. and S. nigra Marshall.

Premise of the ResearchThe prevalence of sexual dimorphisms, which evolve due to contrasting strategies to maximize reproductive success in males and females, is variable among dioecious plant species. In the Salicaceae, many traits have been assessed across many studies, but direct or indirect associations between these traits and reproductive allocation are often neglected. Given the dynamic evolution of sex determination systems and the strong interest in wood production in the family, we wondered whether sexual dimorphisms related to reproduction may have gone unreported. Here, we assess sexual dimorphism in reproductive traits in two species of Salix. Recognition of reproductive sexually dimorphic traits will contribute to understanding the evolution of sex determination systems in the Salicaceae. MethodologyWe conducted observational studies in natural populations to assess the presence of sexual dimorphisms in early spring bud density, catkin number, and flower number per catkin across four sampling periods in Salix exigua. We also analyzed flower number and catkin number per flower in Salix nigra. Pivotal ResultsWe observed no sexual dimorphism in pre-season buds per branch in S. exigua but did find that males produced more flowers per catkin and more catkins per branch than females in both S. exigua and S. nigra. ConclusionsHigher flower numbers in males compared to females is consistent with expectations from intra-sexual selection among males. The presence of reproductive sexual dimorphisms in Salix suggests that sexual selection may affect the evolution of mating strategies in Salix species, and the evolution of the sex determination system within this genus.

plant biology↗

bioRxiv2023

Expansion of Disease Specific Cardiac Macrophages in Immune Checkpoint Inhibitor Myocarditis

BackgroundImmune checkpoint inhibitors (ICIs), antibodies targeting PD-1/PD-L1 or CTLA4 have revolutionized cancer management but are associated with devastating immune-related adverse events (irAEs) including myocarditis. The main risk factor for ICI myocarditis is the use of combination PD-1 and CTLA4 inhibition. ICI-myocarditis is often fulminant and is pathologically characterized by myocardial infiltration of T lymphocytes and macrophages. While much has been learned regarding the role of T-cells in ICI-myocarditis, little is understood regarding the identity, transcriptional diversity, and functions of infiltrating macrophages. MethodsWe employed an established murine ICI myocarditis model (Ctla4+/-Pdcd1-/- mice) to explore the cardiac immune landscape using single-cell RNA-sequencing, immunostaining, flow cytometry, in situ RNA hybridization and molecular imaging and antibody neutralization studies. ResultsWe observed marked increases in CCR2+ monocyte-derived macrophages and CD8+ T-cells in this model. The macrophage compartment was heterogeneous and displayed marked enrichment in an inflammatory CCR2+ subpopulation highly expressing Cxcl9, Cxcl10, Gbp2b, and Fcgr4 that originated from CCR2+ monocytes. Importantly, a similar macrophage population expressing CXCL9, CXCL10, and CD16 (human homologue of mouse FcgR4) was found selectively expanded in patients with ICI myocarditis compared to other forms of heart failure and myocarditis. In silico prediction of cell-cell communication suggested interactions between T-cells and Cxcl9+Cxcl10+ macrophages via IFN-{gamma} and CXCR3 signaling pathways. Depleting CD8+T-cells, macrophages, and blockade of IFN-{gamma} signaling blunted the expansion of Cxcl9+Cxcl10+macrophages in the heart and attenuated myocarditis suggesting that this interaction was necessary for disease pathogenesis. ConclusionThese data demonstrate that ICI-myocarditis is associated with the expansion of a specific population of IFN-{gamma} induced inflammatory macrophages and suggest the possibility that IFN-{gamma} blockade may be considered as a treatment option for this devastating condition.

immunology↗