Biology subjects

Ying, X.

Publications and source records attributed to Ying, X..

8 recordsLinked to original sources

bioRxiv2026

CynoBrain: A unified high-resolution framework for cross-modal data integration for macaque brain mapping

The cynomolgus macaque is a widely used primate model in neuroscience, yet existing brain atlas resources remain limited in resolution and lack a standardized framework for integrating structural, molecular, and connectivity data within a common reference space. Here we present CynoBrain, a high-resolution multi-modal brain atlas constructed from population-averaged MRI data at 9.4 Tesla, yielding a 75 micron isotropic template with significant improvement in resolution. A co-registered whole-brain autofluorescence template resolves cytoarchitectural boundaries not visible in MRI template. Three major parcellation schemes, D99 143-area, M132 105-area, and SARM are incorporated in CynoBrain space. Spatial transcriptomic based laminar definition, reconstructed from 2D sections into volumetric common space, provides a six-layer cortical segmentation spanning the whole cortex. As a demonstration of the integrative capability of this framework, 2D retrograde tracing data is reconstructed and mapped to the Cynobrain space, enabling volumetric compilation of 2D macaque brain mapping data across subjects. An online interactive platform was constructed to allow full access to Cynobrain, establishing it as a scalable, open platform for cross-modal data integration for primate neuroscience. Overall, CynoBrain is a unified framework for cross-modal data integration for macaque brain mapping with high-resolution, multiple atlases and transcriptomic based laminar definition.

neuroscience↗

bioRxiv2025

Angel Wing: a class of alternative splicing regulators in animals

Alternative splicing (AS) regulates the diversity and level of the proteome. The specificity in AS is in turn regulated by RNA binding proteins, but our understanding of how they act is far from complete. Here we identify the Angel wing (Anw) protein as a novel AS regulator. Loss of Anw in Drosophila disrupts splicing in muscle genes and subsequently muscle function. Based on a mini-gene assay in which Anw and its RNA targets are co-expressed in cultured cells, we demonstrated orthologous splicing regulation of the minigene transcripts, interaction between Anw and its RNA targets, and a remarkable functional conservation among Anw homologs. Anw forms nuclear foci, and genetic ablation of Anw domains suggests that maintaining distinctive features of these foci is important for its function. The evolution of Anw is dynamic with gene gains and losses, but preserves a cross-phyla "ultra conserved element" as an alternative exon that potentially regulates its own level by non-sense mediated mRNA decay. As the human anw homolog is a candidate gene for myasthenia gravis, our work suggests a mechanism for cellular dysfunction in this disease.

genetics↗

bioRxiv2024

Continual integration of single-cell multimodal data with MIRACLE

Single-cell sequencing technologies have revolutionized our understanding of cellular heterogeneity and facilitated the construction of multi-omics cell atlases via data integration. However, updating these atlases with new data conventionally requires reintegration of all data and is computationally intensive, hindering timely updates and dynamic adjustments in biological and medical research. To address this challenge, we present Multimodal Integration with Continual Learning (MIRACLE), a novel online learning framework for the adaptive and efficient integration of single-cell multimodal data. MIRACLE employs dynamic architectures and data rehearsal strategies to support continual learning, allowing diverse data to be integrated while minimizing information loss over time. Our evaluations demonstrate that MIRACLE achieves accurate online integration with reduced computational requirements, effectively updating and expanding atlases with new cross-tissue and cross-modal data, and precisely identifying novel cell types and transferring labels across datasets. MIRACLE provides an efficient and flexible tool for single-cell community to integrate, share and explore biological knowledge from single-cell multimodal data.

bioinformatics↗

bioRxiv2024

Regulation of the aurantio-obtusin accumulation by StTCP4.1-mediated StDA1-StHDR1 module in Senna tora seeds

Senna tora (S. tora) is a commonly used Chinese medicinal plant due to the presence of the bioactive compounds anthraquinones in its mature seeds. Seed size is an important factor that affect S. tora yield quantity and quality. However, the mechanism regulating seed size and aurantio-obtusin biosynthesis in S.tora is still unclear. In this study, we identified the ubiquitin receptor StDA1 in S.tora that served as a negative regulator to seed formation and limited seed enlargement. Antisense overexpression of StDA1 led to larger seeds in S. tora and promoted the accumulation of aurantio-obtusin. In contrast, overexpression of StDA1 in S.tora resulted in a relative decrease in aurantio-obtusin accumulation. Moreover, StDA1 can directly bind to StHDR1and regulate its degradation through the 26S proteasome to regulate seed size and aurantio-obtusin accumulation. We also found that the StDA1-StHDR1 module is responsive to the MeJA via StTCP4.1, which in turn affects the accumulation of aurantio-obtusin. Overall, we have identified a protein complex that regulates the accumulation of aurantio-obtusin, StTCP4.1-StDA1-StHDR1, as a potential target for improving S.tora yield quantity and quality.

molecular biology↗

bioRxiv2023

CD8 T-cell dysfunction is linked with CAR T-cell failure and can be mitigated by a non-alpha IL-2 agonist, pegenzileukin

Chimeric antigen receptor (CAR) T-cell therapy has been a breakthrough for relapsed or refractory large B-cell lymphoma (rrLBCL). However, suboptimal CAR T-cell activity can lead to therapeutic failure and dismal outcome. Using single cell RNA-sequencing of rrLBCL tumors, we identify a prominent population of clonally expanded dysfunctional CAR+ CD8 T-cells indicative of ongoing tumor cell engagement, proliferation, and dysfunction at the time of progression from CAR T-cell therapy. Furthermore, we show that rrLBCL patient-derived CAR T-cells are more prone to dysfunction and loss of cytotoxicity compared to healthy donor-derived CAR T-cells. Using both antigen-driven and CAR-driven models of T-cell dysfunction, we show that pegenzileukin, a non-alpha IL2 agonist, can prevent T-cell dysfunction. In both in vitro and in vivo CAR T-cell models, pegenzileukin improved T-cell expansion and tumor control. This provides pre-clinical rational for use of pegenzileukin in combatting T-cell dysfunction, a central mechanism of CAR T-cell failure. HIGHLIGHTSO_LITumor-infiltrating CD8 CAR T-cells show clonal expansion and dysfunction at the time of progression. C_LIO_LIrrLBCL patient-derived CAR T-cells are more prone to dysfunction compared to healthy-donor-derived CAR T-cells. C_LIO_LIPegenzileukin, a non-alpha IL2 agonist, rescues antigen- and CAR-driven CD8 T-cell dysfunction and improves CAR T-cell responses in vivo. C_LI

immunology↗

bioRxiv2022

MIDAS: a deep generative model for mosaic integration and knowledge transfer of single-cell multimodal data

AO_SCPLOWBSTRACTC_SCPLOWRapidly developing single-cell multi-omics sequencing technologies generate increasingly large bodies of multimodal data. Integrating multimodal data from different sequencing technologies, i.e. mosaic data, permits larger-scale investigation with more modalities and can help to better reveal cellular heterogeneity. However, mosaic integration involves major challenges, particularly regarding modality alignment and batch effect removal. Here we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation, and batch correction of mosaic data by employing self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to other methods and reliability by evaluating its performance in full trimodal integration and various mosaic tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells (PBMCs), and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis, and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS.

bioinformatics↗

bioRxiv2022

Ecological Dynamics Imposes Fundamental Challenges in Microbial Source Tracking

Quantifying the contributions of possible environmental sources ("sources") to a specific microbial community ("sink") is a classical problem in microbiology known as microbial source tracking (MST). Solving the MST problem will not only help us understand how microbial communities were formed, but also have far-reaching applications in pollution control, public health, and forensics. Numerous computational methods, referred to as MST solvers hereafter, have been developed in the past and applied to various real datasets to demonstrate their utility across different contexts. Yet, those MST solvers do not consider microbial interactions and priority effects in microbial communities. Here, we revisit the performance of several representative MST solvers. We show compelling evidence that solving the MST problem using existing MST solvers is impractical when ecological dynamics plays a role in community assembly. In particular, we clearly demonstrate that the presence of either microbial interactions or priority effects will render the MST problem mathematically unsolvable for any MST solver. We further analyze data from fecal microbiota transplantation studies, finding that the state-of-the-art MST solvers fail to identify donors for most of the recipients. Finally, we perform community coalescence experiments to demonstrate that the state-of-the-art MST solvers fail to identify the sources for most of the sinks. Our findings suggest that ecological dynamics imposes fundamental challenges in solving the MST problem using computational approaches.

microbiology↗

bioRxiv2021

MetaLogo: a generator and aligner for multiple sequence logos

Sequence logos are used to visually display conservations and variations in short sequences. They can indicate the fixed patterns or conserved motifs in a batch of DNA or protein sequences. However, most of the popular sequence logo generators are based on the assumption that all the input sequences are from the same homologous group, which will lead to an overlook of the heterogeneity among the sequences during the sequence logo making process. Heterogeneous groups of sequences may represent clades of different evolutionary origins, or genes families with different functions. Therefore, it is essential to divide the sequences into different phylogenetic or functional groups to reveal their specific sequence motifs and conservation patterns. To solve these problems, we developed MetaLogo, which can automatically cluster the input sequences after multiple sequence alignment and phylogenetic tree construction, and then output sequence logos for multiple groups and aligned them in one figure. User-defined grouping is also supported by MetaLogo to allow users to investigate functional motifs in a more delicate and dynamic perspective. MetaLogo can highlight both the homologous and nonhomologous sites among sequences. MetaLogo can also be used to annotate the evolutionary positions and gene functions of unknown sequences, together with their local sequence characteristics. We provide users a public MetaLogo web server (http://metalogo.omicsnet.org), a standalone Python package (https://github.com/labomics/MetaLogo), and also a built-in web server available for local deployment. Using MetaLogo, users can draw informative, customized and publishable sequence logos without any programming experience to present and investigate new knowledge on specific sequence sets.

bioinformatics↗