Biology subjects

Ying, X.

Publications and source records attributed to Ying, X..

4 recordsLinked to original sources

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↗