Biology subjects

Chao, B.

Publications and source records attributed to Chao, B..

4 recordsLinked to original sources

bioRxiv2023

stGCL: A versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics

Advances in spatial transcriptomics (ST) technologies have provided unprecedented opportunities to depict transcriptomic and histological landscapes in the spatial context. Multi-modal ST data provide abundant and comprehensive information about cellular status, function, and organization. However, in dealing with the processing and analysis of spatial transcriptomics data, existing algorithms struggle to effectively fuse the multi-modal information contained within ST data. Here, we propose a graph contrastive learning-based cross-modality fusion model named stGCL for accurate and robust integrating gene expression, spatial information as well as histological profiles simultaneously. stGCL adopts a novel histology-based Vision Transformer (H-ViT) method to effectively encode histological features and combines multi-modal graph attention auto-encoder (GATE) with contrastive learning to fuse cross-modality features. In addition, stGCL introduces a pioneering spatial coordinate correcting and registering strategy for tissue slices integration, which can reduce batch effects and identify cross-sectional domains precisely. Compared with state-of-the-art methods on spatial transcriptomics data across platforms and resolutions, stGCL achieves a superior clustering performance and is more robust in unraveling spatial patterns of biological significance. Additionally, stGCL successfully reconstructed three-dimensional (3D) brain tissue structures by integrating vertical and horizontal slices respectively. Application of stGCL in human bronchiolar adenoma (BA) data reveals intratumor spatial heterogeneity and identifies candidate gene biomarkers. In summary, stGCL enables the fusion of various spatial modality data and is a powerful tool for analytical tasks such as spatial domain identification and multi-slice integration.

bioinformatics↗

bioRxiv2023

CG-SLENP: A chemical genetics strategy to selectively label existing proteins and newly synthesized proteins

Protein synthesis and subsequent delivery to the target locations in cells are essential for their proper functions. Methods to label and distinguish newly synthesized proteins from existing ones are critical to assess their differential properties, but such methods are lacking. We describe the first chemical genetics-based approach for selective labeling of existing and newly synthesized proteins that we termed as CG-SLENP. Using HaloTag in-frame fusion with lamin A (LA), we demonstrate that the two pools of proteins can be selectively labeled using CG-SLENP in living cells. We further employ our recently developed selective small molecule ligand LBL1 for LA to probe the potential differences between newly synthesized and existing LA. Our results show that LBL1 can differentially modulate these two pools of LA. These results indicate that the assembly states of newly synthesized LA are distinct from existing LA in living cells. The CG-SLENP method is potentially generalizable to study any cellular proteins. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/565346v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@d18268org.highwire.dtl.DTLVardef@c8b9ddorg.highwire.dtl.DTLVardef@226bf5org.highwire.dtl.DTLVardef@1dda80c_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2023

scTIE: data integration and inference of gene regulation using single-cell temporal multimodal data

Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mecha-nisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal datasets, we demonstrate scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome dataset we generated from differentiating mouse embryonic stem cells over time, we demonstrate scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

bioinformatics↗

bioRxiv2021

Human immunodeficiency reveals GIMAP5 as lymphocyte-specific regulator of senescence

Elucidating the molecular basis of immunodeficiency diseases is a powerful approach to discovering new immunoregulatory pathways in humans. Here we report 10 affected individuals from 4 families with a new immunodeficiency disease comprising of severe progressive lymphopenia, autoimmunity, immunodeficiency, and liver disease due to recessive loss of function variants in "GTPase of immunity-associated proteins" protein 5 (GIMAP5). We show that the disease involves the progressive loss of naive T lymphocytes and a corresponding increase in antigen-experienced, but poorly functional and replicatively senescent T cells. In vivo treatment of Gimap5-deficient mice with rapamycin (an inhibitor of mTORC1) significantly restores the fraction of naive T lymphocytes. Furthermore, a GIMAP5-deficient human patient who was treated with rapamycin (sirolimus) showed a remarkable reduction in spleen/lymph node size. Together, these observations reveal that GIMAP5 plays a critical role in lymphocyte metabolism which is essential for senescence prevention and immune competence, suggesting that an inhibitor of mTORC1 could be a valuable clinical intervention in treating patients deficient for GIMAP5.

immunology↗