Jinmiao Chen

This paper identifies an IFNγ-CXCL9-CXCR3 axis linking T cell recruitment to skeletal muscle remodelling in human physical frailty, showing its association with fibrosis and potential therapeutic targets.

This study identifies lipid-laden macrophages as an immunosuppressive niche in PCNSL, validated through spatial transcriptomics and mouse models, and shows SYK inhibition reverses their tumor-supportive activity.

This paper introduces NTF, a deep learning framework for high-resolution 3D reconstruction of spatial omics data, achieving fast and scalable results across multiple species and datasets.

SpaRank is a transferable spatial deconvolution framework that uses rank-based encodings to robustly infer cell-type compositions across diverse biological contexts and omics modalities.

SpaMosaic integrates spatial multi-omics data using contrastive learning and graph neural networks, improving spatial domain identification and missing modality imputation across diverse datasets.

The paper develops a bioengineered vascularized pancreatic cancer model and uses multi-omics and functional assays to reveal an immunosuppressive and therapy-resistant tumor niche.

This study shows that reducing Complex III in macrophages during viral infection dampens inflammation, improving recovery from H1N1 influenza.

The paper constructs a large-scale heart atlas and identifies cellular changes and communication patterns in aging and disease using a new computational method.

This study shows that co-culturing iPSC-derived macrophages with hepatocytes enhances macrophage maturation and hepatocyte function through direct cell-cell contact.

This paper introduces SpaFoundation, a histology foundation model that predicts spatial gene expression from tissue images, showing strong performance in prediction and transferability.

StereoMM integrates spatial transcriptomic data and histological images to improve spatial domain recognition and tumor heterogeneity analysis in cancer tissues.

ACE provides a contrastive learning framework for mosaic integration of single-cell multi-omics data, improving representation of cellular heterogeneities in datasets with incomplete modalities.

Spatial tri-omic sequencing (DBiT ARP-seq and DBiT CTRP-seq) and CODEX imaging map chromatin, RNA, and protein dynamics across mammalian brain development and lysolecithin-induced neuroinflammation.

This study characterizes the tumor microenvironment of steatotic liver-related hepatocellular carcinoma using single-cell and spatial transcriptomic analyses, identifying immunosuppressive features and Treg-CAF interactions linked to immunotherapy resistance.

FAST improves spatial dimension reduction for multi-section SRT data by modeling count data and spatial smoothness, showing better performance and speed on real datasets.

SpatialGlue integrates spatial multi-omics data using a graph neural network to improve spatial domain detection and cross-modality correlation analysis.

GraphST improves spatial transcriptomics analysis by integrating clustering, integration, and deconvolution through graph self-supervised contrastive learning, achieving higher accuracy than existing methods.

FastIntegration integrates large-scale single-cell RNA-seq data efficiently and improves downstream analysis by providing batch-corrected gene expression values.

SEDR couples a deep autoencoder over expression with a variational graph autoencoder over position, improving clustering in dorsolateral prefrontal cortex and splitting a visually uniform breast tumour region.

The paper compares UMAP and t-SNE for analyzing multiplex-immunofluorescence single-cell data from HCC tissues, finding UMAP faster and better at highlighting biological lineages like hybrid tumor cells.

This study identifies nine distinct microglial states in mice across lifespan and after injury using single-cell RNA sequencing, revealing persistent and age-related changes.

cycleX infers multi-dimensional pseudotimes to reveal relationships between cell cycle and differentiation in dendritic cell progenitors.