Zihan Wu

This study shows how a viral tRNA-like structure recruits host ribosomes for poly(A)-independent translation by binding to ribosomal subunits and substituting for poly(A) tails.

This study uses spatial transcriptomics and snRNA-seq to identify 25 forebrain regions and characterize dynamic changes in the human fetal cortex and striatum during late first and early second trimesters.

This study combines spatial transcriptomics and snRNA-seq to map cell type organization and functional modules in the mouse amygdala, revealing developmental origins and spatial patterns.

Spatial-DC improves spatial proteomics deconvolution by using deep learning to infer cell-type composition and reconstruct proteomic profiles from low-resolution data.

H2O predicts spatial transcriptomics and proteomics from H&E images using a contrastive learning framework, outperforming existing models and recovering biologically relevant cell-cell communication.

This study creates a single-cell spatial transcriptomic atlas of the human cortex, revealing gene expression patterns, aging effects, and disease associations across layers and species.

The paper presents a proteomics and glycoproteomics dataset of 15 human body fluids and provides protocols for their analysis, with results available in an online repository.

The paper introduces tFold-TCR, a model for predicting TCR-pMHC structures, achieving higher accuracy and speed than existing methods and creating a large database of these structures.

The paper creates a high-resolution spatial transcriptomic atlas of the mouse brain using snRNA-seq and Stereo-seq, identifying new cell clusters, region-specific genes, and transcription factor regulons.

Spatial-ID improves cell type annotation in spatial transcriptomics by combining transfer learning and spatial embedding, outperforming existing methods on public datasets.