Ke Zhang

VIP-OT traces drug response heterogeneity in single cells using vibrational imaging and optimal transport to predict post-treatment metabolic states from pre-treatment spectra.

RamanOmics integrates snRNA-seq, spatial transcriptomics, and Raman imaging to map spatial biochemical and molecular changes in aging and repair, identifying senescent cell signatures and their functional roles.

Bering improves cell segmentation and annotation in spatial transcriptomics by using graph embeddings and transfer learning, achieving better accuracy across different technologies and tissues.

SCHAF generates spatially resolved single-cell transcriptomes from H&E histology images using vision transformers and adversarial learning, showing good agreement with ground-truth data.

The paper identifies and characterizes a reference iPSC line, KOLF2.1J, for standardized use in collaborative studies across multiple differentiation protocols and assays.

Raman2RNA predicts single-cell RNA expression profiles from live-cell Raman microscopy images using machine learning, achieving high accuracy in tracking cell state transitions during reprogramming.

This study identifies L1 and B1 retrotransposons as key elements shaping the 3D chromatin structure through spatial compartmentalization in human and mouse cells.