Jian Shu

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.

The paper investigates the cell and molecular origins of the primate ovarian reserve using rhesus macaques, identifying PG2 cells as the primary source of primordial follicles and describing the recruitment of fetal theca cells for hormone production.

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.

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.

ClusterMap is a framework for analyzing spatial gene expression data, clustering RNAs into subcellular structures and tissue regions across multiple dimensions.

Across 1,176,683 airway and lung cells from 22 datasets, ACE2 rises with age, in males and in smokers' multiciliated cells, and is lowest in young children.

Optimal transport converts a 65,781-cell reprogramming time course into probabilistic fates and trajectories, exposing alternative cell fates and a gene, Obox6, that improves reprogramming efficiency.