Sisi Chen

This study generates human hindbrain organoids with segment-specific 5-HT neurons to model serotonergic development and motor modulation, revealing DISC1 deficiency disrupts serotonergic development and motor function.

The paper decodes BMP/TGFβ signaling from single-cell transcriptomes by identifying a low-dimensional transcriptional code and training a model to infer ligand concentrations.

The paper identifies a noise-driven differentiation switch mechanism that controls cell type diversity in neural progenitor cells through combinatorial signaling inputs.

The paper addresses missing gene expression data in scRNA-seq by improving read mapping, revealing more genes and biological insights in mouse and human datasets.

This study uses human pluripotent stem cells to create an in vitro model of early human embryogenesis, recapitulating key developmental events from embryonic day 3 to day 9/10.

ActiveSVM identifies minimal gene sets for accurate cell-type and disease state classification in single-cell mRNA-seq data, achieving 90% accuracy across diverse datasets.

PopAlign identifies subpopulations and tracks gene expression changes in heterogeneous cell mixtures across drug and disease conditions.

The paper shows that signaling environments can control the diversity of neural progenitor cell populations in culture using single-cell RNA-seq and computational analysis.

The paper introduces a multiplexing method for single-cell RNA-seq that enables cost-effective analysis of large-scale perturbation experiments, revealing cell population and transcriptional changes not detectable by bulk methods.