Zhisong He

This study identifies how SALL1 and neural microenvironmental context shape the identity of stem cell-derived human microglia-like cells in 2D and 3D cultures.

The paper uses a multiplexed single-cell transcriptomic screen to study how morphogen cues affect regional specification in human neural organoids, finding that timing, concentration, and combination of morphogens strongly influence cell type and regional composition.

DISCO-seq enables 3D single-cell transcriptomics of whole tissues and organisms by combining tissue-clearing chemistry with imaging and transcriptomic profiling.

This paper introduces a scalable platform to generate regionally specified neural organoids from uniform single-lumen neuroepithelium, improving reproducibility and fidelity to natural brain development.

The paper programs human neuron subtypes using combinatorial patterning and scRNA-seq to identify diverse neuron types and their developmental signaling networks.

The paper uses live imaging and single-cell transcriptomics to study how extracellular matrix influences brain organoid development and regionalization.

This study maps the cell state transitions during human blood vessel organoid development using single-cell transcriptomics and identifies key regulators and disease associations.

The paper creates a multi-omic atlas of human neural organoids modeling the posterior brain and identifies morphogen conditions that expand organoid models.

This study identifies mesenchymal progenitors in mouse limb buds that regulate digit numbers and identities through BMP signaling and GREM1-mediated antagonism.

This paper integrates 36 single-cell transcriptomics datasets from human neural organoids into a unified atlas to assess cell type coverage and compare organoid protocols with primary brain tissue.

This paper creates a human endoderm organoid cell atlas by integrating single-cell transcriptomes from multiple organoid types to compare cell states with primary tissues and disease models.

The paper characterizes RNA content in individual phase-separated coacervate microdroplets using high-throughput sequencing and identifies sequence motifs associated with RNA accumulation.

This paper develops a multimodal approach to study human retinal organoid development, integrating spatial, transcriptomic, and chromatin accessibility data to create a virtual atlas of retinal development.

The paper uses single-cell genomic data from human cerebral organoids to infer gene regulatory networks and assess transcription factor roles in brain development.

This study uses single-cell transcriptomics to analyze liver regeneration after portal vein embolization, identifying changes in cell states and immune interactions.

This study uses single-cell transcriptomics to show that Ngn2 overexpression in human iPSCs generates heterogeneous neurons, including peripheral nervous system-like populations, and that Ngn2 dosage affects neuron fate.

This paper introduces a method to integrate single-cell genomics data across experiments and conditions while preserving biological information, showing it outperforms existing methods in certain scenarios.

The paper uses human intestinal organoids to create a single-cell transcriptome atlas, revealing molecular mechanisms of cell fate specification in the intestine.

The paper uses lineage recording techniques to study brain regionalization in cerebral organoids, finding that iPSC clones accumulate in distinct brain regions and lineage restriction increases over time.

This paper introduces VoxHunt, a computational method that maps single-cell genomic data from brain organoids to spatial reference data to assess patterning and cell composition.

This study uses single-cell genomics to compare brain development in humans, chimpanzees, and macaques, identifying human-specific gene regulatory features.

The paper identifies 33 functional modules associated with human neuron maturation and develops a model to quantify neuron maturity states across species.

The paper quantifies how cell type composition and independent gene expression changes contribute to age-related transcriptome changes in human brains.

The paper evaluates gene expression variation in the human placenta, finding 33.2% within-individual, 58.9% among-individual, and 7.8% among-human-group variation, with selection pressures influencing most genes.