Longqi Liu

This study creates a spatiotemporal atlas to understand mesenchymal-epithelial stem cell dynamics in human hair follicle aging and androgenetic alopecia, identifying PTN as a key regenerative factor.

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.

OrionGeno annotates over 5,300 eukaryotic genomes using a phylogeny-aware deep learning model, outperforming existing methods in gene and protein structure prediction.

This study reconstructs the evolutionary assembly of a conserved cortical axis across vertebrates using spatial transcriptomics and single-nucleus RNA-seq, revealing conserved molecular programs and convergent evolution in brain circuitry.

OneGenome-Rice (OGR) is a genomic foundation model for rice pre-trained on 422 genomes, outperforming existing models in 11 tasks and enabling downstream applications like trait analysis and gene expression prediction.

This study uses single-cell multi-omics to analyze immune reconstitution failure in HIV patients, identifying genetic and epigenetic factors and developing a predictive model.

The paper maps cis-regulatory elements in the macaque cortex using snATAC-seq and links them to disease-related genes and risk SNPs.

This study reconstructs 3D spatial transcriptomic models of planarians to map cell type distribution and positional gene regulation, identifying candidate genes involved in regeneration.

The paper introduces CIMA, a multi-omics atlas of human immune cells, identifying regulatory mechanisms and genetic associations across sex, age, and genetic factors.

This study constructs a 4D spatiotemporal transcriptomic map of planarian regeneration using Stereo-seq, identifying dynamic gradients and an injury-induced regenerative zone.

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 uses single-cell multiomics to study gene regulatory programs in mouse embryonic skin, identifying cis-regulatory elements and transcription factors involved in development.

This study creates spatiotemporal transcriptomic maps of mouse brain after ICH to understand cellular responses and identify therapeutic targets.

The paper creates a single-cell atlas of amniote brains to study cell type diversification and identifies species-specific gene expression patterns and novel cell subtypes.

The paper constructs a multimodal brain cell atlas of adult macaques across their lifespan, revealing aging-related molecular changes and identifying aging hotspots and regulatory loci.

This study uses single-cell spatial transcriptomics to identify molecular and cellular changes in hippocampal sclerosis associated with MTLE, revealing vulnerable subregions and novel pathological features.

This study introduces Stereo-XCR-seq, a method for in situ single-cell T and B cell repertoire sequencing, revealing spatial heterogeneity in lymphoid aggregates in immune disorders.

This study uses multi-omics data to analyze immune and metabolic changes in mountain climbers during acclimatization and extreme altitude exposure.

The paper uses single-cell long-read transcriptome sequencing to study transcript diversity and alternative splicing in zebrafish embryos, identifying cell populations with distinct splicing regulations during early development.

This paper introduces an improved nanopore sequencing platform called CycloneSEQ, demonstrating long-duration sequencing and high yield with applications in genomics.

The paper fills a gap in 3D spatial transcriptomics by providing data from E9.5 and E11.5 mouse embryos, enabling detailed analysis of organogenesis with a new open-access database.

The paper constructs a 3D spatiotemporal multi-omics atlas of Drosophila development, revealing gene regulatory networks and cell type dynamics across embryogenesis to metamorphosis.

STOmicsDB provides a database for spatial transcriptomics data with tools for dataset visualization, publication search, and data archiving.

GraphST improves spatial transcriptomics analysis by integrating clustering, integration, and deconvolution through graph self-supervised contrastive learning, achieving higher accuracy than existing methods.

Spateo provides a framework for spatiotemporal modeling of single-cell spatial transcriptomics data to study cell-cell interactions and organ development in 3D space.

This study maps gene expression and developmental trajectories in zebrafish embryos using Stereo-seq and scRNA-seq, identifying spatial gene modules and signaling interactions.

CDCP is a platform for sharing and analyzing single-cell datasets, offering visualization and analysis tools for researchers.

The paper uses Stereo-seq to map single-cell transcriptomes of axolotl telencephalon during development and regeneration, identifying a key radial glial cell subtype involved in early wound healing.

This study uses Stereo-seq to generate high-resolution spatiotemporal transcriptomic maps of Drosophila embryos and larvae, revealing new subregions and gene regulatory networks.