Welcome! My name is Haikuo Li. I am a Postdoctoral Research Associate at the Rong Fan Laboratory at Yale University.
I got my Ph.D. in Biology & Biomedical Sciences at Washington University in St. Louis (Program in Molecular Genetics and Genomics). At WashU, I worked in the Humphreys Lab studying mechanisms of kidney injury and fibrosis with single-cell technologies. See Thesis Work to learn more about my graduate studies.

Research Interests:
Single-cell genomics; spatial multiomics; systems biology; tissue fibrosis & regeneration; metabolisms; translational medicine; cancer

Publications:
https://www.ncbi.nlm.nih.gov/myncbi/haikuo.li.1/bibliography/public/

Research Skills:
WET LAB:
→ Spatial genomics library generation including spatial-RNA-seq (DBiT-seq), spatial-ATAC-seq, spatial-CUT&Tag and spatial co-profiling
→ single-cell and single-nucleus library generation from diverse technologies, including 10X Genomics, sci-RNA-seq, SHARE-seq and INTACT, as well as multimodal profiling including RNA-seq, ATAC-seq, Hi-C, CUT&RUN, CARLIN and CITE-seq
→ Molecular biology technologies such as cloning, vector construction, qPCR, immunohistology, and in-situ hybridization
→ Tissue culture including primary cell isolation, immunocytochemistry and Seahorse metabolic measurement
→ Animal work such as mouse kidney disease surgery (UUO/IRI) and tumor implantation
→ Clinical sample management and processing such as human kidney dissection
→ Protein chemistry including mass spectrometry sample preparation & recombinant protein preparation and protein liquid chromatography
DRY LAB:
→ Python, R, Shell, Jupyter, PyMOL
→ Single-cell sequencing data preprocessing and analysis including UMAP visualization, data integration, sample demultiplexing, cell trajectory interference, fate mapping, gene activity prediction and multimodal analysis at the million-cell level
→ Spatial transcriptomics, epigenomics and metabolomics data preprocessing and analysis
→ Analysis of bulk RNA-seq, proteomics and metabolomics data
→ Extensive experience in data mining and discovering biological insights

Last update: 12/2024