Shuozhen Bao

Decoding Spatial Programs in Human Glioblastoma Through Surprisal Information-Theoretical Analysis

This paper uses surprisal information-theoretic analysis on spatial transcriptomic data from human glioblastoma to uncover hidden spatial programs and interactions not detected by standard methods.

Single-cell spatial multi-omics molecular pathology enabled by SuperFocus

SuperFocus integrates histopathology with single-cell spatial multi-omics data from spot-based measurements, improving accuracy by 28-73% over existing methods.

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus

Thermostable RNaseH release of barcoded cDNA keeps sections intact for CODEX imaging, yielding same-section whole-transcriptome and protein maps plus CODEX-guided spot deconvolution in FFPE tissues.

Spatial epigenomic niches underlie glioblastoma cell state plasticity

This study identifies two epigenetically-defined spatial niches in glioblastoma that influence cell state plasticity through local and long-range molecular signals.

Spatially Resolved in vivo CRISPR Screen Sequencing via Perturb-DBiT

Perturb-DBiT co-sequences spatial transcriptomes and gRNAs on one tissue section, supporting genome-wide in vivo CRISPR screens in FFPE and fresh-frozen tumors across three cancer models.

Spatially Exploring RNA Biology in Archival Formalin-Fixed Paraffin-Embedded Tissues

This paper introduces Patho-DBiT, a method for spatial transcriptomics in FFPE tissues, enabling RNA biology analysis including splicing, variants, and regulatory networks in clinical samples.