Qi Zou

This paper introduces SIM-fdFLIM, a super-resolution FLIM method that preserves ratio accuracy and enables high-speed, high-resolution live-cell lifetime imaging.

This study benchmarks preprocessing workflows for spatial transcriptomics data across multiple platforms and identifies platform-specific recommendations to improve downstream analysis.

GeneSPOT is a gene-centric framework that characterizes spatial gene relationships and their reorganization across biological conditions and time.

The paper introduces MAPS, a platform for unifying physical and molecular coordinates across modalities in spatial biology, demonstrating its effectiveness on diverse datasets and applications.

This paper introduces Histo3D-MO, a framework for reconstructing 3D spatial multi-omics maps at single-cell resolution, revealing spatial patterns in tumor microenvironments.

NicheTrans translates single-omics data into spatial multi-omics insights by integrating spatial and multi-modal information, outperforming existing methods in disease and tissue analysis.

This paper introduces SpaWeaver, a framework for robust spatial multi-omics integration using weak anchors, showing improved performance in handling real-world challenges and enabling new biological insights.

The paper presents a verified spatial transcriptomics atlas built from public data, showing that increasing study diversity improves model transfer and reproducibility.

SpatialZ generates dense 3D cell atlases from planar spatial transcriptomics data by reconstructing virtual slices, validated with real 3D data and enabling new spatial analyses.

This paper introduces SpatialEx and SpatialEx+ frameworks to integrate spatial multi-omics data using histology as an anchor, achieving high-parameter co-profiling across tissue sections.

stMMR identifies spatial domains from multi-modal spatial transcriptomics data using geometric deep learning, showing improved performance in domain identification and gene discovery across biological contexts.