Qian Zhu

This study identifies stemness features in luminal hormone-responsive cells within DCIS that correlate with invasive progression and shows that targeting FOXA1 reduces tumor growth.

This study identifies OXA1L-dependent mitochondrial translation as a targetable dependency in chemo-refractory TNBC, showing that inhibiting it enhances chemotherapy sensitivity.

CancerSTFormer analyzes spatial transcriptomes to study cancer niches and therapy responses at multiple scales.

This paper identifies AADAT as a metabolic immune checkpoint in TNBC that promotes immune evasion by regulating malate and CoQ10, and shows that malate supplementation can reverse immunotherapy resistance.

The paper shows that combining anti-CSF-1R therapy with chemotherapy and anti-PD-1 improves immune response in metastatic TNBC by reducing macrophages and increasing T/B cell infiltration.

This study uses spatial omics to identify distinct tumor microenvironment profiles in Black and White American TNBC patients, linking them to different immunosuppressive mechanisms and clinical outcomes.

Xenomake processes and sorts xenograft reads from spatial transcriptomic experiments, improving data quality and compatibility with downstream analysis.

The paper shows Matrin3 stabilizes chromatin accessibility and loop-domain interactions, and facilitates YY1-mediated enhancer-promoter interactions during differentiation.

The paper identifies a modular core regulatory circuitry in AML that enforces oncogene expression in KMT2A-rearranged leukemias.

Giotto is a toolbox for analyzing and visualizing spatial transcriptomic and proteomic data, offering integration with single-cell RNA-seq and supporting diverse datasets.

CUT&RUNTools processes CUT&RUN data to identify protein binding and genomic footprints through read analysis and visualization.

The paper shows that enhancer-dependence of gene expression increases with developmental age in mouse and human cells.

The paper identifies 202 variable gene regulatory networks and predicts essential regulators for over 800 cell types in the mouse using the mouse cell atlas data.

The paper integrates single-cell transcriptomic and spatial data to analyze cell-type and spatial domain contributions to heterogeneity in the mouse visual cortex.