Zhiliang Bai

This study enhances CAR-T cell migration through synthetic cytokine receptors, reducing metastasis in TNBC mouse models.

StarTrak infers cellular migration and phylogeny by integrating somatic variants, spatial data, and transcriptomics at single-pixel resolution in spatial genomics.

REDCAT maps single-cell type-specific metabolic activities in tissues using multimodal all-optical imaging, revealing metabolic specialization and reprogramming in immune and liver tissues.

The paper uses single-cell foundation models to predict durable CAR T response from pre-infusion scRNA-seq data, achieving high accuracy without requiring perfect cell annotation.

The paper identifies yeast-derived lipids that extend lifespan in Drosophila by remodeling metabolism in the gut and brain, enhancing tissue homeostasis and communication.

This study maps age-related cellular senescence in human lymph nodes, identifying senescence markers and spatial shifts in germinal center B cells.

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

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.

The paper introduces SuMMIT-SRS, a multiplexed metabolic imaging technique, and demonstrates its application in various cell types to study metabolic rewiring.

This study evaluates SARS-CoV-2 vaccine responses in HSCT recipients, finding memory T cell defects linked to reduced T cell responses despite humoral responses.

ASTRO is an automated pipeline for spatial transcriptomics analysis, optimized for FFPE samples and capable of detecting coding and non-coding RNAs.

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.

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.

This paper presents spatial epigenome profiling using CUT&Tag and microfluidic barcoding to map histone modifications at tissue scale and single-cell level in mouse embryos and olfactory bulbs.

DBiT-seq enables high-spatial-resolution multi-omics mapping of mouse embryos by deterministic barcoding, revealing tissue types and fine features like retinal pigmented epithelium and microvasculature.

The paper was withdrawn due to a duplicate posting and directs readers to the current version on bioRxiv.