Zhenqin Wu

Generalizable virtual staining from histopathology to spatial proteomics across biological scales

This study introduces QUEST, a model that uses H&E histology to predict spatial protein expression, enabling virtual staining and analysis across biological scales.

Modeling patient tissues at molecular resolution with Eva

Eva is a foundation model for tissue imaging data that learns multi-scale spatial representations and performs various tasks like cross-modal inference and patient stratification.

HOPE: Interpretable Histology Analysis with Spatial Omics-Derived Signatures for Precision Oncology

HOPE learns tumor microenvironment signatures from paired H&E and spatial omics data to improve prognostic stratification using H&E images alone.

Mapping single-cell developmental potential in health and disease with interpretable deep learning

The paper introduces CytoTRACE 2, a deep learning framework to assess cell potency from scRNA-seq data, showing improved performance across multiple datasets and applications.

Guess till correct: Gungnir codec enabling high error-tolerance and low-redundancy DNA storage through substantial computing power

Gungnir is a DNA storage codec that corrects errors through hash matching and biochemical constraints, achieving full data recovery from 20% erroneous bases without redundancy or high-coverage sequencing.

ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images

ROSIE generates multiplex immunofluorescence staining from H&E images by imputing protein expression and localization using deep learning.

OmicsNavigator: An auditable scientific partner for scalable hypothesis validation in spatial omics

OmicsNavigator translates spatial omics data into testable biological findings using a large language model for hypothesis validation and annotation.

Spatial multi-omics and deep learning reveal fingerprints of immunotherapy response and resistance in hepatocellular carcinoma

This study uses spatial multi-omics and deep learning to identify immunotherapy response and resistance signatures in hepatocellular carcinoma.

Learning single-cell spatial context through integrated spatial multiomics with CORAL

The paper introduces CORAL, a deep generative model for spatial multiomics data that deconvolves low-resolution data into single-cell profiles and identifies spatial domains and cell-cell interactions.

Spatial proteomics of human diabetic kidney disease, from health to class III

The paper uses spatial proteomics to analyze protein expression changes in human diabetic kidney disease across different stages, revealing compartment-specific cellular and inflammatory changes.

PEPSI: Polarity measurements from spatial proteomics imaging suggest immune cell engagement

The paper introduces a metric to measure surface protein polarity from immunofluorescence imaging and shows it identifies immune cell states linked to tumor engagement, improving survival prediction models.

A Multi-Granularity Approach to Similarity Search in Multiplexed Immunofluorescence Images

The paper introduces MIISS, a framework for similarity search in multiplexed immunofluorescence images, showing domain-specific models outperform others.

Characterizing tissue structures from spatial omics with spatial cellular graph partition

This work introduces SCGP, a method for unsupervised identification of tissue structures from spatial omics data, and demonstrates its application to spatial transcriptomics and proteomics datasets.

7-UP: generating in silico CODEX from a small set of immunofluorescence markers

7-UP generates in silico 40-plex CODEX data from a 7-plex mIF panel using cellular morphology, enabling cell type classification and survival prediction across diverse samples.

SPACE-GM: geometric deep learning of disease-associated microenvironments from multiplex spatial protein profiles.

SPACE-GM identifies spatial motifs in cancer samples linked to recurrence and immunotherapy outcomes using geometric deep learning.

DynaMorph: self-supervised learning of morphodynamic states of live cells

DynaMorph uses self-supervised learning to analyze morphodynamic states of live human microglia, identifying distinct states and transitions in response to disease-relevant stimuli.

Systematic characterization of genome editing in primary T cells reveals proximal genomic insertions and enables machine learning prediction of CRISPR-Cas9 DNA repair outcomes

This study systematically characterizes genome editing in primary T cells, identifies proximal genomic insertions, and develops a machine learning model to predict CRISPR-Cas9 DNA repair outcomes.