Projects
Single Cell Base Editor
Cancer driver mutations, specifically substitutions that lead to an amino acid change, are critical for the progression and maintenance of nearly all malignancies. However, the effect of substitution mutations is mostly inferred computationally rather than functionally tested. In this project we want to develop a high-throughput single cell approach to systematically investigate the functional effects of reported cancers substitutions. This approach enables parallel, highly scalable testing of many cancer mutations across multiple genes in a single experiment. It uses CRISPR base editors to introduce specific cancer mutations into the genome, identifies the newly introduced mutation genotype among individual cells and determines each mutation’s transcriptional phenotype per a given cell. Specifically, long-read targeted sequencing is applied to single cell cDNAs. Single cell long read sequencing identifies the presence of an engineered mutation in each cDNA assigned to an individual cell. To determine phenotype of the mutation, we integrate the short-read transcriptome profile from the same single cells. This integrative approach enables single-cell direct genotyping of the introduced genetic variant and matching phenotype from the same cell.
Mutation genotyping & CAR-T
Somatic mutations in hematopoietic stem and progenitor cells can expand into distinct clones, producing genotypically distinct populations of blood and immune cells. Tracking these mutations during and after CAR T-cell therapy may contextualize treatment related toxicities such as cytokine release syndrome and prolonged cytopenias. To study this we use long-read single-cell RNA sequencing to profile mutations and gene expression at a per-cell resolution. Single-cell RNA sequencing, however, can only detect expressed mutations, leaving much of the genome unresolved. Single-cell DNA sequencing allows for the genotyping of clonal mutations. By integrating single-cell DNA and RNA sequencing we aim to link genotypes to immune cell states and track clone-specific dynamics across treatment and recovery.
GIM Multiomics
Gastric intestinal metaplasia (GIM) is a well-established precursor lesion of gastric cancer and represents a critical pathological state for investigating the early molecular events that drive gastric carcinogenesis. To delineate the genetic and epigenetic features associated with GIM, we established and performed single-cell experimental approaches with computational analyses. In particular, we have established and applied single-cell multi-omic technologies that enable the simultaneous interrogation of multiple molecular modalities within the same cell.
Through this platform, we generate integrated single-cell datasets encompassing gene expression, chromatin accessibility, and isoform-level transcriptomic information. Furthermore, by leveraging advanced bioinformatic pipelines, we infer additional molecular features, including copy number variation, somatic nucleotide alterations, and transcription factor binding activity. By systematically integrating these interconnected genetic and epigenetic layers, we aim to elucidate the molecular mechanisms underlying the emergence of GIM and its progression toward gastric malignancy. Ultimately, this work seeks to provide mechanistic insights that may inform future strategies for gastric cancer prevention, early detection, and therapeutic intervention.
AI Model for Single-Cell Spatial Features from Histopathology Images
Bridging the gap between high-cost spatial transcriptomics and routine histopathology, a new transformer-based AI model predicts single-cell cell types directly from standard H&E cancer slides. Trained and validated on over 40 million single cells, this scalable framework enables automated mapping of tumor-infiltrating lymphocytes and key tumor microenvironment dynamics.
Cellular reprogramming and intercellular interactions in cancer
Tumors are composed not only of cancer cells, but also diverse stromal and immune cells that together form the tumor microenvironment. Through dynamic interactions between these cellular populations, tumors establish spatially organized ecosystems that influence cancer initiation, progression, metastasis, and therapeutic response. Our research focuses on the regulatory mechanisms underlying the cellular reprogramming and multicellular interactions within these ecosystems. Using single-cell and spatial omics together with patient-derived experimental models, we integrate computational and translational approaches to define mechanisms driving cancer progression and identify new opportunities for therapeutic intervention.
Integrating Quantitative Genomics and Spatial Architecture of Tumor-Infiltrating Lymphocytes
TILs are crucial in the tumor microenvironment, influencing survival and immunotherapy response, but their large-scale analysis is challenging. We developed a deep learning framework using molecular label transferring to automate TIL identification from histopathology images. Our project aims to quantify TILs, correlate them with tumor mutation burden, and assess their link to immune checkpoint blockade responses, enabling scalable and integrative cancer analysis.
Identifying Methylation Signatures of Cell free DNA for Early Detection
Distinct cell-free DNA (cfDNA) methylation and fragmentation patterns are identified in cerebrospinal fluid from NSCLC patients with brain metastases. Using nanopore sequencing, researchers found unique 5mC and 5hmC signatures, suggesting potential biomarkers for early detection.
Cell free DNA and RNA sequencing for early cancer detection
An integrated early-detection model combining cell-free RNA and cell-free DNA features extracted from a single patient blood draw. Distinct molecular signatures were identified across colorectal cancer and pancreatic neuroendocrine tumor cohorts compared to healthy controls.
Gastric Cancer Registry
In 2011, Gastric Cancer Foundation launched the first Gastric Cancer Registry, a secure HIPAA-compliant database at Stanford University. Registry researchers in the Ji Research Group gather comprehensive data about people with stomach cancer, including lifestyle, health and family histories, environmental factors as well as physical samples of the tumors themselves.
In order to conduct breakthrough research, we encourage all people with gastric cancer (or their families) to sign up with the registry. It’s an easy process that can make a world of difference.
Immunogenic neo-antigen discovery for personalized immuno therapy
Recent advances in cancer immunotherapy and genomic sequencing technologies have created promising opportunities for precision cancer medicine. Somatic coding mutation in cancer genomes may lead to amino acid alterations that generate immunogenic peptides, called neoantigens.
Intratumor Heterogeneity
Genome perspective: Several cancer types consist of multiple genetically distinct subpopulations. The underlying mechanism for this intra-tumor heterogeneity can be explained by the clonal evolution model, whereby growth advantageous mutations cause the expansion of cancer cell subclones.
Short Tandem Repeat Sequencing (STR-Seq)
Microsatellites, also referred to as short tandem repeats (STRs) are multiallelic in terms of germline variation and have higher mutation rate than single nucleotide polymorphisms (SNPs). Because of the highly polymorphic nature, microsatellites are the most popular and versatile genetic marker with many applications including forensics DNA fingerprinting and population genetics.
Linked Read Sequencing
Developing tools to extend the utility of the novel linked read sequencing technology for more applications.
Single-molecule resolution mutation detection using single-color droplet digital PCR from un-amplified circulating tumor DNA
Our novel single color digital droplet PCR (ddPCR) molecular assay can detect and quantifying cancer mutations directly from poor quality and limited quantity DNA samples such as un-amplified circulating tumor DNA (ctDNA) collected from the plasma of cancer patients.
Identifying targets and resistance mechanisms resulting from intra-tumor heterogeneity
Tumor cells are surrounded by a unique microenvironment. This heterogeneity enables tumors to grow and promotes resistance to treatment but can also serve as a source of novel targets.
