Biology subjects

Ji, H. P.

Publications and source records attributed to Ji, H. P..

At least 19 recordsLinked to original sources

bioRxiv2026

Aneuploidy profiling among different types of brain metastases reveals a colon cancer-specific chromosomal signature

Brain metastasis is a significant cause of patient morbidity and mortality. There is increasing evidence that genomic instability features such as aneuploidy are associated with metastases to the brain and other organs. We analyzed the genomic instability of brain metastases among different cancer types. This included brain metastases from non-small cell lung, breast, melanoma and colorectal cancer. Our study identified a significantly higher degree of genomic instability in colorectal brain metastases compared to the other types of brain metastases. Nex, we examined the brain metastases for the types of aneuploidy events, namely chromosomal arm imbalances that involved gains or losses. Colorectal cancer brain metastases had specific chromosomal arm imbalance profiles that included gains of 13q and 20q which were significantly more frequent compared to the other cancer types. We examined other colorectal cancer metastatic sites in the lung, liver, lymph nodes and the primary colon. Examining colorectal cancer metastases more closely, aneuploidy gains across either arms for chromosomes 7 and 16 were also higher among brain metastases compared to liver, lung and lymph node metastases. Overall, this study shows that colorectal cancer brain metastasis has specific aneuploidy profiles different than other cancer types as well as being different among other metastatic organ sites.

cancer biology↗

bioRxiv2026

Basal gland localization and focal distribution of OLFM4-expressing cells in increasing severity of gastric intestinal metaplasia

Patients with gastric intestinal metaplasia (GIM), a precancerous lesion, are at high risk for progressing to gastric cancer. Identifying these patients is critical to enable gastric cancer interception. Current approaches rely primarily on histologic evaluation of GIM severity and extent, which may be improved by incorporating molecular features that distinguish high-risk lesions. Our prior single-cell and spatial transcriptomics study identified differentially expressed genes associated with the highest-risk category of GIM. They included ANPEP expressed in enterocytes and CPS1 and OLFM4 expressed in intestinal stem-like or progenitor cells. We evaluated the protein expression and localization of these three markers to understand the cellular features associated with GIM risk and their spatial distribution within metaplastic tissues. Using multiplex immunofluorescence, whole slide image analysis and confocal microscopy, we examined protein expression from 100 tissue biopsies annotated for metaplasia severity using the Operative Link on Gastric Intestinal Metaplasia Assessment (OLGIM) system. Tissue samples included control gastric tissue, GIM, dysplasia and adenocarcinoma. Quantitative whole slide image analysis demonstrated that CPS1 expression had a modest association with disease severity. Although ANPEP was strongly associated with GIM severity, it was also frequently expressed in stromal regions outside epithelial glands. In contrast, OLFM4 expression was largely restricted to epithelial glands and showed a strong association with increased OLGIM severity. These OLFM4-positive epithelial cells were present in discrete glandular foci that expanded with increasing severity of metaplasia. Within individual metaplastic glands, OLFM4 expression was highest at the gland base with decreased expression toward the gland surface. Overall, these findings identified OLFM4 as a protein marker associated with high-risk GIM. The spatial organization of OLFM4-expressing cells at the base of metaplastic glands and their focal expansion within tissues suggest the presence of a stem cell-like epithelial compartment that may contribute to the progression of GIM towards gastric cancer.

cancer biology↗

bioRxiv2025

Nanopore-based cell-free DNA fragmentation and methylation profiles from the cerebral spinal fluid of patients with lung cancer brain metastases

BackgroundNon-small cell lung cancer (NSCLC) patients with brain metastases (BMET) have a poor prognosis. Cerebrospinal fluid (CSF) is a source of cell free DNA (cfDNA) from the brain and its methylation and fragmentation properties may be an indicator of NSCLC-BMET. MethodsWe applied a nanopore single-molecule sequencing approach to characterize the fragmentation, methylation and hydroxymethylation patterns present in CSF-derived cfDNA from NSCLC-BMET patients (N=15). We compared the cancer cfDNA finding to non-cancer healthy controls (N=11) and their CSF cfDNA. We also compared the fragmentation patterns between CSF-derived cfDNA and plasma-derived cfDNA. ResultsWe observed enriched mono-nucleosome levels and significantly higher mono-/trinucleosome ratios in cancer patients. Comparison with plasma-derived cfDNA further confirmed the unique fragmentation features of CSF-derived cfDNA. Distinct methylation and hydroxymethylation patterns were observed between cancer and control CSF samples. We observed significantly lower degree of hydroxymethylation in cancer patients compared to healthy controls and the affected genes had different pathway profiles. ConclusionsCSF cfDNA in patients with NSCLC-BMET had a distinct profiles of DNA fragmentation, methylation and hydroxymethylation.

genomics↗

bioRxiv2023

KmerSV: a visualization and annotation tool for structural variants using Human Pangenome derived k-mers

SummaryKmerSV is a visualization and annotation tool for structural variants (SVs). It can be applied to assembly contigs or long-read sequences. Using k-mers it rapidly generates images and provides genome features of SVs. As an important feature, it utilizes the new Human Pangenome reference which provide haploid specific assemblies, addresses limitations in prior references and improves the discovery of SVs. Availability and implementationKmerSV is implemented in Python and available at github.com/sgtc-stanford/kmerSV

bioinformatics↗

bioRxiv2023

Niche differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions

Single cells influence, and are shaped by, their local spatial niche. Technologies for in situ measurement of gene expression at the transcriptome scale have enabled the detailed profiling of the spatial distributions of cell types in tissue as well as the interrogation of local signaling patterns between cell types [1]. Towards these goals, we propose a new statistical procedure called niche-differential expression (niche-DE) analysis. Niche-DE identifies cell-type specific niche-associated genes, defined as genes whose expression within a specific cell type is significantly up- or down-regulated, in the context of specific spatial niches. We develop effective and interpretable measures for global false discovery control and show, through the analysis of data sets generated by myriad protocols, that the method is robust to technical issues such as over-dispersion and spot swapping. Niche-DE can be applied to low-resolution spot- and ROI-based spatial transcriptomics data as well as data that is single-cell or subcellular in resolution. Based on niche-DE, we also develop a procedure to reveal the ligand-receptor signaling mechanisms that underlie niche-differential gene expression patterns. When applied to 10x Visium data from liver metastases of colorectal cancer, niche-DE identifies marker genes for cancer-associated fibroblasts and macrophages and elucidates ligand-receptor crosstalk patterns between tumor cells, macrophages and fibroblasts. Co-detection by indexing (CODEX) was performed on the same patient samples, to corroborate the niche-DE results.

bioinformatics↗

bioRxiv2022

Adaptive nanopore sequencing for single cell characterization of cancer mutations and gene rearrangements

In this proof-of-concept study, we developed a single cell method that identifies somatic alterations found in coding regions of mRNAs and integrates these mutation genotypes with their matching cell transcriptomes. We used nanopore adaptive sampling on single cell cDNA libraries, generated long read sequences from target gene transcripts and identified coding variants among individual cells. Short-read single cell transcriptomes characterized the cell types with mutations. We delineated CRISPR edits from a cancer cell line. From primary cancer samples, we targeted hundreds of cancer genes, identified somatic coding mutations and a gene rearrangement among individual tumor cells.

genomics↗

bioRxiv2022

Single cell CRISPR base editor engineering and transcriptional characterization of cancer mutations

We developed a multiplexed single cell technology to genome engineer mutations, directly delineate their genotype among individual cells and determine each mutations transcriptional phenotype. This approach uses CRISPR base editors to introduce predesignated variants into a target gene. Long-read sequencing of the target genes transcript identifies the engineered mutations among individual cells. Simultaneously, we analyzed the transcriptome profile from the same set of cells by short-read sequencing. By integrating the two types of data, we determined the mutations genotype and expression phenotype at single cell resolution. Using cell lines, we engineered and evaluated the phenotype of more than 100 TP53 mutations. Based on the single cell gene expression, we classified the mutations as having a functionally significant phenotype versus the wild-type state. We validated these results on a subset of mutations using isolated clones analyzed with RNA-seq. Overall, we successfully demonstrated single cell mutation engineering and phenotypic assessment.

genomics↗

bioRxiv2022

The Human Pangenome's sequence conservation reveals a landscape of polymorphic structural variation

The Human Pangenome is a new reference build that addresses many of the limitations of the current reference. The first release is based on 94 high-quality haploid assemblies from individuals with diverse backgrounds. To facilitate the Pangenomes adoption in the wider research community, we conducted a multiple genome comparative analysis against the current GRCh38 reference. We applied a k-mer indexing strategy to identify highly conserved sequences that are omnipresent across all Pangenome assemblies, the reference and CHM13, a reference assembly with telomere-to-telomere chromosomes. Pan-conserved tag segments provided an informative set of universally conserved sequences. Examining the intervals between pairs of these segments defined highly conserved segments of the genome versus ones that have structurally related polymorphisms. We identified a Pangenome landscape of 60,764 polymorphic intervals with unique and geo-ethnic features. Overall, this study of the Pangenome revealed the conserved versus divergent features including the landscape of polymorphic structural variants.

genomics↗

bioRxiv2022

Cancer subclone detection based on DNA copy number in single cell and spatial omic sequencing data

In cancer, somatic mutations such as copy number alterations (CNAs) accumulate during disease progression and lead to functional intra-tumor heterogeneity that can influence the efficacy of cancer therapy. Therefore, studying the functional characteristics and spatial distribution of genetically distinct subclones is crucial to the understanding of tumor evolution and the design of cancer treatment. Here, we present Clonalscope, a method for subclone detection using copy number profiles that can be applied to spatial transcriptomics (ST) data and data from single-cell sequencing platforms such as scRNA-seq and scATAC-seq. Clonalscope implements a nested Chinese restaurant process to identify de novo subclones within one or multiple samples from the same patient. Clonalscope incorporates prior information from paired whole-genome or whole-exome sequencing (WGS/WES) data to achieve more reliable subclone detection and malignant cell labeling. On scRNA-seq and scATAC-seq data from four gastrointestinal tumor samples, Clonalscope successfully labeled malignant cells and identified genetically different subclones, which were validated in detail using matched scDNA-seq data. On ST data from a squamous cell carcinoma and two invasive ductal carcinoma samples, Clonalscope successfully labelled malignant spots, traced subclones between associated datasets, and identified spatially segregated subclones expressing genes associated with drug resistance and survival.

bioinformatics↗

bioRxiv2022

Single cell multi-omic mapping of subclonal architecture and pathway phenotype in primary gastric and metastatic colon cancers

Single cell genomics provides a high-resolution profile of intratumoral heterogeneity and subclonal structure in primary and metastatic tumors. Notably, metastases and therapeutic resistant tumors often originate from distinct subclones. These distinct cellular populations are an important contributor to adaptation and resistance to ongoing therapy. Single cell DNA-sequencing (scDNA-seq) defines subclones but does not provide biological information about cell types. Single cell RNA-sequencing (scRNA-seq) provides biological information but is less useful for identifying different subclones. The integrated scDNA-seq and scRNA-seq data from the same tumor cell population provides both subclone structure and biology. To understand the cellular genomic variation of different subclones in primary and metastatic cancers, we developed an approach to integrate multi-omics data from both types of single cells. This joint data set represented thousands of normal and tumor cells derived from a set of primary gastric and metastatic colorectal cancers. The extensive cellular sampling provided robust characterization of the subclonal architecture of gastric and colorectal cancers. We reconstructed the subclonal architecture using the cells in G0/G1 phase. The scDNA-seq provided a ground truth for copy number-based subclones. From the scRNA-seq data, the epithelial cells in G0/G1 were identified and assigned to specific subclones by a correlation algorithm based on gene dosage. The inferred CNV profiles from the scRNA-seq epithelial cells were assigned subclones identified from the scDNA-seq. Afterward, we determined the biological pathway activities of specific clones. Overall, integrative multi-omics analysis of single-cell datasets is more informative than any individual genomic modality, provides deep insights into the intratumoral heterogeneity, and reveals subclonal biology.

genomics↗

bioRxiv2022

Single molecule methylation profiles of cell-free DNA in cancer with nanopore sequencing

Epigenetic characterization of cell-free DNA (cfDNA) is an emerging approach for characterizing diseases. We developed a strategy using nanopore-based single-molecule sequencing to measure cfDNA methylomes. This approach generated up to hundreds of millions of reads per cfDNA sample from cancer patients, an order of magnitude improvement over existing nanopore methods. Leveraging methylomes of matched tumors and immune cells, we characterized cfDNA methylomes of cancer patients for longitudinal monitoring during treatment.

genomics↗

bioRxiv2022

Distinct cell states define the developmental trajectories of mucinous appendiceal neoplasms towards pseudomyxoma metastases

Appendiceal mucinous neoplasms (AMN) are rare tumors of the gastrointestinal tract. They metastasize with widespread abdominal dissemination leading to pseudomyxoma peritonei (PMP), a disease with poor prognosis. The cellular features of origin, differentiation and progression in AMN and PMP remain largely unknown. We characterized the distinct cell states of AMN and PMP using single-cell RNA-sequencing in 31 samples including AMNs, PMP metastases and matched normal tissues. We identified previously undescribed cellular features and heterogeneity in AMN and PMP. We discovered developmental cell states in tumor epithelial cells that ranged from progenitor phase to goblet cell differentiation, which was accompanied by elevated mucin gene expression. Metastatic PMP cells had a distinct cell state with increased mTOR and RAS signaling pathways and a series of upregulated cancer genes. We observed heterogeneity in a single PMP tumor as well as PMP metastases from the same patient. We validated our findings with immunohistochemistry, mass spectrometry on malignant ascites from PMP patients and gene expression data from an independent set of 63 PMPs.

cancer biology↗

bioRxiv2021

Magnetic DNA random access memory with nanopore readouts and exponentially-scaled combinatorial addressing

The storage of data in DNA typically involves encoding and synthesizing data into short oligonucleotides, followed by reading with a sequencing instrument. Major challenges include the molecular consumption of synthesized DNA, issues with basecalling errors, and limitations with scaling up read access operations for individual data elements. Addressing these challenges, we describe a DNA storage system called MDRAM (Magnetic DNA-based Random Access Memory) that enables repetitive and efficient readouts of targeted files with nanopore-based sequencing. Through conjugation of synthesized DNA to magnetic beads, we enabled repeated readouts of data while preserving the original DNA analyte and maintaining data readout quality. MDRAM also utilizes an efficient convolutional coding scheme that leverages soft information in raw nanopore sequencing signals to achieve information reading costs comparable to Illumina sequencing despite substantially higher error rates. Finally, we demonstrate a proof-of-concept DNA-based proto-filesystem that enables an exponentially-scalable data address space using only small numbers of targeting primers for assembly and readout. ONE-SENTENCE SUMMARYWe demonstrate a novel DNA data storage system that leverages conjugation of DNA onto magnetic beads, new computational advances in data encoding, and exponentially scalable access of individual data elements.

synthetic biology↗

bioRxiv2021

Single cell characterization of CRISPR-modified transcript isoforms with nanopore sequencing

Transcript isoforms are mRNAs that arise from alternative splicing events. During RNA processing, different combinations of a gene's exons lead to a diverse set of isoforms. Polymorphisms or mutations at splice junctions can generate alternative splicing events. Various splicing factors also impact the representation of a gene's transcript isoforms. To assess how these two features contribute to alternative splicing, we developed a single cell approach to introduce CRISPR edits that modify mRNA transcript structure. Our method combines (1) long-read sequencing to characterize the expressed transcripts and identify the edit at single cell resolution; (2) short-read sequencing to match the single cell gene expression profiles of the cells with the altered isoform. First, we modify target exon-intron segments with CRISPR-Cas9. Second, using cDNAs with cell barcodes, we use long read sequencing to directly identify the changes in transcript isoforms from the targeted CRISPR edits. As a variation on this approach, we also determined how modifying specific splicing factors influence isoform expression and structure. Overall, we demonstrate how the integration of single cell long read analysis and CRISPR engineering can be used to directly confirm transcript isoform and target genomic edits at single cell resolution. This approach will improve our understanding of the role of alternative splicing in transcriptional regulation.

genomics↗

bioRxiv2021

Characterization of the consensus mucosal microbiome of colorectal cancer

Recent evidence suggests that dysbiosis, an imbalance of microbiota, is associated with increased risk of colorectal cancer. Diverse microbial organisms are physically associated with the cells found in tumor biopsies. Characterizing this mucosa-associated microbiome through genome sequencing has advantages compared to culture-based profiling. However, there are notable challenges in accurately characterizing the features of tumor microbiomes with methods like transcriptome sequencing. Most sequence reads originate from the host. Moreover, there is a high likelihood of bacterial contaminants being introduced. Another major challenge is the microbiome diversity among different studies. Colorectal tumors demonstrate a significant extent of microbiome variation among individuals from different geographic and ethnic origins. To address these challenges, we identified a consensus microbiome for colorectal cancer through analyzing 924 tumors from eight independent RNA-Seq data sets. A standardized meta-transcriptomic analysis pipeline was established and applied to the complete CRC cohort. Common contaminants were filtered out. Our study involved taxonomic investigation of non-human sequences, linked microbial signatures to phenotypes and the association of microbiome with tumor microenvironment components. Microbiome profiles across different CRC cohorts were compared, and recurrently altered microbial shifts specific to CRC were determined. We identified cancer-specific set of 114 microbial species associated with tumors that were found among all investigated studies. Validating our approach, we found that Fusobacterium nucleatum was one of the most enriched bacterial species in CRC. Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria were among the four most abundant phyla for CRC microbiome. Signficant associations between the consensus species and specific immune cell types were noted. Our results are available as a web data resource for other researchers to explore (https://crc-microbiome.stanford.edu).

genomics↗

bioRxiv2021

KmerKeys: a web resource for searching indexed genome assemblies and variants

K-mers are short DNA sequences that are used for genome sequence analysis. Applications that use k-mers include genome assembly and alignment. Despite these current applications, the wider bioinformatic use of k-mers in has challenges related to the massive scale of genomic sequence data. A single human genome assembly has billions of these short sequences. The sheer amount of computation for effective use of k-mer information is enormous, particularly when involving multiple genome assemblies. To address these issues, we developed a new k-mer indexing data structure based on a hash table tuned for the lookup of k-mer keys. This web application, referred to as KmerKeys (https://kmerkeys.dgi-stanford.org/), provides performant, rapid query speeds for cloud computation on genome assemblies. We enable fuzzy as well as exact k-mer-based searches of assemblies. To enable robust and speedy performance, the website implements cache-friendly hash tables, memory mapping and massive parallel processing. Our method employs a scalable and efficient data structure that can be used to jointly index and search a large collection of human genome assembly information. One can include variant databases and their associated metadata such as the gnomAD population variant catalog. This feature enables the incorporation of future genomic information into sequencing analysis.

bioinformatics↗

bioRxiv2020

Profiling diverse sequence tandem repeats in colorectal cancer reveals co-occurrence of microsatellite and chromosomal instability involving Chromosome 8

Colorectal carcinomas (CRCs) which have lost DNA mismatch repair display hypermutability evident in a molecular phenotype called microsatellite instability (MSI). These mismatch repair deficient tumors are thought to lack widespread genomic instability features, such as copy number changes and rearrangements. To identify MSI for clinical diagnosis, current molecular testing looks for changes in mononucleotide or dinucleotide repeats. However, microsatellites have other types of sequence tandem repeats such as tri- and tetranucleotide motifs. These additional classes of microsatellites are generally not examined for MSI but are known to be unstable in a phenotype known as elevated microsatellite alterations at selected tetranucleotide repeats, or EMAST. We developed a sequencing approach that provides ultra-high coverage (>2500X) of microsatellite targets and cancer genes for profiling genomic instability. We assessed the diverse repeat motifs across 200 microsatellites. Our approach provides highly sensitive detection of MSI with high specificity, evaluates copy number alterations with high accuracy, delineates chromosomal instability (CIN) classification and deconvolutes subclonal architecture. By examining both MSI and CIN, we discovered mutations and copy number alterations that defined mixed genomic instability states of CIN and MSI, which are normally considered exclusive. An increase in copy number of chromosome arm 8q was prevalent among MSI tumors. Moreover, we identified an inter-chromosomal translocation event from a CRC with co-occurrence of MSI. Subclonal analysis demonstrated that mutations which are typically considered to be exclusive in MSI, shows mutual occurrence in MSI tumors with more sensitive characterization. Our approach revealed that MSH3 mutations are a potential source of mixed genomic instability features. Overall, our study demonstrates that some colorectal cancers have features of both microsatellite and chromosomal instability. This result may have implications for immunotherapy treatment.

genomics↗

bioRxiv2020

Alleloscope: Integrative analysis of single cell haplotype-divergent copy number alterations and chromatin accessibility changes reveals novel clonal architecture of cancers

Cancer progression is driven by both somatic copy number aberrations (CNAs) and chromatin remodeling, yet little is known about the interplay between these two classes of events in shaping the clonal diversity of cancers. We present Alleloscope, a method for allele-specific copy number estimation that can be applied to single cell DNA and ATAC sequencing data, either separately or in combination. This approach allows for integrative multi-omic analysis of allele-specific copy number and chromatin accessibility on the same cell. On scDNA-seq data from gastric, colorectal, and breast cancer samples, with extensive validation using matched linked-read sequencing, Alleloscope finds pervasive occurrence of highly complex, multi-allelic copy number aberrations, where cells that carry varying allelic configurations adding to the same total copy number co-evolve within a tumor. The contributions of such allele-specific events to intratumor heterogeneity have been under-reported and under-studied due to the lack of methods for their detection. On scATAC-seq from two basal cell carcinoma samples and a gastric cancer cell line, Alleloscope detects multi-allelic copy number events and copy neutral loss-of-heterozygosity, enabling the dissection of the contributions of chromosomal instability and chromatin remodeling in tumor evolution.

bioinformatics↗