Biology subjects

Lau, B. T.

Publications and source records attributed to Lau, B. T..

13 recordsLinked to original sources

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↗

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

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↗

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↗

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↗

bioRxiv2020

The cellular genomic diversity, regulatory states and networking of the metastatic colorectal cancer microenvironment

PurposeThe liver is the most frequent metastatic site for colorectal cancer (CRC). Its microenvironment is modified to provide a niche that allows CRC cell growth. This study focused on characterizing the cellular changes in the metastatic CRC (mCRC) liver tumor microenvironment (TME). Experimental DesignWe analyzed a series of microsatellite stable (MSS) mCRCs to the liver, paired normal liver tissue and peripheral blood mononuclear cells using single cell RNA-seq (scRNA-seq). We validated our findings using multiplexed spatial imaging and bulk gene expression with cell deconvolution. ResultsWe identified TME-specific SPP1-expressing macrophages with altered metabolism features, foam cell characteristics and increased activity for extracellular matrix (ECM) organization. SPP1+ macrophages and fibroblasts expressed complementary ligand receptor pairs with the potential to mutually influence their gene expression programs. TME lacked dysfunctional CD8 T cells and contained regulatory T cells, indicative of immunosuppression. Spatial imaging validated these cell states in the TME. Moreover, TME macrophages and fibroblasts had close spatial proximity, a requirement for intercellular communication and networking. In an independent cohort of mCRCs in the liver, we confirmed the presence of SPP1+ macrophages and fibroblasts using gene expression data. An increased proportion of TME fibroblasts was associated with worst prognosis in these patients. ConclusionsWe demonstrated that mCRC in the liver is characterized by transcriptional alterations of macrophages in the TME. Intercellular networking between macrophages and fibroblasts supports CRC growth in the immunosuppressed metastatic niche in the liver. These features can be used to target these immune checkpoint resistant MSS tumors. TRANSLATIONAL RELEVANCEThe liver is the commonest site for metastatic colorectal cancer (mCRC). Alterations in the tumor microenvironment (TME) allow metastatic cells to seed the distant liver site and grow. Leveraging single-cell RNA sequencing, we discovered a distinct SPP1+ macrophage cell state with pro-fibrogenic gene expression and altered metabolism. These SPP1+ macrophages communicated with fibroblasts, mutually influencing each others gene expression program. Using spatial imaging, we confirmed proximal colocalization between macrophages and fibroblasts in the mCRC TME, which is required for intercellular communication. These states and intercellular communication promoted immunosuppression in the TME, with a lack of dysfunctional anti-tumor CD8 T cells and prevalence of regulatory T cells. Increased fibroblasts were associated with worst prognosis in an independent patient cohort. Our results identified novel TME features that result in reshaping of the metastatic niche that allows progression of mCRC. These features can be potential targets for mCRC treatment, which is microsatellite stable and resistant to immune checkpoint blockade.

cancer biology↗

bioRxiv2019

Genetic risk of cholangiocarcinoma is linked to the autophagy gene ATG7

Cholangiocarcinoma is an aggressive cancer originating from the bile duct. Although cholangiocarcinoma does occur in families, to date no specific causative gene has been identified. We identified ATG7 as a cancer susceptibility gene using a joint genetic analysis of an extended pedigree with familial cholangiocarcinoma in combination with a population genetic association study. Affected family members had a germline mutation (c.2000C>T [p.Arg659*]) in the autophagy related gene, ATG7, and all of the affected individuals had cholangiocarcinoma tumors harboring somatic genomic deletions of ATG7. From a population genetic study, we identified a germline polymorphism of ATG7 (c.1591C>G [p.Asp522Glu]) associated with increased risk of cholangiocarcinoma. The autophagy substrate p62 demonstrated a higher accumulation in tumors of p.Asp522Glu carriers compared with non-carriers indicating defective autophagy. To determine whether the germline ATG7 mutation had functional consequences, we developed an ATG7-deficient cholangiocyte cell line, derived from human bile duct, to test for autophagy-mediated lipidation activity. The germline mutation from the familial cholangiocarcinoma demonstrated a lack of lipidation activity compared to the wildtype ATG7. Moreover, in zebrafish embryos depleted of atg7, a reproducible necrotic head phenotype was rescued by injection of wildtype ATG7 but not mutant ATG7. Our findings point to ATG7 as a causative genetic risk factor for cholangiocarcinoma and implicate autophagy as a novel cancer driver mechanism.

genomics↗

bioRxiv2019

Single cell genomic characterization reveals the cellular reprogramming of the gastric tumor microenvironment

PurposeThe tumor microenvironment (TME) consists of a heterogenous cellular milieu that can influence cancer cell behavior. The characteristics of the cellular TME have a dramatic impact on treatments such as immunotherapy. These features can be revealed with single-cell RNA sequencing (scRNA-seq). We hypothesized that single cell gene expression studies of gastric cancer (GC) together with paired normal tissue and peripheral blood mononuclear cells (PBMCs) would identify critical elements of cellular dysregulation not apparent with other approaches.\n\nMethodsSingle cell gene expression studies were conducted on seven patients with GC and one patient with intestinal metaplasia. We sequenced 56,167 cells comprising GC (32,407 cells), paired normal tissue (18,657 cells) and PBMCs (5,103 cells). Protein expression of genes of interest was validated by multiplex immunofluorescence.\n\nResultsTumor epithelium had copy number alterations and a distinct gene expression program compared to normal with intra-tumor heterogeneity. The GC TME was significantly enriched for stromal cells, macrophages, dendritic cells (DCs) and Tregs. TME-exclusive stromal cells expressed extracellular matrix components distinct from normal tissue. Macrophages were transcriptionally heterogenous and did not conform to a binary M1/M2 paradigm. Gene expression program of tumor DCs was unique from PBMC DCs. TME-specific cytotoxic T cells comprised of two exhausted heterogenous subsets. Helper, cytotoxic T, Treg and NK cells expressed multiple immune checkpoint or costimulatory molecules. Receptor-ligand analysis revealed TME-exclusive inter-cellular communication.\n\nConclusionsSingle cell gene expression studies revealed widespread reprogramming across multiple cellular elements in the milieu of the GC TME. Cellular remodeling was delineated by changes in cell numbers, transcriptional states and inter-cellular interactions. This characterization facilitates understanding of tumor biology and enables the identification of novel molecular targets including for cancer immunotherapy.\n\nSTATEMENT OF TRANSLATIONAL RELEVANCEWe leveraged the power of single-cell genomics to characterize the heterogenous cell types and states in the tumor microenvironment (TME). By profiling thousands of single cells from surgical resections of gastric cancer together with paired normal mucosa and peripheral blood mononuclear cells (PBMCs), we determined the deviations in the TME from physiological conditions. Our analysis revealed a cellular reprogramming of the TME compared to normal mucosa in immune and stromal lineages. We detected transcriptional heterogeneity within macrophages and a TME-specific gene expression program in dendritic cells. Cytotoxic T cells in the TME had heterogenous profiles of exhaustion and expression of multiple immune checkpoint and costimulatory molecules. We constructed a receptor-ligand based inter-cellular communications network that was exclusive to tumor tissue. These discoveries provide information at a highly granular cellular resolution enabling advances in cancer biology, biomarker discovery and identification of treatment targets such as for immunotherapy.

cancer biology↗

bioRxiv2018

Joint single cell DNA-Seq and RNA-Seq of gastric cancer reveals subclonal signatures of genomic instability and gene expression

Sequencing the genomes of individual cancer cells provides the highest resolution of intratumoral heterogeneity. To enable high throughput single cell DNA-Seq across thousands of individual cells per sample, we developed a droplet-based, automated partitioning technology for whole genome sequencing. We applied this approach on a set of gastric cancer cell lines and a primary gastric tumor. In parallel, we conducted a separate single cell RNA-Seq analysis on these same cancers and used copy number to compare results. This joint study, covering thousands of single cell genomes and transcriptomes, revealed extensive cellular diversity based on distinct copy number changes, numerous subclonal populations and in the case of the primary tumor, subclonal gene expression signatures. We found genomic evidence of positive selection - where the percentage of replicating cells per clone is higher than expected - indicating ongoing tumor evolution. Our study demonstrates that joining single cell genomic DNA and transcriptomic features provides novel insights into cancer heterogeneity and biology. SIGNIFICANCEWe conducted a massively parallel DNA sequencing analysis on a set of gastric cancer cell lines and a primary gastric tumor in combination with a joint single cell RNA-Seq analysis. This joint study, covering thousands of single cell genomes and transcriptomes, revealed extensive cellular diversity based on distinct copy number changes, numerous subclonal populations and in the case of the primary tumor, subclonal gene expression signatures. We found genomic evidence of positive selection where the percentage of replicating cells per clone is higher than expected indicating ongoing tumor evolution. Our study demonstrates that combining single cell genomic DNA and transcriptomic features provides novel insights into cancer heterogeneity and biology.

genomics↗

bioRxiv2017

Single-cell transcriptome analysis identifies distinct cell types and intercellular niche signaling in a primary gastric organoid model

The diverse cellular milieu of the gastric tissue microenvironment plays a critical role in normal tissue homeostasis and tumor development. However, few cell culture model can recapitulate the tissue microenvironment and intercellular signaling in vitro. Here we applied an air-liquid interface method to culture primary gastric organoids that contains epithelium with endogenous stroma. To characterize the microenvironment and intercellular signaling in this model, we analyzed the transcriptomes of over 5,000 individual cells from primary gastric organoids cultured at different time points. We identified epithelial cells, fibroblasts and macrophages at the early stage of organoid formation, and revealed that macrophages were polarized towards wound healing and tumor promotion. The organoids maintained both epithelial and fibroblast lineages during the course of time, and a subset of cells in both lineages expressed the stem cell marker Lgr5. We identified that Rspo3 was specifically expressed in the fibroblast lineage, providing an endogenous source of the R-spondin to activate Wnt signaling. Our studies demonstrate that air-liquid-interface-derived organoids provide a novel platform to study intercellular signaling and immune response in vitro.

genomics↗