Biology subjects

Chen, X. E.

Publications and source records attributed to Chen, X. E..

7 recordsLinked to original sources

bioRxiv2026

High-Specificity Detection of Chromosomal Mosaicism Reveals Cell-Type-Specific Genomic Alteration Patterns in Aging Tissues

Mosaic chromosomal alterations (mCAs) increase with age and are associated with multiple diseases, yet the cell types and states that harbor these alterations remain largely unknown. Because mCAs arise in individual cells prior to clonal expansion, they are typically rare and obscured in bulk data. We develop CHASM, a method for detecting chromosomal copy number alterations (CNA) from single-cell chromatin accessibility (scATAC-seq) data, a scalable modality that captures both cell state and chromosomal alterations. CHASM estimates a CNA-null background for each cell, providing an individualized expectation for chromosomal accessibility, which is critical in non-neoplastic tissues where alteration-carrying cells are not readily distinguishable from normal. By comparing each cell against its expected background, CHASM distinguishes chromosomal alterations from background variation and achieves more stringent control of false positives. We validate CHASM using in silico spike-in experiments, cross-modality comparisons with matched single-cell DNA and RNA data, and established genome-instability contrasts, including p53 deficiency and chromosome Y loss. Applied to multiple aging data sets, CHASM consistently recovers mCA burden in age-susceptible cell populations and reveals aging-associated signatures not detected by existing methods. In a cohort of 99 human kidney samples spanning age and disease conditions, CHASM identifies enrichment of mCAs in injury-associated cell states (VCAM1-high proximal tubule cells). Notably, CHASM detects the age-associated emergence of mCAs in cancer-relevant genomic regions, including chromosomes 3 gains and losses and chromosome 7 gain, in ostensibly normal cell populations. Cells harboring mCAs exhibit activation of injury-response regulatory programs and reduced epithelial identity programs, while elevated mCA burden in specific epithelial populations are associated with increased immune and stromal infiltration. Overall, we develop CHASM for high-specificity detection of CNA at single cell resolution. Applied across tissues, CHASM reveals aging-patterns of genome instability within cell types and implicates mCAs in early, pre-disease cellular states.

bioinformatics↗

bioRxiv2025

Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling

Through natural or synthetic lineage barcodes, single-cell technologies now enable the joint measurement of molecular states and clonal identities, providing an unprecedented opportunity to study cell fate and dynamics. Yet, most computational methods for inferring cell development and differentiation rely exclusively on transcriptional similarity, overlooking the lineage information encoded by lineage barcodes. This limitation is exemplified by T cells, where subtle transcriptional differences mark divergent fates with distinct biological activity. Single-cell RNA and matched TCR sequencing is now ubiquitous in the analysis of clinical samples, where the TCR sequence provides an endogenous clonal barcode and could reveal clonal T cell responses. We present Clonotrace, a computational framework that jointly models gene expression and clonotype information to infer cell state transitions and fate biases with higher fidelity. While motivated by challenges in analyzing T cell populations, especially in the tumor microenvironment and immunotherapy settings, Clonotrace is broadly applicable to any lineage-barcoded single-cell dataset. Across diverse systems including T cells, hematopoietic differentiation, and cancer therapy resistance models, Clonotrace reveals differentiation hierarchies, distinguishes unipotent from multipotent states, and identifies candidate fate-determining genes driving lineage commitment.

bioinformatics↗

bioRxiv2024

An epigenetic basis for sustained inflammatory epithelial progenitor cell states in Crohn's disease

Defining consequential differences in intestinal epithelial stem cells in healthy humans versus those with inflammatory bowel disease (Crohns disease and ulcerative colitis) is essential for the development of much needed therapies to restore the epithelial barrier and maintain its fidelity. Employing single cell transcriptomic/epigenomic approaches and colonoid models from children and adults with Crohns disease led us to identify an inflammatory secretory progenitor (ISP) cell state present almost exclusively in patients with Crohns disease compared to control subjects. ISPs exhibit gene expression profiles consistent with normal secretory progenitor cells but concomitantly express a suite of distinguishing pro-inflammatory genes. Mechanistically, ISPs exhibit open chromatin and gene expression of ISP signature genes. While these ISP-specific genes are not expressed in intestinal stem cells, their chromatin is accessible in Crohns disease stem cells suggesting that ISP genes are epigenetically poised in stem cells and are transcriptionally activated in ISP cells in the presence of inflammatory stimuli. Consistently, Crohns disease colonoids exhibit sustained ISP gene expression that can be elicited further with pro-inflammatory cytokines or via co-culture with pro-inflammatory macrophages. In summary, we define differences in the epithelial stem and progenitor compartment of patients with Crohns disease suggesting aberrant stem cell differentiation and inflammatory gene expression arises during disease. HIGHLIGHTSO_LIWe identify an inflammatory secretory progenitor cell state in endoscopically non-inflamed tissue from pediatric and adult patients with Crohns disease. C_LIO_LICrohns disease epithelial stem and progenitor cells display increased chromatin accessibility preceding the emergence of inflammatory secretory progenitor cell states. C_LIO_LICrohns disease colonoids from non-inflamed regions contain inflammatory secretory progenitor cells in the absence of inflammatory stimuli C_LIO_LIInflammatory secretory progenitor cells expand in colonoid culture in response to cytokine stimulation or co-culture with pro-inflammatory macrophages C_LI

physiology↗

bioRxiv2024

Targeting Interferon-Driven Inflammatory Memory Prevents Epigenetic Evolution of Cancer Immunotherapy Resistance

Acquired resistance is a growing obstacle to durable responses after cancer immune checkpoint blockade (ICB). The mechanisms by which heterogeneous tumors evolve under immunotherapy pressure and strategies targeting key populations to prevent relapse are poorly understood. We show that chronic interferon (IFN) enables a subpopulation of cancer cells to acquire inflammatory memory and express memory ISGs, a subset of IFN-stimulated genes enriched for immune evasion properties, leading to subclonal epigenetic evolution of ICB-resistant states. Inflammatory memory is epigenetically encoded through chronic virus mimicry - feedforward MDA5 signaling likely activated by endogenous retroelements. While JAK inhibitors can improve ICB response, combining them with TBK1 inhibitors collapses this feedforward mechanism, erasing inflammatory memory and preventing differentiation into resistance states. Across human cancers, small subpopulations of memory ISG-expressing cells are prevalent and coupled to T cell exhaustion, suggesting inflammatory memory may be a common mechanism of acquired resistance targetable by JAK plus TBK1 inhibition.

immunology↗

bioRxiv2022

Biochemical activity is the default DNA state in eukaryotes

Genomes encode for genes and the regulatory signals that enable those genes to be transcribed, and are continually shaped by evolution. Genomes, including those of human and yeast, encode for numerous regulatory elements and transcripts that have limited evidence of conservation or function. Here, we sought to create a genomic null hypothesis by quantifying the gene regulatory activity of evolutionarily naive DNA, using RNA-seq of evolutionarily distant DNA expressed in yeast and computational predictions of random DNA activity in human cells and tissues. In yeast, we found that >99% of bases in naive DNA expressed as part of one or more transcripts. Naive transcripts are sometimes spliced, and are similar to evolved transcripts in length and expression distribution, indicating that stable expression and/or splicing are insufficient to indicate adaptation. However, naive transcripts do not achieve the extreme high expression levels as achieved by evolved genes, and frequently overlap with antisense transcription, suggesting that selection has shaped the yeast transcriptome to achieve high expression and coherent gene structures. In humans, we found that, while random DNA is predicted to have minimal activity, dinucleotide content-matched randomized DNA is predicted to have much of the regulatory activity of evolved sequences, including active chromatin marks at between half (DNase I and H3K4me3) and 1/16th (H3K27ac and H3K4me1) the rate of evolved DNA, and the repression-associated H3K27me3 at about twice the rate of evolved DNA. Naive human DNA is predicted to be more cell type-specific than evolved DNA and is predicted to generate co-occurring chromatin marks, indicating that these are not reliable indicators of selection. However, extreme high activity is rarely achieved by naive DNA, consistent with these arising via selection. Our results indicate that evolving regulatory activity from naive DNA is comparatively easy in both yeast and humans, and we expect to see many biochemically active and cell type-specific DNA sequences in the absence of selection. Such naive biochemically active sequences have the potential to evolve a function or, if sufficiently detrimental, selection may act to repress them.

genomics↗

bioRxiv2022

GIL: A Python package for designing custom indexing primers

Next-generation sequencing allows samples to be multiplexed by adding a unique DNA index to each sample. Multiplexing greatly reduces the price of sequencing large numbers of samples, yet the minimum cost per sample remains high when using commercially available indexing kits and designing custom indexes is challenging. To address these issues, we created GIL (Generate Indexes for Libraries), a software tool that designs indexing primers for producing multiplexed sequencing libraries. GIL can be customized in numerous ways to meet user specifications, including index length, sequencing modality, color balancing, and compatibility with existing primers, and produces ordering and demultiplexing-ready outputs. GIL is written in Python and is freely available on GitHub at https://github.com/de-Boer-Lab/GIL. It can also be accessed as a web-application implemented in Streamlit at https://dbl-gil.streamlitapp.com.

bioinformatics↗

bioRxiv2021

Modeling the trajectory of SARS-CoV-2 spike protein evolution in continuous latent space using a neural network and Gaussian process

Viral vaccines can lose their efficacy as the genomes of targeted viruses rapidly evolve, resulting in new variants that may evade vaccine-induced immunity. This process is apparent in the emergence of new SARS-CoV-2 variants which have the potential to undermine vaccination efforts and cause further outbreaks. Predictive vaccinology points to a future of pandemic preparedness in which vaccines can be developed preemptively based in part on predictive models of viral evolution. Thus, modeling the trajectory of SARS-CoV-2 spike protein evolution could have value for mRNA vaccine development. Traditionally, in silico sequence evolution has been modeled discretely, while there has been limited investigation into continuous models. Here we present the Viral Predictor for mRNA Evolution (VPRE), an open-source software tool which learns from mutational patterns in viral proteins and models their most statistically likely evolutionary trajectories. We trained a variational autoencoder with real-time and simulated SARS-CoV-2 genome data from Australia to encode discrete spike protein sequences into continuous numerical variables. To simulate evolution along a phylogenetic path, we trained a Gaussian process model with the numerical variables to project spike protein evolution up to five months in advance. Our predictions mapped primarily to a sequence that differed by a single amino acid from the most reported spike protein in Australia within the prediction timeframe, indicating the utility of deep learning and continuous latent spaces for modeling viral protein evolution. VPRE can be readily adapted to investigate and predict the evolution of viruses other than SARS-CoV-2 in temporal, geographic, and lineage-specific pathways.

bioinformatics↗