Biology subjects

Zhang, N. R.

Publications and source records attributed to Zhang, N. R..

At least 19 recordsLinked to original sources

bioRxiv2026

A Manifold-Based Measure of Transcriptional Entropy for Quantifying Aging in Single Cells

Aging reshapes tissues through changes in cellular composition, coordinated transcriptional reprogramming, and loss of transcriptional coordination. Whereas the first two have been characterized across aging tissues, the third remains difficult to quantify. We introduce an unsupervised, first-principles framework for measuring transcriptional dyscoordination in single cells as deviation from a learned, predictable structure accounting for technical noise. Orthogonal validation links transcriptional dyscoordination to classical intrinsic noise and distinguishes it from coordinated change. In controlled perturbations, dyscoordination rises after genotoxic injury and senescence induction, then falls following senolytic depletion. Across mouse, rat, and human tissues, dyscoordination increases with chronological age, especially in regenerative compartments. In human T cells, dyscoordination increases with clonal expansion and effector function yet declines within persisting clones after checkpoint blockade. Cross-modal analyses further link dyscoordination to chromatin-based mitotic age and genome instability. These results identify loss of transcriptional coordination as a distinct and dynamic feature of cellular aging.

genomics↗

bioRxiv2025

Dual checkpoint blockade of glioblastoma with Anti-PD-1 and Anti-LAG-3 promotes expansion of tumor-reactive T cell clones along a unique pathway of differentiation

IDH-wildtype grade IV glioblastoma is the most aggressive adult primary brain tumor and remains refractory to anti-PD-1 monotherapy despite evidence of limited tumor-specific T cell induction. To determine the impact of immune checkpoint inhibitors (ICIs) on glioblastoma T cell transcriptional landscape and repertoire, we conducted paired single-cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) of tumor-infiltrating lymphocytes (TILs) from patients with untreated, newly diagnosed glioblastoma and from recurrent glioblastoma treated with dual checkpoint blockade targeting PD-1 and LAG-3. Using a validated transcriptional signature, we found that predicted tumor-reactive T cells (TRC) in untreated glioblastomas reside almost exclusively in a clonally expanded GZMKhi population with developmental plasticity, affording them the potential to differentiate into both tissue-resident and terminal effector T cells. Dual ICI therapy induced substantial clonal remodeling, characterized by the recruitment of new TRC from the periphery into the tumor microenvironment (TME) and differentiation into transitional effectors and ultimately terminal effectors along a gradient characterized by simultaneous acquisition of cytotoxic and exhaustion genes, regulated by specific transcriptional, metabolic, and epigenetic programs. Longitudinal clonal tracking in peripheral blood confirmed that with ICI treatment, most TRC expand transiently in circulation prior to tumor infiltration, with peripherally derived clones becoming the major contributor to the GZMKhi TRC that further expand in the tumor. Our study provides the first comprehensive map of T cell clonal dynamics and differentiation in glioblastoma following dual ICIs and highlights a potential mechanism of immune activation and peripheral recruitment of TRC in glioblastoma not previously described. Our results suggest that therapeutic strategies to sustain these GZMKhi early effector and transitional effector T cells may further enhance ICI therapeutic efficacy in glioblastoma.

immunology↗

bioRxiv2025

Mitochondrial clone tracing within spatially intact human tissues

Understanding tissue development and intra-tissue evolution requires the ability to trace clones in intact tissues coupled with high-plex molecular profiling preserving spatial context. However, current lineage tracing tools are incompatible with spatial omics. Here, we present SUMMIT (Spatially Unveiling Mitochondrial Mutations In Tissues), a spatially-resolved lineage tracing technology that integrates gene expression profiling with mitochondrial mutation-based clone identification. Unlike synthetic lineage recording methods, SUMMIT relies only on endogenous mutations and thus can be applied to human tissues. To address the compositional mixing of cell types within spatial spots, SUMMIT includes a rigorous statistical framework to confidently assign variants to specific cell subpopulations and achieves high power for spatially localized clones by pooling information across neighboring spots. We validated SUMMIT using a controlled model in which we mixed two cancer cell lines in a mouse tumor, then demonstrated it on multiple human tissues including Barretts esophagus, gastric cardia, small bowel, and colorectal cancer. Across these samples, we distinguished between global mutations and mutations marking locally restricted clones. The coupled transcriptomic data allowed us to characterize the cell type composition within each clone and delineate their spatial configuration. This integrated approach provides a framework to understand spatially-defined clonal evolution in preserved human tissue.

genomics↗

bioRxiv2024

All-Optical Multimodal Mapping of Single Cell Type-Specific Metabolic Activities via REDCAT

Metabolism underlies cell growth, survival, and function, yet its activities vary widely across cell types and tissue environments. Spatially resolving these processes in situ at single-cell resolution is essential to advance our understanding of cellular function and tissue physiology in health and disease. However, existing approaches are limited by either destructive workflows, insufficient spatial resolution and biochemical specificity, or lack of direct linkage to cell identity. Here, we present Raman Enhanced Delineation of Cell Atlases in Tissues (REDCAT), a multimodal all-optical platform that integrates stimulated Raman scattering, autofluorescence redox imaging, second harmonic generation, and high-plex immunofluorescence to co-map metabolic activities and cell types within the same tissue section. REDCAT achieves subcellular resolution profiling of protein, lipid, redox, and nuclear acid metabolism, together with extracellular matrix composition, in both FFPE and fresh-frozen human tissues. Applied to normal lymph nodes, REDCAT delineated distinct redox and lipid remodeling programs across germinal center B-cell zones and immune subsets, highlighting cell-type-specific metabolic specialization. In lymphoma, it revealed profound metabolic reprogramming, including extensive lipid accumulation, nuclear metabolic heterogeneity, and a transitional metabolic state associated with transformation from chronic lymphocytic leukemia to diffuse large B-cell lymphoma, thereby illuminating tumor evolution in situ. In human liver, REDCAT resolved cell-type-specific lipid droplet diversity and zonation-dependent nuclear metabolic gradients, uncovering new principles of spatial metabolic organization. By directly linking cell identity with spatial metabolic states at single-cell or subcellular resolution, REDCAT establishes a broadly applicable framework for studying immune function, tumor progression, and tissue physiology, and offers a new path to deciphering the metabolic basis of health and disease.

cell biology↗

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↗

bioRxiv2023

emObject: domain specific data abstraction for spatial omics

Recent advances in high-parameter spatial biology have yielded a rapidly growing new class of biological data, allowing researchers to more comprehensively characterize cellular state and morphology in native tissue context. However, spatial biology lacks a cohesive data abstraction on which to build novel computational tools and algorithms, making it difficult to fully leverage these emergent data. Here, we present emObject, a domain-specific data abstraction for spatial biology data and experiments. We demonstrate the simplicity, flexibility, and extensibility of emObject for a range of spatial omics data types, including the analysis of Visium, MIBI, and CODEX data, as well as for integrated spatial multiomic experiments. The development of emObject is an essential step towards building a unified data science ecosystem for spatial biology and accelerating the pace of scientific discovery.

bioinformatics↗

bioRxiv2023

Signal recovery in single cell batch integration

Data integration to align cells across batches has become a cornerstone of single cell data analysis, critically affecting downstream results. Yet, how much biological signal is erased during integration? Currently, there are no guidelines for when the biological differences between samples are separable from batch effects, and thus, data integration usually involve a lot of guesswork: Cells across batches should be aligned to be "appropriately" mixed, while preserving "main cell type clusters". We show evidence that current paradigms for single cell data integration are unnecessarily aggressive, removing biologically meaningful variation. To remedy this, we present a novel statistical model and computationally scalable algorithm, CellANOVA, to recover biological signal that is lost during single cell data integration. CellANOVA utilizes a "pool-of-controls" design concept, applicable across diverse settings, to separate unwanted variation from biological variation of interest. When applied with existing integration methods, CellANOVA allows the recovery of subtle biological signals and corrects, to a large extent, the data distortion introduced by integration. Further, CellANOVA explicitly estimates cell- and gene-specific batch effect terms which can be used to identify the cell types and pathways exhibiting the largest batch variations, providing clarity as to which biological signals can be recovered. These concepts are illustrated on studies of diverse designs, where the biological signals that are recovered by CellANOVA are shown to be validated by orthogonal assays. In particular, we show that CellANOVA is effective in the challenging case of single-cell and single-nuclei data integration, where the recovered biological signals are replicated in an independent study.

bioinformatics↗

bioRxiv2023

Clonal cell states link Barrett's esophagus and esophageal adenocarcinoma

Barretts esophagus is a common type of metaplasia and a precursor of esophageal adenocarcinoma. However, the cell states and lineage connections underlying the origin, maintenance, and progression of Barretts esophagus have not been resolved in humans. To address this, we performed single-cell lineage tracing and transcriptional profiling of patient cells isolated from metaplastic and healthy tissue. Our analysis revealed discrete lineages in Barretts esophagus, normal esophagus, and gastric cardia. Transitional basal progenitor cells of the gastroesophageal junction were unexpectedly related to both esophagus and gastric cardia cells. Barretts esophagus was polyclonal, with lineages that contained all progenitor and differentiated cell types. In contrast, precancerous dysplastic foci were initiated by the expansion of a single molecularly aberrant Barretts esophagus clone. Together, these findings provide a comprehensive view of the cell dynamics of Barretts esophagus, linking cell states along the full disease trajectory, from its origin to cancer.

genomics↗

bioRxiv2022

Quantifying common and distinct information in single-cell multimodal data with Tilted-CCA

Multimodal single-cell technologies profile multiple modalities for each cell simultaneously and enable a more thorough characterization of cell populations alongside investigations into cross-modality relationships. Existing dimension-reduction methods for multimodal data focus on capturing the "union of information," producing a lower-dimensional embedding that combines the information across modalities. While these tools are useful, we develop Tilted-CCA to quantify the "intersection and difference of information", that is, a decomposition of a paired multimodal dataset into common axes of variation that is shared between both modalities and distinct axes of variation that is found only in one modality. Through examples, we show that Tilted-CCA enables meaningful visualization and quantification of the cross-modal information overlap. We also demonstrate the application of Tilted-CCA to two specific types of analyses. First, for single-cell experiments that jointly profile the transcriptome and surface antibody markers, we show how to use Tilted-CCA to design the target antibody panel to best complement the transcriptome. Second, for single-cell multiome data that jointly profiles transcriptome and chromatin accessibility, we show how to use the common embedding given by Tilted-CCA to identify development-informative genes and distinguish between transient versus terminal cell types.

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

A comparative study of structural variant calling strategies using the Alzheimer's Disease Sequencing Project's whole genome family data

BackgroundReliable detection and accurate genotyping of structural variants (SVs) and insertion/deletions (indels) from whole-genome sequence (WGS) data is a significant challenge. We present a protocol for variant calling, quality control, call merging, sensitivity analysis, in silico genotyping, and laboratory validation protocols for generating a high-quality deletion call set from whole genome sequences as part of the Alzheimers Disease Sequencing Project (ADSP). This dataset contains 578 individuals from 111 families. MethodsWe applied two complementary pipelines (Scalpel and Parliament) for SV/indel calling, break-point refinement, genotyping, and local reassembly to produce a high-quality annotated call set. Sensitivity was measured in sample replicates (N=9) for all callers using in silico variant spike-in for a wide range of event sizes. We focused on deletions because these events were more reliably called. To evaluate caller specificity, we developed a novel metric called the D-score that leverages deletion sharing frequencies within and outside of families to rank recurring deletions. Assessment of overall quality across size bins was measured with the kinship coefficient. Individual callers were evaluated for computational cost, performance, sensitivity, and specificity. Quality of calls were evaluated by Sanger sequencing of predicted loss-of-function (LOF) variants, variants near AD candidate genes, and randomly selected genome-wide deletions ranging from 2 to 17,000 bp. ResultsWe generated a high-quality deletion call set across a wide range of event sizes consisting of 152,301 deletions with an average of 263 per genome. A total of 114 of 146 predicted deletions (78.1%) were validated by Sanger sequencing. Scalpel was more accurate in calling deletions [≤]100 bp, whereas for Parliament, sensitivity was improved for deletions > 900 bp. We validated 83.0% (88/106) and 72.5% (37/51) of calls made by Scalpel and Parliament, respectively. Eleven deletions called by both Parliament and Scalpel in the 101-900 bin were tested and all were confirmed by Sanger sequencing. ConclusionsWe developed a flexible protocol to assess the quality of deletion detection across a wide range of sizes. We also generated a truth set of Sanger sequencing validated deletions with precise breakpoints covering a wide spectrum of sizes between 1 and 17,000 bp.

genetics↗

bioRxiv2022

Systematic Single Cell Pathway Analysis (SCPA) reveals novel pathways engaged during early T cell activation

Next generation sequencing technologies have revolutionized the study of T cell biology, capturing previously unrecognized diversity in cellular states and functions. Pathway analysis is a key analytical stage in the interpretation of such transcriptomic data, providing a powerful method for detecting alterations in important biological processes. Current pathway analysis tools are built on models developed for bulk-RNA sequencing, limiting their effectiveness when applied to more complex single cell RNA-sequencing (scRNA-seq) datasets. We recently developed a sensitive and distribution-free statistical framework for multisample distribution testing, which we implement here in the open-source R package Single Cell Pathway Analysis (SCPA). After demonstrating the effectiveness of SCPA over commonly used methods, we generate a scRNA-seq T cell dataset and characterize pathway activity over early cellular activation and between T cell populations. This revealed unexpected regulatory pathways in T cells, such as an intrinsic type I interferon system regulating T cell survival and a reliance on arachidonic acid metabolism throughout T cell activation. A systems level characterization of pathway activity in T cells across multiple human tissues also revealed alpha defensin expression as a hallmark of bone marrow derived T cells. Overall, our work here provides a widely applicable tool for single cell pathway analysis, and highlights unexpected regulatory mechanisms of T cells using a novel T cell dataset.

immunology↗

bioRxiv2021

Nonparametric Interrogation of Transcriptional Regulation in Single-Cell RNA and Chromatin Accessibility Multiomic Data

Epigenetic control of gene expression is highly cell-type- and context-specific. Yet, despite its complexity, gene regulatory logic can be broken down into modular components consisting of a transcription factor (TF) activating or repressing the expression of a target gene through its binding to a cis-regulatory region. Recent advances in joint profiling of transcription and chromatin accessibility with single-cell resolution offer unprecedented opportunities to interrogate such regulatory logic. Here, we propose a nonparametric approach, TRIPOD, to detect and characterize three-way relationships between a TF, its target gene, and the accessibility of the TFs binding site, using single-cell RNA and ATAC multiomic data. We apply TRIPOD to interrogate cell-type-specific regulatory logic in peripheral blood mononuclear cells and contrast our results to detections from enhancer databases, cis-eQTL studies, ChIP-seq experiments, and TF knockdown/knockout studies. We then apply TRIPOD to mouse embryonic brain data during neurogenesis and gliogenesis and identified known and novel putative regulatory relationships, validated by ChIP-seq and PLAC-seq. Finally, we demonstrate TRIPOD on SHARE-seq data of differentiating mouse hair follicle cells and identify lineage-specific regulation supported by histone marks for gene activation and super-enhancer annotations.

bioinformatics↗

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

Dimension reduction and denoising of single-cell RNA sequencing data in the presence of observed confounding variables

Confounding variation, such as batch effects, are a pervasive issue in single-cell RNA sequencing experiments. While methods exist for aligning cells across batches, it is yet unclear how to correct for other types of confounding variation which may be observed at the subject level, such as age and sex, and at the cell level, such as library size and other measures of cell quality. On the specific problem of batch alignment, many questions still persist despite recent advances: Existing methods can effectively align batches in low-dimensional representations of cells, yet their effectiveness in aligning the original gene expression matrices is unclear. Nor is it clear how batch correction can be performed alongside data denoising, the former treating technical biases due to experimental stratification while the latter treating technical variation due inherently to the random sampling that occurs during library construction and sequencing. Here, we propose SAVERCAT, a method for dimension reduction and denoising of single-cell gene expression data that can flexibly adjust for arbitrary observed covariates. We benchmark SAVERCAT against existing single-cell batch correction methods and show that while it matches the best of the field in low-dimensional cell alignment, it significantly improves upon existing methods on the task of batch correction in the high-dimensional expression matrix. We also demonstrate the ability of SAVERCAT to effectively integrate batch correction and denoising through a data down-sampling experiment. Finally, we apply SAVERCAT to a single cell study of Alzheimers disease where batch is confounded with the contrast of interest, and demonstrate how adjusting for covariates other than batch allows for more interpretable analysis.

genomics↗

bioRxiv2020

Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments

Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that "all genes affect every complex trait" complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing MR methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using GWAS summary statistics, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, determine the causal direction and perform multivariable MR to adjust for confounding risk factors. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and the potential pleiotropic pathways.

genetics↗

bioRxiv2019

The role of lipoprotein subfractions in coronary artery disease: A Mendelian randomization study

Recent genetic data can offer important insights into the roles of lipoprotein subfractions and particle sizes in preventing coronary artery disease (CAD), as previous observational studies have often reported conflicting results. In this study, we first used the LD score regression to estimate the genetic correlation of 77 subfraction traits with traditional lipid profile and identified 27 traits that may involve distinct genetic mechanisms. We then used Mendelian randomization (MR) to estimate the causal effect of these traits on the risk of CAD. In univariable MR, the concentration and content of medium high-density lipoprotein (HDL) particles showed a protective effect against coronary artery disease. The effect was not attenuated in multivariable MR that adjusted for traditional lipid profile. The multivariable MR analyses also found that small HDL particles and smaller mean HDL particle diameter may have a protective effect. We identified four genetic markers for HDL particle size and CAD.

genetics↗

bioRxiv2018

Transfer learning in single-cell transcriptomics improves data denoising and pattern discovery

Single-cell RNA sequencing (scRNA-seq) data is noisy and sparse. Here, we show that transfer learning across datasets remarkably improves data quality. By coupling a deep autoencoder with a Bayesian model, SAVER-X extracts transferable gene-gene relationships across data from different labs, varying conditions, and divergent species to denoise target new datasets.

bioinformatics↗