About

Xiao Dong, PhD, is the Bioinformatics Core Director and Assistant Professor at the University of Minnesota Twin Cities. He obtained his PhD in bioinformatics from the Shanghai Institutes of Biological Sciences at the Chinese Academy of Sciences in 2013 and completed postdoctoral training in the Department of Genetics at the Albert Einstein College of Medicine in 2021. Since 2021, he has been leading the Xiao Dong laboratory within the Masonic Institute on the Biology of Aging and Metabolism (MiBAM) and the Department of Genetics, Cell Biology and Development (GCD). His research focuses on discovering causal mechanisms of human aging, specifically testing the mutation theory of aging, which posits that the accumulation of DNA mutations in normal somatic cells is a causal factor in age-related functional decline. His laboratory approaches this by developing and applying advanced single-cell multi-omics technologies and machine learning algorithms.

Research topics

• Genetics
• Biology
• Cell biology
• Medicine
• Biochemistry
• Gerontology
• Computational biology

Selected publications

• Visium (with probes) data from the liver of a (FVB x C57Bl/6)F1 female mouse

  Cellular Senescence Network (SenNet) · 2026-04-27

  datasetOpen access
  PublisherDOI

• Visium (with probes) data from the liver of a (FVB x C57Bl/6)F1 male mouse

  Cellular Senescence Network (SenNet) · 2026-04-27

  datasetOpen access
  PublisherDOI

• A transcriptomic analysis reveals shared and inducer-specific expression patterns of cellular senescence

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-23

  articleOpen access

  Abstract Cellular senescence is a heterogeneous cell state induced by diverse stressors, including telomere attrition, genotoxic agents, oxidative damage, and inflammation. Despite ongoing efforts to identify conserved senescence biomarkers, it remains unclear whether senescence-inducing stimuli converge at the level of individual genes or broader molecular processes. Here, we profiled transcriptomic changes in human primary lung fibroblasts (IMR-90) driven toward senescence by replicative exhaustion, bleomycin, H 2 O 2 , or ionizing radiation under matched, dose- or time-resolved conditions. Across all four senescent inducers, global transcriptomic variation aligned along a shared axis of senescence progression, consistent with established machine learning-based senescence classifiers. However, overlap at the level of individual genes was limited, with most responses being inducer-specific or only partially conserved. In contrast, pathway-level analysis revealed far more consistent enrichment across all conditions, including downregulation of proliferation-associated pathways and activation of stress-related and pro-inflammatory pathways, accompanied by distinct inducer-specific patterns. These results support a hierarchical organization of the senescent transcriptome, in which diverse senescence inducers converge on shared pathway-level features while maintaining gene-level heterogeneity. These results provide a foundational basis for interpreting senescence signatures and may facilitate the development of more robust transcriptome-based markers of cellular senescence in aging and disease.

  PublisherDOI

• Evidence for negative selection against somatic mutations induced in normal fibroblasts by <i>N</i> -ethyl- <i>N</i> -nitrosourea

  Genome Research · 2026-05-15

  preprint

  Mutations accumulate with age in most human tissues. While some undergo clonal expansion and contribute to disease, the mutational burden tolerated by a normal cell without functional decline remains unknown. Here, we repeatedly treat proliferating human primary fibroblasts with the point mutagen N -ethyl- N -nitrosourea, and analyze mutation burden by single-cell whole-genome sequencing. Mutation burden increases linearly to ~56,000 single-nucleotide variants per cell, with only a modest reduction in growth rate. We detect negative selection against potentially deleterious coding and noncoding variants, including mutations affecting pathways important for cell growth and maintenance. These findings suggest that selective depletion of harmful variants helps proliferating cells maintain function despite an extreme mutation burden. Because most adult tissues are largely nondividing and cannot remove damaging mutations through a growth disadvantage, somatic mutations that accumulate during aging may have pronounced functional consequences in vivo.

  PublisherDOI

• STEVE: Single-cell Transcriptomics Expression Visualization and Evaluation

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-13

  articleOpen accessSenior authorCorresponding

  Single-cell RNA sequencing (scRNA-seq) has become a key technology for characterizing cell-type heterogeneity in complex tissues. However, its utility depends on accurate and reproducible cell-type annotation, which remains a major analytical challenge. Although hundreds of computational tools have been developed for automated annotation, there is currently no systematic framework to evaluate annotation robustness in a dataset-specific manner or within the context of complete analytical pipelines. Here, we present STEVE (Single-cell Transcriptomics Expression Visualization and Evaluation), a quantitative framework designed to assess the accuracy, robustness, and reproducibility of cell-type annotation in scRNA-seq studies. STEVE implements three complementary in silico evaluation modules: (i) Subsampling Evaluation to quantify annotation stability under varying reference sizes and data partitions; (ii) Novel Cell Evaluation to assess the ability to detect previously unseen cell types; and (iii) Annotation Benchmarking to compare alternative annotation tools against ground-truth labels. In addition, STEVE includes a Reference Transfer Annotation module that enables cross-dataset cell-type mapping using external reference datasets. All modules are built upon a unified probabilistic framework that provides consistent confidence estimation across evaluation scenarios. We evaluated STEVE across four independent scRNA-seq datasets with experimentally defined or expert-curated cell-type labels. Our results show that annotation robustness is strongly influenced by the annotation method, biological separability, dataset complexity, and batch effects. STEVE provides a practical framework for quantifying annotation uncertainty and improving reproducibility in single-cell transcriptomic analyses. STEVE is freely available at GitHub (https://github.com/XiaoDongLab/STEVE).

  PublisherOA PDFDOI

• Visium (with probes) data from the brain of a (FVB x C57Bl/6)F1 female mouse

  Cellular Senescence Network (SenNet) · 2026-04-27

  datasetOpen access
  PublisherDOI

• Visium (with probes) data from the liver of a (FVB x C57Bl/6)F1 female mouse

  Cellular Senescence Network (SenNet) · 2026-04-27

  datasetOpen access
  PublisherDOI

• Visium (with probes) data from the liver of a (FVB x C57Bl/6)F1 female mouse

  Cellular Senescence Network (SenNet) · 2026-04-27

  datasetOpen access
  PublisherDOI

• Visium (with probes) data from the liver of a (FVB x C57Bl/6)F1 female mouse

  Cellular Senescence Network (SenNet) · 2026-04-27

  datasetOpen access
  PublisherDOI

• Visium (with probes) data from the liver of a (FVB x C57Bl/6)F1 female mouse

  Cellular Senescence Network (SenNet) · 2026-04-27

  datasetOpen access
  PublisherDOI

Recent grants

• Computational evaluation of the causal role of somatic mutations in human aging

  NIH · $747k · 2018–2025

• The role of senescent cells in dysregulating immune responses and pathogen control

  NIH · $16.3M · 2024–2029

• Computational evaluation of the causal role of somatic mutations in human aging

  NIH · $272k · 2018–2021

Frequent coauthors

• Yixue Li

  Guangzhou Medical University

  87 shared

• Jan Vijg

  Albert Einstein College of Medicine

  78 shared

• Guohui Ding

  40 shared

• Alexander Y. Maslov

  Voronezh State University of Engineering Technologies

  38 shared

• Moonsook Lee

  Albert Einstein College of Medicine

  27 shared

• Rudong Li

  Chinese Academy of Sciences

  20 shared

• Lei Zhang

  Northeast Forestry University

  19 shared

• Brandon Milholland

  Albert Einstein College of Medicine

  18 shared

Education

• PhD, Shanghai Institutes for Biological Sciences

  University of the Chinese Academy of Sciences

  2013

• Bachelor's Degree

  Zhejiang University

  2008

Awards & honors

• Dr. James E. Rubin Medical Memorial Award
• Graduating Medical Student Research Award
• Veneziale-Steer Award
• Dr. Marvin and Hadassah Bacaner Research Awards
• Schmidt Steer Award