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Pan-organ Immunosenescence Atlas

A story about how cells change over time

Created by Fan Lab at Yale University and the Cyberinfrastructure for Network Science Center at Indiana University

What is immunosenescence?

The immune system is a complex network of cells, tissues, organs, and the substances they make. It helps the body fight infections and other diseases. As we age, our immune system function becomes impaired. Our bodies have a harder time fighting off infections, controlling malignancies, and maintaining immune tolerance. This age-related deficiency in immune system function is called immunosenescence.

Illustrations of immune cells with a variety of shapes and internal structures

A key contributor to the immune system’s deterioration is cell senescence. This occurs when stress or damage to the cell causes it to exit the cell cycle. In this state of cell cycle arrest, the cell stops dividing but remains metabolically active. The senescent cell secretes a combination of bioactive molecules known as SASP—the senescence-associated secretory phenotype.

Stress or damage changes a normal cell into a senescent cell that secretes molecules associated with inflammation

In this state of cell cycle arrest, the cell stops dividing but remains metabolically active. The senescent cell secretes a combination of bioactive molecules known as SASP—the senescence-associated secretory phenotype. Cell senescence has some very important benefits for our health. It aids in embryo development, helps our bodies respond to injury, and works to keep cancer cells from replicating.

However, as we age, senescent cells accumulate. This may contribute to “inflammaging,” a chronic, low-grade inflammation that can damage healthy tissue, promote disease progression, and accelerate the aging process. In many cases, the body relies on immune cells to clear senescent cells and prevent them from accumulating.

This function is impaired when immune cells themselves become senescent. Immune-cell senescence can also weaken the immune system’s ability to detect disease and migrate to sites of infection or injury. A better understanding of cellular senescence is crucial to developing treatments that could improve our quality of life as we age.

Comparison of a normal cell and a senescent cell, whose changes include cell-cycle arrest, secretions, enlarged shape, organelle dysfunction, and molecular damage

Researchers in the field of senescence biology work to identify the many varieties of senescent cells, locate where they reside in the body, and understand the effects they have on their surrounding environments. A cell’s senescence phenotype—the physical and biochemical traits the cell exhibits once it becomes senescent—can vary widely depending on the cell type and the tissue microenvironment. These diverse and context-specific phenotypes make research on immune cell senescence a particular challenge.

For starters, the immune system is distributed throughout the body. It consists of many organs and multiple tissue types with their own distinct structures and functions. Immune cell researchers often find that existing datasets (whether bulk or single-cell sequenced) underrepresent immune cells or lack them entirely. When immune cells are present, they typically lack robust data on senescence. Just as there is no single “immune organ” there is no single “immune cell” with one particular function.

Examples of immune-related cell types: basophil, natural killer cell, B cell, plasma cell, T cell, neutrophil, dendritic cell, mast cell, eosinophil, endothelial cell, macrophage, and fibroblast

Instead, there are many cell types with different structures and roles. Different immune cell types exhibit different senescence patterns/features. Senescence can be difficult to detect in immune cells since some of the recognized markers of senescence are part of the normal functioning of many immune cells. Since context matters in the study of immune-cell senescence, research needs to analyze many tissue types across various anatomical structures.

To study how senescent cells behave in a living organism, scientists often conduct their research using samples from laboratory mice.

Mice have long been prized by medical researchers due to the many biological and genetic features they share with humans.

They are valuable to senescence research because their short lifespans allow scientists to observe the mechanics of aging over the space of months rather than decades.

This 3D mouse model shows the thymus, liver, spleen, and pancreas.

Keep scrolling to explore mouse anatomy.

The Cellular Senescence Network (SenNet) Program is one such effort that seeks to identify and characterize the differences in senescent cells across the body, across various states of human health, and across the lifespan. They are creating atlases of senescent cells, the differences among them, and the molecules they secrete, using data collected from multiple human and model organism tissues.

SenNet is teaming with the Human Reference Atlas (HRA) to study the possibility that senescent cells may alter their tissue microenvironments.

What changes can we see between young, aged, and treated tissue?

Cell sample comparison

Compare tissue across three organs

Researchers collected tissue from young mice, aged mice, and aged mice treated with dasatinib and quercetin (D&Q), drugs that target senescent cells.

In this comparison, young mice were 2 months old. Aged mice were 24 months old, including those treated with D&Q.

By mapping individual cells within liver, spleen, and thymus samples, researchers can compare how cellular patterns change with age—and whether those patterns change after treatment.

Each color represents a cell population. Compare where those populations appear, how densely they cluster, and which populations share the same neighborhoods across the three conditions.

Liver

  1. Young

    Cell population map of liver tissue from a young mouse, showing a rounded section divided by a large white channel

  2. Aged

    Cell population map of liver tissue from an aged mouse, showing a rounded section dominated by peach and brown regions

  3. Aged + D&Q

    Cell population map of D and Q-treated liver tissue from an aged mouse, showing peach and pale blue regions around an open center

Spleen

  1. Young

    Cell population map of spleen tissue from a young mouse, showing light blue clusters and a dark blue and pink region

  2. Aged

    Cell population map of spleen tissue from an aged mouse, showing multiple blue clusters surrounded by pink regions

  3. Aged + D&Q

    Cell population map of D and Q-treated spleen tissue from an aged mouse, showing blue clusters with green centers among pale pink regions

Thymus

  1. Young

    Cell population map of thymus tissue from a young mouse, showing broad pale blue, green, yellow, and orange regions

  2. Aged

    Cell population map of thymus tissue from an aged mouse, showing a narrow curved section with turquoise, dark blue, brown, and pink regions

  3. Aged + D&Q

    Cell population map of D and Q-treated thymus tissue from an aged mouse, showing a broad yellow and peach center bordered by turquoise and pink

What software exists to visualize cell differences over time?

The HRA Cell Distance Explorer tool can be used to measure how close cells are to one another, compare cell distances over time, and analyze cell neighborhoods in spatially resolved omics data.

The neighborhood-level analysis produced by the Cell Distance Explorer can be combined with more macro-level efforts to quantify spatial distribution to establish a metric and baseline that enable comparison across organs with different morphology.

  1. Let’s get familiar with the Cell Distance Explorer app.
  2. Show/hide cell types in the visualization and plots. Hide cell links from this table view. Update colors for individual cell types. Download CSVs for the current configurations of cell types, cell links, and cell type color map formatting.
  3. Use the Visualization panel to navigate the cells spatially:
    • Zoom in and out with the mouse pinwheel or pinch using the trackpad
    • Pan using CTRL/CMD + mouse drag, right click + mouse drag, or use the keyboard arrows
    • Rotate the visualization using CTRL/CMD + keyboard arrows or with a mouse drag
  4. This violin plot shows cell-to-nearest-anchor cell distance distributions categorized by each cell type in the dataset.
  5. This histogram plot shows the cell-to-nearest-anchor cell distance distributions categorized by each cell type in the dataset.

There is strong external evidence that immune senescence is implicated in tissue reorganization. Thus, monitoring the effects of senescence on the surrounding tissue, evaluating them, and treating them could vastly improve our health.

Continue exploring

Resources

Explore the data, tools, and research communities behind this story.

Acknowledgments

  • DataAnthony Fung and Rong Fan (Yale University), Andreas Bueckle (Indiana University)
  • Story authorsAnthony Fung (Yale University), Todd Theriault, Elizabeth Maier, and Katy Börner (Indiana University)
  • Cell Distance Explorer design and implementation Yashvardhan Jain, Elizabeth Maier, Bruce W. Herr II, Daniel Bolin, and Edward Lu (Indiana University)
  • Web design and developmentEdward Lu and Elizabeth Maier (Indiana University)

References

  1. Bueckle, Andreas, Bruce W. Herr II, Lu Chen, Daniel Bolin, Danial Qaurooni, Michael Ginda, Yashvardhan Jain, Aleix Puig-Barbe, Kristin Ardlie, Fusheng Wang, and Katy Börner. “Cell Type Populations for 3D Anatomical Structures of the Human Reference Atlas.” Scientific Data 13, no. 1 (2026): 716. https://doi.org/10.1038/s41597-026-06642-4.

  2. Jain, Yashvardhan, Jodie Jepson, Roy Chen, Elizabeth Maier, Bruce W. Herr II, Aleix Puig-Barbe, Ellen M. Quardokus, Danial Qaurooni, Clarence Yapp, Samuel L. Ewing, Archibald Enninful, Negin Farzad, Andreas Bueckle, Quinn T. Easter, Bruno Matuck, Chenchen Zhu, Emma Marie Monte, Jeffrey M. Purkerson, Matthew Jehrio, Ravi S. Misra, Rong Fan, Fiona Ginty, Arivarasan Karunamurthy, Jean Fan, Martha Campbell-Thompson, Gloria S. Pryhuber, Kevin M. Byrd, John W. Hickey, and Katy Börner. “Exploring Endothelial Cell Environments across Organs in Spatially Resolved Omics Data.” bioRxiv preprint, September 25, 2025. https://doi.org/10.1101/2025.09.23.678129.