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We investigated the impact of CMV serostatus on cell composition and immune aging and found no significant associations with CD4 T cell, CD8 T cell or B cell frequencies (Supplementary Fig. 10a,b ).
|
[
{
"end": 157,
"label": "CellType",
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"text": "B cell"
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{
"end": 135,
"label": "CellType",
"start": 125,
"text": "CD4 T cell"
},
{
"end": 147,
"label": "CellType",
"start": 137,
"text": "CD8 T cell"
}
] |
Single_Cell
|
Two CD8 T cell signatures were associated with CMV serostatus after regression of other covariates: the GZMK signature and a GNLY signature ( GNLY , FGFBP2 and CX3CR1 ) (Supplementary Fig. 10c–f ).
|
[] |
Single_Cell
|
The GNLY signature was enriched across all CD8 T cell subsets in CMV donors, while the GZMK signature was variably enriched in different sites and subsets of CMV donors by GSEA (Supplementary Fig. 10d,f and Supplementary Tables 21 and 22 ).
|
[
{
"end": 61,
"label": "CellType",
"start": 43,
"text": "CD8 T cell subsets"
}
] |
Single_Cell
|
Therefore, CMV infection drives T cell gene signature changes that overlap with, but are distinct from, age-related immune alterations.
|
[] |
Single_Cell
|
Source paper: PMC12396968
CD4 T cells are highly heterogeneous and exhibit functional and phenotypic continuums , suggesting that age effects could differentially manifest within or across subsets.
|
[
{
"end": 39,
"label": "CellType",
"start": 28,
"text": "CD4 T cells"
}
] |
Single_Cell
|
We applied an annotation-independent analysis of aging in CD4 T cells in the lung, JEJ and LN, leveraging a per-cell estimation of age effects using counterfactual analysis with MrVI, which separately considers each cell and controls for covariates .
|
[
{
"end": 81,
"label": "Tissue",
"start": 77,
"text": "lung"
},
{
"end": 69,
"label": "CellType",
"start": 58,
"text": "CD4 T cells"
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{
"end": 86,
"label": "Tissue",
"start": 83,
"text": "JEJ"
},
{
"end": 93,
"label": "Tissue",
"start": 91,
"text": "LN"
},
{
"end": 220,
"label": "CellType",
"start": 216,
"text": "cell"
}
] |
Single_Cell
|
This analysis identified groups of cells with similar predicted age-associated changes in gene expression (‘modules’), which we interrogated by DE analysis across age (Fig. 7 , Extended Data Fig. 6 , and Supplementary Tables 23 and 24 ).
|
[
{
"end": 40,
"label": "CellType",
"start": 25,
"text": "groups of cells"
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] |
Single_Cell
|
In the lung, a fraction of CD4 T cells (~25%, comprising T EM cells, T RM cells and T EMRA cells) exhibited decreased cytotoxicity ( GZMH , GNLY , GZMA ) and increased cytokine receptor ( IL18R1 , IFNGR1 ) genes with age (Fig. 7a–c and Extended Data Fig. 6a,b ).
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[
{
"end": 11,
"label": "Tissue",
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"text": "lung"
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{
"end": 38,
"label": "CellType",
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"text": "CD4 T cells"
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"end": 67,
"label": "CellType",
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"text": "T EM cells"
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{
"end": 79,
"label": "CellType",
"start": 69,
"text": "T RM cells"
},
{
"end": 96,
"label": "CellType",
"start": 84,
"text": "T EMRA cells"
}
] |
Single_Cell
|
Similar upregulation of cytokine responsiveness with age occurred in some CD4 T cells in blood, BM, LN and spleen, while decreased cytotoxicity was unique to the lungs (Fig. 7c,d and Extended Data Fig. 6b ).
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[
{
"end": 94,
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"start": 89,
"text": "blood"
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{
"end": 167,
"label": "Tissue",
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"text": "lungs"
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{
"end": 113,
"label": "Tissue",
"start": 107,
"text": "spleen"
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{
"end": 85,
"label": "CellType",
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"text": "CD4 T cells"
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{
"end": 98,
"label": "Tissue",
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"text": "BM"
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{
"end": 102,
"label": "Tissue",
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"text": "LN"
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] |
Single_Cell
|
CD4 T cells in the JEJ (mainly T RM cells) exhibited an age-related decline in T H 17-associated genes ( IL17A , IL17F , IL22 , RORC , CCR6 ) and increase in pro-inflammatory cytokines ( IFNG , TNF ) (Fig. 7e–g and Extended Data Fig. 6c,d ); age-associated downregulation of IL-17-associated genes was also observed in CD4 T cells in lung and blood (Fig. 7g–h and Extended Data Fig. 6d ).
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[
{
"end": 348,
"label": "Tissue",
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"text": "blood"
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{
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"label": "Tissue",
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"text": "lung"
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{
"end": 11,
"label": "CellType",
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"text": "CD4 T cells"
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"text": "JEJ"
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{
"end": 41,
"label": "CellType",
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"text": "T RM cells"
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{
"end": 330,
"label": "CellType",
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"text": "CD4 T cells"
}
] |
Single_Cell
|
CD4 T cells in the LN (also in spleen and lungs) exhibited reduced expression of genes associated with regulation ( IL10 , TIGIT , CTLA4 , CD27 ) and increased expression of inflammation and activation markers ( IL18BP , TNFRSF4 , TNF ) with age (Fig. 7i–l and Extended Data Fig. 6e,f ).
|
[
{
"end": 47,
"label": "Tissue",
"start": 42,
"text": "lungs"
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{
"end": 37,
"label": "Tissue",
"start": 31,
"text": "spleen"
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{
"end": 11,
"label": "CellType",
"start": 0,
"text": "CD4 T cells"
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{
"end": 21,
"label": "Tissue",
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"text": "LN"
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] |
Single_Cell
|
These results revealed age-associated transcriptional changes in tissue CD4 T cells associated with site-specific functions.
|
[
{
"end": 83,
"label": "CellType",
"start": 65,
"text": "tissue CD4 T cells"
}
] |
Single_Cell
|
Source paper: PMC12396968
We present a comprehensive analysis of the human immune system across tissues and ages through multimodal profiling of blood and tissues from organ donors spanning six decades of adult life.
|
[
{
"end": 152,
"label": "Tissue",
"start": 147,
"text": "blood"
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{
"end": 90,
"label": "Tissue",
"start": 71,
"text": "human immune system"
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{
"end": 105,
"label": "Tissue",
"start": 98,
"text": "tissues"
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{
"end": 164,
"label": "Tissue",
"start": 157,
"text": "tissues"
}
] |
Single_Cell
|
We found that tissue localization was a dominant driver of the immune landscape, determining immune cell composition, cell states and functional capacity.
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[
{
"end": 20,
"label": "Tissue",
"start": 14,
"text": "tissue"
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{
"end": 104,
"label": "CellType",
"start": 92,
"text": " immune cell"
}
] |
Single_Cell
|
With age, these tissue-specific properties were largely maintained, although certain subsets and sites showed altered function, migration and regulation.
|
[] |
Single_Cell
|
Our results reveal that the human immune system is highly specialized for diverse tissue environments to maintain homeostasis and mount effective immune responses.
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[
{
"end": 47,
"label": "Tissue",
"start": 28,
"text": "human immune system"
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{
"end": 101,
"label": "Tissue",
"start": 82,
"text": "tissue environments"
}
] |
Single_Cell
|
Source paper: PMC12396968
We demonstrated that each tissue imposed site-specific immune cell compositions and adaptations that varied by lineage, and these tissue effects were conserved across donors.
|
[
{
"end": 60,
"label": "Tissue",
"start": 54,
"text": "tissue"
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{
"end": 94,
"label": "CellType",
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"text": "immune cell"
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] |
Single_Cell
|
Although we realized and reinforced site-specific features for T RM cells at barrier sites and lymphoid organs , whether these adaptations applied to other immune cells remained unknown.
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[
{
"end": 73,
"label": "CellType",
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{
"end": 110,
"label": "Tissue",
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"text": "lymphoid organs"
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{
"end": 168,
"label": "CellType",
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"text": "immune cells"
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] |
Single_Cell
|
Here, we found that site-specific signatures for T cells in the gut (high tissue residency, low cytotoxicity), lungs (high effector function, increased regulation) and lymphoid organs (stem-like features) were not exclusive to the canonical resident populations, were shared across NK cell and ILC subsets and were absent from B cell and myeloid lineages.
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[
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"text": "B cell"
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"label": "Tissue",
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"text": "lungs"
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{
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"text": "gut"
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"text": "lymphoid organs"
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"text": "NK cell"
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"text": "ILC subsets"
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{
"end": 354,
"label": "CellType",
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"text": "myeloid lineages"
}
] |
Single_Cell
|
The enhanced expression of stem-like transcription factors TCF-1 and LEF-1 by LN memory T cells suggests that lymphoid organs may serve as reservoirs for long-lived memory cells, given these factors’ requirement for memory T cell generation .
|
[
{
"end": 95,
"label": "CellType",
"start": 78,
"text": "LN memory T cells"
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{
"end": 125,
"label": "Tissue",
"start": 110,
"text": "lymphoid organs"
},
{
"end": 177,
"label": "CellType",
"start": 154,
"text": "long-lived memory cells"
}
] |
Single_Cell
|
Macrophages and plasma cells also exhibited site-specific features in the gut, lungs and lymphoid organs through distinct subset-specific pathways, such as alveolar macrophages in the lungs and red pulp macrophages in the spleen.
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[
{
"end": 11,
"label": "CellType",
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"text": "Macrophages"
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{
"end": 84,
"label": "Tissue",
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"text": "lungs"
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{
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"label": "Tissue",
"start": 184,
"text": "lungs"
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{
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"label": "Tissue",
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"text": "spleen"
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{
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"label": "Tissue",
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"text": "gut"
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{
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"text": "alveolar macrophages"
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"text": "plasma cells"
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{
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"label": "Tissue",
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"text": "lymphoid organs"
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{
"end": 214,
"label": "CellType",
"start": 194,
"text": "red pulp macrophages"
}
] |
Single_Cell
|
These lineage-dependent tissue adaptations probably reflect niche localization and interactions with distinct structural and immune cells within each tissue.
|
[
{
"end": 156,
"label": "Tissue",
"start": 150,
"text": "tissue"
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{
"end": 137,
"label": "CellType",
"start": 125,
"text": "immune cells"
},
{
"end": 120,
"label": "CellType",
"start": 110,
"text": "structural"
}
] |
Single_Cell
|
Source paper: PMC12396968
Age-associated gene signatures identified for macrophages, T cells and B cells were intrinsic to the subset and site.
|
[
{
"end": 85,
"label": "CellType",
"start": 74,
"text": "macrophages"
},
{
"end": 106,
"label": "CellType",
"start": 99,
"text": "B cells"
},
{
"end": 94,
"label": "CellType",
"start": 87,
"text": "T cells"
}
] |
Single_Cell
|
The APOE–TREM2 gene signature, essential for crucial macrophage functions , was reduced with age by lung macrophages.
|
[
{
"end": 116,
"label": "CellType",
"start": 99,
"text": " lung macrophages"
}
] |
Single_Cell
|
APOE–TREM2 expression in microglia is associated with neurodegeneration in Alzheimer’s disease and in other macrophage types with cardiovascular diseases .
|
[
{
"end": 118,
"label": "CellType",
"start": 108,
"text": "macrophage"
},
{
"end": 34,
"label": "CellType",
"start": 25,
"text": "microglia"
}
] |
Single_Cell
|
TREM2 can have different effects on macrophage functions; promoting anti-inflammatory ‘M2-like’ function in some contexts and phagocytosis and sustained inflammation in others .
|
[] |
Single_Cell
|
The age-associated loss of TREM2 in lung macrophages could thus account for compromised immunity to respiratory pathogens and increased lung cancer susceptibility observed in the aging population.
|
[
{
"end": 52,
"label": "CellType",
"start": 36,
"text": "lung macrophages"
}
] |
Single_Cell
|
TREM agonists that enhance phagocytic function are being tested in clinical trials in Alzheimer’s disease and could be considered in the rejuvenation of aging macrophages in other sites.
|
[
{
"end": 170,
"label": "CellType",
"start": 153,
"text": "aging macrophages"
}
] |
Single_Cell
|
Source paper: PMC12396968
Other age-associated features were specific to lymphocyte lineages.
|
[
{
"end": 85,
"label": "CellType",
"start": 75,
"text": "lymphocyte"
}
] |
Single_Cell
|
T cells in circulation expressed higher levels of genes associated with inflammation, cytotoxicity and NK-like markers with age, as previously reported .
|
[
{
"end": 7,
"label": "CellType",
"start": 0,
"text": "T cells"
}
] |
Single_Cell
|
Circulating T EMRA cells and T EM cells upregulated GZMK and other markers, similar to senescent GzmK CD8 T cells found in mice and human blood .
|
[
{
"end": 24,
"label": "CellType",
"start": 0,
"text": "Circulating T EMRA cells"
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{
"end": 39,
"label": "CellType",
"start": 29,
"text": "T EM cells"
},
{
"end": 113,
"label": "CellType",
"start": 87,
"text": "senescent GzmK CD8 T cells"
},
{
"end": 127,
"label": "Tissue",
"start": 123,
"text": "mice"
},
{
"end": 143,
"label": "Tissue",
"start": 132,
"text": "human blood"
}
] |
Single_Cell
|
T RM cells in the lungs and intestines did not exhibit this age-associated gene signature, suggesting that the tissue environment may insulate them from signals that promote cellular aging or that cellular aging is tissue-specific.
|
[
{
"end": 38,
"label": "Tissue",
"start": 28,
"text": "intestines"
},
{
"end": 23,
"label": "Tissue",
"start": 18,
"text": "lungs"
},
{
"end": 10,
"label": "CellType",
"start": 0,
"text": "T RM cells"
},
{
"end": 129,
"label": "Tissue",
"start": 111,
"text": "tissue environment"
}
] |
Single_Cell
|
However, both circulating (T EM , T EMRA ) and T RM cells had increased expression of genes for pro-inflammatory cytokines and chemokines with age, consistent with inflammaging implicated in cardiovascular diseases and metabolic dysregulation .
|
[
{
"end": 31,
"label": "CellType",
"start": 27,
"text": "T EM"
},
{
"end": 40,
"label": "CellType",
"start": 34,
"text": "T EMRA"
},
{
"end": 57,
"label": "CellType",
"start": 47,
"text": "T RM cells"
}
] |
Single_Cell
|
Our findings suggest that human T cells may be more prone to innate functions such as cytokine-driven activation (for example, via IL-18) with age.
|
[
{
"end": 39,
"label": "CellType",
"start": 26,
"text": "human T cells"
}
] |
Single_Cell
|
We also identified an age-associated increase in IL-18 expression and reduced BCR-mediated signaling within tissue B cells, which is a feature of NK-like B cell subsets identified in disease contexts .
|
[
{
"end": 122,
"label": "CellType",
"start": 108,
"text": "tissue B cells"
},
{
"end": 160,
"label": "CellType",
"start": 146,
"text": "NK-like B cell"
}
] |
Single_Cell
|
Thus, aging may reflect a broader age-related shift to innate-like functions in both T cells and B cells.
|
[
{
"end": 104,
"label": "CellType",
"start": 97,
"text": "B cells"
},
{
"end": 92,
"label": "CellType",
"start": 85,
"text": "T cells"
}
] |
Single_Cell
|
Source paper: PMC12396968
Our findings have important implications for immune monitoring, therapeutic modulation and clinical advancement.
|
[] |
Single_Cell
|
The compartmentalization of immune subsets across tissues emphasizes the importance of site-specific immune monitoring in disease states, as exemplified in severe COVID-19, in which immune dynamics in the respiratory tract rather than blood correlated with infection outcome .
|
[
{
"end": 222,
"label": "Tissue",
"start": 205,
"text": "respiratory tract"
},
{
"end": 240,
"label": "Tissue",
"start": 235,
"text": "blood"
},
{
"end": 42,
"label": "CellType",
"start": 28,
"text": "immune subsets"
},
{
"end": 57,
"label": "Tissue",
"start": 50,
"text": "tissues"
}
] |
Single_Cell
|
The distinctness of gut-specific subsets provides rationale for targeted intestinal interventions, as demonstrated by rotavirus vaccines .
|
[
{
"end": 40,
"label": "CellType",
"start": 20,
"text": "gut-specific subsets"
}
] |
Single_Cell
|
The identification of stem-like profiles (marked by TCF7 and LEF1 expression) in LN T cells and NK cells has direct relevance to adoptive CAR-T immunotherapies, in which stemness is associated with remission .
|
[
{
"end": 91,
"label": "CellType",
"start": 81,
"text": "LN T cells"
},
{
"end": 104,
"label": "CellType",
"start": 96,
"text": "NK cells"
}
] |
Single_Cell
|
LNs may thus represent an optimal source of NK and T cells for engineering adoptive cell therapies against cancer, infections and autoimmunity .
|
[
{
"end": 58,
"label": "CellType",
"start": 51,
"text": "T cells"
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{
"end": 3,
"label": "Tissue",
"start": 0,
"text": "LNs"
},
{
"end": 46,
"label": "CellType",
"start": 44,
"text": "NK"
}
] |
Single_Cell
|
Source paper: PMC12396968
Our study has several limitations.
|
[] |
Single_Cell
|
The low frequency of certain immune subsets in tissues, including DCs, macrophages in lymphoid organs and hematopoietic progenitors, precluded aging analysis and will require sorting for future studies.
|
[
{
"end": 82,
"label": "CellType",
"start": 71,
"text": "macrophages"
},
{
"end": 43,
"label": "CellType",
"start": 29,
"text": "immune subsets"
},
{
"end": 54,
"label": "Tissue",
"start": 47,
"text": "tissues"
},
{
"end": 69,
"label": "CellType",
"start": 66,
"text": "DCs"
},
{
"end": 101,
"label": "Tissue",
"start": 86,
"text": "lymphoid organs"
},
{
"end": 131,
"label": "CellType",
"start": 106,
"text": "hematopoietic progenitors"
}
] |
Single_Cell
|
Similarly, an in-depth analysis of TCR and BCR across sites and age would require isolating memory T cells and B cells from each site.
|
[
{
"end": 118,
"label": "CellType",
"start": 111,
"text": "B cells"
},
{
"end": 106,
"label": "CellType",
"start": 92,
"text": "memory T cells"
}
] |
Single_Cell
|
Finally, although we identified age-associated changes in 24 donors, additional donors would increase power and probably reveal additional aging signatures.
|
[] |
Single_Cell
|
In conclusion, this dataset, along with the models and analyses presented, can serve as a valuable and actionable resource, informing targeted immune modulation by site and age in future treatments for infectious, neoplastic and inflammatory diseases.
|
[] |
Single_Cell
|
Source paper: PMC12396968
The study analyzed immune cells from multiple tissue samples obtained from 24 organ donors.
|
[
{
"end": 59,
"label": "CellType",
"start": 47,
"text": "immune cells"
},
{
"end": 88,
"label": "Tissue",
"start": 74,
"text": "tissue samples"
}
] |
Single_Cell
|
No statistical method was used to predetermine sample size.
|
[] |
Single_Cell
|
No data were excluded from the analysis.
|
[] |
Single_Cell
|
Investigators were not blinded to allocation during experiments and outcome assessment, as this is a profiling study.
|
[] |
Single_Cell
|
Source paper: PMC12396968
Tissues were obtained from deceased organ donors (Supplementary Table 1 ) at the time of organ acquisition for clinical transplantation.
|
[
{
"end": 122,
"label": "Tissue",
"start": 117,
"text": "organ"
},
{
"end": 35,
"label": "Tissue",
"start": 28,
"text": "Tissues"
}
] |
Single_Cell
|
In the USA, this was done through an approved protocol and material transfer agreement via LiveOnNY, the organ procurement organization for the New York metropolitan area .
|
[
{
"end": 110,
"label": "Tissue",
"start": 105,
"text": "organ"
}
] |
Single_Cell
|
In the UK, tissues were obtained through the Cambridge Biorepository for Translational Medicine (CBTM), REC 15/EE/0152, as previously described .
|
[
{
"end": 18,
"label": "Tissue",
"start": 11,
"text": "tissues"
}
] |
Single_Cell
|
Owing to the different amounts of tissues and some distinct samples (for example, skin, liver and colon) obtained at each location, protocols for processing may differ, as described below.
|
[
{
"end": 103,
"label": "Tissue",
"start": 98,
"text": "colon"
},
{
"end": 93,
"label": "Tissue",
"start": 88,
"text": "liver"
},
{
"end": 86,
"label": "Tissue",
"start": 82,
"text": "skin"
},
{
"end": 41,
"label": "Tissue",
"start": 34,
"text": "tissues"
},
{
"end": 67,
"label": "Tissue",
"start": 51,
"text": "distinct samples"
}
] |
Single_Cell
|
Source paper: PMC12396968
Each tissue was subjected to a tissue-specific protocol to maximize MNC recovery and viability across a diversity of sites .
|
[
{
"end": 39,
"label": "Tissue",
"start": 33,
"text": "tissue"
}
] |
Single_Cell
|
Detailed, step-by-step protocols for immune cell isolation from blood, BM, spleen, LNs, lungs (parenchyma and airway or BAL) and JEJ (JLP and JEL) are presented elsewhere .
|
[
{
"end": 69,
"label": "Tissue",
"start": 64,
"text": "blood"
},
{
"end": 105,
"label": "Tissue",
"start": 95,
"text": "parenchyma"
},
{
"end": 93,
"label": "Tissue",
"start": 88,
"text": "lungs"
},
{
"end": 81,
"label": "Tissue",
"start": 75,
"text": "spleen"
},
{
"end": 48,
"label": "CellType",
"start": 37,
"text": "immune cell"
},
{
"end": 73,
"label": "Tissue",
"start": 71,
"text": "BM"
},
{
"end": 86,
"label": "Tissue",
"start": 83,
"text": "LNs"
},
{
"end": 116,
"label": "Tissue",
"start": 110,
"text": "airway"
},
{
"end": 123,
"label": "Tissue",
"start": 120,
"text": "BAL"
},
{
"end": 132,
"label": "Tissue",
"start": 129,
"text": "JEJ"
},
{
"end": 137,
"label": "Tissue",
"start": 134,
"text": "JLP"
},
{
"end": 145,
"label": "Tissue",
"start": 142,
"text": "JEL"
}
] |
Single_Cell
|
All single-cell suspensions from each site were centrifuged (400 g , 10 min at 4 °C) and washed twice with PBS containing 5% (v/v) FBS and 2 mM EDTA.
|
[] |
Single_Cell
|
Cells were counted using the NC-2000 Cell Counter (Chemometec), and 50 million viable cells from each site were treated with TruStain FcX (BioLegend) and FcR Blocking Reagent (Miltenyi).
|
[
{
"end": 5,
"label": "CellType",
"start": 0,
"text": "Cells"
},
{
"end": 91,
"label": "CellType",
"start": 79,
"text": "viable cells"
}
] |
Single_Cell
|
Cells were subsequently labeled for 30 min at 4 °C with biotinylated anti-CD66B, anti-CD235ab and anti-CD326 to remove granulocytes, red blood cells and epithelial cells, respectively, by streptavidin-coated magnetic particles and negative selection (Bangs Laboratories).
|
[
{
"end": 169,
"label": "CellType",
"start": 153,
"text": "epithelial cells"
},
{
"end": 131,
"label": "CellType",
"start": 119,
"text": "granulocytes"
},
{
"end": 5,
"label": "CellType",
"start": 0,
"text": "Cells"
},
{
"end": 148,
"label": "CellType",
"start": 133,
"text": "red blood cells"
}
] |
Single_Cell
|
All single-cell suspensions were subjected to dead cell removal using a Dead Cell Removal Kit (Miltenyi).
|
[] |
Single_Cell
|
Source paper: PMC12396968
Each single-cell suspension was hashtagged to allow pooling of samples for loading on the 10× Genomics Chromium instrument.
|
[] |
Single_Cell
|
MNCs from each site (10 per site) were transferred into 4 ml flow cytometry tubes, pelleted by centrifugation as above and resuspended in PBS containing 5% (v/v) FBS and 2 mM EDTA and then incubated with TruStain FcX (BioLegend) and FcR Blocking Reagent (Miltenyi) at 4 °C for 10 min to reduce background labeling.
|
[] |
Single_Cell
|
Each hashtag was spun at 14,000 g for 10 min, added to each sample (1 µl hashtag per tube), incubated at 4 °C for 30 min, pelleted and washed 3 times with PBS containing 5% (v/v) FBS and 2 mM EDTA.
|
[] |
Single_Cell
|
For CITE-seq antibody staining, 200,000 cells from each sample were resuspended in reconstituted TotalSeq-A Universal Cocktail (BioLegend) (donors 496 and 503) and TotalSeq-C Universal Cocktail (BioLegend) (remaining 10 donors) in PBS containing 5% (v/v) FBS and 2 mM EDTA, incubated at 4 °C for 30 min and washed 3 times with PBS containing 5% (v/v) FBS and 2 mM EDTA before resuspension in a final volume of 1 ml.
|
[
{
"end": 45,
"label": "CellType",
"start": 40,
"text": "cells"
}
] |
Single_Cell
|
CITE-seq antibody panels are listed in Supplementary Table 1 .
|
[] |
Single_Cell
|
Source paper: PMC12396968
Each tissue was subjected to a tissue-specific protocol to generate a single-cell suspension of immune cells that has been published in detail elsewhere .
|
[
{
"end": 39,
"label": "Tissue",
"start": 33,
"text": "tissue"
},
{
"end": 136,
"label": "CellType",
"start": 124,
"text": "immune cells"
}
] |
Single_Cell
|
Immune cells were isolated from blood, BM aspirates (sternum), spleen, LNs, lungs, liver, JEJ (JEL and JLP) and skin.
|
[
{
"end": 37,
"label": "Tissue",
"start": 32,
"text": "blood"
},
{
"end": 60,
"label": "Tissue",
"start": 53,
"text": "sternum"
},
{
"end": 81,
"label": "Tissue",
"start": 76,
"text": "lungs"
},
{
"end": 69,
"label": "Tissue",
"start": 63,
"text": "spleen"
},
{
"end": 88,
"label": "Tissue",
"start": 83,
"text": "liver"
},
{
"end": 116,
"label": "Tissue",
"start": 112,
"text": "skin"
},
{
"end": 12,
"label": "CellType",
"start": 0,
"text": "Immune cells"
},
{
"end": 41,
"label": "Tissue",
"start": 39,
"text": "BM"
},
{
"end": 74,
"label": "Tissue",
"start": 71,
"text": "LNs"
},
{
"end": 93,
"label": "Tissue",
"start": 90,
"text": "JEJ"
},
{
"end": 98,
"label": "Tissue",
"start": 95,
"text": "JEL"
},
{
"end": 106,
"label": "Tissue",
"start": 103,
"text": "JLP"
}
] |
Single_Cell
|
Each single-cell suspension was hashtagged to allow pooling of samples for loading on the 10× Genomics Chromium instrument.
|
[] |
Single_Cell
|
Approximately 500,000 MNCs per tissue were transferred into 1.5 ml Lo-Bind DNA tubes.
|
[
{
"end": 37,
"label": "Tissue",
"start": 31,
"text": "tissue"
},
{
"end": 26,
"label": "CellType",
"start": 22,
"text": "MNCs"
}
] |
Single_Cell
|
Cells were centrifuged at 400 g for 5 min, the supernatant removed and resuspended in 50 μl PBS containing 0.04% BSA.
|
[
{
"end": 5,
"label": "CellType",
"start": 0,
"text": "Cells"
}
] |
Single_Cell
|
Cells were treated with 5 μl TruStain FcX (BioLegend) to reduce background labeling and incubated at 4 °C for 10 min, then each hashtag was added to the sample (1 µl hashtag per tube).
|
[
{
"end": 5,
"label": "CellType",
"start": 0,
"text": "Cells"
}
] |
Single_Cell
|
Samples were incubated at 4 °C for 30 min, washed three times with PBS containing 0.04% BSA and equal numbers of cells from each tissue were pooled based on the number processed per donor.
|
[
{
"end": 118,
"label": "CellType",
"start": 113,
"text": "cells"
}
] |
Single_Cell
|
Cells were incubated with TotalSeq-C Human Universal Cocktail (BioLegend) (Supplementary Table 2 ) for 30 min at 4 °C and subsequently washed 3 times with PBS containing 0.04% BSA.
|
[
{
"end": 5,
"label": "CellType",
"start": 0,
"text": "Cells"
}
] |
Single_Cell
|
Cells were resuspended in 500 µl PBS containing 0.04% BSA and passed through a 40 µm Flowmi pipette tip filter to remove any clumps of cells.
|
[
{
"end": 5,
"label": "CellType",
"start": 0,
"text": "Cells"
},
{
"end": 140,
"label": "CellType",
"start": 135,
"text": "cells"
}
] |
Single_Cell
|
Source paper: PMC12396968
For scRNA-seq experiments, single cells were loaded onto the channels of a Chromium chip (10× Genomics).
|
[
{
"end": 67,
"label": "CellType",
"start": 55,
"text": "single cells"
}
] |
Single_Cell
|
cDNA synthesis, amplification and sequencing libraries were generated using either the Single Cell 5′ Reagent (v1 and v2) or 3′ Reagent (v3) Kits.
|
[] |
Single_Cell
|
TCRαβ and BCR paired VDJ libraries were prepared from samples made with the 5′ Reagent Kit.
|
[] |
Single_Cell
|
All libraries were sequenced on either an Illumina HiSeq 4000, NextSeq or NovaSeq 6000 instrument.
|
[] |
Single_Cell
|
Source paper: PMC12396968
Alignment was performed using Cell Ranger (v6.0.0) from 10× Genomics with the appropriate chemistry option ( fiveprime or SC3Pv3 ).
|
[] |
Single_Cell
|
We added the cell hashing antibody and the protein antibody fastqs to a single call of cellranger count .
|
[] |
Single_Cell
|
Immune receptors (TCR and BCR) were aligned using cellranger vdj .
|
[] |
Single_Cell
|
TCR and BCR alignment results from Cell Ranger were used for quality control and filtering of low-quality cells (individual cells with both TCR and BCR detected).
|
[
{
"end": 111,
"label": "CellType",
"start": 94,
"text": "low-quality cells"
},
{
"end": 129,
"label": "CellType",
"start": 113,
"text": "individual cells"
}
] |
Single_Cell
|
In cases where a single cell had both TCR and BCR reads, the immune receptor data were discarded, and the cell was labeled as a multiplet.
|
[
{
"end": 28,
"label": "CellType",
"start": 17,
"text": "single cell"
},
{
"end": 110,
"label": "CellType",
"start": 106,
"text": "cell"
}
] |
Single_Cell
|
For all alignments, we used reference genome refdata-gex-GRCh38-2020-A and immune receptor reference refdata-cellranger-vdj-GRCh38-alts-ensembl-5.0.0 .
|
[] |
Single_Cell
|
Source paper: PMC12396968
Samples were demultiplexed by hashtag expression using hashsolo with default parameters .
|
[] |
Single_Cell
|
Cells that were not uniquely assigned to an individual sample were removed from downstream analysis.
|
[
{
"end": 5,
"label": "CellType",
"start": 0,
"text": "Cells"
}
] |
Single_Cell
|
Filtering was performed to remove cells with fewer than 50 unique genes detected.
|
[
{
"end": 39,
"label": "CellType",
"start": 34,
"text": "cells"
}
] |
Single_Cell
|
Mitochondrial counts were quantified, summing all genes starting with ‘ MT- ’, and ribosomal counts were quantified using all genes starting with ‘ RPS ’ and ‘ RPL ’.
|
[] |
Single_Cell
|
For erythrocyte-related counts, all genes starting with ‘ HB ’ as well as ALAS2 and EPOR (to detect erythrocyte precursors) were quantified.
|
[
{
"end": 122,
"label": "CellType",
"start": 100,
"text": "erythrocyte precursors"
}
] |
Single_Cell
|
Cells with more than 20% mitochondrial counts were flagged as potentially low-quality for later filtering.
|
[
{
"end": 5,
"label": "CellType",
"start": 0,
"text": "Cells"
}
] |
Single_Cell
|
Counts for mitochondrial genes and MALAT1 were subsequently removed from the gene expression object and downstream analysis.
|
[] |
Single_Cell
|
To exclude contamination from ambient RNA, we processed the data using DecontX .
|
[] |
Single_Cell
|
Two samples (one from liver and one from skin) with abnormally high ambient counts were removed, as DecontX could not correct the ambient counts (for example, plasma cell genes like ALB in all immune cells).
|
[
{
"end": 27,
"label": "Tissue",
"start": 22,
"text": "liver"
},
{
"end": 45,
"label": "Tissue",
"start": 41,
"text": "skin"
},
{
"end": 205,
"label": "CellType",
"start": 193,
"text": "immune cells"
}
] |
Single_Cell
|
All downstream analysis was performed on uncorrected counts, as we found few ambient counts in other samples.
|
[] |
Single_Cell
|
We used a CellTypist model (at https://cog.sanger.ac.uk/celltypist/models/Red_Blood_CZI/v1/Red_Blood_CZI.pkl ) to detect erythrocytes.
|
[
{
"end": 133,
"label": "CellType",
"start": 121,
"text": "erythrocytes"
}
] |
Single_Cell
|
Doublets were additionally detected using Scrublet with a sim_doublet_ratio of ten.
|
[] |
Single_Cell
|
For each unique tissue site, we performed an initial integration across all samples by training a single-cell variational inference (scVI) model on the gene expression with following parameters: 10,000 highly variable genes using the seurat_v3 option in Scanpy, early stopping enabled and 50 epochs, 10 epochs for n_epochs_kl_warmup , two layers in encoder and decoder, nb gene likelihood and a mini-batch size of 256.
|
[
{
"end": 27,
"label": "Tissue",
"start": 16,
"text": "tissue site"
}
] |
Single_Cell
|
Source paper: PMC12396968
To perform filtering of low-quality events, we used the following quality metrics: the probability of a doublet predicted by Scrublet, the probability of a doublet from HashSolo, the percentage of erythrocyte genes as described above, whether a cell contained both TCR and BCR, whether the CellTypist erythrocyte model predicted a cell to be an erythrocyte, as well as cells with a total count below 2,000 unique molecular identifiers, 1,200 unique genes or 200 protein counts.
|
[
{
"end": 384,
"label": "CellType",
"start": 373,
"text": "erythrocyte"
},
{
"end": 277,
"label": "CellType",
"start": 273,
"text": "cell"
},
{
"end": 363,
"label": "CellType",
"start": 359,
"text": "cell"
},
{
"end": 402,
"label": "CellType",
"start": 397,
"text": "cells"
}
] |
Single_Cell
|
All scores were added to generate a per-cell quality metric.
|
[] |
Single_Cell
|
To perform filtering, we argued that cells that group together and have evidence of low quality should be removed from downstream analysis.
|
[
{
"end": 42,
"label": "CellType",
"start": 37,
"text": "cells"
}
] |
Single_Cell
|
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