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We first used Louvain clustering on the coordinates from scVI latent space using 15 nearest neighbors to cluster the per-tissue integrated data with a resolution of 5.0.
|
[] |
Single_Cell
|
Every cluster with a median low-quality score (described above) of at least one was removed from downstream analysis.
|
[] |
Single_Cell
|
Although some low-quality events were retained with this filtering, their frequency was drastically reduced.
|
[] |
Single_Cell
|
We additionally established tissue-specific cut-offs to remove additional events and removed clusters with a mean low-quality score of 0.3 from all tissues, except for the lung LN and JEJ, for which the threshold was manually increased to recover higher-quality cells.
|
[
{
"end": 176,
"label": "Tissue",
"start": 172,
"text": "lung"
},
{
"end": 155,
"label": "Tissue",
"start": 148,
"text": "tissues"
},
{
"end": 179,
"label": "Tissue",
"start": 177,
"text": "LN"
},
{
"end": 187,
"label": "Tissue",
"start": 184,
"text": "JEJ"
},
{
"end": 267,
"label": "CellType",
"start": 247,
"text": "higher-quality cells"
}
] |
Single_Cell
|
Using a course cell-type annotation based on manual annotation of clusters, we identified cell types that were consistently filtered out, even though their quality did not appear to be spuriously low by manual inspection.
|
[
{
"end": 100,
"label": "CellType",
"start": 90,
"text": "cell types"
}
] |
Single_Cell
|
We retained mast cells and hematopoietic stem cells from all tissues, all macrophages from LNs and spleen, all erythrocytes and platelets from BM and all monocytes from liver.
|
[
{
"end": 22,
"label": "CellType",
"start": 12,
"text": "mast cells"
},
{
"end": 85,
"label": "CellType",
"start": 74,
"text": "macrophages"
},
{
"end": 163,
"label": "CellType",
"start": 154,
"text": "monocytes"
},
{
"end": 123,
"label": "CellType",
"start": 111,
"text": "erythrocytes"
},
{
"end": 137,
"label": "CellType",
"start": 128,
"text": "platelets"
},
{
"end": 105,
"label": "Tissue",
"start": 99,
"text": "spleen"
},
{
"end": 174,
"label": "Tissue",
"start": 169,
"text": "liver"
},
{
"end": 51,
"label": "CellType",
"start": 27,
"text": "hematopoietic stem cells"
},
{
"end": 68,
"label": "Tissue",
"start": 61,
"text": "tissues"
},
{
"end": 94,
"label": "Tissue",
"start": 91,
"text": "LNs"
},
{
"end": 145,
"label": "Tissue",
"start": 143,
"text": "BM"
}
] |
Single_Cell
|
Source paper: PMC12396968
We concatenated cells from all tissues and computed the 10,000 top highly variable genes using the seurat_v3 option in Scanpy and used the same parameters as described above, but with a mini-batch size of 1,024 to accelerate the training process.
|
[
{
"end": 49,
"label": "CellType",
"start": 44,
"text": "cells"
},
{
"end": 66,
"label": "Tissue",
"start": 59,
"text": "tissues"
}
] |
Single_Cell
|
We used this integrated latent space to assign initial cell types and removed all cell types that were not labeled as immune cells.
|
[
{
"end": 65,
"label": "CellType",
"start": 55,
"text": "cell types"
},
{
"end": 92,
"label": "CellType",
"start": 82,
"text": "cell types"
},
{
"end": 130,
"label": "CellType",
"start": 118,
"text": "immune cells"
}
] |
Single_Cell
|
Additionally, we removed all cells for which manual labeling and automatic labeling using MMoCHi (see below) were inconclusive about coarse cell-type identity (for example, B cell, myeloid, T cell).
|
[
{
"end": 179,
"label": "CellType",
"start": 173,
"text": "B cell"
},
{
"end": 34,
"label": "CellType",
"start": 29,
"text": "cells"
},
{
"end": 149,
"label": "CellType",
"start": 133,
"text": "coarse cell-type"
},
{
"end": 188,
"label": "CellType",
"start": 181,
"text": "myeloid"
},
{
"end": 196,
"label": "CellType",
"start": 190,
"text": "T cell"
}
] |
Single_Cell
|
These events were of low quality by manual inspection.
|
[] |
Single_Cell
|
We performed post hoc manual removal of these and other clusters of low-quality cells after integrating all cells.
|
[
{
"end": 85,
"label": "CellType",
"start": 68,
"text": "low-quality cells"
},
{
"end": 113,
"label": "CellType",
"start": 108,
"text": "cells"
}
] |
Single_Cell
|
Source paper: PMC12396968
To identify canonical immune cell subsets, we used a recently reported, supervised machine learning algorithm, MMoCHi) (v0.2.1) .
|
[
{
"end": 69,
"label": "CellType",
"start": 50,
"text": "immune cell subsets"
}
] |
Single_Cell
|
We first normalized the gene expression (GEX) count matrix using log(10,000 C g,i / T G,i + 1), where C g,i represents the counts for GEX feature g in cell i , and T G,i is the total counts for all GEX features in cell i .
|
[] |
Single_Cell
|
Similarly, we normalized the antibody-derived tag (ADT) count matrix using log(1,000 C a,i / T A,i + 1), where C a,i represents the counts for ADT feature a in cell i and T A,i is the total counts for all ADT features in cell i .
|
[] |
Single_Cell
|
We applied landmark registration (MMoCHi) to batch-correct the ADT expression across experimental batches.
|
[] |
Single_Cell
|
In brief, we applied automatic detection of landmarks (peaks) in the expression distributions for each ADT feature in a given sample, applying manual adjustments as needed using the graphical user interface, then performed curve registration and warping to align the positive and negative peaks for each ADT feature across batches.
|
[] |
Single_Cell
|
Source paper: PMC12396968
We provided MMoCHi with a hierarchy of immune cell subsets and their canonical surface protein-level and RNA-level markers (Supplementary Fig. 2 ) and used the markers to identify high-confidence members (cells) of each subset for training.
|
[
{
"end": 86,
"label": "CellType",
"start": 67,
"text": "immune cell subsets"
},
{
"end": 238,
"label": "CellType",
"start": 233,
"text": "cells"
}
] |
Single_Cell
|
For each classification level, automatic thresholds for high-confidence ADT or GEX marker-positive and marker-negative cells were manually adjusted as needed using the supplied GUI (Supplementary Table 3 ).
|
[
{
"end": 98,
"label": "CellType",
"start": 83,
"text": "marker-positive"
},
{
"end": 124,
"label": "CellType",
"start": 103,
"text": "marker-negative cells"
}
] |
Single_Cell
|
Following MMoCHi’s internal training data refinement, we applied an 80–20 train–test split and trained a random forest classifier, sklearn.ensemble.
|
[] |
Single_Cell
|
RandomForestClassifier , on both gene and protein expression .
|
[] |
Single_Cell
|
For 2 of the 24 organ donors and a subset of samples from a third donor, we did not perform CITE-seq and only had scRNA-seq profiles.
|
[] |
Single_Cell
|
Thus, these samples were excluded from the MMoCHi classification described here.
|
[] |
Single_Cell
|
However, we used a k -nearest neighbors approach to transfer the classifier labels to individual cells profiled from these two organ donors.
|
[
{
"end": 102,
"label": "CellType",
"start": 86,
"text": "individual cells"
}
] |
Single_Cell
|
Specifically, we used the sklearn.neighbors.
|
[] |
Single_Cell
|
KNeighborsClassifier with n_neighbors = 10 to construct a k -nearest neighbors graph in the mrVI embedding of the dataset (see below) and classify the remaining cells.
|
[
{
"end": 166,
"label": "CellType",
"start": 161,
"text": "cells"
}
] |
Single_Cell
|
Of the subsets, pDCs were identified using two separate nodes on the hierarchy (Supplementary Fig. 1 ), as pDCs shared expression with both B cells and myeloid cells.
|
[
{
"end": 147,
"label": "CellType",
"start": 140,
"text": "B cells"
},
{
"end": 165,
"label": "CellType",
"start": 152,
"text": "myeloid cells"
}
] |
Single_Cell
|
Once classified, the two subsets were merged into a single population of pDCs.
|
[] |
Single_Cell
|
The MMoCHi annotation was used at two separate levels throughout the paper, defined as either one of the 34 fine-grained subsets or grouped into CD4 T cells, CD8 and unconventional T cells (including γδ T cells and CD8 MAIT cells), B cells, NK cells and ILCs or myeloid cells (including monocytes, macrophages, cDCs, migratory DCs and pDCs).
|
[
{
"end": 309,
"label": "CellType",
"start": 298,
"text": "macrophages"
},
{
"end": 296,
"label": "CellType",
"start": 287,
"text": "monocytes"
},
{
"end": 239,
"label": "CellType",
"start": 232,
"text": "B cells"
},
{
"end": 128,
"label": "CellType",
"start": 108,
"text": "fine-grained subsets"
},
{
"end": 156,
"label": "CellType",
"start": 145,
"text": "CD4 T cells"
},
{
"end": 161,
"label": "CellType",
"start": 158,
"text": "CD8"
},
{
"end": 188,
"label": "CellType",
"start": 166,
"text": "unconventional T cells"
},
{
"end": 210,
"label": "CellType",
"start": 200,
"text": "γδ T cells"
},
{
"end": 229,
"label": "CellType",
"start": 214,
"text": " CD8 MAIT cells"
},
{
"end": 249,
"label": "CellType",
"start": 241,
"text": "NK cells"
},
{
"end": 258,
"label": "CellType",
"start": 254,
"text": "ILCs"
},
{
"end": 275,
"label": "CellType",
"start": 262,
"text": "myeloid cells"
},
{
"end": 315,
"label": "CellType",
"start": 311,
"text": "cDCs"
},
{
"end": 330,
"label": "CellType",
"start": 317,
"text": "migratory DCs"
},
{
"end": 339,
"label": "CellType",
"start": 335,
"text": "pDCs"
}
] |
Single_Cell
|
Source paper: PMC12396968
Owing to the breadth of tissues and human subjects sampled in our dataset and high-resolution annotation of immune subsets, we anticipated that our immune atlas would be useful to the research community as a reference for performing cell-type label transfer.
|
[
{
"end": 59,
"label": "Tissue",
"start": 52,
"text": "tissues"
},
{
"end": 150,
"label": "CellType",
"start": 136,
"text": "immune subsets"
}
] |
Single_Cell
|
To facilitate this application, we trained a model using popV , a tool developed for cell-type label transfer that uses several annotation algorithms and consensus voting to determine annotations and evaluate their confidence.
|
[] |
Single_Cell
|
popV also calculates joint embeddings of the query and reference datasets, which can be used for visualization of the query data and other analysis tasks.
|
[] |
Single_Cell
|
A popV model was trained using the tissues and MMoCHi annotations (Fig. 2 ) as the reference dataset.
|
[
{
"end": 42,
"label": "Tissue",
"start": 35,
"text": "tissues"
}
] |
Single_Cell
|
Label transfer performance was evaluated using the Human Lung Cell Atlas as a query dataset .
|
[] |
Single_Cell
|
To visualize the data, we computed UMAP embeddings as described above on joint scVI embeddings, which were calculated as part of the popV pipeline.
|
[] |
Single_Cell
|
Source paper: PMC12396968
To evaluate the importance of ADT information in the MMoCHi classification performance, we additionally applied a pre-trained CellTypist model (Immune_All_Low; https://celltypist.cog.sanger.ac.uk/models/Pan_Immune_CellTypist/v2/Immune_All_Low.pkl ) using default settings to the tissue immune cells (Fig. 2 ).
|
[
{
"end": 326,
"label": "CellType",
"start": 307,
"text": "tissue immune cells"
}
] |
Single_Cell
|
Source paper: PMC12396968
To integrate scRNA-seq profiles of immune cells in our study, we first used scVI, which did not yield a fully integrated latent space and clustered by site of collection (for example, US or UK).
|
[
{
"end": 75,
"label": "CellType",
"start": 63,
"text": "immune cells"
}
] |
Single_Cell
|
We next leveraged MrVI, which uses a mixture-of-Gaussian as a prior and enforces stronger separation of true cell state and effect of donors on gene expression, as has been recently demonstrated .
|
[] |
Single_Cell
|
MrVI takes advantage of a prior based on a multimodal variational mixture of posteriors (similar to a VampPrior ), which have been shown to outperform Gaussian priors for scRNA-seq integration in benchmarking studies .
|
[] |
Single_Cell
|
In brief, MrVI finds a sample-agnostic latent space, U , and computes a sample-specific embedding.
|
[] |
Single_Cell
|
A second latent space, Z , is defined by adding an attention-based concatenation between U and the sample embedding space to the original U -space.
|
[] |
Single_Cell
|
Another layer of attention is used to incorporate an embedding of 10× Genomics chemistry and experimental site (Cambridge, UK versus Columbia, NY), and this third latent space is decoded using a linear decoder to yield the rate of a negative binomial distribution.
|
[] |
Single_Cell
|
We use a cell-type-aware Gaussian mixture prior in U -space.
|
[] |
Single_Cell
|
To introduce cell type awareness, we use a bias to the mixture proportions that makes it likely for cells of the same type to be sampled from the same Gaussian.
|
[
{
"end": 122,
"label": "CellType",
"start": 100,
"text": "cells of the same type"
}
] |
Single_Cell
|
Source paper: PMC12396968
For the latent embedding highlighted throughout the article and used for manual cell-type curation, we used the donor identities as the sample keys and used the output of MMoCHi classification (see above) as the cell-type prior in MrVI.
|
[
{
"end": 249,
"label": "CellType",
"start": 240,
"text": "cell-type"
}
] |
Single_Cell
|
We used default parameters except n_epochs_kl_warmup of 25, n_latent_u of 20, n_latent in Z -space of 200, dropout in qz as well as pz of 0.03 (adopted from a previous publication ).
|
[] |
Single_Cell
|
To visualize cells (either the total immune component or individual major lineages), we computed nearest neighbors ( scanpy.pp.neighbors ) on the MrVI U latent space and calculated UMAP embeddings ( scanpy.tl.umap ) using the 15 nearest neighbors, a minimum distance of 0.4, a spread of 1.0 and initialization with PAGA after running scanpy.tl.paga .
|
[] |
Single_Cell
|
To identify additional heterogeneity in cell states within samples in addition to the cell-type annotation provided by MMoCHi, we performed manual annotation.
|
[] |
Single_Cell
|
For each MMoCHi annotated population, a new scVI model was trained with donor as the batch key, then Leiden clustering ( scanpy.tl.leiden ) was performed on the lineage-specific neighbors graph at an appropriate resolution, selected to minimize over-clustering (ranging from 1 to 15).
|
[] |
Single_Cell
|
Markers for each cluster were computed by scanpy.tl.rank_genes_groups , and clusters with similar marker expression were merged.
|
[] |
Single_Cell
|
To annotate proliferating cells, scanpy.tl.score_genes_cell_cycle was run, and the output was used in combination with the gene expression of MKI67 and TOP2A .
|
[
{
"end": 31,
"label": "CellType",
"start": 12,
"text": "proliferating cells"
}
] |
Single_Cell
|
Source paper: PMC12396968
We focused our DE analysis on immune lineages and cell types with sufficient representation across experimental sites, tissues and donor ages.
|
[
{
"end": 73,
"label": "CellType",
"start": 58,
"text": "immune lineages"
},
{
"end": 88,
"label": "CellType",
"start": 78,
"text": "cell types"
},
{
"end": 154,
"label": "Tissue",
"start": 147,
"text": "tissues"
}
] |
Single_Cell
|
This included six tissue groups: blood, BM, spleen, gut (JLP and JEL), LNs (ILN, LLN and MLN) and lungs (consisting of BAL and parenchyma); six immune lineages: myeloid, CD4 T cells, CD8 T cells, invariant T cells (that is, γδ T cells and MAIT cells), B cells and ILC/NK cells; and 26 individual cell types within all lineages.
|
[
{
"end": 259,
"label": "CellType",
"start": 252,
"text": "B cells"
},
{
"end": 38,
"label": "Tissue",
"start": 33,
"text": "blood"
},
{
"end": 137,
"label": "Tissue",
"start": 127,
"text": "parenchyma"
},
{
"end": 103,
"label": "Tissue",
"start": 98,
"text": "lungs"
},
{
"end": 50,
"label": "Tissue",
"start": 44,
"text": "spleen"
},
{
"end": 55,
"label": "Tissue",
"start": 52,
"text": "gut"
},
{
"end": 42,
"label": "Tissue",
"start": 40,
"text": "BM"
},
{
"end": 60,
"label": "Tissue",
"start": 57,
"text": "JLP"
},
{
"end": 68,
"label": "Tissue",
"start": 65,
"text": "JEL"
},
{
"end": 74,
"label": "Tissue",
"start": 71,
"text": "LNs"
},
{
"end": 79,
"label": "Tissue",
"start": 76,
"text": "ILN"
},
{
"end": 84,
"label": "Tissue",
"start": 81,
"text": "LLN"
},
{
"end": 92,
"label": "Tissue",
"start": 89,
"text": "MLN"
},
{
"end": 122,
"label": "Tissue",
"start": 119,
"text": "BAL"
},
{
"end": 168,
"label": "CellType",
"start": 161,
"text": "myeloid"
},
{
"end": 181,
"label": "CellType",
"start": 170,
"text": "CD4 T cells"
},
{
"end": 194,
"label": "CellType",
"start": 183,
"text": "CD8 T cells"
},
{
"end": 213,
"label": "CellType",
"start": 196,
"text": "invariant T cells"
},
{
"end": 234,
"label": "CellType",
"start": 224,
"text": "γδ T cells"
},
{
"end": 249,
"label": "CellType",
"start": 239,
"text": "MAIT cells"
},
{
"end": 276,
"label": "CellType",
"start": 264,
"text": "ILC/NK cells"
},
{
"end": 306,
"label": "CellType",
"start": 296,
"text": "cell types"
}
] |
Single_Cell
|
Covariates included 10× Genomics chemistry (3′ versus 5′), sex (male versus female), laboratory (Cambridge, UK versus Columbia, NY) and CMV status (positive versus negative).
|
[] |
Single_Cell
|
For aging analyses, donors were categorized as being <40 or >40 years of age.
|
[] |
Single_Cell
|
Source paper: PMC12396968
Variance decomposition and pseudobulk DE analysis were performed using LMM through the dreamlet R package (v1.4.1) .
|
[] |
Single_Cell
|
Depending on the resolution of the analysis, DE was performed separately either for each immune lineage (for example, myeloid cells, B cells, and so on) or for each immune subset (for example, macrophages, naive B cells and so on) using the cluster_id parameter in dreamlet .
|
[
{
"end": 204,
"label": "CellType",
"start": 193,
"text": "macrophages"
},
{
"end": 140,
"label": "CellType",
"start": 133,
"text": "B cells"
},
{
"end": 103,
"label": "CellType",
"start": 89,
"text": "immune lineage"
},
{
"end": 131,
"label": "CellType",
"start": 118,
"text": "myeloid cells"
},
{
"end": 219,
"label": "CellType",
"start": 206,
"text": "naive B cells"
},
{
"end": 178,
"label": "CellType",
"start": 165,
"text": "immune subset"
}
] |
Single_Cell
|
The raw GEX count matrix was pseudobulked across samples, and each tissue in each donor was treated as a separate sample.
|
[
{
"end": 73,
"label": "Tissue",
"start": 67,
"text": "tissue"
}
] |
Single_Cell
|
Before performing DE, samples and genes with poor representation were filtered using dreamlet::processAssays .
|
[] |
Single_Cell
|
Samples with fewer than 50 cells and genes not represented in at least 40% of the samples with at least five counts were excluded.
|
[
{
"end": 32,
"label": "CellType",
"start": 27,
"text": "cells"
}
] |
Single_Cell
|
To confirm findings by MrVI counterfactual analysis (see below), these thresholds were reduced to a minimum of ten cells for a sample to be included, and at least 10% of samples with at least five counts.
|
[
{
"end": 120,
"label": "CellType",
"start": 115,
"text": "cells"
}
] |
Single_Cell
|
DE for a subset was not performed when fewer than three or four samples (for tissue and age analysis, respectively) met the minimum cell thresholds.
|
[] |
Single_Cell
|
Variance decomposition was performed for age analysis for each lineage using dreamlet::fitVarPart with sex, sequencing chemistry, CMV serostatus, age group, processing site and tissue as covariates (Supplementary Table 13 ).
|
[
{
"end": 183,
"label": "Tissue",
"start": 177,
"text": "tissue"
}
] |
Single_Cell
|
LMM was performed using dreamet::dreamlet with eBayes estimation enabled.
|
[] |
Single_Cell
|
Tissue effects (Figs. 3 and 4 , Extended Data Figs. 3 and 4 , and Supplementary Tables 4 , 7 and 10 ) were modeled by comparing each lineage/subset in one tissue group to the same lineage/subset in the remaining tissue groups, with donor identity encoded as a random effect.
|
[
{
"end": 167,
"label": "Tissue",
"start": 155,
"text": "tissue group"
},
{
"end": 147,
"label": "CellType",
"start": 133,
"text": "lineage/subset"
},
{
"end": 194,
"label": "CellType",
"start": 180,
"text": "lineage/subset"
},
{
"end": 225,
"label": "Tissue",
"start": 212,
"text": "tissue groups"
}
] |
Single_Cell
|
Age effects (Figs. 5 – 7 , Extended Data Fig. 5 , and Supplementary Tables 15 and 24 ) were modeled across each tissue-group–age-group combination while controlling for CMV serostatus and sex as fixed effects and with sequencing chemistry and processing site as random effects.
|
[] |
Single_Cell
|
Age effects within each tissue group were then measured using the contrasts parameter in dreamlet::dreamlet between old and young for each tissue group (for example, the effect of age in the gut was computed as ‘ old-gut − young-gut ’).
|
[
{
"end": 194,
"label": "Tissue",
"start": 191,
"text": "gut"
},
{
"end": 36,
"label": "Tissue",
"start": 24,
"text": "tissue group"
},
{
"end": 220,
"label": "Tissue",
"start": 213,
"text": "old-gut"
},
{
"end": 232,
"label": "Tissue",
"start": 223,
"text": "young-gut"
}
] |
Single_Cell
|
CMV effects (Supplementary Fig. 10 and Supplementary Table 21 ) were modeled across each tissue-group–CMV serostatus combination while controlling for age and sex as fixed effects and with sequencing chemistry and processing site as random effects.
|
[] |
Single_Cell
|
CMV effects within each tissue group were then measured using the contrasts parameter in dreamlet::dreamlet between CMV and CMV for each tissue group.
|
[
{
"end": 36,
"label": "Tissue",
"start": 24,
"text": "tissue group"
},
{
"end": 149,
"label": "Tissue",
"start": 137,
"text": "tissue group"
}
] |
Single_Cell
|
Source paper: PMC12396968
For identifying cross-tissue and cross-donor gene signatures for each major immune lineage, we constructed probabilistic factor models directly from scRNA-seq count matrices using scHPF.
|
[
{
"end": 118,
"label": "CellType",
"start": 98,
"text": "major immune lineage"
}
] |
Single_Cell
|
The output of scHPF includes two matrices: an M × K gene score matrix containing weights for each of M genes in each of K factors and a K × N cell score matrix containing weights for each of N cells in each of K factors.
|
[] |
Single_Cell
|
In the original report of scHPF, the algorithm required a user-supplied value of K , the number of factors in the model .
|
[] |
Single_Cell
|
Here, we use a new consensus factorization implementation of scHPF, in which the user specifies a broad range of K values from which many scHPF models are generated .
|
[] |
Single_Cell
|
The gene score matrices for these models are then clustered to identify K recurrent factors, which are combined to seed a final round of training to construct a final consensus model with K factors.
|
[] |
Single_Cell
|
Source paper: PMC12396968
We constructed two types of scHPF models: a tissue-level model (Extended Data Fig. 4 ), in which the number of cells from each of three tissue groups was balanced by random sub-sampling (gut: JEL and JLP; lung parenchyma; and LNs: MLN and LLN), and a donor-level model (Figs. 5 and 6 ), in which the number of cells from each organ donor was balanced.
|
[
{
"end": 248,
"label": "Tissue",
"start": 233,
"text": "lung parenchyma"
},
{
"end": 218,
"label": "Tissue",
"start": 215,
"text": "gut"
},
{
"end": 144,
"label": "CellType",
"start": 139,
"text": "cells"
},
{
"end": 177,
"label": "Tissue",
"start": 164,
"text": "tissue groups"
},
{
"end": 223,
"label": "Tissue",
"start": 220,
"text": "JEL"
},
{
"end": 231,
"label": "Tissue",
"start": 228,
"text": "JLP"
},
{
"end": 257,
"label": "Tissue",
"start": 254,
"text": "LNs"
},
{
"end": 262,
"label": "Tissue",
"start": 259,
"text": "MLN"
},
{
"end": 270,
"label": "Tissue",
"start": 267,
"text": "LLN"
},
{
"end": 343,
"label": "CellType",
"start": 338,
"text": "cells"
}
] |
Single_Cell
|
We constructed both types of models for the major immune lineages: CD4 T cells, CD8 T cells (including all invariant T cells), NK cells, ILCs, B cells and macrophages.
|
[
{
"end": 166,
"label": "CellType",
"start": 155,
"text": "macrophages"
},
{
"end": 150,
"label": "CellType",
"start": 143,
"text": "B cells"
},
{
"end": 65,
"label": "CellType",
"start": 44,
"text": "major immune lineages"
},
{
"end": 78,
"label": "CellType",
"start": 67,
"text": "CD4 T cells"
},
{
"end": 91,
"label": "CellType",
"start": 79,
"text": " CD8 T cells"
},
{
"end": 124,
"label": "CellType",
"start": 107,
"text": "invariant T cells"
},
{
"end": 135,
"label": "CellType",
"start": 127,
"text": "NK cells"
},
{
"end": 141,
"label": "CellType",
"start": 137,
"text": "ILCs"
}
] |
Single_Cell
|
For donor models, donors with fewer than 300 cells for a given lineage were removed.
|
[
{
"end": 50,
"label": "CellType",
"start": 45,
"text": "cells"
}
] |
Single_Cell
|
In both models, the count matrices were randomly downsampled such that the average number of transcripts per cell was the same for each donor to avoid coverage bias.
|
[
{
"end": 113,
"label": "CellType",
"start": 109,
"text": "cell"
}
] |
Single_Cell
|
scHPF models considered only protein-coding genes (excluding TCR and immunoglobulin cassettes) detected in at least 1% of cells across the final subsampled and downsampled training matrix.
|
[] |
Single_Cell
|
Source paper: PMC12396968
Immune subset composition within each lineage across tissues was visualized by violin plots or box plots (using seaborn ).
|
[
{
"end": 41,
"label": "CellType",
"start": 28,
"text": "Immune subset"
},
{
"end": 88,
"label": "Tissue",
"start": 81,
"text": "tissues"
}
] |
Single_Cell
|
Tissue-specific enrichment of immune subset frequencies in specific tissues was also assayed within each major lineage using scCODA for Bayesian inference.
|
[
{
"end": 75,
"label": "Tissue",
"start": 59,
"text": "specific tissues"
}
] |
Single_Cell
|
Significant enrichment of an immune subset in one tissue over the rest was determined using sccoda.util.comp_ana.
|
[
{
"end": 56,
"label": "Tissue",
"start": 50,
"text": "tissue"
},
{
"end": 42,
"label": "CellType",
"start": 29,
"text": "immune subset"
}
] |
Single_Cell
|
CompositionalAnalysis to detect credible effects, and was run sequentially, selecting each cell type as the reference.
|
[
{
"end": 100,
"label": "CellType",
"start": 91,
"text": "cell type"
}
] |
Single_Cell
|
Majority voting was then used to identify cell types that are credibly changing more than half the time with automatic reference-subset selection and the default false discovery rate of 0.05.
|
[
{
"end": 52,
"label": "CellType",
"start": 42,
"text": "cell types"
}
] |
Single_Cell
|
Source paper: PMC12396968
To determine tissue-specific gene expression signatures across immune lineages (Fig. 3 ), significant DEGs were defined as adjusted P < 0.05 and log 2 (FC) > 1 by pseudobulk DE across tissues at the lineage level (see above).
|
[
{
"end": 219,
"label": "Tissue",
"start": 212,
"text": "tissues"
}
] |
Single_Cell
|
Mean z -score gene expression was calculated for each pairing of tissue group and lineage.
|
[] |
Single_Cell
|
Genes and samples were both hierarchically clustered using scipy.cluster.hierarchy.linkage with Ward’s method and Euclidean distance.
|
[] |
Single_Cell
|
Discrete clusters of genes with similar expression patterns were calculated using scipy.cluster.hierarchy.fcluster with the ‘ maxclust ’ method (Supplementary Table 6 ).
|
[] |
Single_Cell
|
Source paper: PMC12396968
For each gene cluster, association with specific tissue groups or lineages could arise from DE within one or more specific subsets of that lineage or from compositional shifts in subsets across tissues.
|
[
{
"end": 90,
"label": "Tissue",
"start": 77,
"text": "tissue groups"
},
{
"end": 229,
"label": "Tissue",
"start": 222,
"text": "tissues"
}
] |
Single_Cell
|
To disentangle these possibilities, we first used pre-ranked GSEA to compare the gene clusters identified via lineage-level DE to the effect size (that is, log(FC)) of DE across tissues in the subset-level DE.
|
[
{
"end": 185,
"label": "Tissue",
"start": 178,
"text": "tissues"
}
] |
Single_Cell
|
To visualize potential effects caused by compositional shifts across tissues, we computed the average frequency of the subset (as a proportion of the total cells within that lineage group) within a tissue, the FC of that frequency over the frequency in the remaining tissue groups and the average expression of the gene cluster.
|
[
{
"end": 76,
"label": "Tissue",
"start": 69,
"text": "tissues"
},
{
"end": 204,
"label": "Tissue",
"start": 198,
"text": "tissue"
},
{
"end": 280,
"label": "Tissue",
"start": 267,
"text": "tissue groups"
}
] |
Single_Cell
|
Source paper: PMC12396968
To assess whether differential transcript expression was reflected in the surface protein profiling (Supplementary Tables 11 and 12 ), we selected ADTs corresponding to DEGs in at least one tissue.
|
[
{
"end": 224,
"label": "Tissue",
"start": 218,
"text": "tissue"
}
] |
Single_Cell
|
To identify enrichment in one tissue group over the other tissue groups, we used scanpy.tl.rank_genes_groups on the normalized expression with Wilcoxon and tie-correction enabled.
|
[
{
"end": 42,
"label": "Tissue",
"start": 30,
"text": "tissue group"
},
{
"end": 71,
"label": "Tissue",
"start": 58,
"text": "tissue groups"
}
] |
Single_Cell
|
To minimize the influence of technical staining artifacts or donor covariates, analysis was conducted separately within each donor.
|
[] |
Single_Cell
|
Donors with fewer than 50 cells for a particular tissue-group–lineage combination or tissue-group–lineage combinations with fewer than four suitable donors were excluded from analysis.
|
[] |
Single_Cell
|
Before DE analysis, the ADT count matrix was subsampled to equalize cell numbers and randomly downsampled such that the average number of transcripts per cell was the same for each group to avoid coverage bias.
|
[] |
Single_Cell
|
Source paper: PMC12396968
We next sought to identify factors from the tissue-level scHPF models of each major immune lineage that were shared across cell types.
|
[
{
"end": 126,
"label": "CellType",
"start": 106,
"text": "major immune lineage"
},
{
"end": 161,
"label": "CellType",
"start": 151,
"text": "cell types"
}
] |
Single_Cell
|
As described above, we first constructed consensus scHPF models for CD4 T cells, CD8 T cells, macrophages, NK cells, ILCs and B cells with equal representation of cells from each of three major tissue groups (gut, lung and LNs).
|
[
{
"end": 105,
"label": "CellType",
"start": 94,
"text": "macrophages"
},
{
"end": 133,
"label": "CellType",
"start": 126,
"text": "B cells"
},
{
"end": 218,
"label": "Tissue",
"start": 214,
"text": "lung"
},
{
"end": 212,
"label": "Tissue",
"start": 209,
"text": "gut"
},
{
"end": 79,
"label": "CellType",
"start": 68,
"text": "CD4 T cells"
},
{
"end": 92,
"label": "CellType",
"start": 81,
"text": "CD8 T cells"
},
{
"end": 115,
"label": "CellType",
"start": 107,
"text": "NK cells"
},
{
"end": 121,
"label": "CellType",
"start": 117,
"text": "ILCs"
},
{
"end": 168,
"label": "CellType",
"start": 163,
"text": "cells"
},
{
"end": 226,
"label": "Tissue",
"start": 223,
"text": "LNs"
}
] |
Single_Cell
|
From each model, we removed probable nuisance factors containing heat shock protein-encoding genes (common dissociation artifact, >1 gene), ribosomal protein-encoding genes (common coverage artifact, >10 genes), genes from the highly inducible metallothionein cluster (>1 gene), hemoglobin transcripts (red blood cell contamination, >0 genes) and genes in a previously published signature of dissociation-induced cell stress in scRNA-seq (>7 genes) among the 30 top-weighted genes.
|
[] |
Single_Cell
|
Next, we computed the average cell score for each factor in each of the three major tissue groups and identified all factors with an average tissue-group score that was at least 80% higher in one tissue group than the average of the remaining two.
|
[
{
"end": 97,
"label": "Tissue",
"start": 84,
"text": "tissue groups"
},
{
"end": 208,
"label": "Tissue",
"start": 196,
"text": "tissue group"
}
] |
Single_Cell
|
Thus, the resulting set of 53 scHPF factors from across all 6 lineage-specific models exhibits some degree of tissue specificity.
|
[] |
Single_Cell
|
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