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. 2021 Mar 17:12:636720.
doi: 10.3389/fimmu.2021.636720. eCollection 2021.

Single Cell Analysis of Blood Mononuclear Cells Stimulated Through Either LPS or Anti-CD3 and Anti-CD28

Nathan Lawlor  1 , Djamel Nehar-Belaid  1 , Jessica D S Grassmann  1 , Marlon Stoeckius  2 , Peter Smibert  2 , Michael L Stitzel  1   3   4 , Virginia Pascual  5 , Jacques Banchereau  1 , Adam Williams  1   3   4 , Duygu Ucar  1   3   4
Affiliations

Single Cell Analysis of Blood Mononuclear Cells Stimulated Through Either LPS or Anti-CD3 and Anti-CD28

"V体育ios版" Nathan Lawlor et al. Front Immunol. .

Abstract

Immune cell activation assays have been widely used for immune monitoring and for understanding disease mechanisms VSports手机版. However, these assays are typically limited in scope. A holistic study of circulating immune cell responses to different activators is lacking. Here we developed a cost-effective high-throughput multiplexed single-cell RNA-seq combined with epitope tagging (CITE-seq) to determine how classic activators of T cells (anti-CD3 coupled with anti-CD28) or monocytes (LPS) alter the cell composition and transcriptional profiles of peripheral blood mononuclear cells (PBMCs) from healthy human donors. Anti-CD3/CD28 treatment activated all classes of lymphocytes either directly (T cells) or indirectly (B and NK cells) but reduced monocyte numbers. Activated T and NK cells expressed senescence and effector molecules, whereas activated B cells transcriptionally resembled autoimmune disease- or age-associated B cells (e. g. , CD11c, T-bet). In contrast, LPS specifically targeted monocytes and induced two main states: early activation characterized by the expression of chemoattractants and a later pro-inflammatory state characterized by expression of effector molecules. These data provide a foundation for future immune activation studies with single cell technologies (https://czi-pbmc-cite-seq. jax. org/). .

Keywords: CITE-seq; LPS; antiCD3/CD28; immune cell activation; immune responses; peripheral blood mononuclear cells; single cell profiling V体育安卓版. .

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"V体育2025版" Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Graphical Abstract
Graphical Abstract
Overview of the study design and analysis methods.
Figure 1
Figure 1
CITE-seq data before and after immune cell activation uncover distinct cell types and cell states. (A) Fluorescence-activated cell sorting (FACS) and enrichment of activated T cells at baseline and anti-CD3/CD28 conditions. T cells were gated as CD3+ T cells. (B) Flow cytometry data quantifying the proportion of activated cells (CD25+ CD69+ or CD25− CD69+) after anti-CD3/CD28 stimulation. All cells = number of activated T cells as a percentage of all PBMCs; T cells = numbers of activated T cells as a percentage of all CD3+ T cells. (C) Distributions of CD80 expression in sorted monocytes and all PBMCs at baseline and LPS conditions. (D) Flow cytometry proportions of activated monocytes (CD80+) before and after LPS stimulation. All cells = number of activated monocytes as a percentage of all PBMCs; Monocytes = number of activated monocytes as a percentage of CD14+CD16+ monocytes. (E) Sample multiplexing was performed using cell hashtag oligonucleotide (HTO) antibodies, where a different HTO barcode was used for a specific treatment condition (e.g., HTO A for cells under baseline condition). (F) De-multiplexing of treatment information using HTOs (left), and donor of origin information using genetic variation across donors in the form of single nucleotide variations (SNVs)(right). (G) Numbers and (H) proportions of singlet cells retained per donor per condition after removing multiplet cells.
Figure 2
Figure 2
Anti-CD3/CD28 stimulation activates all classes of lymphocytes. (A) Clustering of PBMCs at baseline and anti-CD3/CD28 conditions separates lymphocytes (NK, T, and B cells) primarily by cell type and secondarily by condition. Cells are color-coded based on activation condition (left) and cell types inferred via CITE-seq antibodies (right). (B) Annotation of cells at baseline was performed using protein expression levels of CD3, CD4, CD8, CD45RA, CD45RO, and additional annotation of activated T cells was done using expression of CD25 and CD69. (C) Cell type proportions across 10 donors at baseline (top) and anti CD3/CD28 condition (bottom). Note the decline in CD14+ monocytes (red bars) upon anti-CD3/CD28 condition. (D) Unsupervised clustering of all CD4+ T cells separates anti-CD3/CD28-stimulated cells from those at baseline and LPS activation. Dimension reduction plot with CD4+ T cells colored by their resultant cell type identity. (E) Average scaled expression of protein markers used to identify and annotate CD4+ T cell subsets. (F) Average scaled expression of marker genes associated with naive, MAIT, NK, TEMRA, and senescence functions.
Figure 3
Figure 3
Annotation of CD8+T cell subsets and trajectory inference. (A) Unsupervised clustering of CD8+ T cells from all conditions reveals a separation of anti-CD3/CD28-stimulated cells from all others. Dimension reduction plot with CD8+ T cells colored by their resultant cell-type identity. (B) Average scaled expression of protein markers used to also identify CD8+ T cell subpopulations. (C) Average scaled expression of marker genes associated with naïve, MAIT, NK, TEMRA, and senescence functions. Pseudo-temporal ordering of (D) CD4+ and (E) CD8+ T cells at baseline and anti-CD3/CD28 conditions. Boxplots show the distributions of pseudotime states for each of the baseline/activated T cell subsets.
Figure 4
Figure 4
Indirect activation of NK cells. (A) Baseline and activated T cell and NK cell subsets were clustered together and the top 50 principal components were extracted. Pearson correlation values were then computed for each baseline and activated cell type and summarized in the heatmap. (B) Scoring of all T and NK cells was determined using transcripts associated with MAIT cell identity, cytotoxicity, and senescence (genes used are shown in Supplementary Table 3). (C) Clustering of NK cells at baseline, and anti-CD3/CD28 conditions identifies 7 different subgroups (middle). In particular, anti-CD3/CD28-stimulated NK cells clustered distinctly from those at baseline (left). Distribution of NK cells in each cluster across three conditions (left). Note the enrichment of activated NK cells in clusters 0, 1, 4, and 5. (D) Heatmap of interferon stimulated genes (ISGs), activation-associated genes, and others used to annotate the NK sub-clusters. Heatmap values represent the average scaled expression of each gene for cells in the corresponding cluster. (E) (left) Pseudotime trajectory inference of NK cells at baseline and anti-CD3/CD28 conditions. (right) Genes and proteins whose expression patterns change with pseudotime in NK cells.
Figure 5
Figure 5
Indirect activation of B cells. (A) Volcano plot summarizing the genes induced in anti-CD3/CD28-activated B cells relative to those at baseline. (B) Analysis of B cells at baseline and after anti-CD3/CD28 stimulation revealed six subgroups (middle). Primarily, anti-CD3/CD28-stimulated B cells clustered separately from those at baseline (left). Distribution of B cells in each cluster across the two conditions (right). Activated B cells were mostly in clusters 0 and 3. (C) Heatmap of age-associated B cell (ABC)/double negative 2 (DN2), interferon stimulated genes (ISGs), and other genes used to annotate the distinct B cell subpopulations. Values in each heatmap represent average scaled gene expression of all cells in the corresponding cluster. (D) (left) Pseudotime trajectory inference of B cells at baseline and anti-CD3/CD28 conditions. (right) Genes and proteins with temporal patterns of expression in pseudo-temporal ordered B cells.
Figure 6
Figure 6
LPS stimulation specifically activates monocytes and induces pro-inflammatory responses. (A) Clustering of PBMCs under baseline and upon LPS stimulation (left). Cell types were annotated using the expression of cell surface proteins (right). (B) Cell type compositions at baseline (top) and LPS condition (bottom) across 10 donors. Refer to (A) for cell types' color coding. (C) Clustering of CD14+ monocytes separates baseline and LPS-stimulated cells. (D) Inflammation scores of all cells under two conditions. LPS-activated monocytes have the highest expression levels of inflammation-associated transcripts. (E) Differential expression analysis of LPS-stimulated and baseline monocytes revealed 125 induced and 94 reduced genes, respectively (FDR 5%), as well as 3 induced and 3 reduced proteins, respectively. (F) Enriched KEGG pathways (FDR 5%) for LPS response genes. Node size for each pathway represents the number of genes in the pathway that were also induced in LPS-stimulated monocytes. Node color for genes indicate induction (red) or reduction (blue) upon LPS treatment. LPS-activated monocytes upregulate genes associated with pattern recognition receptor signaling pathways (Toll-like receptor, NOD-like receptor) and inflammation (NF-kB signaling), but also downregulate genes associated with antigen presentation and phagosome activity. (G) Trajectory (pseudotime) inference of monocytes under baseline and LPS conditions identified genes with heterogeneous activity in stimulated monocytes. Cells are color-coded with respect to activation status (top left) and inferred pseudotimes (top right). Expression levels of selected genes were overlaid on cells sorted in the activation trajectory (bottom).
Figure 7
Figure 7
Pseudo-temporal ordering of monocytes reveals distinct LPS-activation states. (A) K-means clustering (n = 4) of the monocyte trajectory divides baseline cells into one group and activated cells into three groups (LPS1, LPS2, LPS3). (B) Proportions of monocytes found in each of the four states across the 10 donors. (C) Inflammation scores of monocytes in distinct states. LPS3 state monocytes show the highest average inflammation score. (D) Network of genes induced in activated monocytes from LPS2 state using Ingenuity Pathway Analysis (IPA). Molecules in bold are associated with immune cell migration and chemotaxis. (E) Network of genes induced in LPS3 activated monocytes. Bolded molecules are associated with inflammatory responses. For both networks, molecules with different functions were depicted with different shapes, as provided by IPA. Molecules are colored based on their log2 fold change in expression in that state vs. all other baseline and activated states. Direct and indirect gene interactions are depicted by solid and dashed lines, respectively.
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