IMMUNOLOGY. An interactive reference framework for modeling a dynamic immune system

Abstract

Immune cells function in an interacting hierarchy that coordinates the activities of various cell types according to genetic and environmental contexts. We developed graphical approaches to construct an extensible immune reference map from mass cytometry data of cells from different organs, incorporating landmark cell populations as flags on the map to compare cells from distinct samples. The maps recapitulated canonical cellular phenotypes and revealed reproducible, tissue-specific deviations. The approach revealed influences of genetic variation and circadian rhythms on immune system structure, enabled direct comparisons of murine and human blood cell phenotypes, and even enabled archival fluorescence-based flow cytometry data to be mapped onto the reference framework. This foundational reference map provides a working definition of systemic immune organization to which new data can be integrated to reveal deviations driven by genetics, environment, or pathology.

Figure 1. Scaffold Maps Reveal Immune Organization of the Bone Marrow

(A) Schematic of the Scaffold map algorithm. (i) Bone marrow from C57BL/6 mice was chosen as the reference sample. (ii) Leukocytes were grouped according to prior knowledge to define landmark cell populations as reference points on the map. The same leukocytes were subjected to unsupervised clustering to provide an objective view of the tissue composition and organization. An illustration is provided with the two major lineages of mature T cells, which express either the cell-surface co-receptor cluster of differentiation 4 (CD4) or cluster of differentiation 8 (CD8). (iii-iv) Both landmark populations (red nodes) and unsupervised clusters (blue nodes) were utilized to generate a force-directed graph in which similar nodes are located close together according to the similarity of their protein expression. Thus, similar nodes fall in proximity to one another while disparate nodes segregate apart from one another. Size of unsupervised clusters denotes the relative number of cells in that grouping. (v) Landmark populations from the bone marrow were fixed in place for subsequent maps to provide points of reference for rapid human interpretation. (vi) Additional samples were each subjected to unsupervised clustering via the same clustering algorithm. (vii) The resulting clusters for each sample were overlaid onto the original landmark nodes to generate tissue-specific Scaffold maps. (B) Bone marrow Scaffold map for C57BL/6 mice. Red nodes denote landmark manually-gated cell populations; blue nodes represent unsupervised cell clusters from the same data. Insert: median frequencies of cell populations defined by conventional criteria from the bone marrow of C57BL/C mice, n = 14. (C) Scaffold map showing only the position of the landmark nodes with arrows annotating established maturation relationships in hematopoietic development.

Figure 2. Mapping Systemic Immune Organization by Tissue

Scaffold maps for lymphoid organs and peripheral solid organs from C57BL/6 mice using bone marrow as the reference sample to define landmark nodes (red). (A) Blood (B) Spleen (C) Skin- Draining (Inguinal) Lymph Node (SLN) (D) Mesenteric Lymph Node (MLN) (E) Thymus (F) Lungs (G) Liver, n = 14. Grey double bars denote a cluster extending behind another map for visualization purposes. Inserts, from top to bottom: Cells comprising B cell clusters from the spleen and SLN are visualized by 2D scatter plot. Schematic of immune cell circulation through and within the tissues characterized by mass cytometry. Cells comprising a deviant thymic T cell population cluster are visualized by 2D scatter plot.

Figure 3. Immune Organization Across Inbred Mouse Strains

Scaffold maps for several tissues from 129S1/Sv and Balb/c mice using C57BL/6 bone marrow as the reference sample to define landmark nodes (red). (A) Bone Marrow (B) Skin-Draining (Inguinal) Lymph Node (C) Liver, n = 3. Histograms of Fcγ receptor I (CD64) and major histocompatibility complex class II (MHC II) expression on liver macrophages from representative mice of each strain.

Figure 4. Mapping Circadian Changes in the Lungs

(A) Scaffold maps of lungs of representative animals collected in the morning (8-9am; left) or the afternoon (1-2 pm; right). (B) Population frequencies in the lungs between morning and afternoon as defined by traditional criteria from both the original mass cytometry dataset (n=7 morning and afternoon) and a follow-up fluorescence experiment (n=7 morning; n=8 afternoon). Bars represent mean ± SEM, and p-values result from one-sided t-test.

Figure 5. Mapping Human and Archival Data onto the Reference Map

(A) Original mass cytometry whole blood Scaffold map from C57BL/6 mice, n = 14. (B) Scaffold map of human whole blood interrogated by 15-parameter mass cytometry with distance measured using only those 15 dimensions for layout of unsupervised clusters onto the reference. Human parameters were assigned to murine correlate markers with similar cellular distribution, including canonical surface markers used for identification of cell populations by conventional criteria as well as several orthologous proteins, n = 4. (C) Scaffold map of original murine blood mass cytometry data with distance measured using only the same 15 dimensions for layout of unsupervised clusters onto the reference. (D) Original mass cytometry bone marrow Scaffold map from C57BL/6 mice. (E) Scaffold map of C57BL/6 bone marrow interrogated by 8-color fluorescence-based flow cytometry from a previously published dataset (Qiu et al., 2011) with distance measured using only those 8 dimensions (B cell isoform of CD45 (B220), integrin α_M (CD11b), T cell receptor β chain (TCRβ), cluster of differentiation 4 (CD4), cluster of differentiation 8 (CD8), stem cell growth factor receptor (c-Kit), stem cell antigen 1 (Sca-1), signaling lymphocytic activation molecule 1 (CD150)) for layout of unsupervised clusters onto the reference. (F) Scaffold map of original mass cytometry data with distance measured using only the same 8 dimensions for layout of unsupervised clusters onto the reference.

Figure 6. Defining the Landscape of Immune Cell Populations

Population-specific landscapes were generated as follows: Cell populations were manually gated, subjected to unsupervised clustering and laid out in an unsupervised force-directed graph. Clusters are colored according to tissue of origin and sized by the number of cells in each cluster as a percent of the total number of leukocytes in the tissue of origin. Each plot is scaled independently. (A) T cell landscape including Lineage marker (Lin)⁻ cluster of differentiation 3 (CD3)⁺ cells. Cells comprising T cell clusters from the colon and small intestine falling within the red box are visualized by 2D scatter plot, n = 14. (B) B cell landscape including Lin⁻ B cell isoform of CD45 (B220)⁺ and Lin⁻ syndecan-1 (CD138)⁺ cells, n = 14. (C) NK cell landscape including Lin⁻ cluster of differentiation 49b (CD49b)⁺ cells, n = 14. (D) cDC landscape including Lin⁻ integrin α_x (CD11c)^hi major histocompatibility complex class II (MHC II)^hi cells, n = 14. (E)Macrophage cell landscape including Lin⁻ Fcγ receptor 1 (CD64)⁺ EGF-like module-containing mucin-like hormone receptor-like 1 (F4/80)⁺ cell, n = 14. Lineage markers (Lin) defined in Materials and Methods.