Abstract

The immune system is unique in its dynamic interplay between numerous cell types. However, a system-wide view of how immune cells communicate to protect against disease has not been established. Here, we applied high-resolution mass spectrometry-based proteomics to characterize 28 primary human hematopoietic cell populations in steady and activated states at a depth of > 10,000 proteins in total. Protein copy numbers reveal a specialization of immune cells for ligand and receptor expression, thereby connecting distinct immune functions. By integrating total and secreted proteomes, we establish a ‘social’ network of communicating cells and discover fundamental structures of intercellular information exchange as well as novel connections between cell types. Our publicly accessible proteomic resource provides a framework for the orchestration of cellular interplay and a reference for altered communication associated with pathology. For more details click here

 





Protein Copy Numbers
Upper left panel: Protein rank plot. Proteins are ranked by copy number with the selected protein highlighted in blue. 
Upper right panel: Copy number profile plot. Horizontal line indicates mean copy number. Error bars are +/- SD.
Lower left panel: Module expression profile of selected protein. Colors represent correlation to module eigengene. Selected protein is displayed in black.
Lower right panel: Module cell-type piechart. Colors represent corresponding cell type and area relative expression.

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Communication Structures
Communication wheel with cell types (outer circle); receptor and ligand gene names (middle circle) and their expression levels (inner circle). Connecting lines indicate intercellular connections. Connection color code: Ligand-receptor connections above the threshold are colored by the cell type color of the cell type expressing the ligand; connection below the threshold are in grey; receptor-receptor connections are in black.

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Pairwise cell-type comparisons
Pairwise comparisons of complete proteomes visualized as Volcano plots. T- test differences are plotted as fold-change (x-axis, in log2) and p-value (y-axis, in -log10). Proteins with significantly different expression levels (two-tailed Welch’s t-test, S0=1, FDR<5%) are indicated in red. The false discovery rate (FDR) can be adjusted. Selected proteins are named and visualized by blue dot.
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Protein Copy Numbers

On this page you can visualize individual protein abundances. Select your favorite protein from the drop-down menu or search for the gene name in the textbox (‘look up protein’). On the page you see four panels.
The first panel displays the median copy number of all proteins (grey) derived from all different cell types in log10 scale. The selected protein is highlighted by a blue dot. In case the protein was assigned to a module, the module number is displayed below the gene name. The second panel shows the mean copy number of the selected protein across all measured cell types. Error bars are +/- sd (n=4, or n=3, see Online Methods for more details). In the third panel the module profile is shown. The black line represents the profile of the selected protein, all other proteins are colored according to their distance to the selected profile eigengene (blue to red, low to high Pearson correlation coefficient). In the fourth panel, the module profile is displayed as a pie. The slice sizes scale according to the module eigengene values and are colored according to the cell type color code on the left.

 

Communication Structures

Menu 1: Plot

On this page you can visualize communication structures as circos plots using the ‘circlize’ R package. Select your favorite protein from the drop-down menu or search for the gene name in the textbox. After your selection, the left panel is updated with potential connection partners, ranked by the STRINGdatabase  score in decreasing order. By default, the protein with the highest STRING score is preselected, but you can of course select a different interacting protein to be visualized. If you want to add another protein, use the tick-box ‘add another protein?’, For timely visualization we recommend to not choose too many proteins at the same time. After selecting all proteins press ‘update wheel’ to display the communication wheel. You can also modify the connection threshold (‘cut-off’) to visualize only a subset of all possible connection. The threshold ranges from 0 to 1 (0 will display all connections and 1 no connections; 0.47 was used in the publication and is set as default). Ligand-receptor connections above the threshold are colored by the cell type color of the cell type expressing the ligand; connection below the threshold are indicated in grey; receptor-receptor connections in black. You can download the communication wheel plot in pdf format (‘download plot’).

Menu 2: Table

On this page, the information of the communication wheel is stored, which you can download (‘download table’ bottom, top left). The table contains all edge information of the selected proteins and consists of 6 columns (CellType, Gene.names, rank, experiment score, Gene.names_2, CellType_2). Rank: connection score (see Online Methods for detailed description); experiment score: STRINGdatabase score (experimental evidence only). You can search the table for specific connections (top), filter individual columns for specific terms (bottom), and specify or sort the number of entries and columns.

 

Pairwise Comparisons

Menu 1: Plot

On this page you can explore and visualize proteome differences by volcano plots (x-axis: fold change [log2], y-axis: p-value [-log10]). Select your two favorite cell types from the drop-down menu or search for their names in the textbox. (‘first cell type’ and ‘second cell type’). You can adjust the false discovery rate (FDR) (‘FDR cutt-off’, the default setting of 0.05 is a good starting point) for pairwise proteome comparisons. By ticking ‘show outlier names’ all proteins below the FDR cut-off are shown with their gene name - by default this option is disabled. For a timely use of this option, we recommend settings with not too many significant proteins (red dots). You can further highlight individual proteins (‘indicate protein’) in the plot with blue dots, but not more than two at a time (‘add another protein?’). After setting the parameters, update the volcano plot (‘update volcano’).
Manual selection tool: You can select proteins directly inside the plot: Just draw a rectangle inside the volcano plot with the mouse cursor and move it around. Points inside the rectangle are listed in the table (‘manual selection’) below the volcano plot. The table contains 5 columns: Gene.names, Majority.protein.IDs (Uniprot IDs), p.value, q.value, diff (fold change).

Menu 2: Table

The information of all proteins in the displayed volcano plot is stored in this table, which you can download (‘download table’, topleft). The table contains 5 columns: Gene.names, Majority.protein.IDs (Uniprot IDs), p.value, q.value, diff (foldchange). You can search the table for specific connections (top), filter individual columns for specific terms (bottom) and specify or sort the number of entries and columns.

 

Suggestions, questions or just want to chat with us?

www.biochem.mpg.de/meissner

For more analyses and details on the methods, please read our paper

Daniel Hornburg 'Dansen'
hornburg [at] biochem [dot] mpg [dot] de
hornburg [at] stanford [dot] edu

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