worksheet

Run this code for webr setup

We are covering the manuscript T cell cytolytic capacity is independent of initial stimulation strength by (Richard et al. 2018).

The manuscript abstract:

How cells respond to myriad stimuli with finite signaling machinery is central to immunology. In naive T cells, the inherent effect of ligand strength on activation pathways and endpoints has remained controversial, confounded by environmental fluctuations and intercellular variability within populations. Here we studied how ligand potency affected the activation of CD8+ T cells in vitro, through the use of genome-wide RNA, multi-dimensional protein and functional measurements in single cells. Our data revealed that strong ligands drove more efficient and uniform activation than did weak ligands, but all activated cells were fully cytolytic. Notably, activation followed the same transcriptional pathways regardless of ligand potency. Thus, stimulation strength did not intrinsically dictate the T cell-activation route or phenotype; instead, it controlled how rapidly and simultaneously the cells initiated activation, allowing limited machinery to elicit wide-ranging responses.

We will explore a single cell experiment (sce) object from richard et al, that I called sce.richard in the set up code.

The manuscript determines CD69, CD25, CD44 and CD62L by flow cytometry before single cell analysis, and stored the data as metadata per cell. The plots for CD69 and CD25 were provided by the authors as follows:

The peptides

In the manuscript, naive OT-I T cells are given peptide that has different stimulation strengths

In class we went over code to re-make figure 3:

sce_richard |>
1  colData() |>
  dplyr::filter(time==6) |>
2  ...

1

get the column data

2

you pick it up from here

Here is the colData

sce_richard |>
  colData() |> view()

data = FileAttachment("sce_richard_summary.csv").csv({ typed: true })
Inputs.table(data, {
  columns: [
    "peptide",
    "time",
    "CD69protein",
    "CD25protein",
    "CD44protein",
    "CD62Lprotein",
    "Cd69transcript",
    "Cd25transcript",
    "Cd44transcript",
    "Sell (CD62L)",
    "PC1",
    "PC2",
    "TSNE1",
    "TSNE2",
    "UMAP1",
    "UMAP2",
    "post.analysis.well.quality",
    "single.cell.quality"
  ]
})

And if plotted:

Question 1

The manuscript determines CD69, CD25, CD44 and CD62L by flow cytometry before single cell analysis, but only shows CD69 and CD25.

How does CD44, CD25, and CD62L surface staining reflect peptide stimulation strength at six hours?

Question 2

A practical consideration is if gene transcripts match up to protein surface expression. On a per cell basis, compare CD69 protein vs Cd69 transcript

Warning: Removed 44 rows containing missing values or values outside the scale range
(`geom_point()`).

Hint: To get help with ggplot2, you can look at the ggplot2 cheat sheet.

For a more comprehensive tutorial, you can read R for data science, chapter 3 visualization.

Question 3

Question 4

The reducedDims slot is specially designed to store reduced dimensional representations of the primary data obtained by methods such as PCA and t-SNE (see Basic Chapter 4 for more details).

I ran the following:

sce.richard |> 
1  scater::logNormCounts() |>
  scater::runPCA() |>
2  scater::runTSNE() -> sce.richard

1

logcounts are stored in assay

2

pca and tSNE is stored in reducedDims

So, you can plot the results and compare to the original data. Try coloring the reducedDimension plot by a) stimulus, and b) CD69

How does it compare to the published figure:

Published PCA

Question 5

References

Richard, Arianne C., Aaron T. L. Lun, Winnie W. Y. Lau, Berthold Göttgens, John C. Marioni, and Gillian M. Griffiths. 2018. “T Cell Cytolytic Capacity Is Independent of Initial Stimulation Strength.” Nature Immunology 19 (8): 849–58. https://doi.org/10.1038/s41590-018-0160-9.