In tnaake/apoptosisQuantification: Assessment of apoptosis and necroptosus unsing proteomics

BiocStyle::markdown()

library("knitr")
opts_chunk$set(stop_on_error = 1L)
suppressPackageStartupMessages(library("apoptosisQuantification"))

Questions and bugs {-}

apoptosisQuantification is currently under active development. If you discover any bugs, typos or develop ideas of improving apoptosisQuantification feel free to raise an issue via GitHub or send a mail to the developer.

Prepare the environment and load the data {#sec-prepare}

To install apoptosisQuantification enter the following to the R console

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
if (!requireNamespace("devtools", quietly = TRUE))
    BiocManager::install("devtools")
devtools::install("tnaake/apoptosisQuantification")

Introduction {#sec-intro}

When performing cell line experiments it is of outermost interest to capture variation originating from the treatment, while controlling external effects. One such effect is the onset of apoptosis that results in cell death and might overlay the biological effects of interest.

We present here the apoptosisQuantification package that allows to quantify the level and effects of apoptosis (and necroptosis). We use here a list of markers that are deregulated in apoptosis and necroptosis.

Classical necrotic markers include lactate deydrogenase (LDH),high-mobility group B1 (HMGB1), myoglobin, enolase, and 14-3-3 proteins. Commonly, a protein release is observed during apoptosis and necroptosis for human myeloid/lymphoma cell lines and human primary macrophage cells as observed time course experiments (cf. @Tanzer2020). The work of @Tanzer2020 looked at differential expression of proteins during a time-course experiments where histiocytic lympoma cell lines (U937) were induced to apoptosis (TNF+SM treatment, SM: birinapant, TNF: tumor necrosis factor) and necroptosis (TNF+SM+Idun-6556, Idun-6556: caspase inhibitor IDUN-6556).

Load marker proteins

The readMarkers function loads a list of apoptosis or necroptosis markers taken from the time-course experiment of @Tanzer2020.

We first load a tibble that stores the marker proteins together with the observed maximum absolute fold change for significant time points and the number of time points when the difference between treated and control cell lines were significant.

markers_apoptosis <- readMarkers(type = "apoptosis", fc = 2, n = 1)
markers_necroptosis <- readMarkers(type = "necroptosis", fc = 2, n = 1)

The returned tibble contains three columns:

• feature: name of the marker,
• change: maximum/minimum fold change from the time-course experiment (absolute value is taken to define the maximum change),
• significant_n: number of significant time-points.

Alternatively, we load a list of hallmark genes that are changed in programmed cell death. The list is taken from @Liberzon2015 (accessed via GSEA). In the following, we create a tibble containing these proteins in the feature column.

## truncate the markers with
f <- system.file("protein_markers/geneset_HALLMARK_APOPTOSIS.RDS", 
    package = "apoptosisQuantification")
hallmark_genes <- readRDS(f)
hallmark_genes <- tibble(feature = hallmark_genes)

Assessing apoptosis and necroptosis via loadings

This approach looks at the distribution of the loadings (from PCA) from markers and non-markers. If there are effects between the different samples caused by apoptosis/necroptosis we expect that the distribution of marker loadings is higher than the distribution of non-marker loadings.

We first load the data set.

f <- system.file("protein_datasets/tanzer2020.RDS", 
    package = "apoptosisQuantification")
tanzer2020 <- readRDS(f)

## obtain the assay slot (contains the transformed intensities)
library(SummarizedExperiment)
prot <- assay(tanzer2020)

We then calculate the loadings of markers and non-markers. We will look here at loadings of principal component 1 and visualize the results (loadings of markers vs. loadings of non-markers).

## plot the distribution as ECDF
plotECDF(values = prot, markers = markers_apoptosis, PC = "PC1")

## histogram (distribution of loadings)
plotHistogram(values = prot, markers = markers_apoptosis, PC = "PC1")

## violin plot (distribution of loadings)
plotViolin(values = prot, markers = markers_apoptosis, PC = "PC1")

We perform a Wilcoxon rank sum test. If significant, it is indicated that the mean of the marker loadings are higher than the mean of the non-marker loadings.

performWilcoxonTest(values = prot, markers = markers_apoptosis, PC = "PC1")

Assessing apoptosis and necroptosis via mitochondrial protein quantification

Apoptotic cells show an increase in the proportion of gene expression which originates from the mitochondria, which occurs after the opening of the MPT pore as apoptosis begins. This gradually results in a decrease in the total expression of chromosomal genes as the DNA is degraded. In late apoptosis, there is typically a drastically reduced total count of genes in a cell, with a large proportion of the gene expression stemming from mitochondria.

Note, that the proportions of proteomics datasets might not directly interpreted as done in transcriptomics due to the specificities of proteomics data acquisition.

The function \code{calculateProportionOfMitochondrialProteins} calculates proportions of the intensities of mitochondrial proteins (i.e. proteins that are encoded in the mitochondrial DNA) to all intensities. The function returns the proportions in percent.

Mitochondrial proteins are defined by the Human Protein Atlas (1139 proteins found by searching the Human Protein Atlas database for mitochondrial sublocalization). These proteins are matched against the proteins found in prot. Currently, two types of ids are encoded: either rownames(prot) have to be "Symbol" or "Ensembl" ids.

Protein intensities have to in untransformed format such that proportions can be calculated as are.

## first back-transform to raw-values
prot_raw <- exp(prot)

The protein ids of tanzer2020 are SYMBOL. We specify the id argument as "Symbol".

prop_mito <- calculateProportionOfMitochondrialProteins(values = prot_raw, 
    id = "Symbol")

Using the function plotProportionOfMitochondrialProteins we visualize the proportion of mitochondrial proteins.

plotProportionOfMitochondrialProteins(proportion = prop_mito)

Assessing apoptosis and necroptosis via decoupleR

Another approach is to calculate contamination scores using the decoupleR package [@Badia2021]. decouple calculates the source activity per sample by coupling a regulatory network with the weighted mean (run_wmean) statistic. The marker proteins correspond to the target nodes, while the contamination ("apoptosis" or "necroptosis") corresponds to the source nodes. The fold changes are taken as the mode of regulation (mor). Signature scores represent the number of standard deviations away from the mean of an empirical null distribution of scores.

The contamination scores are calculated per sample. Per default, the markers are taken from readMarkers when there is no signatures argument passed to scoreSamples. Alternatively, a tibble can be passed to the argument signatures that contains the columns:

• feature: name of marker proteins,
• change: associated negative or positive fold change associated with the marker.

If signatures is passed as an argument to scoreSamples the contamination scores are calculated based on the markers defined in this object.

scores <- scoreSamples(values = prot, contamination = "apoptosis", n_perm = 100)

We visualize the scores using the plotSampleScores function.

scores$treatment <- tanzer2020$treatment
plotSampleScores(scores = scores) +
    ggplot2::facet_wrap(~ treatment, scales = "free_x") +
    ggplot2::theme(legend.position = "none")

Assessing apoptosis and necroptosis via differences in marker expression to protein set

Alternatively, the contaminations can also be assessed in a relative manner by looking at the distributions of differences between the marker proteins and a random subset (possibly including the marker proteins). To do this, a signatures object (tibble) has to be passed that does not contain the column change (i.e. whenenver there is the column change in signatures the decoupleR approach will be taken).

As an example we use here the hallmark_genes object that contains a list of proteins (stored in the column feature).

hallmark_genes

We then call the function scoreSamples. We specify that we draw 100 random proteins from prot (n_perm) and specify the marker gene set by the signatures argument.

scores <- scoreSamples(values = prot, contamination = "apoptosis", n_perm = 100,
    signatures = hallmark_genes)

Finally, we plot the scores.

plotSampleScores(scores = scores)

Appendix {-}

Session information {-}

All software and respective versions to build this vignette are listed here:

sessionInfo()

References

tnaake/apoptosisQuantification documentation built on Feb. 20, 2022, 5:37 p.m.