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Biological information flow
Information flow in biological systems (Source Central dogma of biology on Wikipedia).
There are three main biological entities that respectively store
information, act as data intermediates, and the functional units, and
the figure below show how information flows between these three
levels.
DNA, that lives in the nucleus of cells, is the central
information storage mechanism, and encodes the blueprint of the
functional units as genes. DNA is transcribed into messenger
RNA (mRNA), that re-localises outside the nucleus and is further
processed into its mature exon-only form after removal of the
non-coding introns sequences. Finally, the mRNA is translated by the
ribosomial machinery into proteins directly into the endoplasmic
reticulum (ER) where they are then redirected to their final
destination.
Sub-cellular structure of an animal cell (Source Cell biology on Wikipedia).
In addition to the standard information worflow where DNA is
transcribed into RNA that itself is translated into proteins,
there is also reverse transcription, that generates
(complementary) DNA from and RNA molecule, as well as replication of
DNA (during cell division) and RNA molecules.
Why studying proteins
Proteins as the functional units in all living organisms, and they are
highly dynamic. The caterpillar and the resulting butterfly have the
same genome. The complement of all the expressed proteins, termed the
proteome is however very different.
The metamorphosis from a caterpilar to a monarch butterfly. (Image from Phys.org)
There are different modalities of the proteome that are of
interest. In addition to the presence or amount of protein in a
biological samples, it is also important to study the interactions
between proteins forming protein-protein complexes, the presence of
post-transcriptional modification (such as, for example,
phosphorylations), the rate at which proteins are produced and
degraded, or where the proteins reside inside a cell.
The technique of choice to study proteins in a high throughput way is
mass spectrometry.
A beginner’s guide to mass spectrometry–based proteomics(Sinha and Mann 2020) is an approachable introduction to sample preparation,
mass spectrometry and data analysis.
Setup
The MSnbase package.
We are going to use the Bioconductor (Huber et al. 2015)MSnbase package
(Gatto and Lilley 2012; Gatto, Gibb, and Rainer 2020), which can be install with the BiocManager
package, available from CRAN. If BiocManager isn’t available on your
computer, install it with:
install.packages("BiocManager")
Now, install MSnbase and its dependencies with
BiocManager::install("MSnbase")
For additional information on how to analyse mass spectrometry-based
proteomics data, refer to (Gatto and Christoforou 2014) and (Gatto 2019), or explore
the the
proteomics-
and mass
spectrometry-related
packages on the Bioconductor page
The recent R for Mass
Spectrometry initiative, and
the documetation book
provide further details and state-of-the-art infrastructure for MS and
proteomics data analysis.
The R for Mass Spectrometry intiative.
How does mass spectrometry work?
Mass spectrometry (MS) is a technology that separates charged
molecules (ions) based on their mass to charge ratio (M/Z). It is
often coupled to chromatography (liquid LC, but can also be gas-based
GC). The time an analytes takes to elute from the chromatography
column is the retention time.
A chromatogram, illustrating the total amount of analytes over the retention time.
An mass spectrometer is composed of three components:
The source, that ionises the molecules: examples are Matrix-assisted
laser desorption/ionisation (MALDI) or electrospray ionisation.
(ESI)
The analyser, that separates the ions: Time of flight (TOF) or Orbitrap.
The detector that quantifies the ions.
When using mass spectrometry for proteomics, the proteins are first
digested with a protease such as trypsin. In mass shotgun proteomics,
the analytes assayed in the mass spectrometer are peptides.
Often, ions are subjected to more than a single MS round. After a
first round of separation, the peaks in the spectra, called MS1
spectra, represent peptides. At this stage, the only information we
possess about these peptides are their retention time and their
mass-to-charge (we can also infer their charge be inspecting their
isotopic envelope, i.e the peaks of the individual isotopes, see
below), which is not enough to infer their identify (i.e. their
sequence).
In MSMS (or MS2), the settings of the mass spectrometer are set
automatically to select a certain number of MS1 peaks (for example
20). Once a narrow M/Z range has been selected (corresponding to one
high-intensity peak, a peptide, and some background noise), it is
fragmented (using for example collision-induced dissociation (CID),
higher energy collisional dissociation (HCD) or electron-transfer
dissociation (ETD)). The fragment ions are then themselves separated
in the analyser to produce a MS2 spectrum. The unique fragment ion
pattern can then be used to infer the peptide sequence using de novo
sequencing (when the spectrum is of high enough quality) of using a
search engine such as, for example Mascot, MSGF+, …, that will match
the observed, experimental spectrum to theoratical spectra (see
details below).
Schematics of a mass spectrometer and two rounds of MS.
The animation below show how 25 ions different ions (i.e. having
different M/Z values) are separated throughout the MS analysis and are
eventually detected (i.e. quantified). The final frame shows the
hypothetical spectrum.
Separation and detection of ions in a mass spectrometer.
The figures below illustrate the two rounds of MS. The spectrum on the
left is an MS1 spectrum acquired after 21 minutes and 3 seconds of
elution. 10 peaks, highlited by dotted vertical lines, were selected
for MS2 analysis. The peak at M/Z 460.79 (488.8) is highlighted by a
red (orange) vertical line on the MS1 spectrum and the fragment
spectra are shown on the MS2 spectrum on the top (bottom) right
figure.
Parent ions in the MS1 spectrum (left) and two sected fragment ions MS2 spectra (right).
The figures below represent the 3 dimensions of MS data: a set of
spectra (M/Z and intensity) of retention time, as well as the
interleaved nature of MS1 and MS2 (and there could be more levels)
data.
MS1 spectra (blue) over retention time (left). MS2 spectra (pink) interleaved between two MS1 spectra (right),
Reading and accessing MS data
Let’s read a very small raw MS data file into R using the readMSData
from the MSnbase package. The file that we are going to load is also
available in the package.
## MSn experiment data ("MSnExp")
## Object size in memory: 0.18 Mb
## - - - Spectra data - - -
## MS level(s): 2
## Number of spectra: 5
## MSn retention times: 25:01 - 25:02 minutes
## - - - Processing information - - -
## Data loaded: Mon Apr 29 12:11:17 2024
## MSnbase version: 2.28.1
## - - - Meta data - - -
## phenoData
## rowNames: dummyiTRAQ.mzXML
## varLabels: sampleNames
## varMetadata: labelDescription
## Loaded from:
## dummyiTRAQ.mzXML
## protocolData: none
## featureData
## featureNames: F1.S1 F1.S2 ... F1.S5 (5 total)
## fvarLabels: spectrum
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
The object that is returned by readMSData is of class MSnExp, that
can store, access and manipulate raw MS data. Note that here we are
focusing on MS-based proteomics data, but this also applied to
MS-based metabolomics data.
We can find out how many spectra are available in that data using
the function length. Full MS acquisitions would contain hundreds
of thousands spectra.
length(x)
## [1] 5
We can use various accessor function to get the MS level of these
spectra, their retention time, or the M/Z and intensity of the
precursor peaks of the ion corresponding to the MS2 spectra.
We can also extract individual spectra using [[ and plot them.
x[[3]]
## Object of class "Spectrum2"
## Precursor: 645.3741
## Retention time: 25:02
## Charge: 2
## MSn level: 2
## Peaks count: 2125
## Total ion count: 150838188
plot(x[[3]])
Visualisation of the 3rd MS spectrum in our small test data set.
Exercise
For the rest of this tutorial, we will be using a
slightly larger dataset (still tiny compared to full acquisitions)
that is distributed with the MSnbase package. Load it as shown below
and compute the number of spectra available in that dataset, their MS
level, and the retention time range over which these spectra have been
acquired.
data(itraqdata)
length(itraqdata)
## [1] 55
unique(msLevel(itraqdata))
## [1] 2
formatRt(range(rtime(itraqdata)))
## [1] "19:09" "50:18"
This object also contains additional metadata for each spectrum, that
can be accessed, as a data.frame, with fData.
Identification
The raw data is still a long way of obtaining biologically relevant
proteomics data. The first step to obtain proteomics data is to
identify the peptides that have been acquired in the MS. Peptide
identification work by comparing expected and observed spectra. As
shown below, when a precursor peptide ion is fragmented in a CID cell,
it breaks at specific bonds, producing sets of peaks (a, b, c
and x, y, z) that can be predicted.
Peptide fragmentation.
It is thus possible to calculate the expected set of fagment peaks for
a given peptide, such as SIGFEGDSIGR below.
calculateFragments("SIGFEGDSIGR")
## mz ion type pos z seq
## 1 88.03931 b1 b 1 1 S
## 2 201.12337 b2 b 2 1 SI
## 3 258.14483 b3 b 3 1 SIG
## 4 405.21324 b4 b 4 1 SIGF
## 5 534.25583 b5 b 5 1 SIGFE
## 6 591.27729 b6 b 6 1 SIGFEG
## 7 706.30423 b7 b 7 1 SIGFEGD
## 8 793.33626 b8 b 8 1 SIGFEGDS
## 9 906.42032 b9 b 9 1 SIGFEGDSI
## 10 963.44178 b10 b 10 1 SIGFEGDSIG
## 11 175.11895 y1 y 1 1 R
## 12 232.14041 y2 y 2 1 GR
## 13 345.22447 y3 y 3 1 IGR
## 14 432.25650 y4 y 4 1 SIGR
## 15 547.28344 y5 y 5 1 DSIGR
## 16 604.30490 y6 y 6 1 GDSIGR
## [ reached 'max' / getOption("max.print") -- omitted 16 rows ]
The last step is to compare obseved and expected peaks. If there is a
good match, the MS2 spectrum is assigned the peptide sequence.
itraqdata2 <- pickPeaks(itraqdata, verbose = FALSE)
s <- "SIGFEGDSIGR"
plot(itraqdata2[[14]], s, main = s)
Matching observed and expected peaks.
It is also possible to plot 2 spectra to compare them directly.
In a full experiment, all possible peptides from the known (or
relevant) proteome of interest (such as databases that can be
downloaded from the UniProt
site1 The Universal Protein Resource (UniProt) is a freely and
accessible comprehensive resource for protein sequence and
annotation data.) are compared to the
millions of observed spectra.
Statistical challenge The matching between millions of observed and possible spectra causes real challenges due to the large search space and the risk of false positives. See (Käll et al. 2008) for further reading.
From the list of identified peptides, it is then necessary to infer the
most provable proteins that were present in the biological sample.
Statistical challenge Protein inference is a difficult task, as peptides often match multiple proteins (either different isoforms or proteins stemming from different gene but with identical domains), which leads to the definition of protein groups, i.e. sets of proteins that can’t be distinguished with the set of identified peptides at hand. See (Nesvizhskii and Aebersold 2005) for further reading.
Exercise
Plot the 44th spectrum of the itraqdata2 experiment. The sequence
can be accessed in the feature metadata with
plot(itraqdata2[[44]], s,
main = fData(itraqdata2)$PeptideSequence[[44]])
Quantitation
The last step of MS data processing is to quantify peptide abundances
in the biological samples. The table below summarises the different
possibilites depending whether the proteins or peptides are labelled,
and whether the quantitation is performed in MS1 or MS2.
Label-free
Labelled
MS1
XIC
SILAC, 15N
MS2
Counting
iTRAQ, TMT
Label-free MS2: Spectral counting
In spectral counting, on simply counts the number of quantified
peptides that are assigned to a protein.
Labelled MS2: Isobaric tagging
Isobaric tagging refers to the labelling using isobaric tags,
i.e. chemical tags that have the same mass and hence can’t be
distinguish by the spectrometer. The peptides of different samples (4,
6, 10 or 11) are labelled with different tags and combined prior to
mass spectrometry acquisition. Given that they are isobaric, all
identical peptides, irrespective of the tag and this the sample of
origin, are co-analysed, up to fragmentation prior to MS2
analysis. During fragmentation, the isobaric tags fall of, fragment
themselves, and result in a set of sample specific peaks. These
specific peaks can be used to infer sample-specific quantitation,
while the rest of the MS2 spectrum is used for identification.
Label-free MS1: extracted ion chromatograms
In label-free quantitation, the precursor peaks that match an
identified peptide are integrated of retention time and the area under
that extracted ion chromatogram is used to quantify that peptide in
that sample.
In SILAc quantitation, sample are grown in a medium that contains
heavy amino acids (typically arginine and lysine). All proteins gown
in this heavy growth medium contain the heavy form of these amino
acids. Two samples, one grown in heavy medium, and one grown in normal
(light) medium are then combined and analysed together. The heavy
peptides precursor peaks are systematically shifted compared to the
light ones, and the ratio between the height of a heavy and light
peaks can be used to calculate peptide and protein fold-changes.
Figure: credit Wikimedia Commons.
Statistical challenge These different quantitation techniques come with their respective and distinct challenges, such as large quantities of raw data processing, data transformation and normalisation, and different underlying statistical models for the quantitative data (count data for spectral counting, continuous data for the others).
Exercise
As its name implies, the itraqdata is an iTRAQ-based isobar
quantitation experiment. We can visualise the reporter peaks as
follows:
plot(itraqdata[[14]], reporters = iTRAQ4, full = TRUE)
Visualisation of the iTRAQ reporter peaks.
Interpret the figure above.
From the closed-up panel, we see that the 4 reporter ions indicate that there
was slightly less of that peptide in the two first samples, i.e. those labelled
with the 114 and 115 reporters.
The rest of the spectrum (i.e m/z > 120) correspond to the dissociated
fragment ions, used for identification.
Quantification
We can quantify these four peaks with the quantify method, to
produce and object of class MSnSet containing quantitation data. The
quantitation values can be accessed with exprs. This data also
contains feature metadata that can be accessed with the fData
function.
Statistical challenge Quantitative data processing come with numerous statistical challenges such as missing data handling, aggregation of quantitation data, data normalisation, … and the effect of these on the downstream statistical tests.
In our examples, we not have processing data for the 55 peptides and 4
samples. In this data, there is only 1 missing value, corresponding to
an absent reporter peak. We are going to simply drop that feature.
table(is.na(exprs(msnset)))
##
## FALSE TRUE
## 219 1
msnset <- filterNA(msnset)
In MS1 label-free experiments, given that each sample is acquired
independently, the proportion of missing values can be as high several
tens of percent. In such situations, removing rows with missing values
isn’t possible at all. Imputation is possible, albeit tricky, as
different mechanisms can be responsible for missing value that appear
either at random or not at random (Lazar et al. 2016).
Next, we aggregate the spectrum-level quantitation values into
protein-level data using the median and the combineFeatures
function:
Following on from here, many data processing such as normalisation,
non-specific filtering, and hypothesis testing is very similar to
other omics data.
Applications in statistical learning
The pRoloc package.
In this section, I illustrate a use case of mass spectrometry-based
proteomics to infer sub-cellular protein localisation and the
application of statistical learning. This section requires the
pRoloc and pRolocdata packages (Gatto et al. 2014; Breckels et al. 2016). Both
packages can be installed with the BiocManager::install function, as
illustrated above.
library("pRoloc")
library("pRolocdata")
The hyperLOPIT2015 data contains localisation data for 5032 proteins
in mouse embryonic stem cells (Christoforou et al. 2016). The protein
information is collected along a 20 fraction density gradient - our
data matrix has dimensions 5032 rows and 20 columns. We use PCA to
easily visualise it in 2 dimensions.
data(hyperLOPIT2015)
## set colours
setStockcol(paste0(getStockcol(), 80))
## produce PCA plot
plot2D(hyperLOPIT2015)
Mouse stem cell spatial proteomics data from Christoforou et al.
Each dot on the PCA plot represents a single protein. The protein is
either of unknown localisation, and represented by a grey circle, or
is of known localisation (and called an organelle marker) and is
coloured according to its expected sub-cellular localisation.
In the next figure, we have trained an support vector machine (SVM)
model and classified proteins of unknown localisation using an SVM
model. The size of the dots are scaled according to the classifier
score and only assignments corresponding to a false discovery rate of
5% have been considered.
Breckels, L M, C M Mulvey, K S Lilley, and L Gatto. 2016. “A Bioconductor Workflow for Processing and Analysing Spatial Proteomics Data.” F1000Res 5: 2926. https://doi.org/10.12688/f1000research.10411.2.
Christoforou, A, C M Mulvey, L M Breckels, A Geladaki, T Hurrell, P C Hayward, T Naake, et al. 2016. “A Draft Map of the Mouse Pluripotent Stem Cell Spatial Proteome.” Nat Commun 7 (January): 8992. https://doi.org/10.1038/ncomms9992.
Gatto, Laurent, Sebastian Gibb, and Johannes Rainer. 2020. “MSnbase, Efficient and Elegant R-Based Processing and Visualisation of Raw Mass Spectrometry Data.” bioRxiv. https://doi.org/10.1101/2020.04.29.067868.
Gatto, Laurent, and Kathryn S Lilley. 2012. “MSnbase-an R/Bioconductor Package for Isobaric Tagged Mass Spectrometry Data Visualization, Processing and Quantitation.” Bioinformatics 28 (2): 288–89.
Gatto, L, L M Breckels, S Wieczorek, T Burger, and K S Lilley. 2014. “Mass-Spectrometry-Based Spatial Proteomics Data Analysis Using pRoloc and pRolocdata.” Bioinformatics 30 (9): 1322–4. https://doi.org/10.1093/bioinformatics/btu013.
Gatto, L, and A Christoforou. 2014. “Using R and Bioconductor for Proteomics Data Analysis.” Biochim. Biophys. Acta 1844 (1 Pt A): 42–51.
Huber, W, V J Carey, R Gentleman, S Anders, M Carlson, B S Carvalho, H C Bravo, et al. 2015. “Orchestrating High-Throughput Genomic Analysis with Bioconductor.” Nat. Methods 12 (2): 115–21.
Käll, L, J D Storey, M J MacCoss, and W S Noble. 2008. “Posterior Error Probabilities and False Discovery Rates: Two Sides of the Same Coin.” J Proteome Res 7 (1): 40–44. https://doi.org/10.1021/pr700739d.
Lazar, C, L Gatto, M Ferro, C Bruley, and T Burger. 2016. “Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies.” J Proteome Res 15 (4): 1116–25. https://doi.org/10.1021/acs.jproteome.5b00981.
Nesvizhskii, A I, and R Aebersold. 2005. “Interpretation of Shotgun Proteomic Data: The Protein Inference Problem.” Mol Cell Proteomics 4 (10): 1419–40. https://doi.org/10.1074/mcp.R500012-MCP200.
Sinha, Ankit, and Matthias Mann. 2020. “A beginner’s guide to mass spectrometry–based proteomics.” The Biochemist, September. https://doi.org/10.1042/BIO20200057.
Information flow in biological systems (Source Central dogma of biology on Wikipedia).
The 
The R for Mass Spectrometry intiative.
The 
