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Transcript of Single cell (mass) cytometry · Nebulizer –Single cell droplets Bandura D, et al. Anal Chem. 2009...
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Single cell (mass) cytometryIntroduction to R for Bioinformatics
Presented by: Giorgos Papoutsoglou, PhD
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Background: Cell development
stimuli
receptors
proteins
death/proliferation
differentiation
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Why single-cell data ?
• Find new cell populations / functions
• Decipher system heterogeneity and
general system monitoring
• Disease measurement, prediction,
understanding
• Network / inference / co-variance
analysis
• Single cell systems ordering
(revealing unanticipated order within
a mixutre)
Ye, J. Hematol. Oncol., 2017
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Study cell populations
Use of fluorescent tags coupled to antibodies,
able to bind to specific cell targets
For example, in immunophenotyping the
targets are specific surface proteins
en.wikipedia.org
www.bdbiosciences.com
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Fluorescent cytometry
https://commons.wikimedia.org
wikipedia.org
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Data files
• FCS 2.0 and FCS 3.0 conventions
• Contain all of the measurements (FSC, SSC, FL1…) for each
individual cell processed in a given sample
FSC SSC FL1 …
Cell 1 ## ## ##
…
cell N ## ## ##
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Bottlenecks
Channel Overlap (spillover)
• Up to 12 colors can be
“routine”
• 17 colors have been
reported
• High background
Variable dynamic range
www.bdbiosciences.com
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Compensation
• Mathematical procedure to remove the spillover i.e. to account for spectral overlap and to measure
the photons deriving from one fluorophore into multiple detectors
• Single stained controls are required for all fluorophores used to reveal the level of spectral overlap
for each detector
www.bio-rad-antibodies.com
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Data analysis: Gating
Papoutsoglou, et. al., NAR, 2017
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Mass cytometry
• Up to 100 non-biological
elemental mass
channels
• No compensation
required
• Zero background
140 145 150 155 160 165 170 175
Isotopic mass (Da)
100 -
90 -
80 -
70 -
60 -
50 -
40 -
30 -
20 -
10 -
0 -
Adapted from S. Bendall, http://web.stanford.edu/class/immunol206a/
wikipedia.org
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Europium
“152” is
actually a
50/50 mix of 151Eu and 153Eu
Each vertical bar in these elements is a different
isotope that can be separately measured
How do you get > 50 parameters?
Adapted from S. Bendall, http://web.stanford.edu/class/immunol206a/
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Data preparation
• Tag antibodies with different metals (chelation)
• Crosslink the proteins (freeze the inner of the cell)
• Permeabilize the membrane (make holes)
• Label cell proteins with chelated antibodies
• Spike beads in the samples for internal standard correction
(normalization)
• Label samples with different barcodes to use them together
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Perturbations
Cross-link
Proteins
Permeabilize
Cell MembraneMetal-chelated
Antibody Stain
Nebulize To Single Cell DropletsIonize In Plasma (7500K)ToF Mass Spec
Workflow
Beads
Adapted from R. Finck, http://web.stanford.edu/class/immunol206a/
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TOF (Time of Flight)
ICP
CyTOF: A prototype schematic
Occurs at a rate of ~1000 cells per second
Nebulizer – Single cell droplets
Bandura D, et al. Anal Chem. 2009
Adapted from S. Bendall, http://web.stanford.edu/class/immunol206a/
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Data collection
.IMD file .FCS file
time(s)
ch. 160
ch. 170
ch. 180
10 25 11 2
11 20 18 4
12 28 16 3
... ... ... ...
59 2 3 3
… … … …
time(s)
Celllen.
ch. 160
ch. 170
ch. 180
10 48 1402 563 15
189 42 1212 481 36
302 51 1934 787 29
... ... ... ...
Detection threshold
3
time(s)
ch. 160
ch. 170
ch. 180
10 22 8 -1
11 17 15 1
12 25 13 0
... ... ... ...
59 -1 0 0
… … … …
if <0, set to 0
find event
“start”, “end”
cell length =
“end”-“start”
integrate
from “start”
to “end”
Nolan’s Lab does
(still?) randomization
here by shifting the
axis to +100. In this
way they maintain
some of the the
negative values.
!
cell length
time (s)
signal intensity
M/z
160
170
180
“start” “end”
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Fresh PBMc stained with 27 markers (mix I):
Lymp B
Lymp CD4+T CD2 CD3 CD4CD45
CD45RA; CD20; CD45; CD38; CD19; CD40 CD49dCD71
CD2 175LuCD3 152SmCD4 142NdCD7 139LaCD8 146NdCD10 168ErCD11b 158Gd
CD13 166ErCD15 170ErCD19 171YbCD20 156GdCD31 144NdCD33 141PrCD34 169Tm
CD36 150NdCD38 165HoCD40 172YbCD44 151EuCD45 159TbCD45RA 153EuCD49d 145Nd
Example reads per isotope
M/z
time
Adapted from S. Bendall, http://web.stanford.edu/class/immunol206a/
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Advantages:
1. Uniform Staining
2. Reduced Antibody
Consumption
3. Reduced Acquisition Time
4. Improved Singlet Detection
Krutzik PO, Nolan GP. , Nat Methods. 2006
Cell Multiplexing/Barcoding
Adapted from R. Finck, http://web.stanford.edu/class/immunol206a/
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21=2
22=4
23=8
24=16
25=32
26=64
27=128
Binary Cell Labeling Schemes for n-well MCB Multiplexing
Bodenmiller, et. al., Nature Biotech., 2012
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Before Analysis
1. Doublet filtering
2. Bead normalization
3. Debarcoding (if needed)
4. Randomization (visualization)
5. Transformation(arcsinh)
6. Gating
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Bead doublets
Events that result from a bead
combined with
• a cell (cell-bead doublets) or
• another bead (bead-bead
doublets).
To find bead doublets make a biaxial
plot of the bead channel (x-axis) and
of the DNA channel (y-axis) and
create a bead gate.
Finck, et. al., Cytometry A, 2013
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Goal: Reliably compare mass cytometry data
across patients, conditions, tissues, etc.
Problem: Drifts in mass cytometry instrument
sensitivity over time due to cellular debris,
fluctuations in plasma temperature, and
calibrations.
Solution: Normalization using internal bead
standards measured concurrently with cell
samples.
Normalization of Mass Cytometry Data
Adapted from R. Finck, http://web.stanford.edu/class/immunol206a/
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Normalization procedure: smoothing
• Bead smoothing removes
local variance (in a single
experiment)
• use the median of a
sliding window of 500
bead-associated events
10
100
1000Day A Day B Day C Day D
time
Smoothed Beads
Lo
ca
l M
ed
ian
Be
ad
In
ten
sity
Bead Masses
139
141
159
169
175
Adapted from R. Finck, http://web.stanford.edu/class/immunol206a/
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Normalization: slope correction
• Fitted Slopes Define a
Correction Function
• Multiple
days/experiments: use
the slope of the line
through the origin and
the point of intersection
of the bead intensity at
every time point and
the mean smoothed
bead intensities across
all experiments.
10
100
1000Day A Day B Day C Day D
time
Smoothed Beads
Lo
ca
l M
ed
ian
Be
ad
In
ten
sity
Bead Masses
139
141
159
169
175
0 200 400 600 800 10000
200
400
600
800
1000
Smoothed Bead Intensity at Single Time−Point
Base
line
(G
lobal M
ean
) B
ea
d I
nte
nsity
139
141
159
169
175
Fitted Line
x
0
1
2
3
4
time
Fitted Slopes Across All Time−Points
Fitte
d S
lop
e
Adapted from R. Finck, http://web.stanford.edu/class/immunol206a/
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Debarcoding
• Each perturbation
experiment is placed
in a separate well
• Each well receives a
unique combination of
barcodes
6-choose-3 MCB-multiplexing example, [Zunder et. al., Nat. Prot., 2015]
redro.pl
different isotope
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Randomization of integer values
Avoid having large peaks (usually at zero) and create better scatterplot visualizations
• Automatic: using a negative uniform distribution
• Manual: add a Gaussian distributed random value (tunable variance)
• (separate option for zero values): scatter using the negative half of a tunable Gaussian
0 5 0 5-1 4 0 5-1 4
automatic manual
proteinabundance (a.u.)
6
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FCS conversion settings (CyTOF)
• Transformation
• Linear, Arcsinh, Log10
• Scaling
• Randomization (only if Linear
data)
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Adapted from S. Bendall, http://web.stanford.edu/class/immunol206a/
Can we create 2D maps representing higher dimensional data?
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Possible Solutions
SPADE
www.cytospade.org / www.cytobank.org
P. Qiu et al. Nature Biotechnology, 2011
SPADE
ViSNE
Wanderlust
ViSNE
Amir et al. Nature Biotechnology, 2013
Adapted from S. Bendall, http://web.stanford.edu/class/immunol206a/
Wanderlust
Bendall et al. Cell, 2014
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Causalpath
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Biological Background
• Multiple Sclerosis (MS)
• naïve T cells become T-helper
(Th) cells in the blood
• Pathogenic Th cells attack the
nervous system
• Secrete cytokines damaging
brain cells
Du and Xie, Cell Research (2012), doi:10.1038/cr.2012.87
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From Naïve CD4+ T cells to GM-CSF+ cells
Herndler-Brandstetter, Cell Research, 2014
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Biological questions
• How human CD4+ T cells differentiate to become GM-CSF+
cells?
• Under which cytokine signals?
• Under what stimulation conditions (signaling pathways involved)?
• Which are the cell characteristics? (co-expression of other T-cell
markers (CD))
• Which are the Th subsets (Th1, Th2, Th17) present?
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Experimental Design
• 2 donors
• 34 distinct experiments (>100K cells/exp.)
• 48 protein markers: 27 surface, 11 signaling, 10 cytokine
0
PMA/IONO.
CD3/CD28
Time2 5 15 30 3d 5d
cytokine activation
TGF-β1
TGF-β3
signaling activation
T-cell activation
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Data example (density)
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Data example (acquisition time vs abundance)
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Thank you !