Poster, WCBP 2005

1. 1
Sensitivity and Reproducibility of Protein Aggregate Analysis by
Sedimentation Velocity
John S. Philo, Alliance Protein Laboratories
Although use of sedimentation velocity (SV) for aggregate analysis is becoming fairly
common, there is little data available about the sensitivity and reproducibility of this method.
Replicate samples demonstrate that the standard deviation for the fraction main peak can be
0.3% or less, with comparable or lower uncertainties for individual aggregate peaks. In some
cases larger aggregate species that are well separated from the main peak can be detected at
levels well below 0.1%, as demonstrated by the presence of the same aggregate species in
multiple lots and/or increases in the fraction of that species with time or stress.
While modern SV data analysis methods can provide size distributions that resemble
chromatograms, that similarity can be deceptive because the nature of the “noise” in the data
is often quite different than for chromatography. Examples will illustrate that it is normal for
the peak positions for very minor peaks to shift from sample to sample. It is also normal for
peaks near the threshold of detection to appear or disappear in different replicates of the
same material, or for two minor peaks to sometimes be merged into one average species by
the software. Those features can make it difficult to reproducibly assign and quantitate
individual aggregate components when there are many minor species, but those features do
not compromise the assessment of fraction main peak. Further, it is nonetheless always clear
whether large aggregate species are present which are lost or poorly resolved by SEC.
all slides © copyright 2005 Alliance Protein Laboratories

2. 2
Modern methods of sedimentation velocity data
analysis1 allow us to separate and quantitate long-lived
aggregates with high resolution and sensitivity, giving a
distribution much like a chromatogram.
1Schuck, P. (2000). Size-distribution analysis of macromolecules by
sedimentation velocity ultracentrifugation and Lamm equation modeling.
Biophys. J. 78, 1606-1619.
5.9 6.0 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7.0 7.1 7.2
Radius (cm)
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
Absorbanceat280nm
0 2 4 6 8 10 12 14 16 18 20 22 24
0.0
0.2
0.4
0.6
0.8
1.0
1.2
c(s),normalized
sedimentation coefficient (Svedbergs)
raw data sedimentation coefficient distribution

3. 3
THE PROMISE
These data for a highly stressed antibody sample illustrate excellent
resolution, sensitivity, and range of size
0 2 4 6 8 10 12 14 16 18 20 22 24
0.0
0.2
0.4
0.6
0.8
1.0
heptamer,0.1%
hexamer,0.4%
pentamer1.4%
tetramer5.3%
trimer14.6%
dimer30.6%
main peak (monomer), 45.5%
?HLhalfmolecule,0.8%
?freelightchain,1.4%
c(s),normalized(totalarea=1)
sedimentation coefficient (Svedbergs)

4. 4
THE PERIL
The similarity of these distributions to chromatograms
can be deceptive, and the software is easily miss-applied
1. the effective resolution is a function of signal/noise
ratio and goes down as the fraction of minor peaks
goes down
– the resolution you can achieve for a 150 kDa antibody is
also much greater than for a 20 kDa cytokine
2. the nature of the noise (variability) is very different
than in chromatography
3. false peaks are possible, especially when the
software is applied inappropriately

5. 5
Three measurements of the same monoclonal antibody stock
diluted into 3 different buffers give 0.25% ± 0.05% aggregate
0 2 4 6 8 10 12 14 16 18 20
0
1
2
3
0 2 4 6 8 10 12 14 16 18 20
0.00
0.02
0.04
0.06
buffer 1, buffer 2, buffer 3
normalizedc(s)
sedimentation coefficient (Svedbergs)
X 50
0 2 4 6 8 10 12 14 16 18 20
0
1
2
3
0 2 4 6 8 10 12 14 16 18 20
0.00
0.02
0.04
0.06
dilution buffer 1
99.77% main peak at 6.271 S
normalizedc(s)
sedimentation coefficient (Svedbergs)
99.77%
0.23%
X 50
0 2 4 6 8 10 12 14 16 18 20
0
1
2
3
0 2 4 6 8 10 12 14 16 18 20
0.00
0.02
0.04
0.06
99.79% dilution buffer 2
99.79% main peak at 6.020 S
normalizedc(s)
sedimentation coefficient (Svedbergs)
0.21%
X 50
0 2 4 6 8 10 12 14 16 18 20
0
1
2
3
0 2 4 6 8 10 12 14 16 18 20
0.00
0.02
0.04
0.06
99.70%
dilution buffer 3
99.70% main peak at 6.053 S
normalizedc(s)
sedimentation coefficient (Svedbergs)
0.30%
X 50

6. 6
Three replicates of Protein X (monomer < 20 kDa) give 0.48% ±
0.06% total aggregate, 0.14% ± 0.07% species 1, 0.19% ± 0.04%
species 2, 0.12% ± 0.07% species 3, 0.07% ± 0.04% species 4
0 2 4 6 8 10 12 14 16 18 20 22 24
0
4
8
0 2 4 6 8 10 12 14 16 18 20 22 24
0.00
0.04
0.08
replicate 1, replicate 2, replicate 3
normalizedc(s)
sedimentation coefficient (Svedbergs)
X 100
0 2 4 6 8 10 12 14 16 18 20 22 24
0
4
8
0 2 4 6 8 10 12 14 16 18 20 22 24
0.00
0.04
0.08
99.57%
replicate 1
99.57% main peak at 1.702 S
normalizedc(s)
sedimentation coefficient (Svedbergs)
0.02%
0.15%
0.18%
0.08%
X 100
0 2 4 6 8 10 12 14 16 18 20 22 24
0
4
8
0 2 4 6 8 10 12 14 16 18 20 22 24
0.00
0.04
0.08
99.45%
replicate 2
99.45% main peak at 1.696 S
normalizedc(s)
sedimentation coefficient (Svedbergs)
0.10%
0.09%
0.23%
0.12%
X 100
0 2 4 6 8 10 12 14 16 18 20 22 24
0
4
8
0 2 4 6 8 10 12 14 16 18 20 22 24
0.00
0.04
0.0899.45%
replicate 3
99.45% main peak at 1.696 S
normalizedc(s)
sedimentation coefficient (Svedbergs)
0.08%
0.11%
0.16%
0.21%
X 100

7. 7
Three replicates of Protein Y (monomer < 50 kDa) give 7.6% ± 0.55% total
aggregate, 5.4% ± 0.15% dimer, 1.33% ± 0.25% tetramer, 0.47% ± 0.23%
hexamer, 0.27% ± 0.09% octamer, 0.09% ± 0.09% larger aggregates
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0
1
2
3
0 2 4 6 8 10 12 14 16 18 20
0.00
0.05
0.10
0.15
93.0%
replicate 1
93.0% main peak at 3.23 S
normalizedc(s)
sedimentation coefficient (Svedbergs)
0.38%
0.19%
1.10%
5.30%
X 20
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0
1
2
3
0 2 4 6 8 10 12 14 16 18 20
0.00
0.05
0.10
0.15
replicate 1, replicate 2, replicate 3
normalizedc(s)
sedimentation coefficient (Svedbergs)
X 20
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0
1
2
3
0 2 4 6 8 10 12 14 16 18 20
0.00
0.05
0.10
0.15
92.4%
replicate 2
92.4% main peak at 3.22 S
normalizedc(s)
sedimentation coefficient (Svedbergs)
0.18%
0.30%
0.27%
1.59%
5.31%
X 20
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0
1
2
3
0 2 4 6 8 10 12 14 16 18 20
0.00
0.05
0.10
0.15
91.9% replicate 3
91.9% main peak at 3.23 S
normalizedc(s)
sedimentation coefficient (Svedbergs)
0.10%
0.73%
0.36%
1.31%
5.56%
X 20

8. 8
Protein Z: trace peaks (<0.05%) in multiple lots fall at
similar positions, implying these are real species
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
0
2
4
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
0.00
0.01
0.02
normalizedc(s)
sedimentation coefficient (Svedbergs)
lot 1
lot 2
0.03%
0.03%
0.04%
0.04%
0.70%
0.16%
X 200

9. 9
Changes in a lyophilized protein over time after re-hydration imply
a peak initially detected at a level of only 0.007% is a real species
1 10 100
0.00
0.06
0.12
0 hr
2 hr
8 hr
s×c(s)
sedimentation coefficient (Svedbergs)
0.02%
0.07%
0.38%
0.007%
X200

10. 10
How can we predict the expected detection limits
and reproducibility? We can exploit the fact that
sedimentation velocity has a robust theory!
1. use the theory to simulate data for a known mixture, for
example monomer + known percentages of irreversible
aggregates
mimic the data acquisition schedule, loading concentration, and
noise level of real experiments
2. analyze the simulated data applying the same procedure
as in real experiments, and see how well the results
match the mixture that was simulated
3. repeat 1 & 2 several times to evaluate reproducibility

11. 11
Analysis of simulated data for antibody + 2% dimer + 1%
trimer + 0.5% tetramer
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
0
4
8
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
0.00
0.08
0.16
96.62% simulation antibody + 2% dimer + 1% trimer
+ 0.5% tetramer (96.5% main peak)
normalizedc(s)
sedimentation coefficient (Svedbergs)
0.46%
0.96%
1.96%
X 50

12. 12
What sources of systematic error can prevent
achieving high reproducibility and sensitivity?
1. Detergent micelles may produce peaks that overlap with
species of interest
Tween-80 micelles sediment around 2 S, and can often be detected
by absorbance, particularly at wavelengths below 280 nm
poor cancellation of detergent signals when detergent is present in
both sample and reference channels is common
2. High levels of excipients (sucrose, sorbitol, amino acids)
may produce significant density gradients at high r.p.m.,
slowing the sedimentation as the run proceeds
in principal new models in the software can handle this, but we
often lack the required data on the density and viscosity effects of
the excipients

13. 13
Other problems and sources of error, continued
3. Some proteins are easily damaged by UV exposure
SV protocols now typically involve many more scans than they did
in the past and hence much more UV exposure
remember, the entire sample is exposed to light with every flash, not
just the radius being measured at that time
4. Aggregates are generally quite sticky, and may get lost
during transfer (on surfaces of vials, pipette tips, or the
centrifuge cell)
in theory multiple rinses should minimize this problem, if sufficient
sample is available

14. 14
Conclusions
1. Both experiment and theory indicate that individual
aggregate species can be measured with a reproducibility
of ±0.1% or better in some cases
• resolution and reproducibility is generally higher for larger
proteins like antibodies than for small cytokines
• precision is poorer when many poorly-resolved aggregate species
are present
• it is normal for the positions of minor peaks to shift from sample
to sample, and the software sometimes merges or omits minor
peaks that are near the detection threshold
2. Large aggregates that sediment > ~3 times faster than
monomer can generally be detected at levels of 0.1% or
below
3. The levels of precision and sensitivity illustrated here
cannot necessarily be achieved for all proteins, or by
relatively inexperienced analysts