Deep interrogation of plasma proteins on a large scale is a challenge due to the number
and concentration of proteins, which span a dynamic range of over 10 orders of magnitude.
Current plasma proteomics workflows employ labor-intensive protocols combining
abundant protein depletion and sample fractionation. We previously demonstrated
the superiority of multinanoparticle (multi-NP) coronas for interrogating the plasma
proteome in terms of proteome depth compared to simple workflows. Here we show
the superior depth and precision of a multi-NP workflow compared to conventional
deep workflows evaluating multiple gradients and search engines as well as datadependent
and data-independent acquisition. We link the physicochemical properties
and surface functionalization of NPs to their differential protein selectivity, a key feature
in NP panel profiling performance. We find that individual proteins and protein
classes are differentially attracted by specific surface properties, opening avenues to
design multi-NP panels for deep interrogation of complex biological samples.
proteomics j nano–bio interaction j nanoparticle j mass spectrometry j machine learning
Nanoparticles (NPs) have expanding utility in many fields, including drug therapy,
where they are being utilized as targeting and delivery vehicles (1–3). Upon contact
with biofluids like blood plasma, a thin biomolecule layer termed the corona forms specifically
and reproducibly on the surface of NPs and is composed of proteins, metabolites,
and nucleic acids (4, 5). Recent investigations of nano–bio interactions have
sought to improve the targeting abilities of nanomaterials and reduce their toxicity by
leveraging the predictable process of corona formation (1, 6, 7).
Corona composition is driven by the complex interplay between the physicochemical
properties of the NP surface and biomolecules including proteins (8–12). For instance,
cross-linked N-isopropylacrylamide and butylacrylamide polymer NPs and graphene
nanoflakes are initially bound to high-abundance molecules (e.g., serum albumin) but
subsequently at equilibrium are replaced by lower-abundance, higher–binding affinity
apolipoproteins AI, All, AIV, and E (13, 14). Depending on the NP type, the number
and quantity of proteins in the protein corona change as a function of protein concentrations
in serum/plasma (4). Understanding what drives the formation of the protein
corona will not only improve the efficacy of nanomedicine (e.g., for drug delivery) but
also enable the use of NP coronas to interrogate the complex proteomes of biofluids
for basic science and biomarker discovery.
In genomics, scalable whole-genome and transcriptome sequencing workflows developed
over the past 2 decades have advanced our understanding of basic biology and
translational medicine through untargeted, hypothesis-free data generation approaches
such as genome-wide association studies and colocalization analyses. However, even
though the proteome is downstream from the genome and transcriptome, and therefore
in principle more directly connected to cellular and organismal state and phenotype,
hypothesis-free data generation approaches, in particular for deep plasma proteomics,
have fallen behind the pace of genomics, primarily because of the large number of protein
variants present in biofluids, their highly diverse structures, their wide-ranging
concentrations, and the lack of suitable molecular processing tools. Despite a molecular
inventory of blood plasma comprising tens of thousands of different proteins and
metabolites, which offers a detailed profile of the current status of an organism, there
have been relatively few tests developed from this information over the past century of
effort (15). The dynamic range of protein concentrations in plasma exceeds 10 orders
of magnitude, with only a few highly abundant proteins making up most of the protein
mass, presenting a challenge to the identification of novel low-abundance protein biomarkers
(15–18). Over the years, complex and resource-intensive workflows have been
Significance
Deep profiling of the plasma
proteome at scale has been a
challenge for traditional
approaches.We achieve superior
performance across the dimensions
of precision, depth, and throughput
using a panel of surfacefunctionalized
superparamagnetic
nanoparticles in comparison to
conventional workflows for deep
proteomics interrogation. Our
automated workflow leverages
competitive nanoparticle–protein
binding equilibria that quantitatively
compress the large dynamic range
of proteomes to an accessible scale.
Usingmachine learning, we dissect
the contribution of individual
physicochemical properties of
nanoparticles to the composition of
protein coronas. Our results
suggest that nanoparticle
functionalization can be tailored to
protein sets. This work
demonstrates the feasibility of
deep, precise, unbiased plasma
proteomics at a scale compatible
with large-scale genomics enabling
multiomic studies.
Competing interest statement: S.F., B.T., T.R.B., P.A.E.,
M.F., M. McLean, E.M.E., X.Z., V.J.G., T.W., K.R., J.C., M.
Mahoney, H.X., E.S.O., C.S., D. Harris, T.L.P., P.M., M.G.,
R.L., A.S., R.B., J.C.C., S.B., J.E.B., D. Hornburg, and
O.C.F. have financial interest in PrognomIQ and Seer.
Only Seer was involved in the study design, data
collection and analysis, and manuscript writing/editing.
This article is a PNAS Direct Submission.
Copyright   2022 the Author(s). Published by PNAS.
This open access article is distributed under Creative
Commons Attribution-NonCommercial-NoDerivatives
License 4.0 (CC BY-NC-ND).
1To whom correspondence may be addressed. Email:
asiddiqui@seer.bio, dhornburg@seer.bio, or of@seer.bio.
This article contains supporting information online at
http://www.pnas.org/lookup/suppl/doi:10.1073/pnas.
2106053119/-/DCSupplemental.
Published March 11, 2022.
PNAS 2022 Vol. 119 No. 11 e2106053119 https://doi.org/10.1073/pnas.2106053119 1 of 11
RESEARCH ARTICLE | APPLIED BIOLOGICAL SCIENCES
ENGINEERING
OPEN ACCESS
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developed to address these challenges, but their complexity has
prevented application to studies with large numbers of samples
and large patient cohorts (19).
To enable broad, deep, fast, and unbiased proteomics, we
recently demonstrated the utility of the nano–bio interface for
plasma profiling in a study identifying candidate biomarkers for
early nonsmall cell lung cancer (NSCLC) (20). In that work we
showed that proteins have a reproducible and specific binding
affinity to different NPs. This attribute facilitates compression
of the large dynamic range of the plasma proteome by exploiting
the competitive binding equilibria, effectively normalizing
the protein concentration by NP binding affinity. We previously
showed that the properties of the protein–NP interface
allow differential interrogation of complex plasma proteomics
samples with superior performance compared to simple workflows
based on neat and depleted plasma (20). This facilitated
the discovery of NSCLC protein signatures comprising a novel
combination of both known and unknown protein biomarkers.
Our findings prompted us to compare our multi-NP workflow
to the common, labor-intensive high-pH peptide fractionation
of depleted plasma as well as to a commercially available deep
proteomics workflow. Furthermore, here we investigated the
mechanisms by which different NPs differentially interrogate
the proteome of complex biological samples, thus enabling
broad protein profiling with a multi-NP panel.
High-pH fractionation exploits the hydrophobicity of peptides
to reduce the complexity of a sample prior to liquid chromatography
coupled with tandem mass spectrometry (LC-MS/MS) injection
by spreading the peptides across multiple fractions. Those
fractions can then be recombined to optimize use of the LC-MS/
MS gradient. Although this approach achieves significantly deeper
measurement of the measured proteome compared to simpler
methods (e.g., neat or depleted plasma), it also increases overall
sample acquisition time, effectively reducing practical study size;
it may also increase variation by requiring additional processing
steps (21). Here we compared NPs to high-pH fractionation and
found that our NP workflow provides better performance in
terms of precision and depth. To dissect mechanisms that contribute
to protein corona formation on NPs, we modeled protein
abundance in the corona as a function of NP physicochemical
properties including charge and decoration with functional
groups. Connecting the physicochemical makeup of NPs with
the specific distribution pattern of proteins across NP coronas
provides an avenue for rational NP designs tailored to further
improve the depth and breadth of proteomic interrogations.
Results
Comparing a Multi-NP Workflow to Conventional Deep Workflows.
A common workflow for deep plasma proteomic profiling
is to fractionate peptides according to their hydrophobicity
prior to LC-MS/MS. We compared a panel of five NPs to a
high-pH fractionation of plasma depleted for high-abundance
proteins (deep fractionation) in which 19 fractions were skipconcatenated
to 9 final fractions, depleted plasma (without any
fractionation), and neat plasma (Fig. 1A) using 30-min gradients
and data-independent acquisition (DIA). To facilitate
comparison, we used a common pooled plasma sample, the
same MS instruments and gradients, and the same downstream
processing of acquired MS data. Using the five-NP panel, we
identified over 1,700 protein groups across three assay replicates,
which translates to 2.5 , 4.4 , and 5.5  more protein
groups compared to deep fractionation, depleted, and neat
plasma, respectively, for the same gradient (Fig. 1B). The four
workflows (Fig. 1B) were processed in independent analysis
batches, which was necessary to compare the independent identification
performance by each workflow. Since multiple deep
workflow variants exist, and data-dependent acquisition (DDA)
has been reported to benefit from fractionation more than DIA
(23), we performed additional comparisons using the same
pooled plasma and longer gradients with DDA as well as a
commercially available deep DDA LC-MS/MS pipeline. While
the absolute number of detected proteins varied significantly
depending on gradient lengths and MS instrumentation, our
five-NP workflow achieved an overall superior performance
across all conditions. Comparing the precision of peptide
quantification between different workflows, five NPs, depleted,
and neat plasma yielded median coefficient of variation (CV)
<20% (16.9, 19.8, and 8.7%, respectively), while deep
fractionation yielded 2  higher median CV (34.2%) compared
to the five-NP workflow (Fig. 1C). The neat plasma workflow
has the lowest CV across workflows because most of the
proteins identified in neat plasma are abundant and easier
to measure.
To determine the dynamic range covered by each workflow,
the identified proteins were mapped to previously reported
deep plasma proteome data (22) and their respective normalized
intensities. The panel of five NPs covers more proteins at
lower intensity than alternative workflows (Fig. 1C), extending
nearly throughout the database’s entire dynamic range, with
about 10  greater median depth compared to the conventional
deep workflows using the same 30-min gradient and DIA.
Moreover, comparing complete identified features (those
detected in all three assay replicates), five NPs detected more
complete features than the other methods, especially at lower
intensity levels (Fig. 1D). To further investigate how each
workflow covers the database, we examined the percent
coverage at each intensity range, ranking from high- to lowabundance
proteins. Deep fractionation covers 18% more highabundance
proteins (top 50% intensity) than the five NPs,
while the five NPs produce up to 10  higher coverage than
deep fractionation across the lowest two orders of magnitude,
capturing 62% more proteins at the lower 50% intensity levels
compared to the alternative deep workflow (Fig. 1E). Intriguingly,
the proteome coverage for five NPs stays stable within
the low-abundance range, supporting the utility of NPs for
compression and sampling across the entire dynamic range.
We examined the overlap between identified protein groups in
different workflows (Fig. 1F). Out of the 1,706 identified protein
groups across the three assay replicates using five NPs, 900 are
uniquely identified by the five NPs, 184 are common between all
methods, and 169 are common only between NPs and deep fractionation.
Comparing five NPs and deep fractionation, the latter
contributes only 172 unique protein groups, compared to 979
identified by the five NPs. We next grouped proteins uniquely
and commonly identified by five NPs and the deep workflow
based on their functional annotations (Fig. 1G). Compared to
deep fractionation, five NPs cover up to 4  more proteins annotated
in UniProt keywords as putatively phosphorylated (2.8 ),
glycosylated (1.1 ), acetylated (3.3 ), and methylated (4 ) as
well as other functionally relevant classes, including secreted
(1.2 ) proteins and lipoproteins (2.6 ) (Fig. 1G).
To show that the performance differences between workflows
are independent from the processing software, we reprocessed
the data with a recently published neural network-based proteomics
search engine, Data-Independent Acquisition Neural Network
[DIA-NN (24)], in library-free mode (SI Appendix, Fig.
S1). This analysis identified over 3,000 protein groups across
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three replicates of the five-NP panel (SI Appendix, Fig. S1A).
Compared to alternative data processing workflows, the five-NP
workflow has a consistently superior performance in terms of
protein group coverage, quantification reproducibility, and
dynamic range coverage (SI Appendix, Fig. S1).
Next, we compared the automated five-NP workflow to the
above-described deep fractionation workflow using DDA with
the injection-to-injection time of 2 h for five-NP samples and
4 h for deep fractionated samples (SI Appendix, Fig. S2). Moreover,
to evaluate our own implementation of a conventional
D
E
500 191 58
Phosphoprotein
383
312
0
500
1000
1500
5 NPs
high-pH
depleted
neat plasma
Protein group IDs
high-pH 19.8
8.7
0
50
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Peptide 5 NPs
high-pH
depleted
neat plasma
5 NPs
depleted
neat plasma
8
−−
5 NPs
high-pH
depleted
neat plasma
end peptide cleanup
denaturation
reduction/alkylation
protein fractionation
denaturation
reduction/alkylation
protein digestion
5 NPs
denaturation
denaturation
pooled
~1,700 PGs ~700 PGs ~400 PGs ~300 PGs
B
C
A
deep shallow
9 concatenated fractions
peptide
fractionation
protein depletion
pooling
1656
670
−
−
−
−
−−
Relative log10 intensity
Coverage [%] Protein groups
5 NPs vs high-pH
coverage ratio
proteome depth [-log10 intensity] 5 NPs high-pH depleted neat plasma
protein abundance rank [high to low] −8
−6
−4
−2
−3.6 −3.7 −3.5
2 4 6 8
0
50
0
10
5
5 NPs
high-pH
0
300
600
900
900
184 169
114 96 71 66
21 14 8 7 2
F
G
1 1
110 234 62
Secreted
74 18
16
Lipoprotein
49 1
15
Methylation
416
25
148
Acetylation
190 265 133
Glycoprotein
100
5 NPs / high-pH
5 NPs
high-pH
depleted
neat plasma
5 NPs
high-pH
depleted
neat plasma
ptide CVs [%] end-to-automated workflow
protein digestion
reduction/alkylation
protein digestion
protein depletion
reduction/alkylation
protein digestion
blood plasma
proteins
19 high-pH fractions
Fig. 1. DIA workflow comparison. Comparing a five-NP workflow (red) to a 19-concatenated-into-9 high-pH fractionation of depleted plasma strategy (blue),
a plasma depletion strategy (green), and neat plasma (purple). (A) Step-by-step comparison of five-NP, high-pH fractionation, depleted, and neat plasma
workflows. (B) Median number of protein groups identified by each workflow. Error bars denote SDs of assay replicates. The top dash depicts the number
of identified proteins in any of the samples, and the lower dash represents the number of identified proteins in three out of three assay replicates (defined
as the complete features). For this comparison, samples belonging to the respective workflows were processed as independent Spectronaut runs. When
processed together, the median numbers of protein groups were 1,615, 862, 461, and 375 for five-NP, high-pH, depleted, and neat plasma, respectively. (C)
CV of median-normalized peptide intensities filtered for three out of three identifications across assay replicates. Median CV is depicted on each plot. (D)
Dynamic range of identified proteins matched with normalized protein intensities from a plasma protein database (22). Protein groups were filtered for
complete features. Median log10 intensity of complete features is shown on each boxplot, and the outliers are removed. (E) Percent coverage of the plasma
protein database in each workflow (Top) and relative coverage of plasma protein database by the five-NP method to high-pH fractionation (Bottom) over negative
protein log10 intensities. The 95% interval is shown in gray. Protein groups were filtered for complete features. (F) UpSet plot showing the protein
group overlap between the five-NP and high-pH workflows. Protein groups are filtered for complete features. The workflows included in each bar are shown
as colored labels. (G) Comparison of number of proteins with specified functional annotations covered exclusively with five-NP (red), exclusively with highpH
workflow (blue), or both (overlapped). Protein groups were filtered for complete features. The Venn diagrams are proportional to the number of protein
groups. Workflows were processed together using Spectronaut for all analyses except for A, in which each workflow was processed separately. All proteins
and peptides were conservatively filtered at 1% protein and peptide FDR.
PNAS 2022 Vol. 119 No. 11 e2106053119 https://doi.org/10.1073/pnas.2106053119 3 of 11
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deep workflow, we asked a commercial proteomics service facility
to perform its deepest plasma proteome workflow on the
same plasma pool as an external reference. The outsourced
workflow entailed depletion of the 12 most abundant plasma
proteins followed by high-pH fractionation into 96 fractions
that were skip-concatenated to 12 final fractions, which were
then analyzed by DDA over a total of 48 h (2 h for each sample).
Using either the same gradient length (outsourced) or longer
gradient length (in-house) for deep fractionation workflows,
five NPs maintained considerably higher protein group coverage
with higher precision across a larger dynamic range (SI
Appendix, Fig. S2).
Differential Interrogation of the Plasma Proteome Using NPs.
To gain a deeper understanding of the superior performance of
the multi-NP panel, we evaluated the individual performance
of an extended group of 10 NPs. Compared to neat and
depleted plasma, each NP yielded significantly more protein
groups with consistently greater depth, ranging from 205 (SP-
333) to 363% (SP-339) (neat plasma; Fig. 2A). To determine
the extent of overlap between proteins identified by different
NPs, as well as both neat and depleted plasma, we calculated
the Jaccard index (JI), which is the ratio of the size of the
intersection of two sets to the size of the union of the sets.
This measure is particularly useful when combining different
NPs with the goal of increasing proteome coverage and is
informative on the degree of exclusivity and redundancy (though
redundancy is not necessarily undesirable, because quantification
across multiple NPs could increase precision). NPs exhibited
consistent identification patterns in assay replicates with distinct
populations of proteins identified across NPs (Fig. 2B).
Confidence and precision of quantification can be increased
by measuring proteins across multiple NPs. To estimate the
degree to which NPs differentially enrich and deplete commonly
detected proteins, we calculated correlation coefficients
between protein coronas for the same plasma. The correlation
of NPs to neat as well as depleted plasma ranges between 0.3
and 0.6, consistent with the particles’ ability to differentially
enrich and deplete subsets of the proteome and compress
dynamic range (20). Across NPs within our workflow, proteins
are more closely correlated with coefficients between 0.5 and
0.9 (Fig. 2C), indicating similarities in protein corona composition
across individual NPs. Assay triplicates exhibited high
reproducibility (Fig. 2D), as measured by the mean correlation
coefficient (close to 1) and the CV (median CVs below 25%
for all NPs, with the exception of SP-333 with CV 25.7%).
To further map similarity across all NPs as well as neat and
depleted plasma, we calculated the Gower distance (Fig. 2E),
which combines qualitative and quantitative similarities to
determine sample relations. We observed clustering that coincides
with negative and positive charge for NPs SP-347, SP-
003, and SP-339 and NPs SP-353 and SP-007, respectively.
However, some NPs that cluster together (like SP-365 and SP-
373) do not strictly follow that rule (Fig. 2E), suggesting that
protein abundance signatures on NPs are driven by more complex
dependencies beyond charge.
S−007
S−353
S−006
S−010
S−365
S−373
S−339
S−003
S−347
depleted plasma
neat plasma
S−333
− −
− − − −
−
− − − − −
− −
− − − − −
461 − − − − − 375
1123 1138 1048 1077
770
1361 1232 1166 1310 1200
0
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1500
0.7
0.7
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0.3 0.4 0.4 0.5 0.4 0.5
0.2 0.2 0.7 0.7 0.5 0.7 0.4
0.2 0.2 0.8 0.7 0.5 0.8 0.4 0.8
0.3 0.3 0.6 0.7 0.8 0.6 0.5 0.6 0.6
0.2 0.2 0.8 0.7 0.5 0.7 0.4 0.7 0.8 0.6
0.2 0.2 0.8 0.7 0.5 0.7 0.4 0.7 0.8 0.6 0.8
0.6 0.2 0.4 0.3 0.3 0.5 0.3 0.2 0.4 0.4 0.2
0.3 0.5 0.5 0.4 0.6 0.3 0.2 0.6 0.6 0.5
0.8 0.7 0.9 0.6 0.9 0.9 0.7 0.9 0.9
0.8 0.8 0.7 0.8 0.8 0.8 0.8 0.8
0.7 0.5 0.6 0.7 0.9 0.7 0.7
0.7 0.8 0.9 0.7 0.8 0.8
0.7 0.6 0.6 0.6 0.5
0.9 0.7 0.8 0.8
0.7 0.8 0.9
0.7 0.7
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19 17 13 16 21 21 26 13 14 20 14 12
0
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A
B
C
D
positive charge
negative charge
neutral
zeta potential
E
Gower Distance
IDs
depl. plasma
neat plasma
S−003
S−006
S−007
S−010
S-333
S−339
S−347
S−353
S−365
S−373
CV [%] depl. plasma
neat plasma
S−003
S−006
S−007
S−010
S−333
S−339
S−347
S−353
S−365
S−373
0.00
0.50
1.00
JI & Cor
Jaccard Index
Correlation
Fig. 2. Interrogating the human plasma proteome with a multi-NP panel. (A) Median number of protein groups identified (1% protein, 1% peptide FDR for
neat and depleted plasma, with the respective NPs shown as bar plots. Error bars denote SDs of protein IDs in assay replicates. The lower dash represents
the number of identified proteins in three out of three assay replicates (complete features). The top dash depicts the number of proteins identified in any
of the samples. (B) The top left triangular area shows the JI indicating degree of overlapping identifications as the mean JI across assay replicates (left column)
and comparing individual NPs, depleted, and neat plasma (filtering for proteins quantified in three out of three assay replicates). Color and box size
are scaled by the magnitude of JI. (C) The bottom right triangular area shows the Pearson correlation coefficient (r) indicating correlation of median normalized
log10 intensities as the mean r across assay replicates (right column) and comparing individual NPs and neat plasma (filtering for proteins quantified in
three out of three assay replicates, using median normalized intensity comparing pairwise common detections). Color and circle size are scaled by the magnitude
of r. (D) Assay precision (CV) calculated for proteins quantified in three out of three assay replicates. Protein intensities were median normalized.
Inner boxplots report the 25 (lower hinge), 50, and 75% quantiles (upper hinge). Whiskers indicate observations equal to or outside hinge   1.5   interquartile
range (IQR). Outliers (beyond 1.5   IQR) are not plotted. Violin plots display all data points. (E) Gower distance mapped as distance tree for median protein
intensities (filtered for three out of three identifications in assay replicates).
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Linking NPs’ Physicochemical Properties to Protein Abundance.
Prompted by the observation that zeta-potential does not fully
predict protein corona composition, we further explored the protein
corona composition as a function of individual NP properties.
We expanded the number of chemically distinct NPs (37
NPs) and interrogated a common pooled plasma, as well as a
cohort of human subjects (10 NPs; SI Appendix, Fig. S3), using
an automated sample preparation workflow (20) in conjunction
with LC-MS/MS. Each NP was annotated to describe its functionalization,
including charge; hydrophobicity; and the presence
of amine, carboxylate, sugar, phosphate, hydroxyl, polymeric
structures, or aromatic groups (Fig. 3A). NPs with exposed
amines or pyridyl groups were marked as coordinating. To
account for the possibility that reactions necessary to functionalize
a particle may not result in complete surface coverage, exposing
the functionality of a precursor particle, we also categorized
particles according to their reaction class, which is the type of
chemical reaction used in the last step of the particle’s synthesis.
On the level of individual protein intensities, we observed distinct
intensity patterns for groups of NPs (Fig. 3B).
We next used a one-dimensional (1D) annotation enrichment
analysis to evaluate how these physicochemical properties differentially
associate categories of NPs with functional annotations.
Hierarchical clustering of NPs based on their 1D enrichment
scores yielded five distinct groups of NPs (Fig. 3C). Fisher’s exact
test was applied to each of these clusters to highlight the dominant
distinguishing NP properties, as a fingerprint of the cluster’s character.
Cluster 1 (Fig. 3C, red) is composed of sponge or silicacoated
superparamagnetic iron oxide nanoparticles treated with
succinic anhydride, which is ring-opened to expose a carboxylate
group. Several members of this group (S-182 through S-186) have
had other groups (butyl, pyridyl, and hydroxyethyl) subsequently
tethered to them via amide coupling, with mixed results in coverage.
Cluster 2 (Fig. 3C, yellow) includes the core sponge particle
(S-113), as well as several other particles derived from this core,
some of which may have hydrophobic or amphiphilic character.
Two of the particles (P-039 and S-179) in this cluster have polystyrene
surfaces functionalized with acidic/anionic groups (carboxylate
and sulfonate). Cluster 3 (Fig. 3C, green) consists almost
entirely of amines, with the exceptions being a hydroxyethyl group
and an isopropylamide attached via activators regenerated by electron
transfer – atom transfer radical polymerization (ARGETATRP).
Clusters 4 (Fig. 3C, blue) and 5 (Fig. 3C, purple) are
composed entirely of hydrophilic groups, including most of the
hydroxyl-functionalized particles and all sugar-functionalized ones.
These two clusters have similar NP characteristics despite exhibiting
some distinct protein enrichment patterns. Clusters 4 and 5
also include several carboxylate-functionalized particles (S-169,
S-170, and S-181), the methylated version of amine S-006
(S-156), and a cationic tetraalkylammonium salt particle (S-180).
These clusters of NPs were found to differentially enrich or
deplete groups of UniProt keywords. Proteins associated with
cytolysis, innate immunity, the membrane attack complex, the
complement pathway, amyloidosis, and hormones are heavily
enriched by clusters 1 and 2 and depleted by clusters 3, 4,
and 5. These keywords are generally enriched for anionic
carboxylate-containing particles and depleted for cationic
amines. These proteins may also exhibit an affinity for hydrophobic
or amphiphilic functionalization, but these observations
are confounded by a correlation with reaction class. Proteins
associated with cholesterols and steroid metabolism exhibit a
similar hydrophobicity pattern but the opposite charge effect:
they are enriched by cluster 3 and depleted by several of the
anionic particles in cluster 2. Aromatic groups further enhance
their enrichment, suggesting the possibility of a pi-stacking
effect. The proteins with gamma-carboxyglutamic acid domains
follow a charge-driven process, being enriched by all the cationic
particles and depleted by most of the anionic ones. The
sulfation and proteoglycan proteins follow a similar pattern.
DNA-binding and chromosomal proteins show an increased
association with hydrophilic clusters 4 and 5, but this association
is weaker with the sugar-functionalized NPs. In contrast,
the sugar-functionalized particles heavily enrich immunoglobulins
and adaptive immunity proteins. Proteins associated with
mitochondria, membranes, cell shape, and muscular and motor
proteins were enriched by hydrophilic cluster 4 but not cluster 5.
Several keywords that exhibited lesser association with protein surface
moieties (secreted, disulfide bond, and cytoplasm) showed
little enrichment or depletion across the different NPs.
NP research and development can be guided by this type of
analysis. In particular, to enrich for specific protein classes or
proteins of specific characteristics, NPs should be explored that
have fingerprints similar to those of the clusters above, which
enrich for respective protein annotations. For example, class
architecture topology/fold homologous superfamily (CATH)
architectures (25), representing the secondary structures of proteins,
are enriched as a function of the charge of the NP surface
functionalization (SI Appendix, Fig. S4). In particular, beta-rich
secondary structures (e.g., 4 Propeller, Aligned Prism, and Beta
Barrel) are associated with NPs containing anionic functional
groups, like carboxylate. This pattern aligns with previous
observations of lysine and arginine being preferentially exposed
in beta-rich structures (26), putatively attracting those to
anionic NP functionalizations.
Given the intricate physicochemical makeup of NPs, our next
goal was to explore to what degree individual functionalization
(e.g., charge, amine groups, or aromatic groups) of NPs affects
the protein corona composition and thus abundance of each protein
for a respective NP. Across the 37 NPs, some functional
characteristics are correlated; for example, the reaction class is
not entirely independent from downstream functionalization (SI
Appendix, Fig. S5). While confounded variables may obscure the
estimated effect sizes, we evaluated the degree to which these pilot
data can provide insights into corona formation on a protein-byprotein
level.
We conducted variance decomposition to quantify the fraction
of observed variance in protein abundance (approximated
by intensities) that can be explained by individual covariates
(i.e., NP properties) for 32 out of the 37 NPs for which data
on the hydrodynamic radius were available (Fig. 3D). On average,
more than 50% of the variance in each protein intensity
across particles can be explained by this selection of NP properties.
A small fraction of the variance is associated with the LCMS/
MS run batch (the defining set of samples run within the
same maintenance cycle) and NP lot, and ∼25% of the variance
(residuals) remains unexplained (Fig. 3D). NP properties
split into reaction class (the final step in functionalization),
accounting for ∼20% and the final functionalization, accounting
for more than 25% of the variance (Fig. 3E). Among
functional groups, charge and carboxylate showed the largest
individual contribution to protein intensity differences (Fig. 3F).
We depict three proteins that are differentially interrogated by a
combination of physicochemical NP characteristics: the inflammation
marker and acute phase response protein CRP, the serintype
endopeptidase KLKB1, and the innate immune response
protein PTX3. Many proteins are associated with multiple functional
groups, which we illustrated for a subset of NP characteristics
in a directional network analysis in SI Appendix, Fig. S6.
PNAS 2022 Vol. 119 No. 11 e2106053119 https://doi.org/10.1073/pnas.2106053119 5 of 11
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C5
A B
C
50 – 300 nm
100 nm
1 nm
O
O
Sugar
–
Aromatic
Amine
Charge + 0 –
Carboxylate Polymeric
Coordinating
Hydroxyl
N
:N-R
R—OH
R
R
R”
R’
P
Phosphate
O O
O
O
C
O
O
–
R
H
H
O
+ 0 –
N
R
R”
R’
R
:N-R
R—OH
O
C
O
O
–
R
–
protein intensities
4
2
0
-2
-4
4
2
0
-2
-4
4
2
0
-2
-4
4
2
0
-2
-4
2
0
-2
MS Batch
NPGroup
NP Lot (nested)
NP property
Residuals
Explained Variance
functional group
reaction class
E F G Wilcox test
Variation Fraction
high
low
mid
Variance Fraction [charge] abs(Isoelectric Point -7.4)
S−182
S−183
S−184/6
S−185
S−128
S−172
S−176
P−039
S−179
S−173
S−145
S−192
S−146
S−113
S−190
S−191
S−007
S−006
S−151
S−155
S−047
S−153
S−154
S−159
S−162
S−118
S−003
S−181
P−073
S−180
S−106
S−147
S−174
S−115
S−156
S−169
S−170
Cytolysis
Membrane attack complex
Hormone
Amyloidosis
Innate immunity
Complement pathway
charge
hydrophobicity
amine
coordinating
sugar
carboxylate
hydroxyl
aromatic
phosphate
polymeric
reaction_class
Log Intensity
& 1D Score
Low High
charge
negative
neutral
positive
hydrophobicity
hydrophobic
amphiphilic
hydrophilic
reaction_class
Amide SA coupling
Amino silane mod.
ARGET−ATRP poly.
Coprecipitation
DCC coupling
Epoxy reaction
Free radical poly.
LBL deposition
Ligand exchange
Methylation
P−039 synthesis
P−073 synthesis
SA ring−opening
St ber process
P−039
S−179
S−173
S−192
S−190
S−191
S−145
S−113
S−146
S−115
S−156
S−169
S−170
S−174
S−106
S−147
S−153
S−154
S−159
S−162
S−047
S−155
S−007
S−006
S−151
S−183
S−182
S−184/6
S−185
S−176
S−128
S−172
S−180
P−073
S−003
S−118
S−181
charge
hydrophobicity
amine
coordinating
sugar
carboxylate
hydroxyl
aromatic
phosphate
polymeric
reaction_class
charge_negative
charge_neutral
charge_positive
hydrophilic
amine
hydrophobic
amphiphilic
hydroxyl
carboxylate
sugar
coordinating
polymeric
phosphate
aromatic
Log2 odds
*
*
* **
* *
*
Steroid metabolism
Cholesterol metabolism
Sulfation
Proteoglycan
Chromosome
DNA-binding
Immunoglobulin
Adaptive immunity
Cell shape
Motor protein
Muscle protein
Membrane
Mitochondrion
Secreted
Disulfide bond
Cytoplasm
Gamma-carboxyglutamic acid
amine
aromatic
carboxylate
charge
coordinating
hydrophobicity
hydroxyl
phosphate
polymeric
sugar
proteins
KLKB1 (E9PBC5)
CRP (P02741)
PTX3 (P26022)
D
O
O P O
O
O
hydro. radius
9.3e−05
0.02
0.02
0
2
4
high
low
mid
Hydrophobicity
C1
C2
C3
C4
0.00
0.25
0.50
0.75
1.00
Explained Variance
0.00
0.25
0.50
0.75
1.00
Explained Variance
0.00
0.25
0.50
Fig. 3. Effect of NP surface functionalization on protein corona composition. (A) NPs are classified based on a variety of physicochemical properties and
functional groups including charge, polymer, sugar, aromatic systems, phosphates, amines, hydrophobicity, hydroxyl groups, coordinating property, and initial
reaction class. (B) Unsupervised hierarchical clustering of median-normalized log10 protein intensities (1% FDR on protein and peptide level). Assay replicates
of NP classes are median averaged. Missing values were filtered and imputed according to Materials and Methods. (C) The 1D annotation enrichment
scores (heat map color ramp) for NPs. The 1D score was calculated for UniProt keywords as described in Materials and Methods. Enriched annotations are
indicated in red; depleted annotations are indicated in blue. NPs are clustered based on the 1D score distributions. The log2–odds ratios of the NPs characteristic
for each cluster are depicted as fingerprint diagrams on the right, with starred results indicating significance (P < 0.05) in Fisher’s exact test. (D) Variance decomposition analysis modeling normalized protein intensities as a function of NP’s physicochemical makeup (A). Explained variance by each variable was estimated using a linear mixed effects model and variancePartition package in R. The explained variance in protein intensities across NPs and the unexplained variance (residuals) are depicted as a density distribution. Variances explained for each protein across NP’s reaction class and functional groups are summed (turquoise distribution). (E) NP specific variance broken down into reaction class and functional groups. (F) Functional groups broken down into contribution of individual physicochemical properties. (G) Explained variance for functional group “charge” split into high (explained variance >30%), middle
(explained variance <25 and >10%), and low (explained variance <10%). Wilcox test was used to determine P values. y axis depicts the absolute of predicted isoelectric point of each protein – 7.4 (pH of the assay). The larger that value, the more likely the protein has a net charge in the assay and can be affected by NP charge. Inner boxplots report 25 (lower hinge), 50, and 75% quantiles (upper hinge). Whiskers indicate observations equal to or outside hinge 1.5   IQR. Outliers (beyond 1.5   IQR) are not plotted. Violin plots capture all data points. 6 of 11 https://doi.org/10.1073/pnas.2106053119 pnas.org Downloaded from https://www.pnas.org by 72.207.85.20 on March 15, 2022 from IP address 72.207.85.20. Protein abundances that are driven by charge should on average have isoelectric points (pI) farther away from the pH during protein corona formation. The protein coronas in these studies were generated at pH 7.4. We observed significantly more extreme pIs for those proteins that have a particularly high degree of variance explained by charge (Fig. 3G). In addition, carboxyl functionalization and amine functionalization correlated as expected with higher and lower pI, respectively (SI Appendix, Fig. S6B). In summary, this analysis indicates that the physicochemical properties of NPs can be predicted, guiding the design of complementary NPs for deep proteome-wide interrogations or tuning NPs to target specific protein classes. Discussion One of the great challenges in improving our understanding of the molecular landscape of health and disease is the lack of large-scale proteomics data that could be useful in developing better tests and therapeutics and that could also be used to give functional context to the vast amount of accumulating genomics data. The wide variety of deep proteomics workflows designed to address this challenge usually sacrifice sample throughput for within-sample detection depth. Here we quantify the performance of a multiple NP proteomics workflow that combines scalability and deep proteome coverage. The depth of a proteome analysis is not only a function of sample preparation but is also influenced by plasma source, the downstream LC-MS/MS method, and the data processing pipeline. Even supposedly similar plasma extraction methods can be a significant source of variation, yielding different protein identification numbers (27). Thus, absolute numbers across workflows and studies cannot be compared directly without providing context. Extensive multistep fractionation, isobaric labeling, and optimized pooling can yield more than 5,000 proteins (19, 22) in plasma, while most deep sample fractionations that combine multiple lengthy depletion steps to remove highabundance proteins yield 1,000 to 2,000 proteins (18, 28–30). When we compared our five-NP workflow to a depletion and high-H peptide fractionation workflow, depleted and neat plasma using the same pooled plasma injected back-to-back with various gradients and processed with two different search engines, we found the NP had consistently higher workflow performance. Using two different downstream data processing approaches, the five-NP workflow yielded 1,706 to 3,088 protein groups, and deep fractionation yielded 684 to 1,855 protein groups. To provide an independent external reference, we asked a commercial proteomics service facility to perform a deep fractionation and long-gradient acquisition of the same samples. The different versions of internally and externally performed deep workflows using the same plasma pool yielded between 684 (30 min per fraction on DIA) and 1,761 (4 h for each of the nine fractions on DDA) protein groups; the five NPs yielded between 1,706 (30 min per NP on DIA) and 2,014 (2 h per NP on DDA), outperforming neat, depleted, and fractionated plasma even when using considerably shorter gradients and fewer hands-on requirements. The conceptual difference between a high-pH fractionation deep proteomics workflow and the NP workflow is that the former fractionates each sample at the peptide level according to hydrophobicity. Multiple fractions are pooled (e.g., 24 fractions are pooled nonsequentially into 9 final fractions) to make optimal use of the LC-MS/MS gradient in which peptides are also separated by hydrophobicity. In contrast, NPs deplete and enrich at the protein level, gaining dynamic range for all peptides belonging to the proteoforms bound to each NP. The effective dynamic range compression at the proteoform level might prevent loss of very low-abundance proteins compared to strategies like high pH that require proteins to be retained until peptide fractionation. Our data suggest that NP corona-driven protein-level interrogation is a significantly more efficient strategy to increase depth in complex proteomes compared to high-pH fractionation workflows. An important feature in our development of optimized NPs and panels of NPs is our ability to draw on the extreme diversity of potential chemical modifications that can be designed and engineered into NPs as well as combined into complementary panels to improve overall coverage or performance in a given application. In addition, larger and smaller NP panels (e.g., 2 orthogonal NP) could be designed and engineered to prioritize throughput or coverage of the proteomics workflow depending on study requirements. Given that NPs and conventional deep methods address the dynamic range issue on different levels, future studies aiming for deep proteome mapping could combine the dynamic range compression benefit of NPs with high-pH fractionation to achieve unprecedentedly deep plasma proteomics. To further optimize and develop new varieties of NPs for deep proteome interrogation, quantifying similarities and dissimilarities between protein corona compositions is key. The large dynamic range of protein abundance in biospecimens (>10 orders of magnitude) combined with the detection range
of mass spectrometers (three to four orders of magnitude) and
the unique protein–NP interaction results in variable intensity
measurements and presence–absence patterns of proteins across
NPs. We showed the robustness of each NP’s measurements
across replicates of the same sample, while a larger subset of
the proteome was captured in combination across many NPs
(Fig. 2).
Previous studies have explored how distinct NP properties
affect protein corona formation with the goal of understanding
NP uptake, blood circulation time, immune system interaction,
and recognition (7, 31), as well as protein corona formation ex
vivo (32, 33). Recently, Ban et al. aggregated multiple published
datasets and employed a random forest model to identify
factors in protein corona formation across NP physicochemical
properties and experimental conditions (34). A more targeted
approach (35) showed the utility of highly specific NPs for targeted
interrogation of very low abundance (<1 ng/mL) biomarkers
in serum such as the cardiac biomarker troponin I
(cTnI). Here, we aimed to identify some of the physicochemical
properties that drive protein corona composition at the resolution
of individual proteins in our well-defined, automated
workflow. Our analysis indicates that altering the bulk physicochemical
properties of NPs can be utilized to target specific
protein subsets. In contrast to prior work predicting a general
inhibition of protein corona formation by negatively charged or
long-chained groups (36), our experimentally observed protein
intensities did not follow this trend, illustrating the limitations
of ab initio computational models in the complex space of NP
functional group variants.
Using variance decomposition analysis, we quantified associations
between distinct physicochemical properties of NPs and
their protein corona makeup at the protein level. While this
provides a proof of concept to connect high-level NP functionalization
to protein corona composition, some limitations apply
to our modeling approach. The model evaluates correlation,
not causation within the dataset presented here. There can be
hidden properties that correlate with the evaluated covariates
and thus drive the observed coefficients. In addition, we did
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not directly determine the moieties on the NPs that bind specific
proteins. However, the correlation between the physicochemical
properties of NPs and their protein corona is evidence
against the possibility of these proteins coming to a large degree
from NP-unrelated sources such as entrapped proteins, protein
attachment to well plates, etc. Some effects driving protein
corona formation are likely unresolved when looking at bulk
NP properties and bulk protein abundance without specifying
their individual interaction interface. This includes secondary
interactions, such as protein–protein interactions, which may
affect protein abundance as a function of the primary
protein–NP interaction. Therefore, the determined dependencies
must be further explored in larger datasets expanding both
the number of NPs and the details of their physicochemical
characterization (i.e., quantitative information on functional
group density) to establish underlying causalities.
The present study demonstrates the feasibility of designing
panels of NPs that together to interrogate classes of protein
ligands. We clustered NPs according to their protein-binding
behavior and described the fingerprints of some types of NPs
that enrich for the binding of specific protein classes. Additional
studies employing a larger set of NPs and more detailed
and quantitative surface characterizations will enable predictive
models mapping out the relation of protein abundance in the
corona to the physicochemical properties of NPs. Such studies
will also improve the NP engineering and proteomics workflow
quality control process by highlighting critical and noncritical
NP property ranges at the resolution of individual proteins and
protein classes. Recent work on protein structure and surface
property prediction such as molecular surface interaction fingerprinting
(37) and AlphaFold (38) also presents an intriguing
opportunity to identify and understand physicochemical protein
properties that drive specific nano–bio interactions. Ultimately,
our findings could enable optimization and design of
NP and surfaces for interrogating more complete segments of
the proteome, prevention or enhancement of specific molecular
absorption, or drug delivery in nanomedicine.
Materials and Methods
Synthesis and Characterization of NP Physicochemical Properties. The
NPs used in this study were synthesized as previously described (20). The data
from dynamic light scattering, scanning electron microscopy, transmission electron
microscopy, and X-ray photoelectron spectroscopy (XPS) were also gathered
as previously described.
Biological Samples. The common plasma sample used in the workflow comparison
was pooled from K2 EDTA plasma samples of 153 deidentified healthy
and lung cancer patients with various ages, genders, and disease stages. Details
of the individual sample acquisition and processing were previously described
(20). In brief, samples were collected after confirmed diagnosis of stage 1, 2, 3,
or 4 NSCLC but prior to treatment. The samples were not controlled for fed/fasting
state.
Sample Preparation.
Protein corona preparation and proteomic analysis. NPs were synthesized as
described previously (20). NP powder was reconstituted in deionized water to a
final concentration of 5 mg/mL for all NPs except for SP-339-008 and SP-353-
002 (final concentration 2.5 mg/mL) and SP-373-007 (final concentration 10
mg/mL), followed by 10 min sonication and vortexing for 2 to 3 s. To form the
protein corona, 100 μL of NP suspension wasmixed with 100 μL of plasma samples
in microtiter plates. The plates were sealed and incubated at 37  C for 1 h
with shaking at 300 rpm. After incubation, the plate was placed on top of a
magnetic collection device for 5 mins to draw down the NPs. The supernatant
containing the noncorona, unbound proteins was aspirated by pipetting. The
protein corona was washed three times with 200 μL of wash buffer, which
contains 150 mM KCl and 0.05% CHAPS in a Tris EDTA buffer with pH of 7.4.
Note that EDTA is already present in the samples collected in K2 EDTA tubes.
To digest the proteins bound onto NPs, a trypsin digestion kit (iST 96X, PreOmics)
was used according to protocols provided by the vendor. Briefly, 50 μL of
Lyse buffer was added to each well and heated at 95  C for 10 min with agitation
at 1,000 rpm. After the plates were cooled to room temperature, trypsin
digestion buffer was added, and the plates were incubated at 37  C for 3 h with
shaking at 500 rpm. After stopping the digestion process by addition of the supplied
stop buffer, the NPs were removed from the reaction by magnetic collection,
and the remaining reaction supernatant was cleaned up with the supplied
filter cartridge (styrenedivinylbenzene reversed-phase sulfonate [SDB-RPS]) kit.
The peptide was eluted with 75 μL of elution buffer twice and combined. Peptide
concentration was measured by a quantitative colorimetric peptide assay kit
from Thermo Fisher Scientific.
Plasma depletion. Plasma samples were subjected to depletion (immunoaffinity-
based removal of abundant proteins) using an Agilent 1260 Infinity II Bioinert
high-performance liquid chromatography (HPLC) system consisting of autosampler,
pumps, column compartment, UV detector, and fraction collector. Plasma
depletion was conducted by first diluting 20 μL of plasma to a final volume of
100 μL using Agilent Buffer A plasma depletion mobile-phase. Each diluted
sample was filtered through an Agilent 0.22 μ cellulose acetate spin filter to
remove any particulates and transferred to a 96-well plate. The plate was then
placed in an autosampler and held at 4  C until further processing. Eighty microliters
of the diluted plasma was then injected onto an Agilent 4.6   50 mm
Human 14 Multiple Affinity Removal System (MARS 14) depletion column
housed in the HPLC column compartment at a constant temperature of 20  C.
Mobile-phase conditions used during protein depletion consisted of 100% Buffer
A mobile-phase flowing at a rate of 0.125 mL/min. Proteins eluting from the column
were detected using the Agilent UV absorbance detector operated at 280 nm
with a bandwidth of 4 nm. The early eluting peak for each injection, representing
the depleted plasma proteins, was collected using a refrigerated fraction collector
with peak intensity–based triggering (200 mAu threshold with a maximum peak
width of 3 min). After peak collection, the fractions were held at 4  C. The sample
volume was then reduced to ∼20 μL using an Amicon Centrifugal Concentrator
(Amicon Ultra-0.5 mL, 3k molecular weight cut-off [MWCO]) with a centrifuge operating
at 4  C and 14,000   g. Each depleted sample was then reduced, alkylated,
digested, desalted, and analyzed according to the sample preparation and MS
analysis protocols described below. During each sample depletion cycle, the MARS
14 column was regenerated with the Agilent Buffer B mobile-phase for ∼4 1/2
min at a flow rate of 1 mL/min and equilibrated back to the original protein capture
condition by flowing Buffer A at 1 mL/min for ∼9 min.
Peptide fractionation. A total of 100 μL of reconstituted peptides was loaded
to a Waters XBridge column (2.1 mm   250 mm, BEH C18, 3.5 mm, 300 A )
using the Agilent 1260 Infinity II HPLC system. The peptides were separated at a
flow rate of 350 mL/min using a gradient of 3 to 30% in 30 min, with a total
run time of 47 min, and the fractions were collected every 1.5 min. The fractions
were then dried using a Speed Vac. Finally, the dried peptides were reconstituted
in a solution of 0.1% formic acid (FA) and 3% acetonitrile (ACN), spiked
with 5 pmol/mL PepCalMix from SCIEX and concatenated to 9 fractions according
to the following scheme: fractions 1, 10, and 19 were pooled; fractions 2
and 11 were pooled; fractions 3 and 12 were pooled; and so on to create 9
concatenated samples.
Outsourced peptide fractionation. The common plasma sample was sent to a
commercial service laboratory with a proteomics facility to perform the deepest
plasma proteome workflow available. Briefly, plasma was depleted in triplicate
using Top12 immunodepletion columns (Pierce, Thermo Scientific) according to
the manufacturer’s protocol. After concentrating proteins using a 5 kDa MWCO
spin filter and trypsin digestion, high-pH fractionation was carried out using a
Waters XBridge C18 (2.1 mm ID   150 mm, 3.5 μm) and Agilent 1100 HPLC,
yielding 96 fractions in total, which were then concatenated to 12 final fractions.
Mass Spectrometry.
DIA. LC-MS/MS. For DIA analyses using sequential window acquisition of all
theoretical fragment ion spectra (SWATH), peptides were reconstituted in a solution
of 0.1% FA and 3% ACN spiked with 5 fmol/μL PepCalMix from SCIEX. Five
μg of peptides in 10 μL of reconstitution buffer was used for each constant mass
MS injection. Each sample was analyzed by an Eksigent nanoLC system coupled
with a SCIEX TripleTOF 6600+ mass spectrometer equipped with an OptiFlow
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source using a trap-and-elute method. First, the peptides were loaded on a
ChromXP C18CL (0.3 mm ID   10 mm) trap column and then separated on a
Phenomenex Kinetex analytical column (150 mm   0.3 mm, C18, 2.6 μm, 100
A ) at a flow rate of 5 μL/min using a gradient of 3 to 32% solvent B (0.1% FA,
100% ACN) mixed into solvent A (0.1% FA, 100% water) over 20 min, resulting
in a 33-min total run time. The mass spectrometer was operated in SWATH
mode using 100 variable windows across the 400 to 1,250 mass-to-charge
ratio range.
Data analysis for library generation. In DIA MS methods, intentionally convoluted
MS2 spectra are resolved by algorithmic comparison to predefined prototypical
spectra libraries. The samples used to generate the spectral library of
this analysis were previously described (20). Briefly, multiple pooled plasma
samples processed by Proteograph as well as the common pooled plasma of
this study (processed through both Proteograph and deep fractionation) were
used to build a spectral library. All the DDA data were first searched against the
human UniProt database using the Pulsar search engine in Spectronaut (Biognosys).
Then the library, including 3,242 protein groups and 25,773 peptides,
was generated using Spectronaut with 1% false discovery rate (FDR) cutoff at
peptide and protein level.
DIA raw data processing. The SWATH data were processed using the Spectronaut
analytical software (version 13.8.190930.43655). The default settings were
used for the analysis except for quantification, for which no cross-run normalization
was chosen. The Q-value cutoff at precursor and protein level was set
to 0.01.
We also processed these data by DIA-NN software (24) (version 1.8) in libraryfree
mode. The neural network classifier was set to double-pass mode. The rest
of the parameters were set to default. The FDR cutoff at both precursor and protein
level (Lib.Q.Value and Lib.PG.Q.Value) was set to 0.01.
DDA. LC-MS/MS (in house). The peptide eluates were lyophilized and reconstituted
in 0.1% TFA. A 2-μg aliquot from each sample was analyzed by nano LCMS/
MS with Thermo Scientific UltiMate 3000 RSLCnano system interfaced to an
Orbitrap Fusion Lumos Tribrid Mass Spectrometer from Thermo Scientific (the
LC-MS parameters for 4-h runs are shown in brackets). Peptides were loaded on
an AcclaimPepMap 100 C18 (0.3 mmID   5 mm) trapping column and eluted
over a Waters Acquity M-Class (75 μm   200 mm) analytic column at 250 nL/
min [300 nL/min] using a gradient of 2 to 35% [2 to 30%] acetonitrile over 113
[200] min, for a total time between injections of 125 [240] min. The mass spectrometer
was operated in DDA mode, with MS and MS/MS performed at 60,000
[120,000] full width at half maximum (FWHM) resolution and 15,000 [30,000] FWHM resolution, respectively.
LC-MS/MS (outsource). The peptide eluates were lyophilized and reconstituted
in 0.1% TFA. Based on the service facility’s protocol, 20% of each reconstituted
sample was analyzed by nano LC-MS/MS with a Waters NanoAcquity HPLC
system interfaced to an Orbitrap Fusion Lumos Tribrid Mass Spectrometer from
Thermo Scientific. Peptides were loaded on a trapping column and eluted over a
75-μm analytic column at 350 nL/min; both columns were packed with Luna
C18 resin (Phenomenex). A 2-h gradient was employed. The mass spectrometer
was operated in data-dependent mode, with MS and MS/MS performed in the
Orbitrap at 60,000 FWHM resolution and 15,000 FWHM resolution,
respectively.
DDA data processing. MS raw files were processed as described previously
(20), in brief, with MaxQuant (v. 1.6.7) and Andromeda (39, 40), searching MS/
MS spectra against the UniProtKB human FASTA database (UP000005640,
74,349 forward entries; version from August 2019) employing standard settings.
Enzyme digestion specificity was set to trypsin, allowing cleavage N-terminal to
proline and up to two miscleavages. Minimum peptide length was set to seven
amino acids and maximum peptide mass to 4,600 Da. Methionine oxidation
and protein N-terminal acetylation were configured as a variable modification,
and carbamidomethylation of cysteines was set as a fixed modification. “Match
between runs” was disabled. Identifications were quantified based on protein
intensities (only proteins with q value <1%) requiring at least one razor peptide.
Proteins that could not be discriminated based on unique peptides were assembled
in protein groups. Furthermore, proteins were filtered for a list of common
contaminants included in MaxQuant. Proteins identified only by site modification
were strictly excluded from analysis.
Database coverage analysis. To understand in more detail how each workflow
covers the database, we divided the database (22) into multiple bins, with
stepping size of 0.5 across log10 intensities starting from zero. Then, the percentage
of coverage for each workflow was calculated in each bin and plotted against
the negative log10 intensities (Fig. 1E).
NP property modeling (10-NP experiment). We used a previously published
dataset comparing 141 healthy and early NSCLC subjects across 10 NPs.
These data were median normalized as described previously (20). To compare
only valid protein quantifications across subjects and all 10 NPs, we removed
subjects and all of their associated samples if for one or more NPs, fewer than
750 proteins were detected. The remaining 157 protein groups were consistently
quantified across 45 subjects and all 10 NPs. To determine to what degree individual
physicochemical properties correlate with protein abundances comparing
profiles across the 157 protein groups, 10 NPs, and 45 subjects, we trained a linear
mixed effects model (LMM; lme4) with
ProteinIntensity   ZetaPotential   PolyDispersityIndex
HydrodynamicDiameter    1jSubject ,
setting RMFL = FALSE, which corresponds to a maximum likelihood estimation
that models each protein’s intensity of the form
y   Xβ   Zμ   ε,
where y is an n   1 column vector of the observed protein intensities; X is an
n   4 matrix for the constant and fixed effects of ZetaPotential (charge), PolyDispersityIndex
(PDI), and HydrodynamicDiameter; and β is a 4   1 column vector
of the fixed-effects regression coefficients. Z is an n   45 design matrix for the
random effects of subject. μ is a 45   1 vector of random effects for subject,
which is assumed to be distributed as N 0, σ2u
Iu .
To determine functional annotations associated with the results from the
LMM, annotations were matched to UniProt identifiers and enrichments calculated
based on the coefficient distributions using the R AnnoCrawler package
and implementation of the 1D annotation enrichment (41). To estimate the coefficient
stability when building models on subsets of the subjects, the data were
split into random (nonoverlapping) sets of 12 subjects. Coefficients calculated
with the above-mentioned strategy (this time accepting singular fits) and similarities
of model coefficients were quantified using Pearson correlation.
NP property modeling (37-/32-NP experiment). We used a dataset generated
from a single biological sample (PC3) measured across 81 NP lots. The data
associated with 10 NP lots were removed due to synthetic uncertainty. MS runs
where fewer than 200 protein groups were detected were discarded as outliers.
Across the 71 remaining NP lots, 1,559 protein groups were detected, comprising
37 structurally distinct NPs.
The log10 protein intensity data were median normalized as described previously.
We filtered for consistent identifications of protein groups by removing
protein groups from the entire dataset if they were not detected with at least
one NP at ≥50% identification rate throughout assay replicates. In order to
estimate the differences between NPs that may deplete proteins below the
instrument detection limits, intensities were imputed for protein groups that
were not detected in any replicate of a given NP, based on the distribution of
the entire population (mean downshift 2.0, width 0.2). The log10 protein
group intensities were plotted and clustered (hclust) using ComplexHeatmap
(Fig. 3B), after filtering out protein groups that were imputed for more than
75% of the NPs.
For 1D annotation enrichment (Fig. 3C), an intensity difference metric for
each protein group was determined by calculating the difference between the
median log10 intensity for each NP (e.g., across NP lots) and the median intensity
of that protein group across all other NPs. The protein group intensity metrics
were associated by their constituent UniProt protein IDs to UniProt keywords,
counting each constituent keyword once per protein group. A Wilcoxon–
Mann–Whitney signed-rank test for annotation enrichment was then applied,
and the results were filtered for a Benjamini–Hochberg FDR <2% (41, 42). The
1D enrichment results were plotted and clustered (hclust) using ComplexHeatmap,
and the top five hierarchical clusters were labeled. We used Fisher’s exact
test to help identify the enrichment of NP properties based on their clustering in
the UniProt keyword analysis.
To quantify the degree to which individual protein intensities correlate with
individual NP properties (Fig. 3 D–F) we employed a linear-mixed-effects model
and performed variance decomposition analysis (R, variancePartition package). A
model was encoded for each protein as
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ProteinIntensity ∼ charge   polymeric   sugar   phosphate   amine
hydrophobicity   hydroxyl   coordinating   aromatic
carboxylate   dls   reactionclass    1jMSBatch
1jNPGroup=NPLot ,
which corresponds to fit a linear mixed effects model of the form
y   Xβ   Zμ   Wv   Qw   ε,
where y is an n   1 column vector of the observed intensities; X is an n   25
matrix for the fixed effects of charge, polymeric, sugar, phosphate, amine, hydrophobicity,
hydroxyl, coordinating, aromatic, carboxylate, hydrodynamic radius,
and reaction class; and β is a 24   1 column vector of the fixed-effects regression
coefficients. Polymeric, sugar, phosphate, amine, hydroxyl, coordinating,
aromatic, and carboxylate are two-level factors, each encoded as a binary indicator
variable for the presence or absence of the factor. Reaction class is a factor
with 14 levels and was encoded using dummy coding with 13 binary indicator
variables. Z is an n   7 design matrix for the random effects of mass spec batch
(MS Batch). μ is a 7   1 vector of random effects for MS Batch (MS maintenance
cycle), which is assumed to be distributed as N 0, σ2u
Iu . W is an n   37
design matrix for the random effects of NP group (NPs with the same physicochemical
makeup). v is a 37   1 vector of random effects for NP group, which is
assumed to be distributed as N 0, σ2v
Iv . Q is the n   q 25  design matrix of
NP lots (a NP synthesis batch) nested in NP group. w is a q 25    1 vector of
random effects for NP lots nested in NP group, which is assumed to be distributed
as N 0, σ2
wIw . The residual error ε ∼ N 0,σ2ε
is a random effect. fitExtractVarPartModel
was run for scaled and centered median-normalized protein
intensities using “weights = T” and “showWarnings = F.” The latter was used to
allow the model to utilize categorical variables as fixed effects. Note that collinearity
of covariates and random effects in the model can cause the warning
“boundary (singular) fit:.” For this dataset, the random-effect variance estimates
for specific proteins can be close to zero, i.e., this protein’s intensity does not
vary as a function of a specific random effect, causing a boundary (singular) fit:
warning. Note that the model used in SI Appendix, Fig. S6 includes only the following
terms: sugar + amine + polymeric + aromatic + phosphate + hydroxyl +
carboxylate + reaction_class + (1jMS.Batch) + (1jNPGroup/npLot). For this
analysis, we employed the library(lme4) and library(lmerTest) to estimate P values
via Satterthwaite’s method (43).
Network Analysis. For network analysis we extracted the coefficients for the
LMM discussed above. A pruned network was constructed filtering for coefficients
with P value (Satterthwaite’s method) < 0.05 using R libraries (ggraph),
(igraph), and (graphlayouts) with layout = “stress.” FDR was estimated using
p.adjust() with the Benjamini–Hochberg method. Isoelectric points (pI) of proteins
were calculated in R using computePI fseqinrg.
Statistical analysis. Statistical analysis and visualization were performed using
R (v3.5.2) with appropriate packages (44).
Data Availability. The mass spectrometry proteomics data for the NSCLC study
(SI Appendix, Fig. S3) were deposited to the ProteomeXchange Consortium
(proteomecentral.proteomexchange.org) via the Proteomics Identification Database
(PRIDE) partner repository (45) with the dataset PXD017052. Data for Figs.
1–3 are available via the PRIDE partner repository (45) with the dataset
PXD022285 and PXD028634. Annotations used for annotation enrichment analysis
are available as part of the Perseus framework (46). The UniProt FASTA is
available at https://www.uniprot.org/ (retrieved 29 August 2019). All other study
data are included in the article and/or supporting information.