WELCOME TO THE ANTIGEN-SPECIFIC REVOLUTION.

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Understanding Your HuScan™ and VirScan™ Results
Methods summary based on the PhIP-Seq pipeline built in the Laboratory of
H. Benjamin Larman at Johns Hopkins University.
By Tyler Hulett & Daniel Monaco
PhIP-Seq:
HuScan™

Human proteome
phage-display

PhIP-Seq:
VirScan™

Human virome
phage-display

Grand Seromics™

Updated July 21st, 2020

Multi-dimensional
antibody discovery

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TMOverview

T7bacteriophage
immunoprecipitation
sequencing (PhIP-Seq) is a method ofmultiplexed analysis that combines highthroughput DNA sequencing with nextgeneration proteomics to determine anindividual’s unique fingerprint of antigenspecific antibodies. PhIP-Seq was developed
at Harvard University by CDI Scientific Advisory
Board member Ben Larman, Steve Elledge, andcolleagues in 2011. This work created HuScan™,a synthetic representation of the complete
human proteome as a T7 bacteriophagedisplay library. They built upon this workin 2015 by creating VirScan™, a synthetic
representation of the complete human virome:
the universe of known infectious viruses.
Recently, data analysis has been improved by
the AntiViral Antibody Response Deconvolution
Algorithm (AVARDA), which deconvolutes
antibody cross-reactivity between closely
related viruses.

Selected HuScan™ Publications:
Mohan D et al. (2018) PhIP-Seq-characterization of serum antibodies-using oligonucleotide-encoded-peptidomes. Nat
Protoc 3:1958-1978.
Larman et al.. (2013) PhIP-Seq-characterization of autoantibodies from patientswith multiple sclerosis, type 1 diabetes
and rheumatoid arthritis. J Autoimmun
43:1-9.
Larman et al. (2011) Autoantigen discovery with a synthetic human peptidome.
Nat Biotechnol 29:535–541.

Selected VirScan™ Publications:Mina, M. J. et al. (2019) Measles virus infection
diminishes
preexisting-antibodies that offer protection from-otherpathogens. Science 366, 599–606.
Xu GJ et al. (2015) Viral immunology. Comprehensive serological profiling of humanpopulations using a synthetic human virome. Science 348:(6239):aaa0698.
Isnard P et al. (2019) Temporal virus serological profiling of kidney graft recipientsusing VirScan. Proc Natl Acad Sci USA
116:10899-10904.
Monaco, D. et al (2018). Deconvoluting Virome-Wide Antiviral Antibody
Profiling
Data.
BioRxiv.
https://doi.
org/10.1101/333625

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July 21st, 2020

Welcome to the antigen-specific revolution!
If you’re reading this – you know how much remains to be discovered about the adaptive
immune system.
We may have powerful new tools like checkpoint blockade and CAR-T – but they’re crude
compared to the natural symphony of antigen-specific immunity. Unfortunately, immunology
remains art as much as a science.
Why is this? I think our problem is that unlike nucleotide sequencing – high-throughput
monitoring tools for antigen-specific immunity remain underdeveloped. And immunology
without antigen-specificity data is like trying to understand planetary motion without aconcept of gravity.
I believe that given the right antigen-specific tools, immunologists can make profound new
discoveries. I hope that PhIP-Seq becomes one of these tools, and after a few years of hard
work by Scott Paschke, Ignacio Pino, Dan Eichinger, Shaohui Hu, Jose Irizarry, Pedro Ramos,and several others here at CDI Antygen – we are excited to be providing you access to
HuScan & VirScan.
These technologies are the result of a decade’s work by Ben Larman, Steve Elledge, Gabe
Roman-Melendez, Daniel Monaco, and many others at Harvard University & Johns Hopkins
University. I followed this work for years before I joined CDI, and am honored to be helping get
PhIP-Seq out into the hands of more scientists.
If you have any questions about your data or understanding the outputs and processes
described in this document – please don’t hesitate to contact me personally,

Tyler Hulett, PhD
Director, Biomarker Development
tyler@cdi.bio

3Full Human Proteome

Serum Screening(build 35.1)...ATCG...

Uniprot library of human proteins(24,239 individual ORFs)
Incubate the library with a serum
sample containing antibodies(NOTE: can be any sample
containing antibodies)

Massively parallel sequencing of phage
DNA to quantify enrichment of each library
member due to antibody binding

Samples
PeptidesDivide into 259,345 90mer peptide
tiles with 45 residue overlap

Immunoprecipitate the antibodies
using protein A/G coatedmagnetic beads, and remove
unbound phage by washing

PCR 1

Data analysis 1

Probablility

Programmable DNA microarrayused to encode peptides assynthetic oligonucleotides

Sequencing and Analysis

Data analysis 2

PCR 2

T7 phage library construction

Amplify the library of peptide
encoding DNA sequences by PCR
directly from the immunoprecipitate; add sample-specificbarcodes with a second round ofhemi-nested PCR

Overview of HuScan™ methods.
The original HuScan PhIP-Seq library was created bydownloading viral protein sequences from UniProt, using
software to tile overlapping sequences, tuning tiled sequences for translation by the phages and their bacterial hosts, and then ordering the sequences as synthetic
oligonucleotides from a vendor as described (Larman,
et al, 2011). This oligonucleotide library was PCR-amplified with adaptors for cloning and inserted into a T7
phage display vector that was expanded in E. Coli. The
expanded T7 phage library quality is confirmed by sequencing to have >90% of the library within one log of
overall average clonal frequency. An aliquot from this library is then reacted with diluted patient serum or otherantibody-containing fluid. Bound antibodies are immunoprecipitated with protein A/G beads, the precipitate
amplified by PCR, and the sequences quantified by a

Individual or population
autoantibody profile

next-generation sequencing and analysis pipeline thatcompares patient-sample IP read counts to negative
controls with no antibody input (mock-IPs) in the context of overall clonal frequency of individual peptides
in the parent library. Output data are then created atboth the peptide and whole-protein level. A more detailed description of this process is available (Mohan, etal, 2018).
A typical HuScan™ service involves case and control
serum or plasma samples. These are heat deactivatedand undergo a protein A/G pulldown assay, PCR amplification, and next-generation sequencing. Raw sequence
data are run through a normalization and quantitation
pipeline as previously described (Mohan et al., 2018).
Raw pipeline outputs are then provided to customers asnormalized read counts data tables for both whole proteins and individual 90mer peptides.

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VirScan™ Human
Virome Library

UniProt library of all viruses
annotated to have human tropism(206 species; 1000 strains)

Serum Screening

Sequencing and Analysis...ATCG...

Incubate the library with a serum
sample containing antibodies(NOTE: can be any sample
containing antibodies)

Massively parallel sequencing of phage
DNA to quantify enrichment of each library
member due to antibody binding

Peptides

Samples

Immunoprecipitate the antibodies
using protein A/G coatedmagnetic beads, and remove
unbound phage by washing
Programmable DNA microarrayused to encode peptides assynthetic oligonucleotides

PCR 1

Data analysis 1

Probablility

Divide into 56mer peptide tiles
with 28 residue overlaps

Data analysis 2

PCR 2

T7 phage library construction

Amplify the library of peptide
encoding DNA sequences by PCR
directly from the immunoprecipitate; add sample-specificbarcodes with a second round ofhemi-nested PCR

Overview of VirScan™ methods.
The original VirScan PhIP-Seq library was created bydownloading viral protein sequences from UniProt, using software to tile overlapping sequences, tuning tiled
sequences for translation by the phages and their bacterial hosts, and then ordering the sequences as synthetic oligonucleotides from a vendor as described (Xu,et al, 2020). This oligonucleotide library was PCR-amplified with adaptors for cloning and inserted into a T7
phage display vector that was expanded in E. Coli. The
expanded T7 phage library quality is confirmed by sequencing to have >90% of the library within one log of
overall average clonal frequency. An aliquot from this library is then reacted with diluted patient serum or otherantibody-containing fluid. Bound antibodies are immunoprecipitated with protein A/G beads, the precipitate
amplified by PCR, and the sequences quantified by a

Individual or population viral infection
and antiviral response profile

next-generation sequencing and analysis pipeline thatcompares patient-sample IP read counts to negative
controls with no antibody input (mock-IPs) in the context of overall clonal frequency of individual peptides
in the parent library. Output data are then created atboth the peptide and whole-protein level. A more detailed description of this process is available (Mohan, etal, 2018).
A typical VirScan™ service involves case and control
serum or plasma samples. These are heat deactivatedand undergo a protein A/G pulldown assay, PCR amplification, and next-generation sequencing. Raw sequence
data are run through a normalization and quantitation
pipeline as previously described (Mohan et al., 2018).
Raw pipeline outputs are then provided to customers asnormalized read counts data tables for both whole proteins and individual 56mer peptides.

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Analysis Pipeline Summary

The core readout of a PhIP-Seq assay begins
with sequence counts of individual peptidebearing bacteriophages precipitated by
antibodies in your sample; this data is
acquired by a DNA sequencer from PCRamplified sample precipitates. Significant
enrichment is determined in relationship to
mock-immunoprecipitation internal controls –the same assay performed without antibodies.
***Note: Unless specifically describedotherwise, all output file structures are
the same for HuScan™, VirScan™, orboth together.
Each file will contain sample columns androws with unique protein or peptide IDs. All‘protein level’ results represent a sum of peptide
values (prosum) or maximal peptide value(promax) for all peptides across that protein.
In annotated files, these will contain parent
protein gene symbols, full descriptive proteinnames, accession numbers for UniProt, andaccession numbers for NCBI RefSeq. Peptidelevel annotations will include specific peptide
sequences and the sequential position of that
peptide within its parent protein.
We generally recommend you begin youranalysis with the file:*_Hits_foldchange_promax_annotated.tsv

Many other interesting output files areprovided with varying levels of processing.
All are described in the following document.
Single-library HuScan or VirScan Studies: Youwill receive a single folder containing individual*.tsv & *.csv data tables separated by tabs orcolumns. Excepting AVARDA readouts for VirScan
which is in a separate nested folder with uniquefile structure, all files will combine all samples
across your experiment alongside protein A/G‘beads only’ internal controls. Each sample orcontrol will receive its own column. AVARDA is
included in the VirScan folder. Individual data
outputs can then easily be opened in Excel fordata tidying and use in statistical programs.
Combined-library HuScan + VirScan Studies:
You will receive a parent folder including files
combining results from both HuScan & VirScan
assays as individual *.tsv & *.csv data tables
separated by tabs or columns. Excepting AVARDA
readouts for VirScan which is in a separate
nested folder with unique file structure, all files
will combine all samples across your experimentalongside protein A/G ‘beads only’ internal
controls. Each sample or control will receive its
own column. Individual data outputs can theneasily be opened in Excel for data tidying and use
in statistical programs. Individualized HuScan &VirScan results will be in separate nested folders;
AVARDA is included the VirScan folder. Individual
data outputs can then easily be opened in Excel
for data tidying and use in statistical programs.

This file contains protein-level metadataannotations alongside the score for the ‘top’
peptide result from that protein. It has been
filtered to only show results if they have been
deemed statistically significant for that sample
versus the ‘beads only’ mock-IP controls. From
there, you’re likely to wish to proceed to:_Hits_foldchange_annotated.tsv

This provides a peptide-level version of thesame results.

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Full PhIP-Seq Pipeline Details

1. Prior to data processing, a HuScan™
and / or VirScan™ T7 bacteriophage display
immunoprecipitation
wet-labassay
isperformed with provided antibody samples
alongside ‘beads only’ mock-IP (protein A/Gmagnetic beads without antibody present).
2. RAW PhIP-Seq FASTQ data files are acquired
via next generation sequencing of PCRamplified precipitates to determine which
phages are captured in this experiment.
Demultiplexing (demux) of sample barcodes
then creates individual FASTQ files for eachsample.
3. Individualized sample FASTQ results areprocessed with alignment software to create
counts files, a family of output files counting
the number times each peptide encoding
sequence is found in each sample FASTQ file.
This alignment is done using a simplified exact
matching approach to report how many times
a peptide’s first 50 encoding nucleotides wassequenced.
***Note: technical replicates shouldhave higher concordance within these
raw FASTQ counts files than any other
output file.
This step results in tab separated (.tsv) output
data tables. All files have a single column foreach study sample alongside ‘beads only’
mock-IP internal controls:*_Counts.tsv – File listing the number of times a
peptide is found in the sample FASTQ as identified
by its unique 50 nucleotide leader sequence.
*_Counts_annotated.tsv – File listing thenumber of times a peptide is found in the sample
FASTQ as identified by its unique 50 nucleotide
leader sequence with metadata annotations.

*_Counts_prosum_annotated.tsv–
Filelisting sum of all peptide counts for each wholeprotein as identified by accession number withmetadata annotations.

4. Starting from counts files, further processing
is used to generate EdgeR enrichment files
and fold change files from the counts file.
This process uses EdgeR, a software package
developed for RNAseq analysis which uses anegative binomial model. EdgeR uses averageand variance data from the protein A/G ‘beads
only’ mock-IPs to determine a p-value for each
peptide within a sample (ie: how significantly
different a sample peptide count signal isversus decoy immunoprecipitation internal
controls).
This step results in tab separated (.tsv) output
data tables. All files have a single column foreach study sample alongside ‘beads only’
mock-IP internal controls:*_EdgeR.tsv – File listing –log10 of p-value
calculated by edgeR differential algorithm
comparing each sample’s peptide counts tomock IP control counts and identified by its
unique 50 nucleotide leader sequence.
*_EdgeR_annotated.tsv – File listing –log10
of p-value calculated by edgeR differential
algorithm comparing each sample’s peptide
counts to mock IP control counts and identified
by its unique 50 nucleotide leader sequence withmetadata annotations.
*_EdgeR_promax.tsv – File listing maximal
value of –log10 of edgeR p-values found acrosspeptides from an individual protein as identified
by accession number.
*_EdgeR_promax_annotated.tsv – File listing
maximal value of –log10 of edgeR p-values foundacross peptides from an individual protein as
identified by accession number with metadataannotations.

*_Counts_prosum.tsv – File listing sum of
all peptide counts for each whole protein as
identified by accession number.

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*_FoldChange.tsv – File listing fold change of
peptide counts compared against median ofcounts for mock IPs identified by its unique 50
nucleotide leader sequence.
*_FoldChange_annotated.tsv – File listing foldchange of peptide counts compared against
median of counts for mock IPs identified by its
unique 50 nucleotide leader sequence withmetadata annotations.

5. Using the previously generated counts files,
EdgeR enrichment files, and fold change files,
generate the master hits files and derivative
hits files. The pipeline uses three criteria todefine peptide ‘hits:’
1) A raw counts file value >= 15 for that peptide
within that individual sample.
2) An EdgeR enrichment file –log10(p-value) >=3 for that peptide within that individual sample.
3) A fold change file value >=5 for that peptide
within that individual sample.

Peptides which pass the ‘hit’ requirements
are output in the master hits files. This step
results in tab separated (.tsv) output datatables. All files have a single column for eachstudy sample alongside ‘beads only’ mock-IP
internal controls:*_Hits.tsv – File listing which peptides are
significantly enriched compared to noise,
reported as binary output. Value of ‘1’ indicates‘is a hit;’ all other peptides are classified as 0 for‘not a hit.’*_Hits_annotated.tsv – File listing which
peptides are significantly enriched compared
to noise, reported as binary output. Value of‘1’ indicates ‘is a hit;’ all other peptides areclassified as 0 for ‘not a hit.’ Includes metadataannotations.
*_Polyclonal.tsv – File listing the total number ofentirely unique (non-overlapping) significantly
enriched ‘is a hit’ peptides for each protein.
Indicates polyclonality and epitope spreadingof antibody reactivity to different sections of thesame protein; created from *_Hits.tsv file.
*_Polyclonal_annotated.tsv – Filelisting
the total number of entirely unique (nonoverlapping) significantly enriched ‘is a hit’
peptides for each protein. Indicates polyclonality
and epitope spreading of antibody reactivity
to different sections of the same protein;created from *_Hits.tsv file. Includes metadataannotations.

6. The above master hits file *_Hits.tsv is thenused to filter all previous output files to create
derivative hits files. These derivative hits
files preserve their original column values
for all ‘hits’ from previous unfiltered outputs,
but replace all ‘non-hits’ with a placeholder
value that indicates no significant change
from background noise. This results in the hitscounts files, hits EdgeR enrichment files, andfinally hits fold change files.
***Note: Best results are often obtainedby starting downstream analysis fromthe hits fold change files created during
this step of processing.
This step results in tab separated (.tsv) output
data tables. All files have a single column foreach study sample alongside ‘beads only’
mock-IP internal controls:
List of hits counts files:*Hits_counts.tsv – File listing the number oftimes a peptide is found in the sample FASTQ
as identified by its unique 50 nucleotide
leader sequence. Only reported if that result
is significantly enriched compared to noise.
Derived from *_Hits.tsv and *_Counts.tsv* Hits_counts_annotated.tsv – File listing thenumber of times a peptide is found in the sample
FASTQ as identified by its unique 50 nucleotide
leader sequence with metadata annotations.
Only reported if that result is significantly enrichedcompared to noise. Derived from *_Hits.tsv and*_Counts_annotated.tsv

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* Hits_counts_prosum.tsv – File listing sumof all peptide counts for each whole protein as
identified by accession number. Only reported
if that result is significantly enriched compared
to noise. Derived from *_Hits.tsv and *_Counts_
prosum.tsv* Hits_counts_prosum_annotated.tsv – Filelisting sum of all peptide counts for each wholeprotein as identified by accession number withmetadata annotations. Only reported if that
result is significantly enriched compared to noise.
Derived from *_Hits.tsv and *_Counts_prosum_
annotated.tsv

List of hits EdgeR enrichment files:* Hits_enrichment.tsv – File listing –log10
of p-value calculated by edgeR differential
algorithm comparing each sample’s peptide
counts to mock IP controls and identified by its
unique 50 nucleotide leader sequence. Only
reported if that result is significantly enrichedcompared to noise. Derived from *_Hits.tsv and*_EdgeR.tsv*Hits_enrichment_annotated.tsv–
Filelisting –log10 of p-value calculated by edgeR
differential algorithm comparing each sample’s
peptide counts to mock IP controls and identified
by its unique 50 nucleotide leader sequencewith metadata annotations. Only reported ifthat result is significantly enriched compared
to noise. Derived from *_Hits.tsv and *_EdgeR_annotated.tsv*Hits_enrichment_promax.tsv – File listing
maximal value of –log10 of edgeR p-values
found across peptides from an individual protein
as identified by accession number. Only reported
if that result is significantly enriched compared
to noise. Derived from *_Hits.tsv and EdgeR_promax.tsv

reported if that result is significantly enrichedcompared to noise. Derived from *_Hits.tsv and*_EdgeR_promax_annotated.tsv

List of hits fold change files:*_Hits_foldchange.tsv – File listing fold changeof peptide counts compared against medianof counts for mock IPs identified by its unique
50 nucleotide leader sequence. Only reported ifthat result is significantly enriched compared tonoise. Derived from *_Hits.tsv and *_FoldChange.
tsv*_Hits_foldchange_annotated.tsv–
Filelisting fold change of peptide counts comparedagainst median of counts for mock IPs identified
by its unique 50 nucleotide leader sequencewith metadata annotations. Only reported if that
result is significantly enriched compared to noise.
Derived from *_Hits.tsv and *_FoldChange_
annotated.tsv*_Hits_foldchange_promax.tsv – File listing
maximal fold change value of all peptides acrossan individual protein identified by accession
number. Only reported if that result is significantly
enriched compared to noise. Derived from *_Hits.tsv and *_FoldChange.tsv*_Hits_foldchange_promax_annotated.tsv–
File listing maximal fold change value of all
peptides across an individual protein withmetadata annotations. Only reported if that
result is significantly enriched compared to noise.
Derived from *_Hits.tsv and *_FoldChange_
annotated.tsv*Hits_enrichment_promax_annotated.
tsv – File listing maximal value of –log10 ofedgeR p-values found across peptides froman individual protein as identified by accession
number with metadata annotations. Only

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6. In the case of a HuScan™ assay, the above
concludes the list of file processing & delivered
files. For assays involving VirScan™, an output
called AVARDA is next created from the*_Hits_foldchange.tsv file. AVARDA stands
for AntiViral Antibody Response Deconvolution
Algorithm and is a multi-module software
package for analyzing VirScan™ datasets.
The AVARDA output provides a probabilistic
assessment of infection at species-level
resolution by considering alignment of all
library peptides to each other and to all otherhuman viruses. This addresses the issue ofantibody cross-reactivity between closely
related viruses with conserved sequence
domains. The current primary output is a table
listing the most significant viral infections
found within each sample. A full overview of
the AVARDA program can be found here.
This step results in comma separated (.csv)
output data tables nested within the AVARDA
folder of your VirScan™ output. File structures
vary as described:*AVARDA_breadth.csv – Matrix listing thenumber of non-overlapping enriched peptides
that were assigned to a given virus within thissample by the AVARDA algorithm.

*AVARDA_evidence_pep.csv – Matrix listing
identity of peptides associated with a given virus.
*AVARDA_post_p_value.csv – Matrix listing
p-value of evidence for a given viral infection.
*AVARDA_post_p_value_BH.csv–
Matrixlisting p-value of evidence for given viral infection
BH test corrected*AVARDA_unfiltered_evidence_number.csv –Matrix listing total number of enriched peptides
that were assigned to a given virus.
*SAMPLE_NAME.csv – Table listing all AVARDA
metrics for a given individual sample.

7. In the case of a VirScan™ assay, thisconcludes sample processing and outputs.
If you ordered combined HuScan™ and
VirScan™ assay, additional processing then
combines outputs from the two data libraries.
Your data will be delivered with this ‘combined’
output in the main root folder. All file names
and processing methods are identical tothe above VirScan & HuScan outputs except
stitched together to create a combined result.
Individual VirScan & HuScan outputs are still
provided individually within nested folders.

*AVARDA_compiled_full_output.csv–
Compiled tabular version of all sample outputs
for the AVARDA algorithm, reported only cases
where a virus was detected in sample.

Overview of AVARDA.
1) tBlastn align all peptide hits
against all human viruses.
2) Use network graph to identify
greatest set of non-overlapping
peptide enrichments.
3) Remove overlapping peptides
with binomial statistics as nullmodels.

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