Technical Note
ResolveOME: Comprehensive Multi-omic Single-Cell Analysis
Authors
Jeff Blackinton, PhD
Director
New Product Introduction
BioSkryb Genomics

Tatiana Morozova, PhD
Senior Scientist
Research & Development
BioSkryb Genomics

Jon Zawistowski, PhD
Senior Director
Research & Development
BioSkryb Genomics

Isai Sales-Gonzalez, PhD
Computational Biologist
Bioinformatics
BioSkryb Genomics

Durga Arvapalli, PhD
Scientist I
Research & Development
BioSkryb Genomics

Swetha Velivela, PhD
Scientist I
Research & Development
BioSkryb Genomics

Jay A.A. West, PhD

President, CEO, Co-founder
BioSkryb Genomics

The ResolveOME Platform for Comprehensive Analysis ofMulti-omic Layers of Single-cell Biology
BioSkryb Genomics has developed a unified system for single cell whole transcriptome
and whole genome amplification sequencing analysis. We describe here the overall
workflow and essential data metrics enabling the analysis integration of the combined
RNA and DNA components of single cells. The system allows comprehensive delineationof genomic variance at both the transcriptional and translational molecular layers ofindividual cells that comprise mammalian tissues.

Product Highlights

Cell collection

 A multi-omic approach to analyzing thecomplete transcriptome and genome ofsingle cells

Hands ontime

 Whole mRNA transcriptome analysis using
full length transcripts~ 30 min Whole Exome Sequencing (WES) Targeted Enrichment Panels Adaptable to targeted protein analysis
using Oligo-linked Antibodies

Introduction
The central dogma of molecular biology statesthat the genome of every single cell contains thecomplete set of instructions for all biological life.
The omic layers, comprised of the entire genome,transcriptome and proteome, synchronize totranslate and decode this genetic code to dictate
cellular fate. Normal and pathologic cellulardevelopment are dependent on the fidelity of thistransformative process. They are dependent on
the OME, or the sum of ALL of the parts.
Elucidating these individual molecular layers
or OMEs has been of significant interest1,2 fordecades and has driven the development ofmultiple omic based methodologies, including
single cell genome sequencing3-5, transcriptome
sequencing6,7 as well as methods for targeted
protein detection in single cells8-10.

~ 2.5 hrsmRNA transcriptome
1st Strand cDNA Whole Genome Amplification (WGA)
enabling: Whole Genome Sequencing (WGS)

Cytosolic lysis

Total
time

~ 20 minNuclear lysis~ 10 hrs~ 25 minWhole Genome
Amplification

Purification oftranscriptome
cDNA

Purification of
amplified genomicDNA

Pre-Amp andlibrary prep ofcDNA

Library prep ofamplified genomicDNA

NGS of transcriptome
libraries

NGS of genome
libraries

Data Analysis&
Interpretation
Figure 1. ResolveOME System Workflow
enables theintegration of first strand cDNA synthesis and genome
amplification with downstream separation of the multiomic
libraries for NGS analysis.
For Research Use Only. Not for use in diagnostic procedures.

2

ResolveOME: Combined Full Transcriptome and Whole Genome Analysis
However,
combiningthesemethodologies,
particularly with high coverage genome analysis,
has been challenging in terms of the data quality10.
The ResolveOME product solution was developed toresolve this challenge. The workflow (Figure 1) begins
with the isolation of single cells, prior to cytosolic
lysis and reverse transcription. Once complete, thesame samples are processed using Primary Templatedirected Amplification (PTA) to perform whole genome
amplification. After bead-based separation andamplification of the transcriptome cDNA pool, distinct
libraries from both the transcriptome and genome
fraction are prepared for downstream next generation
sequencing and analysis.
ResolveOME is built upon the foundation of
PTA. Previous methodologies for whole genome
amplification of single cells and picogram (pg)quantities of DNA, such as MDA, have not been able
to provide the breadth and uniformity of genomic
coverage required for robust variant detection3-5. With
the development of PTA11 , individual cells and low DNA
inputs can now be amplified by ResolveDNA with highuniformity, providing a broad sequencing breadth andthe highest variant calling sensitivity.

A) REVERSE TRANSCRIPTION
Transcript RNA

Primer (Oligo dT)

Reverse Transcriptase

cDNA

Template swtich Oligo

Primer Extension and template swtiching250~ 2000 bases

B) PRIMARY TEMPLATE DIRECTED AMPLIFICATION
Genomic DNA
Termination basePrimer (6-9mer)

Amplicon DNA

Isothermal Polymerase

PrimerPrimerPrimerPrimer Extension and Termination

Building on the performance of the PTA chemistry,
BioSkryb Genomics has integrated the ability to perform
first strand cDNA synthesis of the entire transcriptome
250-1500 bases
250-1500 bases(Figure 2). After single cell isolation in a standard
Primary template
microtiter plate, the BioSkryb Resolve RT chemistry
reprimed for additional
is applied to each cell to perform cytosolic lysis andamplification (favored)
reverse transcription of the mRNA from each individual
cell (Figure 2A). The synthesized cDNA molecule of eachtranscript remains in the sample during the nuclear
lysis and subsequent steps to amplify the genomes
Genomic DNA
250-1500 bases
250-1500 basesof each cell. Similar to the ResolveDNA product, thegenome of each cell is then denatured in preparation
for random priming based genome amplification(Figure 2B). PTA utilizes isothermal amplification andproprietary termination chemistry to restrict ampliconsize, preferentially redirecting random primers to theprimary template (Figure 2B). This is a critical featureamplification inhibited
Short fragment DNA pool created ~ 500K fold amplificationof the ResolveOME chemistry system, which minimizes Furtherdue
to short sizerecopying of not only synthesized genome amplicons,
but the amplicons generated from the initial template Figure 2. ResolveOME™ Amplification process. Isolated single cells first undergo 1st
strand cDNA synthesis of cystosolic mRNA molecules existing within each cell using a reverse
switch-based first strand cDNA synthesis.
transcription process (A). When this process is complete primary template amplification of the

The result of the combined processes is a pool of nuclear fraction is conducted (B). First strand cDNA products are then enriched from the pool
amplified genome which also contains the complete of amplified genomic DNA, prior to library preparation using the BioSkryb ResolveOME library
preparation system, next generation sequencing, and analysis with BaseJumper software.
diversity of cDNA in a cell. These combined pools arethen separated using a proprietary magnetic bead based reagent system to isolate the cDNAs, and remove any contaminating
amplified genomic DNA from the transcriptome pool. Having created these two distinct fractions, the genomic DNA and thetranscriptomic cDNA, the samples are then converted to sequencing libraries using well defined processes for transcriptome(including pre-amplification) and genome libraries using the optimized ResolveOME library preparation system.

For Research Use Only. Not for use in diagnostic procedures.

ResolveOME: Combined Full Transcriptome and Whole Genome Analysis
A)

700

3

Transcriptome Ampliﬁca�on Yield

cDNA Yield (ng)

600
500
400
300

(-) cDNA enrichment(+) cDNA enrichment

200
100

NTC
NTC
NTC
NTC
RNA 100pg-1
RNA 100pg-2
RNA 100pg-3
RNA 100pg-4
DNA 100pg-1
DNA 100pg-2
DNA 100pg-3
DNA 100pg-4
DNA 100pg-5
DNA 100pg-6
DNA 100pg-7
DNA 100pg-8
SC-1
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SC-32
5C-1
5C-2
5C-3
5C-4
sNTC
sNTC
sNTC
sNTC

0

B)

3500

Genome Ampliﬁca�on Yield

DNA Yield (ng)

3000
2500
2000
1500
1000
500

NTC
NTC
NTC
NTC
RNA 100pg-1
RNA 100pg-2
RNA 100pg-3
RNA 100pg-4
DNA 100pg-1
DNA 100pg-2
DNA 100pg-3
DNA 100pg-4
DNA 100pg-5
DNA 100pg-6
DNA 100pg-7
DNA 100pg-8
SC-1
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5C-1
5C-2
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5C-4
sNTC
sNTC
sNTC
sNTC

0

Figure 3. Amplification yield of Transcriptome and Genome from individual GM12878 cells. To assess ResolveOME chemistry performance, samples are firstquantified after combined transcriptome and genome amplification. In (A), the yield of pre-amplified transcriptome cDNA is assessed with both RNA and DNA as wellas single cells, and wells that contain 5 cells. No template controls were deposited during the chemistry run (NTC) while the FACS sorting negative controls (sNTC)
was deposited in parallel with cell sorting. The yield of amplified genomic DNA (B) is assessed from the same cells. A comparison was made to determine the effect of
cDNA enrichment on the output of either the transcriptome cDNA or amplified genomic DNA yield (orange bars). We found the use of the enrichment process was
critical for achieving acceptable transcriptome cDNA yield, while differences in amplified Genomic DNA were not observed.

This system in its entirety allows the analysis of full lengthmRNA transcript-derived cDNA molecules as well as the ability
to analyze the entire genome of each cell. While WGS analysis
may be a desirable downstream next generation sequencing
application, the genomic fraction can be alternatively enriched
for WES or used for targeted panel enrichment12,13. The use ofvarious enrichment strategies is the same process that has been
employed for the integration of the ResolveDNA WGA withvarious commercial genome enrichment products. Protocols forthese application workflows can be found at BioSkryb.com.

Experimental Methods and Results
To demonstrate the efficiency of amplification we first analyzed
DNA yield (Figure 3) and fragment sizes (data not shown) of theresultant amplification process. To determine performance ofthe combined genome/transcriptome workflow we first utilized
the 1000 Genomes cell line GM12878. While this cell line has
fewer active mRNA transcripts, the use of this highly defined
genome allows the assessment of allelic balance, precision andsensitivity of single nucleotide variation (SNV). We found that
the ResolveOME process allows the creation of separate anddistinct pools of transcriptome cDNA and amplified genomic
DNA (Figure 3) that allowed downstream library creation fornext generation sequencing. We found the average single cell

yield of transcriptome cDNA was ~150 ng after preamplification,
while the same single cells typically generated ~1500 ng of
amplified genomic DNA. We noted some degree of RT activityof purified DNA (Figure 3A). In these reactions (DNA 100 pg),
DNA is available during the RT step, unlike the nuclear DNA of
single cells where nuclear lysis occurs later. The generation of
amplified product is a result of the promiscuity of the reverse
transcriptase, which can and will use DNA as template in theabsence of template mRNA molecules. This is less prevalent
in the RT process of single cells in ResolveOME, as the nuclear
genomic DNA is not denatured prior to the RT step. An importantfeature of the chemistry is the limited amount of amplificationof the transcriptome cDNA from bulk RNA during PTA (Figure
3B). This is a result of the mechanism of PTA, which has limited
recopying of templates that are smaller in fragment size.
A key feature in the ResolveOME process is the bead basedenrichment of the transcriptome-derived cDNA which occurs
at the completion of the genomic amplification process. We
found the enrichment process was critical to achieve acceptableyield for the transcriptome-derived cDNA (Figure 3A), while
not appearing to cause a loss of amplified genomic DNA
yield (Figure 3B). While some variability in cDNA or amplified
genomic DNA yield is expected in the amplification of these twoomic pools from a single cell, we noted high yield consistency,
For Research Use Only. Not for use in diagnostic procedures.

4

ResolveOME: Combined Full Transcriptome and Whole Genome Analysis
with only one cell (Figure 3B - SC-25) that had lower yieldsuggesting a potential FACS drop out or user error. Overall,the ResolveOME system is able to produce pools of amplified
cDNA and genomic DNA with sufficient quantity to prepare next
generation sequencing libraries for downstream analysis. Using
the unified workflow, the transcriptome cDNA and amplified
genomic DNA products from the same single cells were used
for library preparation to compare the performance of a range
of cell types, the detection of expressed genes and, to calculate
the allelic balance, precision and sensitivity of SNV using thereference GM12878 genome.

Transcriptome Sequencing Gene Detection
The ability to combine the transcriptome analysis with the
genome analysis is dependent on high data quality for these
two molecular layers. A key metric is the number of uniquegenes detected within a single cell. To create this assessment
we used SALMON, a bioinformatic algorithm to compare genes
detected between cell types using the ResolveOME chemistry.
Using the GM12878 line as a basis, we compared the numberof genes detected to several cell lines (Figure 4). We foundthe number of genes detected in GM12878 cells using the
ResolveOME chemistry ranged from 2500 to approximately
4500. In alternative cell types, particularly those that are induced

Genome Sequencing Precision and Sensitivity
The integration of the PTA with template switching basedtranscriptome cDNA preparation allows a plethora ofdownstream analysis options. Some of these downstream
bioinformatics workflows may include combining copy number
variation with a set of expressed genes which may by either
deleted or amplified at the chromosome level. At a basic level theability to define cell phenotype and state while delineating thestructure of the genome is critical for various forms of pathology.
Other applications may include the analysis of oncogenic SNV
drivers of pathology combined with structurally modified mRNA
molecules. The possibilities through these combined modalities
are abundant. However, to conduct the synergized analysis, thedata quality must be high.
The metrics indicating the highest quality data in genome
sequencing relate to SNV sensitivity and precision. This is true
for both the sequencing of large populations of cells (referredhere as bulk) as well as in the genome of a single cell. Given thatan individual cell typically contains one genome (diploid), about
6.6 pg of DNA, the amplification of this genome material must
preserve the allelic balance. This is a measure of the uniformity
or evenness of the amplification process. PTA has the highest
allelic balance of any developed whole genome amplification
chemistry11. The development of the ResolveOME system has
the same requirements. Measuring the allelic balance (Figure 5)of single cells is the best method to determine amplification bias.
Using the same cells (Figure 3), libraries were created and QC'dprior to high depth sequencing. To ensure adequate genomic
coverage and uniformity, each library was initially sequenced toa minimum of 2M total reads to establish its Preseq count. Thisis an estimation of library complexity that predicts the ability
to call variants with high sensitivity and precision at the depths
normally required for accurate analysis14. Only those libraries
0.25

H3MFYM-NA-PTA-Pl2-SC1

H3MFYM-NA-PTA-Pl2-SC10

H3MFYM-NA-PTA-Pl2-SC11

H3MFYM-NA-PTA-Pl2-SC12

H3MFYM-NA-PTA-Pl2-SC15

H3MFYM-NA-PTA-Pl2-SC2

H3MFYM-NA-PTA-Pl2-SC3

H3MFYM-NA-PTA-Pl2-SC4

H3MFYM-NA-PTA-Pl2-SC7

0.20
0.15
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0.05
0.00

0.20
0.15
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0.05
0.00
0.25
0.20
0.15
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to be drug resistant, the number of unique genes detected
ranged from ~4000-7000. This was compared further to aclinical breast cancer sample that had been cryopreserved andremained uncultured and found the range of gene detection to
be 500-4000 genes. Taken together, the data suggest unifying
the transcriptome and genome does not require a sacrifice
in data quality. Rather, the analysis of full length transcripts
provides high sensitivity and dynamic range while allowing thedelineation of splice variation and gene fusions within a specifictranscript, even using fragile and precious clinical samples.

0.25

Proportion

Figure 4. Detected Genes as Measured by SALMON. Unique genedetection by ResolveOME for a range of cell types and conditions wasmeasured to determine sequencing performance.



Figure 5. Allelic balance of heterozygous sites was measured inwhole-genome reactions using alternate allele frequency sites with atleast 2X coverage.
For Research Use Only. Not for use in diagnostic procedures.

ResolveOME: Combined Full Transcriptome and Whole Genome Analysis

5

that achieved a Preseq count >3.5 E9 were used for sequencing at full
depth. WGS libraries were sequenced at 2 X 150 on NovaSeq 6000 S4
flowcells.
A key factor driving the precision and sensitivity in the detection of
SNV within single cells and low input samples is the ability to maintainstrict allelic balance of heterozygous sites within the genome. Results
show that allelic balance was high in amplified genomes fromsingle cell samples from ResolveOME (Figure 5) and preserved overall samples. The data demonstrates the ResolveOME WGA processintegrated with ResolveOME library preparation system, maintains atrue representation of the allele frequency from samples with a single
intact genome.

ResolveOME - Single Cell
ResolveDNA - Single Cell
ResolveDNA - 3 Cell
ResolveDNA - 5 Cell
ResolveDNA - 100pg
ResolveDNA - 50pg

Ultimately, the performance of whole-genome amplified libraries
is determined by how faithfully they represent the genome being
amplified. Reference alleles for GM12878 were therefore evaluatedin PTA libraries using the BaseSpace Variant Calling Assessment Tool
with the Genome in a Bottle Consortium’s v3.3.2 truth set.
Importantly, variant detection amongst heterozygous alleles showed
limited allelic bias (Figure 5), and called single nucleotide variants
with high precision and sensitivity (Figure 6). To interpret sensitivity
and precision, ResolveOME single-cell samples analyzed by WGS
were compared to 1, 3, and 5 GM12878 cells using the ResolveDNA
workflow, as well as to 50 and 100 ng of purified genomic GM12878
DNA. ResolveOME single-cell WGS samples overall generated
allele detection sensitivities typically greater than 91% (with the
exception of one drop out cell - intentionally included which had
a low preseq value-Figure 6A) where the single cell had the lowest
sensitivity of 91%. We found the sensitivity and precision of single
cells processed with the ResolveOME system to be comparable to thesame cells processed with the ResolveDNA and library preparation
using Illumina DNA prep (genome only). Not surprisingly, samples
which contained greater than one cell (3 & 5 cell) and those using 10
and 20 cell equivalents (50 & 100 pg) of genomic DNA had slightly
higher sensitivity and precision. Single-cell libraries prepared using
the ResolveOME amplification and library preparation system had
equivalent performance as those processed with ResolveDNA andlibrary preparation using the Illumina DNA prep kit.

Figure 6. Precision and sensitivity of single nucleotide
variation detection was measured in samples containing a single
cell/ reaction. WGS data indicated precision and sensitivity exceed
99% and 91%, respectively, where 5/6 cells displayed sensitivity ofexceeding 93%.

Taken together, these data demonstrate the combination of uniform
amplification and ligation-based library preparation allow for exceptional genome recovery and allelic balance, even whencombined with the first strand mRNA transcriptome in the ResolveOME workflow. This enables highly sensitive and precise single
nucleotide variation detection, at the resolution of a single genome within an individual cell.
The development of the ResolveOME chemistry allows a new class of single-cell analysis. This comprehensive view of themacromolecular dynamics allows further definition and delineation of the factors that influence clonal heterogeneity within acellular population. This is both true in the process of normal development and the evolution of disease states. The chemistrysystem also maintains a significant amount of flexibility to enable the addition of new modalities, such as CITE-seq9 to probe theexpression of translated surface marker proteins. Protein state can then be delineated using genome based mutation profiling with
the BioSkryb BaseJumper analytics platform. BaseJumper uses curated databases (ClinVar, COSMIC) to enable the global annotationof a coding change to provide the inference on the structure and function of every single protein in the genome. This data can befurther used to predict functional changes in protein activity and the risk profile of the modification. This is accomplished using
well established algorithms (SIFT, PROVEAN, FATHOM, etc.) to determine if a functional change is likely, and whether or not thenon-synonymous change is tolerated, neutral or deleterious to cell function. Taken together, ResolveOME enables the study of themulti-omic spectrum of individual cells.
For Research Use Only. Not for use in diagnostic procedures.

ResolveOME: Combined Full Transcriptome and Whole Genome Analysis

6

References:

Figure 7. Molecular Factors Affecting Cell Fate. Many factors
contribute to cell fate including SNV, CNV, translocation, mutation
co-occurrence and mutation rate, as well as gene expression andregulation, protein levels and functional state.

SUMMARY
The creation of the ResolveOME system allows comprehensive
analysis of the transcriptome and genome in parallel from thesame cell. Providing high resolution accuracy of genome analysis
down to the single base level combined with the comprehensive
full length mRNA transcriptome enables the understanding ofinterplay of these omic layers within and between individual
cells. Our current understanding, the multi-ome is limitedby the ability to analyze the complete complement of thesemacromolecular layers in the same single cell. Conventional
multi-omic methods are restricted to decoding a tiny portionof the genome, end-counting a fraction of an expressed gene,or a limited population of proteins. The ResolveOME systemprovides the highest resolution multi-omic view of an individual
cell. The method enables a non-biased approach to detect theexpressed full-length transcripts within a single cell and allow
the analysis of the complete or targeted genome (i.e. exome) of
the same cell. Taking advantage of this flexibility the user canselect a subset of analyses vectors, such as expression, copy
number variation, and single nucleotide variation to allow novel
discovery of new biomarkers and potentially therapeutic targets.
The ResolveOME system enables a newfound level of multiomic breadth, which will enable discovery over a wide set of
application areas, including oncology, neurology, immunology,
reproductive medicine, toxicology, and cardiac research.

1.

Yu, K.H. and M. Snyder, Omics Profiling in Precision Oncology. Mol Cell
Proteomics, 2016. 15(8): p. 2525-36.

2.

Liu, J., et al., Applications of Single-Cell Omics in Tumor Immunology.
Front Immunol, 2021. 12: p. 697412.

3.

Chen, M., et al., Comparison of multiple displacement amplification(MDA) and multiple annealing and looping-based amplification cycles(MALBAC) in single-cell sequencing. PLoS One, 2014. 9(12): p. e114520.

4.

Chen, C., et al., Single-cell whole-genome analyses by Linear
Amplification via Transposon Insertion (LIANTI). Science, 2017. 356(6334):
p. 189-194.

5.

Dean, F.B., et al., Comprehensive human genome amplification using
multiple displacement amplification. Proc Natl Acad Sci U S A, 2002.
99(8): p. 5261-6.

6.

Pollen, A.A., et al., Low-coverage single-cell mRNA sequencing reveals
cellular heterogeneity and activated signaling pathways in developing
cerebral cortex. Nat Biotechnol, 2014. 32(10): p. 1053-8.

7.

Nowakowski, T.J., et al., Spatiotemporal gene expression trajectories
reveal developmental hierarchies of the human cortex. Science, 2017.
358(6368): p. 1318-1323.

8.

Swanson, E., et al., Simultaneous trimodal single-cell measurement oftranscripts, epitopes, and chromatin accessibility using TEA-seq. Elife,
2021. 10.

9.

Stoeckius, M., et al., Simultaneous epitope and transcriptome
measurement in single cells. Nat Methods, 2017. 14(9): p. 865-868.

10.

Macaulay, I.C., et al., Separation and parallel sequencing of the genomes
and transcriptomes of single cells using G&T-seq. Nat Protoc, 2016. 11(11):p. 2081-103.

11.

Gonzalez-Pena, V., et al., Accurate genomic variant detection in single
cells with primary template-directed amplification. Proc Natl Acad Sci U
S A, 2021. 118(24).

12.

Zawistowski, J., et al., ResolveDNA Integration with Illumina DNA Prep
and DNA Prep with Enrichment to Enable Single-cell Genomics. 2022,
BioSkryb Genomics. p. 1-5.

13.

Zawistowski, J., et al., Unprecedented Whole Exome Coverage Uniformity
using ResolveDNA WGA and Twist Human Core Exome Panel, in www.
bioskryb.com, B. Genomics, Editor. 2022.

14.

Daley, T. and A.D. Smith, Modeling genome coverage in single-cellsequencing. Bioinformatics, 2014. 30(22): p. 3159-65.

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For Research Use Only. Not for use in diagnostic procedures.
BIOSKRYB, RESOLVEDNA and RESOLVEOME are trademarks of BioSkryb, Inc.
All other product names and trademarks are the property of theirrespective owners.
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