Next-Generation Sequencing

Comprehensive Capture of FFPE RNA for RNA-Seq Using a Continuous Synthesis
Chemistry and Post-Library Ribodepletion

Srikanth Perike, Alexander Hoefler, Deanna K. Woo, Xiangdong Meng, Linda Lingelbach, Nish Kumar, Cissy Jiang, Angelica Olcott, Elizabeth Jordan Dreskin
Bio-Rad Laboratories, Inc., 2000 Alfred Nobel Drive, Hercules, CA 94547

Abstract

The most common archived biospecimen in oncology, formalin-fixed paraffin-embedded (FFPE) tissue
is considered a key source material for gene expression profiling of tumors using RNA sequencing(RNA-Seq). However, FFPE RNA is highly degraded and is therefore suboptimal for most currently
available RNA-Seq library preparation workflows, which also focus on capturing only long (>200 bp)
RNAs. In this study, we present a novel RNA-Seq library preparation workflow (Figure 1) that overcomesmany of the challenges associated with generating complex, rich data from FFPE RNA and other sample
types with scarce and/or degraded RNA. This solution, the SEQuoia Complete Stranded RNA Library
Prep Kit, leverages a proprietary retrotransposon enzyme in place of the traditional reverse transcriptase
and ligases found in other RNA-Seq library preparation kits, enabling a continuous synthesis reactionthat converts RNA to complementary DNA (cDNA) and adds both sequencing adapters in a single step.
Our data show that using this kit in conjunction with the SEQuoia RiboDepletion Kit (for post-libraryribodepletion) yields a more complex library from FFPE RNA that includes both long (>200 bp) and short(<200 bp) RNA biotypes and is therefore more representative of the complete transcriptome.
Introduction
The ability to reliably investigate the molecular profile of common
malignancies has greatly advanced our understanding of diseaseprogression and enabled the development of diagnostic andprognostic clinical tests. To date, much of this research has utilized
flash-frozen (FF) tumor specimens, which can provide high-quality
yet limited amounts of material to study (The Cancer Genome Atlas
Network 2012). FFPE tissues represent an invaluable alternative
resource for biomedical research because they are more abundant
and readily available compared to FF tissue samples and are often
linked to rich clinical and patient outcome data (Pennock et al. 2019).
However, FFPE RNA tends to be degraded and possesses chemical
modifications that may interfere with downstream enzymatic reactions(Marczyk et al. 2019, Masuda et al. 1999). Moreover, FFPE RNA isoften low in yield (Marczyk et al. 2019).

Bulletin 3421

Technological advances, coupled with reduced operationalcosts, have elevated next-generation sequencing, or massively
parallel sequencing, to be the ideal tool for transcriptome analysis.
The typical RNA-Seq workflow tends to be a lengthy, multistep
process that involves sample RNA extraction, ribosomal RNA(rRNA) removal using either an enrichment or depletion strategy,
chemical or enzymatic fragmentation of the residual RNA into
smaller fragments, reverse transcription of RNA into cDNA, ligationof sequencing adapters onto the 5' and 3' end of the cDNA
fragments, and finally amplification of the library pool to yield aquantity that is sufficient to load onto a sequencing flow cell.
The degraded and heavily modified nature of FFPE RNA presents
key limitations for the traditional library preparation strategy in termsof its high RNA quality and yield requirements as well as its complex
workflow (Palomares et al. 2019, Sarantopoulou et al. 2019).

Comprehensive Capture of FFPE RNA for RNA-Seq Using a Continuous Synthesis Chemistry and Post-Library Ribodepletion

Bulk RNA-Seq library protocols typically require 100 ng to 1 µgof input RNA (Naphade et al. 2022). In total, half a dozen unique
enzymes may be used in the construction of an RNA-Seq library.
The chemical modifications found in FFPE RNA could affect theperformance of any one of these enzymes and lead to reduced
library quality. In addition, most RNA-Seq library preparation
protocols focus on the capture of only long (>200 bp) RNA
fragments unless specifically designed to isolate small (<200 bp)
RNA fragments (Naphade et al. 2022). Evidence suggesting thatboth small and long noncoding RNAs (lnRNAs) are key in gene
expression regulation (Patil et al. 2014, Ransohoff et al. 2018)encourages the capture of both classes of RNAs in RNA-Seq
libraries. However, generating data that represents the complete
transcriptome requires the costly and laborious construction,
sequencing, and analysis of multiple libraries. Furthermore,most library preparation strategies deplete rRNA before reverse
transcription. The smaller RNA fragments that are a hallmark of
FFPE RNA samples may be further degraded and lost throughhandling during the depletion step. In short, obtaining a complete
and biologically representative transcriptomic dataset from FFPE
RNA is a challenge.

Total RNA

3' polyadenylation
Fragmentationand end repair

3'Polyadenylationpolyadenylation

SEQuoia Complete
Stranded RNA
Library Prep Kit

cDNA synthesis and adapter addition

Purification and cleanup

Library amplification
Single or pooled libraries
Probe hybridizationand elongation

SEQuoia RiboDepletion Kit

Bead-mediated capture and depletion

Library amplification

A novel RNA-Seq library preparation workflow (Figure 1) has
recently been introduced that may enable researchers to
overcome many of the challenges associated with generatingcomplex, rich data from FFPE-derived degraded RNA. The
SEQuoia Complete Stranded RNA Library Prep Kit leverages aproprietary retrotransposon enzyme in place of the traditional
reverse transcriptase and ligases found in other RNA-Seq library
preparation kits. This enzyme possesses greater processivity
than a retroviral reverse transcriptase (Bibillo and Eickbush 2002)
and readily conducts end-to-end template jumping (Bibillo andEickbush 2004), enabling a continuous synthesis reaction thatconverts RNA to cDNA and adds both sequencing adapters in asingle step. By first polyadenylating the RNA fragments and thenadding a polythymidine sequence to the 3’ sequencing adapter
to prime the reaction, this enzyme captures long (>200 bp) andshort (<200 bp) RNA biotypes as well as degraded RNA fragments
in a single library. Here, we demonstrate that using this kit inconjunction with the SEQuoia RiboDepletion Kit (for rRNA-derived
fragment depletion) yields a comprehensive library from FFPE
RNA that is comprised of both long and short RNA biotypes andtherefore more representative of the complete transcriptome.

Sequencing-ready
RNA-Seq libraryFig. 1. Novel RNA-Seq library preparation workflow employing the SEQuoia
Complete Stranded RNA Library Prep and SEQuoia RiboDepletion Kits.
cDNA, complementary DNA; RNA-Seq, RNA sequencing.

Materials and Methods
Sample Description

Matched infiltrating ductal carcinoma FFPE and FF tissue samples(T2N2aM0) collected in 2018 were obtained from BioIVT (catalog#HMBREASTDC1 [FFPE] and HMBREASTDC2 [FF]). FFPE tissue
was cut into 10 µm sections and stored at room temperature until
used. FF tissue was stored at –80ºC.
RNA Extraction

Total RNA was extracted from FFPE sections with four commonlyused commercial kits: RecoverAll Multi-Sample RNA/DNA Isolation
Workflow (Thermo Fisher Scientific Inc., #A26135), PureLink FFPERNA Isolation Kit (Thermo Fisher Scientific, #K156002), AllPrep
DNA/RNA FFPE Kit (QIAGEN, #80234); and RNeasy FFPE Kit(QIAGEN, #73504). RNA was purified from the FF sample using
PureZOL RNA Isolation Reagent (Bio-Rad™ Laboratories, Inc.,#7326880). Three replicate extraction reactions were performed foreach kit using equal amounts of starting material.
Preliminary Library Preparation and Sequencing

To assess the suitability of the different RNA extraction kits forwhole transcriptome analysis, 10 ng of total RNA from eachextraction was depleted of ribosomal RNA (rRNA) using NEBNextrRNA Depletion Kit v2 (New England Biolabs [NEB], #E7400)
according to the manufacturer’s instructions. Six microliters ofeach ribodepletion reaction was subsequently used as input to© 2022 Bio-Rad Laboratories, Inc.

2

Bulletin 3421

Comprehensive Capture of FFPE RNA for RNA-Seq Using a Continuous Synthesis Chemistry and Post-Library Ribodepletion

prepare libraries using SEQuoia Complete Stranded RNA Library
Prep Kit (Bio-Rad, #17005726). Libraries were indexed during thefinal amplification step using SEQuoia Dual Indexed Primers Plate(Bio-Rad, #12011930). The quality and quantity of each librarywere evaluated prior to sequencing using the High Sensitivity
DNA Kit (Agilent Technologies, Inc., #50674626) run on the Agilent
2100 Bioanalyzer (Agilent, #G2939BA) and the Qubit 1x dsDNAHigh Sensitivity (HS) Assay Kit (Thermo Fisher Scientific, #Q33231).
Libraries were stored at –20ºC prior to sequencing using theIllumina® NextSeq 500 Sequencing System (Illumina, Inc.,#SY-415-1001).

■

The quality and quantity of each library were evaluated by Agilent
2100 Bioanalyzer and the Qubit as described in the Preliminary
Library Preparation and Sequencing section.

Library Construction

Sequencing and Data Analysis

Three library construction workflows were evaluated to assess
the suitability of pre-library ribodepletion versus post-libraryribodepletion and to compare the ability of different librarychemistries to construct complex libraries that represent the wholetranscriptome (Figure 2). Only FF RNA extracted using the PureZOL
Kit and FFPE RNA extracted using PureLink or RecoverAll Kits
were used in subsequent experiments, as these samples weremost suitable for high-quality library construction. To minimize
variation between extraction replicates, RNA samples from eachof the extraction kits were pooled and quantified prior to library
construction. Libraries were constructed in triplicate using the
manufacturers’ instructions. All incubation and thermal cycling
were performed on a C1000 Touch Thermal Cycler with 96–DeepWell Reaction Module (Bio-Rad, #1851197) using 8-Tube PCR
Strips (Bio-Rad, #TLS0851) and Hard-Shell™ PCR Plates (Bio-Rad,#HSP901). Key differences for each of the three workflows arenoted as follows:■■

Individual libraries were diluted to 2 nM using 10 mM Tris (pH 8.0)
and 0.1 mM EDTA and then pooled and denatured according to the
manufacturer’s instructions for the NextSeq 500/550 Mid Output
Kit v2.5, 150 Cycles (Illumina, #20024904). Denatured libraries
were diluted to 10 pM in HT1 buffer and then further diluted to
1.8 pM by mixing 195 μl of the 10 pM library with 1,100 μl of
HT1 buffer and 5 μl of 12.5 pM PhiX Control v3. Libraries weresequenced on Illumina NextSeq 500 Sequencing System withpaired-end reads and the following cycle length parameters:
read 1, 68; read 2, 8; read 3, 8; read 4, 8.
FASTQ files were generated using the BaseSpace Sequence Hub(Illumina). Results were analyzed using the SeqSense Analysis
Solution Web App or using the SEQuoia Complete Analysis
Toolkit installed on a Linux system. Both analysis options use thesame underlying pipeline (Figure 3) with FASTQ files as input forsecondary analysis: the quality of raw RNA-Seq reads is assessedusing FASTQC. CutAdapt is used to trim the polyA tails and primer
and adapter sequences from sequencing contigs. Using STARaligner, the reads are aligned against the human transcriptome
built from the ENSEMBL Genome Reference Consortium Human
Build 38 (GRCH38). Picard tools are used as quality control (QC)
for the BAM files. The estimated transcripts are summarized to thegene level and the final reports are generated in PDF, HTML, andCVS formats.

 EBNext Workflow (NEB) — For each NEB workflow library,N
10 ng of total RNA was ribodepleted using NEBNext rRNA
Depletion Kit v2 (NEB, #E7400) according to the manufacturer’s
instructions. Six microliters of each ribodepletion reaction
was subsequently used as input to prepare libraries using
NEBNext Ultra II Directional RNA Library Prep Kit for Illumina(NEB, #E7760S). Samples were fragmented by incubating at
94°C for 7 min, per the manufacturer’s protocol for degraded
RNA samples with a RNA integrity number (RIN) value of 2–6.
NEBNext Multiplex Oligos for Illumina (Dual Index Primer Set 1)(NEB, #E7600S) were used to index libraries during the final
amplification step, which included 14 PCR cycles.

Multiplex RiboDepletion

To determine the efficiency of ribodepleting libraries en masse,
48 libraries were constructed using 100 ng Human Placenta Total
RNA (Thermo Fisher Scientific, #AM7950) as input for the SEQuoia
Complete Stranded RNA Library Prep Kit. Libraries were indexedusing SEQuoia Dual Indexed Primers in the final amplification
step. An aliquot of each library was either individually depleted(singleplex) using SEQuoia RiboDepletion Kit or pooled by mixing
an equal molar ratio of the libraries then depleted (multiplex).
Libraries were sequenced on the NextSeq 500 Sequencing
System to a read depth of 3 million reads per library.

 EQuoia Complete Workflow (SEQ) — For each SEQ workflow
Slibrary, an input of 10 ng of total RNA was used to constructlibraries using SEQuoia Complete Stranded RNA Library Prep Kit(Bio-Rad, #17005726). Total RNA was fragmented by incubating
at 70°C for 1 min, per the manufacturer’s recommendation for
degraded samples. Libraries were amplified using SEQuoia
Dual Indexed Primers (Bio-Rad, #12011930) and 12 PCR
cycles. Amplified libraries were depleted of cDNA constructs
derived from rRNA by adding 0.5 ng of a library to a SEQuoia
RiboDepletion Kit (Bio-Rad, #17006487) reaction.

© 2022 Bio-Rad Laboratories, Inc.

 ybrid Workflow (Hybrid) — For each Hybrid workflow library,H
10 ng of total RNA was ribodepleted using NEBNext rRNA
Depletion Kit v2 (NEB, #E7400) according to the manufacturer’s
instructions. Six microliters of each ribodepletion reaction was
subsequently used as input to prepare libraries using SEQuoia
Complete Stranded RNA Library Prep Kit and SEQuoia DualIndexed Primers. A 70°C, 1 min fragmentation step was used.
The final amplification step included 12 PCR cycles.

3

Bulletin 3421

Comprehensive Capture of FFPE RNA for RNA-Seq Using a Continuous Synthesis Chemistry and Post-Library Ribodepletion

A. RNA isolation

x9

x9

NEB

HybridNEBNext rRNA
Depletion Kit v2

NEBNext rRNA
Depletion Kit v2

NEBNext Ultra II
Kit

Bio-Rad SEQuoia
Complete Kit

SEQ

Bio-Rad SEQuoia
Complete Kit

Bio-Rad SEQuoia
RiboDepletion Kit

Post-libraryribodepletion

Bio-Rad PureZOL
Reagent(Flash-frozen
samples)x9Pre-libraryribodepletion

Thermo Fisher
Scientific
PureLink Kit(FFPE samples)

B. Library preparation

Library
preparation

Thermo Fisher
Scientific
RecoverAll Kit(FFPE samples)

NEB: NEBNext rRNA Depletion Kit v2 and NEBNext Ultra II Directional RNA Library
Prep Kit for IlluminaHybrid: NEB NEBNext rRNA Depletion Kit v2 and Bio-Rad SEQuoia Complete
Stranded RNA Library Prep Kit
SEQ: Bio-Rad SEQuoia Complete Stranded RNA Library Prep Kit and SEQuoia
RiboDepletion Kit

Results

using the PureLink Kit (60 ± 12). RNA extracted using the RNeasy
Kit, on the other hand, consistently had the lowest DV200 score(55 ± 0.2). As suspected, the DV200 and RIN scores did notagree with one another for all samples (Figure 4). The Bioanalyzer
electropherogram traces of SEQ workflow libraries using FFPE andFF confirm that the FFPE libraries are comprised of more small
RNA fragments than FF libraries, as indicated by the narrower peakin the FFPE library electropherogram (Figure 5).

RNA Extraction Quality

RNA was extracted from FFPE tissue using four commerciallyavailable extraction kits. Despite each extraction using the sameamount of starting material (four 10 μm sections), the RNA yield
was significantly different between the extraction kits (Table 1). The
RNeasy Kit yielded the most RNA (928 ± 159 ng/μl), whereas theRecoverAll and PureLink Kits yielded a modest amount of RNA(518 ± 72 ng/μl and 371 ± 89 ng/μl, respectively). The AllPrep Kit
consistently yielded the lowest amount of RNA (280 ± 21 ng/μl).
Interestingly, the RIN for all four extraction kits was nearly identical(2.1–2.6), suggesting that the RNA was highly degraded in all FFPE
extractions. While the RIN score is commonly used as a relative
predictor of successful library preparation, its sensitivity comes
into question when measuring RNA from FFPE tissue samples(Illumina 2016). The DV200 score, which reports the percentage of
purified RNA with a length greater than or equal to 200 nucleotide
bases, tends to be a more reliable determinant of RNA quality
and was therefore used to determine the suitability of the RNA
extraction methods. RNA extracted from FFPE tissue using the
RecoverAll and AllPrep Kits produced the highest DV200 scores(92 ± 0.5 and 83 ± 2, respectively) followed by RNA extracted© 2022 Bio-Rad Laboratories, Inc.

Fig. 2. Experimental design to compare
RNA extraction, library preparation, andribosomal depletion strategies. Libraries wereconstructed in triplicate for each RNA extraction
and library preparation/ribodepletion method.
A, RNA extraction methods; B, library
preparation/ribodepetion methods. NEB,
NEBNext rRNA Depletion Kit v2 and NEBNext
Ultra II Directional RNA Library Prep Kit for
Illumina; Hybrid, NEB NEBNext rRNA Depletion
Kit v2 and Bio-Rad SEQuoia Complete Stranded
RNA Library Prep Kit; SEQ, Bio-Rad SEQuoia
Complete Stranded RNA Library Prep Kit andSEQuoia RiboDepletion Kit. FFPE, formalin-fixedparaffin-embedded.

FASTQ File (input)
Validate inputs
Extract UMI
n Trim readnPreprocessingnAlignmentnQuantificationn

ReportnnnnAlign BAM file
Deduplication UMI
Compile gene counts
Normalize gene counts
Generate sample or batch reports
PDF, HTML, CVS

Fig. 3. SEQSense RNA-Seq data analysis pipeline. UMI, unique molecular identifier.
4

Bulletin 3421

Comprehensive Capture of FFPE RNA for RNA-Seq Using a Continuous Synthesis Chemistry and Post-Library Ribodepletion

Fluorescence units

Unexpectedly, the quality of sequencing libraries constructed from
RNeasy Kit–extracted RNA was poor. In fact, fewer than 20% of
the reads were uniquely aligned. In contrast, libraries constructedfrom RNA extracted using the other three kits produced highquality sequencing results (data not shown). Based on the RNA
yield, DV200 scores, and sequencing results, we opted to move
forward with only RNA extracted using the RecoverAll and
PureLink Kits for FFPE. To confirm these two kits yielded RNA
with similar quality, we assessed the correlation coefficientsof lncRNA and protein-coding RNA transcripts detected at ≥1 RPKM
in at least 2 replicates with ≤10% CV for RecoverAll and PureLink
library replicates prepared with each workflow (Figure 6). The
Pearson correlation coefficients between RecoverAll and PureLink
extractions were 0.986–0.999, indicating that the libraries
constructed from FFPE RNA extracted using these two methods
are very similar.

40

SampleType

Replicate

Yield,
ng/µl

RIN

DV200

Parameter
Quality

RecoverAll

FFPE

1

522

2.5

92

High

2

589

2.6

93

High

3

445

2.5

92

High

1

473

2.5

46

Low

2

328

2.3

68

Medium

3

311

2.4

66

Medium

1

298

2.1

82

High

2

257

2.1

83

High

3

285

2.1

86

High

1

995

2.3

55

Medium

2

1,043

2.5

55

Medium

3

746

2.3

55

Medium

1

61

7.6

92*

High*

2

47

7.7

AllPrep

RNeasyPureZOL

FFPE

FFPE

FFPE

FF

1,000,000

140

r = 0.98651

RecoverAll-SEQ

100,000
10,000
1,000
100
10
1
1

100
10,000
PureLink-SEQ

1,000,000

B
100,000

r = 0.99968

10,000
1,000
100
10
1
1

100,000

90.00

2.70

80.00

2.60

70.00

2.50

60.00

2.40

50.00

2.30

40.00

2.20

30.00

2.10

20.00

RecoverAll-Hybrid

2.80

10

100 1,000 10,000 100,000
PureLink-NEB

r = 0.99475

10,000
1,000
100
10
1
1

RIN

DV200

120

C

100.0010

100 1,000 10,000 100,000
PureLink-Hybrid

Fig. 6. FFPE RNA extracted using the RecoverAll and PureLink Kits produces
similar sequencing results. Correlation coefficients of lncRNA and protein coding
genes detected at 1 RPKM in at least two replicates with ≤10% CV between
replicates for libraries constructed from FFPE RNAs using RecoverAll and PureLink
extraction methods. A, SEQ workflow; B, NEB workflow; C, Hybrid workflow.
CV, coefficient of variation; lncRNA, long noncoding RNA; RPKM, reads per kilobaseof transcript per million reads mapped.

2.00
PureLink
AllPrep
370.7
280.0
RNA yield (ng/µl)

100

A

3
54
7.5
* PureZOL DV200 was calculated on a pooled sample due to the low concentrationsof the individual replicates.
FF, flash-frozen; FFPE, formalin-fixed paraffin-embedded; RIN, RNA integrity number.

RecoverAll
518.7

80

Fig. 5. Example of typical Bioanalyzer electropherogram. SEQ workflow libraries
constructed using RNA from FFPE breast cancer tissue (RecoverAll, —) and matched
FF tissue (PureZOL, —). FF, flash-frozen; FFPE, formalin-fixed paraffin-embedded.

RecoverAll-NEB

PureLink

60

Time, sec

Table 1. Quantity and quality metrics of RNA extracted from matched FF
and FFPE breast cancer tissue. Each extraction was performed in triplicate.
Kit

160
140
120
100
80
60
40
20
0

RNeasy928.0

Fig. 4. Poor agreement between DV200 and RIN scores for extracted RNA
samples. The DV200 ( ■ ) and RIN (—) were the average of three independent
extractions using each kit. RNA yield is recorded below the chart. Error bars
represent standard deviation (SD). RIN, RNA integrity number.

© 2022 Bio-Rad Laboratories, Inc.

5

Bulletin 3421

Comprehensive Capture of FFPE RNA for RNA-Seq Using a Continuous Synthesis Chemistry and Post-Library Ribodepletion

As expected, the percentage of reads mapping to rRNA in FFPE
libraries was greater in the SEQ workflow (23.5 ± 0.7) and slightly
higher in the Hybrid workflow (3.5 ± 0.7) compared to the NEB
workflow (0.8 ± 0.6). The small RNA fragments captured by
SEQuoia Complete Stranded RNA Library Prep Kit contain a greater
percentage of constructs that will not efficiently hybridize to probes
in the SEQuoia RiboDepletion kit, resulting in less efficient depletion.

Sequencing Library Quality

To evaluate the performance of the three RNA-seq workflowsthat compare ribodepletion strategy and library chemistry, weconstructed libraries using rRNA-depleted RNA from FFPE
tissue samples (NEB and Hybrid workflows) or total RNA (SEQ
workflow). The libraries constructed using the SEQ workflow
underwent enrichment before sequencing by using the SEQuoia
RiboDepletion Kit to deplete library constructs containing cDNA
inserts that originated from rRNA. After sequencing, the raw dataof all three workflows were downsampled to 5 million reads/library.
Analytical comparisons focused on several key metrics, including
library yield, rRNA depletion efficiency, genome alignment profiles,
transcript coverage, and transcript quantification (Table 2).

The percentage of uniquely mapped reads was higher in FFPE
libraries constructed using the Hybrid workflow (65.5 ± 2) and the
NEB workflow (78.5 ± 10.6). The lower percentage of uniquely
mapped reads in the SEQ workflow FFPE libraries (39 ± 0.45) may
reflect a better capture of small RNA fragments, which tend to map
to multiple loci in the genome.

Library yields were consistently 10-fold greater with the SEQ
workflow, which is not entirely unexpected. The SEQ workflow
used 10 ng of total RNA for preparing the library. Library constructs
were amplified using 12 PCR cycles to increase the amountof library material. Only 0.5 ng of library underwent the postlibrary ribodepletion process before sequencing. In contrast, theHybrid and NEB workflows used 10 ng of total RNA that was
first ribodepleted. Though RNA concentration was not measured
following depletion, typically only 10% of the RNA added to adepletion reaction is recovered. Therefore, we can presume 1 ngof depleted RNA was added to each of the library protocols in theHybrid and NEB workflows.

Capture of RNA Biotypes

A closer examination of the RNA biotypes represented in libraries
from each workflow reveals another significant distinction between
the workflows. Whereas the total number of genes detected
was similar across the workflows and between FFPE and FF
samples (Figure 7A), significantly more small RNA biotypes were
captured in workflows that employed SEQuoia Complete Stranded
RNA Library Prep Kit (Figure 7C). Depleting rRNA before library
construction resulted in a loss of small RNA biotypes (compare
SEQ to Hybrid workflow) but the SEQuoia Complete Stranded
RNA Library Prep Kit still captured more small RNA biotypes thatremained after depletion (compare Hybrid to NEB workflow). Most
notable is the capture of miRNA by the SEQuoia Complete Library
Prep Kit from total RNA (Figure 7B), which is significantly more than
the number of miRNAs captured by the same method using predepleted RNA, indicating loss of valuable material throughpre-library prep sample handling.

The total number of reads for the SEQ (45.5M ± 15) was also
greater than the Hybrid (16.3M ± 2.8) and NEB (6.4M ± .2)
workflows. It is interesting to note that the Hybrid workflow had
more than twice the number of reads as the NEB workflow.
Presumably, this reflects the more efficient capture of small RNA
biotypes by the SEQuoia Complete Stranded RNA Library Prep Kit
that is used in the Hybrid workflow.

Table 2. Sequencing metrics of libraries created with the different library and ribosomal RNA depletion protocols.
SEQ Workflow​

PCR cycles​

Hybrid Workflow

RecoverAll​(FFPE)

PureLink​(FFPE)

PureZOL(FF)

RecoverAll​(FFPE)

PureLink​(FFPE)

PureZOL​
(FF)

12​

12​

12​

12​

12

42.17​

34.97​

1.00​

3.15​

Library yield, nm 44.47​

NEB Workflow​
RecoverAll​(FFPE)

PureLink​(FFPE)

PureZOL​
(FF)

12​

14​

14​

14​

3.41​

1.66​

3.42​

6.11​

Mean librarysize, bp

300

293

453

370​

360​

396

322

321

413

Total reads​

28,600,594​

47,913,879​

60,122,647​

14,809,271​

19,629,284​

14,618,192​

6,637,246​

6,199,090​

7,459,959​

5,000,000

5,000,000

5,000,000

5,000,000

5,000,000

5,000,000

5,000,000

5,000,000

23

13

4​

3​

1​

1.2​

0.4​

0.2​

Uniquely mapped 39.59​
reads, %​40.22​

56.93​

64.08​

66.96​

81.57​

71.00​

86.00​

82.00​

Uniquely mapped 1,979,210​
reads

2,011,183​

2,846,326​

3,203,960​

3,347,894​

4,078,173​

3,527,169​

4,276,429​

4,120,035​

Downsampled to 5,000,000
rRNA, %​

24 ​Transcripts
RPKM ≥ 0.3

16,03916,846

17,13718,487

18,69817,54017,674

18,192

17,574

Transcripts
RPKM ≥ 1.0​

13,187

13,314

13,996

15,58315,701

14,923

14,925

15,245

14,589Transcript
integrity score​

42.9461

39.9025

54.1157

44.253652.607755.250442.433748.052544.8903

FF, flash-frozen; FFPE, formalin-fixed paraffin-embedded; RPKM, reads per kilobase of transcript per million reads mapped; rRNA, ribosomal RNA.

© 2022 Bio-Rad Laboratories, Inc.

6

Bulletin 3421

Comprehensive Capture of FFPE RNA for RNA-Seq Using a Continuous Synthesis Chemistry and Post-Library Ribodepletion

Uniformity and Continuity of CoverageA
16,000To assess the uniformity of transcript coverage, we calculated
the median coefficient of variation (CV) for the top 1,000 highly
expressed transcripts from the libraries constructed using theSEQ, Hybrid, and NEB workflows (Figure 8). We observed thatboth SEQ workflow (CV = 0.71) and NEB workflow (CV = 0.68)
libraries have an increased median CV compared to Hybrid
workflow libraries (CV = 0.63, P < 0.05) but this difference is notstatistically significant. At 10M read depth, the SEQ workflow had
an equivalent CV (CV = 0.56) to the Hybrid workflow (CV = 0.58).

Number of unique genes

14,00012,00010,000
8,000
6,000
4,000

SEQ Workflow

Number of unique small RNAs

B

Hybrid Workflow

PureZOL

PureLink

RecoverAll

PureZOL

PureLink

RecoverAll

PureZOL

PureLink

0

RecoverAll

2,000

To assess the continuity of transcript coverage in SEQ and NEB
workflow libraries using pre– and post–rRNA depletion methods,
we calculated the percentage of gaps (gap %) without readcoverage across the top, middle, and bottom 1,000 expressedtranscripts (Figure 9) using RNA-Seq QC tools from the Broad
Institute (DeLuca 2012). The gap percentage is defined as the total
cumulative gap length divided by the total cumulative transcript
length in each library (RNA-SEQ QC, Broad Institute). Eachcomparison shows the percentage of transcript coverage in each
replicate library. In the top 1,000 expressed transcripts, the gap% for SEQ workflow libraries was 15.8% compared to 8.9% in theHybrid workflow and 9.9% in the NEB workflow libraries (Figure
9A). We have also detected 18.2% of unique small RNAs in the
top 1,000 expressed transcripts of the SEQ workflow libraries but
not in the Hybrid or NEB workflow libraries. This suggests that theincreased gap % in the top 1,000 expressed transcripts of SEQ
workflow libraries might be due to the presence of more uniquesmall RNA biotypes in addition to the key lncRNA and proteincoding genes. In bottom expressed transcripts (Figure 9C), nosmall RNAs were detected in any library preparation. However, inthe middle 1,000 expressed transcripts (Figure 9B), SEQ workflow
libraries detected 0.2% of small RNAs but none were detected in
the NEB or Hybrid workflow libraries. It is notable that in the middle
and bottom 1,000 expressed transcripts, the SEQ workflow library
preparation exhibited the smallest gap % compared with Hybrid
and NEB workflow libraries. Furthermore, the Hybrid workflow
libraries showed less gap % in these same categories than the
NEB workflow libraries, indicating that the SEQuoia Complete
Stranded RNA Library Prep Kit results in better continuity oftranscript coverage than the other workflows.

NEB Workflow

1,200
1,000
800
600
400
200

SEQ Workflow

Hybrid Workflow

PureZOL

PureLink

RecoverAll

PureZOL

PureLink

RecoverAll

PureZOL

PureLink

RecoverAll

0

NEB Workflow

C
Number of miRNAs detected

250
200
150
100
50

SEQ Workflow

Hybrid Workflow

PureZOL

PureLink

RecoverAll

PureZOL

PureLink

RecoverAll

PureZOL

PureLink

RecoverAll

0

NEB Workflow

Fig. 7. More small RNAs were detected with post-library ribodepletion thanpre-library ribodepletion. The number of unique transcripts per RNA biotype
detected in libraries prepared using each of the workflows are shown in the following
panels: A, unique long RNAs (protein coding and lncRNAs) detected at ≥5 reads;
B, combined unique small RNAs, including miRNA (■), tRNA (■), snoRNA (■), andsnRNA (■), detected with ≥5 reads each; C, unique miRNAs detected at ≥5 reads.
Error bars represent SD. lncRNA, long noncoding RNA; miRNA, microRNA; RPKM,
reads per kilobase of transcript per million reads mapped; SD, standard deviation;
snRNA, small nuclear RNA; snoRNA, small nucleolar RNA; tRNA, transfer RNA.

© 2022 Bio-Rad Laboratories, Inc.

7

Bulletin 3421

Comprehensive Capture of FFPE RNA for RNA-Seq Using a Continuous Synthesis Chemistry and Post-Library Ribodepletion

Comprehensive Transcriptome Coverage

There is a significant difference in the RNA biotypes captured byeach of the workflows. Whereas only slightly different subsets oflong RNA biotypes were observed between the different workflows(Figure 10A), the SEQ workflow libraries were much more complex
in terms of the small RNA biotypes. Notably, over 500 unique small
RNAs including miRNA, tRNA, snRNA, and snoRNA subtypes
were detected in SEQ workflow libraries (Figure 10B), comparedwith only seven small RNAs in the NEB workflow libraries.

Median CV coverage

0.8

0.6

0.4

0.2

0.0

A
SEQ

HybridNEB

Fig. 8. The median CV of coverage of the top 1,000 expressed transcripts.
RecoverAll RNA was used in the SEQuoia Complete (SEQ), NEB, and Hybrid
workflows. Results were reported by the CollectRnaSeqMetrics utility from Picard
tools. Data represents mean and SEM from SEQ (n = 2), NEB (n = 3), and Hybrid(n = 3) workflow libraries. * P < 0.05 (Student’s t-test). CV, coefficient of variation;
SEM, standard error of the mean.
A

SEQ

2,509
319
1,614

326

B

NEB

1,455
103

80
Gap % for middle 1,000
expressed transcripts

Gap % for top 1,000
expressed transcripts

80

60

40

20

1,651
60
Hybrid

B

40

SEQ
3

20
455

0

0
SEQ

Hybrid

NEB

4
66

SEQ

Hybrid

NEB

NEB

20
HybridC
Fig. 10. SEQuoia library workflow provides more complete transcriptome
coverage of small RNAs in addition to long RNAs. Venn diagrams demonstrating
number of genes detected at ≥1 RPKM and ≤10% CV in libraries constructedfrom FFPE RNA extracted using the RecoverAll Kit with the same library prep/
ribodepletion. A, lncRNA and protein coding genes; B, small RNAs (miRNA, tRNA,snoRNA, snRNA). CV, coefficient of variation; lncRNA, long noncoding RNA; miRNA,
microRNA; RPKM, reads per kilobase of transcript per million reads mapped;
snRNA, small nuclear RNA; snoRNA, small nucleolar RNA; tRNA, transfer RNA.

Gap % for bottom 1,000
expressed transcripts

80

60

40

20

0
SEQ

HybridNEB

Fig. 9. Percentage of gaps in transcript for the top, middle, and bottom 1,000
expressed genes. A, top 1,000 expressed genes; B, middle 1,000 expressed
genes; C, bottom 1,000 expressed genes. In the top 1,000 expressed transcripts,
18.2% of unique small RNAs were detected in SEQ workflow but not in the NEB or
Hybrid workflows. Data represents mean and SEM from SEQ (n = 2), NEB (n = 3),
and Hybrid (n = 3) workflow libraries. * P < 0.05, *** P < 0.0005 (one-way ANOVA).
ANOVA, analysis of variance; SEM, standard error of the mean.

© 2022 Bio-Rad Laboratories, Inc.

8

Bulletin 3421

Comprehensive Capture of FFPE RNA for RNA-Seq Using a Continuous Synthesis Chemistry and Post-Library Ribodepletion

Multiplexing Ribodepletion Reactions

Discussion
RNA-Seq can be a powerful tool for elucidating the molecularprofile of RNA isolated from FFPE tumor samples. However, torealize the full potential of this sample type many challenges must
be overcome. One significant challenge is the degradation andchemical modifications to nucleic acids caused by the fixation andstorage process, as these interfere with the chemistry involved in
preparing and sequencing samples and thus may confound thefidelity of RNA expression profiles (Groelz et al. 2013). In addition
to the low RNA quality, low RNA yield (<100 ng) is also common
in FFPE samples and can lead to low complexity and suboptimal
results during RNA-Seq (Adiconis et al. 2013). Another challenge
is the decision of which RNA biotypes to study. Biomarker
discovery research using FFPE tissue samples has concentrated
on elucidating differential expression patterns of long RNA
biotypes, due mainly to the preponderance of pre-existing dataobtained from FF tissue samples using PCR techniques (Spencer
et al. 2013). In recent years, there is growing interest in exploringalterations in the expression of small RNA biotypes as potential
biomarkers of cancer diagnosis and progression (Marino et al.
2014). Interrogating the whole transcriptome for both long andshort RNA biotypes requires the construction of multiple libraries,
which is costly and labor intensive both from the bench andbioinformatics perspective. The results of this study demonstrate
a novel approach to the library preparation and ribodepletion that
captures more of the transcriptome in a single library, enablinga researcher to interrogate both long and short RNA biotypes ina single library.

In a separate experiment, the multiplexing capability of the SEQ
workflow was also evaluated. Forty-eight RNA-Seq libraries wereconstructed and indexed using SEQuoia Complete Stranded RNA
Library Prep Kit. Each library was split into two; half of each library
was ribodepleted individually using SEQuoia RiboDepletion Kit, andthe other half of each library was pooled by mixing an equimolar
ratio of libraries and then depleted en masse using the SEQuoia
RiboDepletion Kit. The percentage of reads mapping to rRNA inlibraries that were depleted en masse was consistently within 5%of the same library depleted in a singleplex reaction (Figure 11A).
In addition, the transcriptome profile is preserved when libraries are
depleted en masse, as illustrated in Figure 11B where the Pearson
correlation coefficient of transcripts between the multiplexed andsingle ribodepletion reactions ranged from 0.977 to 0.996, withan average of 0.992.
A
Reads mapped to rRNA, %

40
35
30
25
20
15
10
5
0
3

8

13

18
23
28
Library number

33

38

43

48

B

Using the SEQuoia Complete Stranded RNA Library Prep Kit
greatly improves the number of molecular targets captured evenfrom modest amounts of degraded RNA from FFPE tissues. The
number of unique long transcripts captured by the SEQ andHybrid workflows was similar to the NEB workflow, consistent withwhat others have reported when comparing capture efficiencies
between library prep kits (Ribarska et al. 2022). In contrast, thenumber of small RNA biotypes captured in the SEQ workflow
was significantly greater than in the Hybrid and NEB workflows.
Importantly, over 100 unique miRNAs were consistently detectedin the SEQ workflow from both FFPE and FF RNA samples.
Deregulation of miRNA is thought to be a major determinant for
the initiation and progression of breast cancer and may represent
novel therapeutic targets and biomarkers of early disease (vanSchooneveld et al. 2015). Correlating the dysregulation of miRNA
expression profiles to changes in coding RNA expression has beena challenge up until now because multiple libraries are required:
one that captures long RNA biotypes and one that captures only
miRNAs. Each library is constructed, sequenced, and analyzed
individually. The SEQ workflow represents an opportunity to study
the expression profile of miRNA in parallel with long coding andnoncoding RNAs using data obtained from a single library, which
may reveal new insights.

Pearson correlation coefficient (r)

1.000

0.950

0.900
0

12

24
Library number

36

48

Fig. 11. Multiplexing capability of SEQuoia RiboDepletion Kit. Forty-eight
libraries were constructed using the SEQuoia Complete Stranded RNA Library Prep
Kit. An aliquot of each library was either individually depleted using the SEQuoia
RiboDepletion Kit or pooled by mixing an equal molar ratio of the libraries and thendepleted. A, the percentage of reads mapped to rRNA for each library are plotted( , multiplexed depletion; , singleplex depletion). B, the number of total uniquetranscripts with RPKM > 0.1 in the libraries depleted in the pooled reaction wascompared to the number in the individually depleted libraries. The Pearson correlation
coefficient was calculated between the individually and multiplexed ribodepleted
libraries. RPKM, reads per kilobase of transcript per million reads mapped; rRNA,
ribosomal RNA.

© 2022 Bio-Rad Laboratories, Inc.

9

Bulletin 3421

Comprehensive Capture of FFPE RNA for RNA-Seq Using a Continuous Synthesis Chemistry and Post-Library Ribodepletion

Depleting rRNA-derived library constructs to enrich a library,as opposed to depleting total RNA of rRNA prior to library
construction, is a strategy well-suited to highly fragmented or
low-abundance RNA samples. Depletion of rRNA from total RNA
prior to library construction requires multiple pipetting steps
and bead-based cleanup steps — both considerable risks formaterial loss. A post-library depletion strategy allows RNA, which
is highly susceptible to degradation, to be converted into morestable cDNA early in the workflow. The addition of sequencing
adapters to short RNA fragments lengthens the fragments andreduces the risk of material loss during pipetting and cleanup
steps. Our results support this notion: the SEQ workflow contained
considerably more unique small RNA transcripts than the NEB
and Hybrid workflows. The difference between the SEQ workflow
and the Hybrid workflow, where pre-depleted RNA was the input
for the SEQuoia Complete library preparation, is most striking.
These results clearly show that small RNAs are lost through theribodepletion process when conducted prior to library preparation.

In addition, post-library ribodepletion allows multiplexing of theribodepletion reaction, which can reduce costs considerably.
Whereas depleting rRNA from total RNA before library construction
requires each sample to be depleted separately, one may
consider pooling multiple libraries when using a post-library
depletion strategy. The libraries can be indexed during the library
amplification step prior to depletion. We demonstrate in this study
high concordance in the number of total unique transcripts foundin libraries that are depleted either in bulk (48 pooled libraries) or as
single reactions. Moreover, the depletion efficiency was relatively
similar; the percentage of reads mapping to rRNA in libraries thatwere depleted in bulk was only slightly higher than in the same
libraries depleted in individual reactions. In sum, to constructa more complex library that includes both long and small RNA
biotypes, a post-library ribodepletion strategy is ideal.
Conclusions
The SEQuoia Complete Stranded RNA Library Prep Kit, a novel
library preparation chemistry that leverages a T4 polynucleotide
kinase and a proprietary retrotransposon instead of a retroviral
reverse transcriptase and ligases, comprehensively captures thetranscriptome, enabling the interrogation of both long and short
RNA biotypes in a single library. Furthermore, depleting rRNA after
library construction using SEQuoia RiboDepletion Kit preserves
small RNA biotypes and fragments from FFPE samples, resulting
in a richer, more complex dataset that more accurately represents
the whole transcriptome. The ability to use an input RNA as low as
10 ng for RNA-Seq library preparation expands the opportunities
for RNA-Seq in oncology research using limited and/or degradedtissue samples. Based on the results of this study, we suggest
translational researchers carefully consider the library preparation
strategy employed to ensure successful biomarker discovery fromFFPE tumor samples.

We have also shown that the SEQ workflow more efficiently
captures scarce RNA biotypes. Depleting RNA from total RNA
prior to library construction resulted in bigger gaps in the transcript
profile of lowly expressed genes, consistent with the idea thatconverting RNA to cDNA and increasing the fragment length byadding adapters helps retain more of the transcriptome.
Of course, depletion efficiency will be lower when constructing alibrary with small RNA biotypes or when using highly degraded
RNA, which can be low quality and low yield. The SEQuoia
RiboDepletion Kit utilizes a biotinylated labeled probe andstreptavidin-bead capture technique to remove rRNA-derived
constructs. Short library constructs from highly degraded andsmall RNAs may not bind to the ribodepletion probes as efficiently
as long constructs, resulting in lower depletion efficiency and agreater percentage of sequencing reads mapping to rRNA (Lin et al.
2019, Herbert et al. 2018). While the potential shortcoming ofhigh residual rRNA-derived library constructs may be seen as anegative in that it decreases the amount of relevant sequencing
reads and increases the sequencing cost, one should weigh thisagainst the gain: a more complex dataset that represents more of
the transcriptome, even from highly degraded and low RNA
yield samples.

© 2022 Bio-Rad Laboratories, Inc.

10

Bulletin 3421

Comprehensive Capture of FFPE RNA for RNA-Seq Using a Continuous Synthesis Chemistry and Post-Library Ribodepletion

References
Adiconis X et al. (2013). Comparative analysis of RNA sequencing methods for
degraded or low-input samples. Nat Methods 10, 623–629.
Bibillo A and Eickbush TH (2002). High processivity of the reverse transcriptase froma non-long terminal repeat retrotransposon. J Biol Chem 277, 34,836–34,845.
Bibillo A and Eickbush TH (2004). End-to-end template jumping by the reverse
transcriptase encoded by the R2 retrotransposon. J Biol Chem 279, 14,945–14,953.
DeLuca DS et al. (2012). RNA-SeQC: RNA-seq metrics for quality control andprocess optimization. Bioinformatics 28, 1,530–1,532.
Groelz D et al. (2013). Non-formalin fixative versus formalin-fixed tissue: A comparisonof histology and RNA quality. Exp Mol Pathol 94, 188–194.
Herbert ZT et al. (2018). Cross-site comparison of ribosomal depletion kits forIllumina RNAseq library construction. BMC Genomics 19, 199.
Illumina, Inc. (2016). Evaluating RNA quality from FFPE samples. https://www.
illumina.com/content/dam/illumina-marketing/documents/products/technotes/
evaluating-rna-quality-from-ffpe-samples-technical-note-470-2014-001.pdf,
accessed October 18, 2022.
Lin X et al. (2019). A comparative analysis of RNA sequencing methods withribosome RNA depletion for degraded and low-input total RNA from formalin-fixedand paraffin-embedded samples. BMC Genomics 20, 831.
Marczyk M et al. (2019). The impact of RNA extraction method on accurate RNA
sequencing from formalin-fixed paraffin-embedded tissues. BMC Cancer 19, 1,189.
Marino ALF et al. (2014). MicroRNA expression as risk biomarker of breast cancer
metastasis: A pilot retrospective case-cohort study. BMC Cancer 14, 739.
Masuda N et al. (1999). Analysis of chemical modification of RNA from formalinfixed samples and optimization of molecular biology applications for such samples.
Nucleic Acids Res 27, 4,436–4,443.
Naphade S et al. (2022). Systematic comparative analysis of strand-specific RNA-seq
library preparation methods for low input samples. Sci Rep 12, 1,789.
Palomares MA et al. (2019). Systematic analysis of TruSeq, SMARTer and SMARTer
Ultra-Low RNA-Seq kits for standard, low and ultra-low quantity samples.
Sci Rep 9, 7,550.
Patil VS et al. (2014). Gene regulation by non-coding RNAs. Crit Rev Biochem Mol
Biol 49, 16–32.
Pennock ND et al. (2019). RNA-Seq from archival FFPE breast cancer samples:
Molecular pathway fidelity and novel discovery. BMC Med Genomics 12, 195.
Ransohoff JD et al. (2018). The functions and unique features of long intergenic noncoding RNA. Nat Rev Mol Cell Biol 19, 143–157.
Ribarska T et al. (2022). Optimization of enzymatic fragmentation is crucial tomaximize genome coverage: A comparison of library preparation methods forIllumina sequencing. BMC Genomics 23, 92.
Sarantopoulou D et al. (2019). Comparative evaluation of RNA-Seq library preparation
methods for strand-specificity and low input. Sci Rep 9, 13,477.
Spencer DH et al. (2013). Comparison of clinical targeted next-generation sequence
data from formalin-fixed and fresh-frozen tissue specimens. J Mol Diagn 15, 623–633.
The Cancer Genome Atlas Network. (2012). Comprehensive molecular portraits ofhuman breast tumours. Nature 490, 61–70.
van Schooneveld E et al. (2015). Dysregulation of microRNAs in breast cancer andtheir potential role as prognostic and predictive biomarkers in patient management.
Breast Cancer Res 17, 21.

© 2022 Bio-Rad Laboratories, Inc.

11

Bulletin 3421

Comprehensive Capture of FFPE RNA for RNA-Seq Using a Continuous Synthesis Chemistry and Post-Library Ribodepletion

Visit bio-rad.com/NGS for more information.
BIO-RAD and HARD-SHELL are registered trademarks of Bio-Rad Laboratories, Inc. in
certain jurisdictions. All trademarks used herein are the property of their respective owners.
© 2022 Bio-Rad Laboratories, Inc..

Bio-RadLaboratories, Inc.
Website bio-rad.com USA 1 800 424 6723 Australia 61 2 9914 2800 Austria 00 800 00 24 67 23 Belgium 00 800 00 24 67 23 Brazil 4003 0399
Canada 1 905 364 3435 China 86 21 6169 8500 Czech Republic 00 800 00 24 67 23 Denmark 00 800 00 24 67 23 Finland 00 800 00 24 67 23
France 00 800 00 24 67 23 Germany 00 800 00 24 67 23 Hong Kong 852 2789 3300 Hungary 00 800 00 24 67 23 India 91 124 4029300 Israel 0 3 9636050
Italy 00 800 00 24 67 23 Japan 81 3 6361 7000 Korea 82 2 3473 4460 Luxembourg 00 800 00 24 67 23 Mexico 52 555 488 7670
The Netherlands 00 800 00 24 67 23 New Zealand 64 9 415 2280 Norway 00 800 00 24 67 23 Poland 00 800 00 24 67 23 Portugal 00 800 00 24 67 23
Russian Federation 00 800 00 24 67 23 Singapore 65 6415 3188 South Africa 00 800 00 24 67 23 Spain 00 800 00 24 67 23 Sweden 00 800 00 24 67 23
Switzerland 00 800 00 24 67 23 Taiwan 886 2 2578 7189 Thailand 66 2 651 8311 United Arab Emirates 36 1 459 6150 United Kingdom 00 800 00 24 67 23

Life ScienceGroup

Bulletin 3421 Ver A

US/EG

22-06571022

Sig 0122