High-throughput Sequencing for Species Authentication and Contamination Detection of 63 Cell Lines

doi:10.21203/rs.3.rs-646865/v1

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High-throughput Sequencing for Species Authentication and Contamination Detection of 63 Cell Lines

https://doi.org/10.21203/rs.3.rs-646865/v1

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Cell lines are widely used in research and for diagnostic tests and are often shared between laboratories. Lack of cell line authentication can result in the use of contaminated or misidentified cell lines, potentially affecting the results from research and diagnostic activities. Cell line authentication and contamination detection based on metagenomic high-throughput sequencing (HTS) was tested on DNA and RNA from 63 cell lines available at the Canadian Food Inspection Agency’s National Centre for Foreign Animal Disease. Through sequence comparison of the cytochrome c oxidase subunit 1 (COX1) gene, the species identity of 53 cell lines was confirmed, and eight cell lines were found to show a greater pairwise nucleotide identity in the COX1 sequence of a different species within the same expected genus. Two cell lines, LFBK-αvβ6 and SCP-HS, were determined to be composed of cells from a different species and genus. Mycoplasma contamination was not detected in any cell lines. However, several expected and unexpected viral sequences were detected, including part of the classical swine fever virus genome in the IB-RS-2 Clone D10 cell line. Metagenomics-based HTS is a useful laboratory QA tool for cell line authentication and contamination detection that should be conducted regularly.

General Microbiology

Bioinformatics

General Cell Biology & Physiology

QA tool

high-throughput sequencing (HTS)

Foreign Animal Disease

LFBK-αvβ6

Cell line authentication is an essential part of ensuring the validity of research and diagnostic results. Misidentified or contaminated cell lines can present irreproducible or inaccurate results which may mislead future research¹. There have been reports where the cell line was misidentified by the source institute, rendering the results of any publication using that cell line questionable. For example, the KB cell line, believed to be oral or squamous cell carcinoma², and the KU7 cell line believed to be derived from bladder cancer cells³, were both found to be HeLa cells. Vaughn et al (2017)² found 631 publications published between the years 2000–2014 that mentioned the use of the KB cell line, of which 574 articles were describing it incorrectly. These, and other papers describing misidentified cell lines, have been and may continue to be cited and used in other studies, thus potentially invalidating the research². As an increasing number of cell lines are reported as being contaminated or misidentified, many scientific journals including Nature and PLOS ONE have now put policies in place for the authentication of cell lines used in their publications^1,4. Two of the main causes of cell line misidentification are cross-contamination between cell lines and mislabeling of tubes or culture flasks⁵. Cross-contamination may occur within cell lines of the same species (intra-species cross-contamination) or between different species (inter-species cross-contamination). Cell lines may also be completely overgrown and replaced by a contaminating cell line⁶. Regularly confirming the identity of cell lines can prevent contamination and mislabeling errors from affecting future research and diagnostic test results.

One of the methods used for cell line identification is short tandem repeat (STR) profiling, which has been widely used for human identification in forensics¹. As each human cell line has originated from a different individual, STR profiling allows for differentiation between them. STRs are composed of 2–7 nucleotides which are repeated due to the duplication of this sequence in the genome⁷. The number of times that this sequence has been repeated consecutively will vary between individuals, and through the analysis of multiple STR loci, an STR profile can be made that will be different for each individual⁷. Single nucleotide polymorphisms (SNPs) are also used for distinguishing between members of the same species⁵. Similar to STR profiling, specific SNP loci in a genome can be analyzed to assemble a profile of SNPs that is unique for each individual⁵. Thus, STR/SNP profiling is commonly used for human cell lines due to the availability of STR/SNP profiles. For non-human cell lines, using genes such as the mitochondrial gene cytochrome c oxidase subunit I (COX1) for DNA barcoding may be used to determine the species of origin. Due to frequent third-position base substitutions in this gene there is a high rate of molecular evolution leading to diversification, which can even differentiate between various phylogeographic groups of the same species⁸. The species can then be determined by comparing the DNA barcode profile of a cell line to databases of these sequences (BOLD, http://www.barcodinglife.org, and NCBI, http://www.ncbi.nlm.nih.gov/genbank/barcode)¹. However, STR/SNP and COX 1-based methods do not provide information on the presence and type of microbial contamination.

Shotgun metagenomic sequencing allows sequencing of a broader spectrum of DNA or RNA in a sample. Thus, it can be used for species identification of cell lines and potentially detect the presence of bacterial, viral, or fungal contamination. If specific STR and SNP loci are amplified prior to sequencing, STR/SNP profiling may be reliably implemented for the confirmation of the specific cell line. In this study, high-throughput sequencing (HTS) was performed on DNA and cDNA extracts from each of 63 cell lines available at the Canadian Food Inspection Agency (CFIA) National Centre of Foreign Animal Disease (NCFAD) to verify the species of origin and presence of microbial contamination.

Cell Culture

A total of 63 cell lines available at the NCFAD were seeded from frozen stocks and grown for 48 hours at 37°C and 5% CO₂, except for Trichoplusia ni cells which were grown with shaking at 27°C for 24 hours before collection. Adherent cell lines were dissociated using 0.25% Trypsin-0.1% EDTA, and cells in suspension were spun down at 4°C for 10 minutes at a relative centrifugal force (rcf) of 600, and re-suspended in culture media. An aliquot of cells was stained with 0.2% trypan blue and counted using a Cellometer Auto T4 counter (Nexcelom Bioscience).

DNA/RNA Extraction

DNeasy Blood and Tissue kit (QIAGEN) was used to isolate DNA and RNA from ~ 2.5 × 10⁶ viable cells using the manufacturer’s recommended protocol. The DNA and RNA were eluted into 50-100uL of AE elution buffer (QIAGEN). Qubit dsDNA Broad Range (BR) and RNA High Sensitivity (HS) kits (Thermo Fisher Scientific) were used to quantify DNA and RNA in the extracts on the DS-11 FX fluorometer (Denovix).

High-throughput sequencing

Invitrogen ezDNase enzyme (Thermo Fisher Scientific) was used for the digestion of cellular DNA within extracted nucleic acid prior to cDNA synthesis. Superscript IV First-Strand Synthesis module (Thermo Fisher Scientific) was used for the synthesis of the first strand of cDNA using a 1:1 ratio of random hexamers and oligo-dTs and 300ng of RNA. The NEBNext Ultra II Second-Strand synthesis module (New England Biolabs) was used according to the manufacturer’s protocol to generate the second strand of cDNA. QIAQuick PCR purification kit (QIAGEN) was used to purify the double-stranded cDNA and eluted in EB buffer (QIAGEN) according to the manufacturer’s recommended protocol. The Qubit dsDNA BR kit (Thermo Fisher Scientific) was then used to quantify the cDNA using a DS11 FX fluorometer.

Sequencing was performed separately on the DNA and cDNA samples with the cDNA samples separated into two runs (Tables 1 and 2). Library preparation for the DNA samples was performed using Riptide High-Throughput Rapid DNA Library prep kit (iGenomX) and the manufacturer’s protocol was followed with a 1:1 ratio of the low GC and high GC primers. The samples were pooled and loaded at a final concentration of 18 pM with 1% PhiX, and sequencing was performed on an Illumina MiSeq using a V2 flow cell with a 300-cycle (2x150bp) cartridge.

Table 1

Cell lines with species identity determined by sequencing that matched institute records or were previously unknown
Cell Line	Species	cDNA Run	Total Reads	Number of Reads Mapped	Breadth of Coverage of COX1 (%)	Mean Depth of Coverage of COX1	Total Variants	GenBank Reference Accession
3T6-Swiss Albino	Mus musculus	2	715,514	128	99.3	12.3	0	KY018919.1
A549	Homo sapiens	2	2,306,896	1,374	100.0	141.8	0	MW389273.1
BHK-21	Mesocricetus auratus	2	706,654	39	99.2	3.4	0	EU660218.1
CEF	Gallus gallus	2	711,596	1,882	100.0	238.5	0	MN013407.1
CHCC-OU2	Gallus gallus	2	2,327,710	9,681	100.0	1,070.4	0	MN013407.1
CHO-K1	Cricetulus griseus	2	3,254,926	300	100.0	20.4	0	KX576660.1
COS-1	Cercopithecus aethiops	2	1,438,372	141	99.5	11.0	0	MN816163.1
CPAE	Bos taurus	1	5,933,398	1,676	100.0	146.6	0	MF663794.1
CV-1	Cercopithecus aethiops	2	923,170	277	100.0	22.3	0	MN816163.1
DE	Anas platyrhynchus	2	840,902	505	100.0	51.8	0	MH744426.1
DF-1	Gallus gallus	2	1,145,492	805	100.0	88.6	0	MK163563.1
Efk-1B	Eptesicus fuscus	2	1,015,466	311	95.5	25.6	2	MF143474.1
Efk-2F	Eptesicus fuscus	2	1,137,244	257	95.5	20.9	2	MF143474.1
Efk-3B	Eptesicus fuscus	2	1,051,616	246	95.4	18.8	2	MF143474.1
EL4-IL2	Mus musculus	2	2,016,368	735	100.0	86.3	1	KY018919.1
H1299	Homo sapiens	2	1,322,326	392	100.0	37.2	0	MW389273.1
HEK-293	Homo sapiens	2	2,997,732	845	100.0	95.0	0	X93334.1
IB-RS-2 Clone D10	Sus scrofa	2	4,731,602	2,846	100.0	282.8	1	MF183225.1
IPAM 3C10	Sus scrofa	2	6,759,802	2,827	100.0	238.2	0	MH603005.1
IPAM 3C8	Sus scrofa	2	1,701,142	229	100.0	17.0	0	MT199606.1
IPAM 3E8	Sus scrofa	2	1,214,488	160	100.0	13.6	0	MG250562.1
IPAM 3F6	Sus scrofa	2	1,948,740	434	100.0	33.5	0	MG250562.1
L929	Mus musculus	2	1,134,052	164	100.0	21.0	0	EU315228.1
LK-W(14)	Ovis aries	2	3,834,432	5,324	100.0	613.8	0	EF490453.1
LLC-PK1	Sus scrofa	2	862,572	423	100.0	48.2	0	AF486866.1
LMH	Gallus gallus	2	847,388	524	100.0	56.5	0	MN013407.1
LT	Ovis aries	2	1,165,940	452	100.0	38.0	0	EF490453.1
MDBK-HS-1	Bos taurus	2	2,071,902	846	100.0	98.2	0	MN714195.1
MDBK-HS-2	Bos taurus	2	658,222	103	100.0	10.9	0	MN714195.1
MDCK SIAT1	Canis familiaris	2	1,270,062	506	100.0	53.6	0	KM061581.1
MDCK2	Canis familiaris	2	1,474,730	2,038	100.0	236.5	0	KM061555.1
MDCK-PGOK	Canis familiaris	2	1,242,568	153	100.0	18.9	0	KM061581.1
OA3.Ts	Ovis aries	2	1,148,750	647	100.0	55.0	0	KU681212.1
OA4K/s1	Ovis aries	2	900,506	182	98.5	13.1	0	MT768116.1
P3X63-Ag8-653	Mus musculus	2	1,999,368	2,896	100.0	305.4	0	AY533105.1
PaKi	Pteropus alecto	2	1,002,720	223	99.9	19.7	0	KF726143.1
PK-15 (PCV-)	Sus scrofa	1	1,116,214	1,123	100.0	98.1	0	KT279758.1
PK-15 (PCV+)	Sus scrofa	1	3,916,490	757	100.0	68.2	0	KT279758.1
QT-35	Coturnix japonica	2	824,636	1,183	100.0	133.8	0	KX712089.1
RK13	Oryctolagus cuniculus	2	729,418	320	100.0	34.3	5	MN296708.1
SC-1	Gallus gallus	2	1,296,288	1,101	100.0	91.0	0	GU261694.1
SIRC	Oryctolagus cuniculus	2	614,400	146	100.0	15.2	1	MN296708.1
SK-6	Sus scofa	2	867,492	162	100.0	18.0	0	MG250562.1
ST	Sus scrofa	1	2,051,240	707	100.0	60.5	0	AF486866.1
TG180	Mus musculus	2	4,749,142	1,263	100.0	130.8	0	KP260515.1
Tni	Trichoplusia ni	2	1,449,444	348	100.0	43.9	0	NC_045936.1
WSL-R-HP	Sus scrofa	2	913,228	804	100.0	65.8	0	MF183225.1
ZZR	Capra aegregrus hircus	2	988,600	262	100.0	28.0	0	MH229952.1
Previously Undefined Cell Lines
BD41/31	Sus scrofa	2	1,020,194	413	100.0	47.5	0	MH603005.1
MARC-145	Chlorocebus pygerythrus	2	594,988	222	100.0	14.6	0	MT481926.1
MRC5	Homo sapiens	2	858,666	249	100.0	23.7	0	MK059615.1
N418	Mus musculus	2	1,180,212	194	100.0	22.2	0	KY018919.1
OUR-1	Mus musculus	2	1,615,188	129	100.0	10.1	0	KP260516.1

Table 2

Cell lines in which a different species from institution records was identified
Cell Line	Species	cDNA Run	Total Reads	Number of Reads Mapped	Breadth of Coverage of COX1 (%)	Mean Depth of Coverage of COX1	Total Variants	Reference Mitogenome Accession
LFBK-αVβ6	Sus scrofa	2	4,351,208	880	100.0	91.0	0	JN601075.1
LFBK-αVβ6	Bos taurus	2	4,351,208	77	44.1	8.2	98	MN200869.1
SCP-HS	Bos taurus	2	511,706	202	100.0	23.7	0	MF663794.1
SCP-HS	Ovis aries	2	511,706	85	100.0	11.1	176	KU681212.1
Same Genus
BGMK	Chlorocebus pygerythrus	2	896,826	501	100.0	50.4	6	EF597501.1
BGMK	Chlorocebus aethiops	2	896,826	484	100.0	48.8	47	MN816163.1
CGBQ	Anser cygnoides	2	2,426,176	10,021	100.0	1,179.6	0	MN356388.1
CGBQ	Anser anser	2	2,426,176	10,010	100.0	1,178.4	9	MN122908.1
MA-104	Chlorocebus pygerythrus	2	947,256	165	100.0	14.6	0	MT481926.1
MA-104	Chlorocebus aethiops	2	947,256	149	100.0	13.2	55	MN816163.1
PaLu	Pteropus ornatus	2	1,030,394	197	100.0	17.2	39	NC_046926.1
PaLu	Pteropus alecto	2	1,030,394	192	100.0	16.9	49	KF726143.1
PaSPT	Pteropus ornatus	2	936,338	195	100.0	25.7	40	NC_046926.1
PaSPT	Pteropus alecto	2	936,338	194	100.0	25.6	50	KF726143.1
Vero	Chlorocebus sabeus	2	461,114	311	100.0	25.1	3	JQ246913.1
Vero	Chlorocebus aethiops	2	461,114	285	100.0	23.4	67	MN816163.1
Vero Nectin-4	Chlorocebus sabeus	2	633,176	238	100.0	28.0	3	JQ256913.1
Vero Nectin-4	Chlorocebus aethiops	2	633,176	207	100.0	26.2	65	MN816163.1
Vero-76	Chlorocebus sabeus	1	1,284,012	618	100.0	50.3	3	EF597503.1
Vero-76	Chlorocebus aethiops	1	1,284,012	583	100.0	47.4	67	MN816163.1
Species names in bold are the observed species while the non-bolded names are the expected species based on laboratory records. Variants were only called if the read depth had a minimum coverage of 3X.

Library preparation for the cDNA samples was performed with the Nextera XT Library Prep kit (Illumina) following the manufacturer’s protocol. In the first run, 26 samples were pooled and loaded at a final concentration of 10 pM with 1% PhiX, and sequencing was performed again on the Illumina MiSeq using a V2 flow cell with a 300-cycle (2x150bp) cartridge. In the second run of cDNA samples, 65 samples were pooled at a final concentration of 18 pM with 1% PhiX, and sequencing was performed on a V3 flow cell with a 600-cycle (2x300bp) cartridge.

Sequence Analysis

iGenomX DNA sequencing reads were demultiplexed using the fgbio⁹ software (v.0.7.0; command used: fgbio DemuxFastqs -i R1.fastq.gz R2.fastq.gz -r 8B12M + T 8M + T -x metadata.csv). To determine the species of the cell line, metagenomic analysis was performed using the nf-villumina¹⁰ (v2.0.0) Nextflow¹¹ workflow on the concatenated DNA and cDNA sequencing data. As part of the nf-villumina workflow, Illumina PhiX Sequencing Control V3 reads were removed using BBDuk¹², and poor quality reads and adaptors were removed using fastp¹³. Taxonomic classification of the filtered reads was performed with Kraken 2¹⁴ using an index of NCBI RefSeq sequences for bacteria, archaea, viruses and the GRCh38 human genome (downloaded and built March 22, 2019), and with Centrifuge using an index of NCBI nt sequences (downloaded and built 2020-02-04). Quality filtered reads were assembled into contigs with Megahit¹⁵, Shovill¹⁶, and Unicycler¹⁷, which were queried against the NCBI nt database (downloaded December 04, 2020) using nucleotide BLAST + ^18,19 (v2.11.0) (default parameters except “-evalue 1e-6”) restricting the search to eukaryotic NCBI nt database entries (i.e. belonging to NCBI taxonomic ID (taxid) 2759).. The processed reads for each cell line were mapped against the top matching COX1 sequence identified by BLAST analysis using Snippy (v4.6.0)²⁰ as part of the nf-illmap Nextflow workflow (v1.0.0)²¹. The resulting BAM alignment file was loaded into Geneious v.9.1.8²² where variants were called using the Find Variations/SNPs tools with default settings except Minimum Coverage = 3 and Minimum Variant Frequency = 0.75. Variants were only called if the read depth had a minimum coverage of 3X. MDBK-HS-1 is from the cell lines available at CFIA NCFAD in Winnipeg, Manitoba, Canada while MDBK-HS-2 came from the CFIA NCAD laboratory in Lethbridge, Alberta, Canada.

RNA and DNA viruses and bacteria were identified from the cell line DNA and cDNA sequencing data using DAMIAN²³. As part of DAMIAN analysis, raw sequencing reads were trimmed using Trimmomatic²⁴ with default settings and assembled using SPAdes²⁴. Contigs were taxonomically classified using nucleotide BLAST+ (v2.11.0) (DAMIAN BLAST + option “progressive”) and the NCBI nt database (downloaded December 04, 2020). Trimmed reads were mapped to the viral genomes identified by DAMIAN and additional nucleotide BLAST analysis using the nf-illmap workflow. Variants were called in Geneious V.9.1.8²² using the method described above.

Cell line authentication

Table 1 lists the cell lines for which the expected species identity was confirmed by mapping the combined reads from the cDNA and DNA sequences to the reference COX1 gene of the top mitochondrial genome BLAST match for each cell line. All observed species in this list matched the species recorded in the institute’s cell line inventory list. This list also includes five archived cell lines that have been documented as “unknown” which did not have a defined species listed.

Two cell lines, LFBK-αvβ6 and SCP-HS, were determined to be composed of cells from a different species than expected. According to institute documentation, LFBK-αvβ6 was a continuous bovine kidney cell line that constitutively expresses αvβ6 integrin^25,26; however, there were no BLAST results from the LFBK-αvβ6 de novo assembled contigs that corresponded to the Bos taurus genome or mitogenome. All BLAST results matched sequences from the Sus scrofa genome and mitogenome. Figure 1 shows the DNA and cDNA reads mapped to reference B. taurus and S. scrofa COX1 sequences. A total of 880 reads from LFBK-αvβ6 mapped to the S. scrofa COX1 gene and had a breadth of coverage of 100% with 0 total variants (i.e., SNPs, MNPs, and INDELs) between the mapped reads and the reference, while only 77 reads mapped to the B. taurus reference COX1 gene with a breadth of coverage of 44.1% and 98 total variants. (See Table 2 for reference accession numbers and results).

According to documentation, SCP-HS is an ovine brain choroid plexus cell line adapted for growth in horse serum; however, the top BLAST results from the de novo assembled contigs were to B. taurus and not to Ovis aries. Figure 1shows the coverage of the SCP-HS reads mapped across reference COX1 sequences from O. aries and B. taurus. In the B. taurus assembly, 202 reads mapped with 100% breadth of coverage across the COX1 gene with 0 total variants, while in the O. aries assembly, 85 reads mapped with 100% breadth of coverage across the COX1 gene with 176 total variants. (See Table 2 for reference accession numbers and results).

Eight cell lines (CGBQ, BGMK, MA-104, PaLu, PaSPT, Vero, Vero Nectin-4, Vero-76) were found to align better to the COX1 sequence from a different species (within the same genus) than the expected species based on available documentation. For these samples, reads were mapped against the COX1 sequences from both the expected and observed species. This analysis showed that, when reads were mapped against a reference sequence representing the expected species, more variants were observed than when they were mapped against a reference representing the observed species, suggesting that the cell line is derived from a different species than was expected (Table 2).

Detection of bacterial and viral sequences

Upon identifying the species of the 63 cell lines, a separate workflow was used to identify bacterial and viral DNA and cDNA sequences. Some viral sequences were expected in the cell lines including human adenovirus C used for the transformation of HEK-293, the common FBS contaminant bovine viral diarrhea virus 2 (BVDV2) in CPAE, and the common porcine circovirus 1 (PCV1) in swine-derived PK-15 (PCV+) cells. Sequences matching these viruses were detected as expected, and PCV1 was also found in all four IPAM clones (Table 3). Retroviral sequences, including murine leukemia virus (MuLV), were also found in some of the cell lines (Table 3). Only viruses with a complete or near complete viral genome (> 98% breadth of coverage) are listed, as incomplete cancer-causing retroviral sequences can be expected within the genomes of tumor-derived cell lines²⁷. Reads that were classified as classical swine fever virus (CSFV) were also found in the IB-RS-2 Clone D10 cell line with a 39.5% breadth of coverage across the viral genome with 7 total variants relative to the reference genome. In the T. ni insect cell line, reads identified as Flock House virus had a breadth of coverage of 85.1% across the reference genome with two total variants (Table 3).

Table 3

List of viral genomes detected in the cell lines
Cell Line	Species	Virus	Total Reads	Number of Reads Mapped	Breadth of Coverage (%)	Mean Depth of Coverage	Total Variants	Viral Reference Genome Accession
RNA Viruses
CPAE	Bos taurus	Bovine viral diarrhea virus 2 (BVDV)	5,933,398	173	82.8	2.1	3	MN824468.1
OA3.Ts	Ovis aries	Bovine viral diarrhea virus 2 (BVDV)	1,148,750	27	18.0	0.3	7	MH806437.1
IB-RS-2 Clone D10	Sus scrofa	Classical swine fever virus (CSFV)	4,731,602	53	39.5	0.8	7	X96550.1
P3X63-Ag8-653	Mus musculus	Murine leukemia virus (MuLV)	1,999,368	3,470	99.3	76.3	124	KY574512.1
Tni	Trichoplusia ni	Flock house virus	1,449,444	67	87.4	4.5	8	EF690537.1
DNA Viruses
A549	Homo sapiens	Human adenovirus C	2,306,896	17	3.5	0.1	0	KF429754.1
HEK-293	Homo sapiens	Human adenovirus C	2,997,732	221	8.8	1.3	1	KF429754.1
PK-15 (PCV+)	Sus scrofa	Porcine circovirus 1 (PCV1)	3,916,490	217,311	100	15,571.2	5	MK770354.1
IPAM 3C10			6,759,802	1,566	100	80.0	6	MK770354.1
IPAM 3C8			1,701,142	3,390	100	186.9	6	MK770354.1
IPAM 3E8			1,214,488	2,939	100	174.3	6	MK770354.1
IPAM 3F6			1,948,740	2,384	100	143.1	5	AY754015.1
Only retroviruses with > 98% genome coverage were included. Variants were only called if the read depth had a minimum coverage of 3X.

The aim of this study was to authenticate the species identity of cell lines available for use at the Canadian Food Inspection Agency’s National Centre for Foreign Animal Disease, and to establish methods that can be integrated into the laboratory quality assurance system. Confirming cell line species at our laboratory was previously conducted by comparing the electrophoretic migratory patterns of common intracellular enzymes (isoenzymes). Examining the polymorphic isoenzyme profiles between species for cell line confirmation has limitations including limited species range, low sensitivity of detection, and complex data interpretation.

In this study, 53 of the 63 cell lines had a COX1 sequence that was consistent with the expected species; the reads from each of these cell lines had a breadth of coverage of > 95% across the COX1 gene, and no more than five variants compared to the reference. LFBK-αvβ6 and SCP-HS cells were found to be from a different genus than expected, suggesting that the cell lines had been misidentified, contaminated, or mislabeled. When reads from the LFBK-αvβ6 and SCP-HS cell lines were mapped to the COX1 genes corresponding to the species identified by BLAST analysis, no variants were observed in either sample. The porcine DNA found within the LFBK-αvβ6 cell line is consistent with a published erratum that this cell line is of porcine origin^25,26. LFBK-αvβ6 isoenzyme patterns are also consistent with cultures of porcine origin (unpublished results).

Assembled sequences from eight of the cell lines showed a higher pairwise nucleotide identity to a different species within the same genus than what was expected (Table 2). Five of the cell lines were of primate origin, two were of bat (flying foxes) origin, and one was of goose origin. The number of variants (i.e., SNPs, MNPs, INDELs) between the mapped reads and the COX1 gene was used as an indication of how similar the cell line was to a particular species. The difference in the number of variants between the expected and observed species varied for each cell line (between 9–64 variants); however, in each case, the number of variants was higher when aligned to the expected species as compared to the observed species. Turner et al. (2018)²⁸ describes the morphological differences between species of the Chlorocebus genus of Old World monkeys, and reported that various geographical locations may permit deviation from the predicted morphology of these species. Thus, the species of the individual animal from which each of these cell lines originated was likely misidentified. It was also noticed that the number of variants in the bat cell lines (PaLu and PaSPT) was considerably higher in the observed species (39 and 40 variants, respectively) compared to all of the other cell lines (6 or fewer variants). The genus Pteropus is known to be very diverse with a large number of species²⁹, therefore, additional investigation will be required to determine if the cell lines are, in fact P. ornatus, as identified here, or if there was a misidentification between closely related species when the cell line was originally created.

The current gold standard for the authentication of human cell lines is STR profiling³⁰, while non-human cell lines are best identified using DNA barcoding with the COX1 gene⁸. The International Cell Line Authentication Committee (ICLAC) keeps a Register of all known misidentified or cross-contaminated cell lines. As of this study, the Register was last updated March 25, 2020 and contains a total of 509 cell lines that are misidentified; of these only 38 were nonhuman cell lines³¹. This is likely not because human cell lines are more susceptible to contamination compared to nonhuman cell lines, but rather, because there is more information available for human cell lines in addition to the limitations of STR profiling which is only applicable for single species differentiation³¹. Thus, the method described here is useful since it can identify the species as well as the presence of contaminants such as other cell lines, mycoplasma, or viruses¹.

Experimental results can be negatively impacted due to mycoplasma contamination of cell lines. Depending on the species of mycoplasma, the effects on the cells vary from changes in protein and nucleic acid synthesis levels to a complete loss of the culture³². Detection of contamination is difficult, due in part to the small size (0.3–0.8µM)³³ of the mycoplasma cells, which allows them to pass through filters^33,34. Additionally, high concentrations of mycoplasma are possible without any obvious visual signs³⁴. In this study, mycoplasma was not detected in any of the 63 cell lines tested. This result was expected as the NCFAD currently has quality control procedures in place to check for mycoplasma contamination in their cultures, and the results here are consistent with the systems in place.

The presence of certain viruses was expected in some of the cell lines. Bovine viral diarrhea virus 2 (BVDV2) is a common contaminant in fetal bovine serum³⁵ and was present in the CPAE and OA3.Ts cell lines. Human adenovirus C was found in both HEK-293 and A549 cells. PCV1, a ubiquitous virus in pigs, was found as expected in the PK-15 (PCV+) cell line and in all four of the IPAM clones tested. Retroviral sequences are common in the genomes of their hosts due to insertion into the host genome²⁷. The near-complete genome (99.3% breadth of coverage with 77 variants) of murine leukemia virus (MuLV) was detected in the P3X63-Ag8 cell line. Partial genomes from retroviruses such as avian leukosis virus (ALV) and porcine endogenous retrovirus (PERV) were detected in some cell lines.

Sequencing reads covering 39.5% of the CSFV genome were found in the cell line IB-RS-2 Clone D10 with 7 total variants shared between the reads mapped and the reference genome. This clone was originally determined to be free of CSFV contamination³⁰, however, testing of this cell line obtained from the American Type Culture Collection (ATCC) by Bolin, et al. (1994)³⁶ detected the virus in this clone. The presence of the entire CSFV genome was also found in the same cell line used by the Pirbright Institute, UK (Don King, personal communication).

Cell line authentication is important for the reproducibility and accuracy of research and diagnostics involving cell lines as it can help identify unexpected errors and contamination in archived material and cell lines obtained from other sources. This study confirmed the species identity of 48 cell lines that are available at the Canadian Food Inspection Agency’s National Centre for Foreign Animal Disease, and determined the species identity of five additional cell lines that were previously undefined. In addition, it was determined that eight cell lines were derived from a different species within the same genus than was expected. The methods described in this study or other comparable methods can be useful as they provide a single approach for species identification, as well as for the detection of contamination (e.g., mycoplasma) or the presence of unexpected viruses.

The authors declare no competing interests.

Contributions

O.L. designed the experiment and wrote the manuscript with M.N.. R.C. performed the experimental work with cell lines and sample treatment. R.C., P.K. and M.N. performed the analysis. C.B. performed the library preparation and sequencing on the MiSeq sequencer. M.M. provided the cell lines and documentation of the cell lines. All authors reviewed and approved the manuscript.

Acknowledgements

The authors acknowledge funding from Canadian Food Inspection Agency (CFIA) project WIN-A-1808 for B.C. The authors would also like to acknowledge June Larence, Hilary Bittner for technical assistance.

Almeida, J. L., Cole, K. D. & Plant, A. L. Standards for Cell Line Authentication and Beyond. PLOS Biology 14, e1002476 (2016).
Vaughan, L., Glänzel, W., Korch, C. & Capes-Davis, A. Widespread Use of Misidentified Cell Line KB (HeLa): Incorrect Attribution and Its Impact Revealed through Mining the Scientific Literature. Cancer Res 77, 2784–2788 (2017).
Jäger, W. et al. Hiding in plain view: Genetic profiling reveals decades old cross-contamination of bladder cancer cell line KU7 with HeLa. J Urol 190, 1404–1409 (2013).
Freedman, L. P. et al. The culture of cell culture practices and authentication—Results from a 2015 Survey. BioTechniques 59, 189–192 (2015).
Freedman, L. P. et al. Reproducibility: changing the policies and culture of cell line authentication. Nature Methods 12, 493–497 (2015).
Yu, M. et al. A resource for cell line authentication, annotation and quality control. Nature 520, 307–311 (2015).
Lin, L.-C. et al. Cross-contamination of the human salivary gland HSG cell line with HeLa cells: A STR analysis study. Oral Diseases 24, 1477–1483 (2018).
Hebert, P. D. N., Cywinska, A., Ball, S. L. & deWaard, J. R. Biological identifications through DNA barcodes. Proc Biol Sci 270, 313–321 (2003).
fulcrumgenomics/fgbio. (Fulcrum Genomics, 2021).
Kruczkiewicz, P. peterk87/nf-villumina. https://github.com/peterk87/nf-villumina (2020).
Tommaso, P. D. et al. Nextflow enables reproducible computational workflows. Nat Biotechnol 35, 316–319 (2017).
Bushnell, B. BBMap. https://sourceforge.net/projects/bbmap/.
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biology 20, 257 (2019).
Li, D. et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102, 3–11 (2016).
Seemann, T. tseemann/shovill. https://github.com/tseemann/shovill (2020).
Wick, R. R., Judd, L. M., Gorrie, C. L. & Holt, K. E. Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol 13, (2017).
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
Seemann, T. Snippy (v4.4.1): Rapid haploid variant calling and core genome alignment. (2019).
Kruczkiewicz, P. peterk87/nf-illmap. (2020).
Kearse, M. et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).
Alawi, M. et al. DAMIAN: an open source bioinformatics tool for fast, systematic and cohort based analysis of microorganisms in diagnostic samples. Scientific Reports 9, 16841 (2019).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
LaRocco, M. et al. A Continuous Bovine Kidney Cell Line Constitutively Expressing Bovine αVβ6 Integrin Has Increased Susceptibility to Foot-and-Mouth Disease Virus. Journal of Clinical Microbiology 51, 1714–1720 (2013).
LaRocco, M. et al. Correction for LaRocco et al., A Continuous Bovine Kidney Cell Line Constitutively Expressing Bovine αVβ6 Integrin Has Increased Susceptibility to Foot-and-Mouth Disease Virus. J Clin Microbiol 53, 755 (2015).
Bamunusinghe, D. et al. Recombinant Origins of Pathogenic and Nonpathogenic Mouse Gammaretroviruses with Polytropic Host Range. Journal of Virology 91, (2017).
Turner, T. R. et al. Morphological variation in the genus Chlorocebus: Ecogeographic and anthropogenically mediated variation in body mass, postcranial morphology, and growth. American Journal of Physical Anthropology 166, 682–707 (2018).
Almeida, F. C., Giannini, N. P., Simmons, N. B. & Helgen, K. M. Each flying fox on its own branch: A phylogenetic tree for Pteropus and related genera (Chiroptera: Pteropodidae). Molecular Phylogenetics and Evolution 77, 83–95 (2014).
American Type Culture Collection Standards Development Organization Workgroup ASN-0002. Cell line misidentification: the beginning of the end. Nature Reviews Cancer 10, 441–448 (2010).
Capes‐Davis, A. et al. Check your cultures! A list of cross-contaminated or misidentified cell lines. International Journal of Cancer 127, 1–8 (2010).
Drexler, H. G. & Uphoff, C. C. Mycoplasma contamination of cell cultures: Incidence, sources, effects, detection, elimination, prevention. Cytotechnology 39, 75–90 (2002).
Olarerin-George, A. O. & Hogenesch, J. B. Assessing the prevalence of mycoplasma contamination in cell culture via a survey of NCBI’s RNA-seq archive. Nucleic Acids Res 43, 2535–2542 (2015).
Young, L., Sung, J., Stacey, G. & Masters, J. R. Detection of Mycoplasma in cell cultures. Nature Protocols 5, 929–934 (2010).
Kozasa, T. et al. Methods to select suitable fetal bovine serum for use in quality control assays for the detection of adventitious viruses from biological products. Biologicals 39, 242–248 (2011).
Bolin, S. R. et al. Detection of a cell line contaminated with hog cholera virus. J Am Vet Med Assoc 205, 742–745 (1994).

No competing interests reported.

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High-throughput Sequencing for Species Authentication and Contamination Detection of 63 Cell Lines

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High-throughput sequencing

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