Laboratory contamination in airway microbiome studies
Abstract Background The low bacterial load in samples acquired from the lungs, have made studies on the airway microbiome vulnerable to contamination from bacterial DNA introduced during sampling and laboratory processing. We have examined the impact of laboratory contamination on samples collected...
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| Format: | Article |
| Language: | English |
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BMC
2019-08-01
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| Series: | BMC Microbiology |
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| Online Access: | http://link.springer.com/article/10.1186/s12866-019-1560-1 |
| _version_ | 1819205653658337280 |
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| author | Christine Drengenes Harald G. Wiker Tharmini Kalananthan Eli Nordeide Tomas M. L. Eagan Rune Nielsen |
| author_facet | Christine Drengenes Harald G. Wiker Tharmini Kalananthan Eli Nordeide Tomas M. L. Eagan Rune Nielsen |
| author_sort | Christine Drengenes |
| collection | DOAJ |
| description | Abstract Background The low bacterial load in samples acquired from the lungs, have made studies on the airway microbiome vulnerable to contamination from bacterial DNA introduced during sampling and laboratory processing. We have examined the impact of laboratory contamination on samples collected from the lower airways by protected (through a sterile catheter) bronchoscopy and explored various in silico approaches to dealing with the contamination post-sequencing. Our analyses included quantitative PCR and targeted amplicon sequencing of the bacterial 16S rRNA gene. Results The mean bacterial load varied by sample type for the 23 study subjects (oral wash>1st fraction of protected bronchoalveolar lavage>protected specimen brush>2nd fraction of protected bronchoalveolar lavage; p < 0.001). By comparison to a dilution series of know bacterial composition and load, an estimated 10–50% of the bacterial community profiles for lower airway samples could be traced back to contaminating bacterial DNA introduced from the laboratory. We determined the main source of laboratory contaminants to be the DNA extraction kit (FastDNA Spin Kit). The removal of contaminants identified using tools within the Decontam R package appeared to provide a balance between keeping and removing taxa found in both negative controls and study samples. Conclusions The influence of laboratory contamination will vary across airway microbiome studies. By reporting estimates of contaminant levels and taking use of contaminant identification tools (e.g. the Decontam R package) based on statistical models that limit the subjectivity of the researcher, the accuracy of inter-study comparisons can be improved. |
| first_indexed | 2024-12-23T04:55:08Z |
| format | Article |
| id | doaj.art-06db223ee85240f6b13a537aed9b4dd1 |
| institution | Directory Open Access Journal |
| issn | 1471-2180 |
| language | English |
| last_indexed | 2024-12-23T04:55:08Z |
| publishDate | 2019-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Microbiology |
| spelling | doaj.art-06db223ee85240f6b13a537aed9b4dd12022-12-21T17:59:21ZengBMCBMC Microbiology1471-21802019-08-0119111310.1186/s12866-019-1560-1Laboratory contamination in airway microbiome studiesChristine Drengenes0Harald G. Wiker1Tharmini Kalananthan2Eli Nordeide3Tomas M. L. Eagan4Rune Nielsen5Department of Thoracic Medicine, Haukeland University HospitalDepartment of Clinical Science, Faculty of Medicine, University of BergenDepartment of Microbiology, Haukeland University HospitalDepartment of Thoracic Medicine, Haukeland University HospitalDepartment of Thoracic Medicine, Haukeland University HospitalDepartment of Thoracic Medicine, Haukeland University HospitalAbstract Background The low bacterial load in samples acquired from the lungs, have made studies on the airway microbiome vulnerable to contamination from bacterial DNA introduced during sampling and laboratory processing. We have examined the impact of laboratory contamination on samples collected from the lower airways by protected (through a sterile catheter) bronchoscopy and explored various in silico approaches to dealing with the contamination post-sequencing. Our analyses included quantitative PCR and targeted amplicon sequencing of the bacterial 16S rRNA gene. Results The mean bacterial load varied by sample type for the 23 study subjects (oral wash>1st fraction of protected bronchoalveolar lavage>protected specimen brush>2nd fraction of protected bronchoalveolar lavage; p < 0.001). By comparison to a dilution series of know bacterial composition and load, an estimated 10–50% of the bacterial community profiles for lower airway samples could be traced back to contaminating bacterial DNA introduced from the laboratory. We determined the main source of laboratory contaminants to be the DNA extraction kit (FastDNA Spin Kit). The removal of contaminants identified using tools within the Decontam R package appeared to provide a balance between keeping and removing taxa found in both negative controls and study samples. Conclusions The influence of laboratory contamination will vary across airway microbiome studies. By reporting estimates of contaminant levels and taking use of contaminant identification tools (e.g. the Decontam R package) based on statistical models that limit the subjectivity of the researcher, the accuracy of inter-study comparisons can be improved.http://link.springer.com/article/10.1186/s12866-019-1560-1MicrobiomeContaminationLow biomassRespiratory16S rRNA gene |
| spellingShingle | Christine Drengenes Harald G. Wiker Tharmini Kalananthan Eli Nordeide Tomas M. L. Eagan Rune Nielsen Laboratory contamination in airway microbiome studies BMC Microbiology Microbiome Contamination Low biomass Respiratory 16S rRNA gene |
| title | Laboratory contamination in airway microbiome studies |
| title_full | Laboratory contamination in airway microbiome studies |
| title_fullStr | Laboratory contamination in airway microbiome studies |
| title_full_unstemmed | Laboratory contamination in airway microbiome studies |
| title_short | Laboratory contamination in airway microbiome studies |
| title_sort | laboratory contamination in airway microbiome studies |
| topic | Microbiome Contamination Low biomass Respiratory 16S rRNA gene |
| url | http://link.springer.com/article/10.1186/s12866-019-1560-1 |
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