User:Vivek Rai/Spike-in controls
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Spike-in controls or spike-ins are known quantities of molecules—such as oligonucleotide sequences (RNA, DNA), proteins, or metabolites—added to a biological sample for more accurate quantitative estimation of the molecule of interest across samples and batches. It is particularly used in high-throughput sequencing assays. These controls act as an internal reference to monitor and normalize technical and biological biases introduced during sample processing such as library preparation, handling, and measurement.[1]
Carefully designed spike-ins can adjust for specific technical biases and enable accurate estimation of the endogenous molecules of interest, resulting in improved data quality and standardization across different samples or experiments. Spike-ins can be synthetic or exogenous material (not originally part of the sample). In sequencing-based assays, exogenous material is typically derived from the genome of a different species such as Drosophila melanogaster or Arabidopsis thaliana.[2]
Design
[edit]Spike-ins are subjected to the same experimental steps and and potential biases as the native molecules within a sample after they have been added. They are added early in the experimental workflow, often during or immediately after sample lysis or extraction.[3] As such, the suitability of spike-ins, their design, and subsequently analysis should allow accounting for as many sources of experimental variation as possible. Ideally, the spike-ins closely resemble the input material containing epitopes of interest but allow clear differentiation from the native molecules. Since the initial amount of each spike-in molecule is known, its measured quantity at the end of the experiment reflects the cumulative effects of technical factors, such as extraction efficiency, enzymatic reaction efficiencies (e.g., reverse transcription, ligation, amplification), sample loss, and measurement sensitivity.
In sequencing assays, spike-ins can further be combined with unique molecular identifiers to increase sensitivity and specificity.
Methods
[edit]The information obtained from spike-ins is typically leveraged after initial bioinformatics analyses have been carried out — with the final output of such analyses being absolute counts of different spike-in controls for each library. Various spike-in normalization or calibration methods then utilize this information as baseline to adjust the primary signal of interest.
Spike-in normalization
[edit]The choice of a normalization method can significantly influence the post-normalization conclusions drawn from an experiment.[4]
Applications
[edit]Several types of spike-in controls are used depending on the application:
- RNA spike-ins: Commonly used in gene expression studies like RNA-Seq and Microarray analysis.[5] Synthetic RNA molecules of defined sequences and lengths are added, often in predefined mixtures covering a wide concentration range. A well-known example is the set developed by the External RNA Controls Consortium (ERCC).[1][3]
- DNA spike-ins: Used in genomics applications such as ChIP-Seq (Chromatin Immunoprecipitation Sequencing),[6] DNA methylation analysis (e.g., bisulfite sequencing),[6] or other genomic assays.[7] These can be synthetic DNA fragments or genomic DNA from an unrelated species (e.g., adding fly DNA to human samples for ChIP-Seq).[8]
- Protein/peptide spike-ins: Used in proteomics, often involving stable isotope-labeled synthetic peptides (e.g., AQUA peptides), or purified proteins added in known amounts for quantification and normalization.[9]
- Metabolite spike-ins: Used in metabolomics, typically involving stable isotope-labeled versions of endogenous metabolites or non-endogenous small molecules added for normalization and quantification.[10]
See also
[edit]References
[edit]- ^ a b Jiang, L; Schlesinger, F; Davis, CA; Zhang, Y; Li, R; Salit, M; Gingeras, TR; Oliver, B (September 2011). "Synthetic spike-in standards for RNA-seq experiments". Genome Research. 21 (9): 1543–1551. doi:10.1101/gr.121095.111. PMC 3166838. PMID 21816910.
- ^ Shen, Shu Yi; Burgener, Justin M.; Bratman, Scott V.; De Carvalho, Daniel D. (2019-08-30). "Preparation of cfMeDIP-seq libraries for methylome profiling of plasma cell-free DNA". Nature Protocols. 14 (10): 2749–2780. doi:10.1038/s41596-019-0202-2. ISSN 1754-2189.
- ^ a b Baker, S C; Petrov, S R; Riley, D R; Dafforn, A; Salit, M L (November 2005). "The External RNA Controls Consortium: a progress report". Nature Methods. 2 (10): 731–734. doi:10.1038/nmeth1005-731. PMID 16200073.
- ^ Patel, Lauren A.; Cao, Yuwei; Mendenhall, Eric M.; Benner, Christopher; Goren, Alon (2024-09). "The Wild West of spike-in normalization". Nature Biotechnology. 42 (9): 1343–1349. doi:10.1038/s41587-024-02377-y. ISSN 1546-1696.
{{cite journal}}: Check date values in:|date=(help) - ^ Jiang, Lichun; Schlesinger, Felix; Davis, Carrie A.; Zhang, Yu; Li, Renhua; Salit, Marc; Gingeras, Thomas R.; Oliver, Brian (2011-08-04). "Synthetic spike-in standards for RNA-seq experiments". Genome Research. 21 (9): 1543–1551. doi:10.1101/gr.121095.111. ISSN 1088-9051.
- ^ a b Orlando, David A; Chen, Mei Wei; Brown, Victoria E; Solanki, Snehakumari; Choi, Yoon J; Olson, Eric R.; Fritz, Christian C.; Bradner, James E.; Guenther, Matthew G. (2014). "Quantitative ChIP-Seq Normalization Reveals Global Modulation of the Epigenome". Cell Reports. 9 (3): 1163–1170. doi:10.1016/j.celrep.2014.10.018. ISSN 2211-1247.
{{cite journal}}: no-break space character in|first2=at position 4 (help); no-break space character in|first3=at position 9 (help); no-break space character in|first5=at position 5 (help); no-break space character in|first6=at position 5 (help); no-break space character in|first7=at position 10 (help); no-break space character in|first8=at position 6 (help); no-break space character in|first9=at position 8 (help); no-break space character in|first=at position 6 (help) - ^ Chen, Kaifu; Hu, Zheng; Xia, Zheng; Zhao, Dongyu; Li, Wei; Tyler, Jessica K. (2016-03-01). "The Overlooked Fact: Fundamental Need for Spike-In Control for Virtually All Genome-Wide Analyses". Molecular and Cellular Biology. 36 (5): 662–667. doi:10.1128/mcb.00970-14. ISSN 1098-5549.
- ^ Egan, Brent; Yuan, Chao-cheng; Craske, Michael; Labhart, Paul; Miller, Christopher; Papin, Candice; Johnson, Dane; Schrader, Marc (26 March 2012). "Utilizing Spike-in standards for normalization and quality control of ChIP-seq experiments". BMC Bioinformatics. 13: 54. doi:10.1186/1471-2105-13-54. PMC 3359226. PMID 22448910.
{{cite journal}}: CS1 maint: unflagged free DOI (link) - ^ Kettenbach, Arminja N.; Rush, John; Gerber, Scott A. (2011-02). "Absolute quantification of protein and post-translational modification abundance with stable isotope-labeled synthetic peptides". Nature Protocols. 6 (2): 175–186. doi:10.1038/nprot.2010.196. ISSN 1750-2799. PMC 3736726. PMID 21293459.
{{cite journal}}: Check date values in:|date=(help) - ^ Chokkathukalam, Achuthanunni; Kim, Dong-Hyun; Barrett, Michael P.; Breitling, Rainer; Creek, Darren J. (2014-02). "Stable isotope-labeling studies in metabolomics: new insights into structure and dynamics of metabolic networks". Bioanalysis. 6 (4): 511–524. doi:10.4155/bio.13.348. ISSN 1757-6199. PMC 4048731. PMID 24568354.
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