Tutorial: Guidelines for Single-Cell RT-qPCR

. 2021 Sep 30 ; 10 (10) : . [epub] 20210930

Jazyk angličtina Země Švýcarsko Médium electronic

Typ dokumentu časopisecké články, práce podpořená grantem, přehledy

Perzistentní odkaz   https://www.medvik.cz/link/pmid34685587

Grantová podpora
GACR 20-05770S Grantová Agentura České Republiky
GACR 19-02046S Grantová Agentura České Republiky
CZ.1.05/1.1.00/02.0109 Ministerstvo Školství, Mládeže a Tělovýchovy
RVO 86652036 Ministerstvo Školství, Mládeže a Tělovýchovy
EJP RD COFUND-EJP N° 825575 Horizon 2020

Reverse transcription quantitative PCR (RT-qPCR) has delivered significant insights in understanding the gene expression landscape. Thanks to its precision, sensitivity, flexibility, and cost effectiveness, RT-qPCR has also found utility in advanced single-cell analysis. Single-cell RT-qPCR now represents a well-established method, suitable for an efficient screening prior to single-cell RNA sequencing (scRNA-Seq) experiments, or, oppositely, for validation of hypotheses formulated from high-throughput approaches. Here, we aim to provide a comprehensive summary of the scRT-qPCR method by discussing the limitations of single-cell collection methods, describing the importance of reverse transcription, providing recommendations for the preamplification and primer design, and summarizing essential data processing steps. With the detailed protocol attached in the appendix, this tutorial provides a set of guidelines that allow any researcher to perform scRT-qPCR measurements of the highest standard.

Zobrazit více v PubMed

Swarup V., Hinz F.I., Rexach J.E., Noguchi K., Toyoshiba H., Oda A., Hirai K., Sarkar A., Seyfried N.T., Cheng C., et al. Identification of evolutionarily conserved gene networks mediating neurodegenerative dementia. Nat. Med. 2019;25:152–164. doi: 10.1038/s41591-018-0223-3. PubMed DOI PMC

Maniatis S., Äijö T., Vickovic S., Braine C., Kang K., Mollbrink A., Fagegaltier D., Andrusivová Ž., Saarenpää S., Saiz-Castro G., et al. Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science. 2019;364:89–93. doi: 10.1126/science.aav9776. PubMed DOI

Kelley K.W., Nakao-Inoue H., Molofsky A.V., Oldham M.C. Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes. Nat. Neurosci. 2018;21:1171–1184. doi: 10.1038/s41593-018-0216-z. PubMed DOI PMC

Habib N., McCabe C., Medina S., Varshavsky M., Kitsberg D., Dvir-Szternfeld R., Green G., Dionne D., Nguyen L., Marshall J.L., et al. Disease-associated astrocytes in Alzheimer’s disease and aging. Nat. Neurosci. 2020;23:701–706. doi: 10.1038/s41593-020-0624-8. PubMed DOI PMC

Mathys H., Davila-Velderrain J., Peng Z., Gao F., Mohammadi S., Young J.Z., Menon M., He L., Abdurrob F., Jiang X., et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature. 2019;570:332–337. doi: 10.1038/s41586-019-1195-2. PubMed DOI PMC

Sala Frigerio C., Wolfs L., Fattorelli N., Thrupp N., Voytyuk I., Schmidt I., Mancuso R., Chen W.T., Woodbury M.E., Srivastava G., et al. The Major Risk Factors for Alzheimer’s Disease: Age, Sex, and Genes Modulate the Microglia Response to Aβ Plaques. Cell Rep. 2019;27:1293–1306. doi: 10.1016/j.celrep.2019.03.099. PubMed DOI PMC

Liddelow S.A., Guttenplan K.A., Clarke L.E., Bennett F.C., Bohlen C.J., Schirmer L., Bennett M.L., Münch A.E., Chung W.S., Peterson T.C., et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature. 2017;541:481–487. doi: 10.1038/nature21029. PubMed DOI PMC

Guttenplan K.A., Stafford B.K., El-Danaf R.N., Adler D.I., Münch A.E., Weigel M.K., Huberman A.D., Liddelow S.A. Neurotoxic Reactive Astrocytes Drive Neuronal Death after Retinal Injury. Cell Rep. 2020;31:107776. doi: 10.1016/j.celrep.2020.107776. PubMed DOI PMC

Keren-Shaul H., Spinrad A., Weiner A., Matcovitch-Natan O., Dvir-Szternfeld R., Ulland T.K., David E., Baruch K., Lara-Astaiso D., Toth B., et al. A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease. Cell. 2017;169:1276–1290. doi: 10.1016/j.cell.2017.05.018. PubMed DOI

Willis E.F., Macdonald K.P.A., Nguyen Q.H., Rose-John S., Ruitenberg M.J., Correspondence J.V. Repopulating Microglia Promote Brain Repair in an IL-6-Dependent Manner. Cell. 2020;180:833–846. doi: 10.1016/j.cell.2020.02.013. PubMed DOI

Wheeler M.A., Clark I.C., Tjon E.C., Li Z., Zandee S.E.J., Couturier C.P., Watson B.R., Scalisi G., Alkwai S., Rothhammer V., et al. MAFG-driven astrocytes promote CNS inflammation. Nature. 2020;578:593. doi: 10.1038/s41586-020-1999-0. PubMed DOI PMC

Friedman B.A., Srinivasan K., Ayalon G., Meilandt W.J., Lin H., Huntley M.A., Cao Y., Lee S.H., Haddick P.C.G., Ngu H., et al. Diverse Brain Myeloid Expression Profiles Reveal Distinct Microglial Activation States and Aspects of Alzheimer’s Disease Not Evident in Mouse Models. Cell Rep. 2018;22:832–847. doi: 10.1016/j.celrep.2017.12.066. PubMed DOI

Andersson A., Bergenstråhle J., Asp M., Bergenstråhle L., Jurek A., Fernández Navarro J., Lundeberg J. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 2020;3:1–8. doi: 10.1038/s42003-020-01247-y. PubMed DOI PMC

Chung W., Eum H.H., Lee H.O., Lee K.M., Lee H.B., Kim K.T., Ryu H.S., Kim S., Lee J.E., Park Y.H., et al. Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat. Commun. 2017;8:1–12. doi: 10.1038/ncomms15081. PubMed DOI PMC

Milich L.M., Choi J.S., Ryan C., Cerqueira S.R., Benavides S., Yahn S.L., Tsoulfas P., Lee J.K. Single-cell analysis of the cellular heterogeneity and interactions in the injured mouse spinal cord. J. Exp. Med. 2021;218:1–25. doi: 10.1084/jem.20210040. PubMed DOI PMC

Bayraktar O.A., Bartels T., Holmqvist S., Kleshchevnikov V., Martirosyan A., Polioudakis D., Ben Haim L., Young A.M.H., Batiuk M.Y., Prakash K., et al. Astrocyte layers in the mammalian cerebral cortex revealed by a single-cell in situ transcriptomic map. Nat. Neurosci. 2020;23:500–509. doi: 10.1038/s41593-020-0602-1. PubMed DOI PMC

Ståhlberg A., Thomsen C., Ruff D., Åman P. Quantitative PCR Analysis of DNA, RNAs, and Proteins in the Same Single Cell. Clin. Chem. 2012;58:1682–1691. doi: 10.1373/clinchem.2012.191445. PubMed DOI

Bustin S.A., Mueller R. Real-time reverse transcription PCR (qRT-PCR) and its potential use in clinical diagnosis. Clin. Sci. 2005;109:365–379. doi: 10.1042/CS20050086. PubMed DOI

Kubista M., Andrade J.M., Bengtsson M., Forootan A., Jonák J., Lind K., Sindelka R., Sjöback R., Sjögreen B., Strömbom L., et al. The real-time polymerase chain reaction. Mol. Aspects Med. 2006;27:95–125. doi: 10.1016/j.mam.2005.12.007. PubMed DOI

Tichopad A., Kitchen R., Riedmaier I., Becker C., Ståhlberg A., Kubista M. Design and optimization of reverse-transcription quantitative PCR experiments. Clin. Chem. 2009;55:1816–1823. doi: 10.1373/clinchem.2009.126201. PubMed DOI

Bar T., Kubista M., Tichopad A. Validation of kinetics similarity in qPCR. Nucleic Acids Res. 2012;40:1395–1406. doi: 10.1093/nar/gkr778. PubMed DOI PMC

Ståhlberg A., Kubista M. The workflow of single-cell expression profiling using quantitative real-time PCR. Expert Rev. Mol. Diagn. 2014;14:323–331. doi: 10.1586/14737159.2014.901154. PubMed DOI PMC

Ståhlberg A., Kubista M. Technical aspects and recommendations for single-cell qPCR. Mol. Aspects Med. 2018;59:28–35. doi: 10.1016/j.mam.2017.07.004. PubMed DOI

Svec D., Tichopad A., Novosadova V., Pfaffl M.W., Kubista M. How good is a PCR efficiency estimate: Recommendations for precise and robust qPCR efficiency assessments. Biomol. Detect. Quantif. 2015;3:9–16. doi: 10.1016/j.bdq.2015.01.005. PubMed DOI PMC

Bustin S., Huggett J. qPCR primer design revisited. Biomol. Detect. Quantif. 2017;14:19–28. doi: 10.1016/j.bdq.2017.11.001. PubMed DOI PMC

Ståhlberg A., Kubista M., Åman P. Single-cell gene-expression profiling and its potential diagnostic applications. Expert Rev. Mol. Diagn. 2011;11:735–740. doi: 10.1586/erm.11.60. PubMed DOI

Ståhlberg A., Bengtsson M. Single-cell gene expression profiling using reverse transcription quantitative real-time PCR. Methods. 2010;50:282–288. doi: 10.1016/j.ymeth.2010.01.002. PubMed DOI

Ståhlberg A., Rusnakova V., Kubista M. The added value of single-cell gene expression profiling. Brief. Funct. Genom. 2013;12:81–89. doi: 10.1093/bfgp/elt001. PubMed DOI

Lafzi A., Moutinho C., Picelli S., Heyn H. Tutorial: Guidelines for the experimental design of single-cell RNA sequencing studies. Nat. Protoc. 2018;13:2742–2757. doi: 10.1038/s41596-018-0073-y. PubMed DOI

Andrews T.S., Kiselev V.Y., McCarthy D., Hemberg M. Tutorial: Guidelines for the computational analysis of single-cell RNA sequencing data. Nat. Protoc. 2021;16:1–9. doi: 10.1038/s41596-020-00409-w. PubMed DOI

van den Brink S.C., Sage F., Vértesy Á., Spanjaard B., Peterson-Maduro J., Baron C.S., Robin C., van Oudenaarden A. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods. 2017;14:935–936. doi: 10.1038/nmeth.4437. PubMed DOI

Marsh S.E., Kamath T., Walker A.J., Dissing-Olesen L., Hammond T.R., Young A.M.H., Abdulraouf A., Nadaf N., Dufort C., Murphy S., et al. Single Cell Sequencing Reveals Glial Specific Responses to Tissue Processing & Enzymatic Dissociation in Mice and Humans Single Cell Sequencing Reveals Glial Specific Responses to Tissue Processing & Enzymatic Dissociation in Mice and Humans. bioRxiv. 2020:1–13.

Hodne K., Weltzien F.-A. Single-Cell Isolation and Gene Analysis: Pitfalls and Possibilities. Int. J. Mol. Sci. 2015;16:26832–26849. doi: 10.3390/ijms161125996. PubMed DOI PMC

Abaffy P., Lettlova S., Truksa J., Kubista M., Sindelka R. Preparation of single-cell suspension from mouse breast cancer focusing on preservation of original cell state information and cell type composition. bioRxiv. 2019 doi: 10.1101/824714. DOI

O’Flanagan C.H., Campbell K.R., Zhang A.W., Kabeer F., Lim J.L.P., Biele J., Eirew P., Lai D., McPherson A., Kong E., et al. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol. 2019;20:1–13. doi: 10.1186/s13059-019-1830-0. PubMed DOI PMC

Wu Y.E., Pan L., Zuo Y., Li X., Hong W. Detecting Activated Cell Populations Using Single-Cell RNA-Seq. Neuron. 2017;96:313–329. doi: 10.1016/j.neuron.2017.09.026. PubMed DOI

Adam M., Potter A.S., Potter S.S. Psychrophilic proteases dramatically reduce single-cell RNA-seq artifacts: A molecular atlas of kidney development. Development. 2017;144:3625–3632. doi: 10.1242/dev.151142. PubMed DOI PMC

Valihrach L., Androvic P., Kubista M. Platforms for Single-Cell Collection and Analysis. Int. J. Mol. Sci. 2018;19:807. doi: 10.3390/ijms19030807. PubMed DOI PMC

Kuhn A., Kumar A., Beilina A., Dillman A., Cookson M.R., Singleton A.B. Cell population-specific expression analysis of human cerebellum. BMC Genom. 2012;13:610. doi: 10.1186/1471-2164-13-610. PubMed DOI PMC

Datta S., Malhotra L., Dickerson R., Chaffee S., Sen C.K., Roy S. Laser capture microdissection: Big data from small samples. Histol. Histopathol. 2015;30:1255–1269. PubMed PMC

Adan A., Alizada G., Kiraz Y., Baran Y., Nalbant A. Flow cytometry: Basic principles and applications. Crit. Rev. Biotechnol. 2017;37:163–176. doi: 10.3109/07388551.2015.1128876. PubMed DOI

Tan S.J., Li Q., Lim C.T. Manipulation and Isolation of Single Cells and Nuclei. Methods Cell Biol. 2010;98:79–96. PubMed

Lee L.M., Liu A.P. The application of micropipette aspiration in molecular mechanics of single cells. J. Nanotechnol. Eng. Med. 2015;5 doi: 10.1115/1.4029936. PubMed DOI PMC

Dzamba D., Valihrach L., Kubista M., Anderova M. The correlation between expression profiles measured in single cells and in traditional bulk samples. Sci. Rep. 2016;6:37022. doi: 10.1038/srep37022. PubMed DOI PMC

Svec D., Andersson D., Pekny M., Sjöback R., Kubista M., Ståhlberg A. Direct Cell Lysis for Single-Cell Gene Expression Profiling. Front. Oncol. 2013;3:274. doi: 10.3389/fonc.2013.00274. PubMed DOI PMC

Wang Y., Zheng H., Chen J., Zhong X., Wang Y., Wang Z., Wang Y. The Impact of Different Preservation Conditions and Freezing-Thawing Cycles on Quality of RNA, DNA, and Proteins in Cancer Tissue. Biopreserv. Biobank. 2015;13:335–347. doi: 10.1089/bio.2015.0029. PubMed DOI

Ji X., Wang M., Li L., Chen F., Zhang Y., Li Q., Zhou J. The Impact of Repeated Freeze-Thaw Cycles on the Quality of Biomolecules in Four Different Tissues. Biopreserv. Biobank. 2017;15:475–483. doi: 10.1089/bio.2017.0064. PubMed DOI

Marinov G.K., Williams B.A., McCue K., Schroth G.P., Gertz J., Myers R.M., Wold B.J. From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing. Genome Res. 2014;24:496–510. doi: 10.1101/gr.161034.113. PubMed DOI PMC

Kubista M., Dreyer-Lamm J., Ståhlberg A. The secrets of the cell. Mol. Aspects Med. 2018;59:1–4. doi: 10.1016/j.mam.2017.08.004. PubMed DOI

Lindén J., Ranta J., Pohjanvirta R. Bayesian modeling of reproducibility and robustness of RNA reverse transcription and quantitative real-time polymerase chain reaction. Anal. Biochem. 2012;428:81–91. doi: 10.1016/j.ab.2012.06.010. PubMed DOI

Sieber M.W., Recknagel P., Glaser F., Witte O.W., Bauer M., Claus R.A., Frahm C. Substantial performance discrepancies among commercially available kits for reverse transcription quantitative polymerase chain reaction: A systematic comparative investigator-driven approach. Anal. Biochem. 2010;401:303–311. doi: 10.1016/j.ab.2010.03.007. PubMed DOI

Bustin S., Dhillon H.S., Kirvell S., Greenwood C., Parker M., Shipley G.L., Nolan T. Variability of the reverse transcription step: Practical implications. Clin. Chem. 2015;61:202–212. doi: 10.1373/clinchem.2014.230615. PubMed DOI

Ståhlberg A., Håkansson J., Xian X., Semb H., Kubista M. Properties of the Reverse Transcription Reaction in mRNA Quantification. Clin. Chem. 2004;50:509–515. doi: 10.1373/clinchem.2003.026161. PubMed DOI

Bengtsson M., Hemberg M., Rorsman P., Ståhlberg A. Quantification of mRNA in single cells and modelling of RT-qPCR induced noise. BMC Mol. Biol. 2008;9:63. doi: 10.1186/1471-2199-9-63. PubMed DOI PMC

Schwaber J., Andersen S., Nielsen L. Shedding light: The importance of reverse transcription efficiency standards in data interpretation. Biomol. Detect. Quantif. 2019;17:100077. doi: 10.1016/j.bdq.2018.12.002. PubMed DOI PMC

Zucha D., Androvic P., Kubista M., Valihrach L. Performance Comparison of Reverse Transcriptases for Single-Cell Studies. Clin. Chem. 2020;66:217–228. doi: 10.1373/clinchem.2019.307835. PubMed DOI

Ståhlberg A. Comparison of Reverse Transcriptases in Gene Expression Analysis. Clin. Chem. 2004;50:1678–1680. doi: 10.1373/clinchem.2004.035469. PubMed DOI

Miranda J.A., Steward G.F. Variables influencing the efficiency and interpretation of reverse transcription quantitative PCR (RT-qPCR): An empirical study using Bacteriophage MS2. J. Virol. Methods. 2017;241:1–10. doi: 10.1016/j.jviromet.2016.12.002. PubMed DOI

Nardon E., Donada M., Bonin S., Dotti I., Stanta G. Higher random oligo concentration improves reverse transcription yield of cDNA from bioptic tissues and quantitative RT-PCR reliability. Exp. Mol. Pathol. 2009;87:146–151. doi: 10.1016/j.yexmp.2009.07.005. PubMed DOI

Levesque-Sergerie J.-P., Duquette M., Thibault C., Delbecchi L., Bissonnette N. Detection limits of several commercial reverse transcriptase enzymes: Impact on the low- and high-abundance transcript levels assessed by quantitative RT-PCR. BMC Mol. Biol. 2007;8:93. doi: 10.1186/1471-2199-8-93. PubMed DOI PMC

Bagnoli J.W., Ziegenhain C., Janjic A., Wange L.E., Vieth B., Parekh S., Geuder J., Hellmann I., Enard W. Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq. Nat. Commun. 2018;9:2937. doi: 10.1038/s41467-018-05347-6. PubMed DOI PMC

Skirgaila R., Pudzaitis V., Paliksa S., Vaitkevicius M., Janulaitis A. Compartmentalization of destabilized enzyme-mRNA-ribosome complexes generated by ribosome display: A novel tool for the directed evolution of enzymes. Protein Eng. Des. Sel. 2013;26:453–461. doi: 10.1093/protein/gzt017. PubMed DOI

Mohr S., Ghanem E., Smith W., Sheeter D., Qin Y., King O., Polioudakis D., Iyer V.R., Hunicke-Smith S., Swamy S., et al. Thermostable group II intron reverse transcriptase fusion proteins and their use in cDNA synthesis and next-generation RNA sequencing. Rna. 2013;19:958–970. doi: 10.1261/rna.039743.113. PubMed DOI PMC

Arezi B., Hogrefe H. Novel mutations in Moloney Murine Leukemia Virus reverse transcriptase increase thermostability through tighter binding to template-primer. Nucleic Acids Res. 2009;37:473–481. doi: 10.1093/nar/gkn952. PubMed DOI PMC

Nolan T., Hands R.E., Bustin S.A. Quantification of mRNA using real-time RT-PCR. Nat. Protoc. 2006;1:1559–1582. doi: 10.1038/nprot.2006.236. PubMed DOI

Álvarez M., Menéndez-Arias L. Temperature effects on the fidelity of a thermostable HIV-1 reverse transcriptase. FEBS J. 2014;281:342–351. doi: 10.1111/febs.12605. PubMed DOI

Forootan A., Sjöback R., Björkman J., Sjögreen B., Linz L., Kubista M. Methods to determine limit of detection and limit of quantification in quantitative real-time PCR (qPCR) Biomol. Detect. Quantif. 2017;12:1–6. doi: 10.1016/j.bdq.2017.04.001. PubMed DOI PMC

Svensson V., Natarajan K.N., Ly L.-H., Miragaia R.J., Labalette C., Macaulay I.C., Cvejic A., Teichmann S.A. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods. 2017;14:381–387. doi: 10.1038/nmeth.4220. PubMed DOI PMC

Baranauskas A., Paliksa S., Alzbutas G., Vaitkevicius M., Lubiene J., Letukiene V., Burinskas S., Sasnauskas G., Skirgaila R. Generation and characterization of new highly thermostable and processive M-MuLV reverse transcriptase variants. Protein Eng. Des. Sel. 2012;25:657–668. doi: 10.1093/protein/gzs034. PubMed DOI

Ziegenhain C., Vieth B., Parekh S., Reinius B., Guillaumet-Adkins A., Smets M., Leonhardt H., Heyn H., Hellmann I., Enard W. Comparative Analysis of Single-Cell RNA Sequencing Methods. Mol. Cell. 2017;65:631–643. doi: 10.1016/j.molcel.2017.01.023. PubMed DOI

Bustin S.A., Nolan T. Pitfalls of quantitative real- time reverse-transcription polymerase chain reaction. J. Biomol. Tech. 2004;15:155–166. PubMed PMC

Picelli S., Faridani O.R., Björklund A.K., Winberg G., Sagasser S., Sandberg R. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 2014;9:171–181. doi: 10.1038/nprot.2014.006. PubMed DOI

Attwater J., Wochner A., Pinheiro V.B., Coulson A., Holliger P. Ice as a protocellular medium for RNA replication. Nat. Commun. 2010;1:1–9. doi: 10.1038/ncomms1076. PubMed DOI

Aicher T.P., Carroll S., Raddi G., Gierahn T., Wadsworth M.H., Hughes T.K., Love C., Shalek A.K. Seq-Well: A sample-efficient, portable picowell platform for massively parallel single-cell RNA sequencing. Methods Mol. Biol. 2019;1979:111–132. PubMed PMC

Hashimshony T., Senderovich N., Avital G., Klochendler A., de Leeuw Y., Anavy L., Gennert D., Li S., Livak K.J., Rozenblatt-Rosen O., et al. CEL-Seq2: Sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 2016;17:1–7. doi: 10.1186/s13059-016-0938-8. PubMed DOI PMC

Nolan T., Hands R.E., Ogunkolade W., Bustin S.A. SPUD: A quantitative PCR assay for the detection of inhibitors in nucleic acid preparations. Anal. Biochem. 2006;351:308–310. doi: 10.1016/j.ab.2006.01.051. PubMed DOI

External RNA Controls Consortium Proposed methods for testing and selecting the ERCC external RNA controls. BMC Genomics. 2005;6:150. doi: 10.1186/1471-2164-6-150. PubMed DOI PMC

Livak K.J., Wills Q.F., Tipping A.J., Datta K., Mittal R., Goldson A.J., Sexton D.W., Holmes C.C. Methods for qPCR gene expression profiling applied to 1440 lymphoblastoid single cells. Methods. 2013;59:71–79. doi: 10.1016/j.ymeth.2012.10.004. PubMed DOI PMC

Andersson D., Akrap N., Svec D., Godfrey T.E., Kubista M., Landberg G., Ståhlberg A. Properties of targeted preamplification in DNA and cDNA quantification. Expert Rev. Mol. Diagn. 2015;15:1085–1100. doi: 10.1586/14737159.2015.1057124. PubMed DOI PMC

Kroneis T., Jonasson E., Andersson D., Dolatabadi S., Ståhlberg A. Global preamplification simplifies targeted mRNA quantification. Sci. Rep. 2017;7:45219. doi: 10.1038/srep45219. PubMed DOI PMC

Korenková V., Scott J., Novosadová V., Jindřichová M., Langerová L., Švec D., Šídová M., Sjöback R. Pre-amplification in the context of high-throughput qPCR gene expression experiment. BMC Mol. Biol. 2015;16:5. doi: 10.1186/s12867-015-0033-9. PubMed DOI PMC

Laurell H., Iacovoni J.S., Abot A., Svec D., Maoret J.J., Arnal J.F., Kubista M. Correction of RT-qPCR data for genomic DNA-derived signals with ValidPrime. Nucleic Acids Res. 2012;40:e51. doi: 10.1093/nar/gkr1259. PubMed DOI PMC

Okello J.B.A., Rodriguez L., Poinar D., Bos K., Okwi A.L., Bimenya G.S., Sewankambo N.K., Henry K.R., Kuch M., Poinar H.N. Quantitative assessment of the sensitivity of various commercial reverse transcriptases based on armored HIV RNA. PLoS ONE. 2010;5:e13931. doi: 10.1371/journal.pone.0013931. PubMed DOI PMC

Suslov O., Steindler D.A. PCR inhibition by reverse transcriptase leads to an overestimation of amplification efficiency. Nucleic Acids Res. 2005;33:1–12. doi: 10.1093/nar/gni176. PubMed DOI PMC

Ye J., Coulouris G., Zaretskaya I., Cutcutache I., Rozen S., Madden T.L. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinform. 2012;13:134. doi: 10.1186/1471-2105-13-134. PubMed DOI PMC

Burns M., Valdivia H. Modelling the limit of detection in real-time quantitative PCR. Eur. Food Res. Technol. 2008;226:1513–1524. doi: 10.1007/s00217-007-0683-z. DOI

Ståhlberg A., Rusnakova V., Forootan A., Anderova M., Kubista M. RT-qPCR work-flow for single-cell data analysis. Methods. 2013;59:80–88. doi: 10.1016/j.ymeth.2012.09.007. PubMed DOI

Bergkvist A., Rusnakova V., Sindelka R., Garda J.M.A., Sjögreen B., Lindh D., Forootan A., Kubista M. Gene expression profiling—Clusters of possibilities. Methods. 2010;50:323–335. doi: 10.1016/j.ymeth.2010.01.009. PubMed DOI

Riedmaier I., Pfaffl M.W. Transcriptional biomarkers—High throughput screening, quantitative verification, and bioinformatical validation methods. Methods. 2013;59:3–9. doi: 10.1016/j.ymeth.2012.08.012. PubMed DOI

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...