Skip to main content


Log in

Using RNA-seq to Assess Off-Target Effects of Antisense Oligonucleotides in Human Cell Lines

  • Original Research Article
  • Published:
Molecular Diagnosis & Therapy Aims and scope Submit manuscript



The field of antisense oligonucleotide therapeutics is rapidly growing and in addition to small molecules and therapeutic antibodies, oligonucleotide-based gene expression modifiers have been developed as fully accepted therapeutics. Antisense oligonucleotides are designed to modify gene expression of their specific target genes. However, as their effect relies on Watson–Crick base pairing, they could also bind to other unintended complementary RNAs showing sufficient sequence homology, which in turn could lead to off-target effects. It is assumed that these off-target effects depend on the degree of complementarity between the antisense oligonucleotides and off-target sequences.


Aim of this study was the investigation of the effects of antisense oligonucleotides on the expression of potential off-targets having a defined number of mismatches to the oligonucleotide sequence.


We extend recent studies by investigating the off-target profile of two 17-mer antisense oligonucleotides in two distinct human cell lines by a whole-transcriptome study using RNA sequencing.


The relatively high percentage of significantly downregulated off-target genes for which one mismatch is present corroborates the requirement for intense bioinformatic screens and stringent specificity criteria to design antisense oligonucleotides with only minimal sequence complementarity to any non-target sequence.


Avoiding suppression of off-target genes by a thorough bioinformatics screen should strongly reduce the risk for toxicities caused by antisense oligonucleotide-mediated off-target RNA suppression and finally result in safer antisense oligonucleotide-based therapeutics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others


  1. Keam SJ. Inotersen: first global approval. Drugs. 2018;78:1371–6.

    Article  CAS  Google Scholar 

  2. Paik J, Duggan S. Volanesorsen: first global approval. Drugs. 2019;79:1349–54.

    Article  CAS  Google Scholar 

  3. Li Q. Nusinersen as a therapeutic agent for spinal muscular atrophy. Yonsei Med J. 2020;61:273–83.

    Article  CAS  Google Scholar 

  4. Raal FJ, Santos RD, Blom DJ, et al. Mipomersen, an apolipoprotein B synthesis inhibitor, for lowering of LDL cholesterol concentrations in patients with homozygous familial hypercholesterolaemia: a randomised, double-blind, placebo-controlled trial. Lancet. 2010;375:998–1006.

    Article  CAS  Google Scholar 

  5. Syed YY. Eteplirsen: first global approval. Drugs. 2016;76:1699–704.

    Article  CAS  Google Scholar 

  6. Vitravene Study Group. A randomized controlled clinical trial of intravitreous fomivirsen for treatment of newly diagnosed peripheral cytomegalovirus retinitis in patients with AIDS. Am J Ophthalmol. 2002;133:467–74.

    Article  Google Scholar 

  7. Kim J, Hu C, Moufawad El Achkar C, et al. Patient-customized oligonucleotide therapy for a rare genetic disease. N Engl J Med. 2019;381:1644–52.

    Article  CAS  Google Scholar 

  8. Eckstein F. Nucleoside phosphorothioates. Annu Rev Biochem. 1985;54:367–402.

    Article  CAS  Google Scholar 

  9. Lind KE, Ferguson DM, Mohan V, Manoharan M. Structural characteristics of 2’-O-(2-methoxyethyl)-modified nucleic acids from molecular dynamics simulations. Nucleic Acids Res. 1998;26:3694–9.

    Article  CAS  Google Scholar 

  10. Frieden M, Ørum H. Locked nucleic acid holds promise in the treatment of cancer. Curr Pharm Des. 2008;14:1138–42.

    Article  CAS  Google Scholar 

  11. Benson MD, Waddington-Cruz M, Berk JL, et al. Inotersen treatment for patients with hereditary transthyretin amyloidosis. N Engl J Med. 2018;379:22–31.

    Article  CAS  Google Scholar 

  12. Lindow M, Vornlocher H-P, Riley D, et al. Assessing unintended hybridization-induced biological effects of oligonucleotides. Nat Biotechnol. 2012;30:920–3.

    Article  CAS  Google Scholar 

  13. Yoshida T, Naito Y, Yasuhara H, Sasaki K, Kawaji H, Kawai J, et al. Evaluation of off-target effects of gapmer antisense oligonucleotides using human cells. Genes Cells. 2019;24:827–35.

    Article  CAS  Google Scholar 

  14. Greenberger LM, Horak ID, Filpula D, Sapra P, Westergaard M, Frydenlund HF, et al. A RNA antagonist of hypoxia-inducible factor-1, EZN-2968, inhibits tumor cell growth. Mol Cancer Ther. 2008;7:3598–608.

    Article  CAS  Google Scholar 

  15. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    Article  CAS  Google Scholar 

  16. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417–9.

    Article  CAS  Google Scholar 

  17. Frankish A, Diekhans M, Ferreira A-M, et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 2019;47:D766–73.

    Article  CAS  Google Scholar 

  18. Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 2015;4:1521.

    Article  Google Scholar 

  19. Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, et al. Software for computing and annotating genomic ranges. PLoS Comput Biol. 2013;9:e1003118.

    Article  CAS  Google Scholar 

  20. Nowicka M, Robinson MD. DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics. FRes. 2016;5:1356.

    Google Scholar 

  21. Anders S, Reyes A, Huber W. Detecting differential usage of exons from RNA-seq data. Genome Res. 2012;22:2008–17.

    Article  CAS  Google Scholar 

  22. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  Google Scholar 

  23. Wickham H. ggplot2. 2016. Accessed 30 Nov 2020.

  24. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 2001;25:402–8.

    Article  CAS  Google Scholar 

  25. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3:RESEARCH0034.

    Article  Google Scholar 

  26. Valente V, Teixeira SA, Neder L, Okamoto OK, Oba-Shinjo SM, Marie SKN, et al. Selection of suitable housekeeping genes for expression analysis in glioblastoma using quantitative RT-PCR. BMC Mol Biol. 2009;10:17.

    Article  Google Scholar 

  27. Hagedorn PH, Hansen BR, Koch T, Lindow M. Managing the sequence-specificity of antisense oligonucleotides in drug discovery. Nucleic Acids Res. 2017;45:2262–82.

    Article  CAS  Google Scholar 

  28. Dieckmann A, Hagedorn PH, Burki Y, Brügmann C, Berrera M, Ebeling M, et al. A sensitive in vitro approach to assess the hybridization-dependent toxic potential of high affinity gapmer oligonucleotides. Mol Ther Nucleic Acids. 2018;10:45–54.

    Article  CAS  Google Scholar 

  29. Kamola PJ, Kitson JDA, Turner G, et al. In silico and in vitro evaluation of exonic and intronic off-target effects form a critical element of therapeutic ASO gapmer optimization. Nucleic Acids Res. 2015;43:8638–50.

    Article  CAS  Google Scholar 

  30. Hagedorn PH, Pontoppidan M, Bisgaard TS, et al. Identifying and avoiding off-target effects of RNase H-dependent antisense oligonucleotides in mice. Nucleic Acids Res. 2018;46:5366–80.

    Article  CAS  Google Scholar 

  31. Godoy P, Schmidt-Heck W, Natarajan K, et al. Gene networks and transcription factor motifs defining the differentiation of stem cells into hepatocyte-like cells. J Hepatol. 2015;63:934–42.

    Article  CAS  Google Scholar 

  32. Godoy P, Widera A, Schmidt-Heck W, et al. Gene network activity in cultivated primary hepatocytes is highly similar to diseased mammalian liver tissue. Arch Toxicol. 2016;90:2513–29.

    Article  CAS  Google Scholar 

  33. Sonawane AR, Platig J, Fagny M, Chen C-Y, Paulson JN, Lopes-Ramos CM, et al. Understanding tissue-specific gene regulation Cell Rep. 2017;21:1077–88.

    CAS  PubMed  Google Scholar 

  34. Shen W, De Hoyos CL, Migawa MT, et al. Chemical modification of PS-ASO therapeutics reduces cellular protein-binding and improves the therapeutic index. Nat Biotechnol. 2019;37:640–50.

    Article  CAS  Google Scholar 

  35. Watt AT, Swayze G, Swayze EE, Freier SM. Likelihood of nonspecific activity of gapmer antisense oligonucleotides is associated with relative hybridization free energy. Nucleic Acid Ther. 2020;30:215–28.

    Article  CAS  Google Scholar 

Download references


The authors thank Lisa Hinterwimmer, Monika Schell, and Stefanie Raith as well as the sequencing platform of the Centre National de Recherche en Génomique Humaine (CNRGH) for their excellent technical support.

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Sven Michel or Frank Jaschinski.

Ethics declarations


No funding was received for the conduct of this study or the preparation of this article.

Conflict of interest

Sven Michel, Ksenia Schirduan, Richard Klar, and Frank Jaschinski are or were at the time the experiments were performed employees of Secarna Pharmaceuticals GmbH & Co. KG. Yimin Shen and Jörg Tost have no conflicts of interest that are directly relevant to the content of this article.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for Publication

Not applicable.

Code Availability

R code that was used do conduct the analysis is available on request from the corresponding author.

Data availability

The sequence data analyzed during the current study are available at the NCBI Sequence Read Archive under the BioProject accession number PRJNA668062.

Author contributions

SM planned the study and experiments, analyzed the data, performed the bioinformatic analysis, and wrote the manuscript. KS planned the study and experiments and performed the experiments. YS planned the RNA-seq experiments and performed the bioinformatic analysis. RK planned and performed the qPCR experiments and analysis. JT planned the RNA-seq experiments, interpreted the data, and wrote the manuscript. FJ planned the study, interpreted the data, and wrote the manuscript.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file 1 (PDF 982 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Michel, S., Schirduan, K., Shen, Y. et al. Using RNA-seq to Assess Off-Target Effects of Antisense Oligonucleotides in Human Cell Lines. Mol Diagn Ther 25, 77–85 (2021).

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: