Abstract
Small noncoding RNAs (sncRNAs) are a diverse group of RNAs including small interfering RNAs, microRNAs, PIWI-interacting RNAs and RNAs derived from structured RNAs such as transfer RNAs, ribosomal RNAs and others. These sncRNAs have varied termini and RNA modifications, which can interfere with adaptor ligation and reverse transcription during cDNA library construction, hindering detection of many types of sncRNA by standard small RNA sequencing methods. To address this limitation, PANDORA sequencing introduces a refined methodology. The procedure includes sequential enzymatic treatments of size-selected RNAs with T4PNK and AlkB, which effectively circumvent the challenges presented by the ligation-blocking termini and reverse transcription-blocking RNA modifications, followed by tailored small RNA library construction protocols and deep sequencing. The obtained datasets are analyzed with the SPORTS pipeline, which can comprehensively analyze various types of sncRNA beyond the traditionally studied classes, to include those derived from various parental RNAs (for example, from transfer RNA and ribosomal RNA), as well as output the locations on the parental RNA from which these sncRNAs are derived. The entire protocol takes ~7 d, depending on the sample size and sequencing turnaround time. PANDORA sequencing provides a transformative tool to further our understanding of the expanding small RNA universe and to explore the uncharted functions of sncRNAs.
Key points
PANDORA sequencing employs sequential enzymatic treatment steps to modify the termini of small noncoding RNAs and remove reverse-transcription blocking methylation for optimized in depth genome-wide profiling of these diverse RNA species that extends beyond well-studied classes of small RNAs.
Complemented by a custom analytical pipeline (SPORTS), the protocol characterizes the features and relative proportions of different sncRNAs and maps them to the parental RNAs they derive from.
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Fig. 1: Overview of the PANDORA-seq protocol.
Fig. 2: SDS–PAGE analysis and quantification of purified AlkB protein.
Fig. 3: Demethylation activity of purified AlkB protein.
Fig. 4: RNA size selection from mouse sperm and liver total RNA via PAGE gel electrophoresis.
Fig. 5: Evaluation of mouse sperm and liver PANDORA-seq DNA libraries.
Fig. 6: Comparison of the sequence length distribution of different types of sncRNA between PANDORA-seq and traditional small RNA-seq.
Data availability
The mouse liver and mature sperm small RNA-seq datasets can be accessed through the Gene Expression Omnibus under the accession code GSE144666 (ref. 6). Source data are provided with this paper.
Code availability
The sncRNA annotation pipeline SPORTS1.1 is available via GitHub at https://github.com/junchaoshi/sports1.1.
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Acknowledgements
The authors thank T. Zhou for discussions. This work was supported by National Key Research and Development Program of China (grant no. 2019YFA0802600 to Ying Zhang and Yunfang Zhang), Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA0460302 to J.S.), National Natural Science Foundation of China (92357306, 82122027 and 32171110 to Ying Zhang, 32370596 to J.S., 82371727 and 82022029 to Yunfang Zhang), Science and Technology Commission of Shanghai Municipality (23JC1403802 to Yunfang Zhang), Fundamental Research Funds for the Central Universities to Yunfang Zhang.
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Authors and Affiliations
China National Center for Bioinformation and Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
Junchao Shi & Liwen Zhang
Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, China
Yunfang Zhang & Yun Li
Sycamore Research Institute of Life Sciences, Shanghai, China
Yunfang Zhang
Molecular Medicine Program, University of Utah School of Medicine, Salt Lake City, UT, USA
Xudong Zhang & Qi Chen
Division of Urology, Department of Surgery, University of Utah School of Medicine, Salt Lake City, UT, USA
Xudong Zhang & Qi Chen
Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT, USA
Xudong Zhang & Qi Chen
Pudong Medical Center, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, China
Menghong Yan
The Key Laboratory of Cell Proliferation and Regulation Biology, Ministry of Education, Beijing Key Laboratory of Genetic Engineering Drug and Biotechnology, College of Life Sciences, Beijing Normal University, Beijing, China
Ying Zhang
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Junchao Shi
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Contributions
J.S. and Yunfang Zhang developed and optimized the experimental procedures. L.Z. and Y.L. collected mouse samples under the supervision of J.S. and Yunfang Zhang. Y.L. generated the AlkB enzyme and validated the enzyme activity with help from Yunfang Zhang and M.Y. M.Y. and Y.L. performed the LC–MS/MS RNA modification analyses with the help from L.Z. and Ying Zhang. J.S. developed the analysis pipeline and performed data analysis with the help of L.Z. Y.L., L.Z. and X.Z. contributed to the interpretation and discussion of data. J.S., Yunfang Zhang, Q.C. and Ying Zhang wrote the main manuscript and integrated input from all authors.
Corresponding authors
Correspondence to Junchao Shi, Yunfang Zhang or Ying Zhang.
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Key references
Shi, J. et al. Nat. Cell Biol. 23, 424–436 (2021): https://doi.org/10.1038/s41556-021-00652-7
Shi, J. et al. Genom. Proteom. Bioinform. 16, 144–151 (2018): https://doi.org/10.1016/j.gpb.2018.04.004
Shi, J. et al. Nat. Cell Biol. 24, 415–423 (2022): https://doi.org/10.1038/s41556-022-00880-5
Extended data
Extended Data Fig. 1 Demethylation activity of AlkB-WT and AlkB-D135S mutant proteins.
Relative RNA modification levels (m1A, m3C, m1G, and m22G) in total RNAs from mouse liver, following treatments with no AlkB enzyme (-AlkB), wild-type AlkB (AlkB-WT), the AlkB-D135S mutant, and a 1:1 combination of AlkB-WT and the AlkB-D135S mutant, as quantified using LC–MS/MS. Data are presented as RNA modification levels relative to the -AlkB group, with n = 3 or 4 biologically independent samples per group, shown as mean ± s.e.m. Statistical analysis was performed using Dunnett’s multiple comparison test. ****P < 0.0001. n.s., not significant.
Source data
Extended Data Table 1 Concentration gradients of nucleoside standards A, C, G, m1A, m3C, m1G, and m22G
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Supplementary Tables
Supplementary Tables 1 and 2.
Source data
Source Data Fig. 2
Unprocessed gels.
Source Data Fig. 3
Statistical source data.
Source Data Fig. 4
Unprocessed gels.
Source Data Fig. 5
Unprocessed gels.
Source Data Extended Data Fig. 1
Statistical source data.
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Shi, J., Zhang, Y., Li, Y. et al. Optimized identification and characterization of small RNAs with PANDORA-seq. Nat Protoc (2025). https://doi.org/10.1038/s41596-025-01158-4
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Received:12 June 2024
Accepted:11 February 2025
Published:03 April 2025
DOI:https://doi.org/10.1038/s41596-025-01158-4
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