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PANDORA-seq expands the repertoire of regulatory small RNAs by overcoming RNA modifications

Nature Cell Biology volume 23, pages 424–436 (2021)Cite this article

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An Author Correction to this article was published on 29 April 2021

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Abstract

Although high-throughput RNA sequencing (RNA-seq) has greatly advanced small non-coding RNA (sncRNA) discovery, the currently widely used complementary DNA library construction protocol generates biased sequencing results. This is partially due to RNA modifications that interfere with adapter ligation and reverse transcription processes, which prevent the detection of sncRNAs bearing these modifications. Here, we present PANDORA-seq (panoramic RNA display by overcoming RNA modification aborted sequencing), employing a combinatorial enzymatic treatment to remove key RNA modifications that block adapter ligation and reverse transcription. PANDORA-seq identified abundant modified sncRNAs—mostly transfer RNA-derived small RNAs (tsRNAs) and ribosomal RNA-derived small RNAs (rsRNAs)—that were previously undetected, exhibiting tissue-specific expression across mouse brain, liver, spleen and sperm, as well as cell-specific expression across embryonic stem cells (ESCs) and HeLa cells. Using PANDORA-seq, we revealed unprecedented landscapes of microRNA, tsRNA and rsRNA dynamics during the generation of induced pluripotent stem cells. Importantly, tsRNAs and rsRNAs that are downregulated during somatic cell reprogramming impact cellular translation in ESCs, suggesting a role in lineage differentiation.

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Fig. 1: Schematic overview, validation of AlkB and T4PNK enzyme activity, and protocol optimization of PANDORA-seq.
Fig. 2: Read summaries and length distributions of different sncRNA categories under traditional RNA-seq, AlkB-facilitated RNA-seq, T4PNK-facilitated RNA-seq and PANDORA-seq.
Fig. 3: Dissecting the effects of AlkB, T4PNK and PANDORA-seq on different sncRNA populations in ESCs.
Fig. 4: Tissue- and cell type-specific expression of tsRNAs and rsRNAs in mice and humans.
Fig. 5: PANDORA-seq reveals that tsRNAs and rsRNAs are dynamically regulated during MEF reprogramming to iPSCs (day 0) to intermediate (day 3) and iPSC stages.
Fig. 6: Transfection of tsRNA or rsRNA impacts mESC lineage differentiation and cell translation.

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Data availability

RNA-seq datasets have been deposited in the Gene Expression Omnibus under the accession code GSE144666. LC-MS/MS data have been deposited in Figshare (https://figshare.com/articles/dataset/_/14033003). All other data supporting the findings of this study are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

The sncRNA annotation pipeline SPORTS1.1 is available from GitHub (https://github.com/junchaoshi/sports1.1). The scripts used for data processing and statistical analysis were written in Perl or R and are available upon reasonable request.

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Acknowledgements

We thank T. Lowe at the University of California, Santa Cruz for early discussion on the project, and Z. Li from the Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences for assistance with operating the mass spectrometer. This work is in part supported by MOST (2019YFA0802600 to Ying Zhang (Chinese Academy of Sciences) and Yunfang Zhang; 2018YFC1004500 to Ying Zhang (Chinese Academy of Sciences) and M.Y.), startup funds from the University of California, Riverside (to Q.C. and S.C.) and the NIH (R01HD092431 to Q.C.; R01ES032024 to Q.C. and T.Z.; P50HD098593 to T.Z. and Q.C.; R35GM128854 to L. Zhao). This work includes data generated at the University of California, San Diego IGM Genomics Center funded by the NIH (P30DK063491, P30CA023100 and P30DK120515). Q.Z. is funded by the NSFC (31630037). Ying Zhang (University of California, Riverside) is funded by a State Scholarships Fund (201908500039). Yunfang Zhang is funded by the NSFC (82022029) and the Natural Science Foundation of Chongqing (cstc2019jcyjjqX0010). M.Y. is funded by the NSFC (31670830) and is a fellow of the Innovative Research Team of High-Level Local Universities in Shanghai. M.S. is funded by an Advanced EMBO fellowship. K.M. is funded by a BBSRC scholarship. Work in the laboratory of M.Z.-G. is funded by the Wellcome Trust (207415/Z/17/Z), ERC (669198) and Open Philanthropy. R.F. is supported by UC Riverside’s Eugene Cota-Robles Fellowship.

Author information

Author notes

  1. Dongmei Tan

    Present address: Laboratory Animal Center, Chongqing Medical University, Chongqing, China

  2. Ying Zhang

    Present address: Department of Biochemistry and Molecular Biology, School of Basic Medicine, Chongqing Medical University, Chongqing, China

  3. Ying Zhang

    Present address: The Key Laboratory of Cell Proliferation and Regulation Biology, Ministry of Education, College of Life Sciences, Beijing Normal University, Beijing, China

  4. These authors contributed equally: Junchao Shi, Yunfang Zhang, Dongmei Tan, Xudong Zhang, Menghong Yan, Ying Zhang, Reuben Franklin.

Authors and Affiliations

  1. Division of Biomedical Sciences, School of Medicine, University of California, Riverside, Riverside, CA, USA

    Junchao Shi, Xudong Zhang, Ying Zhang, Shichao Liu, Changcheng Zhou & Qi Chen

  2. Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV, USA

    Yunfang Zhang, Dongmei Tan, Menghong Yan & Tong Zhou

  3. Pudong Medical Center, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, China

    Menghong Yan

  4. Department of Biochemistry, Stem Cell Center, University of California, Riverside, Riverside, CA, USA

    Reuben Franklin, Jernej Murn & Sihem Cheloufi

  5. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK

    Marta Shahbazi, Kirsty Mackinlay & Magdalena Zernicka-Goetz

  6. MRC Laboratory of Molecular Biology, Cambridge, UK

    Marta Shahbazi

  7. Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, USA

    Bernhard Kuhle, Xiang-lei Yang & Paul Schimmel

  8. Division of Urology, Department of Surgery, University of Utah School of Medicine, Salt Lake City, UT, USA

    Emma R. James, Jingtao Guo & Douglas T. Carrell

  9. State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Liwen Zhang, Yongcun Qu & Ying Zhang

  10. CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS Center for Excellence in Molecular Cell Sciences, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China

    Qiwei Zhai

  11. Department of Chemistry, University of California, Riverside, Riverside, CA, USA

    Wenxin Zhao, Linlin Zhao & Yinsheng Wang

  12. Department of Molecular, Cell and Systems Biology, University of California, Riverside, Riverside, CA, USA

    Weifeng Gu

  13. Howard Hughes Medical Institute, Department of Oncological Sciences and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT, USA

    Jingtao Guo & Bradley R. Cairns

  14. Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA

    Xuemei Chen

  15. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Magdalena Zernicka-Goetz & Ying Zhang

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  1. Junchao Shi
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Q.C., T.Z. and J.S. designed the project. Yunfang Zhang, D.T. and J.S. developed and optimized the enzymatic treatment protocol for PANDORA-seq. J.S., T.Z., Ying Zhang (Chinese Academy of Sciences) and Q.C. designed and developed the scope of data analyses. S.C., J.M. and R.F. generated iPSCs from MEFs and contributed to related analyses. X.Z. and R.F. performed the functional assays of mESCs under the supervision of S.C. and Q.C. J.S. and T.Z. developed the computational tools and analysed all of the datasets with input from Ying Zhang (Chinese Academy of Sciences) and Q.C. X.Z. and Ying Zhang (University of California, Riverside) developed and performed northern blot analyses for tissues/cells with help from D.T. and S.L. Yunfang Zhang tested and validated T4PNK’s effect in improving adapter ligation. M.Y. and X.Z. contributed to the LC-MS/MS RNA modification analyses with the help from Y.W. M.Y. designed and generated the AlkB plasmid and generated the AlkB enzyme with help from W.Z., Q.Z. and L. Zhao. L. Zhang and Y.Q. collected mature sperm samples under the supervision of Ying Zhang (Chinese Academy of Sciences). M.S., K.M. and M.Z.-G. performed experiments to contribute mESCs, primed hESCs and naive hESCs for analyses. B.R.C. contributed to data interpretation in regard to piRNAs and rsRNAs and the Discussion section, with input from D.T.C., J.G. and E.R.J. X.C. contributed to data interpretation in regard to miRNA and miRBase. P.S., X.-l.Y. and B.K. contributed to data interpretation of mitochondrial tsRNAs and discussed the evolutionary aspects. L. Zhao, C.Z., W.G., D.T.C., J.G. and E.R.J. contributed to the interpretation and discussion of data. Q.C. T.Z., Ying Zhang (Chinese Academy of Sciences) and J.S. wrote the main manuscript and integrated input from all authors.

Corresponding authors

Correspondence to Sihem Cheloufi, Ying Zhang, Tong Zhou or Qi Chen.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature Cell Biology thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Reads summary and length distributions of different sncRNA category under Traditional RNA-seq, AlkB-facilitated RNA-seq, T4PNK-facilitated RNA-seq, and PANDORA-seq.

Showing Reads summary and length distributions of different sncRNA category in six tissue/cell types that are not shown in Fig. 3 because of space limitation. (a-c) Cells during mouse somatic cell reprogramming to iPSC: (a) MEFs (day 0), (b) intermediates (day 3), (c) iPSCs; (d) mouse spleen, (e) primed human embryonic stem cells (hESCs-primed), and (f) naïve human embryonic stem cells (hESCs-naïve) (g-l) the relative tsRNA/miRNA ratio under different protocols. for g,h,I,k, mean ± SEM, n=3 biologically independent samples in each bar; for j,l, n=2 biologically independent samples in each bar; different letters above bars indicate statistical difference, P < 0.05; same letters indicate P ≥ 0.05 (two-sided, one-way ANOVA, uncorrected Fisher’s LSD test). Statistical source data and the precise P values are provided in Source Data Extended Data Fig. 1.

Source data

Extended Data Fig. 2 Evaluation of Northern blot probe efficiency on synthesized targets (that is, rsRNA-28S-1, 5’tsRNAGlu, let-7i, mir-122, mir-21).

The Northern blot probes used for each target are the same as used in main Fig. 2g-i. a, each synthetic sncRNAs are individually loaded on PAGE followed by Northern blots analyses. b, the five synthetic sncRNAs were mixed together with the amount tested in (a) and then equally separated and loaded on PAGE followed by Northern blots analyses. The relative efficiency of each NB probe can be shown: the probe efficiency between let-7i, tsRNAGlu and rsRNA-28 are similar; the probe for mir-122 is highest, while the probe for mir-21 has the lowest efficiency. Similar results were obtained in 3 independent experiments. The unprocessed blots are provided in Source Data Extended Data Fig. 2.

Source data

Extended Data Fig. 3 Annotation of mouse piRNA in non-germ cell tissue/cell types is not stable when 1–3 mismatches are allowed.

When 1–3 mismatches are allowed for sncRNAs matching, the piRNA annotation rate (but not other sncRNAs types) show significant decrease in mouse tissue/cell types (a) mouse brain, (b) mouse liver, (c) mouse spleen, (d) mouse embryonic stem cells, (e) mouse mature sperm, (f) mouse mature sperm heads, (g) mouse MEFs (day 0), (h) mouse intermediate cells (day 3), (i) mouse iPSCs. Very few piRNAs were annotated for human cell lines (j) human HeLa cells, (k) human hESCs-primed, and (l) human hESCs-naïve. These data suggest the annotated piRNAs in non-germ cell tissue/cell types could be due to database quality issue and their true identity awaits to be verified.

Extended Data Fig. 4 Scattered plot comparison of profile changes in tsRNAs and rsRNAs compared to miRNAs under different treatment protocol.

Scattered plot comparison of profile changes in tsRNAs (red dots) and rsRNAs (blue dots) compared to miRNAs (gray dots) under AlkB vs traditional, T4PNK vs traditional and PANDORA-seq vs traditional in (a) mouse brain, (b) mouse liver, (c) mouse spleen, (d) mouse mature sperm, (e) mouse MEFs (day 0), (f) mouse intermediate cells (day 3), (g) mouse iPSCs, (h) human HeLa cells, (i) human hESCs-primed, (j) mouse mature sperm heads, and (k) human hESCs-naïve.

Extended Data Fig. 5 The tsRNA responses to AlkB, T4PNK and PANDORA-seq in regard to different tsRNA origin (5’tsRNA, 3’tsRNA, 3’tsRNA with CCA end, and internal tsRNAs).

a, mouse brain, (b) mouse liver, (c) mouse spleen, (d) mouse mature sperm, (e) mouse mature sperm heads, (f) mouse MEFs (day 0), (g) mouse intermediate cells (day 3), (h) mouse iPSCs, (i) human HeLa cells, (j) human hESCs-primed, and (k) human hESCs-naïve. For a-b,d-j, data are plotted as mean ± SEM (n=3 biologically independent samples in each bar); for c,k, n=2 biologically independent samples in each bar. Different letters above bars indicate statistical difference, P < 0.05; same letters indicate P ≥ 0.05 (two-sided, one-way ANOVA, uncorrected Fisher’s LSD test). Statistical source data and the precise P values are provided in Source Data Extended Data Fig. 5.

Source data

Extended Data Fig. 6 Overall length mapping of tsRNA reads in genomic and mitochondrial tRNA under different RNA-seq protocol.

Overall mapping of all tsRNAs on a tRNA length scale revealed the preferential loci from which tsRNAs are derived from the mature full tRNA under traditional protocol and different enzymatic treatments. a, mouse brain, (b) mouse liver, (c) mouse spleen, (d) mouse mature sperm, (e) mouse MEFs (day 0), (f) mouse intermediate cells (day 3), (g) mouse iPSCs, (h) human HeLa cells, (i) human hESCs-primed, (j) mouse mature sperm heads, and (k) human hESCs-naïve. Mapping plots are presented as mean ± SEM.

Extended Data Fig. 7 The miRNAs that showing sensitive response to PANDORA-seq are in fact rsRNAs.

Previously annotated miRNAs from miRbase that showing upregulation under PANDORA-seq could also annotated to rsRNAs (with one mismatch tolerance), as shown in (a) mouse brain, (b) mouse liver, (c) mouse spleen, (d) mouse mature sperm, (e) mouse mature sperm heads, (f) mouse MEFs (day 0), (g) mouse intermediate cells (day 3), (h) mouse iPSCs, (i) human HeLa cells, and (j) human hESCs-naïve.

Extended Data Fig. 8 The pairwise comparison matrices showing the differential expression pattern of rsRNAs under different RNA-seq protocol across tissues and cells.

a, Pairwise comparison matrices for six mouse tissue/cell types, including 5S rRNA, 5.8S rRNA, mitochondrial 12S rRNA, mitochondrial 16S rRNA, 28S rRNA and 45S rRNA. Color bar: from blue (more similar) to red (more different). b, Pairwise comparison matrices for three human cell types, including 5S rRNA, 5.8S rRNA, mitochondrial 12S rRNA, mitochondrial 16S rRNA, 28S rRNA and 45S rRNA. Color bar: from blue (more similar) to red (more different). c, Pairwise comparison matrices for during mouse iPSC reprogramming, including 5S rRNA, 5.8S rRNA, mitochondrial 12S rRNA, mitochondrial 16S rRNA, 18S rRNA, 28S rRNA and 45S rRNA. Color bar: from blue (more similar) to red (more different).

Extended Data Fig. 9 Northern blot analyses of tsRNA/rsRNA (that is, tsRNAAla, tsRNAArg, tsRNAGlu, tsRNAHis, tsRNALys and rsRNA-28S-1) changes during mESC to EB differentiation.

a, mESC vs Day6 EB; (b) mESC vs Day10 EB. Red arrowhead: tsRNAs; Blue arrowhead: rsRNAs. Similar results were obtained in 3 independent experiments for rsRNA-28S-1; and in 2 independent experiments for tsRNAAla, tsRNAArg, tsRNAGlu, tsRNAHis, and tsRNALys. The unprocessed blots are provided in Source Data Extended Data Fig. 9.

Source data

Extended Data Fig. 10 Expression heatmap of the differentially expressed genes from representative GOBP terms in Day6 and Enriched GOBP terms of differential expressed genes in Day3 EBs after tsRNA/rsRNA transfection.

a,b,c,d, Expression heatmap of the differentially expressed genes from the representative GOBP terms in Day3 EBs from Fig. 6b,c: (a) Neurological development; (b) Muscle/heart development; (c) Oxidative phosphorylation; (d) Translation/ribosome. Venn-diagram beneath each heatmap shows the numbers of overlapped dysregulated genes under different tsRNA/rsRNA transfection. e, Top-ranked upregulated GOBP terms in Day3 EBs after each tsRNA/rsRNA transfection compared to control. f, Top-ranked downregulated GOBP terms in Day3 EBs after each tsRNA/rsRNA transfection compared to control.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3.

Reporting Summary

Supplementary Tables

Supplementary Table 1. RNA-seq read summaries and differentially expressed sncRNAs by pairwise comparison between individual RNA-seq protocols. Supplementary Table 2. Alternative annotation for miRNA fragments based on miRBase among mouse and human tissues/cells. Supplementary Table 3. Statistics of probes targeting small RNA expression between MEFs and iPSCs under traditional treatment. Supplementary Table 4. List of differentially expressed genes in day 1, 3 and 6 embryoid bodies after tsRNA/rsRNA transfection. Supplementary Table 5. Gene set scores for GOBP terms.

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Shi, J., Zhang, Y., Tan, D. et al. PANDORA-seq expands the repertoire of regulatory small RNAs by overcoming RNA modifications. Nat Cell Biol 23, 424–436 (2021). https://doi.org/10.1038/s41556-021-00652-7

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