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A microRNA-inducible CRISPR–Cas9 platform serves as a microRNA sensor and cell-type-specific genome regulation tool

Abstract

microRNAs (miRNAs) are small noncoding RNAs that play important regulatory roles in plants, animals and viruses. Measuring miRNA activity in vivo remains a big challenge. Here, using an miRNA-mediated single guide RNA (sgRNA)-releasing strategy and dCas9–VPR to drive a transgene red fluorescent protein, we create an miRNA sensor that can faithfully measure miRNA activity at cellular levels and use it to monitor differentiation status of stem cells. Furthermore, by designing sgRNAs to target endogenous loci, we adapted this system to control the expression of endogenous genes or mutate specific DNA bases upon induction by cell-type-specific miRNAs. Finally, by miRNA sensor library screening, we discover a previously undefined layer of heterogeneity associated with miR-21a activity in mouse embryonic stem cells. Together, these results highlight the utility of an miRNA-induced CRISPR–Cas9 system as miRNA sensors and cell-type-specific genome regulation tools.

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Data availability

RNA-sequencing data have been deposited in the Gene Expression Omnibus (GEO) under accession code GSE122683. All relevant sequences of primers and miRNA mimics have been provided in Supplementary Table 1. FPKM values are shown in Supplementary Table 2. Source data for Figs. 1d–f, 2a,c,d, 3c,f, 4b,d, 5e and 6b–d,f,g and Supplementary Figs. 2a,b, 3c,d, 4, 5b and 6b have been provided in Supplementary Table 3. Unprocessed gel images for Fig. 5c have been provided in Supplementary Fig. 7. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank members of the Wang laboratory for critical reading and discussion of the manuscript. We thank National Center for Protein Sciences Beijing (Peking University) for assistance with flow cytometry. This study was supported by The National Key Research and Development Program of China (2016YFA0100701 and 2018YFA0107601) and the National Natural Science Foundation of China (31471222, 91640116, 31821091 and 31622033) to Y.W.

Author information

X.-W.W. and L.-F.H. performed all experiments with help from others. J.H. performed RNA sequencing analysis. L.-Q.L., Y.-T.C. and M.S. helped with constructing plasmids and cell culture experiments. All authors were involved in the interpretation of data. Y.W. conceived and supervized the project. Y.W. wrote the manuscript with help from X.-W.W. and L.-F.H.

Competing interests

Two patent applications have been filed relating to the data presented here.

Correspondence to Yangming Wang.

Integrated supplementary information

  1. Supplementary Figure 1 MICR-ON activates transgene expression upon induction by miRNAs.

    (a) FACS plot showing the gating strategy used in flow cytometry analysis to define RFP positive cells. (b) Dosage response of MICR-on RFP to miR-294 in miR-294-sensing-CRISPR-on Dgcr8-/- ESCs. Representative microscopic images for experiment 1 in Fig. 1d are shown. Scale bars: 100 μm. (c, d) RFP expression in miR-294-sensing and miR-20b-sensing CRISPR-on Dgcr8-/- ESCs upon transfection of 50 nM miR-294 or miR-20b mimics. (c) Representative microscopic images. Scale bars: 100 μm. (d) Representative flow cytometry scatter plots. Fraction of RFP positive cells using indicated cut-off (dotted line) is shown at the top right of each plot. (e) The RFP expression in the presence of different miRNAs in miR-294-sensing-CRISPR-on Dgcr8-/- ESCs. Shown are representative flow cytometry scatter plots of 3 independent experiments for Fig. 1e. Fraction of RFP positive cells using indicated cut-off (dotted line) is shown at the top right of each plot. For b-d, experiments were repeated twice independently with similar results.

  2. Supplementary Figure 2 MICR-ON activates transgene expression upon induction by siRNAs.

    (a) RFP expression in siHNRNPA0-sensing-CRISPR-on HEK-293T cells. Top: Representative microscopic images. Bottom: Relative fluorescence intensities. Data were normalized to siNC. Shown are mean ± SD, n = 3 independent experiments. Scale bars: 200 μm. (b) RFP expression in siPABPC1-sensing-CRISPR-on HEK-293T cells. Top: Representative microscopic images. Bottom: Relative fluorescence intensities. Data were normalized to siNC. Shown are mean ± SD, n = 3 independent experiments. Scale bars: 200 μm. All P values were calculated using two-tailed unpaired Student’s t-test. Statistical source data are provided in Supplementary Table 3.

  3. Supplementary Figure 3 MICR-ON-RFP system can be used to track the differentiation status of stem cells.

    (a) Flow cytometry analysis of undifferentiated, differentiated and miR-294 transfected differentiated cells. (b) Representative microscopic images showing RFP expression. Scale bars: 100 μm. Each experiment was repeated three times independently with similar results in a and b. (c) Mean fluorescence intensities of undifferentiated, differentiated and miR-294 transfected differentiated cells. Shown are mean ± SD, n = 3 independent experiments. (d) miR-1 expression is correlated with RFP intensity in differentiating miR-1 sensor C2C12 cells. Top, flow cytometry scatter plot and sorting gates. Bottom, correlation between miR-1 expression and RFP intensity in sorted cell populations. Shown is Pearson correlation coefficient. Three independent experiments were performed and similar results were obtained as shown. Statistical source data for c and d are provided in Supplementary Table 3.

  4. Supplementary Figure 4 On- and off-target editing frequencies of MICR-BE.

    Heat maps showing cellular C to T conversion percentages for each sample in the on-and off-targets loci. Shown is the mean value from 3 independent experiments. The percentages for the on-target sites are listed. Statistical source data are provided in Supplementary Table 3.

  5. Supplementary Figure 5 MICR screen for the heterogeneity of miRNA activity in mouse ESCs.

    (a) The design of screening strategy. (b) Representative flow cytometry histograms of RFP intensity in 10 different miRT-sensing-CRISPR-on mouse ESC cells. The experiment was repeated three times independently with similar results. (c) The % coefficient of variation (CV) of RFP intensity. Shown are mean ± SD, n = 3 independent experiments. Source data for b are provided in Supplementary Table 3.

  6. Supplementary Figure 6 miR-21a displays heterogeneity of miRNA activity in mouse ESCs.

    (a) GSEA for all conserved mRNA targets of miR-21a expressed in mouse ESCs (n=129). For x axis, genes are ranked based on the expression ratio of RFP low versus RFP high mouse ESCs. The nominal P value was determined by an empirical gene set-based permutation test. (b) qRT-PCR analysis of selected conserved miR-21a targets which are ranked 1–5 and 16–20 based on the FPKM fold change in RFP low versus RFP high mouse ESCs. Data were normalized to β-actin and then to RFP high ESCs. The bar indicates the average of 3 ESC samples. Each data point represents one ESC sample, independently cultured, sorted and processed separately in the same day. The experiments were performed twice independently with similar results (Data shown represent results from one experiment. Results for another set of experiment were included in Supplementary Table 3). (c) Scatter plot for the expression level of pluripotency related genes in RFP low versus RFP high mouse ESCs. Key pluripotency markers are indicated and labelled in red. All P values were calculated using two-tailed unpaired Student’s t-test. Source data are provided in Supplementary Table 3.

  7. Supplementary Figure 7 Unprocessed gel images.

    Shown are unprocessed gel images for Fig. 5c. The experiment was repeated three times independently with similar results as shown.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–7 and legends for Supplementary Tables 1–3.

  2. Reporting Summary

  3. Supplementary Table 1.

  4. Supplementary Table 2.

  5. Supplementary Table 3.

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Further reading

Fig. 1: MICR-ON activates transgene expression on induction by miRNAs.
Fig. 2: MICR-ON-RFP system visualizes miRNA expression during cell differentiation.
Fig. 3: MICR-ON-OR and dual-colour operators can be used to detect the expression of two miRNAs.
Fig. 4: MICR-ON activates and MICR-i represses the expression of endogenous genes on induction by miRNAs.
Fig. 5: MICR-BE mediates base editing of genomic DNA on induction by miRNAs.
Fig. 6: miR-21a shows dynamic change of activity in mouse ESCs.
Supplementary Figure 1: MICR-ON activates transgene expression upon induction by miRNAs.
Supplementary Figure 2: MICR-ON activates transgene expression upon induction by siRNAs.
Supplementary Figure 3: MICR-ON-RFP system can be used to track the differentiation status of stem cells.
Supplementary Figure 4: On- and off-target editing frequencies of MICR-BE.
Supplementary Figure 5: MICR screen for the heterogeneity of miRNA activity in mouse ESCs.
Supplementary Figure 6: miR-21a displays heterogeneity of miRNA activity in mouse ESCs.
Supplementary Figure 7: Unprocessed gel images.