2 users have reported that they have successfully carried out the experiment using this protocol.
Dual Fluorescence Reporter Based Analytical Flow Cytometry for miRNA Induced Regulation in Mammalian Cells

引用 收藏 提问与回复 分享您的反馈 Cited by



Scientific Reports
Mar 2017



MicroRNA-induced gene regulation is a growing field in basic and translational research. Examining this regulation directly in cells is necessary to validate high-throughput data originated from RNA sequencing technologies. For this several studies employ luciferase-based reporters that usually measure the whole cell population, which comes with low resolution for the complexity of the miRNA-induced regulation. Here, we provide a protocol using a dual-fluorescence reporter and flow cytometry reaching single cell resolution; the protocol contains a simplified workflow that includes: vector generation, data acquisition, processing, and analysis using the R environment. Our protocol enables high-resolution measurements of miRNA induced post-transcriptional gene regulation and combined with system biology it can be used to estimate miRNAs proficiency.

Keywords: miRNA (miRNA), Flow-Cytometry (流式细胞术), Dual fluorescence reporter (双荧光报告因子), Functional assay (功能分析), ncRNA (非编码RNA), Small RNA (小分子RNA), non-protein-coding RNA (非编码RNA), Gene regulation (基因调控), Reporter gene system (报告基因系统), Single cell analysis (单细胞分析), Flow cytometry (流式细胞术)


MicroRNAs (miRNA) are highly conserved small non-protein-coding RNAs (21-22 nt) that regulate post-transcriptionally gene expression and modulate fundamental biological processes like development and cell homeostasis ( Lagos-Quintana et al., 2001; Fabian et al., 2010; Bartel, 2018), including as well several pathologies where miRNA expression correlates with tumor progression and aggressiveness (Lu et al., 2005; Di Leva and Croce, 2013; Krishnan et al., 2015; Bertoli et al., 2015 and 2016). The study of miRNAs has boosted the evolution of numerous biochemical and computational techniques that unravel new mechanisms and networks applicable to clinical scenarios. These high-throughput procedures include profiling methods (microarrays and NGS), in vivo target validation and single-molecule imaging (Steinkraus et al., 2016). In contrast, functional reporters that directly assess miRNA-target repression have been kept simple and with low resolution.

Deep sequencing and mass spectrometry have increased the possibilities to assess miRNAs and targets expression (Ender et al., 2008; Yang et al., 2009; Brameier et al., 2011; Schramedei et al., 2011; Bai et al., 2014a and 2014b; Muller et al., 2015; Yu et al., 2015), since quantifying the number of RNA molecules and running statistical analyses for the differential expression (miRNAs or mRNA) can generate hypotheses that need further experimental evaluation, using, for example, miRNA exogenous expression or inhibition. In brief mRNA analysis on cDNA level (qPCR or next-generation sequencing) estimates RNAs degradation, mass spectrometry defines the affected coding targets (Yang et al., 2009), and ribosome profiling determines which miRNAs targets are translationally repressed (Bazzini et al., 2012).

Moreover, to narrow down the analysis to the Argonaute bound miRNA captured-based profiling has been used, these profiles rely on RNA isolation from ribonucleoproteins, cDNA synthesis and its quantification (qPCR, microarrays or NGS). Crosslink-immunoprecipitation (CLIP) together with high-throughput sequencing increase enormously our understanding of miRNA regulation (Hafner et al., 2010), to the extent that including an RNA-RNA ligation step (in the capture) allows detecting miRNA-mRNA hybrids called chimeras (Helwak et al., 2013; Grosswendt et al., 2014; Moore et al., 2015).

FRET-based assays in vitro have calculated kinetic parameters for AGO-miRNA binding and RISC formation, increasing the structural basis of miRNA target recognition and suggesting that AGO-miRNA behaves more like RNA binding domain rather than RNA-RNA solely interaction (Wee et al., 2012; Salomon et al., 2015). Using this vast amount of information computational scientist developed algorithms to understand miRNA as an intricate network (Vera et al., 2013; Lai et al., 2016), expanding further with these integrative models the concepts of miRNA as fine-tuners and switches of gene expression (Bartel and Chen, 2004).

The increased resolution of sequencing methods did not boost the development of better in vivo reporters. Usually, studies involved in exhaustive biochemical characterizations (Bait profiling) tested functionally miRNA-mRNA interaction using luciferase reporter assays (Helwak et al., 2013; Hasler et al., 2016; Steinkraus et al., 2016). These experiments require two proteins (a reference protein and another with a miRNA response element) to create quantitative results (ratios) for miRNA regulation, dismissing the intricate functional network behind miRNAs; creating a misbalance between the high-resolution biochemical outputs (NGS, CLIP-Seq, FRET, etc.) and low-resolution reporters.

For that reason, we implemented a system for analytical flow cytometry (Denzler et al., 2016), using a single plasmid reporter system and validated it for miRNAs (Lemus-Diaz et al., 2017). To increase resolution, we used fluorescent proteins instead of luciferases, analyzed single cells by flow cytometry, and processed the data using the R environment. Furthermore, we adopted a titration model for miRNAs regulation, tested and validated its prediction, creating three categorical variables that integrate miRNA binding and expression (Mukherji et al., 2011; Lemus-Diaz et al., 2017), which can be used to estimate miRNA proficiency (Garcia et al., 2011).

Here, we provide a detailed protocol that includes plasmid generation, transfection, data acquisition, data handling and plotting using a simplified code. The reporter described here expresses two fluorescent proteins (CFP and YFP) under control of two constitutive promoters (Figure 1A), while one protein is the reference (YFP); the other has a miRNA response element (CFP). In the empty plasmid (CFP w/o miRNA target site) the two fluorescent proteins are expressed proportionally (Figure 1B), while in a plasmid with a miRNA response element this proportionality is shifted (Figure 1C).

To tidy up the raw events and analyze them using a threshold model for miRNA regulation, we process the data into a transfer function (called here Analytical function) by binning the reference protein intensities and calculating the mean for sensor protein (Bosson et al., 2014; Denzler et al., 2016). For our construct, we transfer the raw data in FCS 2.0 format into the R environment using FlowCore Bioconductor package (Gentleman et al., 2004; Hahne et al., 2009; Huber et al., 2015), then we logarithmically transform the YFP relative intensities, bin them at 0.05 intervals, and calculate the average of the log CFP intensities of each range.

Figure 1. Dual fluorescence-reporter analytical flow cytometry. A. The plasmid contains two fluorescent proteins (YFP and CFP) with two constitutive promoters and a miRNA response element. B and C. To generate analytical functions using raw cytometry data (Grey dots), the YFP relative intensities are binned at 0.05 intervals, and the average CFP intensity per bin is calculated (Green dots). Transfection of HEK 293 with two constructs: (B) Non-cognate and (C) miR-27-3p targeted insert.

Materials and Reagents

  1. Plasmids, primers and cells
    1. p.UTA.2.0 Empty (Addgene, catalog number: 82446 )
    2. p.UTA.2.0 miR 19b-3p (Addgene, catalog number: 82442 )
    3. Sequencing Primers:
      T7 Universal Primer
    4. HEK293 cells

  2. Other materials
    1. Pipette tips 
      1. 1,000 μl Filter Tips (SARSTEDT, catalog number: 70.762.211 )
      2. 100 μl Filter Tips (SARSTEDT, catalog number: 70.760.212 )
      3. 10 μl Filter Tips (SARSTEDT, catalog number: 70.1116.210 )
    2. 1.5 ml microfuge tubes (Eppendorf, catalog number: G_0030108116 )
    3. 2.0 ml microfuge tubes (SARSTEDT, catalog number: 72.695.200 )
    4. 24-well plates (Greiner Bio One International, catalog number: 662160 )
    5. 5 ml Ploystyrene Round-Bottom Tube (Corning, Falcon®, catalog number: 352052 )
    6. PCR tubes 0.2 ml (SARSTEDT, catalog number: 72.737.002 )
    7. Parafilm (Sigma-Aldrich, Bemis, catalog number: P7793-1EA )
    8. HEK293 Human embryonic kidney cell line (ATCC, catalog number: CRL-1573TM )
    9. One Shot® TOP10 Chemically Competent Escherichia coli (Thermo Fisher Scientific, catalog number: C404010 )
    10. Sense and antisense strands of oligonucleotides (more information below)
    11. Gel extraction kit (QIAquick gel extraction kit, QIAGEN, catalog number: 28704 )
    12. GibcoTM Opti-MEMTM (+ L-Glutamine, + phenol red) (Thermo Fisher Scientific, catalog number: 31985047 )
    13. Lipofectamine® 2000 (Thermo Fisher Scientific, catalog number: 11668027 )
    14. NotI restriction enzyme (New England Biolabs, catalog number: R0189S )
    15. XhoI restriction enzyme (New England Biolabs, catalog number: R0146S )
    16. EcoRI (New England Biolabs, catalog number: R0101S )
    17. HindIII (New England Biolabs, catalog number: R0104S )
    18. Plasmid DNA Midiprep kit (QIAquick Plasmid Midi Kit, QIAGEN, catalog number: 10023 )
    19. QIAprep Spin Miniprep Plasmid mini kit (QIAGEN, catalog number: 27104 )
    20. Paraformaldehyde 4% solution (Santa Cruz Biotechnology, catalog number: sc-281692 )
    21. T4 DNA ligase kit (New England Biolabs, catalog number: M0202S )
    22. T4 Polynucleotide kinase (PNK) (New England Biolabs, catalog number: M0201S )
    23. Agarose (Carl Roth, catalog number: 3810.3 )
    24. PBS (PAN-Biotech, catalog number: P04-36500 )
    25. Trypsin-EDTA (PAN-Biotech, catalog number: P10-023100 )
    26. DMEM (Thermo Fisher Scientific, Life Technologies, catalog number: 41965062 )
    27. Fetal Bovine Serum (Thermo Fisher Scientific, Life Technologies, catalog number: 10500-064 )


  1. Pipettes
  2. Fridge
  3. Agarose gel electrophoresis chamber
  4. BD LSR II Flow Cytometer (BD, model: LSR II )
    1. YFP: Laser 488 nm and 550LP-BP575/26 filters
    2. CFP: Laser 408 nm and BP450/50 (Pacific Blue)
  5. Benchtop refrigerated microcentrifuge (Thermo Fisher Scientific, model: HeraeusTM FrescoTM 21 , catalog number: 75002555)
  6. Universal Centrifuge (Thermo Fisher Scientific, model: HeraeusTM MegafugeTM 16 , catalog number: 75004230; Rotor: Thermo Fisher Scientific, model: TX-150, catalog number: 75005701 )
  7. Gel documentation system (INTAS Gel iX Imager) (INTAS Science Imaging Instruments, model: FACE )
  8. NanoDropTM 2000 (Thermo Fisher Scientific, model: NanoDropTM 2000 , catalog number: ND-2000)
  9. Thermoblock (Eppendorf, model: ThermoStat Plus , catalog number: 5352 000.010)
  10. Thermocycler (Labcycler Sensoquest)
  11. Vortexer (Scientific Industries, model: Vortex-Genie 2 , catalog number: SI-0236)


  1. BD-FACSDIVA (BD Biosciences)
  2. R (https://www.r-project.org)
  3. R studio (https://www.rstudio.com)
  4. Bioconductor (https://www.bioconductor.org)
  5. Serial Cloner (http://serialbasics.free.fr/Serial_Cloner.html)


This protocol describes two blocks (Figure 2), one specifies how to generate a reporter plasmids with a miRNA response element, while the second outlines data collection from FACS and processing in the R environment, the second block includes the gates used and a simplified code to generate analytical functions.

Figure 2. Procedure scheme. Dual reporter plasmid is linearized with XhoI and NotI and ligated with annealed oligonucleotides. Proper oligonucleotides ligation eliminates one of two EcoRI restriction sites in the plasmid; hence positive clones are screened using endonuclease restriction. After the clones are sequenced and transfected, the fluorescence intensities are measured by Flow Cytometry, and the raw data is processed using the R programing language to generate analytical functions.

  1. Oligonucleotide designing and annealing
    Here, we describe how to make a p.UTA.2.0 plasmid with 1 microRNA-binding site, using miR-451a as an example.
    Note: We use here perfect miRNA complementarity, but endogenous 3’ UTR can be used.
    1. Design and order oligonucleotides as separate strands (Our oligonucleotides are ordered from Sigma-Aldrich) (Table 1).

      Table 1. Oligonucleotides design with XhoI and NotI 

    2. Prepare in 0.2 ml PCR tubes the following reaction to anneal the oligonucleotides (Table 2).

      Table 2. Oligonucleotides annealing and phosphorylation 

    3. Incubate at 37 °C for 1 h in a thermocycler, then cool it down in a ramp from 95 to 25 °C for 5 min (5 °C/min).
      1. Alternatively, you can heat up the oligonucleotides and let them cool down at room temperature for several hours.
      2. Normally phosphorylation is not necessary; however it is required if the plasmid is dephosphorylated.
      3. You can check if the oligonucleotides are annealed through running a 2% agarose gel using the individual oligonucleotides as controls. Normally we skip this step and have no issues with the ligation afterward.
    4. Store the annealed oligonucleotides at -20 °C or proceed to the ligation step.

  2. p.UTA.2.0 plasmid Linearization
    1. Prepare the digestion in a 1.5 ml microfuge tube (Table 3). Scale up accordingly for multiple reactions.

      Table 3. Digestion of the plasmid p.UTA.2.0 Empty 

      Although not required, we recommend making NotI and XhoI separated controls.
      Usually dephosphorylation is not necessary; however, it reduces the background after ligation (see below), in that case, the annealed oligonucleotides need to be phosphorylated.
    2. Vortex briefly to mix.
    3. Spin the tube quickly to collect the liquid at the bottom.
    4. Incubate the mixture at 37 °C for at least 1 h.
      Note: Overnight incubation is possible.
    5. Run the reactions in a 1% agarose gel.
    6. Cut the 5,131 bp band and transfer the piece to a clean microcentrifuge tube.
    7. Extract the DNA from the gel using the QIAquick Gel Extraction Kit following the manufacturer’s protocol.
    8. Measure DNA concentration.
    9. Prepare 50 ng/μl aliquots of the linearized plasmid.

  3. Ligation of p.UTA.2.0 and annealed oligo-duplex
    1. Prepare ligation in a 1.5 ml microfuge tube according to Table 4. Scale up accordingly for multiple reactions.

      Table 4. Ligation reaction p.UTA.2.0 and annealed oligos 

    2. Vortex briefly.
    3. Spin the tube quickly to collect the liquid at the bottom of the tube.
    4. Incubate the mixture at 16 °C for at least 2 h.
      Note: Overnight incubation is possible at 4 °C.
    5. Transform into your favorite competent cells.

  4. Screening of positive clones
    1. To check for the correct oligonucleotide insertion, we performed endonuclease restriction to distinguish empty clones (w/o miRNA response element) from the positive ones. Since the insertion site (XhoI and NotI) contains an EcoRI site that disappears after digestion, this holds true if the oligonucleotides lack EcoRI sequence.
    2. We used HindIII and EcoRI together: the empty vector yields four bands (3,992; 1,010; 732; 111) and the positive clones three (3,292; 1,736; 111).
    3. Since our plasmid lacks a stuffer sequence, we can differentiate background from partially digested DNA; for that reason, we highly recommend performing this screening test (Figure 3).
    4. Sequences can be retrieved from Addgene Page (http://www.addgene.org/82446/).

      Figure 3. Plasmid scheme and restriction sites. Screening of positive clones using HindIII and EcoRI restriction sites. Notice the EcoRI site between the XhoI and NotI that differentiates between inserted oligos and re-ligation background.

    1. Grow your bacteria cultures and isolate plasmid using QIAprep Spin Miniprep.
      Note: It’s also ok to use home-made solutions to isolate plasmids.
    2. Prepare the digestion reaction in a 1.5 ml microfuge tube (Table 5). Scale up accordingly when performing multiple reactions.

      Table 5. Digestion reaction to screen positive clones 

    3. Vortex briefly and spin to collect liquid at the bottom of the tube.
    4. Incubate the mixture at 37 °C for 1 h.
    5. Run the mixtures in a 1% agarose gel.
    6. Visualize the agarose gel.
    7. Analyze your clones.
      Figure 4 depicts the results of an experiment containing several empty clones derived probably from incomplete digestion of the plasmid.
    8. Prepare transfection quality DNA.

      Figure 4. Screening reaction Expected results. A and B. In silico DNA gel electrophoresis of the Empty and the Positive (miRNA target inserted) plasmids. C. Agarose DNA gel electrophoresis from screening reactions, it shows one positive clone (lane 2) and three empty vectors without insert (lanes 4, 6, 8). Non-digested controls (lanes 1, 3, 5, 7).

  5. Transfection of the p.UTA.2.0 plasmids
    This transfection scheme is optimized for 24-well plates using Hek293 cells; we have evaluated this protocol in HeLa, HTC116, and SH-SY5Y. It showed that the neuroblastoma cell line (SH-SY5Y) is not suitable for this assay because the promoters are not strong enough to produce proper analytical functions.
    1. Seed HEK293 cells (about 100,000 cell per well) in a 24-well plate.
    2. Incubate for 18-24 h at 37 °C and 5% CO2.
    3. Prepare DNA samples as follows (we recommend at least triplicates per experimental unit. Scale up accordingly):

    4. Prepare Lipofectamine® 2000 reagent as follows:

    5. Add the DNA solution (Step E3) to Lipofectamine® 2000 reagent (Step for E4) in a 1:1 ratio.
    6. Incubate the mixture for at least 20 min at room temperature.
    7. Add the DNA-lipofectamine mixture dropwise to the cells.
    8. Incubate for 72 h at 37 °C and 5% CO2.

  6. Sample preparation for flow cytometry
    1. Discard medium from the well.
    2. Wash 1 time with PBS (~500 μl).
    3. Add 100 μl trypsin per well.
    4. Incubate for 5 min at 37 °C and 5% CO2.
    5. Add 400 μl DMEM medium + 15% FCS.
    6. Transfer the cell suspension to a FACS-tube.
    7. Centrifuge at 300 x g for 5 min.
    8. Discard the supernatant.
    9. Add 1 ml PBS.
    10. Centrifuge at 300 x g for 5 min.
    11. Repeat Steps F9 and F10 two more times.
    12. Discard the supernatant.
    13. Spin down briefly.
      Note: Cells can be documented at this point without fixing.
    14. Add 100 μl PBS.
    15. Add 100 μl PFA 4% (Paraformaldehyde 4%).
    16. Incubate for 30 min at 4 °C.
    17. Wash three times as before (Steps F9 and F10).
    18. Add 200 μl PBS.
    19. Seal the FACS-tubes with parafilm.
    20. Store at 4 °C (fridge).
      Note: The fixed cells can be stored up to 1 week without affecting the output of the transfer functions.

Data analysis

  1. Data Acquisition
    The following steps describe the process of gating, and how to export the FCS files to be analyzed using R (It’s assumed that the readers have a basic knowledge of flow cytometry data acquisition). We performed the measurements using a BD LSR II instrument with filters 550LP-BP575/26 (PE) for YFP and BP450/50 (Pacific Blue) for CFP.
    1. Analyze your negative control.
    2. Set up the single cells using FSC.A and FSC.H scatter plot (Called here P1 or single cells).
    3. Select the main population of cells using the FSC.A and SSC.A scatter. (Called here P2 or main population).
    4. Define the YFP positive cells using the histogram visualization (Called here P3 or YFP positive cells). (Figure 5 and Video 1)
      Note: Figure 5 and video EmptyFACSdiva (Video 1) show the gates we used for a mock control, and the layout we kept on the acquisition software.

      Figure 5. Negative control acquisition (screenshot). Panels show the gates used for HEK293 cells: scatter plots for singlets (FSC.A vs. FSC.H), size and complexity (FSC.A vs. SSC.A); histograms and scatter plot for PE.A (550LP-BP575/26), and the output for CFP and YFP (Scatter plot PE.A vs. Pacific blue).

      Video 1. FACS Diva acquisition Non-Transfected negative control. Panels show the gates used for HEK293 cells: scatter plots for singlets (FSC.A vs. FSC.H), cell population (FSC.A vs SSC.A), histograms and scatter plot for PE.A (550LP-BP575/26), and the analytical output for CFP and YFP (Scatter plot PE.A vs. Pacific blue).

    5. Run the p.UTA.2.0 empty vector and create a gate with the Relative Fluorescence Units of PE.A from 10,000 till the end of the x-axis. (Called here P4 or acquisition gate) (Figure 6).

      Figure 6. Empty vector control acquisition screenshot. Panels show the gates used for HEK293 cells transfected with the empty vector (p.UTA.2.0 empty). Scatter plots for singlets (FSC.A vs. FSC.H), main cell population (FSC.A vs. SSC.A), histograms and scatter plot for PE.A (550LP-BP575/26), and the analytical output for CFP and YFP (Scatter plot PE.A vs. Pacific blue).

    6. Acquire between 1,000 and 3,000 events from the P4 population.
      Note: We used the P4 population as acquisition gate to obtain enough data points at high YFP intensities, reducing the scattering of the analytical function, which essential for the regression analysis.
    7. Read your samples.
      Figures 6 and 7 show acquisition panels of HEK293 cells transfected with p.UTA.2.0 Empty and p.UTA.2.0 miR 19b-3p.

      Figure 7. p.UTA.2.0 miR 19b-3p acquisition screenshot. Panels show the gates used for HEK293 cells: scatter plots for singlets (FSC.A vs. FSC.H), size and complexity (FSC.A vs. SSC.A); histograms and scatter plot for PE.A (550LP-BP575/26), and the output for CFP and YFP (Scatter plot PE.A vs. Pacific blue).

    8. Export the P3 gate in FCS format 2.0 (Figure 8).
      Note: P3 includes all the YFP positive cells. We used P4 as the stopping gate to improve the quality of the analytical functions. Normally, we also save the whole data set for our records.

      Figure 8. FACS-Diva export data screenshot. Export FCS format 2.0 for P3 (YFP positive cells including P4) population, screenshot exemplifies the export options used for the FCS files.

  2. Data processing and plotting
    Here is a simplified code to efficiently analyze UTA analytic cytometry written for laboratory scientist with minimum or no coding experience. The example FSC files and the codes used in the following steps are included in Data codes.zip and the Video 2 describes each step.

    Video 2. R studio data analysis

    Step by step guide to generate analytical function from Raw FCS files using the R environment:
    1. Locate the FCS files (containing only the YFP positive population) in the same folder with the R script “Analysis_Functions.R” file.
    2. In R, set your working directory.
    3. Call the functions in the “Analysis_functions_4.0.R” file.
    4. Create a vector containing the FCS files’ names, in this case just files containing YFP positive events, in this example data is the P3 population (Including P4).
      fileNames <-Sys.glob("*_P3.fcs")
    5. Create your working List.
      The following line will create an R object (List) that contains all the analytical functions for your FCS files. You will apply the bin.data function to each file on your working directory; it means that each object inside the list corresponds to an FCS file or experimental unit. Each FCS processed object will have:
      1. Raw data (CFP, YFP, ratio, fold).
      2. Bin data (Analytical Function).
      3. Bin CFP and YFP will be used for Analytical function generation.
      4. Ratio of CFP/YFP per each CFP bin.
      5. Fold Repression per each bin. It just another way to see repression; it means that CFP is being repressed at 100%.
      6. Descriptive statistics with quartiles median, max, and min.

      Functions<-lapply(fileNames, function(x) bin.data.FscH(x,bin.size = 0.05))

      This is a base R loop: it takes each element on fileNames (FCS files) and applies the bin.data.function on them to create a list. The first argument defines the vector (Containing the FCS file labels) and the second includes the bin.data.FscH function where you can modify the bin size.
    6. Write the names of your FCS files, assign them to your list and write them in a text file.


      The List_Index.txt file contains the names of your FCS files and their indexes inside the list; we recommend having a hard copy to plot the data.
    7. Plot the analytical functions in R (Figure 9).

      plot.miR(Functions,fil_num = c(2,4,6,8),control = 1,ylim = c(1,8),l_y = 8,
      Leg = c("Empty","miR 148a 3p","miR 19b 3p","miR 24 3p","miR 29a 3p"),main="miRNA" ,col.m=c("black","blue","red","green3","purple"),
      cex = 0.7,mu.cex = 0.3)
      plot.miR(Functions,fil_num = c(3,5,7),control = 1,ylim = c(1,8),l_y = 8,
      Leg = c("Empty","miR 15a 5p","miR 21 5p","miR_28_3p"),main= "miRNA",col.m=c("black","blue","red","green3","purple"),
      cex = 0.7,mu.cex = 0.3 )

      The List_Index.txt file contains the names of your FCS files and their indexes inside your list; we recommend having a hard copy of it when you plot the data. Table 6 describes each argument.

      Figure 9. Analytical functions output from the plot.miR function. HEK293 cells were transfected with several miRNA sensors, acquired and read by Flow cytometry. FCS 2.0 files were analyzed in the R environment.

      Table 6. The function arguments of plot. miR 

    Optional Steps:
    You can also generate a PDF with the R plots:
    1. Create the PDF device:


      pdf function requires the name of your file in quotes, in this case, “YourName.pdf” do not forget the extension, while the second and third arguments are the width and height of your plot, respectively.
    2. Run your plot codes (more than one is possible, do not expect any output on the R-studio plot layout).
      plot.miR (Functions, fil_num = c (2,3,4,5),control = 1,ylim = c (1,8),l_y = 8,
      Leg = c( "Empty","miR_148a_3p","miR_15a_5p","miR_19b_3p","miR_21_5p"),main= "983" ,col.m=c ( "black","blue","red","green3","purple"),
      cex = 0.7,mu.cex =0.3)

    3. Close the PDF

      You might also use another software to plot your data, using the following function, You will create a CSV file of all your list, the first column has the log values of each YFP bin, and the other columns contain the CFP values of each element in your list (Functions), with the name of your FCS files.
    4. Use the Export function.


      The Export function requires the list containing the bin data, requires the vector containing the names of your FCS files, which is the same you used to define the index for the plots, the third argument is the name of your CSV file.

  3. Data interpretation and statistical analysis.
    To interpret and gain further insight from the analytical functions, you can extract information using the molecular titration model used before (Mukherji et al., 2011; Lemus-Diaz et al., 2017) (Figure 10).

    Figure 10. Titration model of miRNA-induced regulation. This model for miRNA-induced regulation describes the free mRNAs available for translation. In the absence of miRNA, the cell transcribes and degrades mRNA at rates KR and γr respectively. In the presence of miRNAs, the free mRNA available for translation depends on the miRNA-mRNA (r*) complex, its formation depends on on-rate (Kon) and miRNA concentration. Then two mutually exclusive paths occur. Either it induces mRNA degradation (at γr* rate) or disassembles into its components (at Koff rate).

    The model describes the miRNA titration of mRNA where transcribed mRNA interacts with miRNA (r* complex) ruled by the Kon and Koff of the complex, which depends on the miRNA complementary and target sites in the mRNA. The following equations describe the change of mRNA (free to be translated) and r*:
    Equation 1

    Equation 2

    In this context, total miRNA concentration is the free miRNA molecules plus the miRNAs included in the r* complex:
    Equation 3

    Assuming that no translation happens from miRNA-mRNA complexes, and only from the free RNA (r), the solution for steady state of (r) is given as before (Mukherji et al., 2011; Lemus-Diaz et al., 2017):
    Equation 4


    Simulations can be performed using the app (Simulation miRNA function). For instructions, see the supplementary material of Lemus-Diaz et al. (2017).
      Using the analytical functions as input (Excel or R) you can estimate the parameters θ and λ, using non-linear regression with equation 4, while θ corresponds miRNA concentration λ correlates with the binding of the miRNA. Estimated parameters for the examples can be found in the supplementary information of Lemus-Diaz et al. (2017).
      We validated this model for the reporter described here and showed how the parameters (θ and λ) are useful to dissect miRNA activity, giving as an example how localization of a miRNA could be inferred by the analytical functions (Lemus-Diaz et al., 2017, Figure 6). Moreover, using this model, we described three categorical variables (Low, Mid, and High functional groups) to interpret the outputs easily (Lemus-Diaz et al., 2017, Figure 2).
    In the data presented in Figure 9, the miR 24-3p, miR 29a-3p can be classified as low functional; miR 148a-3p and miR 21-5p as mid; and miR 21-5p and miR 19b-3p as highly functional. However, as any qualitative data this classification is arbitrary, so we advise to use the estimate parameters and use them to compare the miRNA functionality (Lemus-Diaz et al., 2017, Figures 3 and 4).


This work was partly supported by the Göttingen Graduate School for Neuroscience, Biophysics, and Molecular Biosciences (DFG grant GSC 226/2), L.T was a master student at the IMPRS for Molecular Biology. This protocol was adapted from Mukherji et al. (2011) and Lemus-Diaz et al. (2017); the term Analytical flow cytometry was adopted from Denzler et al. (2016).

Competing interests

The authors declare that there are no conflicts of interest or competing interest.


  1. Bai, B., Liu, H. and Laiho, M. (2014a). Small RNA expression and deep sequencing analyses of the nucleolus reveal the presence of nucleolus-associated microRNAs. FEBS Open Bio 4: 441-449.
  2. Bai, B., Yegnasubramanian, S., Wheelan, S. J. and Laiho, M. (2014b). RNA-Seq of the nucleolus reveals abundant SNORD44-derived small RNAs. PLoS One 9(9): e107519.
  3. Bartel, D. P. (2018). Metazoan microRNAs. Cell 173(1): 20-51. 
  4. Bartel, D. P. and Chen, C. Z. (2004). Micromanagers of gene expression: the potentially widespread influence of metazoan microRNAs. Nat Rev Genet 5(5): 396-400. 
  5. Bazzini, A. A., Lee, M. T. and Giraldez, A. J. (2012). Ribosome profiling shows that miR-430 reduces translation before causing mRNA decay in zebrafish. Science 336(6078): 233-237. 
  6. Bertoli, G., Cava, C. and Castiglioni, I. (2015). microRNAs: New biomarkers for diagnosis, prognosis, therapy prediction and therapeutic tools for breast cancer. Theranostics 5(10): 1122-1143.
  7. Bertoli, G., Cava, C. and Castiglioni, I. (2016). microRNAs as biomarkers for diagnosis, prognosis and theranostics in prostate cancer. Int J Mol Sci 17(3): 421.
  8. Bosson, A. D., Zamudio, J. R. and Sharp, P. A. (2014). Endogenous miRNA and target concentrations determine susceptibility to potential ceRNA competition. Mol Cell 56(3): 347-359.
  9. Brameier, M., Herwig, A., Reinhardt, R., Walter, L. and Gruber, J. (2011). Human box C/D snoRNAs with miRNA like functions: expanding the range of regulatory RNAs. Nucleic Acids Res 39(2): 675-686.
  10. Denzler, R., McGeary, S. E., Title, A. C., Agarwal, V., Bartel, D. P. and Stoffel, M. (2016). Impact of microRNA levels, Target-Site complementarity, and cooperativity on competing endogenous RNA-regulated gene expression. Mol Cell 64(3): 565-579.
  11. Di Leva, G. and Croce, C. M. (2013). miRNA profiling of cancer. Curr Opin Genet Dev 23(1): 3-11.
  12. Ender, C., Krek, A., Friedlander, M. R., Beitzinger, M., Weinmann, L., Chen, W., Pfeffer, S., Rajewsky, N. and Meister, G. (2008). A human snoRNA with microRNA-like functions. Mol Cell 32(4): 519-528.
  13. Fabian, M. R., Sonenberg, N. and Filipowicz, W. (2010). Regulation of mRNA translation and stability by microRNAs. Annu Rev Biochem 79: 351-379.
  14. Garcia, D. M., Baek, D., Shin, C., Bell, G. W., Grimson, A. and Bartel, D. P. (2011). Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat Struct Mol Biol 18(10): 1139-1146. 
  15. Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., Hornik, K., Hothorn, T., Huber, W., Iacus, S., Irizarry, R., Leisch, F., Li, C., Maechler, M., Rossini, A. J., Sawitzki, G., Smith, C., Smyth, G., Tierney, L., Yang, J. Y. and Zhang, J. (2004). Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10): R80.
  16. Grosswendt, S., Filipchyk, A., Manzano, M., Klironomos, F., Schilling, M., Herzog, M., Gottwein, E. and Rajewsky, N. (2014). Unambiguous identification of miRNA: target site interactions by different types of ligation reactions. Mol Cell 54(6): 1042-1054. 
  17. Hafner, M., Landthaler, M., Burger, L., Khorshid, M., Hausser, J., Berninger, P., Rothballer, A., Ascano, M., Jr., Jungkamp, A. C., Munschauer, M., Ulrich, A., Wardle, G. S., Dewell, S., Zavolan, M. and Tuschl, T. (2010). Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141(1): 129-141. 
  18. Hahne, F., LeMeur, N., Brinkman, R. R., Ellis, B., Haaland, P., Sarkar, D., Spidlen, J., Strain, E. and Gentleman, R. (2009). flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics 10: 106.
  19. Hasler, D., Lehmann, G., Murakawa, Y., Klironomos, F., Jakob, L., Grasser, F. A., Rajewsky, N., Landthaler, M. and Meister, G. (2016). The lupus autoantigen la prevents mis-channeling of tRNA fragments into the human microRNA pathway. Mol Cell 63(1): 110-124. 
  20. Helwak, A., Kudla, G., Dudnakova, T. and Tollervey, D. (2013). Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153(3): 654-665.
  21. Huber, W., Carey, V. J., Gentleman, R., Anders, S., Carlson, M., Carvalho, B. S., Bravo, H. C., Davis, S., Gatto, L., Girke, T., Gottardo, R., Hahne, F., Hansen, K. D., Irizarry, R. A., Lawrence, M., Love, M. I., MacDonald, J., Obenchain, V., Oles, A. K., Pages, H., Reyes, A., Shannon, P., Smyth, G. K., Tenenbaum, D., Waldron, L. and Morgan, M. (2015). Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods 12(2): 115-121.
  22. Krishnan, P., Ghosh, S., Wang, B., Li, D., Narasimhan, A., Berendt, R., Graham, K., Mackey, J. R., Kovalchuk, O. and Damaraju, S. (2015). Next generation sequencing profiling identifies miR-574-3p and miR-660-5p as potential novel prognostic markers for breast cancer. BMC Genomics 16: 735.
  23. Lagos-Quintana, M., Rauhut, R., Lendeckel, W. and Tuschl, T. (2001). Identification of novel genes coding for small expressed RNAs. Science 294(5543): 853-858. 
  24. Lai, X., Wolkenhauer, O. and Vera, J. (2016). Understanding microRNA-mediated gene regulatory networks through mathematical modelling. Nucleic Acids Res 44(13): 6019-6035.
  25. Lemus-Diaz, N., Boker, K. O., Rodriguez-Polo, I., Mitter, M., Preis, J., Arlt, M. and Gruber, J. (2017). Dissecting miRNA gene repression on single cell level with an advanced fluorescent reporter system. Sci Rep 7: 45197.
  26. Lu, J., Getz, G., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., Ebert, B. L., Mak, R. H., Ferrando, A. A., Downing, J. R., Jacks, T., Horvitz, H. R. and Golub, T. R. (2005). MicroRNA expression profiles classify human cancers. Nature 435(7043): 834-838.
  27. Moore, M. J., Scheel, T. K., Luna, J. M., Park, C. Y., Fak, J. J., Nishiuchi, E., Rice, C. M. and Darnell, R. B. (2015). miRNA-target chimeras reveal miRNA 3'-end pairing as a major determinant of Argonaute target specificity. Nat Commun 6: 8864.
  28. Mukherji, S., Ebert, M. S., Zheng, G. X., Tsang, J. S., Sharp, P. A. and van Oudenaarden, A. (2011). MicroRNAs can generate thresholds in target gene expression. Nat Genet 43(9): 854-859.
  29. Muller, S., Raulefs, S., Bruns, P., Afonso-Grunz, F., Plotner, A., Thermann, R., Jager, C., Schlitter, A. M., Kong, B., Regel, I., Roth, W. K., Rotter, B., Hoffmeier, K., Kahl, G., Koch, I., Theis, F. J., Kleeff, J., Winter, P. and Michalski, C. W. (2015). Next-generation sequencing reveals novel differentially regulated mRNAs, lncRNAs, miRNAs, sdRNAs and a piRNA in pancreatic cancer. Mol Cancer 14: 94.
  30. Salomon, W. E., Jolly, S. M., Moore, M. J., Zamore, P. D. and Serebrov, V. (2015). Single-molecule imaging reveals that Argonaute reshapes the binding properties of its nucleic acid guides. Cell 162(1): 84-95.
  31. Schramedei, K., Morbt, N., Pfeifer, G., Lauter, J., Rosolowski, M., Tomm, J. M., von Bergen, M., Horn, F. and Brocke-Heidrich, K. (2011). MicroRNA-21 targets tumor suppressor genes ANP32A and SMARCA4. Oncogene 30(26): 2975-2985. 
  32. Steinkraus, B. R., Toegel, M. and Fulga, T. A. (2016). Tiny giants of gene regulation: experimental strategies for microRNA functional studies. Wiley Interdiscip Rev Dev Biol 5(3): 311-362. 
  33. Vera, J., Lai, X., Schmitz, U. and Wolkenhauer, O. (2013). MicroRNA-regulated networks: the perfect storm for classical molecular biology, the ideal scenario for systems biology. Adv Exp Med Biol 774: 55-76.
  34. Wee, L. M., Flores-Jasso, C. F., Salomon, W. E. and Zamore, P. D. (2012). Argonaute divides its RNA guide into domains with distinct functions and RNA-binding properties. Cell 151(5): 1055-1067. 
  35. Yang, Y., Chaerkady, R., Beer, M. A., Mendell, J. T. and Pandey, A. (2009). Identification of miR-21 targets in breast cancer cells using a quantitative proteomic approach. Proteomics 9(5): 1374-1384.
  36. Yu, F., Bracken, C. P., Pillman, K. A., Lawrence, D. M., Goodall, G. J., Callen, D. F. and Neilsen, P. M. (2015). p53 represses the oncogenic sno-miR-28 derived from a snoRNA. PLoS One 10(6): e0129190.


MicroRNA诱导的基因调控是基础和转化研究中不断增长的领域。 直接在细胞中检查该调节对于验证源自RNA测序技术的高通量数据是必要的。 对于这一研究,一些研究采用基于荧光素酶的报告基因,通常测量全细胞群,其具有低分辨率的miRNA诱导调节的复杂性。 在这里,我们提供使用双荧光报告基因和流式细胞仪达到单细胞分辨率的方案; 该协议包含一个简化的工作流程,包括:使用R环境进行矢量生成,数据采集,处理和分析。 我们的协议可实现miRNA诱导的转录后基因调控的高分辨率测量,并结合系统生物学,可用于估计miRNA的熟练程度。

【背景】MicroRNAs(miRNA)是高度保守的小型非蛋白质编码RNA(21-22nt),可调节转录后基因表达并调节基因生物学过程,如发育和细胞稳态(Lagos-Quintana et al。,2001; Fabian et al。,2010; Bartel,2018),包括miRNA表达与肿瘤进展和侵袭性相关的几种病理(Lu et al。, 2005; Di Leva和Croce,2013; Krishnan et al。,2015; Bertoli et al。,2015和2016)。对miRNA的研究推动了许多生化和计算技术的发展,这些技术解开了适用于临床情景的新机制和网络。这些高通量程序包括分析方法(微阵列和NGS),体内靶标验证和单分子成像(Steinkraus et al。,2016)。相反,直接评估miRNA-靶标抑制的功能性报告基因保持简单且分辨率低。

深度测序和质谱分析增加了评估miRNA和靶标表达的可能性(Ender et al。,2008; Yang et al。,2009; Brameier et al 。,2011; Schramedei et al。,2011; Bai et al。,2014a和2014b; Muller et al。, 2015; Yu et al。,2015),因为量化RNA分子的数量和运行差异表达的统计分析(miRNA或mRNA)可以产生需要进一步实验评估的假设,例如使用,miRNA外源性表达或抑制。简言之,cDNA水平的mRNA分析(qPCR或新一代测序)估计RNAs降解,质谱定义受影响的编码目标(Yang et al。,2009),核糖体分析确定哪些miRNA目标是翻译抑制(Bazzini et al。,2012)。

此外,为了缩小对Argonaute结合的分析,已经使用基于捕获的分析miRNA,这些分布依赖于从核糖核蛋白中分离RNA,cDNA合成及其定量(qPCR,微阵列或NGS)。交联免疫沉淀(CLIP)和高通量测序极大地增加了我们对miRNA调控的理解(Hafner et al。,2010),其程度包括RNA-RNA连接步骤(在捕获中) )允许检测称为嵌合体的miRNA-mRNA杂种(Helwak et al。,2013; Grosswendt et al。,2014; Moore et al。, 2015年)。

基于FRET的分析体外计算了AGO-miRNA结合和RISC形成的动力学参数,增加了miRNA靶识别的结构基础,并表明AGO-miRNA表现得更像RNA结合域而不是RNA- RNA单独相互作用(Wee et al。,2012; Salomon et al。,2015)。利用这一大量信息计算科学家开发的算法将miRNA理解为一个错综复杂的网络(Vera et al。,2013; Lai et al。,2016),进一步扩展这些综合模型将miRNA的概念作为微调和基因表达的转换(Bartel和Chen,2004)。

测序方法的分辨率提高并未促进更好的体内记者的发展。通常,涉及详尽的生化特征(诱饵分析)的研究使用荧光素酶报告分析检测功能性miRNA-mRNA相互作用(Helwak et al。,2013; Hasler et al。,2016 ; Steinkraus et al。,2016)。这些实验需要两种蛋白质(参考蛋白质和另一种具有miRNA反应元件的蛋白质)来创建miRNA调节的定量结果(比率),从而消除miRNA背后复杂的功能网络;在高分辨率生化输出(NGS,CLIP-Seq,FRET,等)和低分辨率报告器之间造成不平衡。

出于这个原因,我们使用单一质粒报告系统实施了分析流式细胞仪系统(Denzler et al。,2016),并对miRNA进行了验证(Lemus-Diaz 等。,2017)。为了提高分辨率,我们使用荧光蛋白代替荧光素酶,通过流式细胞术分析单个细胞,并使用R环境处理数据。此外,我们采用了miRNAs调控的滴定模型,测试并验证了其预测,创建了三个整合miRNA结合和表达的分类变量(Mukherji et al。,2011; Lemus-Diaz et al 。,2017),可用于估计miRNA的熟练程度(Garcia et al。,2011)。在这里,我们提供了一个详细的协议,包括质粒生成,转染,数据采集,数据处理和使用简化代码绘图。这里描述的报告基因在两个组成型启动子的控制下表达两种荧光蛋白(CFP和YFP)(图1A),而一种蛋白质是参考(YFP);另一种具有miRNA反应元件(CFP)。在空质粒(CFP w / o miRNA靶位点)中,两种荧光蛋白按比例表达(图1B),而在具有miRNA反应元件的质粒中,这种比例性被移位(图1C)。

为了整理原始事件并使用miRNA调节的阈值模型对其进行分析,我们通过对参考蛋白质强度进行分级并计算传感器蛋白质的平均值来将数据处理成转移函数(此处称为分析函数)(Bosson et et al。,2014; Denzler et al。,2016)。对于我们的构造,我们使用FlowCore Bioconductor软件包将原始数据以FCS 2.0格式传输到R环境中(Gentleman et al。,2004; Hahne et al。,2009; Huber et al。,2015),然后我们以对数方式转换YFP相对强度,以0.05间隔对它们进行分组,并计算每个范围的log CFP强度的平均值。

图1.双荧光 - 报告分析流式细胞仪。 A.质粒含有两个荧光蛋白(YFP和CFP),两个组成型启动子和一个miRNA反应元件。 B和C.为了使用原始细胞计数数据(灰点)生成分析函数,以0.05间隔对YFP相对强度进行分箱,并计算每个箱的平均CFP强度(绿点)。用两种构建体转染HEK 293:(B)非同源和(C)miR-27-3p靶向插入物。

关键字:miRNA, 流式细胞术, 双荧光报告因子, 功能分析, 非编码RNA, 小分子RNA, 非编码RNA, 基因调控, 报告基因系统, 单细胞分析, 流式细胞术


  1. 质粒,引物和细胞
    1. p.UTA.2.0空(Addgene,目录号:82446)
    2. p.UTA.2.0 miR 19b-3p(Addgene,目录编号:82442)
    3. 测序引物:
      class =“ke-zeroborder”bordercolor =“#000000”style =“width:500px;” border =“0”cellspacing =“0”cellpadding =“2”>psiCHECK2-R
      T7 Universal Primer
    4. HEK293细胞

  2. 其他材料
    1. 移液器提示&nbsp;
      1. 1,000μl过滤嘴(SARSTEDT,目录号:70.762.211)
      2. 100μl过滤嘴(SARSTEDT,目录号:70.760.212)
      3. 10μl过滤嘴(SARSTEDT,目录号:70.1116.210)
    2. 1.5 ml微量离心管(Eppendorf,目录号:G_0030108116)
    3. 2.0毫升微量离心管(SARSTEDT,目录号:72.695.200)
    4. 24孔板(Greiner Bio One International,目录号:662160)
    5. 5毫升聚苯乙烯圆底管(Corning,Falcon ®,目录号:352052)
    6. PCR管0.2 ml(SARSTEDT,目录号:72.737.002)
    7. Parafilm(Sigma-Aldrich,Bemis,目录号:P7793-1EA)
    8. HEK293人胚肾细胞系(ATCC,目录号:CRL-1573 TM )
    9. One Shot ® TOP10化学感受器 Escherichia coli (Thermo Fisher Scientific,目录号:C404010)
    10. 寡核苷酸的有义和反义链(下面有更多信息)
    11. 凝胶提取试剂盒(QIAquick凝胶提取试剂盒,QIAGEN,目录号:28704)
    12. Gibco TM Opti-MEM TM (+ L-谷氨酰胺,+酚红)(Thermo Fisher Scientific,目录号:31985047)
    13. Lipofectamine ® 2000(赛默飞世尔科技,目录号:11668027)
    14. NotI限制酶(New England Biolabs,目录号:R0189S)
    15. XhoI限制酶(New England Biolabs,目录号:R0146S)
    16. EcoRI(New England Biolabs,目录号:R0101S)
    17. HindIII(New England Biolabs,目录号:R0104S)
    18. 质粒DNA Midiprep试剂盒(QIAquick Plasmid Midi Kit,QIAGEN,目录号:10023)
    19. QIAprep Spin Miniprep Plasmid mini试剂盒(QIAGEN,目录号:27104)
    20. 多聚甲醛4%溶液(Santa Cruz Biotechnology,目录号:sc-281692)
    21. T4 DNA连接酶试剂盒(New England Biolabs,目录号:M0202S)
    22. T4多核苷酸激酶(PNK)(New England Biolabs,目录号:M0201S)
    23. 琼脂糖(Carl Roth,目录号:3810.3)
    24. PBS(PAN-Biotech,目录号:P04-36500)
    25. 胰蛋白酶-EDTA(PAN-Biotech,目录号:P10-023100)
    26. DMEM(Thermo Fisher Scientific,Life Technologies,目录号:41965062)
    27. 胎牛血清(Thermo Fisher Scientific,Life Technologies,目录号:10500-064)


  1. 移液器
  2. 冰箱
  3. 琼脂糖凝胶电泳室
  4. BD LSR II流式细胞仪(BD,型号:LSR II)
    1. YFP:激光488 nm和550LP-BP575 / 26滤光片
    2. CFP:激光408 nm和BP450 / 50(太平洋蓝)
  5. 台式冷冻微量离心机(Thermo Fisher Scientific,型号:Heraeus TM Fresco TM 21,目录号:75002555)
  6. 通用离心机(Thermo Fisher Scientific,型号:Heraeus TM Megafuge TM 16,目录号:75004230;转子:Thermo Fisher Scientific,型号:TX-150,目录号:75005701 )
  7. 凝胶文件系统(INTAS Gel iX Imager)(INTAS Science Imaging Instruments,型号:FACE)
  8. NanoDrop TM 2000(Thermo Fisher Scientific,型号:NanoDrop TM 2000,目录号:ND-2000)
  9. Thermoblock(Eppendorf,型号:ThermoStat Plus,目录号:5352 000.010)
  10. 热循环仪(Labcycler Sensoquest)
  11. Vortexer(科学工业,型号:Vortex-Genie 2,目录号:SI-0236)


  1. BD-FACSDIVA(BD Biosciences)
  2. R( https://www.r-project.org
  3. R studio( https://www.rstudio.com
  4. Bioconductor( https://www.bioconductor.org
  5. 串行克隆( http://serialbasics.free.fr/Serial_Cloner.html




  1. 寡核苷酸设计和退火
    1. 设计和订购寡核苷酸作为单独的链(我们的寡核苷酸从Sigma-Aldrich订购)(表1)。


    2. 在0.2ml PCR管中制备以下反应以使寡核苷酸退火(表2)。


    3. 在37℃下在热循环仪中孵育1小时,然后在95至25°C的斜坡中冷却5分钟(5°C / min)。
      1. 或者,您可以加热寡核苷酸,让它们在室温下冷却几个小时。
      2. 通常不需要磷酸化;但是如果质粒去磷酸化则需要它。
      3. 您可以使用单个寡核苷酸作为对照,检查寡核苷酸是否通过运行2%琼脂糖凝胶退火。通常我们会跳过这一步,之后就没有问题。
    4. 将退火的寡核苷酸储存在-20℃或进行连接步骤。

  2. p.UTA.2.0质粒线性化
    1. 在1.5 ml微量离心管中准备消化(表3)。相应地扩大以进行多次反应。


    2. 涡旋短暂混合。
    3. 快速旋转管子以收集底部的液体。
    4. 将混合物在37°C孵育至少1小时。
    5. 在1%琼脂糖凝胶中进行反应。
    6. 切下5,131 bp的条带并将其转移到干净的微量离心管中。
    7. 使用QIAquick凝胶提取试剂盒按照制造商的方案从凝胶中提取DNA。
    8. 测量DNA浓度。
    9. 准备50 ng /μl等份的线性化质粒。

  3. 连接p.UTA.2.0和退火的寡聚双链体
    1. 根据表4在1.5 ml微量离心管中准备结扎。相应地按比例放大以进行多次反应。


    2. 短暂地涡旋。
    3. 快速旋转管子以收集管子底部的液体。
    4. 将混合物在16°C孵育至少2小时。
    5. 转变成你最喜欢的感受态细胞。

  4. 筛选阳性克隆
    1. 为了检查正确的寡核苷酸插入,我们进行了内切核酸酶限制以区分空克隆(没有miRNA反应元件)和阳性克隆。由于插入位点(XhoI和NotI)含有消化后消失的EcoRI位点,如果寡核苷酸缺乏EcoRI序列,则这是正确的。
    2. 我们一起使用HindIII和EcoRI:空载体产生四个条带(3,992; 1,010; 732; 111)和阳性克隆三个(3,292; 1,736; 111)。
    3. 由于我们的质粒缺乏填充序列,我们可以区分背景和部分消化的DNA;因此,我们强烈建议您进行此筛选测试(图3)。
    4. 可以从Addgene页面检索序列( http://www.addgene.org/82446/)。


    1. 使用QIAprep Spin Miniprep培养细菌培养物并分离质粒。
    2. 在1.5ml微量离心管中制备消化反应(表5)。在进行多次反应时相应地放大。


    3. 短暂涡旋并旋转以收集管底部的液体。
    4. 将混合物在37℃孵育1小时。
    5. 在1%琼脂糖凝胶中加入混合物。
    6. 可视化琼脂糖凝胶。
    7. 分析你的克隆。
    8. 准备转染质量的DNA。

      图4.筛选反应预期结果。 A和B. 计算机空白和阳性(插入miRNA靶标)质粒的DNA凝胶电泳。 C.来自筛选反应的琼脂糖DNA凝胶电泳,其显示一个阳性克隆(泳道2)和三个没有插入物的空载体(泳道4,6,8)。未消化的对照(泳道1,3,5,7)。

  5. 转染p.UTA.2.0质粒
    1. 在24孔板中接种HEK293细胞(每孔约100,000个细胞)。
    2. 在37°C和5%CO 2 孵育18-24小时。
    3. 如下准备DNA样本(我们建议每个实验单位至少重复三次。相应地放大):

    4. 按如下方法制备Lipofectamine ® 2000试剂:

    5. 将DNA溶液(步骤E3)以1:1的比例添加到Lipofectamine ® 2000试剂(步骤E4)中。
    6. 在室温下孵育混合物至少20分钟。
    7. 将DNA-lipofectamine混合物滴加到细胞中。
    8. 在37°C和5%CO 2 孵育72小时。

  6. 流式细胞仪的样品制备
    1. 从井中丢弃培养基。
    2. 用PBS(约500μl)洗涤1次。
    3. 每孔加入100μl胰蛋白酶。
    4. 在37℃和5%CO 2 下孵育5分钟。
    5. 加入400μlDMEM培养基+ 15%FCS。
    6. 将细胞悬浮液转移至FACS管。
    7. 在300 x g 下离心5分钟。
    8. 丢弃上清液。
    9. 加入1毫升PBS。
    10. 在300 x g 下离心5分钟。
    11. 重复步骤F9和F10两次。
    12. 丢弃上清液。
    13. 简要地旋转下来。
    14. 加入100μlPBS。
    15. 加入100μlPFA4%(多聚甲醛4%)。
    16. 在4°C孵育30分钟。
    17. 如前洗涤三次(步骤F9和F10)。
    18. 加入200μlPBS。
    19. 用封口膜密封FACS管。
    20. 储存在4°C(冰箱)。


  1. 数据采集
    以下步骤描述了选通过程,以及如何使用R导出要分析的FCS文件(假设读者具有流式细胞仪数据采集的基本知识)。我们使用BD LSR II仪器进行测量,仪器带有用于YFP的过滤器550LP-BP575 / 26(PE)和用于CFP的BP450 / 50(太平洋蓝)。
    1. 分析你的消极对照。
    2. 使用FSC.A和FSC.H散点图设置单个单元格(此处称为P1或单个单元格)。
    3. 使用FSC.A和SSC.A散射选择主要细胞群。 (在这里称为P2或主要人口)。
    4. 使用直方图可视化定义YFP阳性细胞(此处称为P3或YFP阳性细胞)。 (图5和视频1)

      图5.负控制采集(屏幕截图)。面板显示用于HEK293细胞的门:单峰的散点图(FSC.A与FSC.H),大小和复杂性(FSC.A vs. SSC.A); PE.A(550LP-BP575 / 26)的直方图和散点图,以及CFP和YFP的输出(Scatter plot PE.A vs. Pacific blue)。

      视频1. FACS Diva采集非转染阴性对照。小组显示用于HEK293细胞的门:单峰的散点图(FSC.A与FSC.H),细胞群(FSC.A vs SSC.A),PE.A(550LP-BP575 / 26)的直方图和散点图,以及CFP和YFP的分析输出(Scatter plot PE.A vs. Pacific blue)。

    5. 运行p.UTA.2.0空向量并创建一个门,其PE的相对荧光单位为10,000,直到x轴的末端。 (在这里称为P4或采集门)(图6)。

      图6.空载体对照获取截图。面板显示用空载体转染的HEK293细胞的门(p.UTA.2.0为空)。单峰的散点图(FSC.A与FSC.H),主要细胞群(FSC.A与SSC.A),PE.A(550LP-BP575 / 26)的直方图和散点图,以及分析输出CFP和YFP(散点图PE.A与太平洋蓝色)。

    6. 从P4人群中获取1,000到3,000个事件。
    7. 阅读你的样品。
      图6和7显示了用p.UTA.2.0 Empty和p.UTA.2.0 miR 19b-3p转染的HEK293细胞的采集组。

      图7. p.UTA.2.0 miR 19b-3p采集截图。 小组显示用于HEK293细胞的门:单峰的散点图(FSC.A与FSC.H),大小和复杂性(FSC.A与SSC.A); PE.A(550LP-BP575 / 26)的直方图和散点图,以及CFP和YFP的输出(Scatter plot PE.A vs. Pacific blue)。

    8. 以FCS格式2.0导出P3门(图8)。

      图8. FACS-Diva导出数据屏幕截图。导出P3(包括P4的YFP阳性细胞)群体的FCS格式2.0,屏幕截图举例说明了用于FCS文件的导出选项。

  2. 数据处理和绘图
    这是一个简化的代码,可以有效地分析为实验室科学家编写的UTA分析细胞计数,并且编程经验最少或没有。示例FSC文件和以下步骤中使用的代码包含在数据代码.zip 和视频2描述了每个步骤。

    使用R环境从Raw FCS文件生成分析函数的分步指南:
    1. 使用R脚本“Analysis_Functions.R”文件在同一文件夹中找到FCS文件(仅包含YFP阳性群体)。
    2. 在R中,设置您的工作目录。
      的 setwd (“/路径/ toyour / FCSfiles /文件夹”)
    3. 调用“Analysis_functions_4.0.R”文件中的函数。
      的来源( “Analysis_functions_4.0.R”)
    4. 创建一个包含FCS文件名称的向量,在这种情况下只是包含YFP正面事件的文件,在本例中数据是P3种群(包括P4)。
      fileNames&lt; - Sys.glob (“* _ P3.fcs”)
    5. 创建您的工作清单。
      1. 原始数据(CFP,YFP,比率,折叠)。
      2. Bin数据(分析功能)。
      3. Bin CFP和YFP将用于分析函数生成。
      4. 每个CFP箱的CFP / YFP比率。
      5. 每个箱子折叠压制。这只是另一种看待镇压的方式;这意味着CFP被压制为100%。
      6. 四分位数中位数,最大值和最小值的描述性统计数据。

      函数&lt; - lapply (fileNames,函数(x) bin.data.FscH (x,bin.size = 0.05))

    6. 写下FCS文件的名称,将它们分配到列表中并将其写入文本文件中。

      table_names&lt; - data.frame (fileNames)
      write.table (table_names,“List_Index.txt”)
      名称(功能)&lt; -fileNames

    7. 在R中绘制分析函数(图9)。

      plot.miR (函数,fil_num = c (2,4,6,8),control = 1,ylim = c (1, 8),l_y = 8,
      Leg = c (“空”,“miR 148a 3p”,“miR 19b 3p”,“miR 24 3p”,“miR 29a 3p”),main =“miRNA”,col.m = c (“黑色”,“蓝色”,“红色”,“绿色3”,“紫色”),
      cex = 0.7,mu.cex = 0.3)
      plot.miR (函数,fil_num = c (3,5,7),control = 1,ylim = c (1,8) ,l_y = 8,
      腿= c (“空”,“miR 15a 5p”,“miR 21 5p”,“miR_28_3p”),main =“miRNA”,col.m = c (“黑色”,“蓝色”,“红色”,“绿色3”,“紫色”),
      cex = 0.7,mu.cex = 0.3)


      图9.从plot.miR函数输出的分析函数。 HEK293细胞用几种miRNA传感器转染,通过流式细胞术获得并读取。在R环境中分析了FCS 2.0文件。

      表6.绘图的函数参数。 miR &nbsp;

    1. 创建PDF设备:

      pdf (“YourName.pdf”,3.18,6.75)

    2. 运行您的绘图代码(不止一个是可能的,不要指望R-studio绘图布局上的任何输出)。

      plot.miR (函数,fil_num = c (2,3,4,5),control = 1,ylim = c (1, 8),l_y = 8,
      腿= c (“空”,“miR_148a_3p”,“miR_15a_5p”,“miR_19b_3p”,“miR_21_5p”),主要=“983”,col.m = c (“黑色”,“蓝色”,“红色”,“绿色3”,“紫色”),
      cex = 0.7,mu.cex = 0.3)

    3. 关闭PDF。

      dev.off ()
      您还可以使用其他软件绘制数据,使用以下函数,您将创建所有列表的CSV文件,第一列包含每个YFP bin的日志值,其他列包含每个元素的CFP值在列表(功能)中,带有FCS文件的名称。
    4. 使用导出功能。



  3. 数据解释和统计分析。
    为了解释并从分析函数中获得进一步的见解,您可以使用之前使用的分子滴定模型提取信息(Mukherji et al。,2011; Lemus-Diaz et al。 ,2017)(图10)。

    图10. miRNA诱导调节的滴定模型该miRNA诱导调控模型描述了可用于翻译的游离mRNA。在没有miRNA的情况下,细胞分别以K R 和γ r 的速率转录和降解mRNA。在存在miRNA的情况下,可用于翻译的游离mRNA取决于miRNA-mRNA(r *)复合物,其形成取决于接通率(K on )和miRNA浓度。然后出现两条相互排斥的路径。它可以诱导mRNA降解(在γ r * 速率下)或分解成其组分(在K off 速率下)。

    该模型描述了mRNA的miRNA滴定,其中转录的mRNA与miRNA(r *复合物)相互作用,该miRNA由复合物的K 和K off 统治,这取决于miRNA。 mRNA中的互补和靶位点。以下等式描述了mRNA的变化(可自由翻译)和r *:


    在这种情况下,总miRNA浓度是游离miRNA分子加上r *复合物中包含的miRNA:

    假设没有从miRNA-mRNA复合物发生翻译,并且仅从游离RNA(r)发生翻译,(r)的稳态解决方案如前所述(Mukherji et al。,2011; Lemus -Diaz et al。,2017):


    可以使用app(模拟miRNA功能)进行模拟。有关说明,请参阅Lemus-Diaz 等人的补充材料(2017)。
    &NBSP;使用分析函数作为输入(Excel或R),您可以使用等式4的非线性回归估计参数θ和λ,而θ对应miRNA浓度λ与miRNA的结合相关。这些例子的估计参数可以在Lemus-Diaz 等人的补充信息中找到。&nbsp;(2017)。
    &NBSP;我们为这里描述的报告者验证了这个模型,并展示了参数(θ和λ)如何用于剖析miRNA活性,以miRNA的定位为例。由分析函数推断(Lemus-Diaz et al。,2017,图6)。此外,使用这个模型,我们描述了三个分类变量(低,中,高功能组)来轻松解释输出(Lemus-Diaz et al。,2017,图2)。
    在图9中呈现的数据中,miR 24-3p,miR 29a-3p可归类为低功能性; miR 148a-3p和miR 21-5p为中期;并且miR 21-5p和miR 19b-3p具有高度功能性。然而,作为任何定性数据,这种分类是任意的,因此我们建议使用估计参数并使用它们来比较miRNA功能(Lemus-Diaz et al。,2017,图3和4)。


这项工作得到了哥廷根神经科学,生物物理学和分子生物科学研究生院(DFG授予GSC 226/2)的部分支持,L.T是IMPRS分子生物学的硕士生。该协议改编自Mukherji et al。,2011和Lemus-Diaz et al。,2017;术语分析流式细胞术采用Denzler 等人,,2016。




  1. Bai,B.,Liu,H。和Laiho,M。(2014a)。 细胞核的小RNA表达和深度测序分析揭示了核仁相关microRNA的存在。 FEBS Open Bio 4:441-449。
  2. Bai,B.,Yegnasubramanian,S.,Wheelan,S.J。和Laiho,M。(2014b)。 核仁的RNA-Seq揭示了丰富的SNORD44衍生的小RNA。 PLoS One 9(9):e107519。
  3. Bartel,D。P.(2018)。 Metazoan microRNAs。 Cell 173(1):20 -51群组。
  4. Bartel,D。P.和Chen,C。Z.(2004)。 基因表达的微观管理者:后生动物microRNA的潜在广泛影响。 Nat Rev Genet 5(5):396-400。&nbsp;
  5. Bazzini,A.A.,Lee,M。T.和Giraldez,A.J。(2012)。 核糖体分析显示miR-430在斑马鱼中导致mRNA衰变之前减少了翻译。 em> Science 336(6078):233-237。&nbsp;
  6. Bertoli,G.,Cava,C。和Castiglioni,I。(2015)。 microRNAs:用于乳腺癌诊断,预后,治疗预测和治疗工具的新生物标记物。 Theranostics 5(10):1122-1143。
  7. Bertoli,G.,Cava,C。和Castiglioni,I。(2016)。 microRNAs作为前列腺癌诊断,预后和治疗诊断的生物标志物。 Int J Mol Sci 17(3):421。
  8. Bosson,A.D.,Zamudio,J。R.和Sharp,P。A.(2014)。 内源性miRNA和目标浓度决定了对潜在ceRNA竞争的易感性。 Mol Cell 56(3):347-359。
  9. Brameier,M.,Herwig,A.,Reinhardt,R.,Walter,L。和Gruber,J。(2011)。 具有miRNA功能的人盒C / D snoRNA:扩大调控RNA的范围。 Nucleic Acids Res 39(2):675-686。
  10. Denzler,R.,McGeary,S.E.,Title,A.C.,Agarwal,V.,Bartel,D.P。和Stoffel,M。(2016)。 microRNA水平,靶位点互补性和竞争性对竞争性内源性RNA调节基因表达的影响。 Mol Cell 64(3):565-579。
  11. Di Leva,G。和Croce,C。M.(2013)。 癌症的miRNA分析。 Curr Opin Genet Dev 23 (1):3-11。
  12. Ender,C.,Krek,A.,Friedlander,M.R.,Beitzinger,M.,Weinmann,L.,Chen,W.,Pfeffer,S.,Rajewsky,N。和Meister,G。(2008)。 具有类似microRNA功能的人类snoRNA。 Mol Cell 32(4):519-528。
  13. Fabian,M.R.,Sonenberg,N。和Filipowicz,W。(2010)。 通过microRNA调节mRNA翻译和稳定性。 Annu Rev Biochem 79:351-379。
  14. Garcia,D.M.,Baek,D.,Shin,C.,Bell,G.W.,Grimson,A。和Bartel,D.P。(2011)。 弱的种子配对稳定性和高靶位点丰度会降低lsy-6和其他microRNA的熟练程度。 Nat Struct Mol Biol 18(10):1139-1146。&nbsp;
  15. Gentleman,RC,Carey,VJ,Bates,DM,Bolstad,B.,Dettling,M.,Dudoit,S.,Ellis,B.,Gautier,L.,Ge,Y.,Gentry,J.,Hornik,K 。,Hothorn,T.,Huber,W.,Iacus,S.,Irizarry,R.,Leisch,F.,Li,C.,Maechler,M.,Rossini,AJ,Sawitzki,G.,Smith,C。 ,Smyth,G.,Tierney,L.,Yang,JY和Zhang,J。(2004)。 Bioconductor:计算生物学和生物信息学的开放式软件开发。 Genome Biol 5(10):R80。
  16. Grosswendt,S.,Filipchyk,A.,Manzano,M.,Klironomos,F.,Schilling,M.,Herzog,M.,Gottwein,E。和Rajewsky,N。(2014)。 根据不同类型的连接反应明确鉴定miRNA:靶位点相互作用。 Mol Cell 54(6):1042-1054。&nbsp;
  17. Hafner,M.,Landthaler,M.,Burger,L.,Khorshid,M.,Hausser,J.,Berninger,P.,Rothballer,A.,Ascano,M.,Jr.,Jungkamp,AC,Munschauer,M 。,Ulrich,A.,Wardle,GS,Dewell,S.,Zavolan,M。和Tuschl,T。(2010)。 通过PAR-CLIP对RNA结合蛋白和microRNA靶位点进行转录组鉴定。 Cell 141(1):129-141。&nbsp;
  18. Hahne,F.,LeMeur,N.,Brinkman,R.R.,Ellis,B.,Haaland,P.,Sarkar,D.,Spidlen,J.,Strain,E。和Gentleman,R。(2009)。 flowCore:用于高通量流式细胞仪的Bioconductor软件包。 BMC Bioinformatics 10:106。
  19. Hasler,D.,Lehmann,G.,Murakawa,Y.,Klironomos,F.,Jakob,L.,Grasser,F.A.,Rajewsky,N.,Landthaler,M。和Meister,G。(2016)。 狼疮自身抗原可防止tRNA片段误导入人类microRNA途径。 Mol Cell 63(1):110-124。&nbsp;
  20. Helwak,A.,Kudla,G.,Dudnakova,T。和Tollervey,D。(2013)。 通过CLASH映射人类miRNA interactome显示频繁的非经典绑定。 Cell 153(3):654-665。
  21. Huber,W.,Carey,VJ,Gentleman,R.,Anders,S.,Carlson,M.,Carvalho,BS,Bravo,HC,Davis,S.,Gatto,L.,Girke,T.,Gottardo,R 。,Hahne,F.,Hansen,KD,Irizarry,RA,Lawrence,M.,Love,MI,MacDonald,J.,Obenchain,V.,Oles,AK,Pages,H.,Reyes,A.,Shannon, P.,Smyth,GK,Tenenbaum,D.,Waldron,L。和Morgan,M。(2015)。 使用Bioconductor编排高通量基因组分析。 Nat Methods 12(2):115-121。
  22. Krishnan,P.,Ghosh,S.,Wang,B.,Li,D.,Narasimhan,A.,Berendt,R.,Graham,K.,Mackey,JR,Kovalchuk,O。和Damaraju,S。(2015 )。 新一代测序分析将miR-574-3p和miR-660-5p鉴定为潜在的新型预后标志物对于乳腺癌。 BMC Genomics 16:735。
  23. Lagos-Quintana,M.,Rauhut,R.,Lendeckel,W。和Tuschl,T。(2001)。 鉴定编码小表达RNA的新基因。 Science 294(5543):853-858。&nbsp;
  24. Lai,X.,Wolkenhauer,O。和Vera,J。(2016)。 通过数学建模了解microRNA介导的基因调控网络。 Nucleic Acids Res 44(13):6019-6035。
  25. Lemus-Diaz,N.,Boker,K.O.,Rodriguez-Polo,I.,Mitter,M.,Preis,J.,Arlt,M。和Gruber,J。(2017)。 使用先进的荧光报告系统在单细胞水平上解析miRNA基因抑制。 Sci Rep 7:45197。
  26. Lu,J.,Getz,G.,Miska,EA,Alvarez-Saavedra,E.,Lamb,J.,Peck,D.,Sweet-Cordero,A.,Ebert,BL,Mak,RH,Ferrando,AA, Downing,JR,Jacks,T.,Horvitz,HR和Golub,TR(2005)。 MicroRNA表达谱对人类癌症进行分类。 Nature 435( 7043):834-838。
  27. Moore,M.J.,Scheel,T.K.,Luna,J.M.,Park,C.Y.,Fak,J.J.,Nishiuchi,E.,Rice,C.M。和Darnell,R.B。(2015)。 miRNA-target嵌合体显示miRNA 3'-末端配对是Argonaute靶特异性的主要决定因素。< / a> Nat Commun 6:8864。
  28. Mukherji,S.,Ebert,M。S.,Zheng,G。X.,Tsang,J。S.,Sharp,P。A. and van Oudenaarden,A。(2011)。 MicroRNAs可以产生靶基因表达的阈值。 Nat Genet 43(9):854-859。
  29. Muller,S.,Raulefs,S.,Bruns,P.,Afonso-Grunz,F.,Plotner,A.,Thermann,R.,Jager,C.,Schlitter,AM,Kong,B.,Regel,I。 ,Roth,WK,Rotter,B.,Hoffmeier,K.,Kahl,G.,Koch,I.,Theis,FJ,Kleeff,J.,Winter,P。和Michalski,CW(2015)。 新一代测序揭示胰腺癌中新的差异调节mRNA,lncRNA,miRNA,sdRNA和piRNA 。 Mol Cancer 14:94。
  30. Salomon,W.E.,Jolly,S.M.,Moore,M.J.,Zamore,P.D。和Serebrov,V。(2015)。 单分子成像显示Argonaute重塑其核酸指南的结合特性。 Cell 162(1):84-95。
  31. Schramedei,K.,Morbt,N.,Pfeifer,G.,Lauter,J.,Rosolowski,M.,Tomm,J.M。,von Bergen,M.,Horn,F。和Brocke-Heidrich,K。(2011)。 MicroRNA-21靶向肿瘤抑制基因ANP32A和SMARCA4。 Oncogene 30(26):2975-2985。&nbsp;
  32. Steinkraus,B.R.,Toegel,M。和Fulga,T。A.(2016)。 微小的基因调控巨人:microRNA功能研究的实验策略。 Wiley Interdiscip Rev Dev Biol 5(3):311-362。&nbsp;
  33. Vera,J.,Lai,X.,Schmitz,U。和Wolkenhauer,O。(2013)。 MicroRNA调控网络:经典分子生物学的完美风暴,是系统生物学的理想场景。 / a> Adv Exp Med Biol 774:55-76。
  34. Wee,L.M.,Flores-Jasso,C.F.,Salomon,W。E.和Zamore,P.D。(2012)。 Argonaute将其RNA指南划分为具有不同功能和RNA结合特性的结构域。 em> Cell 151(5):1055-1067。&nbsp;
  35. Yang,Y.,Chaerkady,R.,Beer,M。A.,Mendell,J。T. and Pandey,A。(2009)。 使用定量蛋白质组学方法鉴定乳腺癌细胞中的miR-21靶点。 蛋白质组学 9(5):1374-1384。
  36. Yu,F.,Bracken,C.P.,Pillman,K.A.,Lawrence,D.M.,Goodall,G.J.,Callen,D.F。和Neilsen,P.M。(2015)。 p53抑制源自snoRNA的致癌sno-miR-28。 PLoS One 10(6):e0129190。
  • English
  • 中文翻译
免责声明 × 为了向广大用户提供经翻译的内容,www.bio-protocol.org 采用人工翻译与计算机翻译结合的技术翻译了本文章。基于计算机的翻译质量再高,也不及 100% 的人工翻译的质量。为此,我们始终建议用户参考原始英文版本。 Bio-protocol., LLC对翻译版本的准确性不承担任何责任。
Copyright: © 2018 The Authors; exclusive licensee Bio-protocol LLC.
引用:Lemus-Diaz, N., Tamon, L. and Gruber, J. (2018). Dual Fluorescence Reporter Based Analytical Flow Cytometry for miRNA Induced Regulation in Mammalian Cells. Bio-protocol 8(17): e3000. DOI: 10.21769/BioProtoc.3000.