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Oct 2019
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High-throughput Flow Cytometry Assay to Investigate TDP43 Splicing Function
高通量流式细胞术检测TDP43剪接功能   

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Abstract

Mutations in RNA-binding proteins (RBPs) such as TDP43 are associated with transcriptome-wide splicing defects and cause severe neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). The impact of RBP mutations on splicing function is routinely studied using PCR-based bulk measurements. However, the qualitative and low-throughput nature of this assay make quantitative and systematic analyses, as well as screening approaches, difficult to implement. To overcome this hurdle, we have developed a quantitative, high-throughput flow cytometry assay to investigate TDP43 splicing function on a single-cell level

Keywords: RNA splicing (RNA剪接), Minigene (微基因), Flow cytometry (流式细胞术), TDP43 (TDP43), Amyotrophic lateral sclerosis (肌萎缩侧索硬化)

Background

RNA-binding proteins (RBPs) such as TDP43 regulate post-transcriptional gene regulation by orchestrating RNA stability, transport and processing, including mRNA splicing (Gerstberger et al., 2014). Impairment of RBP function as a result of mutations and/or aggregation has been implicated in the etiology of many neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) (Harrison and Shorter, 2017). Notably, ALS-associated mutations in RBPs like TDP43 can cause transcriptome-wide splicing defects, suggesting that misregulation of RBP splicing function may be a key driver of disease (Arnold et al., 2013; Sun et al., 2015). Thus, an important goal in ALS research is to uncover the molecular mechanisms of RBP function, requiring experimental approaches for the systematic interrogation of RBP splicing function.

Widely-used methods for studying splicing are reverse transcription-PCR (RT-PCR) and RNA sequencing (RNA-seq). Both methods can be used to study the splicing of endogenous transcripts; however, typically only as a bulk measurement of an entire population of cells. RT-PCR is simple and affordable, yet efficient only to study splicing of a select few endogenous transcripts. Moreover, RT-PCR read-outs are often only semi-quantitative. In contrast, RNA-seq provides quantitative insights into global splicing, but is time- and cost-intensive and requires sophisticated data analysis pipelines (Wang et al., 2009). Thus, both RT-PCR and RNA-seq are poorly suited for high-throughput applications, for example to systematically compare the splicing function of multiple RBP variants.

A powerful tool to study splicing are minigenes, which typically are plasmid-based, simplified genes containing introns and exons that recapitulate defined splicing events outside of the native gene context (Baralle and Baralle, 2005). In particular, minigenes have been instrumental in discovering cis- and trans-acting splicing elements (Cooper, 2005). Instead of endogenous targets, minigenes are also routinely used to streamline investigation of RBP splicing function (D'Ambrogio et al., 2009; Kino et al., 2011). Conventionally, the splicing of minigenes is evaluated with analytical or quantitative RT-PCR. However, the low-throughput and bulk read-out of this method precludes leveraging the full potential of minigene-based splicing reporters.

To overcome these hurdles, we developed a minigene that encodes for a fluorescent protein (FP minigene), thus allowing us to convert a defined splicing event into a sensitive, inherently quantitative and readily detectable signal on a single-cell level by flow cytometry (Schmidt et al., 2019). To normalize splicing of the FP minigene to the overall abundance of the parent transcript, we fused it to another, intron-free fluorescent protein (constitutive FP). While this approach is broadly applicable (Gurskaya et al., 2012; Sorenson and Stevens, 2014; Gonatopoulos-Pournatzis et al., 2018), we adapted it specifically to study TDP43-dependent splicing by interrupting the FP minigene with exon 9 of the CFTR gene, which is skipped only in the presence of functional TDP43 (Buratti et al., 2001). To rapidly compare the splicing efficiency of multiple TDP43 variants in TARDBP (i.e., the gene encoding TDP43) knock-out cells, we used a bidirectional promoter to drive the coordinated expression of both our splicing reporter and a given BFP-tagged TDP43 variant from the same plasmid. A graphical overview of our splicing reporter design in shown in Figure 1.



Figure 1. Splicing reporter design.
A. The fluorescent protein (FP)-based splicing module and BFP-tagged TDP43 variants are expressed from the same plasmid via a bi-directional promoter (pBI). B. Production of a dually-fluorescent GFP-mCherry fusion protein depends on the presence of functional TDP43, which is required to mediate the skipping of CFTR gene-derived exon (labeled E2).

Here, we describe in detail how to perform and analyze our flow cytometry-based splicing assay. This protocol is optimized for use with HEK-293T wild-type and HEK-293T TARDBP knock-out cells (Schmidt et al., 2019), yet in principle suitable for other recently published constitutive or inducible TARDBP knock-out cell lines as well (Chiang et al., 2010; Gasset-Rosa et al., 2019; Roczniak-Ferguson and Ferguson, 2019).

Materials and Reagents

  1. Cell lines
    1. HEK-293T wild-type cells (ATCC, catalog number: CRL-3216 )
    2. HEK-293T TARDBP knock-out cells (available upon request)

  2. Cell culture
    1. 1.5 ml microtubes (Eppendorf, catalog number: 0 22363204 )
    2. 60 µm scepter tips for cell counting (Milipore-Sigma, catalog number: PHCC60050 )
    3. 10 cm tissue culture plates for propagating cell lines (Thermo Scientific, catalog number: 12-556-002 )
    4. 6-well tissue culture plates for splicing assays (Thermo Scientific, catalog number: 14-832-11 )
    5. 70 µm cell strainer (Corning, Falcon, catalog number: 08-771-2 )
    6. 5 ml round-bottom polystyrene tubes (Corning, Falcon, catalog number: 14-959-1A )
    7. SH800S flow cytometer setup beads (Sony Biotechnology, catalog number: LE-B3001 )
    8. HyClone DMEM high glucose (GE Healthcare, catalog number: SH30081FS ) supplemented with:
      1. 10% FBS (Atlanta Biologicals, catalog number: S11150H )
      2. 1 mM sodium pyruvate (Gibco, catalog number: 11-360-070 )
      3. 2 mM L-glutamine (Gemini Biosciences, catalog number: 400106 )
      4. 1x MEM non-essential amino acids (Gibco, catalog number: 11-140-076 )
      5. 40 U/ml penicillin and 40 µg/ml streptomycin (Gemini Biosciences, catalog number: 400109 )
    9. Sterile 1x PBS at pH 7.4 (Sigma-Aldrich, catalog number: P3813-5x10PAK )
    10. Trypsin-EDTA (Gemini Biosciences, catalog number: 400150 )
    11. X-tremeGENE9 transfection reagent (Roche, catalog number: 0 6365787001 )
    12. OptiMEM (Gibco, catalog number: 31985-070 )

  3. Plasmids (available at https://www.addgene.org/Rajat_Rohatgi/, unless otherwise noted)
    1. Splicing reporter only: pHBS1389 IBB-GFP-mCherry3E (Addgene, catalog number: 118803 )
    2. Positive control: pHBS1503 [IBB-GFP-mCherry3E]-[BFP-TDP43 WT] (Addgene, catalog number: 133327 )
    3. Negative control: pHBS1501 [IBB-GFP-mCherry3E]-[BFP-TDP43 ∆RRM] (Addgene, catalog number: 133325 )
    4. Extensive collection of TDP43 CTD mutants in the splicing reporter (e.g., Addgene, catalog numbers: 133328-133333 )
    5. Backbone for subcloning of CTD mutants into splicing reporter: pHBS1224 [IBB-GFP-mCherry3E]-[BFP-TDP43 ∆CTD EcoRV] (available upon request)
    6. Compensation probes: mammalian expression vectors with BFP, GFP and mCherry inserts (e.g., Addgene, catalog numbers: 54665 , 54759 and 54563 )

Equipment

  1. Single-channel pipettes (Rainin or equivalent)
  2. Cell culture incubator (Thermo Scientific or equivalent)
  3. Scepter cell counter (Milipore-Sigma) or equivalent manual/automatic cell counter
  4. Sony SH800S cell sorter (Sony Biotechnology) or equivalent cell sorter equipped with 405 nm, 488 nm and 561 nm lasers, as well as appropriate filters
  5. Computer for data analysis (e.g., 3.1 GHz Intel i7 processor with 16 GB RAM)

Software

  1. SH800S operating software for data collection (Sony Biotechnology) or operating software for equivalent flow cytometers
  2. Mathematica Version 11.3 or higher (Wolfram Research) + custom data analysis pipelines:
    1. Script ‘CSA_Gating.nb’ (available at https://github.com/RohatgiLab/TDP43-analysis)
    2. Script ‘CSA_Plotting.nb’ (available at https://github.com/RohatgiLab/TDP43-analysis

Procedure

  1. Seeding of cells
    Note: Work in a tissue culture hood to prevent contaminations. Culture all HEK-293T wild-type and TARDBP knock-out cells in DMEM high-glucose plus supplements (see Materials).
    1. Harvest HEK-293T wild-type and TARDBP knock-out cells by trypsinization and determine cell numbers using the Scepter (or equivalent manual/automatic cell counters).
    2. For setup and calibration of the flow cytometer, seed four wells of a standard six-well tissue culture plate with 400,000 HEK-293T wild-type cells each (compensation samples).
    3. For positive and negative controls, seed another four wells of a standard six-well tissue culture plate with 400,000 HEK-293T TARDBP knock-out cells each (control samples).
    4. For each TDP43 variant to be tested, seed two wells of a standard six-well tissue culture plate with 400,000 HEK-293T TARDBP knock-out cells each (experimental samples).
    5. Let the cells adhere overnight in a cell culture incubator at 37 °C and 5% CO2.

  2. Transfection of cells
    General workflow: Work in a tissue culture hood to prevent contaminations. Per transfection, carefully add 3 µl of X-tremeGENE9 transfection to 250 µl OptiMEM without touching the walls of the microtube and incubate for 20 minutes at room temperature. Per transfection, mix 1 µg of reporter plasmid with 250 µl OptiMEM in a separate microtube. After incubation, combine the reactions and carefully spread the mix dropwise across the cells in a given well.
    1. To prepare the compensation samples, transfect one well of HEK-293T cells with 1 µg of either BFP, GFP or mCherry mammalian expression constructs. Note that one well will purposely remain untransfected.
    2. To prepare control samples, transfect two wells of HEK-293T TARDBP knock-out cells with 1 µg each of either pHBS1501 or pHBS1503.
    3. To prepare experimental samples, transfect two wells of HEK-293T TARDBP knock-out cells with 1 µg each of the desired construct. A reference collection of TDP43 CTD mutants in the splicing reporter background is available at https://www.addgene.org/Rajat_Rohatgi/.
    4. Incubate all samples for 24 h in a cell culture incubator at 37 °C and 5% CO2.

  3. Flow cytometer setup and calibration
    Note: Only a basic outline of the SH800S setup and calibration procedure is given below. Please consult the manual or contact the technical service for further questions and troubleshooting.
    1. Before starting the flow cytometer, check the sheath fluid and ddH2O levels (refill if necessary) and empty the waste.
    2. Make sure that the sheath fluid container and lines are sealed, then turn on the compressor.
    3. Turn on the SH800S flow cytometer and start the operating software.
    4. Follow the prompts during the chip loading and fluidics check steps. We use 100 µm chips for analyzing HEK-293T cells. Select the 405 nm, 488 nm and 561 nm lasers when prompted.
    5. Follow the prompts during the chip alignment step. Sort calibration is not required, as analyzer mode is sufficient.
    6. During setup, harvest the four compensation samples by trypsinization. Make sure to keep the sample volumes low to prevent unnecessary dilution of the cell suspensions (we typically use 250 µl trypsin per well and quench by adding 250 µl complete DMEM). Filter each sample through a cell strainer into a 5 ml round-bottom polystyrene tube. Keep tubes on ice until further use.
    7. Once setup is finished, create a new experiment in the operating software. In the measurement settings pane, select only the FSC (forward scatter), BSC (side scatter), FL1 (BFP), FL2 (GFP) and FL3 (mCherry) channels. When prompted, start the compensation wizard and follow the steps. The compensation samples will be needed at this point.
    8. Upon completion of the compensation step, setup is finished and the remaining samples can be prepared.

  4. Prepare and analyze samples by flow cytometry
    1. Harvest the control and experimental samples by trypsinization as described in Step C6. Filter each sample through a cell strainer into a 5 ml round-bottom polystyrene tube. Keep samples on ice until further use.
    2. Start by analyzing the control samples to set the gates and get an idea of the expected effect sizes.
    3. In the BSC-A vs. FSC-A density plot, ensure that the gate for the cell population set during compensation still applies (Gate 1; Figure 2A).
    4. In Gate 1, create a FSC-H vs. FSC-A density plot to further gate for single cells (Gate 2; Figure 2B). This filter makes use of disproportions between the area and height of cell clumps compared to single cells (Hazen et al., 2018).
    5. In Gate 2, create FL1-A (BFP), FL2-A (GFP) and FL3-A (mCherry) histogram plots. The BFP signal is proportional to the total TDP43 amount. The GFP signal scales with total splicing reporter transcript levels, whereas the mCherry signal reflects TDP43 splicing activity.
    6. To get a preview of TDP43 splicing efficiency, first gate for BFP-positive cells in the FL1-A histogram (Gate 3; Figure 2C). This step is to select for successfully transfected cells only. The transfection efficiency (i.e., fraction of BFP-positive cells) should be 50% or greater. Then in Gate 3, create a FL2-A (GFP) vs. FL3-A (mCherry) density plot. There should be a significant difference in the position of the positive and negative control populations in this plot (Figure 2D).
      Note: These gates are only for data preview, but not final data analysis.
    7. Collect at least 50,000 events in Gate 3 for each control and experimental sample. Make sure to hit the ‘Record’ button to ensure that data is saved during data collection. As a reference, we recommend to take a screen shot of the above specified plots for each sample after data collection. Unfortunately, this has to be done manually for each sample. Right-click on the worksheet area to bring up the quick access menu and select ‘Copy Worksheet Picture’. Paste the picture into a simple image processor such as Microsoft Paint and save.
    8. After all samples have been analyzed, export the data as an fcs-file by right-clicking on the experiment in the ‘Active Experiments’ panel.
    9. Shut-down the flow cytometer and software following the instructions of the shutdown wizard, which can be found in the ‘Cytometer’ tab.
    10. After the instrument shut-down, turn-off the compressor and release the pressure from the sheath tank.


      Figure 2. Example data and gating strategy during data acquisition. A. BSC-A vs. FSC-A density plot showing all detected events and Gate 1 that selects for cells. B. FSC-H vs. FSC-A density plot and Gate 2 to select for single cells. C. FL1-A (BFP) histogram and Gate 3 to select for transfected cells. D. FL3-A (mCherry) vs. FL2-A (GFP) density plots for full-length, wild-type (WT) TDP43 and TDP43 lacking its RNA-binding domains (∆RRM).

Data analysis

Note: The data analysis pipeline outlined below uses Mathematica scripts that have been written specifically to analyze fcs-files generated by the Sony SH800 software and may not be immediately compatible with fcs-files from other machines without tweaking the code.

For analysis of the splicing data, the mCherry signal of each cell is normalized to the GFP signal of the same cell to quantify splicing efficiency (Figure 3A). To further normalize to the TDP43 level of that cell, the splicing efficiency is plotted against the BFP signal. We have developed Mathematica scripts (available at https://github.com/RohatgiLab/TDP43-analysis) that extract the raw measurements for each observation from standard fcs-files, gate for cells and calculate the splicing ratio (script ‘CSA_Gating.nb), as well as plot the data (‘CSA_Plotting.nb’).
  First open the ‘CSA_Gating.nb’ script in Mathematica. Under ‘Step 0’, the gates for the analysis are specified (Figure 3B). Try the pre-defined values or refer to the reference screen shots to set appropriate boundary conditions. Confirm by evaluating the cell. In ‘Step 1’, the location of the data files on the hard drive is specified. To the parameter ‘RandomFile’, attribute the file path to a random file in the folder containing the fcs-files from the experiment (Figure 3C). Evaluate the cell to confirm; a list of all fcs-files found will be returned. Finally, evaluate the cell titled ‘Step 2’. In this analysis loop, the data will be loaded, gated and analyzed (Figure 3D). For each sample, a summary of the analysis as well as csv-files with the raw numerical values will be exported.
  For further analysis and plotting of the data, open the ‘CSA_Plotting.nb’ script in Mathematica. Start by attributing the file path to the csv-files from the above analysis to the parameter ‘RandomFile’ (Figure 3E) and evaluate the cell to confirm. Then evaluate the next cell to re-format the data for further analysis. To plot TDP43 splicing efficiency, expressed as the ratio of the mCherry-to-GFP signals, versus the total cellular TDP43 concentration (BFP signal), evaluate the cell labeled ‘Plot the data!’. To change the axis of the plots, update the ‘yScale’, ‘xStart’ and ‘xEnd’ parameters (Figure 3F), then re-evaluate the cell. This step generates both individual plots for each sample that is automatically rendered and saved (see example in Figure 3G). The last two cells in the script allow the comparison of TDP43 splicing efficiency in a range of TDP43 levels defined by the parameters ‘start’ and ‘end’, generating a violin plot (Figure 3G). To manually re-arrange the order of the samples in the plot, specify the desired sample order in the list termed ‘OrderedData’ and evaluate the cell (Figure 3H).


Figure 3. Splicing assay data analysis and plotting. A. Outline of the analysis pipeline. A flow cytometer is used to measure and deconvolute the mCherry, GFP and BFP fluorescence signals of individual cells in a population. Using custom scripts, the splicing efficiency is calculated by taking the ratio of the mCherry to GFP signal of a given cell (Figure 1) and compared to the TDP43 level of the same cell (BFP signal). B. Screenshot highlighting where to change the gate boundaries and cut-offs in the ‘CSA_Gating.nb’ script. C. Screenshot indicating where to provide the file path to the fcs raw data in the ‘CSA_Gating.nb’ script. D. Example density plots generated during evaluation of the ‘CSA_Gating.nb’ script to visualize the gates defined for analysis. E. Screenshot illustrating where in the ‘CSA_Plotting.nb’ script to provide the file path to the csv files generated during evaluation of the ‘CSA_Gating.nb’ script. F. Screenshot highlighting where to adjust the plot scales in the ‘CSA_Plotting.nb’ script. G. Example density plot generated by the ‘CSA_Plotting.nb’ script that depicts splicing efficiency vs. TDP43 levels for wild-type (WT) and ∆RRM TDP43 variants (top) and example violin plot highlighting the splicing efficiencies in the defined BFP signal range (bottom). H. Screenshot of the ‘CSA_Plotting.nb’ script indicating where to define the BFP signal range for the violin plots comparing splicing efficiency.

Acknowledgments

This work has been supported by the Deutsche Forschungsgemeinschaft (SCHM 3082/2-1 to H.B.S.) and the National Institutes of Health (DP2 GM105448 and R35 GM118082 to R. R.). The original protocol was published in Schmidt et al. (2019).

Competing interests

The authors declare no competing interests.

References

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简介

[摘要 ] TDP43等RNA结合蛋白(RBP)突变与转录组范围的剪接缺陷相关,并引起严重的神经退行性疾病,包括肌萎缩性侧索硬化症(ALS)和额颞痴呆(FTD)。RBP突变对剪接的影响通常使用基于PCR的大量测量方法来研究其功能。但是,该方法的定性和低通量性质使得难以进行定量和系统的分析以及筛选方法。为克服这一障碍,我们开发了定量,高通量流式细胞仪检测单细胞水平的TDP43剪接功能

[背景 ] RNA结合蛋白(RBPs)(例如TDP43)通过协调RNA的稳定性,转运和加工(包括mRNA剪接)来调节转录后基因的调控(Gerstberger 等,2014)。突变和/导致RBP功能受损或聚集与许多神经退行性疾病的病因有关,例如肌萎缩性侧索硬化症(ALS)和额颞痴呆(FTD)(Harrison and Shorter,2017)。值得注意的是,与RBPs相关的ALS相关突变如TDP43可以引起转录组范围的剪接。缺陷,提示RBP剪接功能的失调可能是疾病的关键驱动因素(Arnold 等,2013; Sun 等,2015)。因此,ALS研究的一个重要目标是揭示RBP功能的分子机制,需要实验方法来系统询问RBP剪接功能。

研究剪接的广泛使用的方法是逆转录PCR(RT-PCR)和RNA测序(RNA-seq),这两种方法都可用于研究内源性转录本的剪接;但是,通常仅作为一个整体的整体测量RT-PCR简单且价格适中,但仅对研究少数内源转录本的剪接有效,此外,RT-PCR读数通常仅是半定量的。全局剪接,但时间和成本都很高,并且需要复杂的数据分析流程(Wang 等人,2009)。因此,RT-PCR和RNA-seq均不适用于高通量应用,例如系统比较多个RBP变体的剪接功能。

研究剪接的有力工具是小基因,它们通常是基于质粒的简化基因,包含内含子和外显子,可概括天然基因范围之外的已定义剪接事件(Baralle and Baralle,2005)。特别是,小基因在发现中发挥了作用顺式和反式剪接元件(Cooper,2005)。除了内源性靶点外,小基因还经常用于简化对RBP剪接功能的研究(D'Ambrogio 等,2009; Kino 等,2011)。 ,小基因的剪接可通过分析或定量RT-PCR进行评估。但是,这种方法的低通量和大量读数无法充分利用基于小基因的剪接报告基因的潜力。

为了克服这些障碍,我们开发了一种编码荧光蛋白的微型基因(FP微型基因),从而使我们能够通过流式细胞术将定义的剪接事件转换为单细胞水平上敏感,固有定量和易于检测的信号(Schmidt 等,2019 )。为了归拼接FP小基因的整体丰家长成绩单,我们融合到另一个,内含免费荧光蛋白(构FP)中。虽然这种方法可广泛应用于(Gurskaya 等。(2012年; Sorenson和Stevens,2014年; Gonatopoulos-Pournatzis 等人,2018年),我们通过用CFTR基因的第9外显子打断FP小基因,专门针对TDP43依赖性剪接进行了改编,只有在存在CFTR基因的情况下才跳过功能性TDP43 (Buratti 等,2001)。为了快速比较TARDBP(即编码TDP43的基因)敲除细胞中多个TDP43变异体的剪接效率,我们使用了双向启动子来驱动协同的expr 剪接报告基因和给定的BFP标签的TDP43变异体均来自同一质粒,我们的剪接报告基因设计的图解概述如图1所示。



D:\重新格式化\ 2020-3-2 \ 1903009--1363 Hermann Schmidt 837314 \ Figs jpg \ Fig1 .jpg

图1.剪接报告子设计A.基于荧光蛋白(FP)的剪接模块和带有BFP标签的TDP43变体通过双向启动子(pBI )从同一质粒表达。B.产生双荧光GFP -mCherry融合蛋白取决于功能性TDP43的存在,这是介导跳过CFTR基因衍生的外显子(标记为E2)所必需的。



在这里,我们详细描述了如何进行和分析基于流式细胞仪的剪接测定法。该方案针对与HEK-293T野生型和HEK-293T TARDBP敲除细胞一起使用进行了优化(Schmidt 等,2019),但原则上也适合于其他最近发表的组成型或诱导型TARDBP敲除细胞系(Chiang 等,2010; Gasset-Rosa 等,2019; Roczniak-Ferguson和Ferguson,2019)。

关键字:RNA剪接, 微基因, 流式细胞术, TDP43, 肌萎缩侧索硬化

材料和试剂


 


细胞系
HEK-293T野生型细胞(ATCC ,目录号:CRL-3216)
HEK-293T TARDBP敲除细胞(根据要求提供)
 


              细胞培养
1.5毫升微管(Eppendorf,目录号:022363204)
60 µm权杖吸头用于细胞计数(Milipore -Sigma,目录号:PHCC60050)
10 cm组织培养板,用于繁殖细胞系(Thermo Scientific,目录号:12-556-002)
用于剪接测定的6孔组织培养板(Thermo Scientific,目录号:14-832-11)
70 µm细胞过滤器(Corning ,Falcon,目录号:08-771-2)
5 ml圆底聚苯乙烯管(Corning ,Falcon,目录号:14-959-1A)
SH800S流式细胞仪设置磁珠(Sony Biotechnology,目录号:LE-B3001)
HyClone DMEM高葡萄糖(GE Healthcare,目录号:SH30081FS)补充:
10%FBS(亚特兰大生物公司,目录号:S11150H)
1 mM丙酮酸钠(Gibco ,目录号:11-360-070 )。
2 mM L-谷氨酰胺(Gemini Biosciences,目录号:400106)
1个MEM非必需氨基酸(Gibco,目录号:11-140-076)
40 U / ml青霉素和40 µg / ml链霉素(Gemini Biosciences,目录号:400109)
pH 7.4的无菌1x PBS (Sigma-Aldrich,目录号:P3813-5x10PAK)
胰蛋白酶-EDTA(Gemini Biosciences,目录号:400150)
X-tremeGENE9转染试剂(罗氏(Roche),目录号:06365787001)
OptiMEM (Gibco ,目录号:31985-070 )。
 


质粒(除非另有说明,请访问https://www.addgene.org/Rajat_Rohatgi/)
仅剪接报告基因:pHBS1389 IBB-GFP-mCherry3E(Addgene ,目录号:118803)
阳性对照:pHBS1503 [IBB-GFP-mCherry3E]-[BFP-TDP43 WT](Addgene ,目录号:133327)
阴性对照:pHBS1501 [IBB-GFP-mCherry3E]-[BFP-TDP43 ∆RRM](Addgene ,目录号:133325)
收集TDP43广泛的CTD突变体的接续记者(例如,Addgene公司,目录号小号:133328- 133333)
将CTD突变体亚克隆到剪接报告基因的骨架:pHBS1224 [IBB-GFP-mCherry3E]-[BFP-TDP 43 ∆CTD EcoRV ](可根据要求提供)
探头补偿:哺乳动物表达载体和BFP,GFP和mCherry的插件(例如,Addgene公司,目录号:54 665,54759和54563)
配套设备


 


单通道移液器(Rainin 或类似产品)
细胞培养培养箱(Thermo Scientific或同等产品)
权杖细胞计数器(Milipore -Sigma)或等效的手动/自动细胞计数器
Sony SH800S细胞分选仪(Sony Biotechnology)或等效的细胞分选仪,配备405 nm,488 nm和561 nm激光器以及适当的滤光片
用于数据分析的计算机(例如,具有16 GB RAM的3.1 GHz Intel i7处理器)
 


软体类


 


SH800S用于数据收集的操作软件(Sony Biotechnology)或用于等效流式细胞仪的操作软件
Mathematica版本11.3或更高版本(Wolfram Research)+定制数据分析管道:
脚本' CSA_Gating.nb '(可从https://github.com/RohatgiLab/TDP43-analysis获得)。
脚本' CSA_Plotting.nb '(在https://github.com/RohatgiLab/TDP43-analysis上提供)。
 


程序


 


细胞接种
注意:在组织培养罩中工作以防止污染,将所有HEK-293T野生型和TARDBP敲除细胞培养在DMEM高葡萄糖加补充剂中(请参见材料)。


通过胰蛋白酶消化收获HEK-293T野生型和TARDBP敲除细胞,并使用Scepter(或等效的手动/自动细胞计数器)确定细胞数。
为了设置和校准流式细胞仪,将标准六孔组织培养板的四个孔分别接种400,000个HEK-293T野生型细胞(补偿样品)。
对于阳性和阴性对照,在标准六孔组织培养板的另外四孔中分别接种400,000个HEK-293T TARDBP敲除细胞(对照样品)。
对于每个要测试的TDP43变体,在标准六孔组织培养板的两个孔中分别接种400,000个HEK-293T TARDBP敲除细胞(实验样品)。
让细胞在细胞培养箱中于37°C和5%CO 2 下粘附过夜。
 


细胞转染
常规工作流程:在组织培养罩中工作以防止污染。每次转染时,小心地将3 µl X-tremeGENE9转染添加到250 µl OptiMEM中,而不接触微管壁,并在室温下孵育20分钟。每次转染,混合1将微克报告质粒与250微升OptiMEM 置于另一个微管中。孵育后,合并反应并小心地将混合物分散在指定孔中的细胞中。


要制备补偿样品,请用1 µg BFP,GFP或mCherry哺乳动物表达构建体转染一孔HEK-293T细胞。请注意,有意将一孔保持未转染状态。
要制备对照样品,请用pHBS1501或pHBS1503分别用1 µg 转染HEK-293T TARDBP敲除细胞的两个孔。 
为了制备实验样品,将两个孔的HEK-293T TARDBP敲除细胞分别用1 µg所需的构建体转染。可在剪接报告子背景中获得TDP43 CTD突变体的参考收藏,网址为https://www.addgene.org。 / Rajat_Rohatgi /。
将所有样品在细胞培养箱中于37°C和5%CO 2 下孵育24小时。
 


流式细胞仪的设置和校准
注意:以下仅给出了SH800S设置和校准过程的基本概述。有关其他问题和故障排除,请查阅手册或联系技术服务。


在启动流式细胞仪之前,请检查鞘液和ddH 2 O的水平(如有必要,请补充)并排空废物。
确保将鞘液容器和管路密封,然后打开压缩机。
打开SH800S流式细胞仪并启动操作软件。
在芯片加载和流体检查步骤中,按照提示进行操作。我们使用100 µm芯片分析HEK-293T细胞。在出现提示时,选择405 nm,488 nm和561 nm激光器。
在芯片对准步骤中按照提示进行操作。由于分析仪模式已足够,因此无需进行校准。
在设置过程中,通过胰蛋白酶消化收获四个补偿样品。确保将样品量保持在较低水平,以防止细胞悬浮液不必要的稀释(我们通常每孔使用250μl胰蛋白酶,并通过添加250μl完整DMEM淬灭)。将细胞滤网放入5毫升的圆底聚苯乙烯试管中,将试管放在冰上直至进一步使用。
设置完成后,在操作软件中创建一个新实验。在``测量设置''窗格中,仅选择FSC(正向散射),BSC(侧向散射),FL1(BFP),FL2(GFP)和FL3(mCherry)通道出现提示时,启动补偿向导并按照步骤进行操作。此时将需要补偿样品。
补偿步骤完成后,设置完成,可以准备剩余的样品。
 


通过流式细胞仪制备和分析样品
收获控制和实验样品通过胰蛋白酶消化所描述的我Ñ步骤C 6,过滤器的每个样品通过细胞过滤网分为A 5毫升的圆底聚苯乙烯试管,保持样品在冰上直至进一步使用。
首先分析控制样本以设置门并了解预期效果的大小。
在BSC-A对FSC-A密度图中,请确保补偿期间设置的细胞群门仍然适用(门1 ;图2A)。
在Gate 1中,创建FSC-H与FSC-A密度图,以进一步为单个细胞进行浇口(Gate 2 ;图2B)。此过滤器利用了与单个细胞相比的细胞团的面积和高度之间的比例失调(Hazen 等人,2018)。
在Gate 2中,创建FL1-A(BFP),FL2-A(GFP)和FL3-A(mCherry)直方图.BFP信号与总TDP43量成正比.GFP信号与总剪接报告基因水平成比例,反映mCherry信号反映TDP43的剪接活性。
要预览TDP43的剪接效率,请在FL1-A直方图中首先对BFP阳性细胞进行门控(3号门;图2C)。此步骤仅选择成功转染的细胞。转染效率(即BFP的分数) -阳性细胞)应大于或等于50%,然后在3号门中创建FL2-A(GFP)与FL3-A(mCherry)密度图。阳性对照和阴性对照的位置应存在显着差异注意:这些门仅用于数据预览,而不能用于最终数据分析(图2D)。
在3号门中为每个对照和实验样品至少收集50,000个事件。确保单击``记录''按钮以确保在数据收集过程中保存了数据。作为参考,我们建议对上述指定图进行截屏对于数据采集后的每个样本,不幸的是,这必须针对每个样本手动完成,右键单击工作表区域以调出快速访问菜单,然后选择“复制工作表图片”,将图片粘贴到一个简单的图像处理器中,例如作为Microsoft Paint并保存。
分析完所有样本后,通过在“活动实验”面板中右键单击实验,将数据导出为fcs文件。
按照关闭向导的指示关闭流式细胞仪和软件,可以在“细胞计数仪”选项卡中找到。
仪器关闭后,请关闭压缩机并释放鞘管中的压力。
 


D:\重新格式化\ 2020-3-2 \ 1903009--1363 Hermann Schmidt 837314 \图jpg \图2 .jpg


图2数据采集过程中的示例数据和门控策略A.BSC -A与FSC-A密度图显示所有检测到的事件以及为细胞选择的门1 B.FSC-H与FSC-A密度图和门2 选择单个细胞.C.FL1- A(BFP)直方图和Gate 3 选择转染的细胞.D.FL3-A(mCherry)vs.FL2-A(GFP)密度图用于全长野生类型(WT)的TDP43和TDP43缺少其RNA结合域(∆RRM)。


 


资料分析


 


注意:下面概述的数据分析管道使用专门为分析Sony SH800软件生成的fcs文件而编写的Mathematica脚本,并且如果不调整代码,可能无法立即与其他计算机的fcs文件兼容。


 


为了分析剪接数据,将每个细胞的mCherry信号归一化为同一细胞的GFP信号以量化剪接效率(图3A)。为了进一步归一化至该细胞的TDP43水平,相对于BFP信号。我们已经开发了Mathematica脚本(可从https://github.com/RohatgiLab/TDP43-analysis获取),该脚本可从标准fcs文件中提取每个观测值的原始测量值,对细胞进行门控并计算剪接率(脚本' CSA_Gating.nb ),以及绘制数据(“ CSA_Plotting.nb ”)。


  首先在Mathematica中打开' CSA_Gating.nb '脚本,在' Step 0'下指定分析门(图3B)。尝试使用预定义的值或参考参考屏幕截图以设置适当的边界条件。通过评估单元格,在``步骤1''中指定数据文件在硬盘驱动器上的位置。为参数`` RandomFile ''将文件路径分配给包含实验中fcs文件的文件夹中的随机文件(图3C)。评估单元格以确认; 将返回所有找到的fcs文件的列表。最后,评估名为'Step 2'的单元格。在此分析循环中,将加载,门控和分析数据(图3C)3D)。对于每个样本,将导出分析摘要以及带有原始数值的csv文件。


  为了进一步分析和绘制数据,请在Mathematica中打开' CSA_Plotting.nb '脚本,首先将上述分析的csv文件的文件路径归因于参数' RandomFile '(图3E),并评估单元格以要确认,然后评估下一个单元格以重新格式化数据以进行进一步分析。要绘制以mCherry与GFP信号之比表示的TDP43 拼接效率与总细胞TDP43浓度(BFP信号)的关系,请评估该单元格要更改图的轴,请更新“ yScale ”,“ xStart ”和“ xEnd ”参数(图3F),然后重新评估单元格。此步骤将为每个图生成两个单独的图自动渲染和保存的样本(请参见图3G中的示例)。脚本中的最后两个单元允许在参数“开始”和“结束”定义的TDP43级别范围内比较TDP43的拼接效率,从而产生小提琴图(图3G)。要手动重新排列原点 在图中的r个样本中,在称为“ OrderedData ” 的列表中指定所需的样本顺序并评估单元格(图3H)。


 


D:\重新格式化\ 2020-3-2 \ 1903009--1363 Hermann Schmidt 837314 \图jpg \图3 .jpg


图3.剪接分析数据的分析和绘图A.分析管线的概述流式细胞仪用于测量和解聚群体中单个细胞的mCherry,GFP和BFP荧光信号。使用自定义脚本,剪接效率为通过将给定细胞的mCherry与GFP信号之比(图1)与同一个细胞的TDP43水平(BFP信号)进行比较来计算得出.B。屏幕截图突出显示了在其中更改门边界和截止点的位置“ CSA_Gating.Nb ”脚本。C.截图,它指示了提供文件路径FCS原始数据中的“ CSA_Gating.Nb ”脚本。D.实例密度生成的评估中的“情节期间CSA_Gating.Nb ”脚本形象化E. 屏幕截图显示了在CSA_Plotting.nb 脚本中的位置,以提供在评估CSA_Gating.nb 脚本期间生成的csv文件的路径。F .屏幕截图突出显示了在何处调整绘图比例 小号在“ CSA_Plotting.Nb ”脚本。G中密度图生成由“ CSA_Plotting.Nb ”脚本描绘剪接效率与水平TDP43 野生型(WT)和DerutaRRM TDP43变体(顶部)和实例小提琴突出显示已定义的BFP信号范围内的拼接效率的曲线图(底部)H 。“ CSA_Plotting.nb ”脚本的屏幕截图,指示在何处定义用于比较拼接效率的小提琴图的BFP信号范围。


 


致谢


 


这项工作得到了Deutsche Forsc Hungsgemeinschaft (SCHM 3082/2-1 到HBS)和国立卫生研究院(DP2 GM105448和R35 GM118082到RR)的支持。原始协议发表在Schmidt 等人(2019)中。


 


竞争利益


 


作者宣称没有利益冲突。


 


参考文献


 


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引用:Schmidt, H. B. and Rohatgi, R. (2020). High-throughput Flow Cytometry Assay to Investigate TDP43 Splicing Function. Bio-protocol 10(8): e3594. DOI: 10.21769/BioProtoc.3594.
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