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Apr 2019

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Yeast Single-cell RNA-seq, Cell by Cell and Step by Step
单细胞酵母逐个单步RNA-seq操作方法   

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Abstract

Single-cell RNA-seq (scRNA-seq) has become an established method for uncovering the intrinsic complexity within populations. Even within seemingly homogenous populations of isogenic yeast cells, there is a high degree of heterogeneity that originates from a compact and pervasively transcribed genome. Research with microorganisms such as yeast represents a major challenge for single-cell transcriptomics, due to their small size, rigid cell wall, and low RNA content per cell. Because of these technical challenges, yeast-specific scRNA-seq methodologies have recently started to appear, each one of them relying on different cell-isolation and library-preparation methods. Consequently, each approach harbors unique strengths and weaknesses that need to be considered. We have recently developed a yeast single-cell RNA-seq protocol (yscRNA-seq), which is inexpensive, high-throughput and easy-to-implement, tailored to the unique needs of yeast. yscRNA-seq provides a unique platform that combines single-cell phenotyping via index sorting with the incorporation of unique molecule identifiers on transcripts that allows to digitally count the number of molecules in a strand- and isoform-specific manner. Here, we provide a detailed, step-by-step description of the experimental and computational steps of yscRNA-seq protocol. This protocol will ease the implementation of yscRNA-seq in other laboratories and provide guidelines for the development of novel technologies.

Keywords: Yeast (酵母), Single-cell RNA-seq (单细胞RNA-seq), Transcriptomics (转录组), Transcript isoforms (异型转录本), Noncoding RNA (非编码RNA)

Background

The appearance of single-cell-omics has revolutionized our understanding of many biological processes and is a field that is rapidly evolving at both the experimental and computational level. Over the last decade, there has been a rapid increase in the number of features that can be measured within individual cells. Single-cell RNA-seq (scRNA-seq) has pioneered this endeavor, and it has become a routine experiment to perform in higher eukaryotes. While very close to the entirety of the repertoire of bulk experiments in higher eukaryotes now have single-cell counterparts, single-cell tools for microorganisms (beyond fluorescent reporter-based methods) are scarce. This technological gap is mainly due to technical limitations imposed by the intrinsic nature of yeast and other microorganisms.

Unlike the mammalian cells, in which scRNA-seq was developed, yeast cells are smaller in cell, genome and transcriptome size. The presence of a rigid cell wall, which needs to be removed prior to library preparation, has been one of the major technical challenges for single cell analysis. On top of that, the yeast genome is highly condensed and pervasively transcribed. Over 85% of their genome is expressed from both strands with extensive transcript isoform diversity per gene (David et al., 2006; Pelechano et al., 2013, 2014 and 2015), and this requires a sensitive, quantitative and strand-specific approach.

As was the case for higher eukaryotes, initial studies exploring scRNA-seq in yeast focused on the development and optimization of the method. Recently, three yeast single-cell RNA-seq approaches have been reported (Gasch et al., 2017; Nadal-Ribelles et al., 2019; Saint et al., 2019). Interestingly, each one of them uses a different cell-isolation strategy and library-preparation protocol. Cell isolation is one of the main differences across these studies that range from microfluidics (Gasch et al., 2017), micromanipulation (Saint et al., 2019) and index sorting (Nadal-Ribelles et al., 2019), the later described here. Microfluidic and micromanipulation-based approaches require specialized equipment and are labor intensive, but allow imaging of individual cells by microscopy as a way to measure phenotype. The protocol described here for yeast single-cell RNA-seq (yscRNA-seq), relies on index sorting as a strategy for recording phenotypic information such as cell morphology and any Fluorescent-Activated Cell-Sorting-compatible feature (fluorescent proteins, antibodies, etc.). Index sorting provides a seamless strategy for merging techniques for individual phenotyping and transcriptional profiling in cells.

A major tipping point for single-cell isolation was the appearance of droplet-based methodologies (Zhang et al., 2019). These technologies dramatically increase potential throughput by generating and isolating cDNA in barcoded gel emulsions, which encapsulate single cells on the order of thousands of cells per run, and thus the cost significantly decreases. Interestingly, a preliminary droplet-based study in yeast is underway (Gresham et al., 2019). While cell capturing boosts cell numbers to an unprecedented scale, the number of genes detected per cell tends to be much lower (Skinnider et al., 2019) and phenotypic information of individual cells is lost. In addition, customization of library preparation is complex and labor intensive in droplet based-approaches, which will most likely be bypassed as technologies are developed.

While cell-isolation strategies determine the number of cells and phenotypic information that can be recorded, the choice of library-preparation approach defines the kind of transcriptional profiling. Due to the intertwined nature of the yeast transcriptome, and the diversity of isoforms per gene, library preparation requires both gene- and strand-isoform-specific resolutions. YscRNA-seq is based on STRT-seq (Islam et al., 2014), and its library design bypasses the technical limitations of working with yeast, fulfilling the requirements for a high-resolution transcriptome profiling method. First, unique molecular identifiers (UMI) are incorporated during cDNA synthesis via a biotinylated template-switching oligo (TSO) at the 5′-end of the molecule to digitally count the absolute abundance of a gene, along with its transcription start site (TSS) position. Second, the use of a homemade Tn5 enzyme to incorporate cell-specific adaptors provides a cost-effective strategy that greatly reduces experimental costs, compared to commercial Tn5 (Hennig et al., 2018). Finally, biotinylated primers allow selectively recovering 5′ ends. Moreover, the high affinity between streptavidin and biotin leads to a sequestration of the biotinylated strand after a brief denaturing step, which releases the non-biotinylated strand into the supernatant for sequencing. These features make yscRNA-seq one of the most sensitive methods available, and the most sensitive method for yeast reported to date (Nadal-Ribelles et al., 2019).

We anticipate that the development of novel methodologies with different aims will soon start to emerge, and yscRNA-seq will be extended to any yeast species or microorganism. The protocol described here, along with future optimizations, could serve as a starting point for the development of new methods. There are many unanswered biological questions that influence our understanding of human health, such as the emergence of drug-resistant microbial phenotypes. Generation of solid frameworks to profile microorganisms at single-cell resolution promises to expand our understanding, and perhaps answer some of these questions once and for all.

Materials and Reagents

Note: Reagents can be from different suppliers as far as they are only used for single-cell RNA-seq protocols and nuclease free. The ones listed here were used to develop the protocol. The regular molecular biology reagents are assumed to be already present in each laboratory (e.g., water, Tris, or NaCl). For plastic labware (filter tips and tubes), we have used several brands, with identical results as far as the material is certified to be nuclease-free and low binding.


  1. Eppendorf LoBind 1.5 ml (Eppendorf, catalog number: 30108051)
  2. Break-away plates (Thomas Scientific, catalog number: EK-75118)
  3. 96-well plates (Thomas Scientific, catalog number: EK-75012)
  4. Filter Tips (Mettler Toledo, catalog numbers: 17007954, 17014973, 17014973)
  5. qPCR plates (Applied Biosystems, catalog number: 4309849)
  6. Universal PCR plate seal (Sigma-Aldrich, catalog number: Z742420-100EA)
  7. UMI_Oligo dT_T31 (100 μM) (Integrated DNA Technologies)
  8. UMI_TSO6 (Integrated DNA Technologies)
  9. STRT-adaptors 96 different oligos (96 well plate Integrated DNA Technologies)
  10. UMI PCR (96 well plate Integrated DNA Technologies
  11. dNTP 25 mM (Thermo Fisher, catalog number: R0181)
  12. 50x Advantage 2 Polymerase (Takara, catalog number: 639202)
  13. PvuI (Cutsmart 10x provided) (New England Biolabs, catalog number: R3150L)
  14. DynabeadsTM MyOneTM Streptavidin C1 (Thermo Fisher, catalog number: 65001)
  15. Zymolyase 100T (100 mg/ml) (US Bio, catalog number:37340-57-1)
  16. RNase Zap (Thermo Fisher, catalog number: AM9780)
  17. RNase Inhibitor (40 U/ml) (Takara, catalog number: 2313A) 
  18. RNase-nuclease free water (Thermo Fisher, catalog number: 10977035)
  19. ERCC RNA spike ins (Thermo Fisher, catalog number: 4456740)
  20. TAPS (Sigma-Aldrich, catalog number: T5316)
  21. DMF (Sigma-Aldrich, catalog number: 74438)
  22. 5x Superscript First strand buffer (Thermo Fisher, catalog number: 18064014)
  23. MgCl2 (1 M) (Thermo Fisher, catalog number: AM9530G)
  24. Betaine (5 M) (Sigma-Aldrich, catalog number: 61962)
  25. Superscript II (Thermo Fisher, catalog number: 18064014)
  26. 10x Advantage 2 PCR Buffer (Takara, catalog number: 639202)
  27. Tris (Sigma-Aldrich, catalog number: T2319)
  28. Tween-20 (Sigma-Aldrich, P9416-50ML)
  29. Glycerol (Sigma-Aldrich, catalog number: G5516)
  30. KAPA library quantification (Kapa Biosystems, catalog number: KR0405)
  31. Ampure beads (Beckman Coulter, catalog number: A63881)
  32. TE 10x (Sigma-Aldrich, catalog number: T9285)
  33. NaCl (Sigma-Aldrich, catalog number: S5150)
  34. EDTA (Thermo Fisher, catalog number: AM9261)
  35. Elution Buffer (EB) (Qiagen, catalog number: 19086)
  36. PB Buffer (PB) (Qiagen, catalog number: 19066)
  37. Sybergreen 2x mastermix (Invitrogen, catalog number: 4309155)
  38. LNA primers (Exiqon)
  39. Primers for qPCR
    1. SOMN17 Fw_TDH3_probe: TCGTCAAGTTGGTCTCCTGG
    2. SOMN18 Rv_TDH3_probe: GGCAACGTGTTCAACCAAGT
    3. SOMN21 Fw_ADH1_probe: TGGTGCCAAGTGTTGTTCTG
    4. SOMN22 Rv_ADH1_probe: GGCGAAGAAGTCCAAAGCTT
    5. SOMN310 Fw_5_ERCC_00130: CGGAAAAGTACTGACCAGCG
    6. SOMN311 Rv_5_ERCC_00130: TGCCAATGACTTCAGCTGAC样式
  40. Optical lids for qPCR (Applied Biosystems, catalog number: 4311971)
  41. DNA High Sensitivity CHIP (Agilent, catalog number: 5067-4626)
  42. 1% Triton X-100 (Sigma-Aldrich, catalog number: X100-1L)
  43. 100 mM DTT (Thermo Fisher, catalog number: 18064014)
  44. Propidium Iodide (PI) (Sigma Aldrich, P4170-10MG)
  45. Cell capturing and lysis solution (see Recipes)
  46. 2x BWT Buffer (see Recipes)
  47. TNT Buffer (see Recipes)

Equipment

  1. 96-well plate magnet (Thermo Fisher, catalog number: 12331D)
  2. 1.5-2 ml tube magnet (Thermo Fisher, catalog number: 12303D)
  3. Bioanalyzer (Agilent)
  4. HiSeq 2000 (Illumina)
  5. FACS (BD Influx, or Aria II, nozzle 70 and 100 microns)
  6. qPCR (Applied Biosystems) 
  7. Qubit (Thermo Fisher, catalog number: Q32854)
  8. Thermocycler (any vendor)
  9. Thermomixer (any vendor)
  10. Multichannel pipettes (any vendor)
  11. Plate centrifuge (any vendor)

Software

  1. Novocraft (Novocraft Technologies Sdn Bhd, http://www.novocraft.com/)
  2. Samtools v1.3.1 (Li et al., 2009, http://samtools.sourceforge.net/)
  3. R programming language v.3.5.0 (R Core Team, 2019, https://www.r-project.org/)
  4. Genomic Alignments R package v.1.18.1 (Lawrence et al., 2013)

Procedure

Note: If possible, generate a single-cell library preparation space in the laboratory. If not, wash the surface of the bench and all the material with RNase Zap at the beginning and end of each library. All material and reagents are handled with gloves and the standard precautions for RNA work should be taken (RNase free material and filter tips).


  1. Cell growth
    On the day before sorting, grow the desired pre-inoculum of your desired yeast strain in their corresponding media overnight (O/N). To profile exponentially growing cells, we recommend the initial culture not to grow over optical density OD660 = 1.

  2. Cell sorting
    1. The next morning (sorting day), dilute cells to OD660 = 0.05 in the corresponding media and allow for at least 2 cell divisions (3 h approximately for wild type strains) prior to cell isolation.
    2. Prepare 96- or 384-well plates containing 5 µl absolute ethanol in each well to fix cells immediately for sorting.
      Notes: 
      1. During protocol optimization, we recommend using break-away plates. These plates allow breaking 96 well plates by rows, and thus several tests can be done using the same plate. Check with your facility the compatibility of the plates.
      2. We have obtained the same results sorting cells directly into 5 µl of “Cell capturing and lysis solution” (see below). If doing so, prepare plates right before sorting and keep them on 4 °C ice.
    3. Dilute cells prior to sorting to OD = 0.05 in 3 ml of growth media and vortex vigorously to separate cell clumps.
      Note: At this step, propidium iodide (PI) (4 µg/ml) can be added to check for cell viability. Adjust the culture volume to your needs. In our experience, 3 ml is enough to sort at least 10 plates.
    4. At the FACS facility, filter cells with Cell Strainer Tubes (check with your facility which tubes they prefer) and put cells in the appropriate sorting tube for live single-cell sorting.
    5. Check the alignment of the plate with the sorter. For example, this can be done by sorting a drop into a covered plate, and ensuring that the droplet would fall into the center of each well.
    6. Sort live single yeast (propidium iodide (PI) negative) into each well of the plates, being sure to leave one well as a negative control. We index-sorted from the population using the forward and side scatter (FSC and SSC respectively).
      Note: To include a positive control, sort 100 cells into one well.
    7. Cover plates with Universal PCR plate seal.
    8. Quick spin plates to collect cells at the bottom of each well (short spin to collect all liquid to the bottom of the wells).
    9. Let the ethanol evaporate in a sterile environment (i.e., sterile hood) for no more than 45 min.
    10. Once the ethanol is completely evaporated, add 5 µl yeast “Cell capturing and lysis solution”. Spin down and freeze immediately.
      Note: Regardless of whether the cells are sorted into ethanol or directly into “Cell capturing and lysis solution” (see Recipes), frozen plates can be stored at -80 °C for at least 6 months. ERCCs are spike in RNAs that provide an accurate measure of technical noise, while we recommend using them, they can be excluded.
    11. Perform the following lysis cycle from freshly-sorted or frozen plates (Table 1).

      Table 1. Temperature conditions for cell wall digestion and cell lysis

      Note: Digestion can be extended up to 30 min.

    12. Immediately proceed to add the RT reaction for first strand cDNA synthesis. Add 5 µl Reverse Transcription mix (RT mix) (Table 2).

      Table 2. Master mix reagents for first strand cDNA synthesis


    13. Spin down the plate and perform the following cycles (Table 3):

      Table 3. Incubation temperatures for cell lysis


    14. Add 15 µl PCR mix for library amplification to each well (Table 4).

      Table 4. Master mix reagents for library amplification


    15. Spin down the plate and do the following cycles (Table 5):

      Table 5. PCR cycling conditions for library amplification

      Note: This is a safe stopping point (at 4 °C O/N, or frozen -20 °C for 1-2 months).

    16. (Optional) Dilute the amplified library 1:20 and check the percentage of positive cells from Step B15 (Figure 1; Tables 6 and 7).
      Note: Use primers against a housekeeping gene (we use TDH3 but we have obtained same results with ADH1) to measure the number of positive cells per well. Use primers against ERCC as a positive control for amplification that should be even across all reactions. This step is especially useful during the protocol set up as it allows inspecting the samples/libraries before moving forward (check for the number of positive libraries). See Table S1 for primer sequences.

      Table 6. Master mix for qPCR assessment

      Note: Primer mix refers to the mixture of Fw and Rv primers (10 µM each) diluted in TE 1x.

      Table 7. PCR cycling conditions for qPCR


      Primer sequences for qPCR:
      SOMN17 Fw_TDH3_probe: TCGTCAAGTTGGTCTCCTGG
      SOMN18 Rv_TDH3_probe: GGCAACGTGTTCAACCAAGT
      SOMN21 Fw_ADH1_probe: TGGTGCCAAGTGTTGTTCTG
      SOMN22 Rv_ADH1_probe: GGCGAAGAAGTCCAAAGCTT
      SOMN310 Fw_5_ERCC_00130: CGGAAAAGTACTGACCAGCG
      SOMN311 Rv_5_ERCC_00130: TGCCAATGACTTCAGCTGAC
      Note: A good plate will have around 70% positive wells considering the Ct values of the housekeeping gene. Plates with less than 50% positive cells are rare. To determine if low efficiency is due to sorting or due reaction efficiency, perform a qPCR using a 1:10 dilution of the cDNA library against ERCCs. Failure to amplify ERCCs, or uneven amplification (represented by wildly different Ct values), is indicative of incorrect library preparation. In the case of low number of positive cells per plate, you can generate a new plate by combining positive from different plates into a new plate before proceeding to tagmentation and try to improve sorting efficiency.


      Figure 1. Representative example of Ct Values obtained by qPCR. The scatter plot represents the cycle amplification (Ct value) of a yscRNA-seq 96-well plate. Each dot represents the value obtained using a yeast housekeeping gene (TDH3, x-axis) as a function of the index-sorting value for cell size (Forward Scatter (FSC), y-axis). Dotted line (Ct > 25) displays the threshold used to discriminate positive and negative wells. The label displays the Ct value for the not sorted well H12 (which is used as a negative control).

    17. Add 15 µl of room temperature equilibrated and well homogenized Ampure XP beads (1:0.6 sample/ bead ratio) to each well.
      Note: Do not increase the volume of beads in the purification step above the 1:1 ratio. A less-than-standard amount of beads ensures that primer dimer carryover is minimal.
    18. Mix by pipetting up and down ten times, or until the solution appears to be homogeneous. Transfer solutions to a 96-well plate with compatible magnet stand.
    19. Incubate the mixture for 10 min at room temperature to let the DNA bind to Ampure XP beads.
    20. Place the 96-well plate on the magnetic stand for 5 min, or until the solution is clear and beads have been collected.
    21. While samples are on the magnet, carefully remove the liquid without disturbing the beads.
    22. Wash magnet-bound beads with 200 µl of 80% (vol/vol) ethanol solution. Incubate the samples for 30 s and then remove the ethanol with the tube in the magnet, do not overdry the beads.
      Note: It is important that the ethanol solution is freshly prepared every time, as ethanol absorbs moisture from the environment, thus changing the final concentration. Read and follow the manufacturer’s instructions.
    23. Repeat the ethanol wash one more time (repeat Steps B20-B22).
    24. Remove any trace of ethanol and let beads dry completely by leaving the plate at room temperature for 5 min or until ethanol evaporates.
      Note: Cover the plate during this step or protect it from any possible source of contamination or airflows.
    25. Once there is no ethanol left, elute dscDNA libraries from Ampure XP beads with 16.5 µl elution buffer at room temperature (EB buffer Qiagen). 
    26. Remove the plate from the magnet and mix vigorously by pipetting up and down three times or until the solution becomes homogeneous.
    27. Place the plate on a magnetic stand and leave it for 2 min, or until the solution appears clear. 
    28. Recover 15 μl of supernatant from each well and transfer to a new plate. Label the plate correctly, as it will be stored.
      Safe stopping point: cDNA libraries can be stored at -20 °C before proceeding to tagmentation for up to 2 weeks.

  3. Full-length cDNA library quality check
    1. Run 1 µl of dscDNA libraries (Step B28) to check the size distribution and estimate of concentrations using a High Sensitivity DNA ChIP (2100 Bioanalyzer). If available, use the qPCR results from Step B16 to guide the selection of wells for Bioanalyzer (Figure 2).


      Figure 2. Representative Bioanalyzer traces of full-length cDNA obtained with yscRNA-seq (Step C1). cDNA libraries obtained from step (Step B28) were run on a DNA High Sensitivity CHIP (Agilent 5067-4626) for validation. Library concentration was also measured by Qubit High Sensitivity (Thermo Fisher). Left panel (A1) represents the lower limit of library quality that we sequenced while middle (A5) and right (B2) panel represent average libraries (Figure adapted from Nadal-Ribelles et al., 2019)

      Adaptor annealing: In order to generate cell-barcoded libraries, Tn5 needs to be loaded with double stranded DNA (dsDNA) adapters. To do so, 96 different dsDNA adapters need to be annealed.

    2. Thaw the plate with the 96 STRT barcodes (100 µM) in ice.
    3. In a new plate, mix 5 μl UMI-TN5-U (100 µM) and 5 μl UMI-TN5_1 to 96 (µM) in TE 1x to final concentration 50 µM (each), a 1:1 dilution.
    4. Anneal primers by heating the mix at 95 °C for 3 min and gradually cool down to room temperature (0.5 °C/s). Label the plate as “Annealed cell-specific adapters” plate.
      Note: Label plates with the amount of annealed primer and the annealing date. Plates can be prepared in advance and stored at -20 °C for up to six months. The amount of annealed adaptors depends on the number of plates that need to be tagmented and the frequency of usage.
    5. To load cell specific adapters to Tn5, prepare a new plate and label it as “loaded Tn5”. In each well, mix the following reagents. We recommend to make a mix with all reagents and add annealed cell-specific adapters individually (Table 8). 

      Table 8. Master mix to generate 10x transposome


    6. Load Tn5 by incubating at 37 °C for one hour and immediately cool to 4 °C. Freeze the “loaded Tn5” plate at -20 °C.
      Note: Loaded Tn5 plate can be safely stored for 1-2 weeks at -20 °C. Caution, store the plate immediately after use leaving the loaded plate on ice 4 °C will significantly reduce Tn5 activity and will result in inefficient tagmentation.

      Tagmentation

    7. Prepare the following mix in a new plate in ice (Table 9).

      Table 9. Master mix for library tegmantation


    8. Incubate at 55 °C for 5 min and 3 min at 85 °C to inactivate Tn5, then cool to 4 °C in a thermocycler. Tagmentation time depends on Tn5 purification efficiency and activity. We have observed that the enzyme loses activity over time. Titration of each batch of Tn5 is strongly suggested by tagmenting the same amount cDNA (Steps B28 and C1) with increasing concentration of loaded Tn5 (Step C6) and check fragmentation profile in a Bioanalyzer.
    9. Prepare a 1:20 dilution of MyOne C1 Streptavidin per each sample.
    10. Wash MyOne beads 2 x with 2x BWT buffer and dilute with 20x more volume than the original volume of beads with 2x BWT.
      Example: 20 µl beads for 20 samples will be finally diluted with 400 µl 2x BWT.
    11. Add 20 µl of diluted MyOne C1 Streptavidin beads (step C10) to each well and incubate at RT for 5 min at room temperature.
    12. Pool all samples per plate (up to 96) into a single collecting tube (1.5 or 2 ml).
    13. Place collecting tube in magnetic rack and allow enough time for the solution to be completely clear.
    14. While on the magnet, wash beads once with 100 µl of TNT buffer do not mix the beads.
    15. While on the magnet, wash MyOne C1 Streptavidin once with 100 µl PB Buffer and discard the supernatant.
    16. While on the magnet, wash beads 3 x with 100 µl TNT buffer again and the discard supernatant.

      Remove 3´ ends

    17. Add 100 µl of the following mix to the washed beads from Step C14 (Table 10). 

      Table 10. Mastermix for 3´ end removal


    18. Incubate mix for 1 h at 37 °C in a thermomixer. Mix every 2 min for 30 s at 1,000 rpms, to avoid bead sedimentation.
    19. Wash beads 3 x with 100 µl of TNT buffer. 

      Elute single stranded cDNA

    20. Resuspend in 30 µl Nuclease-free water.
    21. Incubate 10 min at 70 °C, 850 rpm mix in a thermomixer.
    22. Immediately bind beads to the magnet and transfer the supernatant to a new tube, which contains the single strand cDNA library in the supernatant (the other strand remains bound to the streptavidin beads).

      Single-strand cDNA cleanup

    23. Add 54 µl of room temperature Ampure XP beads to 30 µl sscDNA library.
    24. Incubate for 10 min at RT.
    25. Bind beads to the magnet for 1 min or until the solution is completely clear and discard supernatant (keep the beads).
    26. Wash once with 200 µl fresh 80% ethanol for 20-30 s. Perform this step with the beads bound to the magnet.
    27. Air dry beads for approximately 2 min.
    28. Resuspend in 30 µl EB buffer and incubate for 5 min at RT.
    29. Bind beads for 2 min, or until the solution is clear, and transfer the supernatant to a new tube.

  4. Assess library concentration
    1. Set up a KAPA quantification reaction with a 1:100, 1:1,000 and 1:10,000 dilutions of the eluted cDNA library (Step C27). All regents except for your DNA library are provided in the kit (Table 11).
      Note: This kit can be substituted by your favorite quantification method or by a qPCR using P5-P7 primer pairs with known standards (PhiX is strongly recommended) SYBRGreen 2x mastermix.

      Table 11. Master mix for qPCR library quantification



    2. qPCR cycling conditions for KAPA and homemade Sybergreen (Table 12).

      Table 12. PCR cycling conditions for qPCR


    3. Use the qPCR to calculate library quantification using the template provided by KAPA biosystems or the instructions provided from your manufacturer.
      Note: We have used KAPA, NEB and homemade systems with similar results.
    4. Set up a separate PCR to run a bioanalyzer to determine the final size distribution. Prepare the following mix, one separate reaction per each library to be loaded (Table 13).

      Table 13. Master mix for to assess library size after tagmentation


    5. Run the same PCR as in Step D2 but for 11 cycles.
    6. Run 1 µl into a High sensitivity DNA CHIP to obtain an average library size based on the Bioanalyzer profile (Figure 3). 


      Figure 3. Representative Bioanalyzer traces obtained from yscRNA-seq. Two representative samples obtained from approximately 80 cells. Concentration of each library is shown and was determined by qPCR (Step D3). Figure from Nadal-Ribelles et al., 2019.

    7. Sequence the library on the HiSeq 2000 High output using the custom Read 1 primer and UMI-TN5-U as the Index read primer.
    8. To run the libraries on the HiSeq rapid run, use lock nucleic acid (LNA) primers. Spike in the primer at 0.5 µM.
      Index 1 primer into HP8
      Read 1 primer into HP9
      Notes:
      1. Double-check this information with your sequencing kit/instrument and/or sequencing core facility.
      2. For a High Output Run, custom primers are required as well, but without LNA due to differences in sequencing chemistry.

      UMI_PCR_read1: +GAATGA+TACGGCG+ACCA +CCGA+T – custom 250 nmole. DNA oligo, HPLC Purification 
      Index1: CTGT+CT+CTT+ATA+CA +CA+TCTGA+CG+C – custom 250 nmole DNA oligo, HPLC Purification

    9. Load around 8-14 pmol of each library per lane. Libraries are single-stranded DNA, thus, no denaturing is required.
      Note: Once the protocol is optimized, there is no need to run a PhiX control, if PhiX is loaded take into consideration a denaturing step for the double-stranded PhIX control, which is not required for yscRNA-seq libraries.

    A schematic representation of the plate processing is shown in Figure 4 as a reference.


    Figure 4. Schematic representation of yscRNA-seq. Images from Smart Medical server (Les Laboratoires Servier, SMART Servier Medical Art.).

Data analysis

The initial steps of yscRNA-seq data analysis are very similar to those applied for bulk RNA-seq (Conesa et al., 2016), but with certain particularities. YscRNA-seq generates two reads per sequenced cDNA molecule. The first read (Index 1) contains the cell-specific barcode that associates a given molecule with a specific cell. The second read (Read 1) contains the UMI, and maps to the TSS of a specific transcript, allowing for absolute molecule-counting and transcript identification, respectively. To properly estimate gene expression from yscRNA-seq data, both reads have to be computationally associated to their respective references. The former association is performed by default in Illumina’s BaseSpace demultiplexing tool. This generates individual FASTQ files (one per each yeast cell) that are the starting point of yscRNA-seq analysis. After this, we further pre-process the FASTQ files to identify the UMIs and perform the alignment and quantification to obtain the gene expression table (Figure 5). Here, we detail the steps that we follow for analyzing yscRNA-seq data, starting from FASTQ files:
Note: The analysis pipeline was executed in a Linux 64 bit machine with 32 Gb of RAM and 6 CPUs. We recommend to have at least 12 Gb of RAM. A machine with several computing cores is also preferable for speeding up the processes.


Figure 5. Schematic representation of analysis pipeline of yscRNA-seq

  1. Read pre-processing
    To extract the UMI from the beginning of the reads, we used a custom JavaScript program (deBCSCell).

    Command: $java -cp directoryOfdeBCSCell rootNameOfdeBCSCell F1=fastqFile BL="”

    This script performs the following actions: 
    1. Extracts the first 6nt (UMI) and incorporates them into the read name for later use.
    2. Extracts and counts N’s and G’s following the UMI, up to 14. The read is discarded if it contains 14 or more N’s or G’s.

  2. Alignment

    Note: In our case, the reference sequences (referenceSequencesName.fa) are a combination of the S. cerevisiae genome (Saccer3, SGD R64 version; www.yeastgenome.org) and the ERCC control sequences. The ERCC control sequences provided by the manufacturer do not include the restriction sites used during the cloning of the sequences downstream of the T7 promoter. Since yscRNA-seq is TSS-specific, this fact considerably decreases the mapping rate due to misalignments at the beginning of the read. To overcome that, we generated a new ERCC reference that includes these sequences. These modified references could be downloaded from the Gene Expression Omnibus (under GSE122392 accession number).

    1. In our case, we have sequenced each library to a median depth of ~720,000 reads. We assessed, with down-sampling, that this depth is substantially above the needed to obtain a high resolution of the yeast transcriptome. In fact, at ~500,000 reads per cell in the number of transcripts detected saturation is reached (Supplementary Figure 2C [Nadal-Ribelles et al., 2019]). In addition, we observe a median of ~74% uniquely mapped reads for all sequenced libraries. These values are useful reference values using this protocol.
    2. We used Novocraft for the alignment of the reads, but open-source aligners such as HISAT2 (Kim et al., 2015) or STAR (Dobin et al., 2013) can be used without obtaining major differences in the results.

    Reads were aligned with Novocraft (http://www.novocraft.com/) using default parameters.
    1. Create the index for Novocraft using novoindex command:

      $novoindex referenceSequencesName.nix referenceSequencesName.fa

    2. Align reads using novoalign command:

      $novoalign -f fastqFileUMIproccesed.fastq -d referenceSequencesName -o SAM |

    3. Convert to BAM format and sort it using Samtools view and Samtools sort: (piped from the previous command)

      | samtools view -bS - | samtools sort - fileName.sorted.bam.

  3. Quantification
    Although we are aware of the existence of public software for quantifying UMI-based single-cell data (such as UMI-tools [Smith et al., 2017]), we performed quantification using R custom scripts to have full flexibility and control over data inspection and data filtering. The starting point of the quantification is the sorted BAM files obtained in the previous step, one for each sequenced cell. 
    1. Process the BAM files.
      Script: readAlignUMI.R. $Rscript readAlignUMI.R

      Each BAM file is loaded into R using the Genomic Alignments package. Then, low quality mapping reads (MAPQ < 30) and reads that map with soft-clipping in the 5′ end are filtered out. Finally, reads that map to the same position and that have the same UMI are grouped for collapsing them in the next steps. 
    2. Process UMI grouped data.

      Script: bard2rds.R. $Rscript bard2rds.R

      At this step, UMIs supported with less than 3 reads are filtered out since they could represent sequencing errors. Then, different UMIs that map to the same genomic position are grouped. Different UMIs mapping to the same position represent different molecules of the same transcript. This information is stored into a RangedData object. 
    3. Overlap with annotation and count table generation.

      Script: txAnno.R (#link). $Rscript txAnno.R.

      If there is overlap, each molecule is assigned to a genomic feature from an annotation. In this study, we used the annotation from (Xu et al., 2009) since it includes a comprehensive categorization of genes into different classes (coding, CUTs, SUTs and others). The output obtained is an absolute gene expression table with genes as rows and cell as columns.

  4. (Optional) Filtering
    To avoid the presence of low-quality cells (dead, stressed or apoptotic), we decided to keep only those with more than 500,000 sequenced reads, and those in which we detect more than 1,000 different transcripts. In our case, this filter eliminates cells with a high ratio of mitochondrial RNA, which has been associated with low quality cells (Ilicic et al., 2016). However, this filter is not imperative and could be tuned depending on the type of experiment and on the biological question under study. 

Recipes

Note: All reagents and water used must be nuclease-free and only used for single cell protocols.

  1. Cell capturing and lysis solution (Table 14)

    Table 14. Composition of “Cell capturing and lysis solution”


  2. 2x BWT Buffer
    10 mM Tris-HCl pH 7.5
    1 mM EDTA
    2 M NaCl
    0.02% Tween-20
  3. TNT Buffer
    20 mM Tris pH 7.5
    50 mM NaCl
    0.02% Tween

Acknowledgments


The authors would like to thank Sten Linnarsson for kindly providing reagents during the initial tests with yscRNA-seq. We thank the Protein Expression and Purification Core Facility at EMBL, Bianca Hennig and Lars Velten for kindly providing in-house purified Tn5. We thank Derek Caetano-Anollés for editing and refining the manuscript. M.N.R. was a recipient of an EMBO long-term fellowship (Stanford University) and later of a Maria de Maeztu Postdoctoral Fellowship (Doctores Banco de Santander-María de Maeztu at Universitat Pompeu Fabra). P.L. is a recipient of an FI Predoctoral Fellowship (Generalitat de Catalunya). This work was supported by the National Institutes of Health (NIH) and a European Research Council Advanced Investigator Grant (AdG-294542) to L.M.S. and the National Key Research and Development Program of China (2017YFC0908405) to W.W. The study was also supported by grants from the Spanish Ministry of Economy and Competitiveness (PGC2018-094136-B-I00 and FEDER to F.P.; BFU2017-85152-P and FEDER to E.N.), the Catalan Government (2017 SGR 799), and the Unidad de Excelencia Maria de Maeztu, MDM-2014-0370. We gratefully acknowledge institutional funding from the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) through the Centres of Excellence Severo Ochoa award, and from the CERCA Programme of the Catalan Government. F.P. is recipient of an ICREA Acadèmia (Generalitat de Catalunya).

Competing interests

The authors declare no financial or non-financial interests.

References

  1. Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szczesniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X. and Mortazavi, A. (2016). A survey of best practices for RNA-seq data analysis. Genome Biol 17: 13.
  2. David, L., Huber, W., Granovskaia, M., Toedling, J., Palm, C. J., Bofkin, L., Jones, T., Davis, R. W. and Steinmetz, L. M. (2006). A high-resolution map of transcription in the yeast genome. Proc Natl Acad Sci U S A 103(14): 5320-5325.
  3. Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M. and Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15-21.
  4. Gasch, A. P., Yu, F. B., Hose, J., Escalante, L. E., Place, M., Bacher, R., Kanbar, J., Ciobanu, D., Sandor, L., Grigoriev, I. V., Kendziorski, C., Quake, S. R. and McClean, M. N. (2017). Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress. PLoS Biol 15(12): e2004050.
  5. Gresham, D., Bonneau, R., Jackson, C. A., Castro, D. M. and Saldi, G. A. (2019). Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments. bioRxiv doi: https://doi.org/10.1101/581678.
  6. Hennig, B. P., Velten, L., Racke, I., Tu, C. S., Thoms, M., Rybin, V., Besir, H., Remans, K. and Steinmetz, L. M. (2018). Large-scale low-cost NGS library preparation using a Robust Tn5 purification and tagmentation protocol. G3 (Bethesda) 8(1): 79-89.
  7. Ilicic, T., Kim, J. K., Kolodziejczyk, A. A., Bagger, F. O., McCarthy, D. J., Marioni, J. C. and Teichmann, S. A. (2016). Classification of low quality cells from single-cell RNA-seq data. Genome Biol 17: 29.
  8. Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lonnerberg, P. and Linnarsson, S. (2014). Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods 11(2): 163-166.
  9. Kim, D., Langmead, B. and Salzberg, S. L. (2015). HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12(4): 357-360.
  10. Lawrence, Michael, Wolfgang Huber, Hervé Pagès, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin T. Morgan, and Vincent J. Carey. (2013). Software for Computing and Annotating Genomic Ranges. PLoS Computational Biology 9(8):e1003118.
  11. Li, Heng, Bob Handsaker, Alec Wysoker, Tim Fennell, Jue Ruan, Nils Homer, Gabor Marth, Goncalo Abecasis, and Richard Durbin. (2009). The Sequence Alignment/Map Format and SAMtools. Bioinformatics 25(16):2078–79.
  12. Nadal-Ribelles, M., Islam, S., Wei, W., Latorre, P., Nguyen, M., de Nadal, E., Posas, F. and Steinmetz, L. M. (2019). Sensitive high-throughput single-cell RNA-seq reveals within-clonal transcript correlations in yeast populations. Nat Microbiol 4(4): 683-692.
  13. Pelechano, V., Wei, W. and Steinmetz, L. M. (2013). Extensive transcriptional heterogeneity revealed by isoform profiling. Nature 497(7447): 127-131.
  14. Pelechano, V., Wei, W. and Steinmetz, L. M. (2015). Widespread co-translational RNA decay reveals ribosome dynamics. Cell 161(6): 1400-1412.
  15. Pelechano, V., Wei, W., Jakob, P. and Steinmetz, L. M. (2014). Genome-wide identification of transcript start and end sites by transcript isoform sequencing. Nat Protoc 9(7): 1740-1759.
  16. Saint, M., Bertaux, F., Tang, W., Sun, X. M., Game, L., Koferle, A., Bahler, J., Shahrezaei, V. and Marguerat, S. (2019). Single-cell imaging and RNA sequencing reveal patterns of gene expression heterogeneity during fission yeast growth and adaptation. Nat Microbiol 4(3): 480-491.
  17. Skinnider, M. A., Squair, J. W. and Foster, L. J. (2019). Evaluating measures of association for single-cell transcriptomics. Nat Methods 16(5): 381-386.
  18. Smith, T., Heger, A. and Sudbery, I. (2017). UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res 27(3): 491-499.
  19. Xu, Z., Wei, W., Gagneur, J., Perocchi, F., Clauder-Munster, S., Camblong, J., Guffanti, E., Stutz, F., Huber, W. and Steinmetz, L. M. (2009). Bidirectional promoters generate pervasive transcription in yeast. Nature 457(7232): 1033-1037.
  20. Zhang, X., Li, T., Liu, F., Chen, Y., Yao, J., Li, Z., Huang, Y. and Wang, J. (2019). Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systems. Mol Cell 73(1): 130-142 e5.

简介

单细胞rna-seq(scrna-seq)已成为揭示种群内在复杂性的有效方法。即使在表面上同质的等基因酵母细胞群体中,也存在高度的异质性,这种异质性起源于一个紧凑且普遍转录的基因组。酵母等微生物的研究是单细胞转录组学面临的一个重大挑战,因为它们体积小,细胞壁坚硬,每个细胞的rna含量低。由于这些技术挑战,酵母特异性的scrna-seq方法最近开始出现,每一种方法都依赖于不同的细胞分离和文库制备方法。因此,每种方法都有其独特的优点和缺点,需要加以考虑。我们最近开发了一种酵母单细胞rna-seq协议(yscrna-seq),它廉价、高通量、易于实现,适合酵母的独特需求。yscrna-seq提供了一个独特的平台,它通过索引分类将单细胞表型与转录本上的独特分子标识符结合起来,允许以链和异构体特定的方式对分子数量进行数字计数。在这里,我们详细、逐步地描述了yscrna-seq协议的实验和计算步骤。该协议将简化yscrna-seq在其他实验室的实现,并为新技术的开发提供指导。
【背景】单细胞组学的出现彻底改变了我们对许多生物学过程的理解,是一个在实验和计算层面都在迅速发展的领域。在过去的十年里,可以在单个细胞内测量的特征数量迅速增加。单细胞rna-seq(single cell rna-seq,scrna-seq)是这项研究的先驱,在高等真核生物中的应用已成为常规实验。虽然在高等真核生物的大量实验中,几乎所有的实验都有单细胞的对应物,但用于微生物的单细胞工具(荧光报告法以外的方法)却很少见。这种技术差距主要是由于酵母和其他微生物的固有特性所造成的技术限制。



与哺乳动物细胞不同,酵母细胞在细胞、基因组和转录组大小上都较小。刚性细胞壁的存在是单细胞分析面临的主要技术挑战之一,需要在制备库前去除刚性细胞壁。除此之外,酵母基因组高度浓缩并被广泛转录。超过85%的基因组表达自每一基因具有广泛转录亚型多样性的两条链(Davidet al>,2006;Pelechanoet al>,2013、2014和2015),这需要一种敏感、定量和链特异性的方法。



与高等真核生物一样,探索酵母中scrna-seq的初步研究集中在方法的开发和优化上。最近,报道了三种酵母单细胞rna序列方法(gasch等,2017;nadal-ribelles等,2019;saint等,2019)。有趣的是,它们中的每一个都使用不同的细胞隔离策略和库准备协议。细胞分离是这些研究的主要区别之一,这些研究涉及微流控(Gasch等人,2017年)、微操作(Saint等人,2019年)和索引排序(Nadal Ribelles等人,2019年),下文将对此进行描述。微流控和基于微操作的方法需要专门的设备和劳动密集型,但允许通过显微镜对单个细胞成像作为测量表型的一种方法。这里描述的酵母单细胞rna-seq(yscrna-seq)的协议依赖于索引排序作为记录表型信息的策略,例如细胞形态和任何荧光激活的细胞排序兼容特征(荧光蛋白、抗体、等)。索引排序提供了一种无缝的策略,用于合并细胞中的个体表型和转录分析技术。单细胞分离的一个主要转折点是基于液滴的方法学的出现(Zhang等人,2019年)。这些技术通过在条形码凝胶乳剂中产生和分离cDNA显著地增加潜在的吞吐量,其将单细胞封装在每个运行的数千个细胞的顺序上,因此成本显著降低。有趣的是,酵母中基于液滴的初步研究正在进行中(Gresham等人,2019年)。当细胞捕获将细胞数量提升到前所未有的规模时,每个细胞检测到的基因数量往往要低得多(Skinnider等人,2019年),单个细胞的表型信息也会丢失。此外,在基于液滴的方法中,库准备的定制是复杂的和劳动密集型的,随着技术的发展,这种方法很可能被绕过。



当细胞分离策略决定可记录的细胞数量和表型信息时,文库制备方法的选择定义了转录谱的类型。由于酵母转录组的交织特性和每个基因的异构体的多样性,文库的制备需要基因和链异构体的特异性分辨率。yscrna-seq基于strt-seq(islamet al>,2014),其库设计绕过了与酵母一起工作的技术限制,满足了高分辨率转录组分析方法的要求。首先,在cdna合成过程中,通过分子5′端的生物素化模板转换寡聚物(tso)结合独特的分子标识符(umi),以数字方式计算基因的绝对丰度及其转录起始位点(tss)位置。其次,与商用TN5相比,使用自制TN5酶结合细胞特异性适配器提供了一种成本效益高的策略,大大降低了实验成本(Hennig等人,2018年)。最后,生物素化引物允许选择性地恢复5′端。此外,链霉亲和素和生物素之间的高亲和力导致生物素化链在经过短暂变性步骤后被隔离,从而将非生物素化链释放到上清液中进行测序。这些特性使yscrna-seq成为目前最敏感的方法之一,也是迄今为止报道的最敏感的酵母方法(nadal-ribelles等,2019)。



我们预计,具有不同目标的新方法的开发将很快开始,并且yscrna seq将扩展到任何酵母物种或微生物。这里描述的协议以及未来的优化可以作为开发新方法的起点。有许多尚未解决的生物学问题影响了我们对人类健康的理解,例如耐药微生物表型的出现。以单细胞分辨率分析微生物的固体框架的产生有望扩大我们的理解,也许可以一劳永逸地回答其中的一些问题。

关键字:酵母, 单细胞RNA-seq, 转录组, 异型转录本, 非编码RNA

材料和试剂

注:试剂可以来自不同的供应商,只要它们只用于单细胞rna-seq协议和无核酸酶。这里列出的那些是用来制定协议的。一般的分子生物学试剂假定已经存在于每个实验室中(例如水、tris或nacl)。对于塑料labware(滤嘴和滤管),我们使用了多个品牌,只要材料被证明不含核酸酶且结合力低,结果都是一样的。>

  1. Eppendorf Lobind 1.5毫升(Eppendorf,目录号:30108051)
  2. 分离板(Thomas Scientific,目录号:EK-75118)
  3. 96孔板(Thomas Scientific,目录号:EK-75012)
  4. 过滤头(梅特勒-托莱多,目录号:17007954、17014973、17014973)
  5. QPCR板(应用生物系统,目录号:4309849)
  6. 通用PCR板密封(Sigma-Aldrich,目录号:Z742420-100EA)
  7. Umi_oligo dt_t31(100μm)(集成DNA技术)
  8. Umi_Tso6(集成DNA技术)
  9. 96种不同寡聚体(96孔板集成DNA技术)
  10. umi-pcr(96孔板集成dna技术
  11. DNTP 25 mm(Thermo Fisher,目录号:R0181)
  12. 50X Advantage 2聚合酶(Takara,目录号:639202)
  13. PVUI(提供Cutsmart 10X)(新英格兰生物实验室,目录号:R3150L)
  14. dynabeadstmmyonetmstreptavidin c1(Thermo Fisher,目录号:65001)
  15. 发酵酶100t(100 mg/ml)(美国生物,目录号:37340-57-1)
  16. RNase-Zap(Thermo-Fisher,目录号:AM9780)
  17. RNase抑制剂(40 U/ml)(Takara,目录号:2313A)
  18. 无核酸酶水(Thermo Fisher,目录号:10977035)
  19. ERCC RNA尖峰蛋白(Thermo Fisher,目录号:4456740)
  20. 丝锥(Sigma-Aldrich,目录号:T5316)
  21. DMF(Sigma-Aldrich,目录号:74438)
  22. 5x上标第一股缓冲器(赛默飞世尔,目录号:18064014)
  23. MGCL2(1米)(赛默飞世尔,产品目录号:AM9530G)
  24. 甜菜碱(5 M)(Sigma-Aldrich,目录号:61962)
  25. 上标II(赛默飞世尔,目录号:18064014)
  26. 10x Advantage 2 PCR缓冲液(Takara,目录号:639202)
  27. Tris(Sigma-Aldrich,目录号:t2319)
  28. 吐温-20(西格玛-奥德里奇,P9416-50ml)
  29. 甘油(Sigma-Aldrich,目录号:G5516)
  30. 卡帕图书馆量化(卡帕生物系统,目录号:KR0405)
  31. 安瓿珠子(贝克曼库尔特,目录号:A63881)
  32. TE 10X(Sigma-Aldrich,目录号:T9285)
  33. NaCl(Sigma-Aldrich,目录号:S5150)
  34. EDTA(赛默飞世尔,目录号:AM9261)
  35. 洗脱缓冲液(EB)(桥根,目录号:19086)
  36. PB缓冲器(PB)(桥根,目录号:19066)
  37. Sybergreen 2x MasterMix(Invitrogen,目录号:4309155)
  38. LNA引物(Exiqon)
  39. qpcr的底漆
    1. SOMN17 FW_U TDH3_U探头:TCGTCAAGTTGGTCTCTGG
    2. SOMN18 RV_TDH3_探头:GGCAACGTTTCAACAAGT
    3. somn21-fw_-adh1_探头:tggtgccaagttgtctg
    4. somn22 rv_adh1_探头:ggcgaagatccaaagct
    5. SOMN310 FW U 5 U ERCC U 00130:CGGAAAGTACTGACCAGCG公司
    6. SOMN311 RV U 5 U ERCC U 00130:TGCCAATGATTCAGTGAC样
  40. QPCR用光学盖(应用生物系统,目录号:4311971)
  41. DNA高灵敏度芯片(安捷伦,目录号:5067-4626)
  42. 1%Triton X-100(Sigma-Aldrich,目录号:X100-1L)
  43. 100 mm DTT(Thermo Fisher,目录号:18064014)
  44. 碘化丙二钠(PI)(西格玛-阿尔德里奇,p4170-10mg)
  45. 细胞捕获和溶解溶液(见配方)
  46. 2x BWT缓冲液(见配方)
  47. tnt缓冲液(见配方)

设备

  1. 96孔板磁铁(Thermo Fisher,目录号:12331D)
  2. 1.5-2毫升管磁铁(Thermo Fisher,目录号:12303D)
  3. 生物分析仪(安捷伦)
  4. HISEQ 2000(照明)
  5. facs(bd inflox,或aria ii,喷嘴70和100微米)
  6. 应用生物系统
  7. Qubit(赛默飞世尔,目录号:Q32844)
  8. 热循环车(任何供应商)
  9. 热混合器(任何供应商)
  10. 多通道移液管(任何供应商)
  11. 平板离心机(任何供应商)

软件

  1. Novocraft(Novocraft Technologies Sdn Bhd,http://www.novocraft.com/“target=”\u blank“>http://www.novocraft.com/)
  2. samtools v1.3.1(li等人>,2009年,http://samtools.sourceforge.net/“target=”\u blank“>http://samtools.sourceforge.net/)
  3. R编程语言V.3.5.0(R核心团队,2019年,https://www.r-project.org/“target=”\u blank“>https://www.r-project.org/)
  4. 基因组比对R包V.1.18.1(Lawrence等人,2013年)

程序

注意:如果可能,在实验室中生成一个单细胞库准备空间。如果没有,用rnase zap在每个库的开始和结束处清洗工作台的表面和所有材料。所有的材料和试剂都是用手套处理的,应采取rna工作的标准预防措施(不含rnase的材料和过滤头)。

  1. 细胞生长
    在分类前一天,将所需酵母菌株的预接种物在相应的培养基中培养过夜(O/N)。为了描述指数增长的细胞,我们建议初始培养不要超过光密度od660=1.
  2. 单元格排序
    1. 第二天早上(分类日),在相应的培养基中将细胞稀释至od660=0.05,并在细胞分离前至少进行2次细胞分裂(野生型菌株大约3小时)。
    2. 准备96或384孔板,每个孔中含有5微升无水乙醇,以便立即固定细胞进行分类。
      注意:
      1. 在协议优化过程中,我们建议使用分离板。这些板可以一排一排地折断96孔板,因此可以使用同一块板进行多次试验。与你的设备检查盘子的兼容性。
      2. 我们获得了相同的结果,将细胞直接分类为5微升的“细胞捕获和溶解溶液”(见下文)。如果这样做,请准备在分类前把盘子放在4℃的冰上。
    3. 在3毫升的生长培养基中稀释细胞至OD=0.05,并大力旋涡以分离细胞团。
      注:在此步骤中,可添加碘化丙二钠(PI)(4微克/毫升)以检查细胞活性。根据需要调整培养液量。根据我们的经验,3ml足够至少分类10个盘子。>
    4. 在FACS设施中,用细胞过滤管过滤细胞(检查你的设施他们喜欢哪种细胞),并将细胞放在合适的分类管中进行活单细胞分类。
    5. 检查板与分拣机的对齐情况。例如,这可以通过将液滴分类到覆盖板中,并确保液滴落入每个井的中心来完成。
    6. 把活的单酵母(碘化丙二钠(pi)阴性)分到盘子的每个孔中,确保留下一个孔作为阴性对照。我们使用正向和侧向散射(分别为fsc和ssc)对总体进行分类。
      注意:要包括阳性对照,请将100个细胞分类为一个孔。>
    7. 带有通用PCR板密封件的盖板。
    8. 快速旋转板在每口井底收集电池(短旋转将所有液体收集到井底)。
    9. 让乙醇在无菌环境(即无菌罩)中蒸发不超过45分钟。
    10. 乙醇完全蒸发后,添加5微升酵母“细胞捕获和裂解溶液”。降速并立即冻结。
      注:无论细胞是被分类成乙醇还是直接进入“细胞捕获和溶解溶液”(见配方),冷冻板都可以在-80°C下保存至少6个月。ercc是rnas中的尖峰,它提供了对技术噪声的精确测量,而我们建议使用它们,它们可以被排除在外。>
    11. 从新分类或冷冻的盘子(表1)中进行以下分析循环。

      表1。细胞壁消化和细胞裂解的温度条件
      注:消化可延长至30分钟。>

      < div >
    12. 立即进行第一链cdna合成的rt反应。添加5微升逆转录混合物(RT混合物)(表2)。

      表2。用于第一链cdna合成的主混合试剂

    13. 向下旋转板并执行以下循环(表3):

      表3。细胞裂解的培养温度

    14. 每口井加入15微升PCR混合物进行库扩增(表4)。

      表4。用于库扩增的主混合试剂

    15. 向下旋转板并执行以下循环(表5):

      表5。库扩增的PCR循环条件 < div >
      注意:这是一个安全的停止点(在4°C O/N或冷冻-20°C 1-2个月)。>>

    16. (可选)稀释扩增库1:20,检查步骤b15中阳性细胞的百分比(图1;表6和表7)。
      注:使用针对管家基因的引物(我们使用TDH3,但我们获得了与ADH1相同的结果)来测量每个井的阳性细胞数。使用针对ercc的引物作为阳性对照进行扩增,扩增应在所有反应中均匀。此步骤在协议设置期间特别有用,因为它允许在前进之前检查样本/库(检查阳性库的数量)。有关引物序列,请参见表s1>
      < div >
      表6。用于QPCR评估的主混音

      注:底漆混合物指稀释在TE 1X中的FW和RV底漆(各10μm)的混合物。>

      表7。qpcr
      的pcr循环条件

      qpcr的引物序列:
      somn17 fw_tdh3_探头:tcgtcaagttgctctctgg
      somn18 rv_tdh3_探头:ggcaacgtttcaaccaagt
      somn21-fw_-adh1_探头:tggtgccaagttgtctg
      somn22 rv_adh1_探针:ggcgaagatccaaagctt
      somn310 fw_5_ercc_00130:cggaaagtactgaccagcg
      somn311 rv_5_ercc_00130:tgccaatgactcagcttgac
      注:考虑到内务基因的CT值,一个好的平板大约有70%的阳性井。阳性细胞少于50%的板是罕见的。为了确定低效率是由于分类还是由于反应效率,使用1:10稀释的cdna文库对erccs进行qpcr。未能放大erccs,或放大不均匀(由不同的ct值表示),表明不正确的库准备。如果每个板上的阳性细胞数较少,则可以在进行标记之前将不同板上的阳性细胞组合成一个新板,从而生成一个新板,并尝试提高分类效率。>
      < div >

      图1。由qpcr获得的ct值的代表性示例。散点图表示yscrna seq 96井板的循环放大(ct值)。每个点表示使用酵母内务基因(tdh3>,x轴)作为细胞大小(前向散射(fsc),y轴)索引排序值的函数而获得的值。虚线(ct>;25)显示用于区分正井和负井的阈值。标签显示未排序井h12的ct值(用作阴性对照)。

    17. 向每个孔中加入15微升室温平衡且均匀的安瓿xp珠(1:0.6样品/珠比)。
      注意:在纯化步骤中,不要将珠的体积增加到1:1以上。少于标准量的珠子可确保底漆二聚体残留量最小。>
    18. 用移液管上下混合十次,或直到溶液看起来均匀为止。将溶液转移到96孔板上,带有兼容的磁铁架。
    19. 将混合物在室温下孵育10分钟,使dna与安瓿xp珠结合。
    20. 将96孔板放置在磁性支架上5分钟,或直到溶液澄清并收集到珠子。
    21. 当样品在磁铁上时,小心地除去液体而不干扰磁珠。
    22. 用200微升80%(vol/vol)乙醇溶液清洗磁珠。将样品孵育30 s,然后用磁铁中的试管除去乙醇,不要使珠子过多。
      注:重要的是每次都要新鲜制备乙醇溶液,如乙醇从环境中吸收水分,从而改变最终浓度。请阅读并遵循制造商的说明。>
    23. 再次重复乙醇清洗(重复步骤B20-B22)。
    24. 去除任何乙醇痕迹,让珠子在室温下放置5分钟或直到乙醇蒸发,使珠子完全干燥。
      注意:在此步骤中盖住板或保护它不受任何可能的污染源或气流的影响。>
    25. 一旦没有乙醇残留,在室温下用16.5微升洗脱缓冲液(eb缓冲液qiagen)从安瓿xp珠中洗脱dscdna文库。
    26. 从磁铁上取下极板,用移液管上下移动三次或直到溶液均匀为止,使极板剧烈混合。
    27. 将金属板放在磁性支架上,放置2分钟,或直到溶液看起来清澈为止。
    28. 从每个孔中回收15μl上清液,并转移到新板上。正确标记标牌,因为它将被存储。
      安全停止点:cDNA文库可在-20°C下保存,然后再进行标记长达2周。>

  3. 全长cdna文库质量检查
    1. 运行1微升的dscdna文库(步骤b28),使用高灵敏度dna芯片(2100生物分析仪)检查大小分布和浓度估计。如果可用,使用步骤b16中的qpcr结果来指导生物分析仪井的选择(图2)。


      图2。使用yscrna seq(步骤c1)获得的具有代表性的全长cdna的生物分析痕迹。在dna高灵敏度芯片(agilent 5067-4626)上运行从步骤(步骤b28)获得的cdna文库进行验证。库浓度也用高灵敏度量子位法(thermo-fisher)测量。左面板(A1)表示库的下限当中间(A5)和右边(B2)面板代表平均库时,我们排序的质量(图改编自Nadal Ribelles等人,2019年)

      适配器退火:要生成细胞条码库,TN5需要加载双链DNA(DSDNA)适配器。为此,需要对96个不同的dsdna适配器进行退火。
    2. 在冰中用96个strt条形码(100μm)解冻板。
    3. 在新的平板中,将5μl Umi-TN5-U(100μm)和5μl Umi-TN5-U 1至96(μm)混合在TE 1X中,以50μm(每个)的最终浓度进行1:1稀释。
    4. 将混合物在95°C下加热3分钟,然后逐渐冷却至室温(0.5°C/s),以退火底漆。将板标记为“退火电池专用适配器”板。
      注:标牌上标明退火底漆的数量和退火日期。可提前制备盘子,并在-20°C下保存6个月。退火适配器的数量取决于需要标记的板的数量和使用频率。
    5. 要将称重传感器专用适配器连接至TN5,准备一块新板并将其标记为“已加载TN5”。在每口井中,混合以下试剂。我们建议与所有试剂混合,并单独添加退火电池专用适配器(表8)。

      表8。主混音产生10x转置


    6. 在37°C下孵育1小时,然后立即冷却至4°C,以装载TN5。在-20°C下冷冻“装载TN5”板。
      注:装载的TN5板可在-20°C下安全存放1-2周。注意,使用后立即将装载板存放在冰上4°C将显著降低TN5的活性,并导致标记效率低下。>

      标记
    7. 在新的冰盘中制备以下混合物(表9)。

      表9。用于库集成的主混音

    8. 在55°C下孵育5分钟,在85°C下孵育3分钟,使TN5失活,然后在热循环器中冷却至4°C。标记时间取决于tn5的纯化效率和活性。我们观察到这种酶随着时间的推移失去活性。强烈建议对每批TN5进行滴定,方法是在生物分析仪中标记相同数量的cDNA(步骤B28和C1)并检查断裂曲线。
    9. 为每个样品制备1:20稀释度肌酮>C1链霉亲和素。
    10. 用2x bwt缓冲液洗涤2倍的肌酐珠,并用比原来体积多20倍的2x bwt珠稀释。
      示例:20个样品的20微升珠子将最终用400微升2x BWT稀释。>
    11. 将20微升稀释的肌酮>C1链霉亲和素珠(步骤C10)添加到每个孔中,并在室温下在室温下培养5分钟。
    12. 将每个板(最多96个)的所有样品汇集到一个单独的收集管(1.5或2毫升)中。
    13. 将收集管放在磁架上,留出足够的时间使溶液完全澄清。
    14. 在磁铁上,用100微升TNT缓冲液清洗珠子一次,不要混合珠子。
    15. 在磁铁上,用100微升铅缓冲液清洗一次myone>c1链霉亲和素,并丢弃上清液。
    16. 在磁铁上,再次用100微升TNT缓冲液和废弃的上清液清洗3个珠子。

      移除3个端部
    17. 将100微升以下混合物添加到步骤C14(表10)中清洗的珠子中。

      表10。用于3'端部移除的MasterMix


    18. 将混合物在37℃下在热混合器中孵育1h。以1000转/分的速度每2分钟搅拌30秒,以避免珠状沉淀。
    19. 用100微升TNT缓冲液清洗3个珠子。

      洗脱单链cdna
    20. 在30微升无核酸酶的水中再悬浮。
    21. 在70°C、850转/分的温度下在热混合器中培养10分钟。
    22. 立即将珠子与磁铁结合,并将上清液转移到一个新的试管中,该试管包含上清液中的单链cdna文库(另一链仍与链霉亲和素珠子结合)。

      单链cdna清除
    23. 将54微升室温安瓿xp珠添加到30微升sscdna文库中。
    24. 在室温下孵育10分钟。
    25. 将珠子绑在磁铁上1分钟或直到溶液完全澄清并丢弃上清液(保留珠子)。
    26. 用200微升新鲜80%乙醇清洗一次,持续20-30秒。将磁珠绑在磁铁上执行此步骤。
    27. 风干珠子约2分钟。
    28. 在30微升EB缓冲液中再培养5分钟。
    29. 将珠子粘合2分钟,或直到溶液澄清,然后将上清液转移到新试管中。

  4. 评估库集中度
    1. 用洗脱cdna文库的1:100、1:1000和1:10000稀释液建立kapa定量反应(步骤c27)。试剂盒中提供了除DNA库以外的所有再生剂(表11)。
      注:本试剂盒可由您最喜欢的量化方法或使用具有已知标准(强烈建议使用Phix)的P5-P7底漆对的QPCR来代替SybrGreen 2x MasterMix。>

      表11.qpcr库量化的主混音


    2. kapa和国产sybergreen的qpcr循环条件(表12)。

      表12.qpcr
      的pcr循环条件

    3. 使用qpcr使用kapa biosystems提供的模板或制造商提供的说明计算库量化。
      注:我们使用了kapa、neb和自制系统,结果相似。>
    4. 建立一个单独的PCR来运行一个生物分析仪来确定最终的大小分布。准备以下混合物,每个要加载的库一个单独的反应(表13)。

      表13。用于在标记后评估库大小的主混音

    5. 运行步骤d2中相同的pcr,但持续11个周期。
    6. 在高灵敏度DNA芯片中加入1微升,以获得基于生物分析仪轮廓的平均文库大小(图3)。


      图3。从yscrna seq.获得的代表性生物分析痕量。从大约80个细胞获得的两个代表性样品。显示每个库的浓度,并通过qpcr测定(步骤d3)。图来自Nadal Ribelles等人,2019年。

    7. 使用自定义read 1>引物和umi-tn5-u作为索引read>引物对hiseq 2000高输出上的库进行排序。
    8. 要在hiseq快速运行上运行库,请使用锁定核酸(lna)引物。在0.5μm时在底漆中刺入。
      索引1引物进入hp8
      将1个底漆读入HP9
      注:
      1. 使用测序工具箱/仪器和/或测序核心设施仔细检查此信息。
      2. 对于高输出运行,也需要自定义引物,但由于测序化学的差异,不需要LNA。

      >umi_pcr_read1:+gaatga+taccggcg+acca+ccga+t–定制250 nmole。DNA寡糖,高效液相色谱纯化
      索引1:ctgt+ct+ctt+ata+ca+tctga+cg+c-定制250 nmol DNA寡糖,高效液相色谱纯化

    9. 每车道每个图书馆的负荷约为8-14 pmol。文库是单链dna,因此不需要变性。注:协议优化后,无需运行phix控件,如果加载phix,请考虑双链phix控件的变性步骤,yscrna seq库不需要此步骤。
      >
    参考图4所示为板材加工的示意图。


    图4。来自Smart Medical的YSCRNA序列图像的示意图Server(The Laboratory Servier,a HREF=“https http://smart.servier.com/”target=“\ \ blank”>smart servier medical art

数据分析

ISCRNA-SEQ数据分析的初始步骤与Bulk RNA-SEQ(Conesaet al>,2016)应用的步骤非常相似,但有一些特殊性。YSCRNA-SEQ发生器每序列CDNA分子读两读。第一个读数(索引1)包含细胞特异性钡代码,该细胞特异性钡代码与特定细胞相关。第二读物(阅读1)包含了UMI,和映射到一个特异的转录、绝对分子计数和转录识别的TSS。为了Properly estimate gene expression from YSCRNA-SEQ data,both reads have to be computationally associated to their references.The former association is default by default in illumina's space>demultiplexing tool.本发明的个体快递文件(每一酵母细胞一次)是YSCRNA-SEQ分析的起始点。此后,我们进一步加速了快速文件的预处理,以识别UMIS,并实现基因表达表的一致性和量化(图5)。在这里,我们详细说明了我们在分析YSCRNA-SEQ数据时采取的步骤,从FASTQ文件开始:
note:the analysis spipeline was executed in a linux 64 bit machine with 32 gb of ram and 6 cpus.我们建议至少有12GB的RAM。一台具有几种计算芯的机器,也比较容易加快过程。
/ 图5YSCRNA-SEQ分析管道的方案代表 strong

  1. 阅读预处理 为了从读者的初期提取UMI,我们使用了一个自定义的Javascript Program(debcscell)。

    command$Java-CP directoryofdebcscell rootnameofdebcsccell f1=fastqfile bl=“>

    这个脚本表述了后续行动:&NBSP;
    1. 提取第一个6nt(UMI)并将其纳入后期使用的阅读名称。
    2. 提取物和计数没有跟踪海事组织,第14页。如果有14个或更多的读者或G's,则该读者将被公开。

  2. 一致性 note:in our case,the reference sequences(referencesequencencesname.fa)are a combination of the S.Cerevisiae genome(SCER3,SGD R64 version;A HREF=“http://www.yeastgenome.org/”target=“\\\\\\\\\\\ \ xBlank”> www.yeastgenome由制造商提供的RCC控制序列不包括在T7推进剂序列克隆过程中使用的限制位置。自从YSCRNA-SEQ是TSS-specific,这一事实考虑到了读者初期因误差而产生的映射速率。我们生成了一个新的ERC参考,包括这些序列。这些修改后的参考文献可从基因表达综合网站下载(在。
    1. 在我们的例子中,我们将每个库的平均读取深度定为72万次。我们评估,通过下采样,该深度基本上高于获得酵母转录组高分辨率所需的深度。事实上,在每个细胞约500000次读取时,检测到的转录本数量达到饱和(补充图2c【Nadal Ribelles等人,2019年】)。此外,我们观察到所有序列库的唯一映射读取的中位数约为74%。这些值是使用此协议的有用参考值。
    2. 我们使用Novocraft对读进行对齐,但是开放源代码的对齐器,例如hisat2(Kim等人,2015)或star(Dobin等人,2013)可以不使用在结果上取得重大差异。

    读取与使用默认参数的Novocraft(http://www.novocraft.com/)对齐。
    1. 使用novoindex命令为novocraft创建索引:

      $novoindex referencesequencesname.nix referencesequencesname.fa>

    2. 使用novoalign命令对齐读取:

      $novoalign-f fastqfileumipcesed.fastq-d引用序列名-o sam
    3. 转换为BAM格式,并使用samtools视图和samtools sort对其进行排序:(从上一个命令中导出)

      samtools视图-bs-samtools sort-文件名.sorted.bam.>

  3. 量化
    虽然我们知道存在用于量化基于umi的单单元数据的公共软件(例如umi tools[Smith等人,2017]),但我们使用r自定义脚本执行了量化,以获得完整的数据检查和数据过滤的灵活性和控制。量化的起点是在上一步中获得的已排序的BAM文件,每个已排序的单元对应一个文件。
    1. 处理BAM文件。
      脚本:readalingumi.r.$r script readalingumi.r>>

      使用基因组比对包将每个bam文件加载到r中。然后,低质量的映射读取(mapq<;30)和5'端带有软剪裁的读取被过滤掉。最后,将地图读取到相同的位置,并将具有相同umi的地图分组,以便在下一步中折叠它们。
    2. 处理umi分组数据。

      脚本:>bard2rds.r.$r script bard2rds.r>>

      在这个步骤中,不到3次读取所支持的umis被过滤掉,因为它们可能表示排序错误。然后,映射到相同基因组位置的不同umi被分组。不同的umis映射到同一位置代表同一转录本的不同分子。此信息存储在rangeddata>对象中。
    3. 与注释和计数表生成重叠。

      脚本:txanno.r(链接)。$rscript txanno.r.>>

      如果有重叠,每个分子都被分配到一个来自注释的基因组特征。在这项研究中,我们使用了来自(Xu等人,2009年)的注释,因为它包含了将基因分为不同类别(编码、剪切、SUTS等)的综合分类。得到的结果是一个以基因为行、细胞为列的绝对基因表达表。

  4. (可选)过滤
    为了避免低质量细胞(死亡、压力或凋亡)的存在,我们决定只保留那些超过500000个序列读取的细胞,以及那些我们检测到超过1000个不同转录本的细胞。在我们的例子中,这种过滤器消除了线粒体RNA比例高的细胞,而线粒体RNA与低质量细胞相关(Ilicic等人,2016)。但是,这种过滤器不是必需的,可以根据实验类型和正在研究的生物问题进行调整。

食谱

注:所有试剂和水必须不含核酸酶,仅用于单细胞方案。

  1. 细胞捕获和裂解溶液(表14)

    表14。“细胞捕获和裂解液”的组成

    >>
  2. 2x bwt缓冲器
    10mmTris HCl,pH 7.5
    1毫米EDTA
    2 m氯化钠
    0.02%吐温-20
  3. tnt缓冲
    20毫米Tris pH 7.5
    50毫米氯化钠
    0.02%吐温

致谢


作者感谢sten linnarsson在与yscrna seq进行初步试验期间提供的试剂。我们感谢EMBL、Bianca Hennig和Lars Velten的蛋白质表达和纯化核心设施提供内部纯化的TN5。我们感谢Derek Caetano Anolls编辑和完善了手稿。M.N.R.曾获得EMBO长期奖学金(斯坦福大学),后来又获得了Maria de Maeztu博士后奖学金(Pompeu Fabra大学的Doctores Banco de Santander Maria de Maeztu)。P.L.是一名金融机构十岁前研究金(加泰罗尼亚将军)的接受者。这项工作得到了美国国立卫生研究院(NIH)和欧洲研究理事会(European Research Council)的高级研究员资助(ADG-294542)和中国国家重点研究与发展计划(2017YFC0908405)的资助。这项研究也得到了西班牙卫生部的资助。《经济与竞争力的尝试》(PGC2018-094136-B-I00和联邦快递到F.P.;BFU2017-85152-P和联邦快递到E.N.),加泰罗尼亚政府(2017 SGR 799)和Unidad de Excelencia Maria de Maeztu,MDM-2014-0370。我们衷心感谢西班牙经济、工业和竞争力部(MINECO)通过“卓越中心”Severo Ochoa奖和加泰罗尼亚政府CERCA计划提供的机构资金。F.P.是ICREA Acad_mia(加泰罗尼亚总司令)的接受者。

相互竞争的利益

作者声明没有财务或非财务利益。

工具书类

  1. Conesa,A.,Madrigal,P.,Tarazona,S.,Gomez Cabrero,D.,Cervera,A.,Mcpherson,A.,Szczesniak,M.W.,Gaffney,D.J.,Elo,L.L.,Zhang,X.和Mortazavi,A.(2016年)。RNA序列数据分析最佳实践调查。基因组生物学>17:13。
  2. David,L.,Huber,W.,Granovskaia,M.,Toedling,J.,Palm,C.J.,Bofkin,L.,Jones,T.,Davis,R.W.和Steinmetz,L.M.(2006年)。酵母基因组中转录的高分辨率图谱。自然科学程序>103(14):5320-5325。
  3. Dobin,A.,Davis,C.A.,Schlesinger,F.,Drenkow,J.,Zaleski,C.,Jha,S.,Batut,P.,Chaisson,M.和Gingeras,T.R.(2013年)。星:超快通用RNA序列比对仪。生物信息学>29(1):15-21。
  4. Gasch,A.P.,Yu,F.B.,Hose,J.,Escalant,L.E.,Place,M.,Bacher,R.,Kanbar,J.,Ciobanu,D.,Sandor,L.,Grigoriev,I.V.,Kendziorski,C.,Quake,S.R.和McClean,M.N.(2017年)。单细胞rna测序揭示内在和外在的调控酵母对应激反应的异质性。plos biol>15(12):e2004050。
  5. Gresham,D.,Bonneau,R.,Jackson,C.A.,Castro,D.M.和Saldi,G.A.(2019年)。利用不同环境中条形码基因型的单细胞rna测序重建基因调控网络。biorxiv>doi:https://doi.org/10.1101/581678。
  6. Hennig,B.P.,Velten,L.,Rake,I.,Tu,C.S.,Thoms,M.,Rybin,V.,Besir,H.,Remans,K.和Steinmetz,L.M.(2018年)。使用强大的TN5纯化和标记协议的大规模低成本NGS文库准备。g3(bethesda)>8(1):79-89。
  7. Ilicic,T.,Kim,J.K.,Kolodziejczyk,A.A.,Bagger,F.O.,McCarthy,D.J.,Marioni,J.C.和Teichmann,S.A.(2016年)。根据单细胞RNA序列数据对低质量细胞进行分类。基因组生物学>17:29。
  8. Islam,S.,Zeisel,A.,Joost,S.,La Manno,G.,Zajac,P.,Kasper,M.,Lonnerberg,P.和Linnarsson,S.(2014年)。具有唯一分子标识符的定量单细胞rna序列。nat方法>11(2):163-166。
  9. Kim,D.,Langmead,B.和Salzberg,S.L.(2015年)。hisat:一种具有低内存要求的快速拼接校准器。nat方法>12(4):357-360。
  10. 劳伦斯、迈克尔、沃尔夫冈·胡贝尔、赫维帕盖斯、帕特里克·阿博扬、马克·卡尔森、罗伯特·绅士、马丁·摩根和文森特·凯里。(2013年)。计算和注释基因组范围的软件。公共科学图书馆计算生物学>9(8):e1003118。
  11. 李,亨,鲍勃·汉德克,亚历克·怀索克,蒂姆·芬内尔,朱阮,尼尔斯·荷马,加博·马尔斯,冈卡洛·阿贝卡西斯和理查德·杜宾。(2009年)。序列比对/地图格式和samtools。生物信息学>25(16):2078–79。
  12. Nadal Ribelles,M.,Islam,S.,Wei,W.,Latorre,P.,Nguyen,M.,de Nadal,E.,Posas,F.和Steinmetz,L.M.(2019年)。酵母群体中敏感的高通量单细胞rna序列显示克隆内转录相关。nat microbiol>4(4):683-692。
  13. Pelechano,V.,Wei,W.和Steinmetz,L.M.(2013年)。亚型分析揭示了广泛的转录异质性。自然>497(7447):127-131。
  14. Pelechano,V.,Wei,W.和Steinmetz,L.M.(2015年)。广泛的共翻译rna衰变揭示核糖体动力学。细胞>161(6):1400-1412。
  15. Pelechano,V.,Wei,W.,Jakob,P.和Steinmetz,L.M.(2014年)。通过转录亚型测序在全基因组范围内识别转录起始和终止位点。nat protoc>9(7):1740-1759。
  16. Saint,M.,Bertaux,F.,Tang,W.,Sun,X.M.,Game,L.,Koferle,A.,Bahler,J.,Shahrezaei,V.和Marguerat,S.(2019年)。单细胞成像和rna测序揭示基因表达模式分裂酵母生长和适应过程中的异质性。nat微生物学>4(3):480-491。
  17. Skinnider,M.A.,Squair,J.W.和Foster,L.J.(2019年)。单细胞转录组学关联性评价方法。NAT方法>16(5):381-386。
  18. Smith,T.,Heger,A.和Sudbery,I.(2017年)。umi工具:在唯一的分子标识符中建模测序错误以提高量化精度。基因组研究>27(3):491-499。
  19. 徐,Z.,魏,W.,加格纳,J.,佩罗奇,F.,克劳德·蒙斯特,S.,坎布伦,J.,古芬蒂,E.,斯图茨,F.,休伯,W.和斯坦梅茨,L.M.(2009)。双向启动子在酵母中产生普遍转录。自然>457(7232):1033-1037。
  20. Zhang,X.,Li,T.,Liu,F.,Chen,Y.,Yao,J.,Li,Z.,Huang,Y.和Wang,J.(2019年)。基于液滴的超高通量单细胞rna-seq系统的比较分析。mol-cell>73(1):130-142 e5。
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引用:Nadal-Ribelles, M., Islam, S., Wei, W., Latorre, P., Nguyen, M., de Nadal, E., Posas, F. and Steinmetz, L. M. (2019). Yeast Single-cell RNA-seq, Cell by Cell and Step by Step. Bio-protocol 9(17): e3359. DOI: 10.21769/BioProtoc.3359.
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