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In This Article

  • Summary
  • Abstract
  • Introduction
  • Protocol
  • Results
  • Discussion
  • Disclosures
  • Acknowledgements
  • Materials
  • References
  • Reprints and Permissions

Summary

The feasibility and effectiveness of high-throughput scRNA-seq methods herald a single-cell era in plant research. Presented here is a robust and complete procedure for isolating specific Arabidopsis thaliana root cell types and subsequent transcriptome library construction and analysis.

Abstract

In multicellular organisms, developmental programming and environmental responses can be highly divergent in different cell types or even within cells, which is known as cellular heterogeneity. In recent years, single-cell and cell-type isolation combined with next-generation sequencing (NGS) techniques have become important tools for studying biological processes at single-cell resolution. However, isolating plant cells is relatively more difficult due to the presence of plant cell walls, which limits the application of single-cell approaches in plants. This protocol describes a robust procedure for fluorescence-activated cell sorting (FACS)-based single-cell and cell-type isolation with plant cells, which is suitable for downstream multi-omics analysis and other studies. Using Arabidopsis root fluorescent marker lines, we demonstrate how particular cell types, such as xylem-pole pericycle cells, lateral root initial cells, lateral root cap cells, cortex cells, and endodermal cells, are isolated. Furthermore, an effective downstream transcriptome analysis method using Smart-seq2 is also provided. The cell isolation method and transcriptome analysis techniques can be adapted to other cell types and plant species and have broad application potential in plant science.

Introduction

Cells are the fundamental unit of all living organisms and perform structural and physiological functions. Although the cells in multicellular organisms show apparent synchronicity, cells of different types and individual cells present differences in their transcriptomes during development and environmental responses. High-throughput single-cell RNA sequencing (scRNA-seq) provides unprecedented power for understanding cellular heterogeneity. Applying scRNA-seq in plant sciences has contributed to successfully constructing a plant cell atlas1, has been used to identify rare cellular taxa in plant tissues2, has provided insight into the composition of cell types in plant tissues, and has been used to identify cellular identity and important functions employed during plant development and differentiation. In addition, it is possible to infer spatiotemporal developmental trajectories in plant tissues1,2,3 to discover new marker genes4 and study the functions of important transcription factors5 using scRNA-seq in order to reveal the evolutionary conservation of the same cell type in different plants3. Abiotic stresses are among the most important environmental influences on plant growth and development. By exploring the changes in the composition of cell types in plant tissues under different treatment conditions through single-cell transcriptome sequencing, one can also resolve the abiotic stress response mechanism6.

The potential for resolving transcriptional heterogeneity between cell types using scRNA sequencing depends on the cell isolation method and sequencing platform. Fluorescence-activated cell sorting (FACS) is a widely used technique for isolating a subpopulation of cells for scRNA-seq based on light scattering and the fluorescence properties of the cells. The development of fluorescent marker lines by transgenic technology has greatly improved the efficiency of cell isolation by FACS7. Conducting scRNA-seq using Smart-seq28 further enhances the ability to dissect the cellular heterogeneity. The Smart-seq2 method has good sensitivity for gene detection and can detect genes even with a low transcript input9. In addition to bulk cell type collection, modern cell sorters provide a single-cell index sorting format, allowing transcriptome analysis at single-cell resolution using Smart-seq210 or other multiplexed RNA-seq methods, such as CEL-seq211. Single-cell or cell-type sorting can be potentially used for many other downstream applications, such as parallel multi-omics studies12,13. Presented here is a robust and versatile protocol for isolating plant cell types, such as xylem-pole pericycle cells, lateral root cap cells, lateral root initial cells, cortex cells, and endodermal cells from the roots of Arabidopsis thaliana marker cell lines by FACS. The protocol further involves constructing the Smart-seq2 library for downstream transcriptome analysis.

Protocol

The following protocol has been optimized for A. thaliana wild-type (WT) seeds with no fluorescence and fluorescent marker lines for the following root cell types: xylem-pole pericycle cells (J0121), lateral root initial cells, lateral root cap cells (J3411), endodermis and cortex cells (J0571) (Figure 1A). All the marker lines were obtained from a commercial source (see Table of Materials), except for the lateral root initiation cell marker line, which was generated by introducing a GATA23 promoter-driven GFP construct into a wild-type Arabidopsis plant following a previously published report14.

1. Preparation of the plant material

  1. Sterilize the A. thaliana WT seeds and fluorescent marker line seeds by incubating the seeds in 20% bleach in a rotating incubator at room temperature for 15 min.
  2. Rinse the seeds in double-distilled water (ddH2O) three to five times. Perform this step on a sterile clean bench.
  3. Plate the WT and reporter line seeds on half-strength Murashige and Skoog (MS) medium with 0.8% agar (w/v)15. Grow the plants vertically for 5 days (16 h light at 23 °C) after stratifying for 2 days at 4 °C.

2. Protoplasting

  1. Prepare the protoplasting solutions2,5, referred to as Solution A and Solution B (see Table of Materials).
    1. Prepare Solution A containing 400 mM mannitol, 0.05% BSA, 20 mM MES (pH 5.7), 10 mM CaCl2, and 20 mM KCl (see Table of Materials). Store Solution A at −20 °C for up to 1 month.
    2. Prepare Solution B by adding 1% (w/v) cellulase R10, 1% (w/v) cellulase RS, 1% (w/v) hemicellulase, 0.5% (w/v) pectolyase, and 1% (w/v) macerozyme R10 in a fresh aliquot of Solution A. Store Solution B at −20 °C for up to 1 month.
  2. Gently thaw Solution A and Solution B on ice before beginning the experiment.
  3. Cut off the roots using a clean blade or scissors, and chop the roots into ~0.5 cm pieces. Submerge the roots in 1.5 mL of Solution B followed by gentle rotation (at approximately 18 rpm) at room temperature for 1.5-2 h.
  4. Filter the root protoplasts through the 40 µm strainer mesh (see Table of Materials).
  5. Rinse the strainer mesh with 1-2 mL of Solution A.
  6. Combine the liquids of step 2.4 and step 2.5, and centrifuge at 300 x g for 5 min at 4 °C. Discard the supernatant with a pipette, resuspend the cell pellet in 500-600 µL of Solution A, and then place it on ice immediately.
  7. Transfer the resuspended cell solution to a new 5 mL test tube for cell sorting.

3. Fluorescence-Activated Cell Sorting (FACS)

  1. Switch on and finish the instrument setup steps on the sorter (see Table of Materials). Select the fluorescence channels, use a WT plant (no fluorescence) as a control to determine the baseline for autofluorescence, and adjust the sorting gate based on the fluorescence intensity and FSC/SSC singlets (Figure 2).
  2. Add 500 µL of Solution A (step 2.1.1) into a 1.5 mL collection tube to prevent the cells from being damaged. Collect 2,000-3,000 cells per tube.
  3. After sorting, immediately place the samples on ice, centrifuge the collection tube containing the cells at 300 x g for 5 min at 4 °C, and remove the supernatant with a pipette.
  4. Take 2 µL of sorted cells, and check for fluorescence using a fluorescence microscope (see Table of Materials).
  5. Store the sorted cells at −80 °C, or use them immediately for library construction (step 4).
  6. For single-cell index sorting, place the 96-well plate into the adapter. Calibrate the position of the plate so that the droplet falls in the center hole of the plate. Select single-cell sorting mode when sorting, enter the target number of sorted cells as 1, and start sorting.

4. Smart-seq2 library preparation

  1. As a result of the ultra-low amount of input, perform single-cell type RNA-seq library construction in a contamination-free environment. Before beginning the experiment, clean the bench with a surface decontaminant8 (see Table of Materials) and 75% ethanol.
  2. Prepare lysis buffer (mixture A) (Table 1) by combining 0.33 µL of 10% Triton X-100, 0.55 µL of RNase inhibitor, and 0.22 µL of 0.1 M DTT (see Table of Materials).
  3. Add 1 µL of mixture A into the sorted sample, and grind with a sterile pestle. The preferable sample volume is ≤0.5 µL; use RNase-free water to make up the volume to 14 µL. Transfer each single cell sample into a 0.2 mL thin-walled PCR tube.
  4. Prepare mixture B containing 0.44 µL of oligo-dT30VN reverse transcription (RT) reaction primer (100 µM) and 4.4 µL of dNTPs (10 mM) (Table 2) (see Table of Materials).
  5. Add 4.4 µL of mixture B to the 14 µL of sample in each tube, pipette gently to mix the sample, and incubate the sample at 72 °C for 3 min. After incubation, immediately put the samples on ice to hybridize the oligo-dT to the poly A tail.
  6. Prepare the reverse transcription reaction mixture (mixture C) (Table 3) (see Table of Materials). Add 21.6 µL of mixture C to each tube containing the samples. Turn on the RT program on a common PCR instrument (Table 4).
  7. Perform the preamplification reaction on ice. Prepare mixture D by combining 44 µL of 2x PCR polymerase mix and 0.88 µL of IS PCR primer (10 µM) (Table 5) (see Table of Materials). Add 40.8 µL of mixture D to the 40 µL of RT reaction product, and run the preamplification program (Table 6).
  8. Purify the preamplification reaction products using Ampure XP beads (see Table of Materials). Add 48 µL of beads (0.6:1 ratio) into each sample from step 4.7, and gently mix the samples by pipetting.
  9. Incubate the samples at room temperature for 10 min. Place the 1.5 mL tubes containing the samples on a magnetic separation stand for 5 min. Carefully discard the supernatant from the samples without disturbing the beads.
  10. Wash the beads by resuspending them in 200 µL of 80% ethanol, and place the samples on the magnetic separation stand (see Table of Materials) for another 3 min before discarding the ethanol-containing supernatant.
  11. Air-dry the samples for 10 min, and cover the tube to prevent contamination and cross-contamination during the air-drying.
  12. Resuspend the beads in 20 µL of ddH2O, incubate the samples at room temperature for 5 min, and then place them on the magnetic separation stand for 5 min.
  13. Pipette out 18 µL of the supernatant from each tube, and transfer the samples into new 1.5 mL centrifuge tubes. Use 1 µL of the sample to assess the quality of the cDNA using a DNA quantification kit, determine the size distribution of each prelibrary using a fragment analyzer (see Table of Materials), and store the remaining sample at −20 °C.
  14. Construct a cDNA library8 for Illumina sequencing16 from the prelibrary product of step 4.13 using a sequencing library preparation kit (see Table of Materials).
  15. Purify the libraries (from step 4.14) using the Ampure XP beads, quantify the purified libraries, and determine the size distribution of each library following step 4.13.
    NOTE: Pool equal nanomoles of each library, ensuring that none of them have the same combination of Illumina index. Otherwise, the libraries can be pooled in a ratio based on the desired sequencing output and sequenced together on the same lane of the Illumina sequencer. Generally, sequencing each library to a depth of 4-6 GB, which yields >10 million mapped reads, provides 20x-30x coverage of the Arabidopsis genome. Lower sequencing depths are also acceptable but might affect the significance of the differential expression analysis.

5. RNA-seq data analysis

  1. Trim the raw reads using Trim-Galore17 followed by mapping to the reference genome using hisat218 (daehwankimlab.github.io/hisat2), and remove the PCR duplicated fragments using Picard19 (broadinstitute.github.io/picard).
  2. Perform the raw count processing and subsequent analysis of the differentially expressed genes (DEG) with DESeq220 using at least three biological replicates for each sample. Perform clustering of the gene expression with the Pheatmap package, and visualize in an expression heatmap.
    NOTE: The RPKM (reads per kilobase per million mapped reads) values of the genes and the TEs (transposable elements) were calculated with Stringtie18 (github.com/gpertea/stringtie) and visualized in a genome browser.

Results

Protoplast isolation
This protocol is effective for the protoplast sorting of fluorescent A. thaliana root marker lines. These markers lines have been developed by the fusion of fluorescent proteins with genes expressed specifically in target cell types, or using enhancer trap lines (Figure 1). Numerous tissues and organs have been dissected into cell types expressing specific fluorescent markers in model plants and crops.

F...

Discussion

The Smart-seq2-based protocol can generate reliable sequencing libraries from several hundreds of cells8. The quality of the starting material is essential for the accuracy of the transcriptome analysis. FACS is a powerful tool for preparing cells of interest, but this procedure, especially the protoplasting step, must be optimized for plant applications. Laser capture microdissection (LCM) or manual dissected cells can also be used as input25,2...

Disclosures

The authors have nothing to disclose.

Acknowledgements

We set up this protocol in the single-cell multi-omics facility of the School of Agriculture and Biology, Shanghai Jiao Tong University, and were supported by the National Natural Science Foundation of China (Grant No. 32070608), the Shanghai Pujiang Program (Grant No. 20PJ1405800), and Shanghai Jiao Tong University (Grant Nos. Agri-X20200202, 2019TPB05).

Materials

NameCompanyCatalog NumberComments
0.22 µm strainerSorfa 622110
AgarYeasen70101ES76
Agilent fragment analyzerAglientAglient 5200
Agilent high-sensitivity DNA kitAglientDNF-474-0500
Ampure XP beadsBECKMANA63881
BetaineyuanyeS18046-100g
BleachMr MuscleFnBn83BK20% (v/v) bleach
BSAsigma9048-46-8
CaCl2yuanyeS24109-500g
Cellulase R10Yakult (Japan)9012-54-8
Cellulase RSYakult (Japan)9012-54-8
Centrifuge tube (1.5 mL)Eppendolf30121589
DNase, RNase, DNA and RNA Away Surface DecontaminantsBeyotimeR0127
dNTPs (10 mM)NEBN0447S
DTT (0.1 M)
invitrogen
18090050
EthanolSinopharm Chemical Reagent Co., Ltd100092680
FACSBD FACS MelodyBD-65745
FACSSonySH800S
Filter tip  (1000 µL)Thermo ScientificTF112-1000-Q
Filter tip  (200 µL)Thermo ScientificTF140-200-Q
Filter tip (10 µL)Thermo ScientificTF104-10-Q
Filter tip (100 µL)Thermo ScientificTF113-100-Q
Fluorescent microscopeNikonEclipse Ni-E
Four-Dimensional Rotating MixerKylin -BellBE-1100
Hemicellulasesigma9025-56-3
IS PCR primer5'-AAGCAGTGGTATCAACGCAGAG
T-3'
KAPA HiFi HotStart ReadyMix(2X)Roche 7958935001
KClSinopharm Chemical Reagent Co., Ltd7447-40-7
Macerozyme R10Yakult (Japan)9032-75-1
Magnetic separation standinvitrogen12321D
Mannitolaladdin69-65-8
MESaladdin145224948
MgCl2 yuanyeR21455-500ml
MicrocentrifugesEppendorfCentrifuge 5425
Micro-mini-centrifugeTitanTimi-10k
MSPhytotechM519
Nextera XT DNA Library Preparation KitilluminaFC-131-1024
oligo-dT30VN primer5'-AAGCAGTGGTATCAACGCAGAG
TACTTTTTTTTTTTTTTTTTTTTTTT
TTTTTTTTTTVN-3'
PCR instrumentThermal cyclerA24811
PectolyaseYakult (Japan)9033-35-6
Plant marker linesNottingham Arabidopsis Stock Centre (NASC)
Qubit 1x dsDNA HS Assay KitinvitrogenQ33231
Qubit 2.0 fluorometerinvitrogenQ32866
RNase inhibitor Thermo ScientificEO0382
RNase-free waterinvitrogen10977023
Solution A400 mM mannitol, 0.05 % BSA , 20 mM MES (pH5.7), 10 mM CaCl2, 20 mM KCl
Solution B1 % (w/v)cellulase R10, 1 % (w/v) cellulase RS, 1 %  (w/v)hemicellulase, 0.5 %  (w/v)pectolyase and 1 %  (w/v) Macerozyme R10 of in a fresh aliquot of solution A
Sterile pestleBIOTREAT453463
Strainer (40 µm )Sorfa 251100
Superscript enzyme (200 U/µL)invitrogen18090050
SuperScript VI buffer (5x)invitrogen18090050
T0est tube (5 mL)BD Falcon352052
Thin-walled PCR tubes with caps (0.5 mL)AXYGENPCR-05-C
Triton X-100Sangon BiotechA600198-0500
TSO primer5'-AAGCAGTGGTATCAACGCAGAG
TACATrGrG+G-3'
VortexTitanVM-T2

References

  1. Zhang, T. -. Q., Chen, Y., Liu, Y., Lin, W. -. H., Wang, J. -. W. Single-cell transcriptome atlas and chromatin accessibility landscape reveal differentiation trajectories in the rice root. Nature Communications. 12 (1), 2053 (2021).
  2. Denyer, T., et al. Spatiotemporal developmental trajectories in the Arabidopsis root revealed using high-throughput single-cell RNA sequencing. Developmental Cell. 48 (6), 840-852 (2019).
  3. Liu, Q., et al. Transcriptional landscape of rice roots at the single-cell resolution. Molecular Plant. 14 (3), 384-394 (2021).
  4. Liu, Z., et al. Global dynamic molecular profiling of stomatal lineage cell development by single-cell RNA sequencing. Molecular Plant. 13 (8), 1178-1193 (2020).
  5. Shahan, R., et al. A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants. Developmental Cell. 57 (4), 543-560 (2022).
  6. Wendrich, J. R., et al. Vascular transcription factors guide plant epidermal responses to limiting phosphate conditions. Science. 370 (6518), (2020).
  7. Carter, A. D., Bonyadi, R., Gifford, M. L. The use of fluorescence-activated cell sorting in studying plant development and environmental responses. The International Journal of Developmental Biology. 57 (6-8), 545-552 (2013).
  8. Picelli, S., et al. Full-length RNA-seq from single cells using Smart-seq2. Nature Protocols. 9 (1), 171-181 (2014).
  9. Wang, X., He, Y., Zhang, Q., Ren, X., Zhang, Z. Direct comparative analyses of 10X genomics chromium and Smart-seq2. Genomics Proteomics Bioinformatics. 19 (2), 253-266 (2021).
  10. Serrano-Ron, L., et al. Reconstruction of lateral root formation through single-cell RNAsequencing reveals order of tissue initiation. Molecular Plant. 14 (8), 1362-1378 (2021).
  11. Hashimshony, T., et al. CEL-Seq2: Sensitive highly-multiplexed single-cell RNA-Seq. Genome Biology. 17, 77 (2016).
  12. Macaulay, I. C., et al. G&T-seq: Parallel sequencing of single-cell genomes and transcriptomes. Nature Methods. 12 (6), 519-522 (2015).
  13. Angermueller, C., et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nature Methods. 13 (3), 229-232 (2016).
  14. De Rybel, B., et al. A novel aux/IAA28 signaling cascade activates GATA23-dependent specification of lateral root founder cell identity. Current Biology. 20 (19), 1697-1706 (2010).
  15. Duncombe, S. G., Barnes, W. J., Anderson, C. T. Imaging the delivery and behavior of cellulose synthases in Arabidopsis thaliana using confocal microscopy. Methods in Cell Biology. 160, 201-213 (2020).
  16. Levy, S. E., Myers, R. M. Advancements in next-generation sequencing. Annual Review of Genomics and Human Genetics. 17 (1), 95-115 (2016).
  17. Ooi, C. C., et al. High-throughput full-length single-cell mRNA-seq of rare cells. PLoS One. 12 (11), e0188510 (2017).
  18. Pertea, M., Kim, D., Pertea, G. M., Leek, J. T., Salzberg, S. L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nature Protocols. 11 (9), 1650-1667 (2016).
  19. Tsyganov, K., Perry, A., Archer, S., Powell, D. RNAsik: A Pipeline for complete and reproducible RNA-seq analysis that runs anywhere with speed and ease. Journal of Open Source Software. 3, 583 (2018).
  20. Love, M. I., Huber, W., Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 15 (12), 550 (2014).
  21. Kamiya, T., et al. The MYB36 transcription factor orchestrates Casparian strip formation. Proceedings of the National Academy of Sciences of the United States of America. 112 (33), 10533-10538 (2015).
  22. Zhang, Y., et al. Two types of bHLH transcription factor determine the competence of the pericycle for lateral root initiation. Nature Plants. 7 (5), 633-643 (2021).
  23. Haecker, A., et al. Expression dynamics of WOX genes mark cell fate decisions during early embryonic patterning in Arabidopsis thaliana. Development. 131 (3), 657-668 (2004).
  24. Chen, Q., et al. Auxin overproduction in shoots cannot rescue auxin deficiencies in Arabidopsis roots. Plant Cell Physiol. 55 (6), 1072-1079 (2014).
  25. Nichterwitz, S., et al. Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Nature Communications. 7, 12139 (2016).
  26. Long, J., et al. Nurse cell--derived small RNAs define paternal epigenetic inheritance in Arabidopsis. Science. 373 (6550), (2021).
  27. Gutzat, R., et al. Arabidopsis shoot stem cells display dynamic transcription and DNA methylation patterns. EMBO Journal. 39 (20), e103667 (2020).

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Transcriptome AnalysisPlant Cell TypesGene ExpressionFluorescence Activated Cell SortingRN seq AnalysisProtoplast SortingRNA seq Library PreparationArabidopsis ThalianaProtoplasting SolutionsCell SortingFACS MachineFluorescence ChannelsSorting GateMulti omic Approaches

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