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).

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&#160;cells&#160;(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
<ol>
	<li>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.</li>
	<li>Rinse the seeds in double-distilled water (ddH2O) three to five times. Perform this step on a sterile clean bench.</li>
	<li>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 &#176;C) after stratifying for 2 days at 4 &#176;C.</li>
</ol>
2. Protoplasting
<ol>
	<li>Prepare the protoplasting solutions2,5, referred to as Solution A and Solution B (see Table of Materials).
	<ol>
		<li>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 &#8722;20 &#176;C for up to 1 month.</li>
		<li>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 &#8722;20 &#176;C for up to 1 month.</li>
	</ol>
	</li>
	<li>Gently thaw Solution A and Solution B on ice before beginning the experiment.</li>
	<li>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.</li>
	<li>Filter the root protoplasts through the 40 &#181;m strainer mesh (see Table of Materials).</li>
	<li>Rinse the strainer mesh with 1-2 mL of Solution A.</li>
	<li>Combine the liquids of step 2.4 and step 2.5, and centrifuge at 300 x g for 5 min at 4 &#176;C. Discard the supernatant with a pipette, resuspend the cell pellet in 500-600 &#181;L of Solution A, and then place it on ice immediately.</li>
	<li>Transfer the resuspended cell solution to a new 5 mL test tube for cell sorting.</li>
</ol>
3. Fluorescence-Activated Cell Sorting (FACS)
<ol>
	<li>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).</li>
	<li>Add 500 &#181;L 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.</li>
	<li>After sorting, immediately place the samples on ice, centrifuge the collection tube containing the cells at 300 x g for 5 min at 4 &#176;C, and remove the supernatant with a pipette.</li>
	<li>Take 2 &#181;L of sorted cells, and check for fluorescence using a fluorescence microscope (see Table of Materials).</li>
	<li>Store the sorted cells at &#8722;80 &#176;C, or use them immediately for library construction (step 4).</li>
	<li>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.</li>
</ol>
4. Smart-seq2 library preparation
<ol>
	<li>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.</li>
	<li>Prepare lysis buffer (mixture A) (Table 1) by combining 0.33 &#181;L of 10% Triton X-100, 0.55 &#181;L of RNase inhibitor, and 0.22 &#181;L of 0.1 M DTT (see Table of Materials).</li>
	<li>Add 1 &#181;L of mixture A into the sorted sample, and grind with a sterile pestle. The preferable sample volume is &#8804;0.5 &#181;L; use RNase-free water to make up the volume to 14 &#181;L. Transfer each single cell sample into a 0.2 mL thin-walled PCR tube.</li>
	<li>Prepare mixture B containing 0.44 &#181;L of oligo-dT30VN reverse transcription (RT) reaction primer (100 &#181;M) and 4.4 &#181;L of dNTPs (10 mM) (Table 2) (see Table of Materials).</li>
	<li>Add 4.4 &#181;L of mixture B to the 14 &#181;L of sample in each tube, pipette gently to mix the sample, and incubate the sample at 72 &#176;C for 3 min. After incubation, immediately put the samples on ice to hybridize the oligo-dT to the poly A tail.</li>
	<li>Prepare the reverse transcription reaction mixture (mixture C) (Table 3) (see Table of Materials). Add 21.6 &#181;L of mixture C to each tube containing the samples. Turn on the RT program on a common PCR instrument (Table 4).</li>
	<li>Perform the preamplification reaction on ice. Prepare mixture D by combining 44 &#181;L of 2x PCR polymerase mix and 0.88 &#181;L of IS PCR primer (10 &#181;M) (Table 5) (see Table of Materials). Add 40.8 &#181;L of mixture D to the 40 &#181;L of RT reaction product, and run the preamplification program (Table 6).</li>
	<li>Purify the preamplification reaction products using Ampure XP beads (see Table of Materials). Add 48 &#181;L of beads (0.6:1 ratio) into each sample from step 4.7, and gently mix the samples by pipetting.</li>
	<li>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.</li>
	<li>Wash the beads by resuspending them in 200 &#181;L 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.</li>
	<li>Air-dry the samples for 10 min, and cover the tube to prevent contamination and cross-contamination during the air-drying.</li>
	<li>Resuspend the beads in 20 &#181;L of ddH2O, incubate the samples at room temperature for 5 min, and then place them on the magnetic separation stand for 5 min.</li>
	<li>Pipette out 18 &#181;L of the supernatant from each tube, and transfer the samples into new 1.5 mL centrifuge tubes. Use 1 &#181;L 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 &#8722;20 &#176;C.</li>
	<li>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).</li>
	<li>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 &#62;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.</li>
</ol>
5. RNA-seq data analysis
<ol>
	<li>Trim the raw reads using Trim-Galore17 followed by mapping to the reference genome using hisat218 (daehwankimlab.github.io/hisat2),&#160;and remove the PCR duplicated fragments using Picard19 (broadinstitute.github.io/picard).</li>
	<li>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.</li>
</ol>

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

The authors have nothing to disclose.

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...

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.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>0.22 &#181;m strainer</td>
			<td>Sorfa&#160;</td>
			<td>622110</td>
			<td></td>
		</tr>
		<tr>
			<td>Agar</td>
			<td>Yeasen</td>
			<td>70101ES76</td>
			<td></td>
		</tr>
		<tr>
			<td>Agilent fragment analyzer</td>
			<td>Aglient</td>
			<td>Aglient 5200</td>
			<td></td>
		</tr>
		<tr>
			<td>Agilent high-sensitivity DNA kit</td>
			<td>Aglient</td>
			<td>DNF-474-0500</td>
			<td></td>
		</tr>
		<tr>
			<td>Ampure XP beads</td>
			<td>BECKMAN</td>
			<td>A63881</td>
			<td></td>
		</tr>
		<tr>
			<td>Betaine</td>
			<td>yuanye</td>
			<td>S18046-100g</td>
			<td></td>
		</tr>
		<tr>
			<td>Bleach</td>
			<td>Mr Muscle</td>
			<td>FnBn83BK</td>
			<td>20% (v/v) bleach</td>
		</tr>
		<tr>
			<td>BSA</td>
			<td>sigma</td>
			<td>9048-46-8</td>
			<td></td>
		</tr>
		<tr>
			<td>CaCl2</td>
			<td>yuanye</td>
			<td>S24109-500g</td>
			<td></td>
		</tr>
		<tr>
			<td>Cellulase R10</td>
			<td>Yakult (Japan)</td>
			<td>9012-54-8</td>
			<td></td>
		</tr>
		<tr>
			<td>Cellulase RS</td>
			<td>Yakult (Japan)</td>
			<td>9012-54-8</td>
			<td></td>
		</tr>
		<tr>
			<td>Centrifuge tube (1.5 mL)</td>
			<td>Eppendolf</td>
			<td>30121589</td>
			<td></td>
		</tr>
		<tr>
			<td>DNase, RNase, DNA and RNA Away Surface Decontaminants</td>
			<td>Beyotime</td>
			<td>R0127</td>
			<td></td>
		</tr>
		<tr>
			<td>dNTPs (10 mM)</td>
			<td>NEB</td>
			<td>N0447S</td>
			<td></td>
		</tr>
		<tr>
			<td>DTT (0.1 M)</td>
			<td> 
			invitrogen</td>
			<td>18090050</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethanol</td>
			<td>Sinopharm Chemical Reagent Co., Ltd</td>
			<td>100092680</td>
			<td></td>
		</tr>
		<tr>
			<td>FACS</td>
			<td>BD FACS Melody</td>
			<td>BD-65745</td>
			<td></td>
		</tr>
		<tr>
			<td>FACS</td>
			<td>Sony</td>
			<td>SH800S</td>
			<td></td>
		</tr>
		<tr>
			<td>Filter tip&#160; (1000 &#181;L)</td>
			<td>Thermo Scientific</td>
			<td>TF112-1000-Q</td>
			<td></td>
		</tr>
		<tr>
			<td>Filter tip&#160; (200 &#181;L)</td>
			<td>Thermo Scientific</td>
			<td>TF140-200-Q</td>
			<td></td>
		</tr>
		<tr>
			<td>Filter tip (10 &#181;L)</td>
			<td>Thermo Scientific</td>
			<td>TF104-10-Q</td>
			<td></td>
		</tr>
		<tr>
			<td>Filter tip (100 &#181;L)</td>
			<td>Thermo Scientific</td>
			<td>TF113-100-Q</td>
			<td></td>
		</tr>
		<tr>
			<td>Fluorescent microscope</td>
			<td>Nikon</td>
			<td>Eclipse Ni-E</td>
			<td></td>
		</tr>
		<tr>
			<td>Four-Dimensional Rotating Mixer</td>
			<td>Kylin -Bell</td>
			<td>BE-1100</td>
			<td></td>
		</tr>
		<tr>
			<td>Hemicellulase</td>
			<td>sigma</td>
			<td>9025-56-3</td>
			<td></td>
		</tr>
		<tr>
			<td>IS PCR primer</td>
			<td></td>
			<td></td>
			<td>5&#39;-AAGCAGTGGTATCAACGCAGAG 
			T-3&#39;</td>
		</tr>
		<tr>
			<td>KAPA HiFi HotStart ReadyMix(2X)</td>
			<td>Roche&#160;</td>
			<td>7958935001</td>
			<td></td>
		</tr>
		<tr>
			<td>KCl</td>
			<td>Sinopharm Chemical Reagent Co., Ltd</td>
			<td>7447-40-7</td>
			<td></td>
		</tr>
		<tr>
			<td>Macerozyme R10</td>
			<td>Yakult (Japan)</td>
			<td>9032-75-1</td>
			<td></td>
		</tr>
		<tr>
			<td>Magnetic separation stand</td>
			<td>invitrogen</td>
			<td>12321D</td>
			<td></td>
		</tr>
		<tr>
			<td>Mannitol</td>
			<td>aladdin</td>
			<td>69-65-8</td>
			<td></td>
		</tr>
		<tr>
			<td>MES</td>
			<td>aladdin</td>
			<td>145224948</td>
			<td></td>
		</tr>
		<tr>
			<td>MgCl2&#160;</td>
			<td>yuanye</td>
			<td>R21455-500ml</td>
			<td></td>
		</tr>
		<tr>
			<td>Microcentrifuges</td>
			<td>Eppendorf</td>
			<td>Centrifuge 5425</td>
			<td></td>
		</tr>
		<tr>
			<td>Micro-mini-centrifuge</td>
			<td>Titan</td>
			<td>Timi-10k</td>
			<td></td>
		</tr>
		<tr>
			<td>MS</td>
			<td>Phytotech</td>
			<td>M519</td>
			<td></td>
		</tr>
		<tr>
			<td>Nextera XT DNA Library Preparation Kit</td>
			<td>illumina</td>
			<td>FC-131-1024</td>
			<td></td>
		</tr>
		<tr>
			<td>oligo-dT30VN primer</td>
			<td></td>
			<td></td>
			<td>5&#39;-AAGCAGTGGTATCAACGCAGAG 
			TACTTTTTTTTTTTTTTTTTTTTTTT 
			TTTTTTTTTTVN-3&#39;</td>
		</tr>
		<tr>
			<td>PCR instrument</td>
			<td>Thermal cycler</td>
			<td>A24811</td>
			<td></td>
		</tr>
		<tr>
			<td>Pectolyase</td>
			<td>Yakult (Japan)</td>
			<td>9033-35-6</td>
			<td></td>
		</tr>
		<tr>
			<td>Plant marker lines</td>
			<td>Nottingham Arabidopsis Stock Centre (NASC)</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit 1x dsDNA HS Assay Kit</td>
			<td>invitrogen</td>
			<td>Q33231</td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit 2.0 fluorometer</td>
			<td>invitrogen</td>
			<td>Q32866</td>
			<td></td>
		</tr>
		<tr>
			<td>RNase inhibitor&#160;</td>
			<td>Thermo Scientific</td>
			<td>EO0382</td>
			<td></td>
		</tr>
		<tr>
			<td>RNase-free water</td>
			<td>invitrogen</td>
			<td>10977023</td>
			<td></td>
		</tr>
		<tr>
			<td>Solution A</td>
			<td></td>
			<td></td>
			<td>400 mM mannitol, 0.05 % BSA , 20 mM MES (pH5.7), 10 mM CaCl2, 20 mM KCl</td>
		</tr>
		<tr>
			<td>Solution B</td>
			<td></td>
			<td></td>
			<td>1 % (w/v)cellulase R10, 1 % (w/v) cellulase RS, 1 %&#160; (w/v)hemicellulase, 0.5 %&#160; (w/v)pectolyase and 1 %&#160; (w/v) Macerozyme R10 of in a fresh aliquot of solution A</td>
		</tr>
		<tr>
			<td>Sterile pestle</td>
			<td>BIOTREAT</td>
			<td>453463</td>
			<td></td>
		</tr>
		<tr>
			<td>Strainer (40 &#181;m )</td>
			<td>Sorfa&#160;</td>
			<td>251100</td>
			<td></td>
		</tr>
		<tr>
			<td>Superscript enzyme (200 U/&#181;L)</td>
			<td>invitrogen</td>
			<td>18090050</td>
			<td></td>
		</tr>
		<tr>
			<td>SuperScript VI buffer (5x)</td>
			<td>invitrogen</td>
			<td>18090050</td>
			<td></td>
		</tr>
		<tr>
			<td>T0est tube (5 mL)</td>
			<td>BD Falcon</td>
			<td>352052</td>
			<td></td>
		</tr>
		<tr>
			<td>Thin-walled PCR tubes with caps (0.5 mL)</td>
			<td>AXYGEN</td>
			<td>PCR-05-C</td>
			<td></td>
		</tr>
		<tr>
			<td>Triton X-100</td>
			<td>Sangon Biotech</td>
			<td>A600198-0500</td>
			<td></td>
		</tr>
		<tr>
			<td>TSO primer</td>
			<td></td>
			<td></td>
			<td>5&#39;-AAGCAGTGGTATCAACGCAGAG 
			TACATrGrG+G-3&#39;</td>
		</tr>
		<tr>
			<td>Vortex</td>
			<td>Titan</td>
			<td>VM-T2</td>
			<td></td>
		</tr>
	</tbody>
</table>

isolation and transcriptome analysis of plant cell types

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.

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...

Watch this Scientific Journal Video about Isolation and Transcriptome Analysis of Plant Cell Types at JoVE.com

Isolation and Transcriptome Analysis of Plant Cell Types

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.

In multicellular organisms, developmental programming and environmental responses can be highly divergent in different cell types or even within cells, ...

isolation-and-transcriptome-analysis-of-plant-cell-types

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1.3K Views.  Shanghai Jiao Tong University. Transcriptoomanalyse op enkele cel- of celtypen kan het subtiele verschil van genexpressie detecteren dat wordt gemaskeerd door heterogeniteit, wat een krachtig hulpmiddel is om de uitgebreide intracellulaire regulerende mechanismen bloot te leggen. Fluorescentie geactiveerde celsortering gevolgd door RN-seq-analyse kan worden uitgevoerd op zowel eencellige als bulkceltypemodus met een hoge transcriptdetectiegevoeligheid en compatibiliteit met andere multi-omische benaderingen. Nieuwe gebruik...

Video: Isolation and Transcriptome Analysis of Plant Cell Types

Mating and Tetrad Separation of Chlamydomonas reinhardtii for Genetic Analysis

The unicellular green alga Chlamydomonas reinhardtii (Chlamydomonas) has become a popular organism for research in diverse areas of cell biology and genetics because of its simple life cycle, ease of growth and manipulation for genetic analysis, genomic resources, and transformability of the nucleus and both organelles. Mating strains is a common practice when genetic approaches are used in Chlamydomonass, to create vegetative diploids for analysis of dominance, or following tetrad dissection to ascertain nuclear vs. organellar inheritance, to test allelism, to analyze epistasy, or to generate populations for the purpose of map-based cloning. Additionally, genetic crosses are routinely used to combine organellar genotypes with particular nuclear genotypes. Here we demonstrate standard methods for gametogenesis, mating, zygote germination and tetrad separation. This protocol consists of an easy-to-follow series of steps that will make genetic approaches amenable to scientists who are less familiar with Chlamydomonas. Key parameters and trouble spots are explained. Finally, resources for further information and alternative methods are provided.

Mating and Tetrad Separation of Chlamydomonas reinhardtii for Genetic Analysis

Mating and tetrad separation are required for genetic analysis in Chlamydomonas reinhardtii. Here we demonstrate standard methods for gametogenesis, mating, zygote germination and tetrad dissection. This protocol consists of an easy-to-follow series of steps that will make genetic approaches amenable to scientists who are less familiar with Chlamydomonas.

The unicellular green alga Chlamydomonas reinhardtii (Chlamydomonas) has become a popular organism for research in diverse areas of cell biology and ...

Fluorescence Activated Cell Sorting of Plant Protoplasts

High-resolution, cell type-specific analysis of gene expression greatly enhances understanding of developmental regulation and responses to environmental stimuli in any multicellular organism. In situ hybridization and reporter gene visualization can to a limited extent be used to this end but for high resolution quantitative RT-PCR or high-throughput transcriptome-wide analysis the isolation of RNA from particular cell types is requisite. Cellular dissociation of tissue expressing a fluorescent protein marker in a specific cell type and subsequent Fluorescence Activated Cell Sorting (FACS) makes it possible to collect sufficient amounts of material for RNA extraction, cDNA synthesis/amplification and microarray analysis.
An extensive set of cell type-specific fluorescent reporter lines is available to the plant research community. In this case, two marker lines of the Arabidopsis thaliana root are used: PSCR::GFP (endodermis and quiescent center) and PWOX5::GFP (quiescent center). Large numbers (thousands) of seedlings are grown hydroponically or on agar plates and harvested to obtain enough root material for further analysis. Cellular dissociation of plant material is achieved by enzymatic digestion of the cell wall. This procedure makes use of high osmolarity-induced plasmolysis and commercially available cellulases, pectinases and hemicellulases to release protoplasts into solution.
FACS of GFP-positive cells makes use of the visualization of the green versus the red emission spectra of protoplasts excited by a 488 nm laser. GFP-positive protoplasts can be distinguished by their increased ratio of green to red emission. Protoplasts are typically sorted directly into RNA extraction buffer and stored for further processing at a later time.
This technique is revealed to be straightforward and practicable. Furthermore, it is shown that it can be used without difficulty to isolate sufficient numbers of cells for transcriptome analysis, even for very scarce cell types (e.g. quiescent center cells). Lastly, a growth setup for Arabidopsis seedlings is demonstrated that enables uncomplicated treatment of the plants prior to cell sorting (e.g. for the cell type-specific analysis of biotic or abiotic stress responses). Potential supplementary uses for FACS of plant protoplasts are discussed.

A method for isolating specific cell types from plant material is demonstrated. This technique employs transgenic marker lines expressing fluorescent proteins in particular cell types, cellular dissociation and Fluorescence Activated Cell Sorting. Additionally, a growth setup is established here that facilitates treatment of Arabidopsis thaliana seedlings prior to cell sorting.

High-resolution, cell type-specific analysis of gene expression greatly enhances understanding of developmental regulation and responses to environmental ...

Comprehensive Compositional Analysis of Plant Cell Walls (Lignocellulosic biomass) Part I: Lignin

The need for renewable, carbon neutral, and sustainable raw materials for industry and society has become one of the most pressing issues for the 21st century. This has rekindled interest in the use of plant products as industrial raw materials for the production of liquid fuels for transportation1 and other products such as biocomposite materials7. Plant biomass remains one of the greatest untapped reserves on the planet4. It is mostly comprised of cell walls that are composed of energy rich polymers including cellulose, various hemicelluloses (matrix polysaccharides, and the polyphenol lignin6 and thus sometimes termed lignocellulosics. However, plant cell walls have evolved to be recalcitrant to degradation as walls provide tensile strength to cells and the entire plants, ward off pathogens, and allow water to be transported throughout the plant; in the case of trees up to more the 100 m above ground level. Due to the various functions of walls, there is an immense structural diversity within the walls of different plant species and cell types within a single plant4. Hence, depending of what crop species, crop variety, or plant tissue is used for a biorefinery, the processing steps for depolymerization by chemical/enzymatic processes and subsequent fermentation of the various sugars to liquid biofuels need to be adjusted and optimized. This fact underpins the need for a thorough characterization of plant biomass feedstocks. Here we describe a comprehensive analytical methodology that enables the determination of the composition of lignocellulosics and is amenable to a medium to high-throughput analysis. In this first part we focus on the analysis of the polyphenol lignin (Figure 1). The method starts of with preparing destarched cell wall material. The resulting lignocellulosics are then split up to determine its lignin content by acetylbromide solubilization3, and its lignin composition in terms of its syringyl, guaiacyl- and p-hydroxyphenyl units5. The protocol for analyzing the carbohydrates in lignocellulosic biomass including cellulose content and matrix polysaccharide composition is discussed in Part II2.

Comprehensive Compositional Analysis of Plant Cell Walls Lignocellulosic biomass Part I: Lignin

Plant biomass is a major carbon-neutral renewable resource that could be used for the production of biofuels. Plant biomass consists mainly of cell walls, a structurally complex composite material termed lignocellulosics. Here we describe a protocol for a comprehensive analysis of the content and composition of the polyphenolic lignin.

The need for renewable, carbon neutral, and sustainable raw materials for industry and society has become one of the most pressing issues for the 21st ...

Comprehensive Compositional Analysis of Plant Cell Walls (Lignocellulosic biomass) Part II: Carbohydrates

The need for renewable, carbon neutral, and sustainable raw materials for industry and society has become one of the most pressing issues for the 21st century. This has rekindled interest in the use of plant products as industrial raw materials for the production of liquid fuels for transportation2 and other products such as biocomposite materials6. Plant biomass remains one of the greatest untapped reserves on the planet4. It is mostly comprised of cell walls that are composed of energy rich polymers including cellulose, various hemicelluloses, and the polyphenol lignin5 and thus sometimes termed lignocellulosics. However, plant cell walls have evolved to be recalcitrant to degradation as walls contribute extensively to the strength and structural integrity of the entire plant. Despite its necessary rigidity, the cell wall is a highly dynamic entity that is metabolically active and plays crucial roles in numerous cell activities such as plant growth and differentiation5. Due to the various functions of walls, there is an immense structural diversity within the walls of different plant species and cell types within a single plant4. Hence, depending of what crop species, crop variety, or plant tissue is used for a biorefinery, the processing steps for depolymerisation by chemical/enzymatic processes and subsequent fermentation of the various sugars to liquid biofuels need to be adjusted and optimized. This fact underpins the need for a thorough characterization of plant biomass feedstocks. Here we describe a comprehensive analytical methodology that enables the determination of the composition of lignocellulosics and is amenable to a medium to high-throughput analysis (Figure 1). The method starts of with preparing destarched cell wall material. The resulting lignocellulosics are then split up to determine its monosaccharide composition of the hemicelluloses and other matrix polysaccharides1, and its content of crystalline cellulose7. The protocol for analyzing the lignin components in lignocellulosic biomass is discussed in Part I3.

Comprehensive Compositional Analysis of Plant Cell Walls Lignocellulosic biomass Part II: Carbohydrates

Plant biomass is a major carbon-neutral renewable resource that could be used for the production of biofuels. Plant biomass consists mainly of cell walls, a structurally complex composite material termed lignocellulosics. Here we describe a protocol for a comprehensive analysis of the content and composition of wall derived carbohydrates.

Glycan Profiling of Plant Cell Wall Polymers using Microarrays

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the raw materials for human societies (e.g. wood, paper, textile and biofuel industries)1,2. However, understanding the biosynthesis and function of these components remains challenging.
Cell wall glycans are chemically and conformationally diverse due to the complexity of their building blocks, the glycosyl residues. These form linkages at multiple positions and differ in ring structure, isomeric or anomeric configuration, and in addition, are substituted with an array of non-sugar residues. Glycan composition varies in different cell and/or tissue types or even sub-domains of a single cell wall3. Furthermore, their composition is also modified during development1, or in response to environmental cues4.
In excess of 2,000 genes have Plant cell walls are complex matrixes of heterogeneous glycans been predicted to be involved in cell wall glycan biosynthesis and modification in Arabidopsis5. However, relatively few of the biosynthetic genes have been functionally characterized 4,5. Reverse genetics approaches are difficult because the genes are often differentially expressed, often at low levels, between cell types6. Also, mutant studies are often hindered by gene redundancy or compensatory mechanisms to ensure appropriate cell wall function is maintained7. Thus novel approaches are needed to rapidly characterise the diverse range of glycan structures and to facilitate functional genomics approaches to understanding cell wall biosynthesis and modification.
Monoclonal antibodies (mAbs)8,9 have emerged as an important tool for determining glycan structure and distribution in plants. These recognise distinct epitopes present within major classes of plant cell wall glycans, including pectins, xyloglucans, xylans, mannans, glucans and arabinogalactans. Recently their use has been extended to large-scale screening experiments to determine the relative abundance of glycans in a broad range of plant and tissue types simultaneously9,10,11.
Here we present a microarray-based glycan screening method called Comprehensive Microarray Polymer Profiling (CoMPP) (Figures 1 &amp; 2)10,11 that enables multiple samples (100 sec) to be screened using a miniaturised microarray platform with reduced reagent and sample volumes. The spot signals on the microarray can be formally quantified to give semi-quantitative data about glycan epitope occurrence. This approach is well suited to tracking glycan changes in complex biological systems12 and providing a global overview of cell wall composition particularly when prior knowledge of this is unavailable.

A technique called Comprehensive Microarray Polymer Profiling (CoMPP) for the characterisation of plant cell wall glycans is described. This method combines the specificity of monoclonal antibodies directed to defined glycan-epitopes with a miniature microarray analytical platform allowing screening of glycan occurrence in a broad range of biological contexts.

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the ...

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein1. For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment2. Reliably identifying these regions was the focus of our work.
Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics3-5 to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)6-8. We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized.
Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types.
To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs9, which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor10,11 and epigenetic data12 to illustrate its usefulness.

Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of ...

Efficient and Rapid Isolation of Early-stage Embryos from Arabidopsis thaliana Seeds

In flowering plants, the embryo develops within a nourishing tissue - the endosperm - surrounded by the maternal seed integuments (or seed coat). As a consequence, the isolation of plant embryos at early stages (1 cell to globular stage) is technically challenging due to their relative inaccessibility. Efficient manual dissection at early stages is strongly impaired by the small size of young Arabidopsis seeds and the adhesiveness of the embryo to the surrounding tissues. Here, we describe a method that allows the efficient isolation of young Arabidopsis embryos, yielding up to 40 embryos in 1 hr to 4 hr, depending on the downstream application. Embryos are released into isolation buffer by slightly crushing 250-750 seeds with a plastic pestle in an Eppendorf tube. A glass microcapillary attached to either a standard laboratory pipette (via a rubber tube) or a hydraulically controlled microinjector is used to collect embryos from droplets placed on a multi-well slide on an inverted light microscope. The technical skills required are simple and easily transferable, and the basic setup does not require costly equipment. Collected embryos are suitable for a variety of downstream applications such as RT-PCR, RNA sequencing, DNA methylation analyses, fluorescence in situ hybridization (FISH), immunostaining, and reporter gene assays.

Efficient and Rapid Isolation of Early-stage Embryos from Arabidopsis thaliana Seeds

We report an efficient and simple method to isolate embryos at early stages of development from Arabidopsis thaliana seeds. Up to 40 embryos can be isolated in 1 hr to 4 hr, depending on the downstream application. The procedure is suitable for transcriptome, DNA methylation, reporter gene expression, immunostaining and fluorescence in situ hybridization analyses.

In flowering plants, the embryo develops within a nourishing tissue - the endosperm - surrounded by the maternal seed integuments (or seed coat). As a ...

Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures

Plasma membrane microdomains are features based on the physical properties of the lipid and sterol environment and have particular roles in signaling processes. Extracting sterol-enriched membrane microdomains from plant cells for proteomic analysis is a difficult task mainly due to multiple preparation steps and sources for contaminations from other cellular compartments. The plasma membrane constitutes only about 5-20% of all the membranes in a plant cell, and therefore isolation of highly purified plasma membrane fraction is challenging. A frequently used method involves aqueous two-phase partitioning in polyethylene glycol and dextran, which yields plasma membrane vesicles with a purity of 95% 1. Sterol-rich membrane microdomains within the plasma membrane are insoluble upon treatment with cold nonionic detergents at alkaline pH. This detergent-resistant membrane fraction can be separated from the bulk plasma membrane by ultracentrifugation in a sucrose gradient 2. Subsequently, proteins can be extracted from the low density band of the sucrose gradient by methanol/chloroform precipitation. Extracted protein will then be trypsin digested, desalted and finally analyzed by LC-MS/MS. Our extraction protocol for sterol-rich microdomains is optimized for the preparation of clean detergent-resistant membrane fractions from Arabidopsis thaliana cell cultures.
We use full metabolic labeling of Arabidopsis thaliana suspension cell cultures with K15NO3 as the only nitrogen source for quantitative comparative proteomic studies following biological treatment of interest 3. By mixing equal ratios of labeled and unlabeled cell cultures for joint protein extraction the influence of preparation steps on final quantitative result is kept at a minimum. Also loss of material during extraction will affect both control and treatment samples in the same way, and therefore the ratio of light and heave peptide will remain constant. In the proposed method either labeled or unlabeled cell culture undergoes a biological treatment, while the other serves as control 4.

Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures

Here we describe a robust method for the fractionation of plant plasma membranes into detergent resistant and detergent soluble membranes based on a mixture of unlabeled and in vivo fully 15N labeled Arabidopsis thaliana cell cultures. The procedure is applied for comparative proteomic studies to understand signaling processes.

Plasma membrane microdomains are features based on the physical properties of the lipid and sterol environment and have particular roles in signaling ...

Isolation and Physiological Analysis of Mouse Cardiomyocytes

Cardiomyocytes, the workhorse cell of the heart, contain exquisitely organized cytoskeletal and contractile elements that generate the contractile force used to pump blood. Individual cardiomyocytes were first isolated over 40 years ago in order to better study the physiology and structure of heart muscle. Techniques have rapidly improved to include enzymatic digestion via coronary perfusion. More recently, analyzing the contractility and calcium flux of isolated myocytes has provided a vital tool in the cellular and sub-cellular analysis of heart failure. Echocardiography and EKGs provide information about the heart at an organ level only. Cardiomyocyte cell culture systems exist, but cells lack physiologically essential structures such as organized sarcomeres and t-tubules required for myocyte function within the heart. In the protocol presented here, cardiomyocytes are isolated via Langendorff perfusion. The heart is removed from the mouse, mounted via the aorta to a cannula, perfused with digestion enzymes, and cells are introduced to increasing calcium concentrations. Edge and sarcomere detection software is used to analyze contractility, and a calcium binding fluorescent dye is used to visualize calcium transients of electrically paced cardiomyocytes; increasing understanding of the role cellular changes play in heart dysfunction. Traditionally used to test drug effects on cardiomyocytes, we employ this system to compare myocytes from WT mice and mice with a mutation that causes dilated cardiomyopathy. This protocol is unique in its comparison of live cells from mice with known heart function and known genetics. Many experimental conditions are reliably compared, including genetic or environmental manipulation, infection, drug treatment, and more. Beyond physiologic data, isolated cardiomyocytes are easily fixed and stained for cytoskeletal elements. Isolating cardiomyocytes via perfusion is an extremely versatile method, useful in studying cellular changes that accompany or lead to heart failure in a variety of experimental conditions.

Individual cardiomyocytes from wild type and mutant mice can be isolated from the heart in order to study their contractility and calcium transients. This allows characterization of the contribution of cellular dysfunction to heart dysfunction from any cause.

Cardiomyocytes, the workhorse cell of the heart, contain exquisitely organized cytoskeletal and contractile elements that generate the contractile force ...

An Efficient Method for Quantitative, Single-cell Analysis of Chromatin Modification and Nuclear Architecture in Whole-mount Ovules in Arabidopsis

In flowering plants, the somatic-to-reproductive cell fate transition is marked by the specification of spore mother cells (SMCs) in floral organs of the adult plant. The female SMC (megaspore mother cell, MMC) differentiates in the ovule primordium and undergoes meiosis. The selected haploid megaspore then undergoes mitosis to form the multicellular female gametophyte, which will give rise to the gametes, the egg cell and central cell, together with accessory cells. The limited accessibility of the MMC, meiocyte and female gametophyte inside the ovule is technically challenging for cytological and cytogenetic analyses at single cell level. Particularly, direct or indirect immunodetection of cellular or nuclear epitopes is impaired by poor penetration of the reagents inside the plant cell and single-cell imaging is demised by the lack of optical clarity in whole-mount tissues.
Thus, we developed an efficient method to analyze the nuclear organization and chromatin modification at high resolution of single cell in whole-mount embedded Arabidopsis ovules. It is based on dissection and embedding of fixed ovules in a thin layer of acrylamide gel on a microscopic slide. The embedded ovules are subjected to chemical and enzymatic treatments aiming at improving tissue clarity and permeability to the immunostaining reagents. Those treatments preserve cellular and chromatin organization, DNA and protein epitopes. The samples can be used for different downstream cytological analyses, including chromatin immunostaining, fluorescence in situ hybridization (FISH), and DNA staining for heterochromatin analysis. Confocal laser scanning microscopy (CLSM) imaging, with high resolution, followed by 3D reconstruction allows for quantitative measurements at single-cell resolution.

An Efficient Method for Quantitative, Single-cell Analysis of Chromatin Modification and Nuclear Architecture in Whole-mount Ovules in Arabidopsis

We provide here an efficient and reliable protocol for immunostaining, Fluorescence in&#160;situ Hybridization, DNA staining followed by quantitative, high-resolution imaging in whole-mount Arabidopsis thaliana ovules. This method was successfully used to analyze chromatin modifications and nuclear architecture.

In flowering plants, the somatic-to-reproductive cell fate transition is marked by the specification of spore mother cells (SMCs) in floral organs of the ...

Isolation and Transcriptome Analysis of Plant Cell Types

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