JQW, SZ, SD and KC are supported by grant from the National Institutes of Health and the Staman Ogilvie Fund&#8212;Memorial Hermann Foundation.

1. EML Cell Culture and Separation of Lin-CD34+ and Lin-CD34- Cells Using Magnetic Cell Sorting System and Fluorescence-activated Cell Sorting Method
<ol>
	<li>Preparation of baby hamster kidney (BHK) cell culture medium for stem cell factor collection:
	<ol>
		<li>Culture BHK cells in DMEM medium containing 10% FBS in 25 cm2 flask (Table 1) at 37 &#176;C, 5% CO2 in a cell culture incubator.</li>
		<li>When cells grow to 80 - 90% confluence, wash cells once with 10 ml of PBS. Add 5 ml of 0.25% trypsin-EDTA solution to the monolayer and incubate the cells for 1-5 min at room temperature (RT) until the cells are detached.</li>
		<li>Pipet the solution up and down gently to break up clumps of cells. Add 5 ml of complete DMEM to the flask to stop trypsin activity. Collect cells by centrifugation at 200 x g for 5 min at RT.</li>
		<li>Remove the medium and resuspend the cell pellet in 10 ml of fresh BHK cell culture medium.</li>
		<li>Transfer 2 ml of the cell suspension from the step 1.1.4 to a new 75 cm2 flask and add 48 ml of fresh BHK cell culture medium to the flask.</li>
		<li>Culture the BHK cells for two days and collect the culture medium. Passage the medium through a 0.45 &#956;m filter. Store the medium in -20 &#176;C until further use.</li>
	</ol>
	</li>
	<li>EML cell culture:
	<ol>
		<li>Culture EML cells (in suspension) in EML basic medium containing BHK cell culture medium (Table 1) at 37 &#176;C, 5% CO2 in a cell culture incubator.</li>
		<li>Maintain the EML cells at low cell density (0.5-5 x 105 cells/ml) with the peak density less than 6 x 105 cells/ml. Split the cells every 2-3 days at the ratio of 1:5. Passage EML cells gently and discard the culture after passaging for 10 generations.</li>
	</ol>
	</li>
	<li>Depletion of lineage positive cells:
	<ol>
		<li>Harvest the EML cells by centrifugation at 200 x g for 5 min and wash the cells once with PBS. Collect the cells by centrifugation at 200 x g for 5 min.</li>
		<li>Resuspend the cells with PBS and count the cells with a hemocytometer. Determine the antibody concentration in the subsequent cell separation step according to the number of the cells (please refer to the instructions offered by the provider of the cell isolation system).</li>
		<li>Isolate the lineage negative (Lin-) cells using lineage antibody cocktail (cocktail of biotin-conjugated monoclonal antibodies CD5, CD45R (B220), CD11b, Anti-Gr-1(Ly-6G/C), 7-4 and Ter-119) and a magnetic activated cell sorting system according to manufacturer&#8217;s instructions.</li>
	</ol>
	</li>
	<li>Separation of Lin-CD34+ and Lin-CD34- cells:
	<ol>
		<li>Spin down the Lin- cells from the step 1.3.3 at 200 x g for 5 min. Resuspend the cell pellet with PBS and count the cells with a hemocytometer.</li>
		<li>Wash the cells twice with FACS buffer and pellet the cells at 200 x g for 5 min.</li>
		<li>Label five 1.5 ml microcentrifuge tubes with the number 1, 2, 3, 4, 5 respectively. Resuspend the cells with 100 &#181;l FACS buffer per 106 cells (106 cells per tube).</li>
		<li>Add 1 &#181;g of Anti-Mouse CD34 FITC antibody to tube 1 and tube 2 and mix the tubes gently.</li>
		<li>Incubate all tubes at 4 &#176;C for 1 hr in the dark.</li>
		<li>Add 0.25 &#181;g of PE-conjugated Anti-Sca1 antibody and 20 &#181;l of APC-conjugated Lineage Cocktail antibodies to tube 1, 0.25 &#181;g of PE-conjugated Anti-Sca1 antibody to tube 3, and 20 &#181;l of APC-conjugated Lineage Cocktail antibodies to tube 4.</li>
		<li>Mix all the tubes gently and incubate the cells at 4 &#176;C for an additional 30 min in the dark.</li>
		<li>Add 300 &#181;l of FACS buffer to the cells and spin down the cells at 200 x g for 5 min.</li>
		<li>Wash the cells with 500 &#181;l of FACS buffer for three times.</li>
		<li>Resuspend the cell pellet in 500 &#181;l of FACS buffer.</li>
		<li>Use the cells in tubes 2, 3, 4, and 5 for setting up compensation. Isolate Lin-SCA+CD34+ and Lin-SCA-CD34- cells in tube 1 using FACS Aria.</li>
	</ol>
	</li>
</ol>
2. RNA Preparation and Library Construction for High-throughput Sequencing
<ol>
	<li>Isolation, quality analysis and quantification of RNA:
	<ol>
		<li>Extract total RNA from Lin-CD34+ and Lin-CD34- cells respectively using TRIzol following the manufactures&#8217; protocol.</li>
		<li>Remove the contaminated DNA using deoxyribonuclease I (DNase I) following the manufacture&#8217;s protocol. Optionally, store the RNA at -80 &#176;C at this step for further use.</li>
		<li>Assess the quality of total RNA using Bioanalyzer according to the instructions offered by the supplier. Use RNA sample with RNA Integrity Number (RIN) lager than 9.</li>
	</ol>
	</li>
	<li>Library Construction and high-throughput sequencing: 
	NOTE: This protocol describes RNA-Seq using Illumina platform. For other sequencing platforms, different library preparation methods are required.
	<ol>
		<li>Use 0.1-4 &#181;g of high quality total RNA per sample for library preparation. Normally 2 &#181;g of&#160;total RNA can be extracted from 105 EML cells.</li>
		<li>Use a RNA-sequencing sample preparation system for&#160;RNA purification and fragmentation, first and second strand cDNA synthesis, end repair, 3&#8217; ends adenylation, adapter ligation and PCR amplification, following the detailed standard procedures from the provider&#8217;s instructions.
		<ol>
			<li>Positively select PolyA mRNA&#160;using oligo-dT magnetic beads and fragment the mRNA.</li>
			<li>Perform reverse transcription using random primers to obtain the cDNA and subsequently synthesize the second strand of cDNA to generate double stranded cDNA.</li>
			<li>Remove the 3&#39; overhangs and fill the 5&#39; overhangs by DNA polymerase. Adenylate 3&#39; ends to prevent cDNA fragments from ligating to one another.</li>
			<li>Add multiplex indexing adapters to both ends of the dscDNA. Perform PCR for the enrichment of DNA fragments.</li>
		</ol>
		</li>
		<li>Measure the A260/A280 to obtain information about the concentration of library using a spectrophotometer.</li>
		<li>Assess the library quality and measure the size range of DNA fragments using a Bioanalyzer.</li>
	</ol>
	</li>
</ol>
3. Data Analysis
For reference of software used in this part, please see (Table 2).
<ol>
	<li>Data file processing for downstream analysis:
	<ol>
		<li>Convert .bcl (base call file) file to .fastq file using CASAVA software (Illumina, version 1.8.2).
		<ol>
			<li>Fire up the &#8216;Terminal&#8217; in Linux system. Go to the data folder that contains the data file from an Illumina HiSeq2000 sequencing machine. Suppose the result folder is &#8216;NASboy1/JiaqianLabData/HiSeq_RUN/2013_07_11/130627_SN860_0309_A_2013-166_H0PW9ADXX/&#8217;, type in the command in Figure S1A, and enter the data folder.</li>
			<li>Install CASAVA 1.8.2 in the Linux system. Suppose the outputfolder is &#8216;Unaligned&#8217;, use the command in Figure S1B to prepare the configuration file for converting. Use the option --fastq-cluster-count 0 to ensure only one .fastq file is created for each sample. The generated .fastq file is in .gz format. Unzip it for downstream analysis (Figure S1B).</li>
			<li>After the &#8216;Unaligned&#8217; folder has been generated, go to the &#8216;Unaligned&#8217; folder (Figure S1C).</li>
			<li>Use the command in Figure S1D to begin the converting process. The &#8216;-j&#8217; parameter supplies the cpu number that will be used.</li>
			<li>After the system finished the converting process, go to the result folder under &#8216;Unaligned&#8217; folder (Figure S1E).</li>
			<li>Use the command in Figure S1F to decompress the .fastq.gz file into .fastq file under each sample folder.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Detect novel transcripts and evaluate the expression level using Tuxedo Suite26:
	<ol>
		<li>Map the paired-end RNA-Seq reads to the mouse reference genome (UCSC version mm9, obtained from <a href="http://cufflinks.cbcb.umd.edu/igenomes.html" target="_blank">http://cufflinks.cbcb.umd.edu/igenomes.html</a>) using Tophat software (version 1.3.3)27, which uses the Bowtie read mapper (version 0.12.7)28. Tophat is supplied with &#8220;-no-novel-juncs&#8221; option to improve estimation accuracy of expression level.
		<ol>
			<li>Put the .fastq files in a folder where the mapping process will be implemented. Suppose there are 2 .fastq files (rename to Example1.read1, Example1.read2) for a paired-end sequencing sample, use the command in Figure S2 to do the mapping (adjust the parameters according to the system setting). The &#8220;-p&#8221; parameter supplies the cpu number that will be used. The &#8220;&#8211;r&#8221; and &#8220;&#8211;mate-std-dev&#8221; parameters can be obtained from library QC or inferred from a subset of aligned reads (Figure S2).</li>
		</ol>
		</li>
		<li>Assemble the mapped reads into RNA transcripts using the Cufflinks software (version 1.3.0)29. Run Cufflinks using the annotation file of known genes (same .gtf file used by Tophat) and .bam file produced by Tophat.
		<ol>
			<li>After Tophat finished running, in the same folder, use the command in Figure S3A to run cufflinks to construct transcriptome and estimate transcript expression level. The &#8216;mm9_repeatMasker.gtf&#8217; and genome sequence files in the &#8216;GenomeSeqMM9&#8217; folder can be obtained from UCSC Genome Browser.</li>
			<li>The resulting genes.expr and transcripts.expr files contain the expression value of genes and transcripts (isoforms). Copy and paste the file contents to an Excel file and manipulate with spreadsheet application (Figure S3B).</li>
			<li>Use the command in Figure S3C to compare the resulting &#8216;transcripts.gtf&#8217; file to the reference &#8216;mm9_genes.gtf&#8217; file in order to identify novel transcripts.</li>
			<li>The resulting .tmap file contains the comparison result. Copy and paste the file contents to an Excel file and manipulate with spreadsheet application. Transcripts with class code &#8216;u&#8217; can be considered as &#8216;novel&#8217; compared to the reference .gtf file provided (Figure S3D). 
			NOTE: For downstream analysis convenience, set the FPKM values to 0.1 if the values are under 0.1. 
			NOTE: Step 3.2.3 - 3.2.6 is optional for those who wish to improve accuracy of novel transcripts&#8217; expression estimation. This will take a much longer time, because mapping and transcriptome construction need to be run more than once.</li>
		</ol>
		</li>
		<li>Run Tophat using default parameters and then run cufflinks to generated .gtf file using the command in Figure S3E.</li>
		<li>Compare the resulting .gtf file to the reference genome .gtf file using the command in Figure S3F.</li>
		<li>Parse the resulted .tmap file as described in the step 3.2.2.4. Copy and paste the file contents to an Excel file and manipulate with spreadsheet application. Transcripts with class code &#8216;u&#8217; can be considered as &#8216;novel&#8217; compared to the reference .gtf file provided.</li>
		<li>After the step 3.2.5, there is a .combined.gtf file in the folder which can be used as the reference .gtf file. A second run of Tophat and cufflinks can be performed as described in the step 3.2.1 and 3.2.2 to obtain a more accurate FPKM estimation of novel transcripts.</li>
	</ol>
	</li>
	<li>Detect differentially expressed genes using DESeq package30.
	<ol>
		<li>The input of DESeq is a raw read counts table. To obtain such a table, use the htseq-count script distributed with the HTSeq Python package which can be downloaded from HTSeq website (<a href="http://www-huber.embl.de/users/anders/HTSeq/doc/count.html" target="_blank">http://www-huber.embl.de/users/anders/HTSeq/doc/count.html</a>).
		<ol>
			<li>Ensure that samtools, python, and htseq-count programsare installed in the system. Obtain raw read count numbers from tophat output by using the command in Figure S4A.</li>
			<li>Prepare &#8216;Raw_Count_Table.txt&#8217;, &#8216;ExperimentDesign.txt&#8217; files using Excel. Copy and save the content in .txt format for the DESeq R package (Figure S4B).</li>
			<li>Install R program in the system. In the terminal, type &#8216;R&#8217; and press ENTER.A screen message will appearas showed in Figure S4C.</li>
			<li>Read &#8216;Raw_Count_Table.txt&#8217;, &#8216;ExperimentDesign.txt&#8217; into R using the command in Figure S4D.</li>
			<li>Load DESeq package using the command in Figure S4E.</li>
			<li>Factorize conditions in R (Figure S4F).</li>
			<li>Use the command in Figure S4G to run negative binominal test on the normalized count table.</li>
			<li>Use the command in Figure S4H to output significant differential expressed genes in a .csv file.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Lookup transcription factors&#8217; (TFs) FPKM values across samples using Excel. Intersect DE gene table and TFs table. Genes belong to both table are differentially expressed transcription factors.
	<ol>
		<li>Go to the website <a href="http://www.bioguo.org/AnimalTFDB/download.php" target="_blank">http://www.bioguo.org/AnimalTFDB/download.php</a> and download the transcription factors. Then lookup the DE transcription factors in the Excel (Figure S5).</li>
	</ol>
	</li>
	<li>Generating .bigwig file for UCSC genome browser visualization.
	<ol>
		<li>Download &#8216;bedtools&#8217; software package from the website <a href="https://github.com/arq5x/bedtools2" target="_blank">https://github.com/arq5x/bedtools2</a> and install the software in the system31. Download the UCSC tools &#8216;bedGraphToBigWig&#8217; from the website <a href="http://hgdownload.cse.ucsc.edu/admin/exe/" target="_blank">http://hgdownload.cse.ucsc.edu/admin/exe/</a> and install the software in the system.</li>
		<li>In the folder containing the .bam file, use the command in Figure S6A to convert .bam file generated by tophat into .bed file.</li>
		<li>After the .bed file is produced, use the command in Figure S6B to generate .bigwig file. The file &#8216;ChromInfo.txt&#8217; can be obtained from following url: <a href="http://hgdownload.cse.ucsc.edu/goldenPath/mm9/database/chromInfo.txt.gz" target="_blank">http://hgdownload.cse.ucsc.edu/goldenPath/mm9/database/chromInfo.txt.gz</a>.</li>
		<li>Observe a custom track on UCSC Genome Browser. Refer to the website <a href="http://genome.ucsc.edu/goldenPath/help/customTrack.html" target="_blank">http://genome.ucsc.edu/goldenPath/help/customTrack.html</a> on how to display a custom track using UCSC genome browser.</li>
	</ol>
	</li>
</ol>
<img alt="Figure S1" fo:content-width="5in" src="/files/ftp_upload/52104/52104supplementalfig1.jpg" /> 
Figure S1: Converting .bcl file to .fastq file using CASAVA software.
<img alt="Figure S2" fo:content-width="5in" src="/files/ftp_upload/52104/52104supplementalfig2.jpg" /> 
Figure S2: Mapping reads to reference genome using Tophat.
<img alt="Figure S3" fo:content-width="5in" src="/files/ftp_upload/52104/52104supplementalfig3.jpg" /> 
Figure S3: Detection of novel transcripts and expression level estimation.
<img alt="Figure S4" fo:content-width="3.4in" src="/files/ftp_upload/52104/52104supplementalfig4.jpg" /> 
Figure S4: Calling differential expressed gene using DESeq package.
<img alt="Figure S5" fo:content-width="5in" src="/files/ftp_upload/52104/52104supplementalfig5.jpg" /> 
Figure S5: Identification of differentially expressed transcription factors.
<img alt="Figure S6" fo:content-width="5in" src="/files/ftp_upload/52104/52104supplementalfig6.jpg" /> 
Figure S6: Converting mapping result for data visualization.

<ol>
	<li>Chambers, S. M., Goodell, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hematopoietic+stem+cell+aging:+wrinkles+in+stem+cell+potential.">Hematopoietic stem cell aging: wrinkles in stem cell potential.</a> Stem Cell Rev. 3, 201-211 (2007).</li><li>Wu, J. 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=Tcf7+is+an+important+regulator+of+the+switch+of+self-renewal+and+differentiation+in+a+multipotential+hematopoietic+cell+line.">Tcf7 is an important regulator of the switch of self-renewal and differentiation in a multipotential hematopoietic cell line.</a> PLoS genetics. 8, (2012).</li><li>Ye, Z. 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=Complex+interactions+in+EML+cell+stimulation+by+stem+cell+factor+and+IL-3.">Complex interactions in EML cell stimulation by stem cell factor and IL-3.</a> Proceedings of the National Academy of Sciences of the United States of America. 108, 4882-4887 (2011).</li><li>Tsai, S., Bartelmez, S., Sitnicka, E., Collins, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lymphohematopoietic+progenitors+immortalized+by+a+retroviral+vector+harboring+a+dominant-negative+retinoic+acid+receptor+can+recapitulate+lymphoid,+myeloid,+and+erythroid+development.">Lymphohematopoietic progenitors immortalized by a retroviral vector harboring a dominant-negative retinoic acid receptor can recapitulate lymphoid, myeloid, and erythroid development.</a> Genes Dev. 8, 2831-2841 (1994).</li><li>Weiler, S. 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=D3:+a+gene+induced+during+myeloid+cell+differentiation+of+Linlo+c-Kit++Sca-1(+)+progenitor+cells.">D3: a gene induced during myeloid cell differentiation of Linlo c-Kit+ Sca-1(+) progenitor cells.</a> Blood. 93, 527-536 (1999).</li><li>Ye, Z. J., Kluger, Y., Lian, Z., Weissman, S. M. <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+precursor+cells+in+a+multipotential+hematopoietic+cell+line.">Two types of precursor cells in a multipotential hematopoietic cell line.</a> Proc Natl Acad Sci U S A. 102, 18461-18466 (2005).</li><li>Wang, Z., Gerstein, M., Snyder, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-Seq:+a+revolutionary+tool+for+transcriptomics.">RNA-Seq: a revolutionary tool for transcriptomics.</a> Nature reviews. Genetics. 10, 57-63 (2009).</li><li>Chu, Y., Corey, D. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA+sequencing:+platform+selection,+experimental+design,+and+data+interpretation.">RNA sequencing: platform selection, experimental design, and data interpretation.</a> Nucleic acid therapeutics. 22, 271-274 (2012).</li><li>Hornett, E. A., Wheat, C. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+RNA-Seq+analysis+in+non-model+species:+assessing+transcriptome+assemblies+as+a+scaffold+and+the+utility+of+evolutionary+divergent+genomic+reference+species.">Quantitative RNA-Seq analysis in non-model species: assessing transcriptome assemblies as a scaffold and the utility of evolutionary divergent genomic reference species.</a> BMC genomics. 13, 361 (2012).</li><li>Eswaran, 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=RNA+sequencing+of+cancer+reveals+novel+splicing+alterations.">RNA sequencing of cancer reveals novel splicing alterations.</a> Scientific reports. 3, 1689 (2013).</li><li>Wang, E. 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=Alternative+isoform+regulation+in+human+tissue+transcriptomes.">Alternative isoform regulation in human tissue transcriptomes.</a> Nature. 456, 470-476 (2008).</li><li>Wu, J. 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=Dynamic+transcriptomes+during+neural+differentiation+of+human+embryonic+stem+cells+revealed+by+short,+long,+and+paired-end+sequencing.">Dynamic transcriptomes during neural differentiation of human embryonic stem cells revealed by short, long, and paired-end sequencing.</a> Proceedings of the National Academy of Sciences of the United States of America. 107, 5254-5259 (2010).</li><li>Loraine, A. E., McCormick, S., Estrada, A., Patel, K., Qin, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-seq+of+Arabidopsis+pollen+uncovers+novel+transcription+and+alternative+splicing.">RNA-seq of Arabidopsis pollen uncovers novel transcription and alternative splicing.</a> Plant physiology. 162, 1092-1109 (2013).</li><li>Edgren, H., 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=Identification+of+fusion+genes+in+breast+cancer+by+paired-end+RNA-sequencing.">Identification of fusion genes in breast cancer by paired-end RNA-sequencing.</a> Genome biology. 12, 6 (2011).</li><li>Ilott, N. E., Ponting, C. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predicting+long+non-coding+RNAs+using+RNA+sequencing.">Predicting long non-coding RNAs using RNA sequencing.</a> Methods. 63, 50-59 (2013).</li><li>Sun, 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=Prediction+of+novel+long+non-coding+RNAs+based+on+RNA-Seq+data+of+mouse+Klf1+knockout+study.">Prediction of novel long non-coding RNAs based on RNA-Seq data of mouse Klf1 knockout study.</a> BMC bioinformatics. 13, 331 (2012).</li><li>Luo, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicroRNA+expression+analysis+using+the+Illumina+microRNA-Seq+Platform.">MicroRNA expression analysis using the Illumina microRNA-Seq Platform.</a> Methods in molecular biology. 822, 183-188 (2012).</li><li>Bolduc, F., Hoareau, C., St-Pierre, P., Perreault, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In-depth+sequencing+of+the+siRNAs+associated+with+peach+latent+mosaic+viroid+infection.">In-depth sequencing of the siRNAs associated with peach latent mosaic viroid infection.</a> BMC molecular biology. 11, 16 (2010).</li><li>Chepelev, I., Wei, G., Tang, Q., Zhao, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+of+single+nucleotide+variations+in+expressed+exons+of+the+human+genome+using+RNA-Seq.">Detection of single nucleotide variations in expressed exons of the human genome using RNA-Seq.</a> Nucleic acids research. 37, 106 (2009).</li><li>Djari, 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=Gene-based+single+nucleotide+polymorphism+discovery+in+bovine+muscle+using+next-generation+transcriptomic+sequencing.">Gene-based single nucleotide polymorphism discovery in bovine muscle using next-generation transcriptomic sequencing.</a> BMC genomics. 14, 307 (2013).</li><li>Murphy, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+expression+studies+using+microarrays:+principles,+problems,+and+prospects.">Gene expression studies using microarrays: principles, problems, and prospects.</a> Advances in physiology education. 26, 256-270 (2002).</li><li>Chen, K., 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=RNA-seq+characterization+of+spinal+cord+injury+transcriptome+in+acute/subacute+phases:+a+resource+for+understanding+the+pathology+at+the+systems+level.">RNA-seq characterization of spinal cord injury transcriptome in acute/subacute phases: a resource for understanding the pathology at the systems level.</a> PLoS one. 8, 72567 (2013).</li><li>Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., Gilad, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-seq:+an+assessment+of+technical+reproducibility+and+comparison+with+gene+expression+arrays.">RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays.</a> Genome research. 18, 1509-1517 (2008).</li><li>Ramskold, D., Kavak, E., Sandberg, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+to+analyze+gene+expression+using+RNA-sequencing+data.">How to analyze gene expression using RNA-sequencing data.</a> Methods in molecular biology. 802, 259-274 (2012).</li><li>Glenn, T. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Field+guide+to+next-generation+DNA+sequencers.">Field guide to next-generation DNA sequencers.</a> Mol Ecol Resour. 11, 759-769 (2011).</li><li>Trapnell, 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=Differential+gene+and+transcript+expression+analysis+of+RNA-seq+experiments+with+TopHat+and+Cufflinks.">Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.</a> Nature protocols. 7, 562-578 (2012).</li><li>Trapnell, C., Pachter, L., 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=TopHat:+discovering+splice+junctions+with+RNA-Seq.">TopHat: discovering splice junctions with RNA-Seq.</a> Bioinformatics. 25, 1105-1111 (2009).</li><li>Langmead, B., Trapnell, C., Pop, M., 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=Ultrafast+and+memory-efficient+alignment+of+short+DNA+sequences+to+the+human+genome.">Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.</a> Genome biology. 10, 25 (2009).</li><li>Trapnell, 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=Transcript+assembly+and+quantification+by+RNA-Seq+reveals+unannotated+transcripts+and+isoform+switching+during+cell+differentiation.">Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation.</a> Nature. 28, 511-515 (2010).</li><li>Anders, S., Huber, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+expression+analysis+for+sequence+count+data.">Differential expression analysis for sequence count data.</a> Genome biology. 11, 106 (2010).</li><li>Quinlan, A. R., Hall, I. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BEDTools:+a+flexible+suite+of+utilities+for+comparing+genomic+features.">BEDTools: a flexible suite of utilities for comparing genomic features.</a> Bioinformatics. 26, 841-842 (2010).</li><li>Cheranova, D., 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=RNA-seq+analysis+of+transcriptomes+in+thrombin-treated+and+control+human+pulmonary+microvascular+endothelial+cells.">RNA-seq analysis of transcriptomes in thrombin-treated and control human pulmonary microvascular endothelial cells.</a> J Vis Exp. , (2013).</li><li>Zhang, H. M., 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=AnimalTFDB:+a+comprehensive+animal+transcription+factor+database.">AnimalTFDB: a comprehensive animal transcription factor database.</a> Nucleic acids research. 40, 144-149 (2012).</li><li>Wu, J. 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=Systematic+analysis+of+transcribed+loci+in+ENCODE+regions+using+RACE+sequencing+reveals+extensive+transcription+in+the+human+genome.">Systematic analysis of transcribed loci in ENCODE regions using RACE sequencing reveals extensive transcription in the human genome.</a> Genome Biol. 9, 3 (2008).</li><li>Wu, J. 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=Large-scale+RT-PCR+recovery+of+full-length+cDNA+clones.">Large-scale RT-PCR recovery of full-length cDNA clones.</a> Biotechniques. 36, 690-696 (2004).</li><li>Wu, J. Q., Shteynberg, D., Arumugam, M., Gibbs, R. A., Brent, M. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+rat+genes+by+TWINSCAN+gene+prediction,+RT-PCR,+and+direct+sequencing.">Identification of rat genes by TWINSCAN gene prediction, RT-PCR, and direct sequencing.</a> Genome Res. 14, 665-671 (2004).</li><li>Dewey, 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=Accurate+identification+of+novel+human+genes+through+simultaneous+gene+prediction+in+human,+mouse,+and+rat.">Accurate identification of novel human genes through simultaneous gene prediction in human, mouse, and rat.</a> Genome Res. 14, 661-664 (2004).</li><li>Wu, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Characterize Mammalian Transcriptome Complexity. , (2011).</li></ol>

The authors have nothing to disclose.

Mammalian transcriptome is very complex34-38. RNA-Seq technology plays an increasingly important role in the studies of transcriptome analysis, novel transcripts detection and single nucleotide variation discovery etc. It has many advantages over other methods for gene expression analysis. As mentioned in the introduction, it overcomes the hybridization artifacts of microarray and can be used to identify novel transcripts de novo. One limitation of RNA-sequencing is relative short read length comparing to Sanger sequencing. However, with the rapid improvement of sequencing technology, read length is increasing constantly. In this paper, we provide detailed methods of using this technology to identify potential key regulators in mouse EML cell self-renewal and differentiation.
The first key step for this protocol is EML cell culture. Although EML is a hematopoietic precursor cell line and it can be propagated in large quantity with SCF. The culturing condition of EML cells requires more attention than the usual immortalized cell lines. The cells should be fed and passaged at a regular basis with gentle operation; otherwise the cells could change in their properties of self-renewal and differentiation and undergo cell death. As the first step after collecting enough cells, we isolated lineage negative cells using a magnetic activated cell sorting system. Then we separated CD34+ and CD34- cells using fluorescence-activated cell sorting. The EML cells are normally passaged less than 10 generations before using for RNA extraction and the numbers of CD34+ and CD34- cells should be similar after separation. If the two populations vary greatly in cell number, it is advisable to discard the culture and re-thaw another tube of cell stock for culture.
After separation of CD34+ and CD34- cell, total RNA extraction was performed, another important step for this study. High quality RNA is the base for construction of a high quality library, which promises the accuracy of the sequencing data. In this critical step, any contact with RNase should be avoided. All reagents should be RNase free. It is important to wear gloves at all times while handling RNA. High quality RNA sample has an OD 260/280 value between 1.8 and 2.0. When collecting the aqueous phase containing RNA, be careful not to carry any organic phase with the RNA sample. Any residual organic solvents such as phenol or chloroform in the RNA would result in an OD260/280 value lower than 1.65. If the OD260/280 value is lower than 1.65, precipitate RNA again with ethanol. After washing with 75% ethanol, do not overdry RNA pellet. Drying RNA pellet completely will affect the solubility of RNA and lead to low yield of RNA.
The next key step for this protocol is library preparation. After total RNA extraction, a step of using DNase for removal of contaminated DNA is highly recommended, since DNA contamination might result in wrong estimation of the amount of total RNA used. It is recommended to perform the downstream procedure immediately after RNA isolation, since after long-term storage and freeze-thawing procedure, RNA will degrade to some degree. If the subsequent steps after RNA isolation can not be performed immediately, store the RNA in -80 &#176;C. Before total RNA is used for mRNA purification and cDNA synthesis, the quality should always be checked. Only high quality RNA can be used for library preparation. Using low quality or degraded RNA might lead to over-representation of 3&#39; ends. Before sequencing, library quality was assessed to ensure maximum sequencing efficiency.
In the data analysis part, after performing a run of Cufflinks without a reference transcriptome, we combined the novel transcripts with known transcripts to form a reference .gtf file and run Tophat and cufflinks for the second time. This two-run procedure is recommended, since this provide more accurate FPKM estimation than running only once. After data analysis, the differentially expressed genes were identified. Downstream experiments can be performed to validate the function of genes in vitro and in vivo. In our previous publication2, we chose the significantly differentially expressed transcription factors and identified the genome binding site of these factors by performing chromatin immunoprecipitation and sequencing (ChIP-Seq). In addition, we applied shRNA knockdown assay to test the functional effect of Tcf7. We found that in Tcf7 knockdown cells, up-regulated genes were the genes highly enriched in CD34- cells, while down-regulated genes were found to be significantly enriched in CD34+ cells. Therefore, the gene expression profile of Tcf7 knockdown cells shifted toward a partially differentiated CD34- state.Overall, using EML cell as a model system coupled with RNA-Sequencing technology and functional assays, we identified and confirmed Tcf7 as an important regulator of EML cell self-renewal and differentiation.

Hematopoietic stem cells are rare blood cells that reside mainly in the adult bone marrow niche. They are responsible for the production of cells required to replenish the blood and the immune systems1. As a kind of stem cells, HSCs are capable of both self-renewal and differentiation. Elucidating mechanisms that control the fate decision of HSCs, toward either self-renewal or differentiation, will offer valuable guidance on the manipulation of HSCs for blood disease researches and clinical usage2. One problem faced by the researchers is that HSCs can be maintained and expanded in vitro to a very limited extent; the vast majority of their progeny are partially differentiated in culture2.
In order to identify key regulators that control the processes of self-renewal and differentiation at a genome-wide scale, we used a mouse primitive hematopoietic progenitor cell line EML as a model system. This cell line was derived from murine bone marrow3,4. When fed with different growth factors, EML cells can differentiate into erythroid, myeloid, and lymphoid cells in vitro5. Importantly, this cell line can be propagated in large quantity in culture medium containing stem cell factor (SCF) and still retaining their multipotentiality. EML cells can be separated into subpopulations of self-renewing Lin-SCA+CD34+ and partially differentiated Lin-SCA-CD34- cells based on surface markers CD34 and SCA6. Similar to short-term HSCs, SCA+CD34+ cells are able of self-renewal. When treated with SCF, Lin-SCA+CD34+ cells can rapidly regenerate a mixed population of Lin-SCA+CD34+ and Lin-SCA-CD34- cells and continue to proliferate6. The two populations are similar in morphology and have similar levels of c-kit mRNA and protein6. Lin-SCA-CD34- cells are capable of propagating in media containing IL-3 instead of SCF3. Unveiling the key regulators in the EML cell fate decision will offer better understanding of cellular and molecular mechanisms in early developmental transition during hematopoiesis.
In order to investigate the underlying molecular differences between the self-renewing Lin-SCA+CD34+ and partially differentiated Lin-SCA-CD34- cells, we used RNA-Seq to identify differentially expressed genes. In particular, we focus on transcription factors, as transcription factors are crucial in determining cell fate. RNA-Seq is a recently developed approach that utilizes the capabilities of next-generation sequencing (NGS) technologies to profile and quantify RNAs transcribed from genome7,8. In brief, total RNA is poly-A selected and fragmented as the initial template.The RNA template is then converted into cDNA using reverse transcriptase. In order to map full-length RNA transcripts, using intact, non-degraded RNA for constructing cDNA library is important. For the purpose of sequencing, specific adapter sequences are added to both ends of cDNA. Then, in most cases, cDNA molecules are amplified by PCR and sequenced in a high-throughput manner.
After sequencing, the resulting reads can be aligned to a reference genome and a transcriptome database. The number of reads that map to the reference gene is counted and this information can be used to estimate the gene expression level. The reads can also be assembled de novo without a reference genome, enabling the study of transcriptomes in non-model organisms9. RNA-seq technology has also been used to detect splice isoforms10-12, novel transcripts13 and gene fusions14. In addition to the detection of protein-coding genes, RNA-Seq can also be used to detect novel and analyze transcription level of non-coding RNAs, such as long non-coding RNA15,16, microRNA17, siRNA etc.18. Because of the accuracy of this method, it has been utilized for detection of single nucleotide variations19,20.
Before the advent of RNA-Seq technology, microarray was the main method used for analyzing gene expression profile. Pre-designed probes are synthesized and subsequently attached to a solid surface to form a microarray slide21. mRNA is extracted and converted to cDNA. During the reverse transcription process, fluorescently labeled nucleotides are incorporated into the cDNA and the cDNA can be hybridized onto the microarray slides. The intensity of the signal collected from a specific spot depends on the amount of cDNA binding to the specific probe on that spot21. Compared with RNA-Seq technology, microarray has several limitations. First, microarray relies on the pre-existing knowledge of gene annotation, while RNA-Seq technology is able to detect novel transcripts at relative high background level, which limits its use when gene expression level is low. Besides, the RNA-Seq technology has much higher dynamic range of detection (8,000 fold)7, whereas, due to background and saturation of signals, the accuracy of microarray is limited for both highly and lowly expressed genes7,22. Finally, microarray probes differ in their hybridization efficiencies, which make the results less reliable when comparing relative expression levels of different transcripts within one sample23. Although RNA-Seq has many advantages over microarray, its data analysis is complex. This is one of the reasons that many researchers still use microarray instead of RNA-Seq. Various bioinformatics tools are required for RNA-Seq data processing and analysis24.
Among several next-generation sequencing (NGS) platforms, 454, Illumina, SOLID and Ion Torrent are the most widely used ones. 454 was the first commercial NGS platform. In contrast to the other sequencing platforms such as illumina and SOLID, the 454 platform generates longer read length (average 700 base reads)25. Longer reads are better for initial characterization of transcriptiome due to their higher assemble efficiency25. The main disadvantage of the 454 platform is its high cost per megabase of sequence. The Illumina and SOLID platforms generate reads with increased numbers and short lengths. The cost per megabase of sequence is much lower than the 454 platform. Due to the large numbers of short reads for the Illumina and SOLID platforms, data analysis is much more computationally intensive. The price of the instrument and reagents for sequencing for the Ion Torrent platform is cheaper and the sequencing time is shorter25. However, the error rate and the cost per megabase of sequence are higher compared to the Illumina and SOLID platforms. Different platforms have their own advantages and disadvantages and require different methods for data analysis. The platform should be chosen based on the sequencing purpose and the availability of funding.
In this paper, we take Illumina RNA-Seq platform as an example. We used EML cell as a model system to investigate the key regulators in EML cell self-renewal and differentiation, and provided a detailed methods of RNA-Seq library construction and data analysis for expression level calculation and novel transcript detection. We have shown in our previous publication that RNA-seq study in EML model system2, when coupled with functional test (e.g. shRNA knockdown) provide a powerful approach in understanding the molecular mechanism of the early stages of hematopoietic differentiation, and can serve as a model for the analysis of cell self-renewal and differentiation in general.

<table><tbody><tr><td>Antibiotic-Antimycotic</td><td>Invitrogen</td><td>15240-062</td><td>BHK cell culture</td></tr><tr><td>Anti-Mouse CD34 FITC</td><td>eBioscience</td><td>11-0341-81</td><td>FACS sorting</td></tr><tr><td>Anti-Mouse Ly-6A/E (Sca-1) PE</td><td>eBioscience</td><td>12-5981-81</td><td>FACS sorting</td></tr><tr><td>APC Mouse Lineage Antibody Cocktail</td><td>BD Biosciences</td><td>558074</td><td>FACS sorting</td></tr><tr><td>BD FACSAria Cell Sorter</td><td>BD Biosciences</td><td>Special offer sysmtem</td><td>FACS sorting</td></tr><tr><td>Corning&amp;trade; Cell Culture Treated Flasks 75 cm&lt;sup&gt;2&lt;/sup&gt;</td><td>Corning incorporated</td><td>430641</td><td>Cell culture</td></tr><tr><td>Corning&amp;trade; Cell Culture Treated Flasks 25 cm&lt;sup&gt;2&lt;/sup&gt;</td><td>Corning incorporated</td><td>430639</td><td>Cell culture</td></tr><tr><td>Deoxyribonuclease I, Amplification Grade</td><td>Invitrogen</td><td>18068-015</td><td>Library preparation</td></tr><tr><td>DMEM</td><td>Invitrogen</td><td>11965-092</td><td>BHK cell culture</td></tr><tr><td>DPBS</td><td>Gibco</td><td>14190</td><td>Cell culture</td></tr><tr><td>HI FBS</td><td>Invitrogen</td><td>16140071</td><td>BHK cell culture</td></tr><tr><td>Horse Serum</td><td>Invitrogen</td><td>16050-122</td><td>EML cell culture</td></tr><tr><td>IMDM</td><td>HyClone</td><td>SH30228.02</td><td>EML cell culture</td></tr><tr><td>L-Glutamine</td><td>Invitrogen</td><td>25030-081</td><td>Cell culture</td></tr><tr><td>Lineage Cell Depletion Kit, mouse</td><td>Miltenyi Biotec</td><td>130-090-858</td><td>Isolation of lineage negative cells</td></tr><tr><td>NanoVue Plus spectrophotometer</td><td>GE Healthcare</td><td>28-9569-62</td><td>Quality control</td></tr><tr><td>Thermo Scientific&amp;trade; Napco&amp;trade; 8000 Water-Jacketed CO&lt;sub&gt;2&lt;/sub&gt; Incubators</td><td>Thermo Scientific</td><td>15-497-002</td><td>Cell culture</td></tr><tr><td>Penicillin-Streptomycin</td><td>Invitrogen</td><td>15140-122</td><td>EML cell culture</td></tr><tr><td>TRIzol&amp;reg; Reagent</td><td>Invitrogen</td><td>15596-018</td><td>RNA exraction</td></tr><tr><td>TruSeq&amp;trade; RNA Sample Prep Kit v2 -Set B (48 rxn)</td><td>Illumina</td><td>RS-122-2002</td><td>Library preparation</td></tr><tr><td>2100 Electrophoresis Bioanalyzer Instrument</td><td>Agilent</td><td>G2939AA</td><td>Quality control</td></tr><tr><td>0.25% Trypsin-EDTA</td><td>Gibco</td><td>25200</td><td>Cell culture</td></tr><tr><td>0.45 &amp;micro;m Syringe Filters</td><td>Nalgene</td><td>190-2545</td><td>Cell culture</td></tr></tbody></table>

identification of key factors regulating self-renewal and differentiation in eml hematopoietic precursor cells by rna-sequencing analysis

Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient&#39;s hematopoietic system in many diseases such as leukemia and lymphoma. Elucidating the mechanisms controlling HSCs self-renewal and differentiation is important for application of HSCs for research and clinical uses. However, it is not possible to obtain large quantity of HSCs due to their inability to proliferate in vitro. To overcome this hurdle, we used a mouse bone marrow derived cell line, the EML (Erythroid, Myeloid, and Lymphocytic) cell line, as a model system for this study.
RNA-sequencing (RNA-Seq) has been increasingly used to replace microarray for gene expression studies. We report here a detailed method of using RNA-Seq technology to investigate the potential key factors in regulation of EML cell self-renewal and differentiation. The protocol provided in this paper is divided into three parts. The first part explains how to culture EML cells and separate Lin-CD34+ and Lin-CD34- cells. The second part of the protocol offers detailed procedures for total RNA preparation and the subsequent library construction for high-throughput sequencing. The last part describes the method for RNA-Seq data analysis and explains how to use the data to identify differentially expressed transcription factors between Lin-CD34+ and Lin-CD34- cells. The most significantly differentially expressed transcription factors were identified to be the potential key regulators controlling EML cell self-renewal and differentiation. In the discussion section of this paper, we highlight the key steps for successful performance of this experiment.
In summary, this paper offers a method of using RNA-Seq technology to identify potential regulators of self-renewal and differentiation in EML cells. The key factors identified are subjected to downstream functional analysis in vitro and in vivo.

In order to analyze differentially expressed genes in Lin-CD34+ and Lin-CD34- EML cells, we used RNA-Seq technology. Figure 1 shows the workflow of the procedures. After isolation of lineage negative cells by magnetic cell sorting, we separated Lin-SCA+CD34+ and Lin-SCA-CD34- cells using FACS Aria. Lin-enriched EML cells were stained with anti-CD34, anti-Sca1 and lineage cocktail antibodies. Only Lin- cells were gated for analysis of Sca1 and CD34 expression. Two populations (SCA+CD34+ and SCA-CD34- EML cells) could be observed by FACS analysis (Figure 2)6.
After cell separation, we extracted total RNA from CD34+ and CD34- cells respectively and analyzed the quality of RNA. The accuracy of RNA-Seq data largely relies on the quality of RNA-Seq library and the quality of total RNA is vital for preparing a high quality library. High quality RNA sample should have an OD 260/280 value between 1.8 and 2.0. In addition to using the spectrophotometer, RNA quality was further assessed with greater accuracy by Bioanalyzer. Figure 3 shows a result of a high quality RNA sample with the RIN equal to 9.4. Only high quality total RNA sample with RIN value greater than 9 was used for mRNA extraction and subsequent library construction procedures.
Ribosomal RNA is the most abundant type of RNA in cell. Currently two main strategies, depletion of rRNA or positively selection of polyadenylated mRNA (poly-A mRNA), are used for enrichment of target RNA before library construction. Non polyadenylated RNA species are lost during the selection of poly-A mRNA. In contrast, rRNA depletion methods such as RiboMinus could preserve non polyadenylated RNA species. The purpose of our study is to look for differentially expressed coding genes in two cell types, thus we used the poly-A mRNA selection method for enrichment of target RNAs before library construction. When library construction was finished, the size of DNA fragments in the library was checked before sequencing using Bioanalyzer. Figure 4 shows a good quality library with the fragment size peaks at about 300 bp.
In the subsequent step, the library was subjected to high-throughput sequencing. In principle, longer read length will be helpful for read mapping. It can reduce the probability that the read is mapped to multiple locations due to similarity among duplicate genes or gene family members. As the pair-end sequencing sequences are from both ends of the fragments, the read length chosen should be less than half of the average fragments length. If the main goal of the experiment is to measure the expression level instead of constructing transcript structure, single-end read (75 or 100 bp) can reduce the cost without losing too much information. Paired-end sequencing is more useful for transcript structure construction and shorter read length can be used to reduce cost. Certainly, when sufficient funding is available, longer read length is preferred.
For differential expression analysis, there are many alternative algorithms other than DESeq. There is also one included in cufflinks package named cuffdiff32. DESeq is one of the most widely used count based DE gene analysis algorithms. DESeq method is based on a well characterized statistics model &#8212; negative binomial distribution. In our experience, DESeq is more stable compare to cuffdiff. Early versions of cuffdiff often give significantly different numbers of DE genes. Therefore we used DESeq for DE analysis here.
Because transcription factors are crucial for cell fate determination, we focused on the significantly differentially expressed transcription factors33. The TFs changed &#62;1.5 fold between Lin-CD34+ and Lin-CD34- were found and are shown on the heatmap (Figure 5)2. Notably, the relative expression level of Tcf7 in Lin-CD34+ cells is more than 100 fold higher than that in Lin-CD34- cells. Thus Tcf7 was chosen for further ChIP-Sequencing (Chromatin Immunoprecipitation and sequencing) analysis and functional test to confirm Tcf7&#8217;s function in regulation of EML cell self-renewal and differentiation2.
<img alt="Figure 1" fo:content-width="5in" src="/files/ftp_upload/52104/52104fig1highres.jpg" /> 
Figure 1: Workflow of the procedures. Lin-CD34+ and Lin-CD34- cells were separated by magnetic cell separation system and fluorescence-activated cell sorting method. Total RNA was extracted followed by mRNA purification and library construction. After analysis of library quality, samples were subjected to high throughput sequencing. Data were analyzed and differentially expressed transcription factors were identified.
<img alt="Figure 2" fo:content-width="2.7in" src="/files/ftp_upload/52104/52104fig2highres.jpg" width="300" /> 
Figure 2: Separation of Lin-CD34+ and Lin-CD34- EML cells6. Lin- EML cells were enriched by magnetic cell sorting. Lin- cells were stained with anti-CD34, anti-Sca1 and lineage mixture antibodies. Lin- cells were gated for expression of CD34 and Sca1. Lin-CD34+SCA+ and Lin-CD34-SCA- EML cell populations were sorted.
<img alt="Figure 3" fo:content-width="6in" src="/files/ftp_upload/52104/52104fig3highres.jpg" width="600" /> 
Figure 3: A representative of high-quality total RNA sample. The quality of total RNA was assessed by Bioanalyzer. The RNA Integrity Number is 9.4 (FU, Fluorescence Units).
<img alt="Figure 4" fo:content-width="6in" src="/files/ftp_upload/52104/52104fig4highres.jpg" width="600" /> 
Figure 4: Fragments size range of Paired-End library. The DNA size distribution of the library was analyzed using Bioanalyzer. Most fragments are within the size range of 250-500 bp.
<img alt="Figure 5" fo:content-width="2.8in" src="/files/ftp_upload/52104/52104fig5highres.jpg" width="300" /> 
Figure 5: Differentially expressed transcription factors (&#62;1.5 fold) between Lin-CD34+ cells and Lin-CD34- cells2. For each cell type, two independent experiments were performed. Up-regulated genes are indicated as red color and down-regulated genes are indicated as green color.
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td colspan="2">BHK medium</td>
		</tr>
		<tr>
			<td>100x Antibiotic-Antimycotic&#160;</td>
			<td>10 ml&#160;&#160;</td>
		</tr>
		<tr>
			<td>200 mM L-Glutamine&#160;</td>
			<td>10 ml&#160;&#160;&#160;</td>
		</tr>
		<tr>
			<td>FBS</td>
			<td>100 ml&#160;&#160;</td>
		</tr>
		<tr>
			<td>DMEM</td>
			<td>880 ml</td>
		</tr>
		<tr>
			<td>Total volume</td>
			<td>1,000 ml</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">EML Basic medium</td>
		</tr>
		<tr>
			<td>IMDM</td>
			<td>390 ml</td>
		</tr>
		<tr>
			<td>HI horse serum</td>
			<td>100 ml</td>
		</tr>
		<tr>
			<td>100 x Penicillin-Streptomycin</td>
			<td>5 ml</td>
		</tr>
		<tr>
			<td>200 mM L-Glutamine</td>
			<td>5 ml</td>
		</tr>
		<tr>
			<td>BHK medium</td>
			<td>75 ml</td>
		</tr>
		<tr>
			<td>Total Volume</td>
			<td>575 ml</td>
		</tr>
		<tr>
			<td colspan="2">Filtrate through 0.45 &#956;M filter</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">FACS buffer</td>
		</tr>
		<tr>
			<td>BSA</td>
			<td>0.50%</td>
		</tr>
		<tr>
			<td>EDTA</td>
			<td>1 mM</td>
		</tr>
		<tr>
			<td colspan="2">Dissolved in PBS and filtrate through 0.45 &#956;M filter</td>
		</tr>
	</tbody>
</table>
Table 1: Buffers and Cell culture mediums.
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Software&#160;</td>
			<td>Usage</td>
			<td>Reference</td>
		</tr>
		<tr>
			<td>Bowtie 1.2.7</td>
			<td>Used by Tophat for mapping</td>
			<td>[28]</td>
		</tr>
		<tr>
			<td>Tophat 1.3.3</td>
			<td>Mapping reads back to reference genome</td>
			<td>[27]</td>
		</tr>
		<tr>
			<td>Cufflinks 1.3.0</td>
			<td>Transcripts construction and expression level estimation</td>
			<td>[29]</td>
		</tr>
		<tr>
			<td>DESeq 1.16.0</td>
			<td>Differential expression analysis</td>
			<td>[30]</td>
		</tr>
		<tr>
			<td>Bedtools 2.18</td>
			<td>Convert .bam file into .bed file</td>
			<td>[31]</td>
		</tr>
		<tr>
			<td>bedGraphToBigWig</td>
			<td>Convert .bed file to .bigwig file</td>
			<td><a href="http://genome.ucsc.edu/" target="_blank">http://genome.ucsc.edu/</a></td>
		</tr>
	</tbody>
</table>
Table 2: List of software for data analysis.

Watch this Scientific Journal Video about Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis at JoVE.com

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

RNA-sequencing and bioinformatics analyses were used to identify significantly and differentially expressed transcription factors in Lin-CD34+ and Lin-CD34- subpopulations of mouse EMLcells. These transcription factors might play important roles in determining the switch between self-renewing Lin-CD34+ and partially differentiated Lin-CD34- cells.

Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient&#39;s hematopoietic system in many diseases such as ...

identification-key-factors-regulating-self-renewal-differentiation

Nanyang Technological University

Research

JoVE Journal

Biology

12.3K Views.  The University of Texas Graduate School of Biomedical Sciences at Houston.  RNA-sequencing en bioinformatica analyses werden significant verschillend tot expressie en transcriptiefactoren in Lin-CD34 + en CD34-Lin subpopulaties van muis EMLcells identificeren. Deze transcriptiefactoren kunnen een belangrijke rol spelen bij het bepalen van de switch tussen zelfvernieuwende Lin-CD34 + en gedeeltelijk gedifferentieerd Lin-CD34-cellen. 

Video: Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations.
In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types.
In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.

Bulk gene expression measurements cloud individual cell differences in heterogeneous cell populations. Here, we describe a protocol for how single-cell gene expression analysis and index sorting by Florescence Activated Cell Sorting (FACS) can be combined to delineate heterogeneity and immunophenotypically characterize molecularly distinct cell populations.

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is ...

Genetics

Directed Differentiation of Primitive and Definitive Hematopoietic Progenitors from Human Pluripotent Stem Cells

One of the major goals for regenerative medicine is the generation and maintenance of hematopoietic stem cells (HSCs) derived from human pluripotent stem cells (hPSCs). Until recently, efforts to differentiate hPSCs into HSCs have predominantly generated hematopoietic progenitors that lack HSC potential, and instead resemble yolk sac hematopoiesis.&#160;These resulting hematopoietic progenitors may have limited utility for in vitro disease modeling of various adult hematopoietic disorders, particularly those of the lymphoid lineages. However, we have recently described methods to generate erythro-myelo-lymphoid multilineage definitive hematopoietic progenitors from hPSCs using a stage-specific directed differentiation protocol, which we outline here. Through enzymatic dissociation of hPSCs on basement membrane matrix-coated plasticware, embryoid bodies (EBs) are formed. EBs are differentiated to mesoderm by recombinant BMP4, which is subsequently specified to the definitive hematopoietic program by the GSK3&#946; inhibitor, CHIR99021. Alternatively, primitive hematopoiesis is specified by the PORCN inhibitor, IWP2. Hematopoiesis is further driven through the addition of recombinant VEGF and supportive hematopoietic cytokines. The resulting hematopoietic progenitors generated using this method have the potential to be used for disease and developmental modeling, in vitro.

Here, we present human pluripotent stem cell (hPSC) culture protocols, used to differentiate hPSCs into CD34+ hematopoietic progenitors. This method uses stage-specific manipulation of canonical WNT signaling to specify cells exclusively to either the definitive or primitive hematopoietic program.

One of the major goals for regenerative medicine is the generation and maintenance of hematopoietic stem cells (HSCs) derived from human pluripotent stem ...

Developmental Biology

Pan-myeloid Differentiation of Human Cord Blood Derived CD34+ Hematopoietic Stem and Progenitor Cells

Ex vivo differentiation of human hematopoietic stem cells is a widely used model for studying hematopoiesis. The protocol described here is for cytokine induced differentiation of CD34+ hematopoietic stem and progenitor cells to the four myeloid lineage cells. CD34+ cells are isolated from human umbilical cord blood and co-cultured with MS-5 stromal cells in the presence of cytokines. Immunophenotypic characterization of the stem and progenitor cells, and the differentiated myeloid lineage cells are described. Using this protocol, CD34+ cells may be incubated with small molecules or transduced with lentiviruses to express myeloid disease mutations to investigate their impact on myeloid differentiation.

Pan-myeloid Differentiation of Human Cord Blood Derived CD34+ Hematopoietic Stem and Progenitor Cells

Here, we present a protocol for immunophenotypic characterization and cytokine induced differentiation of cord blood derived CD34+ hematopoietic stem and progenitor cells to the four myeloid lineages. The applications of this protocol include investigations on the effect of myeloid disease mutations or small molecules on myeloid differentiation of the CD34+ cells.

Ex vivo differentiation of human hematopoietic stem cells is a widely used model for studying hematopoiesis. The protocol described here is for cytokine ...

Identification and Isolation of Burst-Forming Unit and Colony-Forming Unit Erythroid Progenitors from Mouse Tissue by Flow Cytometry

Early erythroid progenitors were originally defined by their colony-forming potential in vitro and classified into burst-forming and colony-forming &#34;units&#34; known as BFU-e and CFU-e. Until recently, methods for the direct prospective and complete isolation of pure BFU-e and CFU-e progenitors from freshly isolated adult mouse bone marrow were not available. To address this gap, a single-cell RNA-seq (scRNAseq) dataset of mouse bone marrow was analyzed for the expression of genes coding for cell surface markers. This analysis was combined with cell fate assays, allowing the development of a novel flow cytometric approach that identifies and allows the isolation of complete and pure subsets of BFU-e and CFU-e progenitors in mouse bone marrow or spleen. This approach also identifies other progenitor subsets, including subsets enriched for basophil/mast cell and megakaryocytic potentials. The method consists of labeling fresh bone marrow or spleen cells with antibodies directed at Kit and CD55. Progenitors that express both these markers are then subdivided into five principal populations. Population 1 (P1 or CFU-e, Kit+ CD55+ CD49fmed/low CD105med/high CD71med/high) contains all of the CFU-e progenitors and may be further subdivided into P1-low (CD71med CD150high) and P1-hi (CD71high CD150low), corresponding to early and late CFU-e, respectively; Population 2 (P2 or BFU-e, Kit+ CD55+ CD49fmed/low CD105med/high CD71low CD150high) contains all of the BFU-e progenitors; Population P3 (P3, Kit+ CD55+ CD49fmed/high CD105med/low CD150low CD41low) is enriched for basophil/mast cell progenitors; Population 4 (P4, Kit+ CD55+ CD49fmed/high CD105med/low CD150high CD41+) is enriched for megakaryocytic progenitors; and Population 5 (P5, Kit+ CD55+ CD49fmed/high CD105med/low CD150high CD41-) contains progenitors with erythroid, basophil/mast cell, and megakaryocytic potential (EBMP) and erythroid/ megakaryocytic/ basophil-biased multipotential progenitors (MPPs). This novel approach allows greater precision when analyzing erythroid and other hematopoietic progenitors and also allows for reference to transcriptome information for each flow cytometrically defined population.

Here, we describe a novel flow cytometric method for prospective isolation of early burst-forming unit erythroid (BFU-e) and colony-forming unit erythroid (CFU-e) progenitors directly from fresh mouse bone marrow and spleen. This protocol, developed based on single-cell transcriptomic data, is the first to isolate all the tissue's erythroid progenitors with high purity.

Early erythroid progenitors were originally defined by their colony-forming potential in vitro and classified into burst-forming and colony-forming ...

Identification and Analysis of Mouse Erythroid Progenitors using the CD71/TER119 Flow-cytometric Assay

The study of erythropoiesis aims to understand how red cells are formed from earlier hematopoietic and erythroid progenitors. Specifically, the rate of red cell formation is regulated by the hormone erythropoietin (Epo), whose synthesis is triggered by tissue hypoxia. A threat to adequate tissue oxygenation results in a rapid increase in Epo, driving an increase in erythropoietic rate, a process known as the erythropoietic stress response. The resulting increase in the number of circulating red cells improves tissue oxygen delivery. An efficient erythropoietic stress response is therefore critical to the survival and recovery from physiological and pathological conditions such as high altitude, anemia, hemorrhage, chemotherapy or stem cell transplantation. 
The mouse is a key model for the study of erythropoiesis and its stress response. Mouse definitive (adult-type) erythropoiesis takes place in the fetal liver between embryonic days 12.5 and 15.5, in the neonatal spleen, and in adult spleen and bone marrow. Classical methods of identifying erythroid progenitors in tissue rely on the ability of these cells to give rise to red cell colonies when plated in Epo-containing semi-solid media. Their erythroid precursor progeny are identified based on morphological criteria. Neither of these classical methods allow access to large numbers of differentiation-stage-specific erythroid cells for molecular study. Here we present a flow-cytometric method of identifying and studying differentiation-stage-specific erythroid progenitors and precursors, directly in the context of freshly isolated mouse tissue. The assay relies on the cell-surface markers CD71, Ter119, and on the flow-cytometric 'forward-scatter' parameter, which is a function of cell size. The CD71/Ter119 assay can be used to study erythroid progenitors during their response to erythropoietic stress in vivo, for example, in anemic mice or mice housed in low oxygen conditions. It may also be used to study erythroid progenitors directly in the tissues of genetically modified adult mice or embryos, in order to assess the specific role of the modified molecular pathway in erythropoiesis.

A flow-cytometric method for identification and molecular analysis of differentiation-stage-specific murine erythroid progenitors and precursors, directly in freshly &#x2013;harvested mouse bone marrow, spleen or fetal liver. The assay relies on cell-surface markers CD71, Ter119, and cell size.

The study of erythropoiesis aims to understand how red cells are formed from earlier hematopoietic and erythroid progenitors. Specifically, the rate of ...

Lentiviral-mediated Knockdown During Ex Vivo Erythropoiesis of Human Hematopoietic Stem Cells

Erythropoiesis is a commonly used model system to study cell differentiation. During erythropoiesis, pluripotent adult human hematopoietic stem cells (HSCs) differentiate into oligopotent progenitors, committed precursors and mature red blood cells 1. This process is regulated for a large part at the level of gene expression, whereby specific transcription factors activate lineage-specific genes while concomitantly repressing genes that are specific to other cell types 2. Studies on transcription factors regulating erythropoiesis are often performed using human and murine cell lines that represent, to some extent, erythroid cells at given stages of differentiation 3-5. However transformed cell lines can only partially mimic erythroid cells and most importantly they do not allow one to comprehensibly study the dynamic changes that occur as cells progress through many stages towards their final erythroid fate. Therefore, a current challenge remains the development of a protocol to obtain relatively homogenous populations of primary HSCs and erythroid cells at various stages of differentiation in quantities that are sufficient to perform genomics and proteomics experiments.
Here we describe an ex vivo cell culture protocol to induce erythroid differentiation from human hematopoietic stem/progenitor cells that have been isolated from either cord blood, bone marrow, or adult peripheral blood mobilized with G-CSF (leukapheresis). This culture system, initially developed by the Douay laboratory 6, uses cytokines and co-culture on mesenchymal cells to mimic the bone marrow microenvironment. Using this ex vivo differentiation protocol, we observe a strong amplification of erythroid progenitors, an induction of differentiation exclusively towards the erythroid lineage and a complete maturation to the stage of enucleated red blood cells. Thus, this system provides an opportunity to study the molecular mechanism of transcriptional regulation as hematopoietic stem cells progress along the erythroid lineage. 
Studying erythropoiesis at the transcriptional level also requires the ability to over-express or knockdown specific factors in primary erythroid cells. For this purpose, we use a lentivirus-mediated gene delivery system that allows for the efficient infection of both dividing and non-dividing cells 7. Here we show that we are able to efficiently knockdown the transcription factor TAL1 in primary human erythroid cells. In addition, GFP expression demonstrates an efficiency of lentiviral infection close to 90%. Thus, our protocol provides a highly useful system for characterization of the regulatory network of transcription factors that control erythropoiesis.

Lentiviral-mediated Knockdown During Ex Vivo Erythropoiesis of Human Hematopoietic Stem Cells

An ex vivo protocol to generate mature human red blood cells from hematopoietic stem/progenitors is described. Additionally we describe an efficient lentiviral-delivery method to knockdown the transcription factor TAL1 in primary erythroid cells. The efficiency of lentivirus mediated gene delivery is demonstrated using GFP expressing viruses.

Erythropoiesis is a commonly used model system to study cell differentiation. During erythropoiesis, pluripotent adult human hematopoietic stem cells ...

Oct4GiP Reporter Assay to Study Genes that Regulate Mouse Embryonic Stem Cell Maintenance and Self-renewal

Pluripotency and self-renewal are two defining characteristics of embryonic stem cells (ES cells). Understanding the underlying molecular mechanism will greatly facilitate the use of ES cells for developmental biology studies, disease modeling, drug discovery, and regenerative medicine (reviewed in 1,2).
To expedite the identification and characterization of novel regulators of ES cell maintenance and self-renewal, we developed a fluorescence reporter-based assay to quantitatively measure the self-renewal status in mouse ES cells using the Oct4GiP cells 3. The Oct4GiP cells express the green fluorescent protein (GFP) under the control of the Oct4 gene promoter region 4,5. Oct4 is required for ES cell self-renewal, and is highly expressed in ES cells and quickly down-regulated during differentiation 6,7. As a result, GFP expression and fluorescence in the reporter cells correlates faithfully with the ES cell identity 5, and fluorescence-activated cell sorting (FACS) analysis can be used to closely monitor the self-renewal status of the cells at the single cell level 3,8.
Coupled with RNAi, the Oct4GiP reporter assay can be used to quickly identify and study regulators of ES cell maintenance and self-renewal 3,8. Compared to other methods for assaying self-renewal, it is more convenient, sensitive, quantitative, and of lower cost. It can be carried out in 96- or 384-well plates for large-scale studies such as high-throughput screens or genetic epistasis analysis. Finally, by using other lineage-specific reporter ES cell lines, the assay we describe here can also be modified to study fate specification during ES cell differentiation.

We describe a fluorescence reporter assay to quickly identify and characterize genes that regulate mouse embryonic stem cell maintenance and self-renewal.

Pluripotency and self-renewal are two defining characteristics of embryonic stem cells (ES cells). Understanding the underlying molecular mechanism will ...

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Heterogeneity of stem cell population hampers detailed understanding of stem cell biology, such as their differentiation propensity toward different lineages. A single cell transcriptome assay can be a new approach for dissecting individual variation. We have developed the single cell qRT-PCR method, and confirmed that this method works well in several gene expression profiles. In single cell level, each human embryonic stem cell, sorted by OCT4::EGFP positive cells, has high expression in OCT4, but a different level of NANOG expression. Our single cell gene expression assay should be useful to interrogate population heterogeneities.

Single cell gene expression assay is needed for understanding stem cell heterogeneities.

Heterogeneity of stem cell population hampers detailed understanding of stem cell biology, such as their differentiation propensity toward different ...

Retroviral Infection of Murine Embryonic Stem Cell Derived Embryoid Body Cells for Analysis of Hematopoietic Differentiation

Embryonic stem cells (ESCs) are an outstanding model for elucidating the molecular mechanisms of cellular differentiation. They are especially useful for investigating the development of early hematopoietic progenitor cells (HPCs). Gene expression in ESCs can be manipulated by several techniques that allow the role for individual molecules in development to be determined. One difficulty is that expression of specific genes often has different phenotypic effects dependent on their temporal expression. This problem can be circumvented by the generation of ESCs that inducibly express a gene of interest using technology such as the doxycycline-inducible transgene system. However, generation of these inducible cell lines is costly and time consuming. Described here is a method for disaggregating ESC-derived embryoid bodies (EBs) into single cell suspensions, retrovirally infecting the cell suspensions, and then reforming the EBs by hanging drop. Downstream differentiation is then evaluated by flow cytometry. Using this protocol, it was demonstrated that exogenous expression of a microRNA gene at the beginning of ESC differentiation blocks HPC generation. However, when expressed in EB derived cells after nascent mesoderm is produced, the microRNA gene enhances hematopoietic differentiation. This method is useful for investigating the role of genes after specific germ layer tissue is derived.

Manipulating temporal gene expression in differentiating embryonic stem cells (ESCs) can be achieved using inducible gene systems. However, generation of these cell lines is costly and time consuming. This protocol achieves rapid expression of a transgene in differentiating ES-derived cells and subsequent analysis of downstream hematopoietic differentiation.

Embryonic stem cells (ESCs) are an outstanding model for elucidating the molecular mechanisms of cellular differentiation. They are especially useful for ...

Isolation Method for Long-Term and Short-Term Hematopoietic Stem Cells

Self-renewal capacity and multi-lineage differentiation potential are generally regarded as the defining characteristics of hematopoietic stem cells (HSCs). However, numerous studies have suggested that functional heterogeneity exists in the HSC compartment. Recent single-cell analyses have reported HSC clones with different cell fates within the HSC compartment, which are referred to as biased HSC clones. The mechanisms underlying heterogeneous or poorly reproducible results are little understood, especially regarding the length of self-renewal when purified HSC fractions are transplanted by conventional immunostaining. Therefore, establishing a reproducible isolation method for long-term HSCs (LT-HSCs) and short-term HSCs (ST-HSCs),&#160;defined by the length of their self-renewal, is crucial for overcoming this issue. Using unbiased multi-step screening, we identified a transcription factor, Hoxb5, which may be an exclusive marker of LT-HSCs in the mouse hematopoietic system. Based on this finding, we established a Hoxb5 reporter mouse line and successfully isolated LT-HSCs and ST-HSCs. Here we describe a detailed protocol for the isolation of LT-HSCs and ST-HSCs using the Hoxb5 reporter system. This isolation method will help researchers better understand the mechanisms of self-renewal and the biological basis for such heterogeneity in the HSC compartment.

We present a step-by-step protocol for the isolation of long-term hematopoietic stem cells (LT-HSCs) and short-term HSCs (ST-HSCs) using the Hoxb5 reporter system.

Self-renewal capacity and multi-lineage differentiation potential are generally regarded as the defining characteristics of hematopoietic stem cells ...

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Samenvatting

Meer video's verkennen

Hoofdstukken in deze video