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.

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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

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

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Human primary keratinocytes are often used as in vitro models for studies on epidermal differentiation and related diseases. Methods have been reported for in vitro differentiation of keratinocytes cultured in two-dimensional (2D) submerged manners using various induction conditions. Described here is a procedure for 2D in vitro keratinocyte differentiation method by contact inhibition and subsequent molecular characterization by RNA-seq. In brief, keratinocytes are grown in defined keratinocyte medium supplemented with growth factors until they are fully confluent. Differentiation is induced by close contacts between the keratinocytes and further stimulated by excluding growth factors in the medium. Using RNA-seq analyses, it is shown that both 1) differentiated keratinocytes exhibit distinct molecular signatures during differentiation and 2) the dynamic gene expression pattern largely resembles cells during epidermal stratification. As for comparison to normal keratinocyte differentiation, keratinocytes carrying mutations of the transcription factor p63 exhibit altered morphology and molecular signatures, consistent with their differentiation defects. In conclusion, this protocol details the steps for 2D in vitro keratinocyte differentiation and its molecular characterization, with an emphasis on bioinformatic analysis of RNA-seq data. Because RNA extraction and RNA-seq procedures have been well-documented, it is not the focus of this protocol. The experimental procedure of in vitro keratinocyte differentiation and bioinformatic analysis pipeline can be used to study molecular events during epidermal differentiation in healthy and diseased keratinocytes.

Presented here is a stepwise procedure for in vitro differentiation of human primary keratinocytes by contact inhibition followed by characterization at the molecular level by RNA-seq analysis.

Human primary keratinocytes are often used as in vitro models for studies on epidermal differentiation and related diseases. Methods have been reported ...

Genetics

Direct Lineage Reprogramming of Adult Mouse Fibroblast to Erythroid Progenitors

Erythroid cell commitment and differentiation proceed through activation of a lineage-restricted transcriptional network orchestrated by a group of cell fate determining and maturing factors. We previously set out to define the minimal set of factors necessary for instructing red blood cell development using direct lineage reprogramming of fibroblasts into induced erythroid progenitors/precursors (iEPs). We showed that overexpression of Gata1, Tal1, Lmo2, and c-Myc (GTLM) can rapidly convert murine and human fibroblasts directly to iEPs that resemble bona fide erythroid cells in terms of morphology, phenotype, and gene expression. We intend that iEPs will provide an invaluable tool to study erythropoiesis and cell fate regulation. Here we describe the stepwise process of converting murine tail tip fibroblasts into iEPs via transcription factor-driven direct lineage reprogramming (DLR). In this example, we perform the reprogramming in fibroblasts from erythroid lineage-tracing mice that express the yellow fluorescent protein (YFP) under the control of the erythropoietin receptor gene (EpoR) promoter, enabling visualization of erythroid cell fate induction upon reprogramming. Following this protocol, fibroblasts can be reprogrammed into iEPs within five to eight days. 
While improvements can still be made to the process, we show that GTLM-mediated reprogramming is a rapid and direct process, yielding cells with properties of bona fide erythroid progenitor and precursor cells.

Here we present our protocol for producing induced erythroid progenitors (iEPs) from mouse adult fibroblasts using transcription factor-driven direct lineage reprogramming (DLR).

Erythroid cell commitment and differentiation proceed through activation of a lineage-restricted transcriptional network orchestrated by a group of cell ...

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 ...

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 ...

Modeling Erythropoiesis Using HSPCs from Cord Blood and Bone Marrow

A Comprehensive Pipeline to Assess the Efficiency of Human Erythropoiesis In Vitro and Ex Vivo

Erythropoiesis, a remarkably dynamic and efficient process responsible for generating the daily quota of red blood cells (approximately 280 &plusmn; 20 billion cells per day), is crucial for maintaining individual health. Any disruption in this pathway can have significant consequences, leading to health issues. According to the World Health Organization, an estimated 25% of the global population presents symptoms of anemia. This protocol describes how to generate human erythroid cells both in vitro using hematopoietic stem and progenitor cells (HSPCs) from sources such as umbilical cord blood (UCB) or blood taken from healthy donors and ex vivo with HSPCs isolated from patients' bone marrow. Using genetic approach, genes of interest can be modulated in HSPCs, and their impact on erythropoiesis can be monitored at various stages of the differentiation process. This method allows for the screening of compounds perturbing, enhancing, or rescuing the capacity of HSPCs to differentiate into mature erythroid cells and to investigate the role of genes of interest during the erythroid differentiation process.

Author Spotlight: Advancing Erythropoiesis Research - A Simplified Pipeline for Assessing Hematopoietic Stem Cell Function in Myelodysplastic Syndromes

Modeling erythropoiesis provides a valuable chance to comprehend the biological significance of actors in this process and to evaluate novel therapeutic approaches that may alleviate differentiation defects. Here, we describe a simple and reliable method to efficiently differentiate CD34+ hematopoietic stem and progenitor cells ex vivo.

Erythropoiesis, a remarkably dynamic and efficient process responsible for generating the daily quota of red blood cells (approximately 280 &plusmn; 20 ...

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

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