We are grateful to Mr. Claus Bus and Ms. Rita Rosendahl at iNANO for their technical assistance. Special thanks to Dr. Daniel Otzen for allowing our frequent use of his ultracentrifuge. This study was supported by the Innovation Fund Denmark (MUSTER project).

NOTE: Mesenchymal stem cell growth medium (MSC media) is prepared beforehand as indicated in the Table of Materials.
1. Cell culture
NOTE: Human mesenchymal stem cells can be obtained from the bone marrow, adipose tissue or other sources11. Alternatively, hMSCs can be bought through a supplier. The BMSCs used in this protocol were derived from the bone marrow of patients and bought from a company.
<ol>
	<li>Thaw 1 x 106 BMSCs into a T175 flask containing 20 mL of MSC media. Incubate the cells at 37 &#176;C with 5% CO2 and replace the media every 2-3 days until 80% confluency.</li>
	<li>Wash the cells with 5 mL of 1x PBS and discard the PBS.</li>
	<li>Detach the cells by adding 5 mL of 0.05% Trypsin-EDTA and incubating the cells for 5 min at 37 &#176;C. Tap the sides of the flask to facilitate detachment.</li>
	<li>Add 15 mL of MSC media to inactivate the trypsin, detach the cells from the surface, and pipette up and down to obtain single cell suspensions.</li>
	<li>Collect the cells in a 50 mL tube and spin down for 5 min at 300 x g to pellet the cells.</li>
	<li>Resuspend the cells in 1 mL of MSC media and count the cells using a hemocytometer. 
	NOTE: Primary human bone marrow MSCs at 80% confluency in a T175 flask is around 2 x 106 cells.</li>
	<li>Plate hMSCs at 2,000 cells/cm2 in fresh MSC media in 5 T175 flasks. Grow the cells at 37 &#176;C with 5% CO2 and replace the media every 2-3 days until 5 flasks of 90% confluent T175 flasks are obtained.</li>
</ol>
2. EVs and RNA-associated biomolecules collection
NOTE: EV collection media is prepared beforehand (Dulbecco&#39;s Modified Eagle&#39;s Medium [DMEM] with 10% fetal bovine serum [FBS] and 1% penicillin/streptomycin [P/S]). EV collection media is normal MSC media, but prepared with commercial EV-depleted FBS (Table of Materials). This is to avoid bovine exRNA contamination from FBS, which normally contains exRNAs associated with EVs, ribonucleoproteins, and lipoproteins. For small RNA library preparation, exRNAs derived from 5 confluent flasks of MSCs are required to enable library construction.
<ol>
	<li>Wash the cells 3x with 20 mL of PBS per T175 flask. Add 20 mL of EV collection media per confluent T175 flask of MSCs and incubate at 37 &#176;C with 5% CO2 for 48 h.</li>
	<li>Collect the media and centrifuge the media for 10 min at 300 x g and 4 &#176;C.</li>
	<li>Collect the supernatant and centrifuge the media for 20 min at 2,000 x g and 4 &#176;C.</li>
	<li>Collect the supernatant and centrifuge the media for 30 min at 15,500 x g and 4 &#176;C. Then collect the supernatant.</li>
	<li>Transfer the media to ultracentrifuge tubes and pellet the exRNAs for 90 min at 100,000 x g and 4 &#176;C. A fixed angle rotor is used here and the pellet is anchored to the side of the tube. Mark the side of the lid and draw a circle on the side of the tube where the pellet is expected.</li>
	<li>Remove the supernatant, dry the inside of the tube by inverting the tube on absorbent paper and use small pieces of absorbent paper to remove the liquid inside the tube without touching the bottom of the tube. Resuspend the pellet in 200 &#956;L of PBS by vortexing for 30 s and pipetting up and down 20x.</li>
	<li>Assess the EVs and biomolecules with nanoparticle tracking analysis (NTA) (Figure 2). 
	NOTE: The EVs and biomolecules can be assessed with nanoparticle tracking analysis (NTA), dynamic light scattering (DLS) or transmission electron microscopy (TEM)12.</li>
	<li>Store the EVs and biomolecules at -80 &#176;C until further downstream experiments. 
	NOTE: If EVs are going to be used for functional studies, 20% glycerol must be added to protect them from rupturing. The cells can be collected using standard procedures if necessary.</li>
</ol>
3. EVs and RNA-associated biomolecules collection of differentiated cells
NOTE: EVs and RNA associated biomolecules can also be collected from the cell culture media while the cells undergo differentiation. The example depicted in the protocol describes osteoblastic differentiation and exRNA collection at day 0 and 7 of differentiation. If no differentiation is required, then skip Section 3 and go to Section 4.
<ol>
	<li>Prepare osteoblastic differentiation media (DMEM with 10% FBS, 1% P/S, 10 mM &#946;-glycerophosphate, 10 nM dexamethasone, 50 &#956;M ascorbate-2-phosphate, and 10 mM 1.25-vitamin-D3) fresh every time.</li>
	<li>Once MSCs are 80% confluent, change the MSC media to osteoblastic differentiation media.</li>
	<li>Replenish the osteoblastic differentiation media after 2-3 days.</li>
	<li>On day 5 of differentiation, remove the media and wash the cells 3x with 20 mL of PBS per T175 flask.</li>
	<li>Add 20 mL of EV collection media containing 10 mM &#946;-glycerophosphate, 10 nM dexamethasone, 50 &#956;M ascorbate-2-phosphate and 10 mM 1.25-vitamin-D3 per confluent T175 flask of MSCs. Incubate the cells at 37 &#176;C with 5% CO2 for 48 h to ensure continued differentiation while collecting the EVs and biomolecules.</li>
	<li>Collect the media on Day 7 and proceed to isolate the EVs and biomolecules as described in steps 2.2-2.6.</li>
	<li>For quality control, seed cells in 96-wells or 6-wells to assess differentiation using an alkaline phosphatase (ALP) activity assay or with quantitative polymerase chain reaction (qPCR), respectively. 
	NOTE: Figure 3 is an example showing osteogenic differentiation of the cells.</li>
</ol>
4. RNA extraction and quality control
<ol>
	<li>Thaw samples from step 2.8 on ice. Extract RNA using an RNA isolation kit (Table of Materials).</li>
	<li>Elute the RNA from the column provided in the RNA isolation kit (Table of Materials) in 100 &#956;L of RNase-free water.</li>
	<li>Concentrate the RNA through ethanol precipitation by adding 1 &#956;L of glycogen, 10 &#956;L of 2 M pH 5.5 sodium acetate and 250 &#956;L of pre-chilled 99% ethanol into 100 &#956;L of purified RNA.</li>
	<li>Incubate the samples at -20 &#176;C overnight to precipitate the RNA. Pellet the RNA by centrifuging for 20 min at 16,000 x g and 4 &#176;C. 
	NOTE: The pellet is white due to co-precipitation with glycogen.</li>
	<li>Remove the supernatant and wash the RNA pellet with 1 mL of 75% ethanol. Pellet the RNA again for 5 min at 16,000 x g and 4 &#176;C.</li>
	<li>Remove the ethanol and leave the lid of the RNA tube open for 5-10 min to air dry the RNA pellet. Resuspend the RNA pellet in 7 &#956;L of RNase-free water.</li>
	<li>Check the RNA quality and concentration using a chip-based capillary electrophoresis machine to detect the RNA according to the manufacturer&#8217;s protocol prior to library construction. 
	NOTE: A representative size distribution of the RNA is shown in Figure 4.</li>
	<li>Extract cellular RNA using a commercial purification kit (Table of Materials) if necessary.</li>
</ol>
5. Library construction and quality control
NOTE: Small RNA libraries are constructed using commercial kits (Table of Materials) with adjustments due to the low RNA input. Library construction is performed on the chilled block.
<ol>
	<li>Chill the heating block for 0.2 mL PCR tubes on ice and pipette 5 &#956;L of the RNA from step 4.6 into 0.2 mL RNase-free PCR tubes on a chilled block.</li>
	<li>Dilute 3&#8217; adaptors (1:10) in RNase-free water in a 0.2 mL RNase-free PCR tube. Add 0.5 &#956;L of diluted adaptor and mix with 5 &#956;L of RNA by pipetting up and down 8x and centrifuge briefly to collect all the liquid at the bottom of the tube.</li>
	<li>Incubate the RNA and 3&#8217; adaptor mix at 70 &#176;C for 2 min in a preheated thermal cycler and then place the sample back on the chilled block.</li>
	<li>Add 1 &#956;L of ligation buffer, 0.5 &#956;L of RNase inhibitor, and 0.5 &#956;L of T4 RNA ligase (deletion mutant) into the RNA and 3&#8217; adaptor mixture. Mix by pipetting up and down 8x and centrifuge briefly.</li>
	<li>Incubate the tube at 28 &#176;C for 1 h in the preheated thermal cycler.</li>
	<li>Add 0.5 &#956;L of stop solution into the sample tube with the tube staying in the thermal cycler, mix by pipetting up and down 8x and continue to incubate at 28 &#176;C for 15 min.</li>
	<li>Dilute 5&#8217; adaptors (1:10) in RNase-free water in a 0.2 mL RNase-free PCR tube. Add 0.5 &#956;L of 5&#8217; adaptor into a separate RNase-free 0.2 mL PCR tube, heat the 5&#8217; adaptor at 70 &#176;C for 2 min in the preheated thermal cycler, and then place the sample on the chilled block.</li>
	<li>Add 0.5 &#956;L of 10 nM ATP, 0.5 &#956;L of T4 RNA ligase to the 5&#8217; adaptor tube, mix by pipetting up and down 8x and centrifuge briefly to collect all the liquids into the bottom. 
	NOTE: Keep the 5&#8217; adaptor on chilled block as much as possible.</li>
	<li>Add 1.5 &#956;L of the 5&#8217; adaptor mixture to the sample from step 5.6, and mix very gently by pipetting 8x slowly. Incubate the sample at 28 &#176;C for 1 h in the preheated thermal cycler.</li>
	<li>Dilute RT primer 1:10 in RNase-free water in an RNase-free 0.2 mL PCR tube. Add 0.5 &#956;L of diluted RT primer into the sample from step 5.9, mix very gently by pipetting up and down 8x slowly and centrifuge briefly.</li>
	<li>Incubate the sample at 70 &#176;C for 2 min in the preheated thermal cycler and then place the sample on a chilled block.</li>
	<li>Add 1 &#956;L of 5x first strand buffer, 0.5 &#956;L of 12.5 mM dNTP mix, 0.5 &#956;L of 100 mM dithiothreitol (DTT), 0.5 &#956;L of RNase inhibitor, and 0.5 &#956;L of reverse transcriptase. Mix very gently by pipetting up and down 8x slowly and centrifuge briefly.</li>
	<li>Incubate the sample at 50 &#176;C for 1 h in the preheated thermal cycler to obtain cDNA.</li>
	<li>Add 4.25 &#956;L of ultrapure water, 12.5 &#956;L of PCR mix, 1 &#956;L of index primer, and 1 &#956;L of universal primer into the cDNA. Mix by pipetting up and down 8x and centrifuge briefly.</li>
	<li>Place the sample in a thermal cycler and set up the cycler as follows: 98 &#176;C for 30 s; 15 cycles of 98 &#176;C for 10 s, 60 &#176;C for 30 s and 72 &#176;C for 15 s; and end the cycle with 72 &#176;C for 10 min. 
	NOTE: The prepared library can be stored at -20 &#176;C for one week.</li>
	<li>Purify the RNA library by separating the library on a DNA gel and cutting the gel (gel extraction) between 140 bp and 160 bp and eluting the library from the gel following the protocol given by the library construction kit. 
	NOTE: In this study, the prepared library from step 5.15 is loaded onto a commercial gel (Table of Materials) and the library between 140 bp and 160 bp is purified out by an automated DNA size fractionator (Table of Materials) according to the manufacturer&#8217;s protocol.</li>
	<li>Concentrate and wash the library from step 5.16 using a column-based PCR purification kit (Table of Materials) and elute the library in 10 &#956;L RNase-free water finally.</li>
	<li>Load 1 &#956;L of the purified library onto a DNA chip (Table of Materials) to check the size of the library following the manufacturer&#8217;s protocol.</li>
	<li>Dilute 1 &#956;L of the purified library (1:1000) in 10 mM pH 8.0 Tris-HCl with 0.05% polysorbate 20 and quantify the library by qPCR using a commercial library quantification kit to quantify the library concentration using the DNA standards in the kit.</li>
	<li>Pool equal amounts of the libraries according to the requirements of the sequencing machine and sequence on a high-throughput sequencing system. 
	NOTE: The library construction of cellular RNA can be done using the same kits by following standard protocol of the kits.</li>
</ol>
6. Bioinformatics pipeline
NOTE: This is an in-house pipeline and the programs used here are listed in the Table of Materials. 
<ol>
	<li>Trim away low-quality reads and remove adaptor sequences from the raw reads.</li>
	<li>Map the clean reads to different kinds of RNAs.
	<ol>
		<li>Annotate tRNAs by allowing two mismatches because the heavily modified tRNA sequences cause frequent base misincorporation by reverse transcriptase.</li>
		<li>Annotate miRNAs by mapping to human miRNAs from miRBase v21 allowing zero mismatches. Since miRNAs are subject to A and U nontemplated 3&#8217;end additions, sequences that do not map are 3&#8217;trimmed of up to three A and/or T nts after which reads are mapped to human miRNAs and other miRNAs from miRBase v21 allowing zero mismatches.</li>
		<li>Annotate to other relevant small RNAs (snRNA, snoRNA, piRNA, and Y RNAs) by allowing one mismatch.</li>
		<li>Map the remaining unmapped reads to long RNA datasets (rRNA, other RNA from Rfam, and mRNA) to assess degradation.</li>
		<li>Annotate the sequences to the human genome.</li>
		<li>Annotate the sequences to bacterial genomes.</li>
		<li>Normalize miRNA expression using the following formula: miRNA counts / the total counts of all mapped miRNAs) x 106.</li>
	</ol>
	</li>
</ol>

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A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clinical+utility+of+circulating+non-coding+RNAs+-+an+update.">Clinical utility of circulating non-coding RNAs - an update.</a> Nature Reviews Clinical Oncology. , (2018).</li><li>Li, 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=Advances,+challenges,+and+opportunities+in+extracellular+RNA+biology:+insights+from+the+NIH+exRNA+Strategic+Workshop.">Advances, challenges, and opportunities in extracellular RNA biology: insights from the NIH exRNA Strategic Workshop.</a> JCI Insight. 3 (7), (2018).</li><li>Turchinovich, A., Samatov, T. R., Tonevitsky, A. 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The authors have nothing to disclose.

Here, we describe a protocol for next generation sequencing of exRNAs that enables differential expression analyses from low input samples. Adhering to a specific protocol for EV and exRNA isolation is important because even small alterations (i.e., the ultracentrifugation step or a change in rotor type) can influence the transcriptome and miRNA levels13,14. Thus, regardless of how the exRNA is isolated, it is important to apply the same experimental and bioinformatic procedure to all the samples in an experiment to be able to compare the results.
RNA integrity is usually assessed on the bioanalyzer. However, since exRNAs are devoid of full length rRNA, their RIN value is very low, but this does not necessarily reflect low quality RNA samples. Additionally, the RNA concentration of the exRNAs varied greatly and is often inaccurate. Hence, instead of assessing RNA quality and quantifying the RNA on the bioanalyzer, use the same volume of media each time for further processing. Also, resuspend the extracted RNA in the same volume of resuspension buffer and use the same volume for library construction.
Small RNA library construction was optimized by reducing the amount of adaptors to a tenth of the standard protocol and using one-half of the other reagents. Reducing the adaptors not only prevents adaptor dimers, it also prevents intramolecular RNA circularization15. The reduction in reagents is optimized for the Illumina TruSeq Small RNA Sample Prep Kit. Should other kits be used, it is recommended to also lower adaptor and reagents accordingly, unless the kit specifies that it is tailored to low input samples. Finally, the PCR cycle for library construction was increased from 12 to 15 cycles in this protocol to account for the low concentration of exRNAs. We have found this to be the optimal adjustment without seeing amplification biases15.
Various quality control metrics can be assessed including base quality scores and read length profile. When doing bioinformatics analyses, the base quality of the reads in cellular RNA and exRNAs were similar; however, the length distribution and the annotated origin of sequences were completely different.Most of the reads from cellular RNA mapped to human miRNAs, however, a large proportion of the exRNA reads were unmatched reads. Upon closer inspection, the unmatched reads turned out to be bacterial reads (Table 2). This was surprising because antibiotics were routinely used in our culture. Our result aligns well with other reports that also showed large proportion of unmatched reads when sequencing cell culture derived exRNA16. One of the reasons for these contaminants is that bacteria overlap in size with extracellular complexes or EVs, and hence co-purify during the ultracentrifugation step17. Previous publications have identified widespread contamination of certain bacteria in culture media which remain undetected under normal cell lab procedures18. To date, this problem has largely been ignored but should be taken into account when purifying and analyzing exRNAs.
This protocol details a complete guide to harvesting exRNAs from culture media, optimizing small RNA library preparation and processing the raw library data. This protocol specifically highlights the various quality control checkpoints throughout the process to demonstrate how low input samples (like exRNAs) can deviate from normal sample preparations so that others working with low input samples may know what to expect.

Extracellular and circulating RNAs (exRNAs) are present in various bodily fluids and are resistant towards RNases1,2. Their high abundance, stability and ease of accessibility are attractive for clinical assessment as diagnostic and prognostic markers3. The mode of transport for exRNAs include extracellular vesicles (EVs), association with lipoproteins (such as high-density lipoprotein; HDL) and ribonucleoprotein complexes (such as with Argonaute2 complexes)4.
A subset of exRNAs are microRNAs (miRNAs), which are small non-coding RNAs of about 22 nt that regulate posttranscriptional gene expression. Ex-miRNAs have been implicated in cell-cell communication and regulation of cell homeostasis5. For example, HDL delivers ex-miR-223 to endothelial cells to repress intercellular adhesion molecule 1 (ICAM-1) and inflammation6. Interestingly, miR-223 is also seen transported by extracellular vesicles from leukocytes to lung cancer cells, programming them to take on a more invasive phenotype7. Thus, the transcriptome of ex-miRNAs from various bodily fluids and cell culture medium will greatly improve our understanding of ex-miRNA signaling.
Small RNA sequencing (small RNA seq) is a powerful tool that can be used to understand the transcriptomics of small RNAs. Not only can different samples be compared amongst differentially expressed known RNAs, but novel small RNAs can also be detected and characterized. Consequently, it is also a robust method to identify differentially expressed miRNAs under different conditions. However, one of the hurdles of small RNA seq is the difficulty in generating small RNA seq libraries from low exRNA input fluids like cerebrospinal fluids, saliva, urine, milk, serum and culture media. The TruSeq Small RNA Library Prep protocol from Illumina requires approximately 1 &#181;g of high quality total RNA and the NEBNext Small RNA Library Prep Set protocol from New England Biolabs requires 100 ng-1 &#181;g of RNA8,9. Oftentimes, total RNA from these samples are below detection limit for conventional UV-vis spectrophotometers.
Ex-miRNAs derived from bodily fluids are potentially good prognostic and diagnostic markers. However, in order to study the functional effects or to determine the origin of specific ex-miRNAs, cell culture systems are often used instead. Mesenchymal stem cells (MSCs) have been studied extensively because their EVs have been implicated in alleviating many diseases including myocardial infarction, Alzheimer&#39;s disease and graft versus host disease10. Here, we demonstrate the purification of ex-miRNAs from bone marrow-derived MSCs (BMSCs) and the specific steps used to optimize small RNA library construction, sequencing and data analysis (Figure 1).

<table>
	<tbody>
		<tr>
			<td>Bone Marrow-Derived Mesenchymal Stem Cells</td>
			<td>ATCC</td>
			<td>PCS-500-012</td>
			<td>Cells used in this protocol was bought from ATCC</td>
		</tr>
		<tr>
			<td>MSCGM BulletKit</td>
			<td>Lonza</td>
			<td>PT-3001</td>
			<td>Termed as Mesenchymal Stem Cell Growth Medium (MSC media)</td>
		</tr>
		<tr>
			<td>Exosome-depleted FBS</td>
			<td>Gibco</td>
			<td>A2720801</td>
			<td></td>
		</tr>
		<tr>
			<td>ExRNA collecting media: MSCGM but with the FBS replaced by exosome-depleted FBS</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Trypsin-EDTA</td>
			<td>Gibco</td>
			<td>25200056</td>
			<td></td>
		</tr>
		<tr>
			<td>T175 Flask</td>
			<td>Sarstedt</td>
			<td>833,912</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicillin-Streptomycin</td>
			<td>Gibco</td>
			<td>15140122</td>
			<td></td>
		</tr>
		<tr>
			<td>Phosphate Buffered Saline</td>
			<td>Sigma</td>
			<td>806552</td>
			<td></td>
		</tr>
		<tr>
			<td>Ultracentrifuge</td>
			<td>Beckman Coulter</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Polycarbonate Bottle with Cap Assembly</td>
			<td>Beckman Coulter</td>
			<td>355618</td>
			<td></td>
		</tr>
		<tr>
			<td>Beckman Coulter Type 60 Ti</td>
			<td></td>
			<td></td>
			<td>Rotor used here</td>
		</tr>
		<tr>
			<td>NucleoCounter</td>
			<td>Chemometec</td>
			<td>NC-3000</td>
			<td>Cell Counter</td>
		</tr>
		<tr>
			<td>&#946;-glycerophosphate</td>
			<td>Calbiochem</td>
			<td>35675</td>
			<td>Components of the osteogenic differentiation media</td>
		</tr>
		<tr>
			<td>Dexamethasone</td>
			<td>Sigma</td>
			<td>D4902-25MG</td>
			<td>Components of the osteogenic differentiation media</td>
		</tr>
		<tr>
			<td>2-Phospho-L-ascorbic&#160;acid trisodium salt</td>
			<td>Sigma</td>
			<td>49752-10G</td>
			<td>Components of the osteogenic differentiation media</td>
		</tr>
		<tr>
			<td>1&#945;,25-Dihydroxyvitamin&#160;D3</td>
			<td>Sigma</td>
			<td>D1530-1MG</td>
			<td>Components of the osteogenic differentiation media</td>
		</tr>
		<tr>
			<td>miRNeasy Mini Kit</td>
			<td>Qiagen</td>
			<td>217004</td>
			<td>miRNA and total RNA purification kit for step 4.8</td>
		</tr>
		<tr>
			<td>Agilent RNA 6000 Pico Kit</td>
			<td>Agilent Technologies</td>
			<td>5067-1514</td>
			<td>Chip-based capillary electrophoresis machine and chips for RNA and DNA analysis</td>
		</tr>
		<tr>
			<td>Agilent 2100 Bioanalyzer</td>
			<td>Agilent Technologies</td>
			<td>G2939BA</td>
			<td>Chip-based capillary electrophoresis machine and chips for RNA and DNA analysis</td>
		</tr>
		<tr>
			<td>Agilent High Sensitivity DNA Kit</td>
			<td>Agilent Technologies</td>
			<td>5067-4626</td>
			<td>Chip-based capillary electrophoresis machine and chips for RNA and DNA analysis</td>
		</tr>
		<tr>
			<td>KAPA Library Quantification Kits</td>
			<td>Roche</td>
			<td>KK4824</td>
			<td>Library quantification kit used here</td>
		</tr>
		<tr>
			<td>TruSeq Small RNA Library Prep Kit -Set A (24 rxns) (Set A: indexes 1-12)</td>
			<td>Illumina</td>
			<td>RS-200-0012</td>
			<td>Small RNA library prepartion kit used in this protocol - used in step 5</td>
		</tr>
		<tr>
			<td>Pippin Prep</td>
			<td>Sage Science</td>
			<td></td>
			<td>Automated DNA gel extractor used in this protocol; manual extraction can be done too</td>
		</tr>
		<tr>
			<td>MinElute PCR Purification Kit</td>
			<td>Qiagen</td>
			<td>28004</td>
			<td>PCR purification in step 5.17</td>
		</tr>
		<tr>
			<td>FASTX_Toolkit</td>
			<td>Cold Spring Harbor Lab</td>
			<td></td>
			<td>Trimming low-quality reads in step 6</td>
		</tr>
		<tr>
			<td>cutadapt</td>
			<td></td>
			<td></td>
			<td>Adaptor removal in step 6</td>
		</tr>
		<tr>
			<td>Bowtie</td>
			<td></td>
			<td></td>
			<td>Mapping of clean reads in step 6</td>
		</tr>
		<tr>
			<td>Samtools</td>
			<td></td>
			<td></td>
			<td>To make the expression profile in step 6</td>
		</tr>
		<tr>
			<td>Bedtools</td>
			<td></td>
			<td></td>
			<td>To make the expression profile in step 6</td>
		</tr>
	</tbody>
</table>

isolating, sequencing and analyzing extracellular micrornas from human mesenchymal stem cells

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, milk and urine. One subset of these RNAs are the posttranscriptional regulators &#8211; microRNAs (miRNAs). To delineate the miRNAs produced by specific cell types, in vitro culture systems can be used to harvest and profile exRNAs derived from one subset of cells. The secreted factors of mesenchymal stem cells are implicated in alleviating numerous diseases and is used as the in vitro model system here. This paper describes the process of collection, purification of small RNA and library generation to sequence extracellular miRNAs. ExRNAs from culture media differ from cellular RNA by being low RNA input samples, which calls for optimized procedures. This protocol provides a comprehensive guide to small exRNA sequencing from culture media, showing quality control checkpoints at each step during exRNA purification and sequencing.

The method described in this protocol is optimized to collect exRNA from MSC culture for next generation sequencing. The overall scheme of the workflow is in Figure 1 on the left and the respective quality control checkpoints are on the right.
The morphology of the cells on the day of collection for undifferentiated (Figure 3A) and differentiated (Figure 3B) cells are shown. Furthermore, representative normalized levels of ALP activity of cells induced for 7 days are also shown (Figure 3C). Here, ALP activity is approximately 2.5 times that of the uninduced cells.
Three T175 flasks of confluent MSCs yield 2-5 x 1010 particles/mL (Figure 2). Mean particle size is approximately 160-165 nm while most of the particles are 130-140 nm. There were a few smaller peaks between 200 and 500 nm, which would indicate large complexes or large aggregates.
Since RNA can be degraded quickly, adhere strictly to the conditions of RNA extraction as per the kit or protocol used. Use RNase-free conditions, in particular RNase-free water to resuspend the RNA and keep RNA on ice when possible. After extraction, the RNA can be quantified using a chip-based capillary electrophoresis machine. Representative electropherograms of the RNA after being analyzed are shown in Figure 4. The electropherogram of exRNAs includes a broad range of RNAs (Figure 4A). In contrast, cellular RNA has very distinct peaks at around 70-120, 1800, and 3800 nt, which correspond to tRNA/5S rRNA/5.8S rRNA, 18S rRNA, and 28S rRNA, respectively (Figure 4B). The RNA integrity number (RIN) represents the quality of the RNA. A good RIN of cellular RNA is &#62;8 (indicating almost no RNA degradation). The algorithm on which RIN is based is the ratio between 28S and 18S. This means that RIN is not a good indicator of RNA quality of exRNAs because full-length rRNAs are usually not detectable in exRNAs.
Following RNA extraction, the miRNA libraries were constructed and the cDNA libraries were separated by gel electrophoresis. The adaptor-ligated cDNA of the miRNAs is typically between 140 and 160 nt. The quality of the library was then visualized on a chip-based capillary electrophoresis machine (Figure 5). Figure 5A and Figure 5B are the libraries from the cellular RNA at Day 0 and Day 7, respectively. Figure 5C and Figure 5D are the exRNA libraries at Day 0 and Day 7 respectively. All four samples had peaks at around 140 nt, which indicate successful miRNA library construction. The amount of cDNA in the libraries was quantified by qPCR using a library quantification kit. The quantification cycle (Cq) of the standards was plotted against the log of the concentration to obtain a standard curve (Figure 6). The equation of the standard curve was then used to calculate the concentration of the libraries (Table 1). In our sample, the concentration of the libraries from exRNAs was 5-8 nM, which was significantly lower than libraries from cellular RNAs (8-30 nM). The libraries were then pooled with equal amount and sent for sequencing.
After the libraries were sequenced, the low-quality reads were trimmed away with FASTX-Toolkit and the resulting quality of the library exRNAs was high and comparable to that of the libraries from the cells (Figure 7A,B). The sequence length of the libraries from cells had a single peak at around 22 nt (Figure 7C), which correlates with miRNAs. In contrast, the libraries from exRNAs were more heterogeneous and contained 3 major peaks: 22 nt, 30 nt, and 33 nt (Figure 7D). Similar to their cellular counterparts, the peak at 22 nt is the miRNAs. Closer inspection revealed that the peaks at 30 and 33 nt are tRNAs halves/fragments or piRNAs. The annotations of the mapped reads were then plotted. Most of the reads (65.1%) from cellular RNA mapped to human miRNAs and each of the other small RNAs accounted for a small portion of the mapped reads (Figure 8A). Contrastingly, only 8% of exRNAs mapped to human miRNAs (Figure 8B). The most abundant small RNA to which the reads mapped were tRNAs. Most of the reads were &#34;unmatched reads&#34; (43%). Further examination of the unmatched reads from exRNAs to global databases led to the surprising finding that most of them correspond to bacterial sequences (74% and 85% for D0 and D7 exRNA, respectively; Table 2).&#160;In contrast, only 0.9% of the cellular RNA was unmatched and, of those, only 10% mapped to bacteria. A comparison to the bovine genome showed less than 1% match suggesting that FBS was not a source of contamination (Table 2). Hence, exRNAs exhibit an atypical profile compared to normal cellular RNA.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58655/58655fig1.jpg" /> 
Figure 1: Workflow of exRNA sequencing and analysis. The entire workflow is depicted on the left in the grey boxes and quality control associated with each step is in the red boxes on the right. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58655/58655fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58655/58655fig2.jpg" /> 
Figure 2: Size distribution of particles secreted by the cells at day 0 and day 7 of differentiation. Representative NTA results of particles collected from 3 x T175 flasks at (A)&#160;Day 0 and (B) Day 7 of differentiation. The respective mean and mode sizes as well as the concentration of the particles are tabulated beneath the graphs. SD: standard deviation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58655/58655fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58655/58655fig3.jpg" /> 
Figure 3: Cell morphology and osteogenic differentiation prior to extracellular RNA collection. (A) Micrograph of undifferentiated BMSCs cultured with collection media on the day of collection. (B) Micrograph of BMSCs differentiated for 7 days with collection media on the day of collection. Scale bars = 100 &#181;m. (C) ALP Activities of the cells were normalized to their respective cell viabilities. The values plotted were that of three independent experiments. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58655/58655fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58655/58655fig4.jpg" /> 
Figure 4: Representative electropherogram of RNA integrity after RNA extraction. RNA analyses of extracellular RNA (A) and cellular RNA (B). The x-axis is the length of the RNA (nt) and the y-axis is the fluorescence. The first peak is the ladder at 25 nt. Ribosomal RNAs are shown as 18S (1,800 nt) and 28S (3,800 nt). RNA integrity number (RIN) is an indicator of RNA quality based on the 18S and 28S ribosomal RNA integrity. Higher RIN numbers equal better quality. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58655/58655fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/58655/58655fig5.jpg" /> 
Figure 5: Representative electropherogram of the cDNA libraries after library construction. DNA analyses of the libraries from (A) cells at Day 0, (B) cells at Day 7, (C) extracellular RNA from Day 0, and (D) extracellular RNA from Day 7. Green and purple numbers are the ladders at 35 nt and 10,380 nt, respectively. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58655/58655fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/58655/58655fig6.jpg" /> 
Figure 6: cDNA library quantification and calculation. Libraries were diluted between 500-1,000 times for those from exRNA and between 1,000 and 4,000 times for cell RNA. The libraries were run alongside the standards from the library quantification kit. The average Cq values of the DNA standards were plotted against the log10 concentration (pM) to generate a standard curve, an equation for the standard curve and an R2 value. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58655/58655fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/58655/58655fig7.jpg" /> 
Figure 7: Quality scores and length distribution of sequences. Low quality reads and adaptor sequences were trimmed with FASTX-Toolkit for (A) cells and (B) exRNAs. The length distribution of clean reads for (C) cells and (D) exRNAs. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58655/58655fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/58655/58655fig8.jpg" /> 
Figure 8: Annotation of small RNAs after sequencing. Annotations represent percentages of clean reads for (A) cells and (B) exRNAs. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58655/58655fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Sample </td>
			<td>Dilution factor </td>
			<td>Cq value </td>
			<td>log(pM) </td>
			<td>Concentration (pM) </td>
			<td>Concentration mean (pM) </td>
			<td>Concentration mean * (452/143) </td>
			<td>Multiply by dillution factor (pM) </td>
		</tr>
		<tr>
			<td>BMSC D0 exRNA</td>
			<td>500</td>
			<td>7.72</td>
			<td>0.702</td>
			<td>5.036</td>
			<td>5.276</td>
			<td>16.678</td>
			<td>8338.993</td>
		</tr>
		<tr>
			<td>BMSC D0 exRNA</td>
			<td>500</td>
			<td>7.57</td>
			<td>0.754</td>
			<td>5.677</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BMSC D0 exRNA</td>
			<td>500</td>
			<td>7.7</td>
			<td>0.709</td>
			<td>5.117</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BMSC D0 exRNA</td>
			<td>1000</td>
			<td>8.64</td>
			<td>0.383</td>
			<td>2.416</td>
			<td>2.365</td>
			<td>7.477</td>
			<td>7476.846</td>
		</tr>
		<tr>
			<td>BMSC D0 exRNA</td>
			<td>1000</td>
			<td>8.68</td>
			<td>0.369</td>
			<td>2.340</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BMSC D0 exRNA</td>
			<td>1000</td>
			<td>8.68</td>
			<td>0.369</td>
			<td>2.340</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BMSC D7 exRNA</td>
			<td>500</td>
			<td>8.52</td>
			<td>0.425</td>
			<td>2.659</td>
			<td>3.221</td>
			<td>10.182</td>
			<td>5090.913</td>
		</tr>
		<tr>
			<td>BMSC D7 exRNA</td>
			<td>500</td>
			<td>8.42</td>
			<td>0.459</td>
			<td>2.880</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BMSC D7 exRNA</td>
			<td>500</td>
			<td>7.97</td>
			<td>0.615</td>
			<td>4.125</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BMSC D7 exRNA</td>
			<td>1000</td>
			<td>8.87</td>
			<td>0.303</td>
			<td>2.011</td>
			<td>1.933</td>
			<td>6.110</td>
			<td>6110.391</td>
		</tr>
		<tr>
			<td>BMSC D7 exRNA</td>
			<td>1000</td>
			<td>8.92</td>
			<td>0.286</td>
			<td>1.932</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BMSC D7 exRNA</td>
			<td>1000</td>
			<td>8.97</td>
			<td>0.269</td>
			<td>1.857</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BMSC D0 cell</td>
			<td>1000</td>
			<td>6.86</td>
			<td>1.000</td>
			<td>10.005</td>
			<td>9.810</td>
			<td>31.007</td>
			<td>31006.974</td>
		</tr>
		<tr>
			<td>BMSC D0 cell</td>
			<td>1000</td>
			<td>6.91</td>
			<td>0.983</td>
			<td>9.614</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BMSC D0 cell</td>
			<td>4000</td>
			<td>8.68</td>
			<td>0.369</td>
			<td>2.340</td>
			<td>2.398</td>
			<td>7.581</td>
			<td>30322.041</td>
		</tr>
		<tr>
			<td>BMSC D0 cell</td>
			<td>4000</td>
			<td>8.59</td>
			<td>0.400</td>
			<td>2.514</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BMSC D0 cell</td>
			<td>4000</td>
			<td>8.68</td>
			<td>0.369</td>
			<td>2.340</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BMSC D7 cell</td>
			<td>1000</td>
			<td>8.44</td>
			<td>0.452</td>
			<td>2.834</td>
			<td>2.880</td>
			<td>9.104</td>
			<td>9104.439</td>
		</tr>
		<tr>
			<td>BMSC D7 cell</td>
			<td>1000</td>
			<td>8.43</td>
			<td>0.456</td>
			<td>2.857</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BMSC D7 cell</td>
			<td>1000</td>
			<td>8.39</td>
			<td>0.470</td>
			<td>2.950</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BMSC D7 cell</td>
			<td>4000</td>
			<td>10.36</td>
			<td>-0.213</td>
			<td>0.612</td>
			<td>0.702</td>
			<td>2.218</td>
			<td>8872.689</td>
		</tr>
		<tr>
			<td>BMSC D7 cell</td>
			<td>4000</td>
			<td>10.27</td>
			<td>-0.182</td>
			<td>0.658</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BMSC D7 cell</td>
			<td>4000</td>
			<td>9.97</td>
			<td>-0.078</td>
			<td>0.836</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Calculations:</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td colspan="6">log(pM) = -0.3467 x (Sample Cq value) + 3.3786; use the standard curve and formula</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">concentration (pM) = 10^log(pM)</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td colspan="7">Size adjustment calculation = Size of DNA standard (452 bp) divided by the average fragment length (143 bp)</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 1: miRNA library quantification.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Sample:</td>
			<td># reads not mapping human</td>
			<td># reads mapping to bacteria</td>
			<td>% bacterial reads</td>
			<td># reads mapping to cow</td>
			<td>% bovine reads</td>
		</tr>
		<tr>
			<td>BMSC D0 Cell</td>
			<td>131007</td>
			<td>9858</td>
			<td>8%</td>
			<td>99</td>
			<td>0.08%</td>
		</tr>
		<tr>
			<td>BMSC D7 Cell</td>
			<td>169730</td>
			<td>7935</td>
			<td>5%</td>
			<td>188</td>
			<td>0.11%</td>
		</tr>
		<tr>
			<td>BMSC D0 exRNA</td>
			<td>6122833</td>
			<td>4551477</td>
			<td>74%</td>
			<td>891</td>
			<td>0.01%</td>
		</tr>
		<tr>
			<td>BMSC D7 exRNA</td>
			<td>7046691</td>
			<td>5970086</td>
			<td>85%</td>
			<td>771</td>
			<td>0.01%</td>
		</tr>
	</tbody>
</table>
Table 2: The percentage of unmatched reads that map to bacteria and bovine reads.

Watch this Scientific Journal Video about Isolating, Sequencing and Analyzing Extracellular MicroRNAs from Human Mesenchymal Stem Cells at JoVE.com

Isolating, Sequencing and Analyzing Extracellular MicroRNAs from Human Mesenchymal Stem Cells

This protocol demonstrates how to purify extracellular microRNAs from cell culture media for small RNA library construction and next generation sequencing. Various quality control checkpoints are described to allow readers to understand what to expect when working with low input samples like exRNAs.

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, ...

isolating-sequencing-analyzing-extracellular-micrornas-from-human

Nanyang Technological University

Research

JoVE Journal

Genetics

8.0K Views.  Aarhus University. This protocol demonstrates how to purify extracellular microRNAs from cell culture media for small RNA library construction and next generation sequencing. Various quality control checkpoints are described to allow readers to understand what to expect when working with low input samples like exRNAs.

Video: Isolating, Sequencing and Analyzing Extracellular MicroRNAs from Human Mesenchymal Stem Cells

Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

The processing of most eukaryotic RNAs is mediated by RNA Binding Proteins (RBPs) with modular configurations, including an RNA recognition module, which specifically binds the pre-mRNA target and an effector domain. Previously, we have taken advantage of the unique RNA binding mode of the PUF domain in human Pumilio 1 to generate a programmable RNA binding scaffold, which was used to engineer various artificial RBPs to manipulate RNA metabolism. Here, a detailed protocol is described to construct Engineered Splicing Factors (ESFs) that are specifically designed to modulate the alternative splicing of target genes. The protocol includes how to design and construct a customized PUF scaffold for a specific RNA target, how to construct an ESF expression plasmid by fusing a designer PUF domain and an effector domain, and how to use ESFs to manipulate the splicing of target genes. In the representative results of this method, we have also described the common assays of ESF activities using splicing reporters, the application of ESF in cultured human cells, and the subsequent effect of splicing changes. By following the detailed protocols in this report, it is possible to design and generate ESFs for the regulation of different types of Alternative Splicing (AS), providing a new strategy to study splicing regulation and the function of different splicing isoforms. Moreover, by fusing different functional domains with a designed PUF domain, researchers can engineer artificial factors that target specific RNAs to manipulate various steps of RNA processing.

This report describes a bioengineering method to design and construct novel Artificial Splicing Factors (ASFs) that specifically modulate the splicing of target genes in mammalian cells. This method can be further expanded to engineer various artificial factors to manipulate other aspects of RNA metabolism.

The processing of most eukaryotic RNAs is mediated by RNA Binding Proteins (RBPs) with modular configurations, including an RNA recognition module, which ...

Highly Efficient Gene Disruption of Murine and Human Hematopoietic Progenitor Cells by CRISPR/Cas9

Advances in the hematopoietic stem cell (HSCs) field have been aided by methods to genetically engineer primary progenitor cells as well as animal models. Complete gene ablation in HSCs required the generation of knockout mice from which HSCs could be isolated, and gene ablation in primary human HSCs was not possible. Viral transduction could be used for knock-down approaches, but these suffered from variable efficacy. In general, genetic manipulation of human and mouse hematopoietic cells was hampered by low efficiencies and extensive time and cost commitments. Recently, CRISPR/Cas9 has dramatically expanded the ability to engineer the DNA of mammalian cells. However, the application of CRISPR/Cas9 to hematopoietic cells has been challenging, mainly due to their low transfection efficiencies, the toxicity of plasmid-based approaches and the slow turnaround time of virus-based protocols.
A rapid method to perform CRISPR/Cas9-mediated gene editing in murine and human hematopoietic stem and progenitor cells with knockout efficiencies of up to 90% is provided in this article. This approach utilizes a ribonucleoprotein (RNP) delivery strategy with a streamlined three-day workflow. The use of Cas9-sgRNA RNP allows for a hit-and-run approach, introducing no exogenous DNA sequences in the genome of edited cells and reducing off-target effects. The RNP-based method is fast and straightforward: it does not require cloning of sgRNAs, virus preparation or specific sgRNA chemical modification. With this protocol, scientists should be able to successfully generate knockouts of a gene of interest in primary hematopoietic cells within a week, including downtimes for oligonucleotide synthesis. This approach will allow a much broader group of users to adapt this protocol for their needs.

A protocol for fast CRISPR/Cas9-mediated gene disruption in mouse and human primary hematopoietic cells is described in this article. Cas9-sgRNA ribonucleoproteins are introduced via electroporation with sgRNAs generated through in vitro transcription and commercial Cas9. High editing efficiencies are achieved with limited time and financial cost.

Advances in the hematopoietic stem cell (HSCs) field have been aided by methods to genetically engineer primary progenitor cells as well as animal models. ...

Ultralow Input Genome Sequencing Library Preparation from a Single Tardigrade Specimen

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state when water is supplied. The genomic sequencing of microscopic animals such as tardigrades risks bacterial contamination that sometimes leads to erroneous interpretations, for example, regarding the extent of horizontal gene transfer in these animals. Here, we provide an ultralow input method to sequence the genome of the tardigrade, Hypsibius dujardini, from a single specimen. By employing rigorous washing and contaminant exclusion along with an efficient extraction of the 50 ~ 200 pg genomic DNA from a single individual, we constructed a library sequenced with a DNA sequencing instrument. These libraries were highly reproducible and unbiased, and an informatics analysis of the sequenced reads with other H. dujardini genomes showed a minimal amount of contamination. This method can be applied to unculturable tardigrades that could not be sequenced using previous methods.

Contamination during the genomic sequencing of microscopic organisms remains a large problem. Here, we show a method to sequence the genome of a tardigrade from a single specimen with as little as 50 pg of genomic DNA without whole genome amplification to minimize the risk of contamination.

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state ...

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 ...

Isolation, Characterization and MicroRNA-based Genetic Modification of Human Dental Follicle Stem Cells

To date, several stem cell types at different developmental stages are in the focus for the treatment of degenerative diseases. Yet, certain aspects, such as initial massive cell death and low therapeutic effects, impaired their broad clinical translation. Genetic engineering of stem cells prior to transplantation emerged as a promising method to optimize therapeutic stem cell effects. However, safe and efficient gene delivery systems are still lacking. Therefore, the development of suitable methods may provide an approach to resolve current challenges in stem cell-based therapies.
The present protocol describes the extraction and characterization of human dental follicle stem cells (hDFSCs) as well as their non-viral genetic modification. The postnatal dental follicle unveiled as a promising and easily accessible source for harvesting adult multipotent stem cells possessing high proliferation potential. The described isolation procedure presents a simple and reliable method to harvest hDFSCs from impacted wisdom teeth. Also this protocol comprises methods to define stem cell characteristics of isolated cells. For genetic engineering of hDFSCs, an optimized cationic lipid-based transfection strategy is presented enabling highly efficient microRNA introduction without causing cytotoxic effects. MicroRNAs are suitable candidates for transient cell manipulation, as these small translational regulators control the fate and behavior of stem cells without the hazard of stable genome integration. Thus, this protocol represents a safe and efficient procedure for engineering of hDFSCs that may become important for optimizing their therapeutic efficacy.

This protocol describes the transient genetic engineering of dental stem cells extracted from the human dental follicle. The applied non-viral modification strategy may become a basis for the improvement of therapeutic stem cell products.

To date, several stem cell types at different developmental stages are in the focus for the treatment of degenerative diseases. Yet, certain aspects, such ...

Endogenous Protein Tagging in Human Induced Pluripotent Stem Cells Using CRISPR/Cas9

A protocol is presented for generating human induced pluripotent stem cells (hiPSCs) that express endogenous proteins fused to in-frame&#160;N- or C-terminal fluorescent tags. The prokaryotic CRISPR/Cas9 system (clustered regularly interspaced short palindromic repeats/CRISPR-associated 9) may be used to introduce large exogenous sequences into genomic loci via homology directed repair (HDR). To achieve the desired knock-in, this protocol employs the ribonucleoprotein (RNP)-based approach where wild type Streptococcus pyogenes Cas9 protein, synthetic 2-part guide RNA (gRNA), and a donor template plasmid are delivered to the cells via electroporation. Putatively edited cells expressing the fluorescently tagged proteins are enriched by fluorescence activated cell sorting (FACS). Clonal lines are then generated and can be analyzed for precise editing outcomes. By introducing the fluorescent tag at the genomic locus of the gene of interest, the resulting subcellular localization and dynamics of the fusion protein can be studied under endogenous regulatory control, a key improvement over conventional overexpression systems. The use of hiPSCs as a model system for gene tagging provides the opportunity to study the tagged proteins in diploid, nontransformed cells. Since hiPSCs can be differentiated into multiple cell types, this approach provides the opportunity to create and study tagged proteins in a variety of isogenic cellular contexts.

Described here is a protocol for tagging endogenously expressed proteins with fluorescent tags in human induced pluripotent stem cells using CRISPR/Cas9. Putatively edited cells are enriched by fluorescence activated cell sorting and clonal cell lines are generated.

A protocol is presented for generating human induced pluripotent stem cells (hiPSCs) that express endogenous proteins fused to in-frame&#160;N- or ...

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, and was deployed to West Africa during the recent Ebola virus epidemic, highlighting this possibility. In addition to its practical advantages (low cost, ease of equipment transport and use), this technology also provides fundamental advantages over second-generation sequencing approaches, particularly the very long read length, ability to directly sequence RNA, and real-time availability of data. Raw read accuracy is lower than with other sequencing platforms, which represents the main limitation of this technology; however, this can be partially mitigated by the high read depth generated. Here, we present a field-compatible protocol for sequencing of the mRNAs encoding for Niemann-Pick C1, which is the cellular receptor for ebolaviruses. This protocol encompasses extraction of RNA from animal blood samples, followed by RT-PCR for target enrichment, barcoding, library preparation, and the sequencing run itself, and can be easily adapted for use with other DNA or RNA targets.

Nanopore sequencing is a novel technology that allows cost-effective sequencing in remote locations and resource-poor settings. Here, we present a protocol for sequencing of mRNAs from whole blood that is compatible with such conditions.

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, ...

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to normal and pathological conditions. The goal of this protocol is to generate high-quality, large-scale transcriptome data of each endocrine cell type with the use of a droplet-based microfluidic single-cell RNA sequencing technology. Such data can be utilized to build the gene expression profile of each endocrine cell type in normal or specific conditions. The process requires careful handling, accurate measurement, and rigorous quality control. In this protocol, we describe detailed steps for human pancreatic islets dissociation, sequencing, and data analysis. The representative results of about 20,000 human single islet cells demonstrate the successful application of the protocol.

Here, we present a protocol to generate high-quality, large-scale transcriptome data of single cells from isolated human pancreatic islets using a droplet-based microfluidic single-cell RNA sequencing technology.

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to ...

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Half of all human transcripts are thought to be regulated by microRNAs. Therefore, quantifying microRNA expression can reveal underlying mechanisms in disease states and provide therapeutic targets and biomarkers. Here, we detail how to accurately quantify microRNAs. Briefly, this method describes isolating microRNAs, ligating them to adaptors suitable for high-throughput sequencing, amplifying the final products, and preparing a sample library. Then, we explain how to align the obtained sequencing reads to microRNA hairpins, and quantify, normalize, and calculate their differential expression. Versatile and robust, this combined experimental workflow and bioinformatic analysis enables users to begin with tissue extraction and finish with microRNA quantification.

Here, we describe a step-by-step strategy for isolating small RNAs, enriching for microRNAs, and preparing samples for high-throughput sequencing. We then describe how to process sequence reads and align them to microRNAs, using open source tools.

Half of all human transcripts are thought to be regulated by microRNAs. Therefore, quantifying microRNA expression can reveal underlying mechanisms in ...

Generation of Defined Genomic Modifications Using CRISPR-CAS9 in Human Pluripotent Stem Cells

Human pluripotent stem cells offer a powerful system to study gene function and model specific mutations relevant to disease. The generation of precise heterozygous genetic modifications is challenging due to CRISPR-CAS9 mediated indel formation in the second allele. Here, we demonstrate a protocol to help overcome this difficulty by using two repair templates in which only one expresses the desired sequence change, while both templates contain silent mutations to prevent re-cutting and indel formation. This methodology is most advantageous for gene editing coding regions of DNA to generate isogenic control and mutant human stem cell lines for studying human disease and biology. In addition, optimization of transfection and screening methodologies have been performed to reduce labor and cost of a gene editing experiment. Overall, this protocol is widely applicable to many genome editing projects utilizing the human pluripotent stem cell model.

This protocol provides a method to facilitate the generation of defined heterozygous or homozygous nucleotide changes using CRISPR-CAS9 in human pluripotent stem cells.

Human pluripotent stem cells offer a powerful system to study gene function and model specific mutations relevant to disease. The generation of precise ...