1. Human islet dissociation
<ol>
	<li>Obtain human islets isolated from cadaver organ donors of either sex, ages between 15-80 years, without pre-existing diseases unless islets from donors with specific demographics are required for the study purpose.
	<ol>
		<li>After isolation, have the isolated islets kept in the tissue culture facility for 2-3 days at the supplier. It often takes more than 1 day for islet damages to become visible.</li>
		<li>Place the islets in a bottle and immerse it completely in the islet medium. Get it to the laboratory by overnight shipment.</li>
		<li>Obtain the islet equivalent quantity (IEQ) of the shipped islets from the islet supplier.</li>
	</ol>
	</li>
	<li>Recover islets from the shipment on the day of islet arrival. Perform this step using a hood to minimize the chance of contamination.
	<ol>
		<li>Cool the complete islet media (CMRL-1066, 10% (v/v) FBS, 1x Pen-Strep, 2 mM glutamine) in a refrigerator.</li>
		<li>Transfer the islets from the bottle to a 50 mL conical tube.</li>
		<li>Add 10 mL pre-cooled complete islet media to the emptied bottle to wash out the remaining islets. Transfer the media to the conical tube.</li>
		<li>Centrifuge the tube at 200 x g for 2 min to recover islets. Aspirate the supernatant leaving about 1-2 mL media with the pellet.</li>
		<li>Resuspend the islets with pre-cooled complete islet media. Based on the IEQ provided by the supplier, add 12 mL media per 5000 IEQ.</li>
		<li>Pour the islets in the media on a 10 cm non-treated tissue culture dish. Incubate overnight in a tissue culture incubator at 37 &#176;C with 5% CO2 in atmospheric air.</li>
	</ol>
	</li>
	<li>Dissociate islets as shown below. Perform this step following overnight incubation.
	<ol>
		<li>Pre-warm complete islet media and cell dissociation solution.</li>
		<li>Prepare 1x PBS containing 0.04% BSA at room temperature.</li>
		<li>Count and hand-pick 200-300 islets using a P200 pipette and transfer the islets to a 15 mL conical tube containing 5 mL pre-warmed complete islet media.</li>
		<li>Collect the islets by centrifugation at 200 x g for 2 min. Gently aspirate the supernatant without disturbing the pellet on the bottom.</li>
		<li>Add 1.0 mL pre-warmed cell dissociation solution and disrupt the pellet by pipetting gently up and down. Incubate the islets at 37 &#176;C for 9-11 min. Every 3 min pipette up and down slowly for 10 s to dissociate the cells into single cells.</li>
		<li>Once islet cells are well dissociated and the solution becomes cloudy, add 9 mL complete islet media and filter through a 30 &#181;m cell strainer into a new 15 mL conical tube.</li>
		<li>Wash the tube and cell strainer with 2 mL complete islet media to collect the remaining islets and add to the same tube.</li>
		<li>Collect cells by centrifugation at 400 x g for 5 min.</li>
		<li>Gently aspirate media and resuspend the cell pellet in 5 mL 1x PBS containing 0.04% BSA (PBS-BSA).</li>
		<li>Filter through a new 30 &#181;m cell strainer into a 15 mL conical tube and centrifuge at 400 x g for 5 min to collect cells.</li>
		<li>Aspirate the supernatant and resuspend the cell pellet in 200-300 &#181;L 1x PBS-BSA solution.</li>
		<li>Measure the cell concentration and adjust volume to a final concentration of 400-500 cells/&#181;L.</li>
	</ol>
	</li>
</ol>
2. Single cell suspension quality control
<ol>
	<li>Determine the cell concentration using a fluorescence-based automated cell counter34.
	<ol>
		<li>Mix 10 &#956;L cells with 0.5 &#956;L AO/DAPI. Pipette mix thoroughly. Load 10.5 &#956;L onto the slide and run the cell count assay to determine count and viability.</li>
		<li>Dilute and/or filter the cell suspension as necessary based on the cell count.</li>
	</ol>
	</li>
</ol>
3. Single cell partitioning using a microfluidic chip. Follow protocol from microfluidic chip manufacturer35.
<ol>
	<li>Bring 3&#39; gel beads and reverse transcription (RT) reagents to room temperature (&#62;30 min). Reconstitute the RT primer in TE buffer if needed.</li>
	<li>Prepare RT master mix in a low bind tube as outlined in Table 1.</li>
	<li>Determine the number of cells to be input for each sample. Calculate the cell suspension volume (X) necessary to deliver the desired target cell number. The calculated volume of nuclease-free water to add to each sample will be 33.8-X &#956;L.</li>
	<li>For each sample to be partitioned, add 33.8-X &#956;L nuclease-free water into 0.2 mL PCR strip tube. Then, add 66.2 &#956;L master mix to each strip tube. Do not add the cells to the strip tube at this point. Pipette gently to mix. Place the prepared strip tubes on ice.</li>
	<li>Place a microfluidic chip into a chip case. Orient the chip case ensuring oil wells (row labeled 3) are closest to the person performing the experiment.</li>
	<li>If running less than 8 samples, use 50% glycerol to fill unused channels in the following order:
	<ol>
		<li>Add 90 &#181;L of 50% glycerol into the wells in row 1 for all unused channels.</li>
		<li>Add 40 &#181;L of 50% glycerol into the wells in row 2 for all unused channels.</li>
		<li>Add 270 &#181;L of 50% glycerol into the wells in row 3 for all unused channels.</li>
	</ol>
	</li>
	<li>Snap the gel beads into a vortex. Vortex at full speed for 30 s. Tap the strip on the bench top several times to collect beads. Confirm that there are no bubbles present.</li>
	<li>Add X &#956;L of cells into the prepared strip tubes. Pipette to mix 5 times. Without discarding the pipette tips, transfer 90 &#956;L of cell mixture to row 1 of the chip.</li>
	<li>Wait for 30 s, then load 40 &#956;L of gel beads to row 2. Pipette very slowly for this step. Dispense 270 &#956;L of partitioning oil to the wells of row 3.</li>
	<li>Hook the chip gasket onto the tabs of the chip holder. Place the assembled chip holder into the single cell partitioning device and press the run button.</li>
	<li>Immediately remove the assembled chip holder upon run completion.</li>
	<li>Remove the chip gasket from the holder, open the chip case at a 45&#176; angle, and remove 100 &#956;L of the emulsion from the chip into a blue plastic 96-well plate.</li>
</ol>
4. Single cell cDNA amplification. Follow protocol from microfluidic chip manufacturer35.
<ol>
	<li>Reverse transcription. 
	NOTE: Perform this step under a clean PCR-only hood to prevent microbial and other contamination of the unamplified cDNA.
	<ol>
		<li>Seal the 96-well blue plate with a foil seal on a heated plate sealer.</li>
		<li>Run the reverse transcription reaction in a thermal cycler as follows: 53 &#176;C for 45 min &#224; 85 &#176;C for 5 min &#224; 4 &#176;C hold. 
		NOTE: This is a safe stopping point. Samples can hold at 4 &#176;C for up to 72 h.</li>
	</ol>
	</li>
	<li>Post-RT purification
	<ol>
		<li>Bring the nucleic acid binding magnetic beads and nucleic acid size selection magnetic beads to room temperature and vortex to re-suspend. At this point, thaw the sample cleanup buffer for 10 min at 65 &#176;C. Bring all other reagents to room temperature and vortex.</li>
		<li>Prepare the buffers as shown in Tables 2 and Tables 3.</li>
	</ol>
	</li>
	<li>Chemically break the emulsion and purify.
	<ol>
		<li>To do so, gently remove the foil seal from the plate.</li>
		<li>Dispense 125 &#956;L of pink emulsion-breaking reagent into each emulsion. Wait for 1 min, then transfer the entire volume to a clean 0.2 mL strip tube. Ensure that there is a layer of clear and a layer of pink in the strip tube.</li>
		<li>Remove 125 &#956;L of the pink layer from the bottom of the strip tube without disturbing the clear layer. It is normal for a small volume (~15 &#956;L) of the pink layer to remain in the tube.</li>
		<li>Add 200 &#956;L of cleanup mix from Table 2 to the strip tube and incubate at room temperature for 10 min.</li>
		<li>Transfer the strip tube to a magnetic stand and allow the solution to clear. Remove the supernatant and discard, then wash the beads with 80% ethanol twice. Allow the beads to dry for 1 min.</li>
		<li>Remove the strip tube from the magnet and add 35.5 &#956;L of elution solution from Table 3 to the beads. Pipette to resuspend the beads in the solution. Incubate for 2 min at room temperature.</li>
		<li>Transfer the strip tube to a magnetic stand and allow the solution to clear. Remove the purified cDNA from the strip tube and dispense it to clean 0.2 mL strip tubes.</li>
	</ol>
	</li>
	<li>Amplify the cDNA.
	<ol>
		<li>Prepare amplification master mix in Table 4, below.</li>
		<li>Add 65 &#956;L of cDNA Amplification Master Mix to each sample. Place the strip tube in a thermal cycler and run the following program: 98 &#176;C 3 min &#224; 15 cycles of [98 &#176;C 15 s &#224; 67 &#176;C 20 s &#224; 72 &#176;C 1 min] &#224; 72 &#176;C 5 min &#224; 4 &#176;C hold 
		NOTE: This is a safe stopping point. Samples can hold at 4&#176;C for up to 72 h.</li>
		<li>Purify the amplified cDNA with 0.6x nucleic acid size selection magnetic beads. Wash twice with 80% ethanol and elute with 40.5 &#956;L.</li>
		<li>Run the quality control cDNA using automated gel electrophoresis and fluorescence-based DNA quantitation assay36,37. 
		NOTE: This is a safe stopping point. Samples can hold at 4 &#176;C for up to 72 h or at -20&#176;C indefinitely.</li>
	</ol>
	</li>
</ol>
5. Sequencing library construction
<ol>
	<li>Tagmentation and clean-up of cDNA38.
	<ol>
		<li>Normalize cDNA to 50 ng in 20 &#956;L of the total volume. Exact quantitation is critical in this step.</li>
		<li>Make the tagmentation mix in Table 5 and aliquot 30 &#956;L to each 20 &#956;L cDNA sample on ice. Put the samples in the thermal cycler and run the tagmentation protocol: 55 &#176;C 5 min &#224; 10 &#176;C hold.</li>
		<li>Perform the clean-up of tagmented cDNA using columns38. Add 180 &#956;L DNA binding buffer to each sample. Transfer 230 &#956;L to a spin column.</li>
		<li>Centrifuge at 1300 x g for 2 min and discard the flow-through.</li>
		<li>Wash twice with 300 &#956;L DNA wash buffer. Centrifuge an additional 2 min at 1300 x g to ensure ethanol removal.</li>
		<li>Elute purified tagmented cDNA by adding 31 &#956;L of elution buffer to the column and incubate at room temperature for 2 min.</li>
		<li>Centrifuge for 2 min at 1300 x g to recover the purified product.</li>
	</ol>
	</li>
	<li>Sample index PCR.
	<ol>
		<li>Choose barcodes that do not overlap during a multiplexed sequencing run.</li>
		<li>Make a Sample Index PCR master mix as shown in Table 6.</li>
		<li>Add 60 &#956;L of Sample Index PCR master mix to 30 &#956;L of the purified sample.</li>
		<li>Add 10 &#956;L of a 20 &#956;M, 4-oligo sample index to each sample (record index used). The total reaction volume is now 100 &#956;L.</li>
		<li>Place in a thermal cycler with the lid set to 105 &#176;C. Run the following program: 98 &#176;C 45 s &#224; 12-14 cycles of [98 &#176;C 20 s &#224; 54 &#176;C 30 s &#224; 72 &#176;C 20 s] &#224; 72 &#176;C 1 min &#224; 4 &#176;C hold. 
		NOTE: This is a safe stopping point. Samples can hold at 4 &#176;C for up to 72 h.</li>
	</ol>
	</li>
	<li>Purify libraries with double bead clean up.
	<ol>
		<li>Add 100 &#956;L of nucleic acid size selection magnetic beads to the sample and mix thoroughly with a pipette. Incubate at room temperature for 5 min.</li>
		<li>Transfer to a magnet and let it stand until the solution clears. Remove and discard the supernatant.</li>
		<li>Wash twice with 200 &#956;L of 80% ethanol.</li>
		<li>Dry the beads on the magnet for 2 min. Remove from the magnet and add 50.5 &#956;L of EB Buffer to the bead pellet. Pipette to re-suspend the beads in the buffer.</li>
		<li>Incubate at room temperature for 2 min. Transfer to a magnet and let stand for 2 min. Transfer 50 &#956;L of eluted sample to a clean strip tube.</li>
		<li>Add 40 &#956;L nucleic acid size selection magnetic beads to the sample and incubate at room temperature for 5 min. Transfer to a magnet and let the solution clear. Remove and discard supernatant.</li>
		<li>Wash twice with 125 &#956;L of 80% ethanol.</li>
		<li>Dry the beads on the magnet for 2 min. Remove from the magnet and add 60.5 &#956;L of EB Buffer to the bead pellet. Pipette to re-suspend the beads in the buffer.</li>
		<li>Incubate at room temperature for 2 min. Transfer to a magnet and let it stand for 2 min. Transfer 50 &#956;L of eluted sample to a clean strip tube. This is the final library.</li>
		<li>Hold samples at 4 &#176;C for up to 72 h or at -20 &#176;C indefinitely. Note that this is a safe stopping point.</li>
	</ol>
	</li>
	<li>Quantify and run quality control of final libraries using automated gel electrophoresis and fluorescence-based DNA quantitation assay36,37. Dilute the samples 1:10 prior to running quality control.</li>
</ol>
6. Library sequencing
<ol>
	<li>Normalize each sample to be sequenced to 2 ng/&#956;L and pool 3 &#956;L of each normalized sample together.</li>
	<li>Measure the pool concentration with fluorescence-based DNA quantitation assay37.</li>
	<li>Dilute the pool to 0.25 ng/&#956;L.</li>
	<li>Denature the pool as follows: 12 &#956;L of diluted pooled sample (0.25 ng/&#956;L) + 1 &#956;L DNA control (1 nM), 2 &#956;L EB Buffer + 5 &#956;L NaOH (0.4N). Let this incubate for 5 min, then add 10 &#956;L of 200 mM Tris pH 8.0.</li>
	<li>Load 4.05 &#956;L into 1345.95 &#956;L HT1. Load 1.3 mL into the sequencer&#8217;s cartridge and run according to manufacturer&#8217;s guidelines39 using a sequencing recipe with 26 cycles (Read 1) + 8 cycles (i7 Index) + 0 cycles (i5 Index) + 55 cycles (Read 2).</li>
</ol>
7. Read alignment (Supplemental File 1)
<ol>
	<li>Run Cell Ranger (v2.0.0) to demultiplex raw base call (BCL) files generated by sequencing into FASTQ files. Align FASTQ files to human B37.3 Genome assembly and UCSC gene model to obtain expression quantification.</li>
	<li>Alignment quality control.
	<ol>
		<li>Generate alignment metrics and check Q30 bases, valid barcode fraction, cell-associated read fraction, mapped read fraction and reads detected in each cell.</li>
		<li>Examine the barcode rank plot to make sure the separation of the cell-associated barcodes and the background.</li>
	</ol>
	</li>
</ol>
8. Data analysis (Supplemental File 2)
<ol>
	<li>Cell quality control and preprocess.
	<ol>
		<li>Exclude cells with &#60; 500 detected genes, &#60; 3000 total number of unique molecular identifier (UMI), &#62; 0.2 viability score as previously described32. Adjust the cutoffs according to tissue and cell types.</li>
		<li>Remove doublets.
		<ol>
			<li>Assess the five endocrine hormone genes (glucagon - GCG, insulin - INS, somatostatin - SST, pancreatic polypeptide - PPY, and ghrelin - GHRL) for bimodal expression pattern (high- and low-expression mode) using R package mclust40.</li>
			<li>Remove cells that express more than one hormone gene, i.e., with two or more hormone genes in the high-expression mode.</li>
		</ol>
		</li>
		<li>Normalize gene expression by the total UMI and multiply by the scale factor of 10,000 at cell level using R package Seurat41.</li>
		<li>Remove genes detected in less than 3 cells.</li>
		<li>Detect variable genes using average expression and dispersion of all cells. Adjust the cutoffs according to the tissue and cell types.</li>
	</ol>
	</li>
	<li>Perform the principal component analysis with the variable genes. Cluster cells with the selected number of principal components. Derive cell-cluster enriched genes by comparing one cell cluster with the rest of the cells.</li>
</ol>

All authors are employees and shareholders of Regeneron Pharmaceuticals, Inc.

Single-cell technologies developed in recent years provide a new platform to characterize cell types and study molecular heterogeneity in human pancreatic islets. We adopted a protocol of droplet-based microfluidic single-cell isolation and data analysis to study human islets. Our protocol successfully produced RNA sequencing data from over 20,000 single human islet cells with relatively small variations in sequence quality and batch effects.
In particular, two steps are critical in this proto...

Pancreatic islets release endocrine hormones to regulate blood glucose levels. Five endocrine cell types, which differ functionally and morphologically, are involved in this essential role: &#945;-cells produce glucagon, &#946;-cells insulin, &#948;-cells somatostatin, PP cells pancreatic polypeptide, and &#949;-cells ghrelin1. Gene expression profiling is a useful approach to characterize the endocrine cells in normal or specific conditions. Historically, the whole islet gene expression profiling was generated using microarray and next-generation RNA sequencing2,3,4,5,6,7,8. Although the whole islet transcriptome is informative to identify the organ-specific transcripts and disease candidate genes, it fails to uncover the molecular heterogeneity of each islet cell type. Laser capture microdissection (LCM) technique has been applied to directly obtain specific cell types from islets9,10,11,12 but falls short of purity of the targeted cell population. To overcome these limitations, fluorescence-activated cell sorting (FACS) has been used to select specific endocrine cell populations, such as &#945;- and &#946;-cells13,14,15,16,17,18. Moreover, Dorrell et al. used an antibody-based FACS sorting approach to classify &#946;-cells into four subpopulations19. FACS-sorted islet cells can also be plated for RNA sequencing of single cells; however, the plate-based methods face challenges in scalability20,21,22. 
To generate high quality, large-scale transcriptome data of each endocrine cell type, we applied microfluidic technology to human islet cells. The microfluidic platform generates transcriptome data from a large number of single cells in a high-throughput, high-quality, and scalable manner23,24,25,26,27. In addition to revealing molecular characteristics of a cell type captured in a large quantity, highly-scalable microfluidic platform enables identification of rare cell types when enough cells are provided. Hence, application of the platform to human pancreatic islets allowed profiling of ghrelin-secreting &#949;-cells, a rare endocrine cell type with little known function due to its scarcity28. In recent years, several studies have been published by us and others reporting large-scale transcriptome data of human islets using the technology29,30,31,32,33. The data are publicly available and useful resources for the islet community to study endocrine cell heterogeneity and its implication in diseases. 
Here, we describe a droplet-based microfluidic single-cell RNA sequencing protocol, which has been used to produce transcriptome data of approximately 20,000 human islet cells including &#945;-, &#946;-, &#948;-, PP, &#949;-cells, and a smaller proportion of non-endocrine cells32. The workflow starts with isolated human islets and depicts steps of islet cell dissociation, single-cell capture, and data analysis. The protocol requires the use of freshly isolated islets and can be applied to islets from humans and other species, such as rodents. Using this workflow, unbiased and comprehensive islet cell atlas under baseline and other conditions can be built.

<table>
	<tbody>
		<tr>
			<td>30 &#181;m Pre-Separation Filters</td>
			<td>Miltenyi Biotec</td>
			<td>130-041-407</td>
			<td>Cell strainer</td>
		</tr>
		<tr>
			<td>8-chamber slides</td>
			<td>Chemometec</td>
			<td>102673-680</td>
			<td>Dell counting assay slides</td>
		</tr>
		<tr>
			<td>Bioanalyzer High Sensitivity DNA Kit</td>
			<td>Agilent</td>
			<td>5067-4626</td>
			<td>for QC</td>
		</tr>
		<tr>
			<td>Bovine Serum Albumin</td>
			<td>Sigma-Aldrich</td>
			<td>A9647</td>
			<td>Single cell media</td>
		</tr>
		<tr>
			<td>Chromium Single Cell 3&#39; Library &#38; Gel Bead Kit v2, 16 rxns</td>
			<td>10X Genomics</td>
			<td>120237</td>
			<td>Single cell reagents</td>
		</tr>
		<tr>
			<td>Chromium Single Cell A Chip Kit v2, 48 rx (6 chips)</td>
			<td>10X Genomics</td>
			<td>120236</td>
			<td>Microfluidic chips</td>
		</tr>
		<tr>
			<td>CMRL-1066</td>
			<td>ThermoFisher</td>
			<td>11530-037</td>
			<td>Complete islet media</td>
		</tr>
		<tr>
			<td>EB Buffer</td>
			<td>Qiagen</td>
			<td>19086</td>
			<td>Elution buffer</td>
		</tr>
		<tr>
			<td>Eppendorf twin-tec PCR plate, 96-well, blue, semi-skirted</td>
			<td>VWR</td>
			<td>47744-112</td>
			<td>Emulsion plate</td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum</td>
			<td>ThermoFisher</td>
			<td>16000-036</td>
			<td>Complete islet media</td>
		</tr>
		<tr>
			<td>Human islets</td>
			<td>Prodo Labs</td>
			<td>HIR</td>
			<td>Isolated human islets</td>
		</tr>
		<tr>
			<td>L-Glutamine (200 mM)</td>
			<td>ThermoFisher</td>
			<td>25030-081</td>
			<td>Complete islet media</td>
		</tr>
		<tr>
			<td>Nextera DNA Library Preparation Kit (96 samples)</td>
			<td>Illumina</td>
			<td>FC-121-1031</td>
			<td>Library preparation reagents</td>
		</tr>
		<tr>
			<td>NextSeq 500/550 High Output Kit v2.5 (75 cycles)</td>
			<td>Illumina</td>
			<td>FC-404-2005</td>
			<td>Sequencing</td>
		</tr>
		<tr>
			<td>Penicillin-Streptomycin (10,000 U/mL)</td>
			<td>ThermoFisher</td>
			<td>15140-122</td>
			<td>Complete islet media</td>
		</tr>
		<tr>
			<td>Qubit High Sensitivity dsDNA Kit</td>
			<td>Life Technologies</td>
			<td>Q32854</td>
			<td>for QC</td>
		</tr>
		<tr>
			<td>Solution 18</td>
			<td>Chemometec</td>
			<td>103011-420</td>
			<td>Cell counting assay reagent</td>
		</tr>
		<tr>
			<td>SPRISelect Reagent</td>
			<td>Fisher Scientific</td>
			<td>B23318</td>
			<td>Purification beads</td>
		</tr>
		<tr>
			<td>Tissue Culture Dishes (10 cm)</td>
			<td>VWR</td>
			<td>10861-594</td>
			<td>for islet culture</td>
		</tr>
		<tr>
			<td>TrypLE Express</td>
			<td>Life Technologies</td>
			<td>12604-013</td>
			<td>Cell dissociation solution</td>
		</tr>
		<tr>
			<td>Zymo DNA Clean &#38; Concentrator-5, 50 reactions</td>
			<td>VWR</td>
			<td>77001-152</td>
			<td>Library clean up columns</td>
		</tr>
	</tbody>
</table>

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.

The single-cell RNA sequencing workflow consists of three steps: dissociating intact human islets into single cell suspension, capturing single cells using a droplet-based technology, and analyzing RNA-seq data (Figure 1). Firstly, the acquired human islets were incubated overnight. The intact islets were examined under the microscope (Figure 2A). The integrity of dissociated islet cells has been validated using RNA fluorescence ...

Watch this Scientific Journal Video about Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets at JoVE.com

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

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 ...

single-cell-rna-sequencing-and-analysis-of-human-pancreatic-islets

Universidad de Cartagena UDEC

Research

JoVE Journal

Genetics

16.2K Views.  Regeneron Pharmaceuticals, Inc. 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.

Video: Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.

Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

Genome Editing and Directed Differentiation of hPSCs for Interrogating Lineage Determinants in Human Pancreatic Development

Interrogating gene function in self-renewing or differentiating human pluripotent stem cells (hPSCs) offers a valuable platform towards understanding human development and dissecting disease mechanisms in a dish. To capitalize on this potential application requires efficient genome-editing tools to generate hPSC mutants in disease-associated genes, as well as in vitro hPSC differentiation protocols to produce disease-relevant cell types that closely recapitulate their in vivo counterparts. An efficient genome-editing platform for hPSCs named iCRISPR has been developed through the TALEN-mediated targeting of a Cas9 expression cassette in the AAVS1 locus. Here, the protocols for the generation of inducible Cas9 hPSC lines using cells cultured in a chemically defined medium and a feeder-free condition are described. Detailed procedures for using the iCRISPR system for gene knockout or precise genetic alterations in hPSCs, either through non-homologous end joining (NHEJ) or via precise nucleotide alterations using a homology-directed repair (HDR) template, respectively, are included. These technical procedures include descriptions of the design, production, and transfection of CRISPR guide RNAs (gRNAs); the measurement of the CRISPR mutation rate by T7E1 or RFLP assays; and the establishment and validation of clonal mutant lines. Finally, we chronicle procedures for hPSC differentiation into glucose-responsive pancreatic &#946;-like cells by mimicking in vivo pancreatic embryonic development. Combining iCRISPR technology with directed hPSC differentiation enables the systematic examination of gene function to further our understanding of pancreatic development and diabetes disease mechanisms.

Protocols to generate hPSC mutant lines using the iCRISPR platform and to differentiate hPSCs into glucose-responsive &#946;-like cells are described. Combining genome editing technology with hPSC-directed differentiation provides a powerful platform for the systematic analysis of the role of lineage determinants in human development and disease progression.

Interrogating gene function in self-renewing or differentiating human pluripotent stem cells (hPSCs) offers a valuable platform towards understanding ...

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged as a powerful tool for gene-expression analysis and transcriptome mapping. However, handling RNA-Seq datasets requires sophisticated computational expertise and poses inherent challenges for biology researchers. This bottleneck has been mitigated by the open access Galaxy project that allows users without bioinformatics skills to analyze RNA-Seq data, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID), a Gene Ontology (GO) term analysis suite that helps derive biological meaning from large data sets. However, for first-time users and bioinformatics' amateurs, self-learning and familiarization with these platforms can be time-consuming and daunting. We describe a straightforward workflow that will help C. elegans researchers to isolate worm RNA, conduct an RNA-Seq experiment and analyze the data using Galaxy and DAVID platforms. This protocol provides stepwise instructions for using the various Galaxy modules for accessing raw NGS data, quality-control checks, alignment, and differential gene expression analysis, guiding the user with parameters at every step to generate a gene list that can be screened for enrichment of gene classes or biological processes using DAVID. Overall, we anticipate that this article will provide information to C. elegans researchers undertaking RNA-Seq experiments for the first time as well as frequent users running a small number of samples.

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged ...

Generation of Native Chromatin Immunoprecipitation Sequencing Libraries for Nucleosome Density Analysis

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution based analysis methodology (nucleosome density ChIP-seq; ndChIP-seq) that enables the generation of combined measurements of micrococcal nuclease (MNase) accessibility with histone modification genome-wide. Nucleosome position and local density, and the posttranslational modification of their histone subunits, act in concert to regulate local transcription states. Combinatorial measurements of nucleosome accessibility with histone modification generated by ndChIP-seq allows for the simultaneous interrogation of these features. The ndChIP-seq methodology is applicable to small numbers of primary cells inaccessible to cross-linking based ChIP-seq protocols. Taken together, ndChIP-seq enables the measurement of histone modification in combination with local nucleosome density to obtain new insights into shared mechanisms that regulate RNA transcription within rare primary cell populations.

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) methodology for the generation of sequence datasets suitable for a nucleosome density ChIP-seq analytical framework integrating micrococcal nuclease (MNase) accessibility with histone modification measurements.

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution ...

Semi-quantitative Detection of RNA-dependent RNA Polymerase Activity of Human Telomerase Reverse Transcriptase Protein

Human telomerase reverse transcriptase (TERT) is the catalytic subunit of telomerase, and it elongates telomere through RNA-dependent DNA polymerase activity. Although TERT is named as a reverse transcriptase, structural and phylogenetic analyses of TERT demonstrate that TERT is a member of right-handed polymerases, and relates to viral RNA-dependent RNA polymerases (RdRPs) as well as viral reverse transcriptase. We firstly identified RdRP activity of human TERT that generates complementary RNA stand to a template non-coding RNA and contributes to RNA silencing in cancer cells. To analyze this non-canonical enzymatic activity, we developed RdRP assay with recombinant TERT in 2009, thereafter established in vitro RdRP assay for endogenous TERT. In this manuscript, we describe the latter method. Briefly, TERT immune complexes are isolated from cells, and incubated with template RNA and rNTPs including radioactive rNTP for RdRP reaction. To eliminate single-stranded RNA, reaction products are treated with RNase I, and the final products are analyzed with polyacrylamide gel electrophoresis. Radiolabeled RdRP products can be detected by autoradiography after overnight exposure.

Human telomerase reverse transcriptase (TERT) synthesizes not only telomeric DNA but also double-stranded RNA through RNA-dependent RNA polymerase activity. Here, we describe a newly established assay to detect RNA-dependent RNA polymerase activity of endogenous TERT.

Human telomerase reverse transcriptase (TERT) is the catalytic subunit of telomerase, and it elongates telomere through RNA-dependent DNA polymerase ...

Retrospective MicroRNA Sequencing: Complementary DNA Library Preparation Protocol Using Formalin-fixed Paraffin-embedded RNA Specimens

&#8211;Archived, clinically classified formalin-fixed paraffin-embedded (FFPE) tissues can provide nucleic acids for retrospective molecular studies of cancer development. By using non-invasive or pre-malignant lesions from patients who later develop invasive disease, gene expression analyses may help identify early molecular alterations that predispose to cancer risk. It has been well described that nucleic acids recovered from FFPE tissues have undergone severe physical damage and chemical modifications, which make their analysis difficult and generally requires adapted assays. MicroRNAs (miRNAs), however, which represent a small class of RNA molecules spanning only up to ~18&#8211;24 nucleotides, have been shown to withstand long-term storage and have been successfully analyzed in FFPE samples. Here we present a 3&#39; barcoded complementary DNA (cDNA) library preparation protocol specifically optimized for the analysis of small RNAs extracted from archived tissues, which was recently demonstrated to be robust and highly reproducible when using archived clinical specimens stored for up to 35 years. This library preparation is well adapted to the multiplex analysis of compromised/degraded material where RNA samples (up to 18) are ligated with individual 3&#39; barcoded adapters and then pooled together for subsequent enzymatic and biochemical preparations prior to analysis. All purifications are performed by polyacrylamide gel electrophoresis (PAGE), which allows size-specific selections and enrichments of barcoded small RNA species. This cDNA library preparation is well adapted to minute RNA inputs, as a pilot polymerase chain reaction (PCR) allows determination of a specific amplification cycle to produce optimal amounts of material for next-generation sequencing (NGS). This approach was optimized for the use of degraded FFPE RNA from specimens archived for up to 35 years and provides highly reproducible NGS data.

Formalin-fixed paraffin-embedded specimens represent a valuable source of molecular biomarkers of human diseases. Here we present a laboratory-based cDNA library preparation protocol, initially designed with fresh frozen RNA, and optimized for the analysis of archived microRNAs from tissues stored up to 35 years.

&#8211;Archived, clinically classified formalin-fixed paraffin-embedded (FFPE) tissues can provide nucleic acids for retrospective molecular studies of ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

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 ...

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.

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, ...

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

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Chapters in this video

Detection of Copy Number Alterations Using Single Cell Sequencing

Genome Editing and Directed Differentiation of hPSCs for Interrogating Lineage Determinants in Human Pancreatic Development

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Generation of Native Chromatin Immunoprecipitation Sequencing Libraries for Nucleosome Density Analysis

Semi-quantitative Detection of RNA-dependent RNA Polymerase Activity of Human Telomerase Reverse Transcriptase Protein

Retrospective MicroRNA Sequencing: Complementary DNA Library Preparation Protocol Using Formalin-fixed Paraffin-embedded RNA Specimens

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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

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

Sequencing of mRNA from Whole Blood using Nanopore Sequencing