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Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

doi: 10.3791/59866 Published: July 18, 2019


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 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.


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: α-cells produce glucagon, β-cells insulin, δ-cells somatostatin, PP cells pancreatic polypeptide, and ε-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 α- and β-cells13,14,15,16,17,18. Moreover, Dorrell et al. used an antibody-based FACS sorting approach to classify β-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 ε-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 α-, β-, δ-, PP, ε-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.

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1. Human islet dissociation

  1. 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.
    1. 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.
    2. Place the islets in a bottle and immerse it completely in the islet medium. Get it to the laboratory by overnight shipment.
    3. Obtain the islet equivalent quantity (IEQ) of the shipped islets from the islet supplier.
  2. Recover islets from the shipment on the day of islet arrival. Perform this step using a hood to minimize the chance of contamination.
    1. Cool the complete islet media (CMRL-1066, 10% (v/v) FBS, 1x Pen-Strep, 2 mM glutamine) in a refrigerator.
    2. Transfer the islets from the bottle to a 50 mL conical tube.
    3. 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.
    4. 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.
    5. Resuspend the islets with pre-cooled complete islet media. Based on the IEQ provided by the supplier, add 12 mL media per 5000 IEQ.
    6. Pour the islets in the media on a 10 cm non-treated tissue culture dish. Incubate overnight in a tissue culture incubator at 37 °C with 5% CO2 in atmospheric air.
  3. Dissociate islets as shown below. Perform this step following overnight incubation.
    1. Pre-warm complete islet media and cell dissociation solution.
    2. Prepare 1x PBS containing 0.04% BSA at room temperature.
    3. 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.
    4. Collect the islets by centrifugation at 200 x g for 2 min. Gently aspirate the supernatant without disturbing the pellet on the bottom.
    5. Add 1.0 mL pre-warmed cell dissociation solution and disrupt the pellet by pipetting gently up and down. Incubate the islets at 37 °C for 9-11 min. Every 3 min pipette up and down slowly for 10 s to dissociate the cells into single cells.
    6. Once islet cells are well dissociated and the solution becomes cloudy, add 9 mL complete islet media and filter through a 30 µm cell strainer into a new 15 mL conical tube.
    7. Wash the tube and cell strainer with 2 mL complete islet media to collect the remaining islets and add to the same tube.
    8. Collect cells by centrifugation at 400 x g for 5 min.
    9. Gently aspirate media and resuspend the cell pellet in 5 mL 1x PBS containing 0.04% BSA (PBS-BSA).
    10. Filter through a new 30 µm cell strainer into a 15 mL conical tube and centrifuge at 400 x g for 5 min to collect cells.
    11. Aspirate the supernatant and resuspend the cell pellet in 200-300 µL 1x PBS-BSA solution.
    12. Measure the cell concentration and adjust volume to a final concentration of 400-500 cells/µL.

2. Single cell suspension quality control

  1. Determine the cell concentration using a fluorescence-based automated cell counter34.
    1. Mix 10 μL cells with 0.5 μL AO/DAPI. Pipette mix thoroughly. Load 10.5 μL onto the slide and run the cell count assay to determine count and viability.
    2. Dilute and/or filter the cell suspension as necessary based on the cell count.

3. Single cell partitioning using a microfluidic chip. Follow protocol from microfluidic chip manufacturer35.

  1. Bring 3' gel beads and reverse transcription (RT) reagents to room temperature (>30 min). Reconstitute the RT primer in TE buffer if needed.
  2. Prepare RT master mix in a low bind tube as outlined in Table 1.
  3. 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 μL.
  4. For each sample to be partitioned, add 33.8-X μL nuclease-free water into 0.2 mL PCR strip tube. Then, add 66.2 μL 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.
  5. 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.
  6. If running less than 8 samples, use 50% glycerol to fill unused channels in the following order:
    1. Add 90 µL of 50% glycerol into the wells in row 1 for all unused channels.
    2. Add 40 µL of 50% glycerol into the wells in row 2 for all unused channels.
    3. Add 270 µL of 50% glycerol into the wells in row 3 for all unused channels.
  7. 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.
  8. Add X μL of cells into the prepared strip tubes. Pipette to mix 5 times. Without discarding the pipette tips, transfer 90 μL of cell mixture to row 1 of the chip.
  9. Wait for 30 s, then load 40 μL of gel beads to row 2. Pipette very slowly for this step. Dispense 270 μL of partitioning oil to the wells of row 3.
  10. 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.
  11. Immediately remove the assembled chip holder upon run completion.
  12. Remove the chip gasket from the holder, open the chip case at a 45° angle, and remove 100 μL of the emulsion from the chip into a blue plastic 96-well plate.

4. Single cell cDNA amplification. Follow protocol from microfluidic chip manufacturer35.

  1. Reverse transcription.
    NOTE: Perform this step under a clean PCR-only hood to prevent microbial and other contamination of the unamplified cDNA.
    1. Seal the 96-well blue plate with a foil seal on a heated plate sealer.
    2. Run the reverse transcription reaction in a thermal cycler as follows: 53 °C for 45 min à 85 °C for 5 min à 4 °C hold.
      NOTE: This is a safe stopping point. Samples can hold at 4 °C for up to 72 h.
  2. Post-RT purification
    1. 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 °C. Bring all other reagents to room temperature and vortex.
    2. Prepare the buffers as shown in Tables 2 and Tables 3.
  3. Chemically break the emulsion and purify.
    1. To do so, gently remove the foil seal from the plate.
    2. Dispense 125 μL 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.
    3. Remove 125 μL of the pink layer from the bottom of the strip tube without disturbing the clear layer. It is normal for a small volume (~15 μL) of the pink layer to remain in the tube.
    4. Add 200 μL of cleanup mix from Table 2 to the strip tube and incubate at room temperature for 10 min.
    5. 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.
    6. Remove the strip tube from the magnet and add 35.5 μL of elution solution from Table 3 to the beads. Pipette to resuspend the beads in the solution. Incubate for 2 min at room temperature.
    7. 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.
  4. Amplify the cDNA.
    1. Prepare amplification master mix in Table 4, below.
    2. Add 65 μL of cDNA Amplification Master Mix to each sample. Place the strip tube in a thermal cycler and run the following program: 98 °C 3 min à 15 cycles of [98 °C 15 s à 67 °C 20 s à 72 °C 1 min] à 72 °C 5 min à 4 °C hold
      NOTE: This is a safe stopping point. Samples can hold at 4°C for up to 72 h.
    3. Purify the amplified cDNA with 0.6x nucleic acid size selection magnetic beads. Wash twice with 80% ethanol and elute with 40.5 μL.
    4. 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 °C for up to 72 h or at -20°C indefinitely.

5. Sequencing library construction

  1. Tagmentation and clean-up of cDNA38.
    1. Normalize cDNA to 50 ng in 20 μL of the total volume. Exact quantitation is critical in this step.
    2. Make the tagmentation mix in Table 5 and aliquot 30 μL to each 20 μL cDNA sample on ice. Put the samples in the thermal cycler and run the tagmentation protocol: 55 °C 5 min à 10 °C hold.
    3. Perform the clean-up of tagmented cDNA using columns38. Add 180 μL DNA binding buffer to each sample. Transfer 230 μL to a spin column.
    4. Centrifuge at 1300 x g for 2 min and discard the flow-through.
    5. Wash twice with 300 μL DNA wash buffer. Centrifuge an additional 2 min at 1300 x g to ensure ethanol removal.
    6. Elute purified tagmented cDNA by adding 31 μL of elution buffer to the column and incubate at room temperature for 2 min.
    7. Centrifuge for 2 min at 1300 x g to recover the purified product.
  2. Sample index PCR.
    1. Choose barcodes that do not overlap during a multiplexed sequencing run.
    2. Make a Sample Index PCR master mix as shown in Table 6.
    3. Add 60 μL of Sample Index PCR master mix to 30 μL of the purified sample.
    4. Add 10 μL of a 20 μM, 4-oligo sample index to each sample (record index used). The total reaction volume is now 100 μL.
    5. Place in a thermal cycler with the lid set to 105 °C. Run the following program: 98 °C 45 s à 12-14 cycles of [98 °C 20 s à 54 °C 30 s à 72 °C 20 s] à 72 °C 1 min à 4 °C hold.
      NOTE: This is a safe stopping point. Samples can hold at 4 °C for up to 72 h.
  3. Purify libraries with double bead clean up.
    1. Add 100 μL of nucleic acid size selection magnetic beads to the sample and mix thoroughly with a pipette. Incubate at room temperature for 5 min.
    2. Transfer to a magnet and let it stand until the solution clears. Remove and discard the supernatant.
    3. Wash twice with 200 μL of 80% ethanol.
    4. Dry the beads on the magnet for 2 min. Remove from the magnet and add 50.5 μL of EB Buffer to the bead pellet. Pipette to re-suspend the beads in the buffer.
    5. Incubate at room temperature for 2 min. Transfer to a magnet and let stand for 2 min. Transfer 50 μL of eluted sample to a clean strip tube.
    6. Add 40 μL 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.
    7. Wash twice with 125 μL of 80% ethanol.
    8. Dry the beads on the magnet for 2 min. Remove from the magnet and add 60.5 μL of EB Buffer to the bead pellet. Pipette to re-suspend the beads in the buffer.
    9. Incubate at room temperature for 2 min. Transfer to a magnet and let it stand for 2 min. Transfer 50 μL of eluted sample to a clean strip tube. This is the final library.
    10. Hold samples at 4 °C for up to 72 h or at -20 °C indefinitely. Note that this is a safe stopping point.
  4. 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.

6. Library sequencing

  1. Normalize each sample to be sequenced to 2 ng/μL and pool 3 μL of each normalized sample together.
  2. Measure the pool concentration with fluorescence-based DNA quantitation assay37.
  3. Dilute the pool to 0.25 ng/μL.
  4. Denature the pool as follows: 12 μL of diluted pooled sample (0.25 ng/μL) + 1 μL DNA control (1 nM), 2 μL EB Buffer + 5 μL NaOH (0.4N). Let this incubate for 5 min, then add 10 μL of 200 mM Tris pH 8.0.
  5. Load 4.05 μL into 1345.95 μL HT1. Load 1.3 mL into the sequencer’s cartridge and run according to manufacturer’s guidelines39 using a sequencing recipe with 26 cycles (Read 1) + 8 cycles (i7 Index) + 0 cycles (i5 Index) + 55 cycles (Read 2).

7. Read alignment (Supplemental File 1)

  1. 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.
  2. Alignment quality control.
    1. Generate alignment metrics and check Q30 bases, valid barcode fraction, cell-associated read fraction, mapped read fraction and reads detected in each cell.
    2. Examine the barcode rank plot to make sure the separation of the cell-associated barcodes and the background.

8. Data analysis (Supplemental File 2)

  1. Cell quality control and preprocess.
    1. Exclude cells with < 500 detected genes, < 3000 total number of unique molecular identifier (UMI), > 0.2 viability score as previously described32. Adjust the cutoffs according to tissue and cell types.
    2. Remove doublets.
      1. 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.
      2. Remove cells that express more than one hormone gene, i.e., with two or more hormone genes in the high-expression mode.
    3. Normalize gene expression by the total UMI and multiply by the scale factor of 10,000 at cell level using R package Seurat41.
    4. Remove genes detected in less than 3 cells.
    5. Detect variable genes using average expression and dispersion of all cells. Adjust the cutoffs according to the tissue and cell types.
  2. 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.

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Representative Results

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 in situ hybridization (RNA-FISH). As shown in Figure 2B, dissociated α- and β -cells were visualized using GCG and INS mRNA probes, respectively.

Cell count and viability need to be determined before the single-cell capture step. Cells with low viability or high debris are not suitable for further processing. A good cell concentration usually ranges from 400 to 500 cells/µL. Approximately 6000 cells were loaded to the microfluidic chip in the single-cell partitioning step, and 100 μL of gel beads in emulsion were removed from the chip. Figure 3A exemplifies a successful example of emulsion following the partitioning step. The liquid in each pipette tips is uniform pale cloudy with minimal partitioning oil separated from the gel beads. In contrast, Figure 3B shows a poor-quality emulsion with clear phase separation between the gel beads and oil. This could be due to a clog during the chip run.

Following single cell partitioning, cDNA amplification was performed. Figure 4A illustrates a representative fragment size distribution after cDNA amplification. The typical peak for a good quality cDNA sample resided near 1000-2000 bp. Interestingly, a spike near 600 bp was specific to islet cDNA. The fragment size distribution for the RNA-seq libraries was between 300 and 500 bp (Figure 4B).

After sequencing, we employed a set of read alignment metrics to evaluate single-cell RNA-seq data quality (Table 7). The first three metrics well summarized single-cell sequencing library quality. On an average, 92% of reads were derived from intact cells and 72% of reads were mapped to exons. Out of all exon reads captured in droplets, 90% of them were produced by intact cells and the rest was likely ambient RNAs in cell-free droplets. These alignment metrics suggest good data quality. The ratio between exon reads and UMI was an empirical measurement to evaluate sequencing saturation and usually, 10:1 ratio was a good indicator. Additionally, the number of detected genes (UMI > 0) was a useful feature to characterize different cell types. For human islet cells, the number of detected genes is about 1,900 in each cell.

We sequenced a total of 20,811 islet cells from 12 non-diabetic donors. Expression of more than one hormone was detected in about 6% of the cells. These multi-hormonal cells are most likely doublets because our previous work showed that less than < 0.1% of single islet cells co-expressed more than one endocrine hormone33. We removed all the identified multi-hormonal cells. It is also important to exclude low-quality cells based on total UMI, detected genes, and cell viability33. After these quality control steps, 19,174 remained for further analysis. The clustering analysis revealed 12 cell types: α-, β-, δ-, PP, ε-cells, acinar, ductal, quiescent stellate, activated stellate, endothelial, macrophage, and mast cells (Figure 5). As expected, endocrine cells were the majority (Table 8). The top enriched genes in α-cells (i.e., GCG, TTR, CRYBA2, TM4SF4, TMEM176B) and β-cells (i.e., IAPP, INS, HADH, DLK1, RBP4) are consistent with other studies13,15,16,17,18,20,21,22,29,30,31,33. Interestingly, both α- and β-cells consisted of several subpopulations. Three β-cell subpopulations, Beta sub1, 2, and 3, were similar with small numbers of subpopulation-enriched genes (18 in Beta sub1, 33 in Beta sub 2, and 18 in Beta sub 3). The fourth subpopulation had 488 enriched genes. The small α-cell subpopulation (Alpha sub3) comprised proliferating cells, characterized by enriched expression of MKI67, CDK1, and TOP2A.

Figure 1
Figure 1: Schematic diagram of single-cell RNA sequencing workflow. Please click here to view a larger version of this figure.

Figure 2
Figure 2: Representative images of intact and dissociated human islets. (A) An image of islets taken after overnight incubation. (B) Dissociated islet cells visualized by RNA-FISH staining for INS (white) and GCG (red). Please click here to view a larger version of this figure.

Figure 3
Figure 3: Examination of the quality of single-cell emulsion prior to reverse transcription. (A) A single-cell emulsion of good quality. The liquid in each pipette tip was homogeneously cloudy. (B) A single-cell emulsion of poor quality. The liquid in the pipette tip was not homogeneous and showed separation between oil and the gel beads. Please click here to view a larger version of this figure.

Figure 4
Figure 4: Examination of the quality of single-cell cDNA and library. (A) A representative cDNA traces. This cDNA was of good quality and yield, with the main peak for the sample occurring near 1000-2000 bp. The spike in the trace around 600 bp was typical and distinctive of islet cDNA. (B) A representative final sequencing library trace. This library was of good quality and yield, with the main peak occurring between 300-500 bp. Please click here to view a larger version of this figure.

Figure 5
Figure 5: Cell types and subpopulations identified in single-cell RNA sequencing of human pancreatic islets. Cells were clustered by distinctive cell types in the space of t-distributed stochastic neighbor embedding (tSNE) dimensions. The analysis also revealed three subpopulations in α-cells and four in β-cells. Please click here to view a larger version of this figure.

Reagent Name Vol. to Use (μL) per reaction
RT Reagent Mix 50
RT Primer 3.8
Additive A 2.4
RT Enzyme Mix 10
Total 66.2

Table 1: Reverse transcript mix.

Reagent Name Volume to Use (uL) per reaction
Nuclease-free water 9
Buffer Sample Clean Up 1 182
Dynabeads MyOne Silane 4
Additive A 5
Total 200

Table 2: Cleanup mix.

Reagent Name Volume to Use (uL) per reaction
Buffer EB 98
10% Tween 20 1
Additive A 1
Total 100

Table 3: Elution solution.

Reagent Name Volume to Use (uL) per reaction
Nuclease-free water 8
Amplification Master Mix 50
cDNA Additive 5
cDNA Primer Mix 2
Total 65

Table 4: cDNA amplification mix.

Reagent Name Volume to Use (μL) per reaction
Tagmentation Enzyme 5
Tagmentation Buffer 25
Total 30

Table 5: Tagmentation mix.

Reagent Name Volume to Use (μL) per reaction
Nuclease-free water 8
Amplification Master Mix 50
SI-PCR Primer 2
Total 60

Table 6: Sample index PCR master mix.

Sample ID % Reads with Valid Cell Barcodes % Exon Reads in Captured Cells among Total Cells % Exon Reads among Total Reads Mean Exon Reads per Cell Median UMI per Cell Median Genes per Cell
Sample-1 92% 93% 76% 142,015 10,310 1,747
Sample-2 92% 91% 74% 151,395 11,350 1,754
Sample-3 94% 92% 75% 120,538 19,604 2,180
Sample-4 95% 93% 67% 160,657 11,870 2,111
Sample-5 94% 92% 62% 177,809 13,821 2,288
Sample-6 95% 89% 67% 138,208 8,235 1,296
Sample-7 94% 89% 72% 147,484 13,606 2,272
Sample-8 94% 91% 69% 159,793 9,505 1,865
Sample-9 95% 92% 72% 168,436 12,794 2,389
Sample-10 83% 83% 74% 88,067 13,323 1,805
Sample-11 82% 88% 77% 67,752 9,295 1,278
Sample-12 91% 85% 74% 194,781 14,877 1,746

Table 7: Read alignment metrics.

Cell type Number of cells Ave. cells per donor (standard deviation)
Alpha 6546 546 (258)
Beta 7361 613 (252)
Delta 922 77 (37)
PP 545 45 (25)
Epsilon 11 1 (1)
Acinar 836 70 (71)
Ductal 1313 109 (95)
Quiescent stellate 225 19 (14)
Activated stellate 890 74 (58)
Endothelial 408 34 (21)
Macrophage 80 7 (6)
Schwann 37 3 (3)

Table 8: Cell type composition. Total number of cells for each cell type and average cells for each donor in each cell type.

Supplemental File 1: Commands used for sequence alignment. Please click here to download this file.

Supplemental File 2: R scripts to perform cell quality control, cell clustering, and to identify cell-type enriched genes. Please click here to download this file.

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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 protocol for high-quality outcomes. Caution needs to be taken when dissociating human islets. It is important to not over digest the islets. Single-cell partitioning is another key step for a successful single-cell experiment. We demonstrated examples of good- and poor-quality emulsions in Figure 3. A clear emulsion is usually an indicative of inadequate number of cells being collected in the partitioning step.

The access to isolated primary human islets is a rate-limiting step to generate large-scale human islet single-cell transcriptomes. Isolated islets from individual cadaver donors are usually processed at different times, thus potential sample-dependent batch effects should be carefully examined during data analysis. Integrative analysis can be used to identify common cell types and subpopulations across individual batches41. The batch effect can also be adjusted by batch-corrected expression quantification42. Another challenge to analyze single-cell RNA-seq data is to identify doublets. In the data pre-processing, we took measures to remove endocrine doublets by identifying cells expressing multiple hormone genes (GCG, INS, SST, PPY, and GHRL). Identification of doublets formed by two different cell types is a relatively easy task due to the extremely high expression of endocrine hormones. The real challenge is to identify within-cell-type doublets, e.g., doublets by two α-cells. Because higher UMI and higher number of detected genes are suggestive of potential doublets, one solution is to remove outliers with a high number of genes and UMI during the cell QC step. Additionally, tools to detect doublets are available43,44,45.

A major limitation of single-cell RNA sequencing is low sensitivity. Using spike-in External RNA Controls Consortium (ERCC) RNAs, we estimated that only 10% of all expressed genes were detected using the current protocol and that detected ones were biased toward high abundance genes46. Pancreatic endocrine cells express extremely high-level of hormone genes (i.e., GCG, INS, SST, and PPY). As a result, the mRNAs of these genes have the risk to become ambient RNA. Such background noises cannot be entirely avoided. However, this step-by-step protocol will help researchers minimize undesired experimental noises. The current protocol is designed for freshly isolated tissues. Other technologies, such as single-nucleus RNA sequencing47,48, are available for RNA-seq of fresh, frozen, or lightly fixed tissues. Additionally, a recently developed cell hashing technology49 can be considered as an advanced microfluidic single-cell protocol allowing sample multiplexing.

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




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



  1. Gutierrez, G. D., Gromada, J., Sussel, L. Heterogeneity of the Pancreatic Beta Cell. Frontiers in Genetics. 8, (22), (2017).
  2. Eizirik, D. L., et al. The human pancreatic islet transcriptome: expression of candidate genes for type 1 diabetes and the impact of pro-inflammatory cytokines. PLoS Genetics. 8, (3), e1002552 (2012).
  3. Fadista, J., et al. Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism. Proceedings of the National Academy of Sciences. 111, (38), 13924-13929 (2014).
  4. Gunton, J. E., et al. Loss of ARNT/HIF1beta mediates altered gene expression and pancreatic-islet dysfunction in human type 2 diabetes. Cell. 122, (3), 337-349 (2005).
  5. Kutlu, B., et al. Detailed transcriptome atlas of the pancreatic beta cell. BMC Medical Genomics. 2, (3), (2009).
  6. Maffei, A., et al. Identification of tissue-restricted transcripts in human islets. Endocrinology. 145, (10), 4513-4521 (2004).
  7. Moran, I., et al. Human beta cell transcriptome analysis uncovers lncRNAs that are tissue-specific, dynamically regulated, and abnormally expressed in type 2 diabetes. Cell Metabolism. 16, (4), 435-448 (2012).
  8. van de Bunt, M., et al. Transcript Expression Data from Human Islets Links Regulatory Signals from Genome-Wide Association Studies for Type 2 Diabetes and Glycemic Traits to Their Downstream Effectors. PLoS Genetics. 11, (12), e1005694 (2015).
  9. Ebrahimi, A., et al. Evidence of stress in beta cells obtained with laser capture microdissection from pancreases of brain dead donors. Islets. 9, (2), 19-29 (2017).
  10. Marselli, L., Sgroi, D. C., Bonner-Weir, S., Weir, G. C. Laser capture microdissection of human pancreatic beta-cells and RNA preparation for gene expression profiling. Methods in Molecular Biology. 560, 87-98 (2009).
  11. Marselli, L., et al. Gene expression profiles of Beta-cell enriched tissue obtained by laser capture microdissection from subjects with type 2 diabetes. PLoS One. 5, (7), e11499 (2010).
  12. Sturm, D., et al. Improved protocol for laser microdissection of human pancreatic islets from surgical specimens. Journal of Visualized Experiments. (71), (2013).
  13. Ackermann, A. M., Wang, Z., Schug, J., Naji, A., Kaestner, K. H. Integration of ATAC-seq and RNA-seq identifies human alpha cell and beta cell signature genes. Molecular Metabolism. 5, (3), 233-244 (2016).
  14. Benner, C., et al. The transcriptional landscape of mouse beta cells compared to human beta cells reveals notable species differences in long non-coding RNA and protein-coding gene expression. BMC Genomics. 15, (620), (2014).
  15. Blodgett, D. M., et al. Novel Observations From Next-Generation RNA Sequencing of Highly Purified Human Adult and Fetal Islet Cell Subsets. Diabetes. 64, (9), 3172-3181 (2015).
  16. Bramswig, N. C., et al. Epigenomic plasticity enables human pancreatic alpha to beta cell reprogramming. The Journal of Clinical Investigation. 123, (3), 1275-1284 (2013).
  17. Dorrell, C., et al. Transcriptomes of the major human pancreatic cell types. Diabetologia. 54, (11), 2832-2844 (2011).
  18. Nica, A. C., et al. Cell-type, allelic, and genetic signatures in the human pancreatic beta cell transcriptome. Genome Research. 23, (9), 1554-1562 (2013).
  19. Dorrell, C., et al. Human islets contain four distinct subtypes of beta cells. Nature Communications. 7, 11756 (2016).
  20. Li, J., et al. Single-cell transcriptomes reveal characteristic features of human pancreatic islet cell types. EMBO Reports. 17, (2), 178-187 (2016).
  21. Muraro, M. J., et al. A Single-Cell Transcriptome Atlas of the Human Pancreas. Cell Systems. 3, (4), 385-394 (2016).
  22. Segerstolpe, A., et al. Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes. Cell Metabolism. 24, (4), 593-607 (2016).
  23. Bose, S., et al. Scalable microfluidics for single-cell RNA printing and sequencing. Genome Biology. 16, (120), (2015).
  24. Fan, H. C., Fu, G. K., Fodor, S. P. Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science. 347, (6222), 1258367 (2015).
  25. Klein, A. M., et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 161, (5), 1187-1201 (2015).
  26. Macosko, E. Z., et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 161, (5), 1202-1214 (2015).
  27. Zheng, G. X., et al. Massively parallel digital transcriptional profiling of single cells. Nature Communications. 8, (14049), (2017).
  28. Dominguez Gutierrez, G., et al. Signature of the Human Pancreatic epsilon Cell. Endocrinology. 159, (12), 4023-4032 (2018).
  29. Baron, M., et al. A Single-Cell Transcriptomic Map of the Human and Mouse. Pancreas Reveals Inter- and Intra-cell Population Structure. Cell Systems. 3, (4), 346-360 (2016).
  30. Lawlor, N., et al. Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome Research. 27, (2), 208-222 (2017).
  31. Wang, Y. J., et al. Single-Cell Transcriptomics of the Human Endocrine Pancreas. Diabetes. 65, (10), 3028-3038 (2016).
  32. Xin, Y., et al. Pseudotime Ordering of Single Human beta-Cells Reveals States of Insulin Production and Unfolded Protein Response. Diabetes. 67, (9), 1783-1794 (2018).
  33. Xin, Y., et al. RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes. Cell Metabolism. 24, (4), 608-615 (2016).
  34. Chemometec. Nucleocounter NC-250: Cell count and viability assay. (2015).
  35. 10X Genomics. 10X Genomics: Chromium Single Cell 3’ Reagents Kits v2: User Guide. 10X Genomics. (2017).
  36. Agilent Technologies. Agilent Bioanalyzer: High Sensitivity DNA Kit Guide. Agilent Technologies. (2013).
  37. Thermo Fischer Scientific. Qubit: dsDNA High Sensitivity Assay Kit. Thermo Fischer Scientific. (2015).
  38. Illumina. Illumina Nextera DNA Library Prep Reference Guide. Illumina. (2016).
  39. Illumina. llumina NextSeq 500 System Guide. Illumina. (2018).
  40. Scrucca, L., Fop, M., Murphy, T. B., Raftery, A. E. mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. R J. 8, (1), 289-317 (2016).
  41. Butler, A., Hoffman, P., Smibert, P., Papalexi, E., Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology. 36, (5), 411-420 (2018).
  42. Stuart, T., et al. Comprehensive integration of single cell data. Preprint at bioRixv. (2018).
  43. DePasquale, E. A. K., et al. DoubletDecon: Cell-State Aware Removal of Single-Cell RNA-Seq Doublets. bioRxiv. (2018).
  44. McGinnis, C. S., Murrow, L. M., Gartner, Z. J. DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. bioRxiv. (2018).
  45. Wolock, S. L., Lopez, R., Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. bioRxiv. (2018).
  46. Dominguez Gutierrez, G., et al. Gene Signature of Proliferating Human Pancreatic alpha Cells. Endocrinology. 159, (9), 3177-3186 (2018).
  47. Habib, N., et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nature Methods. 14, (10), 955-958 (2017).
  48. Habib, N., et al. Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science. 353, (6302), 925-928 (2016).
  49. Stoeckius, M., et al. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biology. 19, (1), 224 (2018).
Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets
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Xin, Y., Adler, C., Kim, J., Ding, Y., Ni, M., Wei, Y., Macdonald, L., Okamoto, H. Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets. J. Vis. Exp. (149), e59866, doi:10.3791/59866 (2019).More

Xin, Y., Adler, C., Kim, J., Ding, Y., Ni, M., Wei, Y., Macdonald, L., Okamoto, H. Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets. J. Vis. Exp. (149), e59866, doi:10.3791/59866 (2019).

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