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>

<ol><li>Gutierrez, G. D., Gromada, J., Sussel, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Heterogeneity+of+the+Pancreatic+Beta+Cell%5BTitle%5D)+AND+%22Frontiers+in+Genetics%22%5BJournal%5D)">Heterogeneity of the Pancreatic Beta Cell.</a> Frontiers in Genetics. 8 (22), (2017).</li>
<li>Eizirik, D. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+human+pancreatic+islet+transcriptome%3A+expression+of+candidate+genes+for+type+1+diabetes+and+the+impact+of+pro-inflammatory+cytokines%5BTitle%5D)+AND+%22PLoS+Genetics%22%5BJournal%5D)">The human pancreatic islet transcriptome: expression of candidate genes for type 1 diabetes and the impact of pro-inflammatory cytokines.</a> PLoS Genetics. 8 (3), e1002552(2012).</li>
<li>Fadista, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Global+genomic+and+transcriptomic+analysis+of+human+pancreatic+islets+reveals+novel+genes+influencing+glucose+metabolism%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences%22%5BJournal%5D)">Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism.</a> Proceedings of the National Academy of Sciences. 111 (38), 13924-13929 (2014).</li>
<li>Gunton, J. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Loss+of+ARNT%2FHIF1beta+mediates+altered+gene+expression+and+pancreatic-islet+dysfunction+in+human+type+2+diabetes%5BTitle%5D)+AND+%22Cell%22%5BJournal%5D)">Loss of ARNT/HIF1beta mediates altered gene expression and pancreatic-islet dysfunction in human type 2 diabetes.</a> Cell. 122 (3), 337-349 (2005).</li>
<li>Kutlu, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Detailed+transcriptome+atlas+of+the+pancreatic+beta+cell%5BTitle%5D)+AND+%22BMC+Medical+Genomics%22%5BJournal%5D)">Detailed transcriptome atlas of the pancreatic beta cell.</a> BMC Medical Genomics. 2 (3), (2009).</li>
<li>Maffei, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Identification+of+tissue-restricted+transcripts+in+human+islets%5BTitle%5D)+AND+%22Endocrinology%22%5BJournal%5D)">Identification of tissue-restricted transcripts in human islets.</a> Endocrinology. 145 (10), 4513-4521 (2004).</li>
<li>Moran, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Human+beta+cell+transcriptome+analysis+uncovers+lncRNAs+that+are+tissue-specific%2C+dynamically+regulated%2C+and+abnormally+expressed+in+type+2+diabetes%5BTitle%5D)+AND+%22Cell+Metabolism%22%5BJournal%5D)">Human beta cell transcriptome analysis uncovers lncRNAs that are tissue-specific, dynamically regulated, and abnormally expressed in type 2 diabetes.</a> Cell Metabolism. 16 (4), 435-448 (2012).</li>
<li>van de Bunt, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((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%5BTitle%5D)+AND+%22PLoS+Genetics%22%5BJournal%5D)">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.</a> PLoS Genetics. 11 (12), e1005694(2015).</li>
<li>Ebrahimi, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Evidence+of+stress+in+beta+cells+obtained+with+laser+capture+microdissection+from+pancreases+of+brain+dead+donors%5BTitle%5D)+AND+%22Islets%22%5BJournal%5D)">Evidence of stress in beta cells obtained with laser capture microdissection from pancreases of brain dead donors.</a> Islets. 9 (2), 19-29 (2017).</li>
<li>Marselli, L., Sgroi, D. C., Bonner-Weir, S., Weir, G. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Laser+capture+microdissection+of+human+pancreatic+beta-cells+and+RNA+preparation+for+gene+expression+profiling%5BTitle%5D)+AND+%22Methods+in+Molecular+Biology%22%5BJournal%5D)">Laser capture microdissection of human pancreatic beta-cells and RNA preparation for gene expression profiling.</a> Methods in Molecular Biology. 560, 87-98 (2009).</li>
<li>Marselli, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Gene+expression+profiles+of+Beta-cell+enriched+tissue+obtained+by+laser+capture+microdissection+from+subjects+with+type+2+diabetes%5BTitle%5D)+AND+%22PLoS+One%22%5BJournal%5D)">Gene expression profiles of Beta-cell enriched tissue obtained by laser capture microdissection from subjects with type 2 diabetes.</a> PLoS One. 5 (7), e11499(2010).</li>
<li>Sturm, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Improved+protocol+for+laser+microdissection+of+human+pancreatic+islets+from+surgical+specimens%5BTitle%5D)+AND+%22Journal+of+Visualized+Experiments%22%5BJournal%5D)">Improved protocol for laser microdissection of human pancreatic islets from surgical specimens.</a> Journal of Visualized Experiments. (71), (2013).</li>
<li>Ackermann, A. M., Wang, Z., Schug, J., Naji, A., Kaestner, K. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integration+of+ATAC-seq+and+RNA-seq+identifies+human+alpha+cell+and+beta+cell+signature+genes%5BTitle%5D)+AND+%22Molecular+Metabolism%22%5BJournal%5D)">Integration of ATAC-seq and RNA-seq identifies human alpha cell and beta cell signature genes.</a> Molecular Metabolism. 5 (3), 233-244 (2016).</li>
<li>Benner, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((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%5BTitle%5D)+AND+%22BMC+Genomics%22%5BJournal%5D)">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.</a> BMC Genomics. 15 (620), (2014).</li>
<li>Blodgett, D. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Novel+Observations+From+Next-Generation+RNA+Sequencing+of+Highly+Purified+Human+Adult+and+Fetal+Islet+Cell+Subsets%5BTitle%5D)+AND+%22Diabetes%22%5BJournal%5D)">Novel Observations From Next-Generation RNA Sequencing of Highly Purified Human Adult and Fetal Islet Cell Subsets.</a> Diabetes. 64 (9), 3172-3181 (2015).</li>
<li>Bramswig, N. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Epigenomic+plasticity+enables+human+pancreatic+alpha+to+beta+cell+reprogramming%5BTitle%5D)+AND+%22The+Journal+of+Clinical+Investigation%22%5BJournal%5D)">Epigenomic plasticity enables human pancreatic alpha to beta cell reprogramming.</a> The Journal of Clinical Investigation. 123 (3), 1275-1284 (2013).</li>
<li>Dorrell, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Transcriptomes+of+the+major+human+pancreatic+cell+types%5BTitle%5D)+AND+%22Diabetologia%22%5BJournal%5D)">Transcriptomes of the major human pancreatic cell types.</a> Diabetologia. 54 (11), 2832-2844 (2011).</li>
<li>Nica, A. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cell-type%2C+allelic%2C+and+genetic+signatures+in+the+human+pancreatic+beta+cell+transcriptome%5BTitle%5D)+AND+%22Genome+Research%22%5BJournal%5D)">Cell-type, allelic, and genetic signatures in the human pancreatic beta cell transcriptome.</a> Genome Research. 23 (9), 1554-1562 (2013).</li>
<li>Dorrell, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Human+islets+contain+four+distinct+subtypes+of+beta+cells%5BTitle%5D)+AND+%22Nature+Communications%22%5BJournal%5D)">Human islets contain four distinct subtypes of beta cells.</a> Nature Communications. 7, 11756(2016).</li>
<li>Li, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Single-cell+transcriptomes+reveal+characteristic+features+of+human+pancreatic+islet+cell+types%5BTitle%5D)+AND+%22EMBO+Reports%22%5BJournal%5D)">Single-cell transcriptomes reveal characteristic features of human pancreatic islet cell types.</a> EMBO Reports. 17 (2), 178-187 (2016).</li>
<li>Muraro, M. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+Single-Cell+Transcriptome+Atlas+of+the+Human+Pancreas%5BTitle%5D)+AND+%22Cell+Systems%22%5BJournal%5D)">A Single-Cell Transcriptome Atlas of the Human Pancreas.</a> Cell Systems. 3 (4), 385-394 (2016).</li>
<li>Segerstolpe, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Single-Cell+Transcriptome+Profiling+of+Human+Pancreatic+Islets+in+Health+and+Type+2+Diabetes%5BTitle%5D)+AND+%22Cell+Metabolism%22%5BJournal%5D)">Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes.</a> Cell Metabolism. 24 (4), 593-607 (2016).</li>
<li>Bose, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Scalable+microfluidics+for+single-cell+RNA+printing+and+sequencing%5BTitle%5D)+AND+%22Genome+Biology%22%5BJournal%5D)">Scalable microfluidics for single-cell RNA printing and sequencing.</a> Genome Biology. 16 (120), (2015).</li>
<li>Fan, H. C., Fu, G. K., Fodor, S. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Expression+profiling.+Combinatorial+labeling+of+single+cells+for+gene+expression+cytometry%5BTitle%5D)+AND+%22Science%22%5BJournal%5D)">Expression profiling. Combinatorial labeling of single cells for gene expression cytometry.</a> Science. 347 (6222), 1258367(2015).</li>
<li>Klein, A. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Droplet+barcoding+for+single-cell+transcriptomics+applied+to+embryonic+stem+cells%5BTitle%5D)+AND+%22Cell%22%5BJournal%5D)">Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.</a> Cell. 161 (5), 1187-1201 (2015).</li>
<li>Macosko, E. Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Highly+Parallel+Genome-wide+Expression+Profiling+of+Individual+Cells+Using+Nanoliter+Droplets%5BTitle%5D)+AND+%22Cell%22%5BJournal%5D)">Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.</a> Cell. 161 (5), 1202-1214 (2015).</li>
<li>Zheng, G. X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Massively+parallel+digital+transcriptional+profiling+of+single+cells%5BTitle%5D)+AND+%22Nature+Communications%22%5BJournal%5D)">Massively parallel digital transcriptional profiling of single cells.</a> Nature Communications. 8 (14049), (2017).</li>
<li>Dominguez Gutierrez, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Signature+of+the+Human+Pancreatic+epsilon+Cell%5BTitle%5D)+AND+%22Endocrinology%22%5BJournal%5D)">Signature of the Human Pancreatic epsilon Cell.</a> Endocrinology. 159 (12), 4023-4032 (2018).</li>
<li>Baron, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+Single-Cell+Transcriptomic+Map+of+the+Human+and+Mouse%5BTitle%5D)+AND+%22Pancreas+Reveals+Inter-+and+Intra-cell+Population+Structure.+Cell+Systems%22%5BJournal%5D)">A Single-Cell Transcriptomic Map of the Human and Mouse.</a> Pancreas Reveals Inter- and Intra-cell Population Structure. Cell Systems. 3 (4), 346-360 (2016).</li>
<li>Lawlor, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Single-cell+transcriptomes+identify+human+islet+cell+signatures+and+reveal+cell-type-specific+expression+changes+in+type+2+diabetes%5BTitle%5D)+AND+%22Genome+Research%22%5BJournal%5D)">Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes.</a> Genome Research. 27 (2), 208-222 (2017).</li>
<li>Wang, Y. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Single-Cell+Transcriptomics+of+the+Human+Endocrine+Pancreas%5BTitle%5D)+AND+%22Diabetes%22%5BJournal%5D)">Single-Cell Transcriptomics of the Human Endocrine Pancreas.</a> Diabetes. 65 (10), 3028-3038 (2016).</li>
<li>Xin, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Pseudotime+Ordering+of+Single+Human+beta-Cells+Reveals+States+of+Insulin+Production+and+Unfolded+Protein+Response%5BTitle%5D)+AND+%22Diabetes%22%5BJournal%5D)">Pseudotime Ordering of Single Human beta-Cells Reveals States of Insulin Production and Unfolded Protein Response.</a> Diabetes. 67 (9), 1783-1794 (2018).</li>
<li>Xin, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((RNA+Sequencing+of+Single+Human+Islet+Cells+Reveals+Type+2+Diabetes+Genes%5BTitle%5D)+AND+%22Cell+Metabolism%22%5BJournal%5D)">RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes.</a> Cell Metabolism. 24 (4), 608-615 (2016).</li>
<li>Chemometec. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Nucleocounter+NC-250%3A+Cell+count+and+viability+assay%22">Nucleocounter NC-250: Cell count and viability assay</a>. , (2015).</li>
<li>10X Genomics. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%2210X+Genomics%3A+Chromium+Single+Cell+3%E2%80%99+Reagents+Kits+v2%3A+User+Guide%22">10X Genomics: Chromium Single Cell 3’ Reagents Kits v2: User Guide</a>. , 10X Genomics. (2017).</li>
<li>Agilent Technologies. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Agilent+Bioanalyzer%3A+High+Sensitivity+DNA+Kit+Guide%22">Agilent Bioanalyzer: High Sensitivity DNA Kit Guide</a>. , Agilent Technologies. (2013).</li>
<li>Thermo Fischer Scientific. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Qubit%3A+dsDNA+High+Sensitivity+Assay+Kit%22">Qubit: dsDNA High Sensitivity Assay Kit</a>. , Thermo Fischer Scientific. (2015).</li>
<li>Illumina. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Illumina+Nextera+DNA+Library+Prep+Reference+Guide%22">Illumina Nextera DNA Library Prep Reference Guide</a>. , Illumina. (2016).</li>
<li>Illumina. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22llumina+NextSeq+500+System+Guide%22">llumina NextSeq 500 System Guide</a>. , Illumina. (2018).</li>
<li>Scrucca, L., Fop, M., Murphy, T. B., Raftery, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((mclust+5%3A+Clustering%2C+Classification+and+Density+Estimation+Using+Gaussian+Finite+Mixture+Models%5BTitle%5D)+AND+%22R+J%22%5BJournal%5D)">mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models.</a> R J. 8 (1), 289-317 (2016).</li>
<li>Butler, A., Hoffman, P., Smibert, P., Papalexi, E., Satija, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integrating+single-cell+transcriptomic+data+across+different+conditions%2C+technologies%2C+and+species%5BTitle%5D)+AND+%22Nature+Biotechnology%22%5BJournal%5D)">Integrating single-cell transcriptomic data across different conditions, technologies, and species.</a> Nature Biotechnology. 36 (5), 411-420 (2018).</li>
<li>Stuart, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Comprehensive+integration+of+single+cell+data%5BTitle%5D)+AND+%22Preprint+at+bioRixv%22%5BJournal%5D)">Comprehensive integration of single cell data.</a> Preprint at bioRixv. , (2018).</li>
<li>DePasquale, E. A. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((DoubletDecon%3A+Cell-State+Aware+Removal+of+Single-Cell+RNA-Seq+Doublets%5BTitle%5D)+AND+%22bioRxiv%22%5BJournal%5D)">DoubletDecon: Cell-State Aware Removal of Single-Cell RNA-Seq Doublets.</a> bioRxiv. , (2018).</li>
<li>McGinnis, C. S., Murrow, L. M., Gartner, Z. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((DoubletFinder%3A+Doublet+detection+in+single-cell+RNA+sequencing+data+using+artificial+nearest+neighbors%5BTitle%5D)+AND+%22bioRxiv%22%5BJournal%5D)">DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors.</a> bioRxiv. , (2018).</li>
<li>Wolock, S. L., Lopez, R., Klein, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Scrublet%3A+computational+identification+of+cell+doublets+in+single-cell+transcriptomic+data%5BTitle%5D)+AND+%22bioRxiv%22%5BJournal%5D)">Scrublet: computational identification of cell doublets in single-cell transcriptomic data.</a> bioRxiv. , (2018).</li>
<li>Dominguez Gutierrez, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Gene+Signature+of+Proliferating+Human+Pancreatic+alpha+Cells%5BTitle%5D)+AND+%22Endocrinology%22%5BJournal%5D)">Gene Signature of Proliferating Human Pancreatic alpha Cells.</a> Endocrinology. 159 (9), 3177-3186 (2018).</li>
<li>Habib, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Massively+parallel+single-nucleus+RNA-seq+with+DroNc-seq%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">Massively parallel single-nucleus RNA-seq with DroNc-seq.</a> Nature Methods. 14 (10), 955-958 (2017).</li>
<li>Habib, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Div-Seq%3A+Single-nucleus+RNA-Seq+reveals+dynamics+of+rare+adult+newborn+neurons%5BTitle%5D)+AND+%22Science%22%5BJournal%5D)">Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons.</a> Science. 353 (6302), 925-928 (2016).</li>
<li>Stoeckius, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cell+Hashing+with+barcoded+antibodies+enables+multiplexing+and+doublet+detection+for+single+cell+genomics%5BTitle%5D)+AND+%22Genome+Biology%22%5BJournal%5D)">Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics.</a> Genome Biology. 19 (1), 224(2018).</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 &amp;micro;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&#039; Library &amp;amp; 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 &amp;amp; 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

Research

JoVE Journal

Genetics

16.3K Views.  Regeneron Pharmaceuticals, Inc. Dit protocol genereert hoge doorvoertranscriptiegegevens van individuele menselijke eilandjecellen, die kunnen worden gebruikt om anochrome celheterogeniteit te bestuderen, zeldzame celtypes te identificeren en genregulatie bij ziekten. Deze techniek genereert grotere schaal eencellige gegevens, is gemakkelijk te gebruiken, en heeft vrij korte verwerkingstijd. Deze methode kan worden toegepast op eilandjes van andere soorten en om andere vers geïsoleerde weefsels te profileren.Het pro...

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

Isolating and Analyzing Cells of the Pancreas Mesenchyme by Flow Cytometry

The pancreas is comprised of epithelial cells that are required for food digestion and blood glucose regulation. Cells of the pancreas microenvironment, including endothelial, neuronal, and mesenchymal cells were shown to regulate cell differentiation and proliferation in the embryonic pancreas. In the adult, the function and mass of insulin-producing cells were shown to depend on cells in their microenvironment, including pericyte, immune, endothelial, and neuronal cells. Lastly, changes in the pancreas microenvironment were shown to regulate pancreas tumorigenesis. However, the cues underlying these processes are not fully defined. Therefore, characterizing the different cell types that comprise the pancreas microenvironment and profiling their gene expression are crucial to delineate the tissue development and function under normal and diseased states. Here, we describe a method that allows for the isolation of mesenchymal cells from the pancreas of embryonic, neonatal, and adult mice. This method utilizes the enzymatic digestion of mouse pancreatic tissue and the subsequent fluorescence-activated cell sorting (FACS) or flow-cytometric analysis of labeled cells. Cells can be labeled by either immunostaining for surface markers or by the expression of fluorescent proteins. Cell isolation can facilitate the characterization of genes and proteins expressed in cells of the pancreas mesenchyme. This protocol was successful in isolating and culturing highly enriched mesenchymal cell populations from the embryonic, neonatal, and adult mouse pancreas.

Here, we describe a method for the isolation of cells in the pancreas microenvironment from embryonic, neonatal and adult mouse tissue, focusing on the isolation of mesenchymal cells. This method allows profiling of cell gene expression and protein secretion in order to elucidate the extrinsic signals that regulate pancreas development, function, and tumorigenesis.

The pancreas is comprised of epithelial cells that are required for food digestion and blood glucose regulation. Cells of the pancreas microenvironment, ...

Developmental Biology

Improved Protocol For Laser Microdissection Of Human Pancreatic Islets From Surgical Specimens

Laser microdissection (LMD) is a technique that allows the recovery of selected cells and tissues from minute amounts of parenchyma 1,2. The dissected cells can be used for a variety of investigations, such as transcriptomic or proteomic studies, DNA assessment or chromosomal analysis 2,3. An especially challenging application of LMD is transcriptome analysis, which, due to the lability of RNA 4, can be particularly prominent when cells are dissected from tissues that are rich of RNases, such as the pancreas. A microdissection protocol that enables fast identification and collection of target cells is essential in this setting in order to shorten the tissue handling time and, consequently, to ensure RNA preservation. 
Here we describe a protocol for acquiring human pancreatic beta cells from surgical specimens to be used for transcriptomic studies 5. Small pieces of pancreas of about 0.5-1 cm3 were cut from the healthy appearing margins of resected pancreas specimens, embedded in Tissue-Tek O.C.T. Compound, immediately frozen in chilled 2-Methylbutane, and stored at -80 &deg;C until sectioning. Forty serial sections of 10 &mu;m thickness were cut on a cryostat under a -20 &deg;C setting, transferred individually to glass slides, dried inside the cryostat for 1-2 min, and stored at -80 &deg;C. 
Immediately before the laser microdissection procedure, sections were fixed in ice cold, freshly prepared 70% ethanol for 30 sec, washed by 5-6 dips in ice cold DEPC-treated water, and dehydrated by two one-minute incubations in ice cold 100% ethanol followed by xylene (which is used for tissue dehydration) for 4 min; tissue sections were then air-dried afterwards for 3-5 min. Importantly, all steps, except the incubation in xylene, were performed using ice-cold reagents - a modification over a previously described protocol 6. utilization of ice cold reagents resulted in a pronounced increase of the intrinsic autofluorescence of beta cells, and facilitated their recognition. For microdissection, four sections were dehydrated each time: two were placed into a foil-wrapped 50 ml tube, to protect the tissue from moisture and bleaching; the remaining two were immediately microdissected. This procedure was performed using a PALM MicroBeam instrument (Zeiss) employing the Auto Laser Pressure Catapulting (AutoLPC) mode. The completion of beta cell/islet dissection from four cryosections required no longer than 40-60 min. Cells were collected into one AdhesiveCap and lysed with 10 &mu;l lysis buffer. Each single RNA specimen for transcriptomic analysis was obtained by combining 10 cell microdissected samples, followed by RNA extraction using the Pico Pure RNA Isolation Kit (Arcturus). This protocol improves the intrinsic autofluorescence of human beta cells, thus facilitating their rapid and accurate recognition and collection. Further improvement of this procedure could enable the dissection of phenotypically different beta cells, with possible implications for better understanding the changes associated with type 2 diabetes.

Laser microdissection is a technique that allows the recovery of selected cells from minute amounts of parenchyma. Here we describe a protocol for acquiring human pancreatic islets from surgical specimens to be used for transcriptomic studies. Our protocol improves the intrinsic autofluorescence of human beta cells, thus facilitating their collection.

Laser microdissection (LMD) is a technique that allows the recovery of selected cells and tissues from minute amounts of parenchyma 1,2. The dissected ...

Medicine

Single-cell Transcriptomic Analyses of Mouse Pancreatic Endocrine Cells

Pancreatic endocrine cells, which are clustered in islets, regulate blood glucose stability and energy metabolism. The distinct cell types in islets, including insulin-secreting &#946; cells, are differentiated from common endocrine progenitors during the embryonic stage. Immature endocrine cells expand via cell proliferation and mature during a long postnatal developmental period. However, the mechanisms underlying these processes are not clearly defined. Single-cell RNA-sequencing is a promising approach for the characterization of distinct cell populations and tracing cell lineage differentiation pathways. Here, we describe a method for the single-cell RNA-sequencing of isolated pancreatic &#946; cells from embryonic, neonatal and postnatal pancreases.

We describe a method for the isolation of endocrine cells from embryonic, neonatal and postnatal pancreases followed by single-cell RNA sequencing. This method allows analyses of pancreatic endocrine lineage development, cell heterogeneity and transcriptomic dynamics.

Pancreatic endocrine cells, which are clustered in islets, regulate blood glucose stability and energy metabolism. The distinct cell types in islets, ...

Pancreatic Tissue Dissection to Isolate Viable Single Cells

The pancreas includes two major systems: the endocrine system, which produces and secretes hormones, and the exocrine system, which accounts for approximately 90% of the pancreas and includes cells that produce and secrete digestive enzymes. The digestive enzymes are produced in the pancreatic acinar cells, stored in vesicles called zymogens, and are then released into the duodenum via the pancreatic duct to initiate metabolic processes. The enzymes produced by the acinar cells can kill cells or degrade cell-free RNA. In addition, acinar cells are fragile, and common dissociation protocols result in a large number of dead cells and cell-free proteases and RNases. Therefore, one of the biggest challenges in pancreatic tissue digestion is recovering intact and viable cells, especially acinar cells. The protocol presented in this article shows a two-step method that we developed to meet this need. The protocol can be used to digest normal pancreata, pancreata that include pre-malignant lesions, or pancreatic tumors that include a large number of stromal and immune cells.

Pancreatic metaplastic cells are precursors of malignant cells that give rise to pancreatic tumors. However, isolating intact viable pancreatic cells is challenging. Here, we present an efficient method for pancreatic tissue dissociation. The cells can then be used for single-cell RNA sequencing (scRNA-seq) or for two- or three-dimensional co-culturing.

The pancreas includes two major systems: the endocrine system, which produces and secretes hormones, and the exocrine system, which accounts for ...

Cancer Research

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

The pancreatic islet is a unique micro-organ composed of several hormone secreting endocrine cells such as beta-cells (insulin), alpha-cells (glucagon), and delta-cells (somatostatin) that are embedded in the exocrine tissues and comprise 1-2% of the entire pancreas. There is a close correlation between body and pancreas weight. Total beta-cell mass also increases proportionately to compensate for the demand for insulin in the body. What escapes this proportionate expansion is the size distribution of islets. Large animals such as humans share similar islet size distributions with mice, suggesting that this micro-organ has a certain size limit to be functional. The inability of large animal pancreata to generate proportionately larger islets is compensated for by an increase in the number of islets and by an increase in the proportion of larger islets in their overall islet size distribution. Furthermore, islets exhibit a striking plasticity in cellular composition and architecture among different species and also within the same species under various pathophysiological conditions. In the present study, we describe novel approaches for the analysis of biological image data in order to facilitate the automation of analytic processes, which allow for the analysis of large and heterogeneous data collections in the study of such dynamic biological processes and complex structures. Such studies have been hampered due to technical difficulties of unbiased sampling and generating large-scale data sets to precisely capture the complexity of biological processes of islet biology. Here we show methods to collect unbiased "representative" data within the limited availability of samples (or to minimize the sample collection) and the standard experimental settings, and to precisely analyze the complex three-dimensional structure of the islet. Computer-assisted automation allows for the collection and analysis of large-scale data sets and also assures unbiased interpretation of the data. Furthermore, the precise quantification of islet size distribution and spatial coordinates (i.e. X, Y, Z-positions) not only leads to an accurate visualization of pancreatic islet structure and composition, but also allows us to identify patterns during development and adaptation to altering conditions through mathematical modeling. The methods developed in this study are applicable to studies of many other systems and organisms as well.

Novel computer-assisted methods of large-scale procurement and analysis of immunohistochemically stained pancreatic specimens are described: (1) Virtual Slice capture of the entire section; (2) Mass analysis of large-scale data; (3) Reconstruction of 2D Virtual Slices; (4) 3D islet mapping; and (5) Mathematical analysis.

The pancreatic islet is a unique micro-organ composed of several hormone secreting endocrine cells such as beta-cells (insulin), alpha-cells (glucagon), ...

Biology

From RNA to Metabolites: Multi-Omic Profiling of Non-Human Primate Fetal Islets

Preparation of Frozen Non-Human Primate Fetal Islets for Combined Single Nuclei RNA-Sequencing and ATAC-Sequencing, and Bulk Metabolomics

One challenge in studies using tissue collected from multiple cohorts is avoiding batch effects when preparing for large-scale multi-omic experiments, such as combined single-cell RNA sequencing and metabolomics. The method in the current study utilizes flash-frozen pancreatic islets from fetal non-human primates collected over a span of two years for input into single-nucleus RNA sequencing and ATAC sequencing assays. The cytosolic fraction generated during nuclear extraction was retained for downstream capillary electrophoresis-mass spectrometry and subsequent metabolite quantification. This method allows for bulk analysis of metabolites that contribute to the changing transcriptomic and epigenomic landscapes within experimental conditions. It is applicable to many tissue types and maximizes the amount of information that can be extracted from samples that are not readily available. As the contribution of metabolism to the establishment of cellular identity via epigenetic modifications becomes more appreciated, techniques that allow for identifying the contribution of metabolites in specific cell types are timely and necessary.

Author Spotlight: Multi-Layered Approach to Understand Postnatal Functions of Pancreatic Islets in Non-Human Primates

This protocol describes procedures to isolate high-quality nuclei from frozen non-human primate pancreatic islets for use in single-nucleus simultaneous RNA sequencing and ATAC sequencing while preserving the bulk cytosolic fraction for metabolomic analyses from the same samples.

One challenge in studies using tissue collected from multiple cohorts is avoiding batch effects when preparing for large-scale multi-omic experiments, ...

Computational Reconstruction of Pancreatic Islets as a Tool for Structural and Functional Analysis

Structural properties of pancreatic islets are key for the functional response of insulin, glucagon, and somatostatin-secreting cells, due to their implications in intraislet communication via electric, paracrine, and autocrine signaling. In this protocol, the three-dimensional architecture of a pancreatic islet is firstly reconstructed from experimental data using a novel computational algorithm. Next, the morphological and connectivity properties of the reconstructed islet, such as the number and percentages of the different type of cells, cellular volume, and cell-to-cell contacts, are obtained. Then, network theory is used to describe the connectivity properties of the islet through network-derived metrics such as average degree, clustering coefficient, density, diameter, and efficiency. Finally, all these properties are functionally evaluated through computational simulations using a model of coupled oscillators. Overall, here we describe a step-by-step workflow, implemented in IsletLab, a multiplatform application developed specifically for the study and simulation of pancreatic islets, to apply a novel computational methodology to characterize and analyze pancreatic islets as a complement to the experimental work.

In this protocol, the pancreatic islets are reconstructed and analyzed using computational algorithms implemented in a dedicated multiplatform application.

Structural properties of pancreatic islets are key for the functional response of insulin, glucagon, and somatostatin-secreting cells, due to their ...

Bioengineering

Visualization of Endogenous Mitophagy Complexes In Situ in Human Pancreatic Beta Cells Utilizing Proximity Ligation Assay

Mitophagy is an essential mitochondrial quality control pathway, which is crucial for pancreatic islet beta cell bioenergetics to fuel glucose-stimulated insulin release. Assessment of mitophagy is challenging and often requires genetic reporters or multiple complementary techniques not easily utilized in tissue samples, such as primary human pancreatic islets. Here we demonstrate a robust approach to visualize and quantify formation of key endogenous mitophagy complexes in primary human pancreatic islets. Utilizing the sensitive proximity ligation assay technique to detect interaction of the mitophagy regulators NRDP1 and USP8, we are able to specifically quantify formation of essential mitophagy complexes in situ. By coupling this approach to counterstaining for the transcription factor PDX1, we can quantify mitophagy complexes, and the factors that can impair mitophagy, specifically within beta cells. The methodology we describe overcomes the need for large quantities of cellular extracts required for other protein-protein interaction studies, such as immunoprecipitation (IP) or mass spectrometry, and is ideal for precious human islet&#160;samples generally not available in sufficient quantities for these approaches. Further, this methodology obviates the need for flow sorting techniques to purify beta cells from a heterogeneous islet population for downstream protein applications. Thus, we describe a valuable protocol for visualization of mitophagy highly compatible for use in heterogeneous and limited cell populations.

This protocol outlines a method for quantitative analysis of mitophagy protein complex formation specifically in beta cells from primary human islet samples. This technique thus allows analysis of mitophagy from limited biological material, which are crucial in precious human pancreatic beta cell samples.

Mitophagy is an essential mitochondrial quality control pathway, which is crucial for pancreatic islet beta cell bioenergetics to fuel glucose-stimulated ...

Pancreatic Islet Embedding for Paraffin Sections

Experiments using isolated pancreatic islets are important for diabetes research, but islets are expensive and of limited abundance. Islets contain a mixed cell population in a structured architecture that impacts function, and human islets are widely variable in cell type composition. Current frequently used methods to study cultured islets include molecular studies performed on whole islets, lumping disparate islet cell types together, or microscopy or molecular studies on dispersed islet cells, disrupting islet architecture. For in vivo islet studies, paraffin-embedded pancreas sectioning is a powerful technique to assess cell-specific outcomes in the native pancreatic environment. Studying post-culture islets by paraffin sectioning would offer several advantages: detection of multiple outcomes on the same islets (potentially even the exact-same islets, using serial sections), cell-type-specific measurements, and maintaining native islet cell-cell and cell-substratum interactions both during experimental exposure and for analysis. However, existing techniques for embedding isolated islets post-culture are inefficient, time consuming, prone to loss of material, and generally produce sections with inadequate islet numbers to be useful for quantifying outcomes. Clinical pathology laboratory cell block preparation facilities are inaccessible and impractical for basic research laboratories. We have developed an improved, simplified bench-top method that generates sections with robust yield and distribution of islets. Fixed islets are resuspended in warm histological agarose gel and pipetted into a flat disc on a standard glass slide, such that the islets are distributed in a plane. After standard dehydration and embedding, multiple (10+) 4 - 5 &#181;m sections can be cut from the same islet block. Using this method, histological&#160;and immunofluorescent analyses can be performed on mouse, rat, and human islets. This is an effective, inexpensive, time-saving approach to assess cell-type-specific, intact-architecture outcomes from cultured islets.

Ex vivo pancreatic islet studies are important for diabetes research. Existing techniques to study cultured islets in their native 3-dimensional architecture are time consuming, inefficient, and infrequently used. This work describes a new, simple, and efficient method for generating high-quality paraffin sections of whole cultured islets.

Experiments using isolated pancreatic islets are important for diabetes research, but islets are expensive and of limited abundance. Islets contain a ...

Intracellular Signaling Events and Hormone Secretion in Human Pseudoislets

Human Pseudoislet System for Synchronous Assessment of Fluorescent Biosensor Dynamics and Hormone Secretory Profiles

The pancreatic islets of Langerhans, which are small 3D collections of specialized endocrine and supporting cells interspersed throughout the pancreas, have a central role in the control of glucose homeostasis through the secretion of insulin by beta cells, which lowers blood glucose, and glucagon by alpha cells, which raises blood glucose. Intracellular signaling pathways, including those mediated by cAMP, are key for regulated alpha and beta cell hormone secretion. The 3D islet structure, while essential for coordinated islet function, presents experimental challenges for mechanistic studies of the intracellular signaling pathways in primary human islet cells. To overcome these challenges and limitations, this protocol describes an integrated live-cell imaging and microfluidic platform using primary human pseudoislets generated from donors without diabetes that resemble native islets in their morphology, composition, and function. These pseudoislets are size-controlled through the dispersion and reaggregation process of primary human islet cells. In the dispersed state, islet cell gene expression can be manipulated; for example, biosensors such as the genetically encoded cAMP biosensor, cADDis, can be introduced. Once formed, pseudoislets expressing a genetically encoded biosensor, in combination with confocal microscopy and a microperifusion platform, allow for the synchronous assessment of fluorescent biosensor dynamics and alpha and beta cell hormone secretory profiles to provide more insight into cellular processes and function.

Author Spotlight: Investigating Islet Abnormalities and Function with a Pseudoislet Protocol 

This protocol describes a method for the synchronous acquisition and co-registration of intracellular signaling events and the secretion of insulin and glucagon by primary human pseudoislets using the adenoviral delivery of a cyclic adenosine monophosphate (cAMP) biosensor, a cAMP difference detector in situ (cADDis), and a microperifusion system.

The pancreatic islets of Langerhans, which are small 3D collections of specialized endocrine and supporting cells interspersed throughout the pancreas, ...

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

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