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Use of the Fluidigm C1 Platform for RNA Sequencing of Single Mouse Pancreatic Islet Cells

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Use of the Fluidigm C1 Platform for RNA Sequencing of Single Mouse Pancreatic Islet Cells Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells Yurong Xina, Jinrang Kima, Min Nia, Yi Weia, Haruka Okamotoa, Joseph Leea, Christina Adlera, Katie Cavinoa, Andrew J. Murphya, George D. Yancopoulosa,1, Hsin Chieh Lina, and Jesper Gromadaa,1 aRegeneron Pharmaceuticals, Tarrytown, NY 10591 Contributed by George D. Yancopoulos, February 11, 2016 (sent for review January 21, 2016; reviewed by Philipp Sherer and Lori Sussel) This study provides an assessment of the Fluidigm C1 platform for 1C). Interestingly, we detected few cells that coexpressed Gcg-Ppy RNA sequencing of single mouse pancreatic islet cells. The system (0.8%; n = 125) (Fig. 1D). Using RNA FISH and immunohisto- combines microfluidic technology and nanoliter-scale reactions. chemistry in pancreas sections from mice we confirmed the exis- + + We sequenced 622 cells, allowing identification of 341 islet cells tence of rare Gcg -Ppy cells (SI Appendix,Fig.S1). Consistent with high-quality gene expression profiles. The cells clustered into with the high sensitivity of RNA FISH (14), we detected low levels populations of α-cells (5%), β-cells (92%), δ-cells (1%), and pancre- (0.02–0.3%) of other endocrine hormones in each single hormone- atic polypeptide cells (2%). We identified cell-type–specific tran- expressing cell. These data show that the dissociated islet cell scription factors and pathways primarily involved in nutrient preparations used for single-cell RNA sequencing consist nearly sensing and oxidation and cell signaling. Unexpectedly, 281 cells exclusively of single hormone-expressing cells. had to be removed from the analysis due to low viability, low sequencing quality, or contamination resulting in the detection Viability of Captured Cells. We used two methods to determine of more than one islet hormone. Collectively, we provide a re- viability of the captured cells. The first method is based on source for identification of high-quality gene expression datasets LIVE/DEAD staining in the C1 Single-Cell Auto Prep System. + + to help expand insights into genes and pathways characterizing islet We found 77% live (LIVE ) cells, 2% dead (DEAD ) cells, and + + cell types. We reveal limitations in the C1 Fluidigm cell capture pro- 21% cells that stained positive for both (LIVE /DEAD ). Via- cess resulting in contaminated cells with altered gene expression bility of the islet cells before capture was 78 ± 16% (n = 9 patterns. This calls for caution when interpreting single-cell transcrip- preparations). The second approach uses unsupervised hierar- tomics data using the C1 Fluidigm system. chical clustering of the top 100 variable genes in the sequenced cells. We used 622 cells from nine preparations for the analysis, single-cell RNA sequencing | pancreatic islet cells | Fluidigm C1 | insulin | after excluding 34 cells where debris or contaminating cells were glucagon observed (SI Appendix, Fig. S2). Twelve acinar cells were de- tected [≥1 reads per kilobase per million (RPKM) for ≥2 of the slets of Langerhans are miniature endocrine organs within the following genes: Amy2a5, Amy2b,orPnlip]. This represents an Ipancreas that are essential for control of blood glucose levels exocrine contamination rate of 1.9% (12/622 cells). Two distinct (1). They are composed of four endocrine cell types producing cell clusters were identified: cells with low (cluster 1) or high glucagon (α-cells), insulin (β-cells), somatostatin (δ-cells), and viability (cluster 2) (Fig. 2A). Of note, mitochondrial genome- pancreatic polypeptide (PP cells). Whole-genome transcriptome encoded genes are more abundantly expressed in cells in cluster analysis has been performed on enriched populations of human 1. In particular, ATP6, ATP8, COX1, COX2, COX3, CYTB, ND1, and mouse α-andβ-cells (2–4). These studies report the ensemble Rnr2, and LOC100503946 are highly up-regulated and assigned average on the cell populations and do not report variation in expressed genes among cells. The studies also do not allow study Significance of the low abundant δ-cells and PP cells. In addition, data in- CELL BIOLOGY terpretation in these analyses can be affected by the presence of a Pancreatic islets are complex structures composed of four cell few contaminating cells. Single-cell RNA sequencing circumvents types whose primary function is to maintain glucose homeo- these problems and has recently been applied to a low number of stasis. Owing to the scarcity and heterogeneity of the islet cell human pancreatic islet cells (5) as well as to other cell types in types, little is known about their individual gene expression complex tissues (6–13). Pancreatic islet cells are suited for single-cell profiles. Here we used the Fluidigm C1 platform to obtain high- RNA sequencing because they express high levels of a single hor- quality gene expression profiles of each islet cell type from mone. This allows for unequivocal identification and unbiased un- mice. We identified cell-type–specific transcription factors and derstanding of gene expression in each cell type. pathways providing previously unrecognized insights into genes Here we used the C1 Fluidigm system to analyze the tran- characterizing islet cells. Unexpectedly, our data uncover technical scriptome of dispersed mouse pancreatic islet cells. We also limitations with the C1 Fluidigm cell capture process, which should studied how the capture process affected cell quality and con- be considered when analyzing single-cell transcriptomics data. tamination. We report identification of all islet cells with high- quality gene expression profiles. Unexpectedly, our data uncover Author contributions: Y.X., Y.W., H.O., H.C.L., and J.G. designed research; J.K., M.N., J.L., technical limitations with the cell capture, which should be C.A., and K.C. performed research; Y.X., J.K., Y.W., H.O., J.L., and J.G. analyzed data; and considered when analyzing single-cell transcriptomics data. Y.X., A.J.M., G.D.Y., H.C.L., and J.G. wrote the paper. Reviewers: P.S., University of Texas Southwestern Medical Center; and L.S., Columbia Results University. Islet Cell Identity. Conflict of interest statement: All authors are employees and shareholders of RNA FISH simultaneously using probes to Regeneron Pharmaceuticals. glucagon (Gcg), insulin (Ins2), somatostatin (Sst), and pancreatic = Data deposition: The data reported in this paper have been deposited in the Gene Ex- polypeptide (Ppy) showed that 99.2% (n 15,542) of islet cells pression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE77980). used for single-cell RNA sequencing expressed high levels of one 1To whom correspondence may be addressed. Email: [email protected] or Jesper. hormone (Fig. 1A). The distribution of the cell types is shown in [email protected]. Fig. 1B. The intensity distributions of the fluorescence signal were This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. bell-shaped, suggesting one cell population for each cell type (Fig. 1073/pnas.1602306113/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1602306113 PNAS | March 22, 2016 | vol. 113 | no. 12 | 3293–3298 Downloaded by guest on September 26, 2021 A B Gcg Ins2 Sst Ppy C 4.6% (727) Gcg 100 2000 100 100 Gcg Ins2 Sst Ppy 84.5% (13242) Ins2 r r 80 80 e 80 5.9% (922) Sst e 1500 b b 60 4.2% (651) Ppy m 60 60 1000 0.8% (125) Gcg-Ppy num ll 40 40 ll nu 40 e Ce Cell number Cell Cell number Cell C 500 20 20 20 (15667) Total 0 0 0 0 0.00 0.50 1.00 0.00 0.50 1.00 0.00 0.50 1.00 0.00 0.50 1.00 Fluorescent intensity Fluorescent intensity Fluorescent intensity Fluorescent intensity D Gcg Gcg Ppy Ppy Fig. 1. Mouse islet cells rarely express more than one hormone. (A) Representative RNA FISH images of single mouse islet cells expressing glucagon (Gcg), + + + + insulin (Ins2), somatostatin (Sst), or pancreatic polypeptide (Ppy). (B) Distribution of islet cells. (C) Intensity distribution histograms of Gcg , Ins2 , Sst ,orPpy cells. (D) Representative RNA FISH images of Gcg+-Ppy+ cells. + + as the cell viability gene set (Methods). These genes account for mainly cluster between Gcg and Ins2 cells (SI Appendix,Fig.S7). >30% of total expression in RPKM. Fig. 2B shows that the This, combined with the RNA FISH data of the input islet cell median expression of the cell viability gene set is 12-fold higher suspensions (cf. Fig. 1), suggests that nearly all multiple-hormone– − (P = 5.6e 23) in cluster 1 cells, whereas the expression of all expressing cells are artifacts that arise during the cell capture pro- − other genes is 285-fold (P = 6.0e 23) reduced. Fig. 2C shows the cess due to damage or cell–cell fusion. Therefore, the cells that distribution of the sequenced cells according to their viability coexpress more than one hormone were excluded from sub- score (Methods). Cells with a score >0.3 are likely to be of low sequent analysis (SI Appendix, Fig. S2). Fig. 3C shows the dis- quality and were removed from the analysis. We found no pat- tribution of the remaining single-hormone–expressing islet cells. tern of changes in cell quality throughout the C1 Fluidigm circuit The cells clustered into populations of α-cells (5%), β-cells (SI Appendix, Fig. S3). In total, the assessments of cell quality (92%), δ-cells (1%), and PP cells (2%), matching the distribution resulted in removal of 65 cells (10%; SI Appendix, Fig. S2). in the input islet cell suspensions measured by RNA FISH. Fig. 3C also shows that each cell expresses low levels (0.003–0.27%) Characterization of Sequenced Islet Cells. Each sample was se- of other endocrine hormones. Total number of detected genes quenced to an average depth of 1 million read pairs (SI Ap- varied between 3,900 and 5,300 (SI Appendix, Table S2).
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