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17 protocols using loupe cell browser

1

Single-cell RNA-seq Profiling of Murine Lung Tumors

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Single-cell RNA-seq data from both KP (n=2) and KPF1F2 (n=2) tumors were demultiplexed using the 10x cellranger mkfastq version 3.1.0 to create fastq files with the I1 sample index, R1 cell barcode+UMI, and R2 sequence. Reads were aligned to the mouse genome (mm10 with custom CRE-ERT2 and FoxA1/2 individual exon references) and UMIs were generated using cellranger count 3.1.0 with expected-cells set to 8000 per library. For the KP sample without stromal depletion, expected-cells was set to 7500. QC reporting, clustering, and dimension reduction were performed for initial data evaluation in 10x Genomics’ Cell Loupe Browser (v5.0). For the KP sample without stromal depletion, we captured 5,963 cells total with 48,090 mean reads per cell and 1,488 median genes per cell. For the KP sample with stromal depletion, we captured 8,536 cells total with 25,593 mean reads per cell and 736 median genes per cell. For the KPF1F2 sample without stromal depletion, we captured 4,662 cells total with 55,589 mean reads per cell and 1,552 median genes per cell. For the KPF1F2 sample with stromal depletion, we captured 4,415 cells total with 60,226 mean reads per cell and 1,339 median genes per cell. Additional details of the primary Cell Ranger data processing can be found at: https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/algorithms/overview.
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2

Single-Cell Transcriptome Analysis of Mouse Cells

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Base calls were converted to reads with the software Cell Ranger (10× Genomics; version 2.1) mkfastq. These reads were then aligned against the mouse reference (GENCODE Mouse Release 26 (GRCm38)) using the Cell Ranger 2.1.0 pipeline (an implementation of STAR v2.7.0, 10× Genomics) with SC3Pv2 chemistry and 5,000 expected cells per sample (Dobin et al, 2013 (link)). Cell barcodes representative of quality cells were delineated from barcodes of apoptotic cells or background RNA based on a threshold of having at least 200 unique transcripts profiled, < 10,000 total transcripts and less than 10% of their transcriptome of mitochondrial origin. Potential multiplets were classified as outside three median absolute deviations (MADs) for percentage mitochondrial content, number of genes and number of UMIs and removed. UMIs from each cell barcode were retained for all downstream analysis. Raw UMI counts were normalised with a scale factor of 10,000 UMIs per cell and subsequently natural log transformed.
Clustering of cells based on transcriptome similarities was performed by either graph‐based clustering or k‐means clustering methods (Cell Loupe Browser, 10× Genomics).
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3

Single-cell RNA-seq analysis of BPN treatment

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Single-cell RNA-seq data were demultiplexed with 10x cellranger mkfastq version 3.1.0 to create fastq files with the I1 sample index, R1 cell barcode+UMI and R2 sequence. Reads were aligned to the mouse genome (mm10 with custom tdTomato reference) and feature counts were generated using cellranger count 3.1.0 with expected-cells set to 6000 per library. QC reporting, clustering, dimension reduction, and differential gene expression analysis were performed in 10x Genomics’ Cell Loupe Browser (v3.1.1). For the BPN control sample, we captured 5065 cells and obtained 46,894 mean reads per cell; 3544 median genes per cell. For the BPN treated sample, we captured 5563 cells and obtained 37,488 mean reads per cell; 2771 median genes per cell. For further details of the primary Cell Ranger data processing, see https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/algorithms/.
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4

Gallbladder epithelial cell scRNA-seq

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Whole gallbladder from a non-CF pig was excised and cut open to reveal the lumen. The outermost layer of cells facing the lumen were mechanically scraped into DPBS-DTT. Cells were dissociated for 1 hour in 1.4 mg/ml pronase and 40 U DNase and then filtered through a 40 μm tissue strainer. Density gradient centrifugation was performed with Iodixanol (OptiPrep) to separate live and dead cells. Cells were stained with trypan blue, counted using a hemocytometer, and submitted for sequencing in DPBS with 0.04% BSA. Libraries were prepared by the Iowa Institute of Human Genetics-Genomics Division core facility at the University of Iowa using the Chromium Single Cell 3’ Reagent Kit v3 Chemistry (10x Genomics) and sequenced on the HiSeq 4000 Sequencing System (Illumina). Single-cell RNA sequencing data were assessed for quality using FastQC (version 0.11.9), preprocessed using CellRanger (version 3.0.02), aligned using STAR (version 2.7.3)37 (link), analyzed using Seurat (version 3.0)38 (link), 39 (link), and visualized in the Loupe Cell Browser (10x genomics).
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5

Gallbladder epithelial cell scRNA-seq

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Whole gallbladder from a non-CF pig was excised and cut open to reveal the lumen. The outermost layer of cells facing the lumen were mechanically scraped into DPBS-DTT. Cells were dissociated for 1 hour in 1.4 mg/ml pronase and 40 U DNase and then filtered through a 40 μm tissue strainer. Density gradient centrifugation was performed with Iodixanol (OptiPrep) to separate live and dead cells. Cells were stained with trypan blue, counted using a hemocytometer, and submitted for sequencing in DPBS with 0.04% BSA. Libraries were prepared by the Iowa Institute of Human Genetics-Genomics Division core facility at the University of Iowa using the Chromium Single Cell 3’ Reagent Kit v3 Chemistry (10x Genomics) and sequenced on the HiSeq 4000 Sequencing System (Illumina). Single-cell RNA sequencing data were assessed for quality using FastQC (version 0.11.9), preprocessed using CellRanger (version 3.0.02), aligned using STAR (version 2.7.3)37 (link), analyzed using Seurat (version 3.0)38 (link), 39 (link), and visualized in the Loupe Cell Browser (10x genomics).
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6

Single-cell ATAC-seq on Hic1 reporter cells

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The input for scATAC-seq requires cell nuclei. To isolate single nuclei, ~ 100,000 fresh FACS purified Hic1; tdTomato reporter+ cells were collected as per scRNA-seq sorting conditions and lysed according to the described protocol (10x Genomics, https://support.10xgenomics.com/single-cell-atac) for 5 min. Nuclei were quantified using a Countess II FL automated cell counter (ThermoFisher) and 10,000 nuclei were targeted for transposition and capture using a 10x Chromium controller. ScATAC-seq libraries were prepared according to the Chromium Single Cell ATAC Reagent Kits User Guide (10x Genomics; CG000168 Rev B). Single cell libraries were sequenced on a Nextseq500 (Illumina) using 2 × 75PE kit to produce 2 × 50 reads to a sequencing saturation of > 80%. The cellranger-atac single cell ATAC pipeline version 1.1.0 was used to generate fastq files from the sequencer output bcl files and further perform read filtering and alignment, detection of accessible chromatin peaks, dimensionality reduction, cell clustering and differential accessibility analyses. Quality metrics for the scATAC-seq such as insert size distribution, enrichment around the transcriptional start site and a tSNE heatmap of fragments per cell are shown in Figure S6G. Plots were generated using Loupe Cell Browser (10X genomics, v. 3.1.0).
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7

Single-Cell Sequencing Workflow

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BCL files were converted to FASTQ files using bcl2fastq Conversion Software (Illumina) using the respective sample sheet with the utilized 10x barcodes. The proprietary 10x Genomics CellRanger pipeline (v3.0.2) was used with default parameters except for the setting of expected cells (--expect-cells 10,000). CellRanger was used to align read data to the reference genome provided by 10x Genomics (Human reference dataset 3.0.0; GrCh38) using the aligner STAR, counting aligned reads per gene, and calculating clustering and summary statistics. Finally, the Loupe Cell Browser from 10x Genomics was used to view and revise annotated clusters, on the basis of the implemented t-SNE algorithm.
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8

Profiling Tumor-Infiltrating T Cells

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Data was collected from FACS-sorted CD3+ T cells generating two technical replicates from the BC9 tumor, one replicate from the BC10 tumor (due to a lower number of T cells), and two technical replicates from the BC11 tumor. Technical replicates were run on separate 10× lanes, but originate from the same sample after tissue dissociation. The sequencing data from each replicate was preprocessed separately using Cell Ranger 2.1.1, available from 10× Genomics. The clonotype comparison feature in Loupe Cell Browser (also from 10× Genomics) combined with custom scripts were then used to pool TCR clonotypes across replicates by matching CDR3 sequences from both alpha and beta chains across replicates of the same tumor. For two cells to be assigned to the same clonotype they had to share both alpha and beta sequences. We also present details and statistics on V and J gene usage and alpha and beta sequences in Table S6 (sheet 2). We achieved a paired transcriptome and TCR sequence for 12962, 4677, and 9436 T cells from BC9, 10, and 11, respectively, resulting in a total dataset of 27,075 T cells. The median number of molecules per cell in this dataset was 4780. The frequencies of the most dominant clonotypes per patient are shown in Figure S6D.
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9

Single-cell TCR analysis of CD4+ T cells

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CD4+ T cells were cultured with SmB/B’58-72-pulsed DCs as per Sm epitope immunogenicity in the methods above. Post-staining, CD4+CTVlo cells were sorted using an Aria cell sorter (BD) and concentration adjusted to 0.7–1.2 × 106 cells/mL in 0.04% BSA/PBS before loading into 10x Chromium Controller (10X Genomics) for single-cell RNASeq preparation following the instructions of the manufacturer (10X Genomics) at Micromon Genomics. Gene expression and V(D)J libraries were sequenced on a NextSeq System (Illumina), and single-cell TCR sequencing data were prepared in Cell Ranger (10X Genomics, version 3.0.2) followed by visualization and analysis on Loupe Cell Browser (10X Genomics, version 4.2.0) and Loupe VDJ Browser (10X Genomics, version 3.0.0). ScRNASeq data is uploaded to Gene Expression Omnibus accession number GSE242152.
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10

Differential Gene Expression Analysis of scRNA-Seq Data

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Differential gene expression analysis of scRNA-Seq data was performed using Cell Ranger and Loupe Cell Browser software (10x Genomics), which uses a variant of the negative binomial exact test from sSeq and the asymptotic beta test in edgeR depending on sample size (93 (link), 94 (link)). Statistical analysis and graphs of other data were generated using GraphPad Prism v9. Pairwise comparisons were analyzed using two-tailed Student t test and multiple comparison with one-way analysis of variance (ANOVA). For all graphs and heatmaps, *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001.
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