DOI: 10.5061/dryad.n02v6wx63
title: Bulk and single-cell RNA-seq of human fetal pancreatic organoids

description: The mammalian pancreas consists of three epithelial compartments: the
 acini and ducts of the exocrine pancreas and the endocrine islets of
 Langerhans. Murine studies indicate that these three compartments derive
 from a transient, common pancreatic progenitor. Here, we report the
 derivation of 18 human fetal pancreas organoid (hfPO) lines from
 gestational week 8-17 (8-17GW) fetal pancreas samples. Four of these
 lines, derived from 15-16GW samples, generate acinar-, ductal- and
 endocrine lineage cells while expanding exponentially for &gt;2 years
 under optimized culture conditions. Single-cell RNA sequencing identifies
 rare LGR5+ cells in the fetal pancreas and hfPOs as the root of the
 developmental hierarchy. These LGR5+ cells share multiple markers with
 adult gastrointestinal tract stem cells. Organoids derived from single
 LGR5+ organoid-derived cells recapitulate this tri-potency in vitro. We
 describe a human fetal tri-potent stem/progenitor cell, capable of
 long-term expansion in vitro and of generating all three pancreatic cell
 lineages.
description: <strong>Bulk RNA sequencing <br></strong>Human
 fetal pancreatic organoids were cultured in expansion, endocrine, or
 acinar cell differentiation conditions for 10 days. 
 Organoids were collected and RNA extraction was performed
 according to the manufacturer's instructions. RNA integrity was
 determined using the Agilent RNA 6000 Nano kit with the Agilent 2100
 Bioanalyzer (Agilent). RNA integrity (RIN) values ranged from 9.0–10.0.
 Samples used for bulk RNA sequencing did not have a RIN &lt;9.0. RNA
 concentrations were determined using the Qubit RNA HS Assay Kit (Thermo
 Fisher). RNA libraries were prepared with the TruSeq Stranded messenger
 RNA polyA kit and paired-end (2 × 50 base pairs) sequenced on an Illumina
 NextSeq 2000. Reads were mapped to the human GRCh38 genome assembly using
 STAR (DOI: 10.1093/bioinformatics/bts635).
 <strong>Single-cell RNA
 sequencing<br></strong>For scRNA-seq cells were FACS sorted
 into 384-well plates pre-printed with primers (Single Cell Discoveries)
 and stored at -80°C until used for library preparation. Libraries were
 prepared according to the previously published VASA-Seq protocol for
 VASA-plate (DOI:
 https://doi-org.utrechtuniversity.idm.oclc.org/10.1038/s41587-022-01361-8)
  and sequenced on a NextSeq2000, high-output 100 cycles flowcell
 (Illumina). Processed, normalized RNA-seq data
 performed according to the VASA-Seq workflow
 (DOI: https://doi-org.utrechtuniversity.idm.oclc.org/10.1038/s41587-022-01361-8) of all called genes.
description: # Bulk and single-cell RNA-seq of human fetal pancreatic organoids #
 **Bulk RNA sequencing** Human fetal pancreatic organoids (hfPOs) were
 cultured in expansion, endocrine, or acinar cell differentiation
 conditions for 10 days.  Organoids were collected and RNA extraction was
 performed according to the manufacturer's instructions. RNA integrity
 was determined using the Agilent RNA 6000 Nano kit with the Agilent 2100
 Bioanalyzer (Agilent). RNA integrity (RIN) values ranged from 9.0–10.0.
 Samples used for bulk RNA sequencing did not have a RIN &lt;9.0. RNA
 concentrations were determined using the Qubit RNA HS Assay Kit (Thermo
 Fisher). RNA libraries were prepared with the TruSeq Stranded messenger
 RNA polyA kit and paired-end (2 × 50 base pairs) sequenced on an Illumina
 NextSeq 2000. Reads were mapped to the human GRCh38 genome assembly using
 STAR
 (DOI: [10.1093/bioinformatics/bts635](https://doi.org/10.1093/bioinformatics/bts635)). ## Datasets File: Bulk_OrgsCystVSbud\ Bulk-seq of hfPOs of either cystic or budding morphology cultured in expansion medium. Count matrix, sample identity, and gene names. File: Bulk_OrgsExpVSDiff: \ Bulk-seq of hfPOs cultured in expansion and endocrine differentiation medium. Count matrix, sample identity, and gene names. File: Bulk_OrgsExpVSAciDiff: \ Bulk-seq of hfPOs cultured in expansion and acinar differentiation medium. Count matrix, sample identity, and gene names. ## File details ### Details for: Bulk\_OrgsCystVSbud.csv Description: A comma-delimited file that contains read counts that represent the normalized number of sequencing reads mapped to each gene/transcript. Format(s): .csv Sequencing Technology: Illumina Variables: * Sample names (columns) in the form hfPO_lineNumber \_morphology e.g. hfPO7_cyst * Gene names (rows) ### Details for: Bulk\_OrgsExpVSDiff.csv Description: A comma-delimited file that contains read counts that represent the normalized number of sequencing reads mapped to each gene/transcript. hfPOs are cultured in either expansion or differentiation medium. Format(s): .csv Sequencing Technology: Illumina Variables: * Sample names (columns): * Exp1: Expansion medium biological replicate 1 * Exp2: Expansion medium biological replicate 2 * Exp3: Expansion medium biological replicate 3 * Diff1: Expansion medium biological replicate 1 * Diff2: Expansion medium biological replicate 2 * Diff3: Expansion medium biological replicate 3 * Gene names (rows) #### Details for: Bulk\_OrgsExpVSDiff.csv Description: A comma-delimited file that contains read counts that represent the normalized number of sequencing reads mapped to each gene/transcript. hfPOs are cultured in either expansion or endocrine differentiation medium. Format(s): .csv Sequencing Technology: Illumina Variables: * Sample names (columns): * Exp1: Expansion medium biological replicate 1 * Exp2: Expansion medium biological replicate 2 * Exp3: Expansion medium biological replicate 3 * Diff1: Expansion medium biological replicate 1 * Diff2: Expansion medium biological replicate 2 * Diff3: Expansion medium biological replicate 3 * Gene names (rows) ### Details for: Bulk\_OrgsExpVSAciDiff.csv Description: A comma-delimited file that contains read counts that represent the normalized number of sequencing reads mapped to each gene/transcript. hfPOs are cultured in either expansion or acinar differentiation medium. Format(s): .csv Sequencing Technology: Illumina Variables: * Sample names (columns): * Exp1: Expansion medium biological replicate 1 * Exp2: Expansion medium biological replicate 2 * Exp3: Expansion medium biological replicate 3 * Diff1: Expansion medium biological replicate 1 * Diff2: Expansion medium biological replicate 2 * Diff3: Expansion medium biological replicate 3 * Gene names (rows) # **Single-cell RNA sequencing** For scRNA-seq cells were FACS sorted into 384-well plates pre-printed with primers (Single Cell Discoveries) and stored at -80°C until used for library preparation. Libraries were prepared according to the previously published VASA-Seq protocol for VASA-plate(DOI: [https://doi-org.utrechtuniversity.idm.oclc.org/10.1038/s41587-022-01361-8](https://doi-org.utrechtuniversity.idm.oclc.org/10.1038/s41587-022-01361-8))  and sequenced on a NextSeq2000, high-output 100 cycles flowcell (Illumina). Processed, normalized RNA-seq data performed according to the VASA-Seq workflow (DOI: [https://doi-org.utrechtuniversity.idm.oclc.org/10.1038/s41587-022-01361-8(opens in new window)](https://doi-org.utrechtuniversity.idm.oclc.org/10.1038/s41587-022-01361-8) of all called genes. ## Datasets #### Processed (post-QC and filtering) File: count_norm_processed.csv .gz\ Processed count matrix with normalised reads, post-QC, and filtering. File: obs_metadata_processed.csv\ observation metadata table for the processed count matrix, with information about tissue origin, cluster number cell type, etc. File: var_metadata_processed.csv\ variable metadata table for the processed count matrix, with information about the gene name, total counts, mean counts, etc. #### Unprocessed (pre-QC and filtering) File: counts_unprocessed.csv.gz \ Unprocessed count matrix with normalised reads, post-QC, and filtering. File: obs_metadata_unprocessed.csv\ observation metadata table for the unprocessed count matrix, with information about tissue origin, cluster number cell type, etc. File: var_metadata_unprocessed.csv\ variable metadata table for the unprocessed count matrix, with information about Gene name, total counts, mean counts, etc. ## File details ### Details for: count\_norm\_processed.csv .gz Description: A comma-delimited file that contains read counts that represent the normalized number of sequencing reads mapped to each gene/transcript. Format(s): .csv Sequencing Technology: Illumina ### Details for: obs\_metadata\_processed.csv Description: A comma-delimited file that contains observation metadata, providing information about each cell in the scRNA-seq dataset. Each row corresponds to a single cell, and each column represents a different metadata attribute. Format(s): .csv Variables: * Plate_number: The plate number on which the cells were processed. Pl1- Pl8. * Cell_ID: A unique identifier for each cell. E.g. hfPanc1_4-001.  In the format: hfPanc_seqbatch number- cell number * total_counts: The total number of reads mapped to genes in each cell. * total_counts_mt: The total number of reads mapped to mitochondrial genes in each cell. * pct_counts_mt: The percentage of reads mapped to mitochondrial genes in each cell. * total_counts_ribo: The total number of reads mapped to ribosomal genes in each cell. * pct_counts_ribo: The percentage of reads mapped to ribosomal genes in each cell. * percent_mt2: same as pct_counts_mt * n_counts: The number of counts detected in each cell. * n_genes: The number of genes detected in each cell. * S_score: A score representing the cell cycle S phase activity. * phase: The assigned cell cycle phase based on the scores (e.g., G1, S, G2M). * leiden_0.4 to leiden_4.5: Cluster assignments from the Leiden clustering algorithm at different resolutions (0.4 to 2.0). * louvain_0.4 to louvain_2.0: Cluster assignments from the Louvain clustering algorithm at different resolutions (0.4 to 2.0). * kmeans_5 to kmeans_15: Cluster assignments from the kmeans clustering algorithm at different resolutions (5 to 15). * hclust_5 to kmeans_15: Cluster assignments from the hclust clustering algorithm at different resolutions (5 to 15). * cell type: The annotated cell type for each cell * Acinar * Acinar Progenitor * Ductal * Endocrine ### Details for: count\_norm\_unprocessed.csv .gz Description: A comma-delimited file that contains read counts that represent the unprocessed number of sequencing reads mapped to each gene/transcript. Format(s): .csv Sequencing Technology: Illumina ### Details for: obs\_metadata\_unprocessed.csv Description: A comma-delimited file that contains observation metadata, providing information about each cell in the scRNA-seq dataset. Each row corresponds to a single cell, and each column represents a different metadata attribute. Format(s): .csv Variables: * Plate_number: The plate number on which the cells were processed. Pl1- Pl8. * Condition: Differentiation or Expansion * Cell_ID: A unique identifier for each cell. E.g. hfPanc1_4-001. In the format: hfPanc_seqbatch number- cell number * total_counts: The total number of reads mapped to genes in each cell. * total_counts_mt: The total number of reads mapped to mitochondrial genes in each cell. * pct_counts_mt: The percentage of reads mapped to mitochondrial genes in each cell. * total_counts_ribo: The total number of reads mapped to ribosomal genes in each cell. * pct_counts_ribo: The percentage of reads mapped to ribosomal genes in each cell. * percent_mt2: same as pct_counts_mt * n_counts: The number of counts detected in each cell. ### Details for: var\_metadata\_processed.csv Description: A comma-delimited file that contains observation metadata, providing information about each cell in the scRNA-seq dataset. Each row corresponds to a single cell, and each column represents a different metadata attribute. Format(s): .csv Variables: * Gene_name: Gene names * mt: is the gene a mitochondria gene, TRUE or FALSE * ribo: is the gene a ribosome gene, TRUE or FALSE * n_cells_by_counts: the number of cells in which a particular gene was detected. * mean_counts: the average expression level of a gene across all cells. * pct_dropout_by_counts:  the percentage of cells where a gene has zero counts (not detected). * total_counts: the total number of counts for a gene across all cells. * n_cells: the number of cells in which a gene or feature is present. ### Details for: var\_metadata\_unprocessed.csv Description: A comma-delimited file that contains observation metadata, providing information about each cell in the scRNA-seq dataset. Each row corresponds to a single cell, and each column represents a different metadata attribute. Format(s): .csv Variables: * Gene_name: Gene names - mt: is the gene a mitochondria gene, TRUE or FALSE - ribo: is the gene a ribosome gene, TRUE or FALSE - n_cells_by_counts: he number of cells in which a particular gene was detected. - mean_counts: the average expression level of a gene across all cells. - pct_dropout_by_counts:  the percentage of cells where a gene has zero counts (not detected). - total_counts: the total number of counts for a gene across all cells.