{"database":"biostudies-arrayexpress","file_versions":[],"scores":null,"additional":{"submitter":["Thomas Åskov Pedersen"],"organism":["Homo sapiens"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/E-MTAB-16620"],"description":["Single-nuclei RNA sequencing (snRNA-seq) data of human adipose tissue. The samples are a part of a study investigating the effect of Lifestyle intervention on human adipose tissue. The 10 participants represented in this study were severely obese at project start with a mean BMI of 46.0 ± 3.1 kg/m2 and a BMI range of 31.4–63.0 kg/m2, with a 50/50 female/male ratio. After intervention mean BMI was 40.8 ± 2.8 kg/m2 with BMI range: 27.4–57.5 kg/m2. These participants took part in 15-weeks of consecutive lifestyle intervention consisting of a hypocaloric diet and moderate physical activity. Hypocaloric diet was calculated for the specific participant and calculated to reduce the subject’s body weight by approximately 1% per week. The exercise consisted of 2-3 hours of moderate-intensity physical activity (e.g. walking or swimming) 5 days per week. The specific details are reported in Bruun et al. Am J Physiol Endocrinol Metab, 2006 (https://doi.org/10.1152/ajpendo.00506.200). The snRNA-seq data contains nuclei isolated from 19 available biopsies with 9 samples before intervention and 10 samples after intervention.The RNA quality of one of the baseline samples was too low to proceed with snRNAseq (sample J \"before\" was excluded in the manuscript, but is included here)."],"repository":["biostudies-arrayexpress"],"sample_protocol":["Library Construction - For isolation of single-nucleus RNA, the samples were run using the 10X Genomics Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 on a 10X Chromium Controller according to manufacturer’s instructions (protocol version “CG000204_ChromiumNextGEMSingleCell3_v3.1_Rev_D”)","Sample Collection - The adipose biopsies have already be analysed in a previous study: Bruun et al. Am J Physiol Endocrinol Metab, 2006 (https://doi.org/10.1152/ajpendo.00506.200). Data and human samples were collected before (baseline) and repeated after the 15-weeks of lifestyle intervention. At least 6 hours before sampling the subjects were fasting and had not engaged in vigorous physical activity. Fasting and inactivity periods were standardized between baseline and after intervention.","Sequencing - The libraries were sequenced on an Illumina NovaSeq 6000 sequencing system and run first using the NovaSeq 6000 SP Reagent Kit v1.5 (200 cycles) (Illumina) and then using the NovaSeq 6000 S4 Reagent Kit v1.5 (200 cycles) (Illumina), reads from later runs were pooled. The S4 is estimated to have 16–20 billion Paired-end Reads passing filter, which results in a sequencing depth of approx. 100.000 reads per nucleus. The pooled library with nuclei isolated from 20 samples were loaded onto a single chip and spiked with 1% PhiX (Illumina). The NovaSeq 6000 was run according to the manufacturer’s instructions, with sequencing run set to as described in (“CG000204_ChromiumNextGEMSingleCell3_v3.1_Rev_D”). Briefly, we used paired-end, single indexing with the following cycle settings: Read 1 at 28 cycles (10x Index and UMI), 8 cycles (i7 Index), 0 cycles (i5 Index) and Read 2 at 91 cycles.","Nucleic Acid Extraction - Nuclei were isolated using a modified version of the Nuclei Isolation Kit: Nuclei PURE Prep, cat. no. NUC201 from Sigma-Aldrich. The entire protocol was carried out on ice. The tissue was lysed in ice-cold lysis buffer (Nuclei Pure Lysis Buffer, Sigma cat. no. L9286, with 1 mM DL-Dithiothreitol (DTT), Sigma cat. no. D9779, and 0.01% Triton X-100, Sigma cat. no. T1565). Homogenized for 30 seconds using an ULTRA-TURRAK TP18/10 homogenizer (20,000 rpm) for 10 minutes and filtered. Nuclei were pelleted by centrifugation and washed in wash buffer (1x DPBS, no calcium, no magnesium, Gibco cat. No. 14190, with 1% Human Serum Albumin Sigma cat. No. A1887, and 0.2 u/µL RNasin Plus RNase Inhibitor [40 u/µL], Promega cat. No. 2611). The lysate was then mixed with 1.8 M Su-crose Cushion Solution (mix of Nuclei PURE 2 M Sucrose Cushion Solution, Sigma cat. no. S9308, Nuclei PURE Sucrose Cushion Solution, Sigma cat. no. S9058 and 0.83 mM DTT). The mixture was then loaded on top of 1.8 M Sucrose Cushion Solution, and centrifuged. The supernatant was re-moved and nuclei-containing pellet was resuspended, centrifuged and filtered. The final pellet was resuspended in approximately 200 µL wash buffer and counted using NucleoCounter NC-200 (ChemoMetec cat. no. 900-0201) to determine nuclei concentration."],"figure_sub":["Organization","MINSEQE Score","Assays and Data","Processed Data","MAGE-TAB Files"],"data_protocol":["Data Transformation - BCL files were demultiplexed into FASTQ files using Cellranger mkfastq software (v6.1.1) [1].  Salmon Alevin v1.4.0 [2] and Alevin-fry v0.3.0 [3] were used to perform pseudoalign-ment of the FASTQ files to a customized GRCh38 genome (assembly GCF_000001405.26) [4], modified into discriminating between mature mRNA and pre-mRNA using the make_splici_txome function from alevin-fry-tutorials [5]. Cell calling was done on the unfiltered count matrix with barcodeRanks function from the Drop-letUtils package [6-8]. Ensembl gene IDs were mapped to symbol with the mapIds func-tion from the AnnotationDbi package [9]. The Seurat objects were converted to a single-CellExperiment object. RNA counts were log-normalized and PCA was performed. An individual doublet score for each cell was then computed with the computeDoubletDensi-ty function from the scDblFinder package [10, 11]. After processing the individual se-quence reactions, all results were aggregated into a single object. All individual reactions were filtered for common genes between them and then merged. Genes without any de-tection in the merged objects were then removed, and all blacklisted cells were removed. The object was then normalized using SCTransform [12], and PCA was performed. The Global dimensions were calculated using maxLikGlobalDimEst function from the intrin-sicDimension library [13]. Using the Seurat (v4) package we ran RunUMAP, DimPlot, FindNeighbors, and FindClusters functions (using the Leiden algorithm [14]) [15]. The resulting data was saved as a filtered object containing the filtered RNA and SCT assays. The continued analysis was run on the SCT assays. Upon analyzing the data, two clusters were removed that likely represented doublets, due to the presence of multiple key mark-er genes   1. Zheng, G.X.Y., et al., Massively parallel digital transcriptional profiling of single cells. Nature Communications, 2017. 8(1): p. 14049 DOI: 10.1038/ncomms14049. 2. Srivastava, A., et al., Alevin efficiently estimates accurate gene abundances from dscRNA-seq data. Genome Biology, 2019. 20(1): p. 65 DOI: 10.1186/s13059-019-1670-y. 3. He, D., et al., Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data. Nature Methods, 2022. 19(3): p. 316-322 DOI: 10.1038/s41592-022-01408-3. 4. Genome Reference Consortium. Genome assembly GRCh38. NCBI RefSeq assembly GCF_000001405.26 2017; Available from: https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.26/. 5. alevin-fry-tutorials. Resolving the splicing origins of UMIs to improve the specificity of single-cell RNA-seq transcriptome mapping with alevin-fry. 2021  [cited 2021 22-Nov]; Available from: https://combine-lab.github.io/alevin-fry-tutorials/2021/improving-txome-specificity/. 6. Griffiths, J.A., et al., Detection and removal of barcode swapping in single-cell RNA-seq data. Nature Communications, 2018. 9(1): p. 2667 DOI: 10.1038/s41467-018-05083-x. 7. Lun, A.T.L., et al., EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biology, 2019. 20(1): p. 63 DOI: 10.1186/s13059-019-1662-y. 8. Lun, A., et al. DropletUtils - Utilities for Handling Single-Cell Droplet Data. 2017  [cited 2021 22-Nov]; Available from: https://bioconductor.org/packages/release/bioc/html/DropletUtils.html. 9. Pagès, H., et al. AnnotationDbi - Manipulation of SQLite-based annotations in Bioconductor. 2007  [cited 2021 22-Nov]; Available from: https://bioconductor.org/packages/release/bioc/html/AnnotationDbi.html. 10. Germain, P., et al., Doublet identification in single-cell sequencing data using scDblFinder [version 2; peer review: 2 approved]. F1000Research, 2022. 10(979) DOI: 10.12688/f1000research.73600.2. 11. Germain, P.-L. and A. Lun. scDblFinder. 2019  [cited 2021 22-Nov]; Available from: https://bioconductor.org/packages/release/bioc/html/scDblFinder.html. 12. Hafemeister, C. and R. Satija, Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology, 2019. 20(1): p. 296 DOI: 10.1186/s13059-019-1874-1. 13. Facco, E., et al., Estimating the intrinsic dimension of datasets by a minimal neighborhood information. Scientific Reports, 2017. 7(1): p. 12140 DOI: 10.1038/s41598-017-11873-y. 14. Traag, V.A., L. Waltman, and N.J. van Eck, From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 2019. 9(1): p. 5233 DOI: 10.1038/s41598-019-41695-z. 15. Hao, Y., et al., Integrated analysis of multimodal single-cell data. Cell, 2021. 184(13): p. 3573-3587.e29 DOI: https://doi.org/10.1016/j.cell.2021.04.048."],"omics_type":["Metabolomics","Unknown","Transcriptomics","Genomics","Proteomics"],"instrument_platform":["10X Chromium Controller","Illumina NovaSeq 6000"],"study_type":["RNA-seq of coding RNA from single cells"],"species":["Homo sapiens"],"pubmed_authors":["Thomas Åskov Pedersen"],"additional_accession":[]},"is_claimable":false,"name":"Human adipose tissue single-nucleus RNA-sequencing before and after lifestyle intervention","description":"Single-nuclei RNA sequencing (snRNA-seq) data of human adipose tissue. The samples are a part of a study investigating the effect of Lifestyle intervention on human adipose tissue. The 10 participants represented in this study were severely obese at project start with a mean BMI of 46.0 ± 3.1 kg/m2 and a BMI range of 31.4–63.0 kg/m2, with a 50/50 female/male ratio. After intervention mean BMI was 40.8 ± 2.8 kg/m2 with BMI range: 27.4–57.5 kg/m2. These participants took part in 15-weeks of consecutive lifestyle intervention consisting of a hypocaloric diet and moderate physical activity. Hypocaloric diet was calculated for the specific participant and calculated to reduce the subject’s body weight by approximately 1% per week. The exercise consisted of 2-3 hours of moderate-intensity physical activity (e.g. walking or swimming) 5 days per week. The specific details are reported in Bruun et al. Am J Physiol Endocrinol Metab, 2006 (https://doi.org/10.1152/ajpendo.00506.200). The snRNA-seq data contains nuclei isolated from 19 available biopsies with 9 samples before intervention and 10 samples after intervention.The RNA quality of one of the baseline samples was too low to proceed with snRNAseq (sample J \"before\" was excluded in the manuscript, but is included here).","dates":{"release":"2026-02-11T00:00:00Z","modification":"2026-05-27T12:33:20.837Z","creation":"2026-02-09T14:35:56.045Z"},"accession":"E-MTAB-16620","cross_references":{"ENA":["ERP188894"],"EFO":["EFO_0002944","EFO_0004170","EFO_0005684","EFO_0005518","EFO_0003816","EFO_0004184"]}}