dataesr/scientific-paragraphs-categorization huggingface dataset:
Cell class-based analyses successfully integrates data across studies As much of the biological variability overlapped between cohorts, we performed data integration across studies. In contrast to the labelcentric analysis presented in Fig. 1b , this approach facilitates the identification of rare cell types which risk being omitted in more sparsely sampled datasets. Many tools are available for integrative analyses and all of them introduce different biases where a trade-off between overfitting vs. insufficient integration must be considered  21, 22. In a first step, we applied single-cell annotation using variational inference (scANVI) as prior knowledge of cell type annotations can improve integration results  23. In line with the findings in Fig. 1a , b, data from Hildreth et al. could not be integrated with scANVI and this dataset was therefore excluded from all subsequent analyses (Supplementary Fig.
Our results showed that (i) Harmony performed well for FAP integration, (ii) scVI and rPCA provided the best results across all scores for methods, cohorts, and depots, and (iii) kBET showed overall the lowest rejection rates for scVI (Fig. 1e , Supplementary Fig. 2a, b ). This in combination with a previous report suggesting that scVI works best for complex data  27  , prompted us to apply this method to all four cell classes separately. Given that the included datasets varied in object number, we capped them to contain similar object counts to reduce bias and enhance integration (Supplementary Fig. 2c, d ). Based on this, we included an optimal number of objects for each cell class which resulted in cell clusters represented by most studies and methods (Supplementary Fig. 2d, e ). With these criteria, we created WAT annotation models which are publicly available and can be applied in future studies (see Methods).
Adipocytes display inconsistent heterogeneity between studies In contrast to scSeq, snSeq allows for analyses of mature adipocytes. In the present study, we analyzed snSeq datasets from subcutaneous (n = 4), omental (n = 2), and perivascular (n = 1) fat depots. As with the other cell classes, we jointly analyzed objects classified as fat cells first. However, this resulted in poor ARI, kBET, and LISI indices (Fig. 1e , Supplementary Fig. 2b ), suggesting that complete data integration was not possible to achieve between studies and depots (Supplementary Fig. 5a ). As a next step, we analyzed the depots separately. This, however, did not improve our integration as most clusters separated according to studies rather than biological similarities between datasets (Supplementary Fig. 5b ). A possible reason for incomplete data integration is that there are few common features between studies, even in the low-dimensional space  21.
To test if data harmonization was influenced by a limited degree of overlap, we next analyzed the studies individually. We focused on data produced by Emont et al. 9  and our own results (Massier et al. #1) generated in subcutaneous WAT as they contain the largest number of subjects and objects. We selected the top 50 marker genes for all clusters and found that adipocytes displayed lower foldchanges compared to the other cell classes (Fig. 5a , left panel). This indicates that the degree of cell heterogeneity is less pronounced in adipocytes. However, there may still be consistencies between the studies. We, therefore, transferred cell type classifications between datasets. In comparison with the other cell types where marker genes displayed clear enrichments and overlap between studies, we found a low overlap and a limited set of reproducible adipocyte marker genes across studies (Fig.
5a , right panel). These included a cluster of genes encoding proteins involved in lipid metabolism e. g. , ABCD2, ACACB, CD36, DGAT2, GPAM, HACD2, and LPL (Fig. 5b ). Because of the modest heterogeneity and low reproducibility across studies of adipocytes, we benchmarked the snSeq data and our recently published STx-based results 7 against rRNA-depleted 45 and cap-trapped  41  bulk RNAseq of isolated human mature fat cells. This revealed that the STx data were positioned close in space to the bulk results, while all snSeq datasets clustered together with considerably fewer similarities to the other samples (Fig. 5c , upper panel). To test the reproducibility of these results, we generated snSeq, STx, and bulk RNAseq from the same individual and repeated the analyses. This confirmed that STx correlated better with bulk RNAseq data than snSeq (Fig.
Nevertheless, a limitation with the present work is that we did not investigate longitudinal data following other types of interventions including, life-style-related changes, and/or pharmacological treatments. Taken together, in this meta-analysis of 17 datasets and >800 bulk transcriptional profiles from eight clinical studies, we have comprehensively defined the cellular composition of human WAT in health and metabolic disease. Thus, by jointly analyzing data from multiple types of studies, we have created a framework that is easily accessible and includes additional tools for WAT analyses such as new models in CellTypist. We, therefore, provide a rich resource to facilitate future studies of specific WAT-resident cell types in relation to aspects not investigated herein, such as ethnic differences and the impact of therapeutic interventions.
Clinical studies Inclusion and Ethics. This study was performed in agreement with the Declaration of Helsinki. Studies of cohorts presented for the first time herein (Massier et al. #1-4), were approved by the regional ethics boards in Stockholm (clinical trials identifiers: NCT01785134 and NCT01727245) and Leipzig (approval numbers: 159-12-21052012 and 004/21-ek) and explained in detail to each participant who gave informed written consent. For retrospective analyses of published data, the studies have been approved by the respective ethical boards where informed written consent was obtained from all participants. For data detailed in Fig. 8d , e, primary outcomes for both clinical trials (NCT01785134 (DEOSH) and NCT01727245 (NEFA)) have been described at clinicaltrials. gov and previously published  7, 53, [57] [58] [59]  , and the studies have been completed.
10X-libraries were prepared with the Chromium Single-Cell v3. 1 reagent kit following the manufacturer's protocol (10X Genomics). Suspensions containing around 1200 nuclei per μl were loaded on Chip G followed by reverse transcription to obtain cDNA, which subsequently was amplified and used for library construction. After preparation, the libraries were sequenced on a NovaSeq 6000 platform (Illumina). For data analysis, the human genome assembly GRCh38. p13 was used. Mapping was performed using 10x Genomics Cell Ranger (v6. 0. 2). CellBender (v0. 2. 0) 61 was used on 'raw_feature_bc_matrix' to remove empty droplets and ambient RNA; scDblFinder (v1. 5. 11)  62  was applied to exclude potential doublets. Downstream analyses were performed as all other included studies (see below). Samples of cohort 2 and 4 were collected and processed in Leipzig (Germany) by P.
A. N. N. Paired samples of omental and subcutaneous WAT were obtained from female patients with obesity undergoing different bariatric surgery procedures (Roux-en-Y Gastric Bypass and sleeve gastrectomy). After collection, samples were washed using PBS, placed on ice until the end of the surgery procedure, snap-frozen in liquid nitrogen and stored at -80 °C for later use. Sequencing analyses were performed on isolated nuclei as detailed above. Single-nuclei RNAseq. libraries were generated using the Chromium Single Cell 3′ v3 assay (10× Genomics) and sequenced with NovaSeq 6000 S4 flow cell platform (Illumina). Raw reads were aligned to the human genome (hg38) and cells were called using 10x Genomics Cell Ranger (v. 6. 0. 1). Collection of publicly available datasets. Peer-reviewed WAT datasets containing either snSeq, scSeq or STx with publicly available results published until 31.
03. 2022 were included in the present metaanalysis (Supplementary Table  1 ). If not present in the published articles, the corresponding authors were contacted by email to obtain information regarding sample numbers and gender as well as ranges of age and BMI. Re-analysis of publicly available datasets. Publicly available datasets were re-analyzed with Seurat v4. 1. 0 16 in R v4. 1. 2  63 (pipeline available via GitHub [https://github. com/lmassier/hWAT_singlecell]). Mitochondrial and hemoglobin genes, as well as further confounding transcripts, including MALAT2 and NEAT1, were removed prior to analysis. All data were normalized using sctransform  64  and corrected for subject effects using Harmony v0. 1. 0 25 before performing independent component analysis  65. Clusters were determined using FindNeighbors and FindClusters in Seurat after generating Uniform Manifold Approximation and Projection (UMAP) data projections using RunUMAP 66.
Spatial data were analyzed as described recently  7. Cluster classification and annotation. Cluster classification for individual cohorts was assisted by a supervised network analysis where each cell cluster was represented as a node. Nodes were connected by edges by calculating overlap percentages of positively enriched marker genes (FDR-adjusted p value <0. 05). Minimum requirements for edge connections were >15% genes overlapped in one of the two nodes and >5% in both nodes. Based on this, Jaccard similarity scores were calculated, and edges were created based on the five highest values. The network was built in R v4. 1. 2. using igraph v1. 2. 11 and visualized in Cytoscape 3. 7. 1 using Rcy3 v2. 14. 1  67. Integrated cluster annotations were performed manually using multiple reference datasets (Supplementary Table  2 ).
Data integration and bechmarking We evaluated the following integration tools: rPCA provided with Seurat 16 , Harmony 25 , BBKNN as well as scVI. In addition to identification/ clustering of prominent marker genes in the integrated data, benchmarking included calculations of ARI coefficients, LISI scores, kBET acceptance rates (1-rejection rate) for integration across methods, depots and cohorts. ARI was calculated using the adj. rand. index function in pdfCluster v1. 0-3 by supplying factors of either depots, methods or cohorts in addition to the identified clusters. Average LISI scores were estimated using lisi v1. 0 compute_lisi command adding embeddings of the UMAPs along the meta data. kBET scores were calculated using kBET v0. 99. 6 and the respective integrated reductions (e. g. , Harmony or scVI) in the Seurat object.
24 (setup_anndata, SCVI, train, get_laten-t_representation). Subsequent subcluster analyses were performed based on different depots (omental, perivascular, and subcutaneous WAT) or lineages (e. g. , myeloid and lymphoid cells). When comparing similarity between individual data sets using Jaccard index, marker genes were selected based on a log 2 fold-change > 0. 5, and adjusted p value <0. 05.
Deconvolution Deconvolution of bulk transcriptomic data of human WAT was performed using BisqueRNA v1. 0. 5 68 using the marker gene approach with a minimum gene count of six. To validate depot differences, two cohorts  32, 33  with available data from omental and subcutaneous WAT were used. We also retrieved sequencing as well as clinical phenotype data from six additional published datasets  53, 54, [69] [70] [71] [72]  (Supplementary Table  3 ). Deconvolved data were compared in a meta-approach using Hmisc v4. 6-0 to calculate Spearman correlation and meta v5. 2-0 to calculate and visualize summarized results using both common and random models  73.
0001. d Stability of clusters A and B were determined in the two indicated cohorts where WAT bulk transcriptomes were generated before and two years following bariatric surgery. e Effects of weight loss induced by bariatric surgery in two cohorts. Panels display deconvolution scores for the indicated cell subpopulations. p values were calculated by two-sided paired sample t test (n = 15; Petrus et al. 54  and n = 37; Kerr et al. 53  ) and boxplots are presented as interquartile range plus median and Tukey whiskers with individual, paired data points. Source data are provided as a Source data file. (SVF) were thawed and stained with different antibody combinations prior to analysis with a flow cytometer. Washing steps were performed with wash buffer (PBS supplemented with 0. 5% BSA [#A4503, Sigma-Aldrich] and 2 mM EDTA [#E7889, Sigma-Aldrich]) and the cells were centrifuged at 200 × g for 10 minutes.
Prior to mounting in DAKO Fluorescence mounting media, the samples were washed with PBS and swirled in distilled water. For immunostaining of both myC02 and sfC12/endothelial cells, images were acquired using the NIS Elements software, a CSU-X1 spinning disk confocal (Yokogawa) mounted on an inverted TiE microscope (Nikon) equipped with a ×1. 2 magnification lens and a Kinetix back-illuminated sCMOS camera (pixel size 6. 5 μm QE95%) (Photometrics). A Nikon ×20/0. 75 air objective was used to acquire images.
Bulk RNA sequencing of in vitro adipogenesis To annotate FAP clusters, we retrieved bulk sequencing data of human subcutaneous adipocyte precursor cells undergoing adipogenesis from four model systems: (i) adipose-derived stem cells 41 , (ii) primary SVF-derived cells from subcutaneous WAT 41 , (iii) Simpson-Golabi-Behmel (SGBS) syndrome cells  42  , and (iv) human multipotent adiposederived stem (hMADS) cells  43. Scores were generated for each FAP cluster at each timepoint of adipogenesis by calculating expression fold-changes of top 30 marker genes over the background of the respective bulk datasets.
Acknowledgements We thank the  National Genomics Infrastructure (NGI) in Genomics Production Stockholm , the  Swedish National Infrastructure for Computing (SNIC)  at  UPPMAX , and the  Centre for Bioinformatics and Biostatistics at the Karolinska Institutet  for providing support regarding sequencing and computational infrastructure. We also thank  Drs. Ana Maria Suzuki  and  Thais de Castro Barbosa  for valuable technical help in processing the clinical samples. Fluorescent imaging was performed at the  Live Cell imaging Core facility/Nikon Center of Excellence , at the  Karolinska Institute , supported by grants from the  Swedish Research Council, KI infrastructure, and Centre for Innovative Medicine. This work was supported by grants from  Margareta af Uggla 's foundation (M. R. ),  Knut & Alice Wallenberg 's foundation (M.
Data availability Processed data generated in this study have been deposited in the Mendeley database (https://doi. org/10. 17632/y3pxvr4xbf. 2). The raw sequencing data generated in this study have been deposited in the GEO database under the accession code GSE225700. The publicly available scSeq and snSeq data re-analyzed in this study were obtained through the GEO database under accession codes: GSE155960 11 , GSE156110  11  , GSE136230 10 , GSE128889  13  , GSE151889 14 , GSE128518 6 , GSE164528  15  , GSE135134 9 , GSE128889  9  as well as the ArrayExpress database under accession codes: E-MTAB-9199 8 , E-MTAB-6677  9. Data from Acosta et al. were received after communication with the author. The publicly available RNA sequencing and microarray data of human WAT re-analyzed in this study were obtained through the GEO database under accession codes: GSE25402 69 , GSE113080  70  , GSE199063  53  , GSE76399  32  , GSE59034 54 , GSE141221 71 , GSE95640  72.
This is partly explained because the repeat regions of many minisatellites are GC rich, because a limitation of available markers renders the technique noninformative in a few cases, and because minisatellite alleles are larger than microsatellite alleles, inducing less technical sensitivity  (9). The STRs correspond to DNA sequences made up of repeated ditri-or tetra-nucleotide motifs. As the number of repeats varies from one individual to another, these sequences become very informative polyallelic markers  (10). Separation and detection of the amplified PCR products is performed more frequently on an Applied Biosystems (ABI) automated DNA sequencer, and the analysis of results is performed using Genescan 2. 1 software (ABI)  (11, 12). STR allows to reach a detection sensitivity of between 1 and 5% (Table  1 ).
Flow Cytometry assessed the percentage of different subpopulations cells. The chimerism of each sample had been previously quantified by EFS national public market techniques, qPCR analysis using QTrace kits (JETA ® Molecular, Utrecht, Netherlands) and NGS analysis using AlloSeq HCT kits (CareDx ® , Brisbane, CA). In addition, chimerism was previously assessed by ddPCR (Bio-Rad ® , Hercules, CA) by a method developed  (21, 22)  using KMRtrack kits (GenDx ® , Utrecht, Netherlands). All donors and patients provided written informed consent, after which the clinical data were collected from each patient. The signed informed consent forms were recorded as a part of the patient's clinical record.
QIAcuity ™ dPCR method The QIAcuity is designed as a walk-away instrument that integrates and automates all plate processing steps. Only plate preparation must be performed manually before starting the run. In instrument, the first step consist in filling the plate's microchannels and partitions with the input volume in the wells. The second step is a high-accuracy plate thermocycler that performs the polymerase chain reaction. The cycling profile can be set in the QIAcuity Software Suite or the instrument software. The final step is the image acquisition of all wells. The user can select the detection channels in the experimental setup. The partitions that have a target molecule inside emit fluorescence light and are brighter than those without a target. Depending on the configuration of the instrument (2plex/ 5plex), there are up to 2 or 5 selectable detection channels.
An additional channel is used for detecting the base fluorescence of the master mix to determine the exact number of filled partitions and normalization of fluorescence data. Chimerism was quantified using the QIAcuity One, 5plex Device with a 5-color target multiplexing capability with several emission wavelengths, 518 to 548 nm (FAM, EvaGreen ® ), 550 to 564 nm (HEX, VIC, JOE), 580 to 606 nm (TAMRA, ATTO 550), 611 to 653 nm (ROX, Texas Red) and 654 to 692 nm (Cy5), with ROX dye used The 44-μL PCR mixture was pipetted into each of the inlet ports of the QIAcuity Nanoplate 26k 24 wells, which was then resealed with QIAcuity Nanoplate Seals using a roller. Finally, the plate was installed in the instrument, and all steps (priming, cycling and imaging) were performed automatically. The PCR program had to be adapted for chimerism analysis: 95°C for 2 min, followed by 45 cycles of 95°C for 15 s and 58°C for 1 min.
Image acquisition was performed using the following exposure times: green signal, 400 ms with the gain at 4 and yellow signal, 300 ms with the gain at 3. The time of the complete procedure was 2 h. Data were analyzed using the QIAcuity 2. 0. 20 software suite, which automatically calculates and defines the optimal threshold for discrimination between positive and negative partitions. This threshold can be modified manually. Quantification of donor or recipient chimerism is obtained by the ratio of the number of partitions to the informative target/ nonpolymorphic target.
QuantStudio Absolute Q method The QuantStudio Absolute Q Digital PCR system integrates all the steps required for dPCR, including digitalization, scanning, thermal cycling and data acquisition, into one instrument. The reaction mixture was pipetted into the MAP plate, and isolation buffer was added. The Applied Biosystems ™ QuantStudio ™ Absolute Q ™ MAP16 Digital PCR consumable is used with the QuantStudio Absolute Q Digital PCR system. The system is powered by microfluidic array plate (MAP) technology that enables consistent and automated separation of the sample into 20,480 microchambers, leading to a <1% coefficient of variation (CV). Chimerism was quantified using the QuantStudio Absolute Q, 5plex Device with a 4-color target multiplexing capability with several emission wavelengths, including a peak at 520 nm (FAM), peak at 560 nm (HEX, VIC), peak at 589 nm (ABY), peak at 625 nm (ROX) and peak at 684 nm (JUN, Cy5), with ROX dye used for reference.
For 1 sample and a reaction volume of 10 μL, we used 2 μL of 5x QuantStudio Absolute Q DNA Master Mix (ThermoFisher ® ), 2. 5 μL of 4x KMRtrack FAM, 0. 5 μL of 20x RPP30 gene control HEX and 5 μL of 30 ng/mL DNA (150 ng). MAP16 is partially usable and reusable; it has a format of 4x4 wells and has a barcode for traceability. Nine microliters of the PCR mixture were pipetted into each of the inlet ports of MAP16, and isolation buffer was added to the system. Finally, the plate was installed in the instrument, and all steps (digitization, thermal cycling, and data acquisition) are performed automatically. The PCR program did not need to be adapted for chimerism analysis: 96°C for 10 min, followed by 40 cycles of 96°C for 5 s and 60°C for 15 s. Image acquisition was performed using manufacturing settings.
The sequencing reaction was performed using the MiSeq v3 Reagent kit using 75 paired-end reads and dual barcoding. A modified sample sheet was prepared for the sequencing reaction, and data analysis of the fastq files was performed using AlloSeq HCT Software. The AlloSeq HCT chimerism study is possible with a kit of 24 or 48 (plate with precoated indices) samples. For one sample, the reaction volume was 40 μL with 4 μL of reverse index primers, 4 mL of direct index primers, 13 μL of AlloSeq HCT PCR Mix, 0. 8 μL of AlloSeq HCT PCR Enzyme, 2. 2 μL of AlloSeq HCT SNP Primer Pool and 16 μL of DNA at 0. 625 ng/mL (10 ng input). The unique amplification protocol couples both amplification and indexing into a single reaction, limiting user hands-on time and manipulation errors. The thermocycling protocol is unique and corresponds to 98°C for 3 min, 8 cycles of 96°C for 15 s + 70°C for 5 s + 57°C for 60 s + 72°C for 30 s, followed by 12 cycles of 96°C for 15 s and 72°C for 60 s, 72°C for 2 min and 10°C.
There was then a double purification step on the beads and finally a denaturation step with 0. 2 N sodium hydroxide for the sequencing reaction, which was performed using the MiSeq v3 reagent kit for 150 cycles. The total delivery time was approximately one day, with 2 h of hands-on time and 17 h of NGS sequencing. Data were analyzed using AlloSeq HCT v2 software, which automatically calculates chimerism values and has many alarm QC metrics, some of which are blocking and will prohibit clear display of potentially incorrect results. Genotyping is determined by the % variant allele frequency (VAF) calculation for each marker using the number of reads classified as "reference" (Ref) and "alternative" (Alt) of the reference sequence hg19 (human genome 19; Genome Reference Consortium). The %VAF is calculated as follows: Ref reads/(Ref reads + Alt reads).
By combining the data obtained from multiples of these posttransfer samples, the chimeric status of the obtained samples can be monitored. The NGStrack chimerism study was performed with a 96-well plate but for 12 samples because 7 mixes were needed for one sample. For one mix, the reaction volume was 20 μL with 2 μL of TRK Mix, 4 mL of TRKenzyme, 8 μL of resuspended IndX, 4 μL of nuclease-free water, and 2 μL of DNA (at 10-50 ng/mL for genotyping, at 10 ng/μL for monitoring). The amplification protocol was unique and corresponded to 95°C for 5 min, 35 cycles of 95°C for 15 s + 62°C for 30 s + 72°C for 1 min, followed by a cooling step at 15°C. There was then a purification step on beads at a 0. 6X proportion and finally a denaturation step with sodium hydroxide for the sequencing reaction, which was performed using MiSeq V2 sequencing reagents.