dataesr/scientific-paragraphs-categorization huggingface dataset:
The ATAC sequencing data (shown as bigwig coverage tracks) were retrieved from GEO (GSE77695). For each match of the c-Maf binding motif, numbers in parentheses indicate the log-likelihood ratio score and the raw P-value calculated by the FIMO tool. The transcripts of each gene are shown at the bottom of each panel. Mouse genome = mm9 and Integrative Genomics Viewer = v. 2. 8. 0  (Robinson et al, 2011 ). E ChIP-qPCR analysis of c-Maf occupancy on IL-4, IL-5, and IL-13 promoters in untreated mouse and human ILC2s (IL-2 stimulation) and in mouse and human ILC2s treated with IL-33 (n = 3 biological replicates). F Dot plot showing over-represented Gene Ontology gene sets among genes upregulated in wild-type compared with c-Maf knocked-down ILC2s. The color scale depicts the adjusted P-value, while the size of the dots and x-axis depict the absolute number or the proportion of upregulated genes included in each gene set, respectively.
Base calling, fastq file generation, and demultiplexing were performed using  Illumina RTA v. 1. 18. 66. 3 and Illumina pipeline v. 2. 19. 1. To remove adapter sequences and trim low-quality bases, raw sequencing reads were scanned using AdapterRemoval v. 2. 1. 7 with parameters -trimns and -trimqualities  (Schubert et al, 2016). Reads were aligned against the mouse GRCm38. 95 reference genome using STAR v. 2. 6. 0c (Dobin et al, 2013). Using samtools v. 1. 8 (Li et al, 2009) , we converted sequence alignment map files to bam files and merged the aligned reads spread across 3 lanes of each sample into a single bam file per sample. Numbers of reads per gene were summarized using the htseq-count function of the HTSeq package v. 0. 9. 1  (Anders et al, 2015). Computations were performed on the Vital-IT infrastructure of the Swiss Institute of Bioinformatics.
Subsequent statistical analyses were performed using R v. 3. 5. 3. Raw counts were normalized using the trimmed mean of M values (TMM) method, and normalization factors were calculated using the edgeR package v. 3. 24. 3  (Robinson et al, 2010). Counts were converted to log 2 counts per million (cpm) using the voom function implemented in the limma package v. 3. 38. 3  (Ritchie et al, 2015). Differentially expressed genes between wild-type and knockout mILC2s were determined by fitting a linear model and computing moderated t-statistics using the lmFit and eBayes functions of the limma package. The P-value of each gene was adjusted using the Benjamini-Hochberg (BH) procedure. To determine whether significant genes were enriched in specific functions, we performed overrepresentation analysis of Gene Ontology biological process gene sets using the enricher function of the clusterProfiler package v.
3. 10. 1  (Yu et al, 2012) , separating up-regulated and downregulated genes, and adjusting P-values using the BH procedure. We also used the enricher function to determine whether or not T helper signatures, CD8 T cell and Th2 memory signatures differed between c-Maf-depleted and wild-type mILC2s. Gene signatures consisted of Th1, Th2, and Th17 gene signatures (from Table  EV1  of  Lee et al, 2016) , based on data by  Stubbington et al (2015) , genes up-regulated in memory versus naive CD8 T cells obtained from MSigDB (https://www. gsea-msigdb. org/gsea/msigdb/index. jsp) and genes over-expressed in murine lung ST2 + Th2 cells (see Table  EV2  for gene lists). Finally, a heatmap of gene z-scores across samples was generated using the ComplexHeatmap package v. 1. 20. 0  (Gu et al, 2016).
In silico search of the c-Maf binding motif The sequences of the promoter regions of IL-4, IL-5, and IL-13 (including 100 base pairs upstream and downstream of promoters) were retrieved from the Eukaroytic Promoter Database (EPD  (Dreos et al, 2017) ). Regions of accessible chromatin upstream of these promoters were determined using ATAC-sequencing data obtained from ILC2s and Th2 cells sorted from mouse lungs (bigwig files downloaded from GEO, accession number: GSE77695; Shih et al, 2016). The c-Maf binding motif was retrieved from the MotifMap database  (Daily et al, 2011) , and potential matches for this motif were identified using the FIMO tool of the MEME Suite v5. 3. 3  (Grant et al, 2011).
Statistics GraphPad Prism software was used to perform statistical analyses. Shapiro-Wilk test was applied to determine normal distribution. Tests used are specified in the legend of each Figure. The data are shown by plotting individual data points, and the mean AE SEM. P < 0. 05 was considered statistically significant and labeled with *P < 0. 01 labeled with **P < 0. 001 with *** and P < 0. 0001 with ****. Experiments were analyzed in a blinded way, without knowing the genotype of the animals or the status of the human primary cells used.
Acknowledgments This work was funded by the  University of Lausanne  and grants from the  Max Clo€ etta Foundation  (G. V. ), the  Swiss National Foundation  (G. V. :  310030_182680 ; C. J:  PRIMA PR00P3_179727 ), the  Helmut Horten Foundation  (C. J. ), the  Umberto Veronesi Foundation  (G. E. ),  Bologna AIL  ( Associazione Italiana contro le Leucemie)/Section of Bologna, FATRO/Foundation Corrado  and  Bruno Maria Zaini-Bologna ,  Fabbri1905. Open access funding provided by  Universite de Lausanne.
the single-cell transcriptome in combination with single-cell TCR and surface epitope information. We identified six T H cell clusters displaying a naive phenotype based on RNA (CCR7, CD7, TCF7, LEF1 and SELL) and epitope expression (CD45RA + and CD45R0 low ) (Extended Data Fig. 5a ). CITE-seq confirmed elevated surface expression of CD25 in two clusters of naive T H cells (T na 4 and T na 5), and a cluster of central memory T (T CM ) cells (T CM 4) in twins with MS compared with unaffected twins (Extended Data Fig. 5b ). These clusters mapped in close proximity to the differential CD25 nodes of the mass cytometry dataset of twins with MS, thereby validating the high-throughput discovery CyTOF approach using an independent single-cell technology (Fig. 3c , Extended Data Fig. 5c ). Although the differential state T H cell nodes in twins with MS displayed a naive phenotype, the increased surface expression of CD25 in twins with MS was indicative of cellular activation.
Cytokine dysregulation in MS progression As CD25 has a crucial function regulating T cell proliferation and activation  29  , we next investigated the possible functional implications of increased expression of CD25 in the differential state transitional T H nodes of twins with MS. Polyclonal stimulation of PBMCs from twin pairs discordant for MS using phorbol 12-myristate 13-acetate and ionomycin was followed by analysis of cytokines and trafficking-related markers (Fig. 3f ). To anchor the CD25 differential state nodes in this additional dataset, a similar strategy as described above was applied. This revealed a T H cell node displaying a naive phenotype characterized by increased expression of CD25 as well as its ligand IL-2 (Fig. 3g , Extended Data Fig. 5h, i ). We observed a strong positive correlation between the size of inter-twin difference in IL-2 expression in the transitional T H cell node and the severity of MS in the affected twin, assessed by the expanded disability status scale (Fig.
MS T H cells display a trafficking signature To follow the hypothesis that the proliferative T H cell node in twins with MS could give rise to encephalitogenic T cells, we next measured the expression of trafficking molecules in the differential state T H cell node. Compared with cells from the unaffected twin siblings, the cells from twins with MS expressed significantly higher amounts of VLA4, which was further validated using our CITE-seq dataset (Fig. 3i , Extended Data Fig. 5o ). VLA4 is required for leukocyte migration into the CNS  35  and is directly targeted by natalizumab, an approved therapy for MS  36. Finally, CXCR4 transcripts were enriched in the two T H cell nodes that demonstrated increased expression of CD25 (T na 5 and T CM 4) in twins with MS compared with unaffected twins, further supporting the CNS trafficking potential of the identified transitional T H cells (Extended Data Fig.
Live-cell barcoding for mass cytometry To eliminate technical variability during sample processing and data acquisition, a restricted combinatorial 9-choose-3 live-cell barcoding strategy was applied, as described previously  14. To achieve this, anti-CD45 monoclonal antibodies (mAbs; BioLegend) were conjugated using MaxPar X8 polymers (Fluidigm) and palladium ( 104 Pd,  105  Pd, 106 Pd,  108  Pd and 110 Pd), indium ( 113 In and  115  In; all from Trace Sciences International) and tantalum ( 181 Ta; Sigma) isotopes. In addition, Y89-conjugated anti-CD45 mAbs (Fluidigm) were used. Twin pairs were randomized and PBMCs of twins with MS and unaffected twin siblings were barcoded in two batches before acquisition during two independent mass cytometry runs. After sample thawing and/or ex vivo activation cells were labelled with heavy-metal-tagged CD45 antibodies at 37 °C for 25 min in cell labelling medium (CLM; RPMI-1640, 4% FCS) on an orbital shaker (500 rpm).
In case of intracellular cytokine detection, the sample convolute was fixed in 1. 6% paraformaldehyde (Electron Microscopy Sciences) for 1 h at 4 °C and washed with permeabilization buffer. The sample was labelled with antibodies recognizing intracellular cytokines in 400 μl permeabilization buffer for 1 h at 4 °C. For both surface and nuclear antigen detection and intracellular cytokine detection, the labelled sample convolute was washed and incubated in 1X iridium intercalator solution (Fluidigm) at 4 °C overnight. The sample convolute was washed twice with PBS and twice with MaxPar water (Fluidigm) and following data were acquired.
Mass cytometry data acquisition and preprocessing Data were acquired on a CyTOF 2. 1 mass cytometer (Fluidigm) with daily instrument quality control and tuning. Acquisitions from two independent CyTOF runs, each containing both of the twin siblings, were normalized using five-element beads (Fluidigm)  52. To monitor potential batch effects, each independent run contained two normalization control samples that were present in both runs. Manual gating using FlowJo (TreeStar) was applied to identify live single cells in the sample convolute based on event length, centre, width, DNA ( 191 Ir and 193 Ir) and live/dead ( 195 Pt) channels. Following this, the sample convolute was debarcoded by utilizing Boolean gates of cells exclusively bearing three barcodes to prevent barcode mis-identification and facilitate doublet exclusion.
Mass cytometry data were transformed in the R environment using an inverse hyperbolic sine (arcsine) function with varying cofactors to account for labelling variability between individual markers. To remove residual batch effects between the two independent mass cytometry runs, individual markers of the sample convolute were aligned by modifying the arcsine cofactor to achieve the same mean in labelling intensity for both normalization controls. A marker-based percentile normalization was applied using the 99. 9th percentile of the transformed dataset. Analogously, individual cytokine positivity was determined by calculating the 99th percentile of the residual cytokine intensity using an unstimulated control.
Algorithm-guided analysis of the cohort of twins with MS The entire analysis was carried out in the statistical programming environment R in RStudio and Visual Studio Code (Microsoft). UMAPs were computed using the umap package with default parameters  53. For the generation of the reference framework, FlowSOM was applied  54  in a similar manner as described previously  51, 55. In brief, 100 clusters of the combined dataset were generated and metaclustering was performed based on the elbow criterion determined by the ConsensusClusterPlus package. Resulting metaclusters were manually merged and annotated based on the median expression profile of individual metaclusters and localization on the UMAP. After initial clustering yielded the main populations such as CD4 + T cells, CD8 + T cells, B cells and myeloid cells, each main population was iteratively clustered into subpopulations that depicted the nodes of the reference framework.
The data-driven analysis was carried out using the diffcyt package  56. For this, markers were separated into cell-state and cell-type markers (Supplementary Table  3 ) and the differential state diffcyt analysis was carried out using a design matrix considering the paired design. Significant immune features between all twins with MS and unaffected twin siblings were extracted using the moderated limma-trend method implemented in diffcyt, applying a false discovery correction according to the Benjamini-Hochberg approach  57. To exclude treatment-induced immune alterations, a second filter was applied to identify significant immune features between only untreated twins with MS and unaffected twin siblings. Accordingly, immune features were extracted that fulfilled both conditions: (1) they appeared significantly different between all twins with MS and unaffected twin siblings, and (2) were not elicited by disease-modifying therapy of the twin with MS.
Network visualizations to map diffcyt-generated clusters onto the reference framework or to map different mass cytometry panels, Infini-tyFlow data or CITE-seq data were generated using modifications of the grappolo and vite packages of the Scaffold framework  58. Resulting force-directed graphs were rearranged using the Fruchterman-Reingold and Kamada-Kawai algorithms implemented in the igraph package and modified and visualized using ggraph. Heatmaps were drawn using the pheatmap package. Correlograms were generated using the Hmisc and corrplot packages. All remaining plots were drawn using ggplot2.
Mapping of myeloid cell populations using InfinityFlow To fine-map the differential state phagocyte node, an extended myeloid reference framework was generated using the InfinityFlow data of healthy PBMCs containing 14 backbone markers and 332 predicted markers as published by ref. 24. To generate the extended myeloid reference nodes, FlowSOM clustering was applied as described above using the backbone markers for clustering. The InfinityFlow dataset contained an overlap of 32 markers with the mass cytometry dataset of twins with MS that facilitated the mapping of the diffcyt-differential state node on the extended reference nodes using the grappolo and vite packages as described above.
Single-cell sequencing data processing Cell Ranger software (10x Genomics, v. 6. 1) was used to demultiplex samples, process raw data, align reads to the GRCh38 human reference genome and summarize unique molecular identifier (UMI) counts. Filtered gene-barcode and cell-surface protein expression-barcode matrices that contained only barcodes with UMI counts that passed the threshold for cell detection were used for further analysis. Then, we processed the filtered UMI count matrices using the R package Seurat (version 4. 0. 3)  59. Cells that expressed fewer than 500 genes and/or >15% mitochondrial reads, and genes expressed in fewer than three cells were removed from the count matrix. After quality control, only raw gene counts in high-quality singlets were submitted to: log-normalization; identification of high-variable genes by using the vst method; scaling; and regression against the number of UMIs and mitochondrial RNA content per cell.
Similar to myeloid cells, only CD4 + T cells in which a TCR clonotype was detected, were subsetted and reanalysed. Single-cell TCRs were computed from the TCR enrichment sequencing data using Cell Ranger vdj pipeline (10x Genomics, v. 6. 1). CD4 + T cells containing more than two β-chains were removed. Only samples with data for both the sibling with MS and the unaffected sibling were used for downstream analysis: seven and eight twin pairs for myeloid and T H cell populations, respectively.
Downstream analysis of CITE-seq data Differentially expressed genes between MS-affected and unaffected twin siblings were computed using a logistic regression model using the twinship as a latent variable. Mapping of CITE-seq data onto the mass cytometry data was accomplished by arcsine transformation of the raw counts obtained for each surface marker followed by percentile normalization similarly as performed for mass cytometry data. Resulting transformed and normalized data were combined with mass cytometry data to create a cellular network using the grappolo and vite packages as described above. Transcription factor regulon activity was inferred based on the gene expression levels of its targets using DoRothEA  60. Trajectory and pseudotime were computed based on the corresponding UMAP using Monocle 3 (ref.
Changes in canonical immune composition in twin pairs discordant for MS can be attributed to disease-modifying treatment. a, UMAP of 1500 cells randomly sampled per patient from the MS twin cohort (left panel). Color code indicates FlowSOM clustering and manual annotation using the lineage markers presented in the heatmap (right panel). b, Heatmap showing the combined expression profiles of antigens analyzed by two partially-overlapping mass cytometry panels for the indicated immune populations. c, Expression profiles in the CyTOF dataset of the indicated markers across the immune cell populations associated with each reference node, alongside their relative mean population frequencies across all individuals in the MS twin cohort (n = 114). The FlowSOM-derived clusters were generated by iteratively clustering and manually merging the six main populations described in Extended Data Fig.
ALZ = Alemtuzumab (n = 3); DMF = Dimethyl Fumarate (n = 2); FTY = Fingolimod (n = 4); GLAT = Glatiramer Acetate (n = 8); IFN = Type 1 Interferons (n = 14); NAT = Natalizumab (n = 4); TFM = Teriflunomide (n = 2). d, UMAP of the myeloid cell compartment within healthy PBMCs outside the MS twin cohort from InfinityFlow data used to map the differential state phagocyte node of the MS twin dataset (top left panel). Color code indicates FlowSOM clustering and manual merging of the extended myeloid reference nodes by using the backbone markers presented in the heatmap showing the expression values (top right panel). The heatmap in the bottom panel displays the 100 most differentially expressed markers between the manually-annotated reference nodes. InfinityFlow backbone markers are presented in bold.
e, Mapping of diffcyt-generated myeloid cell clusters in the MS twin dataset (left panel) on InfinityFlow data of healthy PBMCs (right panels) showing the overlay expression of CD88, CD89, FcεR1a, HLA-DQ. Dot size corresponds to population frequency among total leukocytes. Violin plots contain a bold horizontal line depicting the respective group mean. If not indicated, differences between experimental groups were statistically not significant (p > 5%) using a two-sided unpaired nonparametric Mann-Whitney-Wilcoxon test with a false-discovery correction according to the Benjamini-Hochberg approach. Extended Data Fig. 3 | Transcriptional profiling of phagocytes of MS -affected twins and unaffected twin siblings. a, Dot plot showing the expression profile of selected signature markers in CITE-seq data of phagocytes derived from 7 twin pairs discordant for MS (21 043 cells; left panel).
Annotation provided in the publicly available mass cytometry dataset was used to match the main clusters in the reference nodes and indicated by the respective color. Dot size corresponds to population frequency among total leukocytes. Violin plots (right panel) showing the frequency of major canonical immune subsets in the validation cohort for HD (n = 29) and RRMS patients (n = 30). d, Violin plots showing the frequency of annotated Th cell nodes in the validation cohort for HD and RRMS patients (right panel). Heatmap (left panel) displaying the expression profile for the indicated Th cell populations. Violin plots contain a bold horizontal line depicting the respective group mean. If not indicated, differences between experimental groups were statistically not significant (p > 5%) using a two-sided unpaired nonparametric Mann-Whitney-Wilcoxon test with a false discovery correction according to the Benjamini-Hochberg approach.
Data availability Raw mass cytometry data can be accessed at https://doi. org/10. 17632/ fzs5ph5p8s. 1. CITE-seq data are available at https://doi. org/10. 17632/ 278fy5m2yj. 2. Publicly available flow cytometry data of healthy monozygotic and dizygotic twin pairs  17  were accessed at http://www. tinyurl. com/twinsFACSdata. The publicly available cross-sectional MS mass cytometry dataset for non-twins  14  was accessed at http:// flowrepository. org/experiments/2166/.
Extended Data Fig. 1 | 1 Changes in canonical immune composition in twin pairs discordant for MS can be attributed to disease-modifying treatment. a, UMAP of 1500 cells randomly sampled per patient from the MS twin cohort (left panel). Color code indicates FlowSOM clustering and manual annotation using the lineage markers presented in the heatmap (right panel). b, Heatmap showing the combined expression profiles of antigens analyzed by two partially-overlapping mass cytometry panels for the indicated immune populations. c, Expression profiles in the CyTOF dataset of the indicated markers across the immune cell populations associated with each reference node, alongside their relative mean population frequencies across all individuals in the MS twin cohort (n = 114). The FlowSOM-derived clusters were generated by iteratively clustering and manually merging the six main populations described in Extended Data Fig.
Patient Cohorts Within the EuroTARGET project, a collection of blood samples and tissue material of prospectively included patients is available as well as a collection of stored samples of 'historical patients' enrolled in earlier studies  [21]. Patients were recruited at participating centres in the Netherlands, the United Kingdom, Iceland, Germany, Romania and Spain. The enrolment of patients in this study occurred from 2005 until 2015  [21, 22]. Inclusion criteria for the EuroTARGET GWAS were a histologically confirmed diagnosis of mRCC, the use of sunitinib as a first TKI, the availability of germline DNA, and recorded clinical data on PFS and OS. For the RIKEN cohort, a total of 219 mRCC patients treated with sunitinib were recruited from 15 Japanese medical institutes. PFS and OS data were available for 204 individuals.
Patient Selection Patients from the EuroTARGET cohort were considered eligible for our genetic association analysis if genotyping data could be obtained, if the start date of sunitinib and a follow-up date after the start date were available, and if the follow-up after start of sunitinib until study end was more than 24 weeks. To enable informative future analyses, we only focus on the subset of patients for whom outcome could be assessed for at least 6 months (24 weeks). Patients were only included if no TKI or TKI-like anti-tumour treatment was given prior to sunitinib (Table  S1 ). Patients with a clear cell histological subtype as well as those with unknown histology were included, while other histological subtypes were excluded  [21]. For the 'historical patients' whole blood, serum or plasma material had been obtained.
Clinical Data Collection For the analyses, we used the clinical EuroTARGET database version from October 2017. Clinical data from all patients of the EuroTARGET cohort were collected by medical file review and entered in web-based electronic case record forms (eCRFs) (Supplementary File S1a,b). Data included demographic information, baseline clinical characteristics, treatment lines, drug toxicities (Common Terminology Criteria for Adverse Events [CTCAE version 4. 0]), tumour response (i. e. , complete remission, partial remission, stable disease, or progressive disease), and death. Tumour response was defined according to RECIST version 1. 1 and based on patient evaluation by local caregivers as given in the radiology report or medical record (no review of imaging)  [21].