dataesr/scientific-paragraphs-categorization huggingface dataset:
For mTet2 sgRNA (TCAGGGGCGATGATGTA-CAT): FORWARD 5′-Sequencing Primer -Genomic Primer -3′ ACACTCTTTCCCTACACGACGCTCTTC-CGATCT GGTCACCCTCAATAGAGAAGACA REVERSE 5′-Sequencing Primer -Genomic Primer -3′ GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT TGGGCAGCTCTCCTATCCTT. For mStag2 sgRNA(TAACACACAAAGACAGTACG): FORWARD 5′-Sequencing Primer -Genomic Primer -3′ ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTGGCTGCATAATAATAGCCTAAAC REVERSE 5′-Sequencing Primer -Genomic Primer -3′ GTGACTGGAGTTCAGACGTGTGCTCTTC-CGATCT AGTTGATGACTGCTTTGGTAAATG. Hi-C methods and data processing. Hi-C was performed as described previously  (40)  with some minor modifications. Please see Supplemental Methods for details. The data have been deposited in NCBI's Gene Expression Omnibus (and are accessible through GEO Series accession number GSE165038).
Acknowledgments We thank  A. Spektor  and  A. Dall'Agnese  for assistance with confocal microscopy;  G. Cowley ,  S. Younger , and  S. Pantel  for technical advice;  Y. Lee  and  L. Ali  for technical assistance;  N. Abdenur  for helpful discussions; and  D. Neuberg  for help with statistical analysis. This work was supported by the  NIH  ( R01HL082945  and  479 P30CA006516 ), the  Edward P. Evans Foundation , the  Gabrielle's Angel Foundation , the  Leukemia & Lymphoma   Society Scholar Award  to BLE; the  National Human Genome Research Institute  ( HG003143  to JD); the  NIH  ( 5K12CA087723-12  and  1K08HL140138-01 ), the  Leukemia & Lymphoma Society   Special Fellow Award  ( 3363-13 ), the  American Society of Clinical Oncology Young Investigator Award , the  American Society of   Hematology Scholar Award , and the  American Society of Hematology/European Hematology Association Translational Research in Hematology Award  to ZT; and the  NIH  ( T32GM007753 ) to SK.
RESULTS The prognostic value of CINSARC in breast cancer is not independent We analyzed our database of 8930 patients with early breast cancer, including 6,035 treated with primary surgery and with available DFS data (Supplementary Table  1 ). With a median follow-up of 77 months (range 1-382), 1759 experienced a DFS event, while 4276 remained disease-free for a 5-year DFS of 75% (95%CI, 74-76) (Fig. 1a ). Applying CINSARC to this population identified 2945 CINSARC-high risk (49%) and 3090 CINSARC-low risk (51%) breast cancer patients (Fig. 1b ), with significantly different 5-year DFS (67% vs. 83%, respectively; p < 2E-16, log-rank test). In univariate analysis (Table  1 ), CINSARC-high risk patients had an 80% increase in risk of DFS event as compared to the CINSARC-low risk (Hazard Ratio HR = 1.
CINSARC classes and therapeutic vulnerability in the Luminal B subtype Beyond prognostication, multigene signatures may also help identify therapeutic targets that might improve survival of patients with high risk of recurrence. Thus, in the whole population of 2028 Luminal B patients of our database, we wondered whether the two CINSARC classes displayed different probabilities of response to specific systemic therapies routinely used or under development in breast cancer (Table  5 ). Regarding chemotherapy, 94 CINSARC high-risk and 56 CINSARC low-risk cases were informative about achievement or not of a pathological complete response (pCR) after anthracycline/taxane-based neoadjuvant chemotherapy. High-risk patients had a numerical but non-statistically significant increase in pCR rate (20%) as compared to low-risk patients (12%, p = 0.
Biological correlates of CINSARC classes in the Luminal B subtype To further elucidate the biological differences between the two CINSARC classes and identify potential therapeutic targets, we compared their whole-exome mutational, whole-genome copy number and transcriptional, and proteomic (RPPA) profiles by using the TCGA dataset, which included 297 Luminal B cases. No genomic alteration was differentially mutated, deleted or amplified between the two classes (Supplementary Tables  4 5 6 ). A total of 510 genes were differentially expressed between the two classes (Supplementary Fig. 1 , Supplementary Table  7 ). The robustness of this gene list was confirmed in the METABRIC independent validation set, and ontology analysis revealed a large preponderance of mitotic processes, including mitotic spindle assembly and chromosomal segregation, and DNA repair among the genes upregulated in the high-risk class (Supplementary Table  8 ).
Breast cancer samples and molecular profiling We analyzed our breast cancer gene expression database 10 pooled from 36 public datasets (Supplementary Table  10 ), comprising 8982 invasive breast cancer samples. The details of Institutional Review Board and Ethical Committee approval and patients' consent for all 36 studies are present in their corresponding publications listed in Supplementary Table  10. Our study is based upon public data from published studies in which ethics approval and informed consent to participate were already obtained by authors. This study was approved by our institutional review board (Comité d'Orientation Stratégique, COS). Gene expression profiles had been generated using DNA microarrays and RNA-Seq, and collected from the National Center for Biotechnology Information (NCBI)/Genbank GEO and ArrayExpress databases, and authors' website.
The final pooled data set contained 8930 nonredundant non-metastatic, non-inflammatory, primary, invasive breast cancers. Before analysis, data were processed as previously described  10. Briefly, the pre-analytic processing first included normalization of each data set separately, and was done by Robust Multi-Array (RMA) with the oligo R package (version 1. 46. 0) for Affymetrix data and by quantile normalization with the limma R package (version 3. 38. 3) for other microarray platforms. When multiple probes mapped to the same GeneID, we retained the one with the highest variance in each data set. We log2-transformed the already normalized TCGA RNAseq data. We also collected DNA and proteomic processed data from TCGA (whole-exome sequencing (WES), array-CGH and HRD score, and RPPA) and METABRIC (targeted-NGS, array-CGH).
Analysis of molecular profiles To avoid biases related to trans-institutional immunohistochemical analyses and thanks to the bimodal distribution of respective mRNA expression levels, the ER, progesterone receptor (PR), and HER2 statutes (negative/positive) were defined on transcriptional data of ESR1, PGR, and HER2 respectively, as previously described  44. In addition to the CINSARC signature  6  , we applied to each dataset separately several multigene signatures: PAM50 5 allowing to define the Luminal A, Luminal B, ERBB2enriched, Basal, and Normal subtypes, immune signatures including the Palmer's B-cell, T-cell, and CD8+ T-cell signatures  11  , and the Rooney' cytolytic activity score  12  , 107-gene signature predictive for pathological response to anthracycline-based neoadjuvant chemotherapy in breast cancer  13  , E2F4-activation signature predictive for response to hormone therapy in breast cancer  14  , Rbsig 15 and E2F regulon  16  signatures predictive for resistance to CDK4/6 inhibitors on breast cancer pre-clinical models  14  and clinical samples of PALOMA-3 trial  16  , and immune signatures predictive for response to immune checkpoint inhibitors: ICR (Immune Constant of Rejection)  19  and TIS (T cell-inflamed signature)  20  signatures and a TLS (tertiary lymphoid structures) signature  21.
We also compared the molecular profiles of CINSARC high-risk versus low-risk Luminal B samples by applying supervised analyses to TCGA and METABRIC data sets at different levels: WES mutational, copy number alterations (CNA), and RPPA data using logistic regression with significance thresholds of p ≤ 0. 05 and q ≤ 0. 10, and transcriptional data using moderated t-test with significance thresholds of fold-change |FC | > 1. 5, p ≤ 0. 05 and q ≤ 0. 10. This later used the TCGA set as learning set and the METABRIC set as independent validation set. Ontology analysis of the resulting gene list was based on the GO biological processes of the Database for Annotation, Visualization and Integrated Discovery (DAVID; david. abcc. ncifcrf. gov/).
The likelihood ratio (LR) tests were used to assess the prognostic information provided beyond that of a clinical model, assuming a X 2 distribution. Changes in the LR values (LR-ΔX 2 ) measured quantitatively the relative amount of information of one model compared with another. All statistical tests were two-sided at the 5% level of significance. In the case of multiple testing, the p-values were replaced by the corrected q-values. Statistical analysis was done using the survival package (version 2. 30) in the R software (version 2. 9. 1; http://www. cran. r-project. org/). We followed the reporting REcommendations for tumor MARKer prognostic studies (REMARK criteria)  45.
ACKNOWLEDGEMENTS Our work was supported by the the  Ligue Nationale Contre le Cancer  (label F. B. ),  SIRIC (INCa-DGOS-Inserm 6038  grant),  Ruban Rose  (F. B. ),  Association La Marie-Do  (F. B. ), and  Fondation Groupe EDF  (D. B. ). The authors wish to thank the computing facilities DISC (Datacenter IT and Scientific Computing, CRCM) and  DSIO (Institut Paoli Calmettes)  for their technical support. We acknowledge the support of the  Institut Paoli-Calmettes biobank  (authorization number  AC-2018-1905 ).
DATA AVAILABILITY The data generated and analyzed during this study are described in the following data record: https://doi. org/10. 6084/m9. figshare. 14350871  46. All data sets of primary breast cancer were downloaded from the Gene Expression Omnibus (GEO, https:// www. ncbi. nlm. nih. gov/geo/), ArrayExpress (https://www. ebi. ac. uk/arrayexpress/), Genomic Data Commons (GDC, https://portal. gdc. cancer. gov/) and cBioPortal (https://www. cbioportal. org/) databases. All accession IDs are provided in Supplementary Table  10  (Table  S10  revised. xlsx), which is included with the data record. The data underlying the figures and tables are contained in the files 'Goncalves_suppor-ting_data. xlsx' and 'Table  S8. xlsx', which are included with the data record. A detailed list of the data underlying each figure and table is also available in the file.
Spatial multiomics of the liver endothelium Pre-amplification of genetic material facilitates unparalleled sensitivity all the way to the single-cell level in high-throughput sequencing analyses  (van Dijk et al. , 2014). In turn, proteomic analyses by mass spectrometry (MS) inherently require much higher input material than transcriptomic studies  (Marx, 2019). Phosphoproteomic analysis is even more demanding, as it requires a pre-enrichment of the phosphorylated peptides that otherwise represent only a minimal fraction of the total peptides subject to MS measurement  (Fı ´la and Honys, 2012). To overcome these limitations and at the same time preserve tissue spatial resolution, we datamined the published L-EC scRNAseq dataset for surface molecules that may enable the isolation of different L-EC subpopulations from consecutive sections of a liver vessel  (Halpern et al.
10 nM of 8 libraries was pooled using unique dual (UD) i7 index adapters (IDT for Illumina-Nextera DNA UD Indexes) and sequenced with NextSeq 550 Single-Read 75bp High-Output. Illumina output files were demultiplexed with bcl2fastq2 Conversion Software v2. 20 (Illumina). The resulting FASTQ files were analysed on the pipeline built on the DKFZ internal Galaxy  (Afgan et al. , 2018)  instance. Adapter sequences were removed by Cutadapt  (Martin, 2011) v1. 16. 6. The trimmed output sequences were aligned to the transcriptome index of the GRCm38. 92 (Ensembl) using the RNA STAR  (Dobin et al. , 2013) v2. 7. 2b  and gene expression was measured by featureCounts  (Liao et al. , 2014)  v1. 6. 3. Gene count outputs were normalized to the relative gene length and transcripts per million (TPM) were calculated for each sample for further analysis.
Phosphopeptide enrichment An automated phosphopeptide enrichment protocol based on Immobilized metal affinity chromatography (IMAC) using a nitrilotriacetic acid (NTA) chelating ligand functionalized with Fe(III) was applied to the mixture of labelled peptides and internal controls, with AssayMap Bravo platform (Agilent Technologies). Each sample was reconstituted with 110 mL 80% ACN, 0. 1% TFA buffer. They were gently sonicated in water bath until complete dissolution and then they were transferred manually to the Greiner 96well full skirt PolyPro PCR plate. The Agilent AssayMAP Phosphopeptide Enrichment v2. 0 App, included with the Agilent AssayMAP Bravo Protein Sample Prep Workbench v2. 0 software suite, was run using AssayMAP Fe(III)-NTA cartridges (Agilent Technologies). Briefly, the cartridges were firstly primed with 100 mL 50% ACN, 0.
6 m/z), an AGC target value of 100 000 ions and normalized collision energy of 27. Dynamic exclusion was set to 60 s (16 s phospho samples). Undetermined charge states and single charged species were excluded from fragmentation. Peptide and protein identification and quantification Data analysis was carried out by MaxQuant  (Tyanova et al. , 2016a) v1. 6. 3. 3. Carbamidomethylation of cysteines was set as fixed modification. Phosphorylation of serine, threonine or tyrosine as well as oxidation of methionines and N-terminal acetylation were set as variable modifications. Identification FDR cutoffs were 0. 01 on peptide level and 0. 01 on protein level. The LFQ based full proteome analysis was carried out with organism specific UniProt database UP000000589 (Mus musculus; Dec, 2017; 60715 sequences) and enabled 'match-between-runs' function.
The LFQ option was enabled and left at default parameters. The phosphoproteome analysis samples was carried out with organism specific UniProt database UP000000589 (Mus musculus; Feb, 2020; 55421 sequences). The multiplicity was set to '2' with 'DimethLys0' and 'DimethNter0' as light labels and 'DimethLys4' and 'DimethNter4' as heavy labels. The 'match-between-runs' function was enabled and fractions were assigned so that the function applied separately within the phospho fractions (fraction 1) and the full proteome fraction (fraction 11). The 'Re-quantify' option was enabled.
Data analysis Analysis of non-EC contaminants in RNAseq and mass spectrometry In order to assure that the selected FACS gates were greatly enriched for EC and, moreover, that there were no zonation confounding effects due to non-EC contamination, we estimated the fraction of non-EC markers in the RNA and protein samples. To this end, we compiled a list of previously published transcriptomes of different liver cell types. Liver immune cell types and EC expression were taken from  (Halpern et al. , 2018). The hepatocyte transcriptome was retrieved from  (Halpern et al. , 2017). Cholangiocyte data were extracted from averaging expression data of single cholangiocytes resulting from single cell sequencing of mouse healthy liver as reported by  (Xiong et al. , 2019). Hepatic stellate cells and fibroblast expression data were taken from  (Dobie et al.
Dataset filtering and processing For gene expression out of 28,727 identified genes, we considered for further analysis (pathway enrichment, phylogenetic tree, PTR definition) only genes with a mean TPM of the four replicates above 5 in at least one of the four zones (PN, PP, PC, CV). In order to provide an overview of the whole transcriptome zonation (Figures  2A  and 2B ), the TPM cut-off was maintained at 1 and the expression profiles were confirmed by qRT-PCR (Figure  2C ). Of 5,015 detected proteins, only those quantified in at least two replicates in one of the zones were retained for imputation. The label-free quantification (LFQ) intensities were imputed with a constant (the minimal LFQ of each sample) for missing values. To normalize the variability among the experimental replicates, the values of each pool across the four zones were normalized to their mean.
Finally, where protein abundance was required, the normalized value was multiplied for the mean LFQ of all the 16 samples before normalization. Of 19,607 detected phospho-sites (p-site) corresponding to 3,447 proteins, only class I (localization probability > 0. 75) were considered for analysis. Normalized ratio exported from MaxQuant was used for quantification. As described for proteins, only p-sites quantified in at least two replicates in one of the zones were retained for imputation. The sample/spike ratios were first log 2 transformed and then imputed from normal distribution with Perseus v1. 6. 14. 0  (Tyanova et al. , 2016b). Finally, normalization between replicates was performed as described for protein analysis. In order to compare multiple datasets, transcripts, proteins and p-sites were matched with their corresponding ENSEMBL gene ID.
Differential gene expression analysis To investigate the gene regulation induced by the receptor tyrosine kinase Tie1 blockade, L-EC was spatially sorted from C57/B6 mice 2 hours after treatment with anti-Tie1 antibody, and processed for RNAseq as described above. To obtain the differentially expressed genes in each zone, the obtained gene counts from Tie1 treated samples and control samples were analysed with the DESeq2  (Yousif et al. , 2020)  (Table  S7 ). Gene regulation was visualized as volcano plot by plotting the -log 10 q-val against the log 2 fold change with the EnhancedVolcano  (Blighe et al. , 2020)  v1. 4. 0 package in RStudio (Figure  6A ). To compare the regulatory effect of Tie1 on portal and central, genes that were significantly regulated in portal or in central, i. e. , q-value < 0.
Wnt9b promoter analysis Searching for putative transcription factor binding sites in the promoter region of Wnt9b was performed by the Search Motif Tool from the Eukaryotic Promoter Database (SIB)  (Dreos et al. , 2015). The search was based on the library from Transcription Factor Motifs (JASPAR CORE 2018 vertebrates), retrieved from -1000 to 1000 bp relative to the Wnt9b transcription start site with a p-value cut-off at 0. 001. Motif was selected for Foxo1 or STAT3, respectively. The retrieved putative binding sites for FoxO1 and STAT3 were indicated as shown in Figure  S7F. Phosphosite motif analysis 15-mer sequences were extracted from -7 to +7 position flanking the detected phosphosites. Motif analysis was performed using PhosphoSitePlus  (Hornbeck et al. , 2015)  v6. 5. 9. 2. Sequences for p-S, p-T, p-Y were loaded separately as foreground, searched against the corresponding background.
QUANTIFICATION AND STATISTICAL ANALYSIS Kruskal-Wallis tests followed by Benjamini-Hochberg correction were performed when comparing multiple groups. Wilcoxon matched-pairs signed rank test or unpaired two-sided Student's t tests were performed when comparing two groups as indicated in the figure legends. N=4 pools (each 30 mice) were used for RNAseq, label free proteome and phosphoproteome analysis. and S1D (mean ± SEM). Flow cytometry data was analysed with FlowJo v10. Statistics and graphics were performed with GraphPad Prism v8 and Rstudio v1. 2. 5042. Heat maps were generated with Gitools v2. 3. 1. Networks were visualized with Gephi v0. 9. 2. Cartoons were created with Biorender. com.
ACKNOWLEDGMENTS We thank the MS-based Protein Analysis Unit and the  High-Throughput Sequencing Unit of the Genomics and Proteomics , the Flow cytometry, the  Light Microscopy Core Facilities , and the  Laboratory Animal Facility at the German Cancer Research Center (DKFZ)  as well as the  Proteomics Core Facility at the Center for Molecular Biology (ZMBH) at Heidelberg University  for providing excellent support. We thank  Silvia Calderazzo  ( DKFZ Division of Biostatistics ) for biostatistical consulting. This work was supported by grants from the  Deutsche Forschungsgemeinschaft  (projects  A1  and  A2  within  CRC1324  '' Wnt Signaling ' ' [project number  331 , 351 , 713 ] to H. G. A. and M. B. ; projects  A4  and  C3  within  CRC-TR209  ' 'Liver Cancer '' [project number  314905040  to H.
(c) PPC SSU rDNA phylogeny The sequences (electronic supplementary material, table  S4 ) were assembled with representative sequences from the NCBI 'nr' database (August 2019) and automatically aligned in SeaView (v. 4. 6. 2)  [12]  (alignment:  [13] ). The alignment was adjusted manually to maximize accuracy and masked to remove gap-rich and poorly aligned sites. Maximum-likelihood (ML) and Log-Det distance  [14]  neighbour joining  [15]  (a partial correction for compositional bias  [16] ) were performed using IQ-TREE (v. 1. 6. 5) [17]. For ML analysis, a TIM3e + G4 model was used (identified by ModelFinder  [18] ). For both analyses, 1000 bootstrap replicates were performed.
2 2Figure 1. 21 Figure 1. ML phylogeny of PPC SSU rDNA calculated from an alignment of 188 sequences and 288 sites. Bootstrap values are summarized: black circles = node support ≥90%; white circles = node support ≥50%. Asterisks (*) indicates node consistent with the Log-Det neighbour-joining bootstrap analysis at greater than or equal to 50%. Slashes (\) indicate branch lengths reduced by half. Newly generated PPC sequences are labelled with location (PA or UK) and sample ID. The Panama specific subgroup is highlighted by a dark grey box. Reference PPC sequences are labelled with geographic location and GenBank accession number or sample ID (this study). The triangle represents a subset of reference USA-PPC sequences; number of sequences is listed in parentheses. All other parenthesized numbers correspond to the number of sequences represented by each phylotype.
Whole genome sequencing Genomic DNA was isolated from blood or ear punches using an EasySpin Genomic DNA Tissue Kit SP-DT-250 (Citomed, Lisbon, Portugal), and RNA was removed with a RNAse A digestion step. Two DNA pools (sauteur and wild-type) were generated by pooling equimolar amounts of DNA of the different individuals. These two bulks were then used to generate paired-end sequencing libraries using the TruSeq DNA PCR-free Library Preparation Kit (Illumina, San Diego, CA) according to manufacturer's protocols. The resulting libraries were sequenced on an Illumina HiSeq X instrument using 2x150 bp reads. Whole-genome sequencing data are available in the Sequence Read Archive (www. ncbi. nlm. nih. gov/sra) under the bioproject PRJNA559371.
4. 1 flow cell (Oxford Nanopore). To filter out any sequenced non-target DNA, we started by mapping all reads to a custom reference consisting of the transcript sequence using MINIMAP2  [50] , a general purpose aligner that is suited for mapping reads with high error rates and that is also splice-aware (see below). With this approach we identified 58,538 individual sequences mapping to the RORB transcript. Since we did not use standard MinION barcodes, we demultiplexed the samples by re-converting the reads that mapped to the transcript and retaining only those that had an unaltered full 7-bp barcode sequence plus an additional 10-bp of the adapter overhang (to account for the high sequencing error rate of Nanopore sequencing). Reads were then remapped using MINI-MAP2 to a new reference sequence containing the full RORB open reading frame.
We inspected sequencing reads from each sample using IGV (v2. 4. 10)  [46]  and detected four main transcript isoforms resulting from changed splice site locations between exons 9-10 (see Results section). The relative abundance of each transcript was obtained by analyzing sequencing coverage using SAMtools mpileup function, considering the counts of each transcript in positions where two or more transcripts share sequence. Demultiplexed fastq reads from each individual/tissue have been deposited in GenBank under the bioproject PRJNA559371.