dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions from the metabolomics data analysis. These examples show that biologically meaningful conclusions can be drawn from the metabolomics data from volunteers with oily or normal scalps. Despite the richness in feature annotation achieved by the combination of different analytical methods and advanced computational annotation tools, the conclusions drawn from the metabolomics data so far are rather descriptive. Mechanistic insight into the functioning of the skin microbiota is either indirect or speculative. We therefore asked whether linking metabolites to specific microorganisms would elevate the data analysis from a descriptive to a more functional level. This question was addressed by two complementary approaches: 1) Systematic mass spectral search for features in the metabolomics datasets that were also detected in publicly available dataset from microbial monocultures using the yet to be published microbeMASST tool, a domain specific MASST tool  (19) , where one can search MS/MS data against data of ~60,000 taxonomically curated cultures of microbes (https://masst.
ucsd. edu/microbemasst/), and 2) MO integration of the metabolomics datasets with a complementary metagenomic sequencing dataset using MMvec  (6). Search for microbial metabolites. MASST (19)  is a recent addition to the GNPS ecosystem and allows to search whether a fragmentation spectrum in a metabolomics dataset has been previously observed in any dataset from the public data repository part of the GNPS/MassIVE knowledge base. microbeMASST (https://masst. ucsd. edu/microbemasst/) performs a MASST search against a curated set of metabolomics datasets from microbial monocultures. To systematically link metabolites from the skin swabs to specific microorganisms, we searched all feature's fragmentation spectra from networks with at least three connected nodes with microbeMASST in batch mode.
The MO integration was performed with MMvec, a neural network that learns the co-occurrence probabilities of metabolite/microbe pairs  (6). MMvec predicts the abundance of all metabolites, given the presence of each microbial taxon. In doing so, it goes through the microbial taxa one-by-one, each time considering all metabolite features simultaneously. To decrease the risk of overfitting the neural network due to the large number of metabolite features, we performed data reduction on the metabolomics dataset, taking advantage of the molecular networks generated by GNPS. Metabolomics data reduction using molecular networks. We observed that the feature intensities within one molecular network were in many cases positively correlated with each other (Supplementary Figure  S27 ), presumably due to biological similarities between structurally related metabolites, but also due to different features representing the same metabolite (e.
Quality trimming was applied to the ends of reads targeting a Q10 Phred score using bbduk. Reads shorter than 70 bp after trimming were removed. Human reads were removed from the dataset by mapping to the GRCh38 assembly using Bowtie 2  (30)  with the --sensitive option. All remaining reads were processed using MetaPhlAn2  (31)  to generate a taxonomic abundance profile for each sample. The resulting table of relative abundances as well as the corresponding sample metadata can be found in the Supplementary Information.
5 (w/w/w/w, solvent B) from 0% B to 25% B in 0. 5 minutes, then to 90% B in 5. 4 minutes. LC-MS/MS data processing. The mass spectrometry data were centroided and converted from the Sciex proprietary format (. wiff) to the open. mzML format using ProteoWizard 3. 0 (MSConvert tool)  (32). The mzML files were then processed with MZmine toolbox (11) (version 2. 37_corr_17. 7_kai_merge_2, available on https://github. com/robinschmid/mzmine2/) using the Ion Identity Molecular Networking workflow  (33)  that allows advanced feature detection for adduct/isotopologue annotations. The MZmine processing was performed on Ubuntu 18. 04 LTS 64-bits workstation (Intel Xeon E5-2637, 3. 5 GHz, 8 cores, 64 Gb of RAM). The MZmine project, the MZmine batch file (. XML format), and results files (. MGF and. CSV) are available in the MassIVE dataset (MSV000088283).
The MZmine batch files contain all the parameters used during the processing for each datasets. In brief, feature detection and deconvolution was performed with the ADAP chromatogram builder  (34) , and local minimum search algorithm. The isotopologues were regrouped, and the features (specific m/z with retention time peaks) were aligned across samples. The aligned feature list was gap-filled, i. e. , a secondary feature detection step that fills in signals from raw data into gaps of missing features. Filters only retained features with an associated fragmentation spectrum and that occurred in a minimum of 2 files. Retention time peak shape correlation grouped features originating from the same molecule and annotated adduct/isotopologue for SIRIUS. Finally the feature quantification table results (.
Molecular networks were visualized using Cytoscape 3. 8. 1 software  (35). The MS 2 spectra were systematically annotated with the SIRIUS computational tools (4) computational annotation tool (v. 4. 5. 3, headless, Linux). Molecular formulas were computed with the SIRIUS module by matching the experimental and predicted isotopic patterns  (36) , and from fragmentation tree analysis (37) of MS 2. Molecular formula prediction was refined with the ZODIAC module by using Gibbs sampling  (14)  for fragmentation spectra that were not chimeric spectra or had poor fragmentation. In silico structure annotation using structures from bio databases was done with CSI:FingerID  (15). Systematic class annotations were obtained with CANOPUS (8) and used the ClassyFire ontology  (38). The parameters for SIRIUS tools were set as follows, for SIRIUS: molecular formula candidates retained (100), molecular formula database (ALL), maximum precursor ion m/z computed (750), profile (qtof), m/z maximum deviation (25 ppm), ions annotated with MZmine were prioritized and other ions were considered in positive ionisation mode.
([M+H3N+H]+, [M+H]+, [M+K]+,[M+Na]+, [M+H-H2O]+, [M+H-H4O2]+, [M+NH4]+) and in negative ionisation mode ([M-H]-,[M+Cl]-,[M-H2O-H]-,[M-2H2O-H]-,[M+Br]-) ; for ZODIAC: threshold filter (0. 9), minimum local connections (0); for CSI:FingerID: m/z maximum deviation (10 ppm) and biological database (BIO), for CANOPUS: default parameters. The microbeMASST tool is still in development but is available through a web dashboard (https://masst. gnps2. org/microbemasst; in testing phase) and as a Python script for batch processing. Matching against samples from >60,000 microbial monocultures was performed with a Python script using the. mgf spectra file from the IIMN workflow. Matches were achieved with 0. 05 precursor m/z tolerance and fragment m/z tolerance, 0. 7 cosine threshold, 4 minimum matched signals, and analog search off.
The wider m/z tolerances were chosen to also include datasets of instruments with lower resolving powers, represented within public data. Results were overlaid with the molecular networkings using the feature ID provided by MZmine. Briefly, microbeMASST produced tables of spectral matches to specific data files in the public domain which were then mapped to the curated list of samples with microbe metadata. Those tabular results are then visualized onto trees of the NCBI taxonomy. Interactive HTML/javascript files summarize the results and provide links to other GNPS/MassIVE tools and services, e. g. , matching GNPS reference spectra, matching MassIVE datasets, interactive visualization and analysis of MS data files in the GNPS dashboard  (39) , and spectral mirror plots with the Universal Spectrum Identifier (USI)  (40)  and the metabolomics spectrum resolver  (41).
Statistical analysis of LC-MS/MS data. The feature quantification tables from MZmine were filtered to exclude low-quality features based on the quartiles Q1, Q2, and Q3 of the feature intensity distribution across samples. The quartiles were calculated separately for each feature in the Swab, the Blank, and the Pool samples. A feature was excluded for high Blank value if the boxes (interquartile ranges) of the Blank and the Pool samples were overlapping in a box plot, i. e. , Q3 Blank > Q1 Pool. A feature was excluded for high Pool variability if the box of the Pool samples was wider than 0. 5 in a box plot on log 10 scale (IQR log10 = log 10 (Q3 Pool ) -log 10 (Q1 Pool ) = log 10 (Q3 Pool / Q1 Pool ) > 0. 5). The intensities from the 44,570 remaining features (all 4 datasets combined) were log 10 -transformed and normalized to the median of the intensities from the same feature in the Pool samples that were analyzed on the same day.
As expected, the resulting intensities showed an approximate normal distribution centered around zero. The intensities were further normalized to the median of the values from all features within each sample and dataset to compensate for differences in the amount of sampled material. The resulting values were used for all downstream statistical analyses, including the MO integration. The correlation between the log 10 -transformed and normalized feature intensities and Sebumeter values was calculated using the stats::lm function in R  (42)  with age, gender, ethnic group, and skin moisture (Skicon value) as covariates. The annotation results from GNPS and SIRIUS were combined to generate a feature annotation metadata file. The statistical analysis and feature annotation results were formatted and mapped into their respective molecular networks for enhanced visualization.
Multi-omics analysis Microbiome input data. Taxonomic abundance profiles generated with MetaPhlAn2 (see above) were used as input for MMvec. The table of relative abundances can be found in the Supplementary Information together with the corresponding sample metadata. Metabolomic input data. Filtered, log 10 -transformed, and normalized feature intensities (see above) were subjected to data reduction as follows: Features present in molecular networks with less than three nodes were discarded to focus on the larger molecular families in the datasets. For each of the 1225 molecular networks with at least three nodes, the median value of the feature intensities was calculated within each sample, resulting in a reduced dataset with one data point for each molecular network in each sample. The dataset was back-transformed to a linear scale and used as input for MMvec.
02% TritonX-100, and Halt TM Protease & Phosphatase Inhibitor Cocktail (100X) (Thermo Scientific, Fr). Pellets were then resuspended in 650 µl washing buffer, mixed with 0. 1 mm silica beads and subjected to 3 cycles of vigorous shaking (Fast-Prep-24, MP-Bio, Fr). After 10 min centrifugation at 10000 rpm, supernatants were recovered and stored at -80°C prior to analyses. Protein extractions, LC-MS/MS analyses, and bioinformatics, and statistical data analyses were done as described in detail  (68). The reference genome GenBank Nucleotide accession code NC_007793. 1 was used for protein annotation. The bioinformatic tools used for proteomics analysis (X!TandemPipeline C++, MassChroQ, MCQR) are open and free resources available in the following repository: https://forgemia. inrae. fr/pappso.
The mass spectrometry proteomics data was deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository with the dataset identifier PPXD034256. Proteome data analyses. Protein abundance differences were detected by ANOVA tests for all methods used (spectral counting, SC; extracted ion chromatograms, XIC; and peak counting, PC). The abundance of a protein was considered significantly variable when the adjusted p-value was <0. 05. Proteins showing significant statistical abundance differences were represented on heat maps using hierarchical clustering with Euclidean distances as described  (68). K-means clustering analysis was performed using RStudio. Proteins whose abundancies were significantly different in one or more conditions were manually curated and classified according to functional groups.
The resolution of the survey scan was 120,000 at m/z 200 Th and for MS/MS scan the resolution was set to 15,000 at m/z 200 Th. For HCD acquisition, the collision energy = 27 and the isolation width is of 1. 4 m/z. The precursors with unknown charge state, charge state of 1 and 5 or greater than 5 were excluded. Peptides selected for MS/MS acquisition were then placed on an exclusion list for 20 s using the dynamic exclusion mode to limit duplicate spectra. The mass spectrometry proteomics data have been deposited to the Center for Computational Mass Spectrometry repository (University of California, San Diego) via the MassIVE tool with the dataset identifier MassIVE MSV000089781 (accessible at http://massive. ucsd. edu/ProteoSAFe/status. jsp?task=db3f5002e32e4a47a71996358bc6ae8c ). Phosphoproteome data analyses.
Proteins were identified by database searching using SequestHT with Proteome Discoverer 2. 5 software (Thermo Scientific, Fr) against the Uniprot S. aureus USA300 database (2020-01 release, 2607 sequences). Precursor mass tolerance was set at 10 ppm and fragment mass tolerance was set at 0. 02 Da, and up to 2 missed cleavages were allowed. Oxidation (M), acetylation (Protein N-terminus), and phosphorylation (S, T, Y) were set as variable modifications. The dimethyl labeled peptides in primary amino groups K and N-ter (+28. 0313 Da for the SerFA 3h sample, +32. 0564 Da for the SerFA-Tric 6h sample and +36. 0757 Da for the SerFA-Tric 10 h sample), and carbamidomethylation (C) were set as fixed modifications. Peptides and proteins were filtered with a false discovery rate (FDR) at 1% using the Percolator tool  (71).
Acknowledgments The Nebraska Transposon Mutant Library strains were generously provided by  BEI Resources, NIAID, NIH, USA. We acknowledge Micalis colleagues  V. Sanchis  and  C. Nielson-LeRoux  for valuable advice on the G. mellonella infection model, and  C. Buisson  for technical assistance, and access to the insectarium. L. Dupont provided excellent technical assistance in proteomic sample preparations. We thank our P. Gaudu for valuable discussions. We are grateful to  Béatrice Py  ( CNRS UMR7283 ,  Marseille, Fr ) for pointing out the Fe-S cluster proteins in  Staphylococci. F. Delolme  ( Université de Lyon ) provided skillful assistance in phosphoproteome studies.
Statistical analysis GraphPad Prism v9. 2. 0 (283) (GraphPad Software) was used for statistical analysis and graphical representation. Statistical analyses were carried out by Kruskal-Wallis test, with Dunn correction for multiple comparisons or Bonferroni multiple comparison test, as indicated. Results were considered significant at P < _. 05. Heat maps, cluster maps, and correlation plot matrices were generated by 'seaborn,'  20  and logo plots were generated by Logomaker software. 21.
cDNA from each cell was molecularly barcoded with dual unique indices during this final library preparation step, which allowed for NGS reads to be assigned to their single cell of origin. Parallelized cloud-based bioinformatic pipelines were used to independently perform alignment and assembly for each single cell's NGS data. The outputs of these steps were, for each single B cell, full transcriptomic gene expression data as well as paired heavy and light chain IgE or IgG mAb sequences. We obtained more than 2450 paired heavy and light chain antibody sequences, from which we built CFs of related B cells by clustering them according to VH sequence similarity. We subsequently focused on 3 CFs that stood out on the bases of sequence similarity to previously characterized IgE antibodies and a signature of convergent evolution (Fig  1, A ).
As a readout of anaphylaxis, we measured changes in body core temperature. 27  In line with our data in human mast cells, we found that these mAbs were able to induce systemic anaphylaxis; we also found that the severity of the anaphylactic shock correlated with the respective affinity, in rank order (highest affinity to lowest: PA13P1H08, PA12P3F10, PA12P3D08), of the 3 mAbs (Fig 4  , D ). We also found elevated levels of the mast cell-specific chymase MCPT1 (Fig 4  , E ) and tryptase MCPT7 (Fig  4, F ) 1 hour after challenge in serum samples from mice sensitized with PA13P1H08, PA12P3F10, and, to a lesser extent, PA12P3D08, confirming the ability of these mAbs to induce degranulation of both mucosal and connective tissue-type mast cells in vivo. While antibodies in the plasma from allergic individuals are polyclonal and thereby confounded by unknown IgE diversity and mAb affinity, our data obtained using IgE/FcεRI-humanized mice and monoclonal, monospecific IgE mAbs provide a simplified system to understand the functional relevancy of a singular epitope.
IgE antibody expression Anti-Ara h 2 IgE mAbs PA12P3D08 (#Ab01493-14. 0) and PA12P3F10 (#Ab01494-14. 0) were obtained from Absolute Antibody and the anti-Ara h 6 IgE mAb 15C2 (#E-15C2) was obtained from InBio. Anti-Ara h 2 PA13P1H08 VH and VL sequences were reverse transcribed into DNA and codon optimized for expression in human cells using IMGT-V-Quest software. VH and VL DNA fragments were synthesized by Eurofins. The VH sequence was cloned into a human pUC19-Igε-expressing vector (the kind gift of Hugo Mouquet and Pierre Bruhns, Institut Pasteur, Paris) using SalI and AgeI restriction sites, and the VL sequence was cloned into human Igk-expressing vector using AgeI and BsiWI restriction sites, as previously described. E1  Plasmids were sequenced before being used for antibody production.
5 (Carterra), for 7 minutes. Natural Ara h 2 (nAra h 2; InBio) was serially injected at 0. 16, 0. 8, 4, and 20 nmol concentrations using the Carterra LSA single flow cell over the antibody-coupled chip in HEPES-buffered saline-Tween 20-EDTA (HBS-TE), 0. 05% bovine serum albumin (BSA) running buffer (Carterra), without regeneration between injections. Five minutes' association time and 20 minutes' dissociation time were used for each injection. Association and dissociation curves were fit using a 1:1 Langmuir binding model to obtain k d , k a , and K D with Kinetics software (Carterra).
Epitope mapping The target sequence of Ara h 2. 0201 (UniProt accession no. Q6PSU2-1), the long isoform of Ara h 2, was translated into linear 15 amino acid peptides with a peptide-peptide overlap of 14 amino acids. The final several peptides were padded with serineglycine. The peptide array was synthesized using Fmoc-based solidphase peptide synthesis. To verify the quality of the synthesized peptides, a separate set of positive and negative control peptides was synthesized in parallel and screened with commercial antibodies (data not shown). Peptide binding was assessed in 1 of 2 ways. An ELISA-based approach was used for antibodies IGX-3001, IGX-3009, and an isotype control antibody. The peptide arrays were first incubated with primary antibody (overnight at 48C). After washing, the peptide arrays were incubated with a 1/10000 dilution of a secondary antibody peroxidase conjugate for 1 hour at 258C.
DISCLOSURE STATEMENT J. B. J. K acknowledges support from the  French Medical Research Foundation (FRM)  ( SPF202005011962 ). L. L. R acknowledges support from the  INSERM , the  French Medical Research Foundation  ( FRM EQU202103012566 ), the  French National Research Agency  ( ANR-20-CE15-0026 ), and the  European Research Council  ( ERC-2021-CoG 101043749 ). Disclosure of potential conflict of interest:  D. Croote ,  J. J. W. Wong ,  P. Creeks ,  J. Romero ,  S. Sohail. ,  K. C. Nadeau , and  H. B. Lowman  are employees of and/or stakeholders in  IgGenix Inc. K. C. Nadeau  has received grants from the  National Institute of Allergy and Infectious Diseases (NIAID) , the  National Heart, Lung, and Blood Institute , the  National Institute of Environmental Health Sciences , and  Food Allergy Research and Education (FARE) ; has an equity stake in Alladapt Immunotherapeutics,  Before Brands ,  ClostraBio ,  COUR Pharmaceuticals ,  Im-muneID ,  Latitude Food Allergy Care , and  Seed Health ; and has received consulting fees from  Excellergy Therapeutics ,  Red Tree Venture Capital , and  Regeneron.
Expression data We obtained expression data from a scRNA-seq study of murine CD8 T cells responding to an acute viral infection  (Kurd et al. , 2020). Therein, the authors transferred P14 transgenic CD8 T cells that recognize an LCMV epitope to histocompatible hosts, which were acutely immunized with the virus the day after. Single responding CD8 T cells from the spleens of immunized hosts were then sorted at different days post-infection (dpi) and analyzed by scRNA-seq. We performed quality control of the data downloaded from Gene Expression Omnibus (GSE131847) and selected cells and genes (Methods) to generate a final UMI count table of 14,666 genes in 42,025 single cells (Figure  1A ). Dimension reduction and representation on UMAP revealed a correct temporal arrangement of the cells (Figure  1B ), although D4 and D32 cells split in two regions of the projection.
Data collection and processing. Sequencing reads were generated by  Kurd et al. (Kurd et al. , 2020)  and  Milner et al. (Milner et al. , 2020). For these studies, the authors transferred P14 TCR-transgenic CD8 T cells, which recognize a Lymphocytic ChorioMeningitis Virus (LCMV) epitope, to histocompatible hosts that were acutely immunized the day after with 10 5 plaque-forming units of LCMV Armstrong. Single responding P14 CD8 T cells from the spleens of immunized hosts were sorted at different dpi and loaded into Single Cell A chips for partition into Gel Bead In-Emulsions in a Chromium Controller (10x Genomics). Single-cell RNA libraries were prepared according to the 10x Genomics Chromium Single Cell 3′ Reagent Kits v2 User Guide and sequenced (paired-end) on a HiSeq 4000 (Illumina). We used data collected at 0,  3, 4, 5, 6, 7, 10, 14, 21, 32  and 90 dpi and downloaded reads from GEO 'release 2020-10-09' (GSE131847) with fastqdump from the SRA-toolkit suite (https://www.
Metabolic gene selection scFEA uses the expression of a list of 719 metabolic genes relevant to the estimation of the fluxes in all 171 metabolic modules. Among these metabolic genes, 430 are present in the 14,666 genes expressed in at least 20 cells in our data. However, all the genes regulating glycolysis and Oxphos described in Kyoto Encyclopedia of Genes and Genomes database  (Kanehisa and Goto, 2000; Kanehisa et al. , 2023, KEGG: release '101. 4')  have not been included in the initial list. As metabolic switches from Oxphos to aerobic glycolysis and vice versa are crucial for the proper differentiation of CD8 T cells into effectors and memory cells during a viral infection, we complemented the list of 430 metabolic genes from N. Alghamdi et al. (  2021 ) with 4 and 96 extra genes, respectively involved in glycolysis and Oxphos, and present in the 14,666 considered genes (SupplementaryTable 1).
Data filtering and scFEA analysis Raw UMI count data were filtered for metabolic genes in the designed list (SupplementaryTable 1) and the counts for 530 metabolic genes in the 42,025 cells were submitted to scFEA. The algorithm was installed and run at the High-Performance Computing cluster Pôle Scientifique de Modélisation Numérique of the Ecole Normale Supérieure de Lyon, according to the instructions of the authors  (Alghamdi et al. , 2021 , https://github. com/changwn/scFEA). p. 15.