dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The IASI mission is a joint mission of  EU-METSAT  and the  Centre National d'Etudes Spatiales (CNES, France). This study has been financially supported by the  French Space Agency -CNES  (grant no. IASI/TOSCA ). The authors acknowledge the AERIS data infrastructure (https://www. aeris-data. fr, last access:  19 October 2021 ) for providing access to the IASI level 1C data, distributed in near-real time by  EUMETSAT  through the EUMETCast system distribution. The authors acknowledge data set producers and providers used in this study. The ozonesonde data used in this study were mainly provided by the  World Ozone and Ultraviolet Data Centre (WOUDC)  and are publicly available (see http://www. woudc. org,last access:  19 October 2021 ). We acknowledge the  Institut für Meteorologie und Klimaforschung (IMK), Karlsruhe, Germany , for a licence to use the KOPRA radiative transfer model.
i NLRP3-deficient U937 cells reconstituted with NLRP3 were treated with PMA and doxycycline for 16 h followed by D4476 and LPS (4 h). NLRP3 immunoprecipitates were analyzed for phospho-Ser by WB. Data are biological quadruplicates representative of two independent experiment (a, c), biological triplicates representative of four independent experiment (b), 11 technical replicates representative of two independent experiments (d), one representative of two (e, h, i) and three (f, g) independent experiments. Ordinary oneway ANOVA with Dunnett's multiple comparisons (a), ordinary two-way ANOVA with Tukey's multiple comparisons (c, d) to corresponding non-targeting siRNA control; *p-value < 0. 05; **p-value < 0. 01; ***p-value <0. 001; ****p-value < 0. 0001. Molecular weights are indicated in kDa (e-i).
Peptides were extracted, washed (50% acetonitrile, 1% formic acid), dried and reconstituted (2. 5% acetonitrile, 0. 1% formic acid). Samples were loaded via a Famos auto sampler (LC Packings) onto equilibrated nano-scale reverse-phase HPLC capillary columns containing 2. 6 µm C18 spherical silica beads into a fused silica capillary (100 µm inner diameter x ~30 cm length)  39. Peptides were eluted with increasing concentrations of solvent (97. 5% acetonitrile, 0. 1% formic acid), subjected to electrospray ionization and entered into an LTQ Orbitrap Velos Pro ion-trap mass spectrometer (Thermo Fisher Scientific). Eluted peptides were fragmented to produce a tandem mass spectrum of specific fragment ions. Sequences were determined by matching databases with the fragmentation pattern using Sequest (ThermoFinnigan).
The modifications of 79. 9663 mass units to serine, threonine, and tyrosine, and of 114. 0429 Da to lysine were included in the database searches to determine phospho-and ubiquitinated peptides respectively. Phosphorylation assignments were determined by the Ascore algorithm  40. All databases include a reversed version of all the sequences and the data was filtered to between 1-2% peptide false discovery rate. Inflammasome activation. Inflammasome was activated using the following protocols unless stated otherwise. 0. 5 × 10 6 U937 cells/ml were differentiated with PMA (50 ng/ml, 16 h), treated with doxycycline (1 μg/ml, 4 h), primed with LPS (40-50 ng/ml, 3-4 h) or Pam3CSK4 (0. 5 μg/ml), and activated with nigericin (15 μg/ml, 1 h), MSU (250 μg/ml, 16 h) or silica (1 mg/ml, 16 h), or transfected with poly(dA:dT) (1 μg/ml, 6 h).
Typhimurium SL1344 (MOI 234) for 2 h, or addition of E. coli J53 (MOI 100) for 16 h with gentamycin (10 μg/ ml) added 1 h after E. coli J53 infection. ELISA. The following ELISA kits were used: human IL-1β, human TNF, mouse IL-1β, mouse TNF (Duoset, RnD Systems), human IL-18 (MBL), mouse IL-18 (Thermofisher). Data were acquired using i-control (Tecan). Measured values are available in the Source Data file. Cytokines in sera were measured using the following Luminex kits: mouse IL-6, mouse IL-1a, mouse IL-1b, mouse TNF, mouse IL-18 (Bio-Rad), and analyzed with Luminex Bioplex manager (Bio-Rad). Western-blot analysis. To test protein level in lysates, cells were directly lysed in sample buffer 2X. To test protein secretion, cell supernatants were concentrated using Amicon Ultra-0. 5 mL Centrifugal Filters (Millipore) and diluted in sample buffer 2X.
Data have been acquired on ChemiDoc Touch imaging system (BioRad) using Image Lab software (BioRad). Quantifications were performed with ImageJ. Uncropped and unprocessed scans of blots are available in the Source Data file. Antibodies are listed in Supplemental Table  4. Microscopy. For ASC speck assays, cells were fixed with 4% paraformaldehyde (in PBS, 20 min), permeabilized in PBS, 0. 1% Triton X-100, 0. 2% Bovine serum albumin (BSA) (30 min), and stained with anti-ASC antibodies (2 μg/ml, Santa Cruz Biotechnology or 1 μg/ml, Adipogen) in PBS, 5% BSA (in PBS, 1 h) followed by anti-Rabbit IgG(H + L)-Alexa Fluor 660 or anti-Rabbit IgG(H + L)-Alexa Fluor 488 (6. 7 μg/ml in PBS, 0. 2% BSA, 30 min). Cells were imaged using AxioImager Z1 (Zeiss) and ×40 objectives. Images were quantified using ImageJ software.
Caspase-1 activity was stained using FAM-FLICA Caspase-1 Assay kit (ImmunoChemisty Technologies) according to the manufacturer's instructions. Briefly, Hoechst 33342 (0. 4 μg/ml, ImmunoChemisty Technologies) and FAM-FLICA reagent (1/50, 1 h) were added in the culture media upon inflammasome activation. Cells were washed once in DPBS (with calcium/ magnesium pH 7. 1, Gibco), and media was replaced by DPBS/glycerol (1:1). Cells were imaged using CQ1 high content screening microscope (Yokogawa) and 10X objectives. Images were quantified using CQ1 and ImageJ softwares. Values are available in the Source Data file. Antibodies are listed in Supplemental Table  4. Cell death assay. To assess cell permeabilization, cells were cultured in media without phenol red. PI (1. 25 μg/ml, Immunochemistry technologies), YOPRO TM -1 Iodide (1 μM, Invitrogen) and Hoechst (0.
2 μg/ml, Immunochemistry technologies) were added 1 h before time-lapse imaging using CQ1 high content screening microscope (Yokogawa). Twi images/well were taken every 10 min using ×10 objectives. Images were quantified using the CQ1 software (Yokogawa). Pyroptosis was assessed using Cytotoxicity Detection Kit PLUS (LDH) (Roche) according to the manufacturer's instructions. Values are available in the Source Data file. ASC oligomerization assay. BMDMs (0. 3 × 10 6 cells/ml) were resuspended in 20 mM HEPES pH 7. 5, 10 mM KCl, 1. 5 mM MgCl 2 , 1 mM EDTA, 1 mM EGTA, 320 mM sucrose and EDTA-free protease inhibitor cocktail, and mechanically disrupted through 21 G needles 30 times. Lysates were centrifugated at 300×g 8 min and supernatants were lysed in 1 volume of CHAPS buffer (20 mM HEPES, 5 mM MgCl 2 , 0.
5 mM EGTA, 0. 1% CHAPS and EDTA-free protease inhibitor cocktail) and then centrifugated at 5000 × g for 8 min. The pellets were washed once with PBS, resuspended in CHAPS buffer, crosslinked with disuccinimidyl suberate (DSS, 2 mM, 37 °C, 1 h) (Thermofisher), and then quenched in sample buffer 2X. Quantitative PCR. BMDMs (0. 3 × 10 6 cells/ml) were lyzed in TRIzol (Invivogen) for total RNA purification. Single strand cDNAs were synthetized using ImProm-II™ Reverse Transcription System (Promega). QPCR were performed using iTaq™ Universal SYBR Green Supermix (Bio-Rad) on CFX96 Touch Real-Time PCR Detection System (Bio-Rad). Primers are listed in Supplementary Table  3. Data were analyzed with CFX Maestro software (Bio-Rad). Values are available in the Source Data file. Mice. C57BL/6 J mice (Charles River laboratory) were housed at the PBES specific pathogen-free rodent facility (ENS Lyon) with food provided ad libidum, 13 h light/ 11 h dark cycle, 22 °C +/-2 °C, 50% +/-10% humidity.
Mice were backcrossed for 7 generations with C57BL/6J, and genotyped by specific PCR using TCTGGCCTGGTTTGCAGTTT primer with GGAAGTGATGGGTCTGTGTATA (WT) or AAGTGATGGGTCTGTGTAGG (KO) primers. Structural models. Models were based on a resolved structure and proposed models (PDB 6npy, with amino acid numbering changed for consistency, S804 corresponding to S806), and represented using PyMOL software  16. The structures of NLRP3/NEK7 complex with different NLRP3 and NEK7 substitutions have been modeled using CHARMM-gui server (PDB reader)  42, 43. Electrostatic potential surfaces of each complex have been calculated using PBEQ solver from CHARMM-gui server. For the sake of clarity, the surface has been calculated for aa 742-864 of NLRP3 and 116-134 of NEK7. Molecular dynamics (MD) simulations were carried out on graphics processing units (GPUs) by means of high throughput MD (HTMD) software with CHARMM36m force field (trajectory of 100 ns).
At the end of each simulation, the number of hydrogen bonds were extracted using Ligplot software. In vitro kinase assays. GST-NLRP3 (1 μg, Abcam) was incubated of with GST-CSNK1A1 (100 ng, Sino Biological), GST-CSNK2A1 (100 ng, Sino Biological)/ GST-CSNK2B (100 ng, Creative Biomart), or 6-His-GST-CAMK2B (100 ng, Sino Biological)/Calmodulin (1 μg, Enzo) with 50 μM ATP and 4 μCi of [γ 32 P]-ATP (PerkinElmer) in kinase buffer (50 mM HEPES pH 7. 3, 100 mM NaCl, 10 mM MgCl 2 , 0. 05% Triton X-100, 10 mM β -glycerophosphate, 5 mM NaF) at 30 °C for 30 min in a final volume of 39 μL, supplemented with 2 mM CaCl 2 for CAMK2B/ Calmodulin reactions. Reactions were stopped by adding 15 μL of LDS (Thermo-Fisher Scientific) and 6 μL of DTT (Euromedex) 500 mM. Samples were analyzed by electrophoresis using Bolt 4-12% Bis-Tris Plus gel (ThermoFisher Scientific) followed by Coomassie blue gel staining using PageBlue TM Protein staining solution (ThermoFisher Scientific) and autoradiography.
Acknowledgements This work was supported in part by the  European Research Council  ( ERC-2013-CoG_616986  to B. F. P. ), the  Agence Nationale de la Recherche  ( ANR-13-JSV3-0002-01  to B. F. P. ,  IDEXLYON of Université de Lyon Investissements d'Avenir program   ANR-16-IDEX-0005  to B. F. P. , AAPG 2017 LYSODIABETES to R. R. ,  ANR-10-LABX-0030-INRT  to R. R. ,  ANR-10-IDEX-0002-02  to R. R. , and  ANR INGESTEM French National infrastructure  to R. R. ), the  Finovi Fundation  (to B. F. P. ), the  Chinese Scientific Council  ( 201600090079  to T. N. ), the  European Foundation for the Study of Diabetes (EFSD) /  Novo Nordisk Diabetes Research Programme  grant (to R. R. ) and by the  "Fondation Pour La Recherche Médicale (FRM) " ( EQU201903007859 ,  Prix Roger Propice Prize , both to R. R.
The arrest of phloem is linked with strongly downregulated aquaporins Aquaporins are transmembrane channels facilitating the transport of water and small solutes from cell-to-cell and between cell compartments. Noticeably, seven of the ten PIPs identified in V. vinifera  37  were drastically inhibited (Tables  1  and S3 ) simultaneously with growth and phloem arrest. Among them, VviPIP1. 3 (Vitvi02g00310 -LOC 100233001), VviPIP2. 5 (Vitvi13g00605 -LOC100233 002) and VviPIP2. 3 (Vitvi08g01038 -LOC100233094) were the most intensively repressed transcripts (ranked 3rd, 5th and 26th according to their absolute expression changes in Syrah) with a percentage of inhibition ranging from 83% to 93% in Syrah. Data on grapevine aquaporins  37  , including the gene expression atlas  26  and various other RNA-seq datasets, showed that these three genes exhibited quite a common expression pattern and actually decreased at ripe or harvest stages (defined as 20 °Brix), but the inhibition continued in post-harvest withering phases (see Fig.
Single berry sampling Single berry growth and development were monitored through biweekly pictures of selected clusters (up to eight for each genotype) starting a few days before softening day until 2 weeks after maximum berry growth. Pictures were taken using a Lumix FZ100 camera (Panasonic), keeping the focal range and cluster to camera lens distance (30 cm) constant. The volume increment of selected berries was calculated by analysing the pictures with ImageJ  64. The software, after calibrating the images using the 1 cm scale present in each of them, automatically counted the pixels enclosed in each targeted berry area, measured as an ellipsoid. The estimated berry volume was then calculated accordingly using the radius of the previously calculated area. To eliminate the intra-and intercultivars' variability in berry sizes and compare the changes in volume among berries of different clusters, vines and genotypes, each berry growth profile was normalised to the softening volume, set to 1.
Data analysis Raw reads were trimmed for quality and length with Trimmomatic, version 0. 38  65. Reads were aligned against the reference grapevine genome PN40024 12 × 2 66 , using the software Hisat2, version 2. 1. 0 67 yielding an average of 31. 6 M sequence per sample (Table  S6 ). Aligned reads were counted using the last available annotation VCost. v3 with HTSeqcount (version 0. 9. 1) with the "nonunique all" flag 68. Genes were filtered by applying an RPKM > 1 cut-off in at least one experimental condition (Table  S4 ), and the variance stabilising transformation was applied for data visualisation. Genes (TMM-normalised) were tested for multi timeseries significance for finding genes with significant (p < 0. 05) temporal expression changes using the R package MaSigPro  69  , with Syrah dataset used as a control.
A quadratic regression model (rqs = 0. 7) was applied. Significant genes over time were clustered using STEM (short time-series expression miner) software  70  with the maximum number of model profiles set to 24. Filtering or normalisation was not applied by the software since input data were already filtered and normalised, but a meancentred scale was applied to overcome cluster mismatches due to different expression levels. Genes showing the same profiles pattern among the significant clusters were grouped as follows: cluster A = cluster 7 & 9; cluster B = cluster 16, 17 & 14; cluster C = cluster 8; cluster D = cluster 15 (Fig. S3 ). Venn diagrams were drawn with the R package VennDiagram. GO term profiler was retrieved from the web tool gProfiler by applying a significant pvalue of 0. 001.
Acknowledgements We would like to thank the  Poupelain Foundation , the  Comité Interprofessionnel des Vins de Bordeaux (CIVB)  and the  Agence Nationale de la Recherche  ( G2WAS  project,  ANR-19-CE20-0024 ) for providing the financial support of this study. Thanks to  Marc Farnos  for plant management,  Sylvain Santoni  and  Muriel Latreille  for RNA library preparation,  Gauthier Sarah  for bioinformatics support and  Yannick Sire  for cation analysis.
, 2018). A limitation of large-scale classification based on unsupervised grouping of neurons with similar expression profiles is the lack of direct functional correlates. Indeed, this would require a prior functional characterization of individual neurons by patch-clamp experiments, a laborious step incompatible with large samples of neurons. Nevertheless, the direct correlation of single cell transcriptomes with functional mechanosensory properties could serve as a basis for identifying the genes involved in mechanotransduction. Here, we used Patch-seq methodology  (Cadwell et al. , 2016; Fuzik et al. , 2016)  to combine recordings of MS currents in cultured mouse DRG neurons with single cell RNA sequencing. In silico analysis of collected data with a previous large-scale dataset allowed to link MS current subtypes to molecular neuronal clusters.
Mapping Patch-seq transcriptomes of MS neurons to single-cell RNA-seq datasets Previous scRNAseq studies performed on large samples of neurons have used gene expression profile analysis to sort out DRG neurons into molecularly defined neuronal subclasses  (Li et al. , 2016; Sharma et al. , 2020; Usoskin et al. , 2015; Zeisel et al. , 2018). To achieve high-quality classification of our neuron sample, we merged our data with the dataset of 1580 scRNAseq DRG neurons from Zeisel and collaborators, which is available as open source  (Zeisel et al. , 2018). Then, we performed a joint analysis using mutual nearest neighbors (MNN) correction by focusing on the expression magnitude of the 211 most highly enriched genes across DRG neuron clusters  (Zeisel et al. , 2018)  (Fig. S1C ). This procedure allowed to classify each neuron from our sample in transcriptional clusters identified previously  (Zeisel et al.
, 2018)  (Table  S1 ). The merged datasets were mapped using t-distributed stochastic neighbor embedding (tSNE) projection (Fig. 2A ). Neurons from our sample spread across seven out of eight functionally distinct DRG neuron populations, some of which are composed of multiple transcriptional clusters whose functional specificity remains to be determined. Eight neurons were classified into the NF group of neurons typically considered as low-threshold mechanoreceptors (LTMR) and proprioceptors. Six neurons belong to NP1 population related to polymodal nociceptors, whereas eleven neurons were assigned to NP2 population and a single neuron to NP3 population, which are related to itch-specific sensory neurons  (Cavanaugh et al. , 2009; Dong and Dong, 2018; Han et al. , 2013; Wang and Zylka, 2009).
Enrichment of transcripts as a mean to identify MS channel gene candidates The above data failed to reveal specific expression of a given MS channel gene in a particular neuronal group. Therefore, we assessed whether some transcripts showed statistically over-representation (e. g. enrichment) in neurons clustered by current types. We determined the differential expression of a gene by comparing its expression in neurons displaying a specific current type against the neurons that do not express this current type. The plots illustrated in figure 4B provide graphical views of the enrichment of each MS channel gene in neurons with RA, IA, SA and ultra-SA components. Piezo2 is differentially expressed (P = 0. 015) in neurons expressing RA currents, exhibiting a 2. 3-fold enrichment. This result indicates that relative enrichment rather than specific expression may be a pertinent criterion when selecting putative MS current genes.
Inhibition of Tmem150c expression does not impair MS currents in DRG neurons Tmem150c has been proposed to encode SA MS channels in proprioceptive DRG neurons contributing to motor coordination  (Hong et al. , 2016). This conclusion has been challenged by independent groups, because Tmem150c expression by itself does not appear to generate MS current but rather modulates the activity of mechano-gated channels including PIEZO1, PIEZO2 and the two-pore domain K + channel TREK1  (Anderson et al. , 2018; Dubin et al. , 2017). Our scRNAseq data indicated that Tmem150c is not enriched in a particular subset of DRG neurons. Therefore, to gain more understanding about its role in MS current generation we recorded from Tmem150c siRNA electroporated DRG neurons. We found no differences whatsoever regarding either the incidence or the amplitude of the different MS current components in Tmem150c siRNA-treated neurons compared with non-targeting siRNA-treated neurons (Fig.
, 2016; Sharma et al. , 2020; Usoskin et al. , 2015; Zeisel et al. , 2018 ). Yet, 85% of the processed mRNA samples met quality standards for RNAseq. The molecular classification of our small and heterogeneous group of DRG neurons however was challenging because of the resulting low statistical power. Advances in microfluidics for single cell partitioning have enabled scRNAseq studies performed on samples of over one thousand cells, subdividing DRG neurons into a total of up to 17 transcriptional clusters  (Sharma et al. , 2020; Zeisel et al. , 2018). To achieve high-quality classification of our neuron sample, we combined our single-cell transcriptomes with the larger single-DRG neuron dataset from Zeisel and collaborators  (Zeisel et al. , 2018). Therefore, despite its modest size, we succeeded to map all the neurons from our sample into DRG neuron transcriptional clusters.
The situation differs in trigeminal corneal neurons where a non-specific decrease of currents with intermediate and slow kinetics has been reported in Piezo2 KO mice  (Fernandez-Trillo et al. , 2020). We also observed a modest effect of Piezo2 inhibition on the amplitude of ultra-SA currents. It cannot be excluded that this decrease is due to a RA current component masked in neurons with large ultra-SA currents. However, this modest impairment is still observable when current amplitude was measured at the end of the mechanical stimulus, i. e. when RA currents are expected to be fully inactivated (data not shown). Inhibition of a Piezo2 splice variant could be responsible for this effect, as Piezo2 extensive alternative splicing affecting inactivation kinetics has been reported in DRG neurons, although Piezo2 splicing variants displayed inactivation kinetics still in the range of RA current subtype  (Szczot et al.
Single-cell library preparation After electrophysiological recording, the cytoplasmic content of the neuron was harvested into the patch pipette, transferred into lysis solution (RNaseOUT 8 U. µl -1 , 2% Triton X-100) and flash frozen. Reverse transcription was performed using a SMART-seq v4 Low Input Kit (Clontech) directly on the cell lysate according to the manufacturer's protocol. After cDNAds amplification, 50 μl of the sample were subjected to cDNA purification on AMPure XP beads. 0. 5 ng of each purified cDNAds were used to construct the sequencing library using Nextera XT kit (Illumina) with fragments over 300-bp length from each neuron. Processing, quality control and filtering of single-cell RNA-seq data cDNAds libraries were sequenced either in a NextSeq 500 (Illumina) using paired-end reads (76 pb) with an average depth of 29 million reads per cell.
The raw reads (FastQ files) were cleaned by removing adaptor sequences, short sequences (length < 25 bp), low-quality bases (quality score < 30) and ambiguous sequences (i. e. , reads with more than 30% unknown bases 'N') using CutAdapt 1. 9. 1 software  (Martin, 2011). TopHat-2 v2. 1. 0 was used to map the cleaned RNA-seq reads to the mouse genome (mm10 / GRCm38. 90) with two mismatches, two gaps and one multihit allowed. TopHat-2 splicing algorithm was used to map reads covering splice junctions, thereby improving the utilization of reads. After genome mapping, gene counts matrix was determined using GenomicAlignment (version 1. 24. 0) (Lawrence et al. , 2013) using GTF annotation. A set of mitochondrial genes (mt-Atp6, mt-Atp8, mt-Co1, mt-Co2, mt-Co3, mt-Cytb, mt-Nd1, mt-Nd2, mt-Nd3, mt-Nd4, mt-Nd4l, mt-Nd5, mt-Nd6) were used to check viability of each sample: the sample is considered as non-apoptotic if the expression of these genes is less than 10% of total count in the same cell.
Read counts were analyzed in the R/Bioconductor environment (version 3. 12; www. bioconductor. org) with the R package edgeR. The gene expression value was normalized between samples by TMM with a gene detection threshold of 20 counts in at least two cells. For the generation of heat maps shown in Figures  2B, z-transformed  TMM count data was used. Relative expression levels were obtained by sample normalization using TPM (transcripts per kilobase million).
Dorsal root ganglion neuron clustering The mapping of the scRNAseq neurons in DRG neuron transcriptional clusters was done by a crossdataset normalization approach. We used fastMNN, the new implementation of Mutual Nearest Neighbours (MNN) correction  (Haghverdi et al. , 2018)  implemented in the R package batchelor to combine our dataset with Zeisel and collaborators scRNAseq sensory neuron dataset  (Zeisel et al. , 2018). Briefly, a principal component analysis (PCA) was performed on the 211 most highly variable genes in DRG neurons (most highly enriched genes for each cluster)  (Zeisel et al. , 2018)  to correct for batch effect. Identification of MNN was done in this reduced dimension space using the following parameters: dimension = 60 and k = 10. To assign each individual neuron to a specific cluster, we built a k nearest-neighbors (kNN) graph with k = 28 using Euclidean distance.