dataesr/scientific-paragraphs-categorization huggingface dataset:
benthamiana leaf Waadt et al. , 2015 -Nucleus mVenus epidermis B epsilon (B ε) AT5G28850 -Cytoplasm UBQ10 mTurquoise and N. benthamiana leaf Waadt et al. , 2015 *  B beta in -Nucleus mVenus epidermis GenBank and Leivar et al. (2011) B delta (B δ) AT5G28900 -Cytoplasm UBQ10 mTurquoise and N. benthamiana leaf Waadt et al. , 2015 *  B gamma in -Nucleus mVenus epidermis GenBank and Leivar et al. (2011) B gamma (B γ) AT1G54450 -Nucleus UBQ10 mTurquoise and N. benthamiana leaf Waadt et al. , 2015 *  B delta in -Punctate mVenus epidermis GenBank and structures Leivar et al. (2011) B beta (B β) AT1G03960 -Cytoplasm UBQ10 mTurquoise and N. benthamiana leaf Waadt et al. , 2015 *  B epsilon in -Nucleus mVenus epidermis GenBank and Leivar et al. (2011) TON 2 (FASS) AT5G18580 -Cytoplasm - CaMV 35S GFP A.
Methods System set-up and simulations performed. Simulations performed are shown in Table  1  and Table  S1. The Kir2. 2 channel atomistic structure (PDB ID: 3SPI 32 ); was converted to a coarse-grained representation using the martinize. py script (version 2. 4) downloaded from the MARTINI website (http://md. chem. rug. nl/index. php/ tools2/proteins-and-bilayers) with the MARTINI2. 2 force field  87, 88  and the ElNeDyn elastic network model  89. A single CG model of Kir2. 2 was embedded into a phosphatidyl choline (PC) bilayer by allowing lipids to self-assemble around the protein in a short simulation  90. The PC membrane was subsequently converted into a membrane of the desired lipid composition using an in-house exchange lipid methodology  54. The Kir2. 2 channel in a model plasma membrane (PM) was then equilibrated for 100 ns prior to being tessellated, using the GROMACS tool genconf, to form 12 × 12, 6 × 6 and 3 × 3 grids.
Simulation analysis. Radial distribution functions of lipid headgroups around the channels were calculated using the GROMACS tool, g_rdf. Clustering analysis was performed using in-house Python scripts, making use of the NumPy 95 , MDAnalysis  96, 97  and NetworkX  98  Python libraries. In the clustering algorithm, proteins were considered to be interacting when the centroids of the cytoplasmic domains (defined as residues 1-45 and 141-377) were within 8. 2 nm of one another. Protein-protein interactions were identified using in-house clustering scripts, again making use of the NumPy and MDAnalysis modules. Residues of neighboring proteins were considered to be interacting when residue centroids were within 0. 7 nm of one another. Phosphate densities were calculated using the g_density tool from GROMACS.
Diffusion analysis was carried out on trajectories with center of mass motion of the protein and lipids removed, and with the first 50 ns removed. Although the PM simulation was run for 50 µs, only the first 20 µs was used for diffusion analysis, in order to be comparable with analysis of the NoPIP2, PC, PMsparse and small simulations. Time-and ensemble-average mean squared displacement (MSD) for each species was extracted using a time step of 1 ns. Diffusion coefficients (D α ) were obtained by fitting the equation MSD = 4D α Δt α to graphs of MSD vs Δt, with Δt in the range 1 ns to 2 µs. Error bars show the standard deviation for the nonlinear fitting procedure. Scripts to perform this analysis were from https://github. com/tylerjereddy/diffusion_analysis_MD_simulations  99. Alpha values were obtained by fitting a straight line at fixed intervals to the log-log plots of MSD vs Δt.
Power spectra of membrane undulations were estimated as detailed in  23. The surface of the bilayer was calculated using lipid phosphate beads projected onto a grid of size 0. 5 nm. The Fourier transform of the bilayer surface was calculated using FFTW routines in the SciPy Python library 100 , and 1D power spectra obtained by performing radial averaging. The code can be obtained from GitHub (https://github. com/philipwfowler/ calculate-bilayer-power-spectrum). Graphs were plotted using gnuplot 4. 6 (www. gnuplot. info) and Matplotlib  101  and molecular visualization used VMD  102.
No escape platform was present so the animal was removed from the pool by the experimenter when the trial ended. One control mouse (paired with an exploitation mouse) that jumped out of the maze instead of swimming on the final day was excluded from the study. Cage control mice were used to measure baseline expression of c-Fos. They did not perform the task but were brought to the experimental room and handled daily by the experimenter, similarly to the four other groups. Data acquisition was performed by means of a video recording system and tracking software, SMART (BIOSEB, Chaville, France) and data processing was automated via a MATLAB batch program developed in our laboratory (Navigation Analysis Tool 81 ). c-Fos immunohistochemistry. Ninety minutes after the last trial, mice were deeply anesthetized with an intraperitoneal injection of ketamine (150 mg/kg) and xylazine (12 mg/kg) and perfused transcardially with saline (0.
Acknowledgements We thank  S. Laroche ,  B. Poucet ,  M. Khamassi ,  A. Hay ,  H. Sanders ,  M. Amoroso  and members of the  Rondi-Reig laboratory  for discussions and comments on the manuscript. This work was supported by the  Fondation pour la Recherche Médicale DEQ. 20120323730-France, by the  National Agency for Research   ANR-REG-071220-01-01-France  and by the  Ville de Paris HABOT Emergence(s)  project. This work also received support under the program Investissements d' Avenir launched by the  French Government  and implemented by the  ANR , with the references,  PER-SU (LRR ),  ANR-10-LABX-BioPsy  ( LRR ) and  ANR-11-LABX-65-SMART (BG ). The group of LRR is member of the  Labex Bio-Psy  and  ENP Foundation. The group of BG is member of the  Labex SMART. Labex are supported by  French State  funds managed by the  ANR  within the  Investissements d' Avenir programme  under reference  ANR-11-IDEX-0004-02.
, 2016). Data were used to build a high-confidence GRB2 interactor-specific assay library. Visualization of AP-SWATH data, quantification, and statistics analysis were performed in Skyline. (Lower right) Clustering of interaction dynamics and network-based analysis were performed using the Garuda platform, a community-driven software platform that supports reproducibility of computational analysis from complex high-dimensional data. See also Figures  S1  and S2.
Some GRB2 interactors can be grouped in a ''distal module'' (Figure  2 ) that is primarily made of proteins containing nucleotide-binding domains, the function of which remains to be established in the context of TCR signaling. Overall, 22 of the 53 high-confidence interactors have not been previously reported, and 41 of them have not been previously observed in T cells (Figure  2 ; Data S1). Whereas some of the 22 GRB2-interacting partners identified here have been reported to be involved in T cell activation (e. g. , SATB1 and GRAB), several of them (RPN1/2, SH3L1, and SC61B) have not been characterized in the context of the TCR signaling pathway yet. To independently confirm the GRB2 interactions identified through AP-SWATH, we performed co-immunoprecipitation (co-IP) and western blotting experiments and focused on CD5 and ARAP-1, two GRB2 partners identified in T cells for which commercial antibodies were available.
Reproducible Quantitative Measurements of GRB2 Protein Interactions in Peripheral CD4 + T Cells The development of methods that enable precise and robust quantitative measurements of protein abundances is important in facilitating statistics and downstream computational analysis for the identification of meaningful protein interaction patterns  (Collins et al. , 2013; Lambert et al. , 2013). Generation of highly precise and reproducible AP-SWATH data from primary cells is, therefore, crucial for understanding dynamic complex formation at the physiological level. Here, we assessed the reproducibility of AP-SWATH analysis to quantify the dynamics of the GRB2 interactome 0. 5, 2, 5, and 10 min after TCR-mediated T cell activation. Targeted analysis of AP-SWATH data was performed using a refined assay library consisting of 604 peptides and 4,020 transitions specific for GRB2 and 53 high-confidence GRB2 interactors identified earlier, followed by manual validation of the extracted peak groups in Skyline (MacLean et al.
Transparent Analysis of Time-Resolved GRB2 Protein Interactions in Peripheral CD4 + T Cells Generation of robust quantitative measurements, together with transparency and reproducibility of downstream computational analysis-including statistics analysis-is fundamental for rigorous validation of findings within the scientific community from complex high-dimensional data  (Peng, 2011). Toward this goal, AP-SWATH data were analyzed in this study using two open-source software systems: the graphical user interface MSstats in Skyline for raw data analysis  (Choi et al. , 2014)  and the Garuda platform for further data processing and visualization  (Ghosh et al. , 2011) , both supporting reproducibility of statistics and computational analysis. Using these software tools, we generated heatmaps, protein clusters, and statistics demonstrating that protein assemblies of increasing complexity formed around GRB2 within the first seconds of T cell activation (Figures  3A, 3B,  and S4 ).
Considering that the TCR signals driving the differentiation of double-positive thymocytes are conveyed via GRB2  (Radtke et al. , 2016)  and that those thymocytes constitute over 90% of the TCR + CD4 + cells present in a thymus, we tested next whether the dynamics of the GRB2 interactome differ between developing and mature T cells following activation with anti-CD3 plus anti-CD4 antibodies. Thymocytes were purified from GRB2 OST mice and GRB2-OST protein complexes isolated prior to and at various time points after TCR stimulation. Three biological replicates were prepared for each time point as described earlier for mature CD4 + T cells. The corresponding GRB2 OST samples were subjected to SWATH-MS, and we concurrently analyzed the AP-SWATH datasets in Skyline for quantitative comparison using the established high-confidence GRB2-interactor-specific assay library (Data S1).
Specific and dynamic formation of protein-protein interaction represents a key mechanism for regulating properties of cellular signaling. However, building pathway models that correctly reflect the properties of a signaling system in primary cells on the basis of public protein interaction data is compromised by the shortcomings of established methods to systematically retrieve protein interaction data. (A) The GRB2 protein-protein interaction network displays the association of time-dependent proteins following anti-CD3 and anti-CD4 stimulation in mature/peripheral CD4 + T cells (P) and developing/thymocytes (T). Nodes represent 15 proteins, including GRB2, that were consistently detected and reproducibly quantified in both peripheral CD4 + T cells and thymocytes. Rectangles inside the nodes show the normalized (Norm.
) log 2 fold change for each time point as the percentage of the maximal (red) or minimal value (green). The significantly different GRB2 protein interactions between the two cell types, as determined by MSstats, are indicated by an asterisk (*adjusted p < 0. 05). (B) Quantitative temporal profiling of three representative GRB2 interactors upon activation of peripheral T cells (blue) or thymic T cell (orange). The significantly different GRB2-PTPRA interaction, as determined by MSstats, between the two cell types at 0. 5 min post-stimulation is indicated (**adjusted p < 0. 01). Error bars represent SD. See also Figure  S6  and Data S1. Most systematic protein-protein interactions studies, to date, do not address the dynamic nature of protein complex formation. Moreover, the vast majority of data on protein interactions has been obtained by the analysis of a heterogeneous set of transformed cell lines, which complicates the development of coherent pathway models.
MS Data acquisition (DDA and DIA modes), assay library generation, and targeted extraction of AP-SWATH data were performed as described previously  (Collins et al. , 2013; Schubert et al. , 2015) , using the Skyline software. Details of the procedures are provided in the Supplemental Experimental Procedures. Filtering Criteria for the Identification of High-Confidence GRB2 Interactors High-confidence GRB2 interactors were identified using a three-step filtering with both DDA and SWATH AP-MS data, followed by manual validation of high-confidence GRB2 interactors in Skyline (see Figures  S2  and S3 ). Details of the procedures are provided in the Supplemental Experimental Procedures.
Statistics and Computational Analysis Statistical and computational analyses were performed using MSstats in Skyline  (Broudy et al. , 2014; Choi et al. , 2014)  and the Garuda platform  (Ghosh et al. , 2011). Both Skyline and Garuda are open-source software systems supporting the reproducibility of statistics and computational analysis by the scientific community. Details can be found in the Supplemental Experimental Procedures.
ACCESSION NUMBERS The accession number for the MS shotgun data (centroided mzXML and identified peptides in a pepXML report), which were used to generate the spectral and assay libraries reported in this paper is PRIDE: PXD003973  (Vizcaı ´no et al. , 2013). The accession number for the SWATH-MS data (instrument raw/wiff files and identified peptides in an OpenSWATH report) reported in this paper is PRIDE: PXD003972. Skyline files are available for AP-SWATH data analysis at the Panorama Public website: https://panoramaweb. org/labkey/ publication_APSWATH_GRB2. url. Packaged Garuda gadgets and recipe documents are available at https://www. dropbox. com/s/w2xafks09tfwdpk/ GarudaDataRecord. zip?dl=0.
ACKNOWLEDGMENTS We thank  Frederic Fiore ,  Elodie Caccomo-Garcia , and  Fabien Danjan  for the construction of GRB2 OST mice. We thank  Ludovic Gillet  and  Yansheng Liu  for instrument maintenance, as well as all members of the  Aebersold laboratory  for discussions. We thank  Jianmin Wu  for technical support with the PINA database. This work was supported by  CNRS ,  INSERM , the  European Research Council  (FP7/2007-2013 grant no. 322465  [''Integrate''] to B. M. ), and the  Agence Nationale de la Recherche  ( PHENOMIN  project to B. M. ). R. A. acknowledges the following grant support:  TBVAC2020 (2-73838-14 ),  ERC  grant  Proteomics v3. 0 (ERC-2008-AdG_20080422 ),  ERC  Proteomics  4D  ( 670821 ), and the  Swiss National Science Foundation  ( 3100A0-688 107679 ). M. G. acknowledges the support by the  Innovative Medicines Initiative  project  ULTRA-DD  ( FP07/2007-2013 , grant no.
Global transcriptomics Total RNA was isolated using the QIAGEN isolation kit (QIAGEN) and quality-checked by Nanodrop analysis (Nanodrop Technologies, Wilmington, DE, USA). Biotin-labeled cRNA samples were produced and hybridized onto Illumina human-12 BeadChips (Illumina, San Diego, CA, United States). Samples included HFF1, BJ, A1, A2, A3, H1, H9, TDHFF1 (iPS2), TDBJ5 (iB5), TFBJ, TFA2, TDA2. 3, TDA3. 1, NI H1, NI H9, NI TDBJ5, NI TFBJ, NI A2, NI A3, eNPCs, rNPCs_Ar, and rNPCs_NI. Microarray analysis, PCA plot, and heatmaps were performed using the R/Bioconductor packages in the programming language R /version 3. 1 or greater). Genes were considered significantly expressed with detection p values % 0. 01. Differential expression analysis was performed using the Illumina custom method, using differential p values % 0.
01, fold change ratio > 1. 5. Pathway analysis was determined by mapping onto KEGG pathways using Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david. ncifcrf. gov/). We fetched the significance level of normalized expression values corresponding to probe ID using lumi R's (http://bioconductor. org/ packages/release/bioc/html/lumi. html). Variance-stabilizing transformation (VST) was used to deal with sample replicates and robust spline normalization (RSN) for normalization. Significant samples (p value < 0. 05) were further transformed onto log2 scale and their IDs were annotated according to the IlluminaHumanv3. db Bioconductor annotation data package. Expression values of multiple probes for one gene were assigned by their median, resulting in analyzing 34,586 genes for each sample.
The list of genes regulating energy metabolism was based on the Human Glucose Metabolism PCR Array (SA Bioscience, http://www. sabiosciences. com), as previously described  (Prigione et al. , 2011a). Microarray raw data for NPCs from different studies were obtained from the National Center for Biotechnology Information Gene Expression Omnibus database under the series accession GSE25673  (Brennand et al. , 2011) , GSE28595  (Li et al. , 2011) , GSE40556  (Reinhardt et al. , 2013) , GSE55107  (Hargus et al. , 2014)  and GSE57595  (Stein et al. , 2014). Datasets were processed and normalized as above. In order to create a matrix of expression level for unique genes in each sample, the datasets were merged by their gene names representing median of their respective probes resulted in a total of 16,493 genes for 198 samples.
Differential expression between samples from various datasets was performed by correction of batch effect arising from two different platforms and by normalizing each dataset to a sample of the same genotype in order to merge datasets for downstream analysis. The corrected batch effect was confirmed by Principal Component Analysis (PCA). Each gene value was further assigned as their relative abundance value, which corresponded to the expression value of the gene in each sample divided by its mean expression value across the samples. The resulting expression matrix was subjected to hierarchical clustering (Spearman correlation, average linkage) and p value threshold. Fold change of differential expression between samples on log2 scale was analyzed using linear and Bayesian model algorithms from limma (http://bioconductor.
org/packages/release/bioc/html/lumi. html) and pairwise differential. Heatmaps displaying Z-score were generated with matrix hierarchically clustered. Similar comparative analysis was performed on datasets of different stages of brain development. Brain data was downloaded from Allen Brain Atlas data portal (http://human. brain-map. org/static/ download) and a matrix of 17,282 genes and 508 samples was created for further analysis as mentioned above.
1% formic acid). 3kV were applied for nanoelectrospray generation. A cycle of one full FT scan mass spectrum (300À1750 m/z, resolution of 70 000 at m/z 200, AGC target 1e 6 ) was followed by 12 data-dependent MS/MS scans (200-2000 m/z, resolution of 35 000, AGC target 5e 5 , isolation window 2 m/z) with normalized collision energy of 25 eV. Target ions already selected for MS/MS were dynamically excluded for 30 s. In addition, only peptide charge states between two to eight were allowed. The label-free software MaxLFQ  (Cox et al. , 2014) , which is integrated into MaxQuant (version 1. 5. 0. 0), was used for quantification  (Cox and Mann, 2008)  and searched against the human proteome database UniProtKB with 88. 717 entries, released in 11/ 2014. A false discovery rate (FDR) of 0. 01 for proteins and peptides and a minimum peptide length of 7 amino acids were required.
A maximum of two missed cleavages was allowed for the tryptic digest. Cysteine carbamidomethylation was set as fixed modification, while N-terminal acetylation and methionine oxidation were set as variable modifications. For comprehensive proteome data analysis, we applied gene set enrichment analysis (GSEA, v2. 2. 1)  (Subramanian et al. , 2005)  in order to see if priori defined sets of proteins show statistically significant, concordant differences between patients and controls. The intensity of detected proteins of each data group was averaged and zero values were replaced by 2 prior to log2 transformation. We used GSEA standard settings, except the minimum size exclusion was set to 10 or 15 and C2 (collection of pathways) or C5 (1454 gene annotations) were used as gene set databases (http://software.
Data were normalized to the housekeeping genes ACTB and GAPDH and are presented as mean log2 ratios in relation to control lines. For mtDNA copy number, the analysis was carried out with qPCR using LightCycler 480 SYBR Green I Master Mix (Roche) with addition of 10 pM of each primer on a LC480 Roche instrument (Roche) as follows: 95 C for 10 min, 40 cycles (95 C for 15 s, 60 C or 62 C for 30 s, 72 C for 30 s), 1 cycle (95 C for 30 s, 60 C for 30 s, heating to 95 C and cooling to 37 C). mtDNA was quantified as copies per ml by amplification of a fragment of the 12S mtDNA gene, with a standard curve obtained from serial dilutions of the linearized plasmid pGEMÒ-T Easy (Promega) containing the 12S mtDNA gene. Nuclear DNA was quantified as ng per ml by amplification of the 4401 -4601 region of the single-copy 28S nuclear gene, with a standard curve obtained from serial dilutions of DNA from control fibroblasts.
The analysis of D-loop polymorphisms was performed as described before  (Kirches et al. , 2001). Briefly, PCR was performed with a FAM labeled forward-primer and a non-labeled reverse-primer. The products were digested with HaeIII and the fluorescent fragment sizes were determined with the ABI3500 Genetic Analyzer using the GeneMarker v1. 51 software (SoftGenetics). Signals were transformed into curve diagrams, and the integral below the curve was taken as the relative amount of the respective length fragment. mtDNA deletions was investigated using two different long-range PCR sets  (Deschauer et al. , 2004). All primer sequences are reported in Table  S4.
Primary antibodies were diluted in blocking solution and incubated for at least 1 hr at RT. Prior to and following the 1 hr incubation period with the corresponding secondary antibody (Alexa Fluor, 1:300, Life Technologies), cells were washed once in PBS-T and two times in blocking solution, 5 min each. Counterstaining of nuclei was achieved by incubation with 100 ng/ml 4',6-diamidino-2-phenylindole (DAPI, Vector Laboratories) in PBS for 1 hr at RT. All images were acquired using the confocal microscope LSM510 Meta (Zeiss) in combination with the AxioVision V4. 6. 3. 0 software (Zeiss) and further processed with Adobe Photoshop CS-6 (Adobe Systems).
Cells were allowed to attach for one hour, before one plate was fixed (day 0). Additional plates were fixed on the following three days at the same time every day. For fixation, cells were washed with PBS, incubated for 20 min in 4% PFA and 8. 1 mM Hoechst, and washed again with PBS for final storage. The analysis was performed both using fluorescence reading with a Tecan plate reader (Infinite M200) and the count of Hoechst (33342, Invitrogen) -positive spots according to the ''spot detector'' function of the high-content screening (HCS) analyzer Cellomics ArrayScan (see below). All data were normalized to values obtained at day 0 to obtain a relative proliferation curve.
For ultrastructural analysis, areas covering entire neuronal cells were imaged automatically on the Tecnai Spirit microscope using the MSI-Raster application within the Leginon system. Regions of interest (up to 50x50 mm 2 ) were selected at low magnification (560x) and successively imaged at 4400x and 15000x magnification applying a defocus of À100 mm and À4 mm, respectively. Obtained micrographs were then montaged to single images using TrakEM2 within the FIJI software package  (Cardona et al. , 2012).