dataesr/scientific-paragraphs-categorization huggingface dataset:
4  Our results show that Mic1, the human microglial subpopulation that harbors the majority of markers found in the previously described DAM transcriptomic signature,  12  is the main microglial subpopulation that drives microgliosis in ALS. In fact, almost a third of Mic1 (DAM) marker genes are deregulated in the motor cortex of our group of patients with ALS. Of note, Mic1 proportion highly correlated with the expression of TREM2, which is required to enhance the proinflammatory stage of DAM. 4  Our immunohistochemical analyses did not show a relevant increased density of Iba1 (a widely used marker of microglial processes) in ALS-related brain tissue, nor was the expression of its coding gene (AIF1) altered in our RNASeq data set. Previous studies have frequently provided contradictory results when using this marker to assess microgliosis.
, 3 modules associated with disease status (MEblack, p = 0. 003; R = 0. 64; MEyellow, p = 0. 025; R = 0. 57; and MEpink, p = 0. 011; R = -0. 51; figure3). Moreover, MEblack and MEyellow were enriched for inflammatory responses, whereas MEpink had an overrepresentation of genes involved in synaptic and neuronal functions (table e-8, links. lww. com/ NXI/A280). The SynGO database confirmed the postsynaptic signature of this enrichment in MEpink (table e-9, links. lww. com/NXI/A280). Of interest, the 2 inflammatory gene coexpression modules (MEblack and MEyellow) showed a strong negative correlation with the postsynaptic module (MEpink) (p = 8 × 10 -5 ; R = -0. 78 and p = 3 × 10 -4 ; R = -0. 74, respectively) (figure3), suggesting that both inflammation and synaptic alterations are interconnected in ALS pathologic processes.
Study funding The authors are indebted to the " Fundación Española para el Fomento de la Investigación de la Esclerosis Lateral Amiotrófica (FUNDELA) " for funding the present study. O. Dols-Icardo  is a recipient of a grant by  The Association for Frontotemporal Degeneration (Clinical Research Postdoctoral Fellowship ,  AFTD 2019-2021 ). This work was supported by research grants from  Institute of Health Carlos III (ISCIII) ,  Spain PI18/00326  to J. C. ,  PI18/00435  to D. A. , and  INT19/00016  to D. A. , and by the  Department of Health Generalitat de Catalunya  PERIS program  SLT006/17/125  to D. A. This work was also supported in part by  Generalitat de Catalunya  ( 2017 SGR 00547 ) to the " Grup de Recerca en Demències: Sant Pau. " Disclosure The authors report no disclosures relevant to the manuscript.
Immunofluorescence staining and confocal microscopy For microscopic analysis, MCF-7 cells were plated on glass coverslips in a 24-well plate, fixed with 4% paraformaldehyde for 20 min, washed in PBS and then incubated with antimyc antibody (sc-40 AC, Santa Cruz, 1:1000 dilution), anti-HA antibody (901533, clone 16B12, BioLegend, 1:1000 dilution) and appropriated Alexa-conjugated secondary antibodies (Molecular Probes) diluted in PBS containing 1% BSA and 0. 5% Tween 20. Coverslips were mounted on Mowiol-DABCO and examined in a Zeiss Meta confocal microscope (LSM 510 META, Zeiss, France) with a UV laser and x 63 objective. Confocal images were analyzed using ImageJ (National Institutes of Health, Bethesda, MD) and Photoshop (Adobe, San Jose, CA) software.
After preconcentration and washing of the sample on a Acclaim PepMap 100 column (C18, 2 cm × 100 μm i. d. 100 A pore size, 5 μm particle size), peptides were separated on a LC EASY-Spray column (C18, 50 cm × 75 μm i. d. , 100 A, 2 µm, 100A particle size) at a flow rate of 300 nL/min with a two steps linear gradient (2-22% acetonitrile/H20; 0. 1 % formic acid for 220 min and 22-32% acetonitrile/H20; 0. 1 % formic acid for 20 min). For peptides ionization in the EASYSpray source, spray voltage was set at 1. 9 kV and the capillary temperature at 250 °C. All samples were measured in a data dependent acquisition mode. Each run was preceded by a blank MS run in order to monitor system background. The peptide masses were measured in a survey full scan (scan range 375-1500 m/z, with 70 K FWHM resolution at m/z=400, target AGC value of 3.
Mass spectrometry data processing Relative intensity-based label-free quantification (LFQ) was processed using the MaxLFQ algorithm from the freely available MaxQuant computational proteomics platform, version 1. 6. 3. 4. Spectra were searched against the Human database, extracted from UniProt on the 9 th of December 2019 and containing 20379 entries (reviewed) supplemented with 229 major bovine contaminants proteins identified in calf serum used for cells culture. The false discovery rate (FDR) at the peptide and protein levels were set to 1% and determined by searching a reverse database. For protein grouping, all proteins that cannot be distinguished based on their identified peptides were assembled into a single entry according to the MaxQuant rules. The statistical analysis was done with Perseus program (version 1.
6. 2. 1) from the MaxQuant environment (www. maxquant. org). Quantifiable proteins were defined as those detected in above 70% of samples in one condition or more. Protein LFQ normalized intensities were base 2 logarithmized to obtain a normal distribution. Missing values were replaced using data imputation by randomly selecting from a normal distribution centred on the lower edge of the intensity values that simulates signals of low abundant proteins using default parameters (a downshift of 1. 8 standard deviation and a width of 0. 3 of the original distribution). To determine whether a given detected protein was specifically differential, a two-sample t-test were done using permutation-based FDR-controlled at 0. 01 and employing 250 permutations. The p value was adjusted using a scaling factor s0 with a value of 0.
Acknowledgments We thank the  PiCSL-FBI core electron microscopy facility (Aïcha Aouane ,  Fabrice Richard  and  Nicolas Brouilly ,  Aix Marseille Univ, CNRS, IBDM, Marseille, France ), member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-0004 ). We are grateful to  Eric Bailly  for the molecular biology advice. This work was supported by grants from the  Agence Nationale de la Recherche  ( ANR-18-CE13-0017 ,  Project SynTEV ), and the  Fund for Scientific Research-Flanders  ( FWO Grant   G0C5718N ). Antonio Luis EGEA-JIMENEZ was recipient of a postdoctoral fellowship of  La Ligue contre le Cancer (France). This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  747025.
Data at: https://data. mendeley. com/datasets/vb8bjpkbjj/draft?a=1ab12cf3-2277-4c45-b787-61289f451a12. The mass spectrometry proteomics data, including search results, have been deposited to the ProteomeXchange Consortium (http://www. proteomexchange. org/) via the PRIDE  (Vizcaino et al. , 2014)  partner repository with the dataset identifier: Project accession PXD018631, username reviewer96649@ebi. ac. uk, password 3fi54JNb.
mend N/A 16:0-18:1 PE (POPE) pmCherry-C1-VPS28 Avanti Polar Lipids This paper Cat# 850757P eley. com/dataset N/A Lipofectamine™ RNAiMAX Transfection Reagent pmCherry-C1-VPS37B Raw imaging data pmCherry-C1-VPS37C ThermoFisher This paper / Mendeley This paper Data This paper Cat# 13778075 s/vb8bjpkbjj/draft N/A ?a=1ab12cf3-N/A IPTG pmCherry-C1-VPS4A Sigma Aldrich This paper Cat# I6758 2277-4c45-b787-N/A Protease Inhibitor Cocktail pmCherry-C1-VTA1 Sigma Aldrich This paper Cat# P8340 61289f451a12 N/A Mass spectrometry proteomics data This paper / PRIDE Project accession.
Hereby, Gb3-FSL is a synthetic lipid that contains the Gb3 head group linked via a spacer (O(CH 2 ) 3 NH) to a dioleoylphosphatidylethanolamine (DOPE) backbone  52. Furthermore, to investigate how the lipid environment influences LecA and StxB binding, we prepared non-phase-separated and phase-separated GUVs. To measure the binding of the two lectins in a quantitative manner, we used the recently developed FIJI macro GUV-AP that determines protein binding efficiency to GUVs and enables analysis of large datasets  53. For quantifying the binding efficiency, we determined the ratio between the signal of the lectin at the rim of the GUV and free lectin in solution  53. Such ratio represents quantitatively the contrast between bound and free lectin. If the lectin cannot bind to the GUV, the contrast and thus the binding efficiency equals to 0.
Image acquisition and processing was done using the software NIS-Elements (version 4. 5, Nikon). SLBs were imaged with a Nikon Eclipse Ti-E inverted microscope, equipped with a TIRF-illuminator, a 100× oil immersion objective (NA 1. 49), with laser lines at 488 nm, 561 nm and 647 nm, and an EM-CCD camera (Andor Ixon DU-897). Images were acquired using a highly inclined laser beam (HILO-configuration)  67. Image acquisition and processing was done using the software NIS-Elements (version 4. 5, Nikon). TIRF-SIM imaging was performed using a Nikon-N-SIM microscope equipped with laser lines at 488 nm and 561 nm, and a CFI apochromatic TIRF 100x oil immersion objective (NA 1. 49).
Image analysis. Quantification of lectin binding to MDCK cells was conducted by measuring fluorescent intensities in single cells that were not surrounded by labeled cells using a custom-written Matlab program as described before  68. Lectin binding efficiencies to GUVs were quantified using the ImageJ-macro GUV-AP  53. Furthermore, we included a feature in the macro to enable the independent assessment of binding to ld and lo phases in phase-separated GUVs. To this end, we modified the macro with a procedure that recognizes and stores the locations of ld and lo domains from β-BODIPY-FL-C5-HPC staining patterns separately for all GUVs in a given image. The macro source code including the modifications is available at: https://github. com/AG-Roemer/ GUV-AP/releases/tag/v2. 0 Energy depletion.
Acknowledgements This work was supported by the  German Research Foundation  [ EXC 294 ,  RO 4341/2-1 ,  RO 4341/3-1, IRTG 1642 ,  RTG 2202 ,  SFB 803 ], the  Ministry of Science, Research and Art of Baden-Württemberg  [Az:  33-7532. 20 ]and a starting grant from the  European Research Council [Programme "Ideas , " ERC-2011-StG 282105]. Ta. S. acknowledges support by the  Franco-German University  (programs ' Polymer Sciences' and 'Cotutelle de thèse') , the  Collège Doctoral Européen (PDI) , and the  International Graduate Academy, University of Freiburg ,.
Operational calibration is currently performed for two initializations only (06:00 and 18:00 UTC). Moreover, predictors from the deterministic ARPEGE model are available up to the lead time 60 h (except total surface irradiation predictors, which are available from 60 to 78 h every 6 h). We can assume that this data set is less abundant than in  Taillardat et al. (2016). This is mainly due to the number of stations covered and the target grid after interpolation, the kilometric AROME grid in western Europe (EURW1S100), which is composed of more than 4 millon grid points. Since the principle of statistical post-processing is to build a statistical model linking observations and NWP outputs, two strategies can be considered: the first one is to build a gridded observation archive on the target grid, using scattered station data and a spatialization technique, and to estimate statistical models for each grid point or each group of grid points (block-MOS technique,  Zamo et al.
Common methods used to interpolate meteorological variables include inverse distance weighting (IDW;  Zimmerman et al. , 1999)  and thin plate splines (TPS;  Franke, 1982)  -both considered deterministic methods -and kriging  (Cressie, 1988) , including kriging with external drift to take topography effects into account  (Hudson and Wackernagel, 1994). But while IDW suffers from several shortcomings such as cusps, corners, and flat sports at the data points, preliminary tests showed that both TPS and kriging did not satisfy computation time requirements. Therefore, a new technique has been developed, very similar to "regression kriging", based on the following principle: at each station location, perform a regression between post-processed temperatures and raw NWP temperatures, using additional gridded predictors as well.
5 km rather than 1 km). Therefore, PEARP data are projected onto the target grid using the following procedure: for each of 7. 5 km grid points, a linear transfer function is estimated through a simple linear regression between each of the 100 AROME temperature data points (available on the 1 km resolution grid) and the corresponding ARPEGE data point. Since this relationship is likely to change over seasons and time of day, these regressions are computed seasonally, and for every hour of the day, using 1 year of data. This is a crude but quick way to perform downscaling of PEARP data, as will be shown later. Since interpolation is to be performed on a very large domain, with greatly varying data density, several regressions are computed on smaller sub-domains denoted by D, whose boundaries are given in Fig.
AROME and AROME EPS The AROME non-hydrostatic NWP model  (Seity et al. , 2011)  has been in use since 2007 in the limited area of Fig. 3. The associated 16-member EPS, called PEAROME  (Bouttier et al. , 2016) , has been in operational use since the end of 2016. The deterministic model operates on the 1 km EURW1S100 grid, whereas PEAROME runs on a 2. 5 km grid. Forecasts are made four times a day from 0 to 54 h. Data span 2 years from 1 December 2016 to 31 December 2018. Calibration is not performed on the 2. 5 km grid, but on a 10 km grid. Thus we consider PEAROME here as a 16 × 5 × 5 = 400-member pseudo-ensemble on a 10 km grid. We do this for three reasons. -We reduce spatial penalty issues due to the high resolution of the raw EPS (see e. g. Stein and Stoop, 2019). -We improve ensemble sampling and, we hope, the quality of predictors.
LC MS\MS S. pistillata skeletal protein samples were dissolved in 5% SDS and digested with trypsin using the S-trap method overnight at room temperature. We analyzed the resulting peptides using a nanoflow ultra-performance liquid chromatograph (nanoAcquity) coupled to a high resolution, high mass accuracy mass spectrometer (Fusion Lumos). The sample was trapped on a Symmetry C18 0. 18*20mm trap column (Waters, Inc) and separated on a HSS T3 0. 075*250 mm column (Waters, Inc. ) using a gradient of 4-28% (80% acetonitrile, 0. 1% Formic acid) for 150 minutes. Spray voltage was set to +2kV. The data were acquired in the Fusion Lumos using a Top Speed Data-Dependent Acquisition method using a cycle time of 3 s. An MS1 scan was performed in the Orbitrap at 120,000 resolution with a maximum injection time of 60 ms.
The data were scanned between 300-1800 m/z. MS2 was selected using a monoisotopic precursor selection set to peptides, peptide charge states set to +2-+8 and dynamic exclusion set to 30 s. MS2 was performed using HCD fragmentation scanned in the Orbitrap, with the first mass set to 130 m/z at a resolution of 15,000. Maximum injection time was set to 60 ms with automatic gain control of 5x10 -4 ions as a fill target. The resulting data were searched against the NCBI Stylophora pistillata protein database using the Byonic search engine (Protein Metrics Inc. ) -the first search was carried out without any false discovery rate (FDR) filtering, to generate a focused database for a second search. The second search was set to 1% FDR, allowing fixed carbamidomethylation on C and variable oxidation on MW, deamidation on NQ and protein N-terminal acetylation.
Data sorting We used the S. pistillata genome database as a reference peptide database for the mass spectrometry analysis  [49]  (NCBI BioProjects PRJNA281535 and PRJNA415215) appended with known NCBI S. pistillata skeletal proteins[e. x. , 31, 50] S. pistillata carbonic anhydrase, ACE95141. 1). We also included a common contaminants database. Despite this inclusion of a contaminants database, several proteins likely of human origin were sequenced and attributed to S. pistillata. To filter out these potential contaminants from our final list of coral-specific proteins, we BLASTed all sequences against the 'Primates' database in NCBI using Blast2GO. We then examined NCBI-generated sequence alignments of coral versus Homo sapiens proteins with e-values lower than e -50 and percent mean similarity greater than 50%, all sequences with e-values lower than e -100 , and all sequences with percent similarity greater than 80%, and removed from our final list of coral proteins any sequences with three or more peptides each of seven or more amino acids in length that were identical between S.
pistillata and humans. All remaining coral-specific proteins identified by the LC MS\MS analysis were filtered to those with at least two significant peptides or at least one significant peptide with at least 10 spectra and an identification score of 250 or greater. Skeletal proteins were first sorted by fractions and methods (i. e. , SSOM and ISOM for each purification method). Next, we sorted all skeletal proteins by their gene ontology (GO) terms  [51, 52]. Finally, we grouped all by terms of interest: protein modification, transmembrane, and ECM; membrane processing; metal binding and vesicular. Proteins detected by LC-MS/MS were annotated using the Trinotate pipeline which relies on both Pfam and UniProt data  [53]. GO terms and Pfam annotations were assigned to Stylophora pistillata predicted proteins and transcripts using Trinotate 3.
0. 1 (https://github. com/Trinotate). Transmembrane regions were predicted using the TMHMM server v2. 0  [54]. Signal peptides on the N-termini of proteins were predicted using SignalP 5. 0  [55, 56]. Attachment of proteins to the exterior of the cell membrane by glycosylphosphatidylinositol (GPI) anchors was predicted using PredGPI  [57]. These computational analyses used program default settings and cutoffs. Completeness of protein sequences was determined by comparing all returned coral proteins to the Acropora digitifera genome  [58] ; NCBI RefSeq assembly GCF_000222465. 1). Detection of various proteins across solubility and protocol fractions was visualized in Venny 2. 1 online software. To examine the conserved coral biomineralization proteins, we determined orthologous biomineralization genes, known from skeletal proteomic analysis, across coral taxa.
First, we estimated orthology relationships between all non-redundant genes of selected metazoa species using OrthoFinder. We included all genes of all Cnidaria species with known genome-based annotations. OrthoFinder generates orthology groups (Orthogroups) based on normalized reciprocal best BLAST hits' bit scores  [59] , and then estimates orthologues genes pairs within Orthogroups  [60]. We then selected all pairs of Acropora spp. orthologs to S. pistillata (1:1, 1:many, many:many relationships) (  [31] [32] [33] , this study). From these pairs we further selected S. pistillata spectra-based identified proteins, or skeletal proteins known in the literature. Since not all skeletal protein annotations from the literature were included in our reference OrthoFinder proteome datasets, we further found their best matches in the reference OrthoFinder proteome using BLASTP.
Skeletal proteome characterization We interrogated the mechanisms by which proteins may be exported from or attached to the cell (SI Table  2 ). Seventeen coral skeletal proteins with likely complete Nterminus predictions possess signal peptides as a potential mechanism for export from the cell. Eight proteins contain at least one transmembrane span suggesting that they are embedded in the cell membrane. Further, 10 proteins likely interact with the exterior of the cell membrane by GPI anchors. In total, 25 of the 60 sequenced coral skeletal proteins exhibit documented characteristics for localization in the ECM. Because the majority of the proteins sequenced from the S. pistillata skeleton do not possess features for signaling their export from the cell, we queried the data set for further suggestions of positioning the proteins in the membrane or that the proteins may be exported by vesicles such as those that may be involved in calcium concentration.
Additionally, most search engines and FDR-calculating algorithms struggle with very small datasets  [66, 67]. Our use of Byonic helps to alleviate statistical limitations that can result in false negative results. Further differences in proteomes, beyond species differences, are the differing reagents used in precipitation (compared to  [31] ) and our smaller centrifugal filter cutoffs (compared to  [32] ). This second difference is particularly important with respect to protein degradation. Even when embedded in biominerals so that amino acids and even short peptides persist, proteins may still succumb to degradation  [68]. This result in small peptide fragments that may be lost from centrifugal filter units with 10 kDa pore sizes and larger. It is reasonable to assume that coral skeletal proteins go through the same process, and if so, the cutoff of the membrane directly affects the number of peptide spectra matches (PSM).
The biomineral then further develops extracellularly, likely aided by other ECM proteins  [30, 35, 50, 84]  as well as other biomolecules  [85] [86] [87] [88]. At present the processes of calcium delivery to the skeleton and the roles of most of the proteins in coral skeletal deposition remain to be determined. It is difficult to map and characterize proteins in non-model organisms such S. pistillata  [89] [90] [91]. High quality proteomic mapping requires knowledge of phosphorylation, glycosylation, proteolytic cite activities and other modifications  [31, [92] [93] [94] , in order to create a more thorough database  [89] [90] [91]. Further, many of the coral skeletal proteins reported to date remain uncharacterized. Uncharacterized proteins were reported in Acropora skeletons at a rate of approximately 25%  [32, 33]  while, they were previously reported at less than 10% in S.
pistillata skeleton  [31]. Our study revealed a greater proportion, of 28% uncharacterized proteins, in S. pistillata skeleton, in line with Acropora spp. While partially attributed to sample size, it is most likely due to quality of genomic data available, since stony corals are non-model organisms and their genomic libraries are far from complete, resulting in incomplete databases on which to map the proteome and many uncharacterized genes.
Mass-spectrometry Digestion of proteins was achieved using a Filter Aided Sample Preparation (FASP) protocol  (Wiśniewski et al. , 2009). Briefly, Vivacon 500 filters (Sartorius, VN01H02 10 kDa/VNCT01) were washed with 0. 1% trifluoroacetic acid in 50% acetonitrile. The beads were loaded on the filter in 8 M urea in 100 mM AB for 30 minutes at rt. On-bead proteins were reduced (10 mM TCEP, 30 minutes, rt), alkylated (50 mM chloroacetamide, 30 min, rt in the dark) and washed (2 × 1 M urea in 50 mM AB). The proteins were subjected to tryptic digestion (0. 2 µg enzyme, Promega, 1 M urea in 50 mM AB) overnight at 37°C. Trypsinised peptides collected from the filtrate were dried and resuspended in 5% formic acid and 5% DMSO. LC-MS/MS analysis was carried out on an Ultimate 3000 ultra-HPLC system (Thermo Fisher) coupled to a QExactive mass spectrometer (Thermo Fisher).
The peptides were trapped on a C18 PepMap100 pre-column (300 µm i. d. × 5 mm, 100 Å, Thermo Fisher) using solvent A (0. 1% formic acid in water) at a pressure of 500 bar, then separated on an in-house packed analytical column (75 µm i. d. packed with ReproSil-Pur 120 C18-AQ, 1. 9 µm, 120 Å, Dr. Maisch GmbH) using a linear gradient (length: 60 minutes, 15% to 35% solvent B (0. 1% formic acid in acetonitrile), flow rate: 200 nl/min). Data were acquired in a datadependent mode (DDA). Full scan MS spectra were acquired in the Orbitrap (scan range 350-1500 m/z, resolution 70000, AGC target 3 × 106, maximum injection time 50 ms). The 10 most intense peaks were selected for HCD fragmentation at 30% of normalised collision energy (resolution 17500, AGC target 5 × 104, maximum injection time 120 ms) with first fixed mass at 180 m/z.