dataesr/scientific-paragraphs-categorization huggingface dataset:
com/Teichlab/tracer and in  [8]. 2. Reconstruct TRA, TRB, TRD, and TRG rearrangements from paired-end data using one processor core by running the following command (here for a hypothetical example dataset consisting of T cells from humans): 3. Run TraCeR in summarise mode on Docker with the following command, where <input_dir> is the path to the directory containing the output of TraCeR assemble mode for all the cells to be summarized together: docker run -it --rm -v $PWD:/scratch -w /scratch teichlab/ tracer summarise [options] <input_dir> For a hypothetical example dataset consisting of T cells from humans, the following command could be run using one processor core: 2. Remove likely cell doublets/multiplets affecting clonal assignments with more or less strict criteria depending on the dataset and biological questions (see Note 49) by visually inspecting the clonal graph outputs created by TraCeR summarise run with the -u flag.
0. 1) software is a complementary tool provided by 10Â. This tool uses the vloupe. vloupe file and can run on MacOS or Windows. 44. In-depth analysis can be done using various tools, mainly R packages. The Seurat package  [10]  can be used to analyze the gene expression data obtained. The TR sequences can be analyzed using the scRepertoire package  [11]. The input for the scRepertoire R package is the filtered_contig_annotations. csv file from Cell Ranger. The individual files can be combined into the list element, and the list is then used as an input for the combineTCR/BCR function, which combines individual chains into clones for each cell. One can also exclude singlechain clones, or filter the chains based on their UMI count in the case of multiple chains of the same type in a single cell by setting different parameters of the function.
There are several definitions of a clone based on the user's needs, which one can choose with the cloneCall parameter of the various functions. The package provides analysis for a number of modalities, including calling clones, clonal space/homeostasis, clonal diversity, and repertoire overlap between samples. Moreover, the package allows the investigation of the clones with regard to the cellular gene expression by combining the clones with Seurat objects by using combineExpression function. One needs to pay attention to consistent naming of the barcodes between the Seurat and scRepertoire objects. Scirpy is another recently developed Python toolkit that allows simple and straightforward analysis of and visualization of scAIRR data and also allows integration with transcriptome data  [12].
Both productive and nonproductive TR rearrangements are considered clonally related if they have the same sequence identifier (thus the same V gene, J gene, and junctional nucleotide sequence). Clonal relationships are visualized as clonal networks, where each cell is represented by a node in the graph. Reconstructed TR rearrangements are represented within nodes by horizontal lines or sequence identifiers colored according to locus and productivity. Edges between the nodes represent shared TR rearrangements and are also colored according to locus. 46. Data usable in TraCeR needs to fulfil the following demands. TraCeR requires data from a sequencing library protocol that generates sequencing reads from full-length mRNA transcripts, for example, SMART-Seq. Sequencing approaches that only provide partial coverage of transcript ends are not suitable for TR reconstruction by TraCeR.
docker run -it --rm -v $PWD:/scratch -w /scratch teichlab/ tracer assemble cell_1_R1. fq. gz cell_1_R2. fq. gz cell_1 Exp_1 -c my_config_file -s Hsap --loci A B G D -m assembly 3. 4. 4 Identify Clonally Related Cells with the Summarise Mode Remove low-quality cells, defined by standard quality control pipelines for single-cell RNA-seq data, to obtain the most accurate results, before running the TraCeR summarise mode. Define cell populations to analyze. The output of the assemble mode of TraCeR is a directory for each cell. Before running the summarise mode, create a new directory for cells you want to analyze together, and move the relevant TraCeR result directories for these cells into the new directory. If cells from multiple donors are present in the dataset, run the summarise mode separately for each donor in order to define true clonally related cells.
As sequencing is performed in a paired-end manner and on two lanes of the sequencer, the command will generate four FASTQ files for each library (two lanes x two read directions). To increase reproducibility, it is recommended to start the processing from the original BCL files, even if demultiplexed FASTQ files are provided by the sequencing provider. It is recommended to initially perform the analysis strategy separately for each sequencing lane, until quality control steps. If no issues have been found on the different lanes, the analysis can be repeated on the complete dataset. To extract the reads from one or more specific lanes, argument --lanes ¼ 1 (for lane 1) and --lanes ¼ 2 (for lane 2) can be used.
Acknowledgments We thank  Magnolia Bostick ,  Christian Busse ,  Eline T. Luning Prak ,  Gloria Kraus ,  Chaim Schramm ,  Nicolas Tchitchek ,  Ulrik Stervbo , and  Johannes Tru  ¨ck for helpful discussions and inspiration around the manuscript and for editing and proofreading and  Elaine Chen  for contributing figures. EMF and  KME were funded by iMAP  ( ANR-16-RHUS-0001 ),  Transimmunom LabEX  ( ANR-11-IDEX-0004-02 ),  TriPoD ERC   Research Advanced Grant  ( Fp7-I-dEAS-ErC-322856 ),  AIR-MI  ( ANR-18-ECVD-0001 ), iReceptor-Plus ( H2020 Research and Innovation Programme 825821 ), and  SirocCo  ( ANR-21-CO12-0005-01 ) grants. AE is supported by grants from the  Deutsche Forschungsgemeinschaft  ( BO 3429/3-1  and  BO 3429/4-1 ) and the  BMBF (RESET-AID). IL is funded by  KG Jebsen  (project  SKGJ-MED-017 ).
1. 2. 12 docker run -it --rm -v $PWD:/scratch -w /scratch teichlab/ tracer assemble cell_1_R1. fq. gz cell_1_R2. fq. gz cell_1 Exp_1 -c my_config_file -s Hsap --loci A B G D -m assembly 3. 4. 4 Identify Clonally Related Cells with the Summarise Mode Remove low-quality cells, defined by standard quality control pipelines for single-cell RNA-seq data, to obtain the most accurate results, before running the TraCeR summarise mode. Define cell populations to analyze. The output of the assemble mode of TraCeR is a directory for each cell. Before running the summarise mode, create a new directory for cells you want to analyze together, and move the relevant TraCeR result directories for these cells into the new directory. If cells from multiple donors are present in the dataset, run the summarise mode separately for each donor in order to define true clonally related cells.
20. 1 μg/μl control total RNA. 4. Windows or Macintosh operating systems to run Loupe 21. 10Â lysis buffer. Browser. 2. 4 Single-Cell 22. 40 U/μl RNase inhibitor. SMART-Seq Data 23. Nuclease-free water. Processing and 24. SMART-Seq sc TSO. Analysis 25. 3 0 SMART-Seq CDS Primer II A. 8. Strong magnetic separation device for plates that accommodates 96 samples in 96-well V-bottom plates (500 μl; VWR). 9. Low-speed benchtop centrifuge for a 96-well plate. 10. Strong magnetic separation device for tubes. 11. Nuclease-free, PCR-grade 8-tube strips secured in a PCR rack, or 96-well plates. 1. TraCeR, installed as a Docker container, with all of its dependencies installed and properly configured. Alternatively, TraCeR can be installed from GitHub. 2. Dependencies, installed and configured by the user (see https://github.
4. 2 Obtain the 1. Download or clone the TraCeR GitHub repository by run- Software ning: git clone https://github. com/Teichlab/tracer. 2. Pull the Docker container from DockerHub by running: docker pull teichlab/tracer. 3. Increase the Docker memory limit to 6-8 GB to avoid TraCeR running out of memory during the assemble step. Details on how to increase the Docker memory limit are found at https:// docs. docker. com/docker-for-windows/#advanced for Windows and https://docs. docker. com/docker-for-mac/ #advanced for Mac. 4docker run -it --rm -v $PWD:/scratch -w /scratch teichlab/ tracer assemble [options] <file_1> [<file_2>] <cell_name> <output_directory>.
As sequencing is performed in a paired-end manner and on two lanes of the sequencer, the command will generate four FASTQ files for each library (two lanes x two read directions). To increase reproducibility, it is recommended to start the processing from the original BCL files, even if demultiplexed FASTQ files are provided by the sequencing provider. It is recommended to initially perform the analysis strategy separately for each sequencing lane, until quality control steps. If no issues have been found on the different lanes, the analysis can be repeated on the complete dataset. To extract the reads from one or more specific lanes, argument --lanes ¼ 1 (for lane 1) and --lanes ¼ 2 (for lane 2) can be used. Antibody Heavy- 33. Consult Illumina literature (Index Adapters Pooling Guide 1000000041074, available at https://support.
Mollusc Exposition with F1 and F1' Miracidia Albino mice exposed to male S. mansoni × female S. bovis cercariae and vice versa (F1 & F1', see Experiments n • 6, Table  1 ), were euthanized at two months post-cercarial exposure and eggs from the livers were hatched to recover first generation miracidia  [26, 27]. Forty miracidia of each cross were stored on Whatman FTA cards  [30]. The sex of these miracidia was determined by PCR and a part of the ITS2 gene was sequenced for 66 specimens (see molecular biology methods Supplementary Materials for details). Sequences obtained were compared to reference sequences from Genbank database (AF531314. 1 for S. mansoni and FJ588862. 1 for S. bovis). We used 24 well-plates to expose 48 Biomphalaria glabrata snails each of Brazil and Guadeloupe strains individually overnight with 10 F1 or F1' miracidia.
French Research National Agency respectively awarded to J. B. and A. M. O. is funded by  Campus France. M. D. B. and A. D. E. -E. were funded by the  Red de Investigación de Centros de Enfermedades Tropicales-RICET  ( RD16/0027/0023 ) of the  PN de I+D+I, ISCIII-Subdirección General de Redes y Centros de Investigación Cooperativa RETICS, Ministry of Health and Consumption, Madrid ; by  CIBER de Enfermedades Infecciosas  ( CB21/13/00056 ),  ISCIII, Ministry of Science and Education, Madrid, Spain ; by the  PROMETEO Program ,  Programa de Ayudas para Grupos de Investigación de Excelencia  ( 2021/004 ),  Generalitat Valenciana, Valencia, Spain  and by  BEFPI/2021/019. This study is set within the framework of the  "Laboratoires d'Excellence (LABEX) "  TULIP  ( ANR-10-LABX-41 ). Institutional Review Board Statement: Not applicable.
N has a second flexible loop L2 in its C-terminal region (Fig  1A ) which binds the C-terminal domain of P when N is associated with RNA as shown for both RABV and VSV  [22, 23]. We investigated the role of this loop. For this, we constructed the mutants N-373-5L-394, N-373-2L-382, N-381-2L-390 and N-385-2L-394 in which stretches of N residues were replaced by 2 or 5 GGGS linkers (L) ( This indicated that both N loops, 104-117 and 372-398, are required to form biocondensates in the cellular minimal system. C) N/P-mCherry inclusions as well as GFP-N/P, and N-128GFP129/P inclusions (observed in BSR-T7/5 cells cotransfected as in B) were photobleached and the recovery of fluorescence was imaged with a Spinning-Disk microscope. Scale bars: 5 μm. For the plots on the right, FRAP data were corrected for background, normalized to the minimum and maximum intensity.
This might be related to the presence of viral N and N-106GFP107 chimeras) were assessed from experiments such as those presented in Fig 2C. Values are the average of 3 independent experiments ± SD. The total number of cells analyzed was 1555 (353 + 407 + 795) for GFP-N and 1804 (241 + 615 + 948) for N-106GFP107. p-values were calculated using two-tailed Student's t test. E) NBs containing P-mCherry, GFP-N or N-112GFP113 constructs were photobleached and the recovery of fluorescence was imaged with a spinning disk microscope. Whole NBs as well as NBs' subvolumes were photobleached. Scale bars: 5 μm. For the plots on the right, FRAP data were corrected for background, normalized to the minimum and maximum intensity. The mean is shown with error bars representing the SD. https://doi. org/10.
Quantification of P-mCherry and GFP-N mutants concentrations in dense and dilute phases To quantify the concentrations of P-mCherry and different fluorescent N mutants, we measured their areal fluorescence in both dilute (i. e. in the cytosol) and dense (i. e. in an N-P inclusion or in an NB) phases from confocal images. For this, we applied the method described in  [25]  by using Fiji/ImageJ software. We assumed that the local concentration of each protein is proportional to its local fluorescence intensity per pixel. First, for each channel, the background intensity was defined by measuring the areal fluorescence intensity from a 50 by 50 pixels size box in the cytosol of an untransfected/uninfected cell. Then, a representative area of the dense phase was defined as a 7 by 7 pixels size box (in the rare cases of smaller inclusions, a 5 by 5 pixels box was used) in the middle of an inclusion.
FRAP was performed 14 h after infection/transfection. Alternatively, BSR-T7/5 cells were transfected for 24 h with pTit plasmids encoding P, N, P-mCherry and GFP-N chimeras (cellular minimal system). Cells were seeded in 35-mm glass-bottom μdishes (Ibidi) prior to transfection and/or infection. Data acquisition was performed on an inverted Nikon Ti Eclipse E microscope coupled with a spinning disk (Yokogawa CSU-X1) and a 100x objective (Apochromat TIRF oil-immersion, NA: 1. 49). Blue laser (488 nm, Vortran, 150 mW) was used for the excitation of fluorescent N0-P (Atto-488) in the acellular system, and GFP-N (as well as N-128GFP129 and N-112GFP113 chimeras) in transfected and infected cells. Yellow laser (561 nm, Coherent, 100 mW) was used for excitation of P protein (full-length) in the acellular system, and for P-mCherry in transfected and infected cells.
Finally, a red laser (642 nm, Vortran, 100 mW) was used for the excitation of Cy5 RNAs concentrated into P droplets in the acellular minimal system. A quad-band dichroic mirror (405/491/561/641 nm, Chroma) and band-pass filters of 525/45 nm (Semrock), 607/36 nm (Semrock) and 692/40 nm (Semrock) were used respectively to ensure specific detection of the fluorescence. Images were recorded with a Prime 95B sCMOS camera (Photometrics). FRAP experiments were performed using iLas 2 module (GATACA Systems) and the whole system was driven by MetaMorph software version 7. 7 (Molecular Devices). FRAP experiments on cellular NB-like condensates were performed at the frame rate of 1 image every 2 seconds (resp. 1 image every second) for GFP-N chimeras (resp. P-mCherry), using the following sequence: 5 s of pre-bleach, 20 (resp.
The following sequence was used to FRAP Atto-488-N0-P: 10 s of prebleach, 20 ms bleach, and 300 s post-bleach. The following sequence was used to FRAP RNA-Cy5: 10 s of pre-bleach, 100 ms bleach, and 60 s post-bleach. All FRAP experiments were performed with lasers at 100% intensity. For the FRAP data analysis we used a home-made macro on FIJI  [47]  performing the following steps. On every slide, the background was estimated and removed by measuring a region outside the cells. To correct for acquisitional bleaching, the mean fluorescence measured in the bleached region was normalized by the mean value of a region in a neighboring cell (if possible a condensate) or in a nearby droplet (for the acellular system). Then a second normalization was applied by dividing the results by the mean value of the pre-bleach sequence to allow comparison of recovery curves.
Live microscopy Droplets formation and fusion events made by full-length P at the final concentration of 10 to 20 μM (in 5% PEG 8000, 20 mM Tris-HCl pH 7. 4, 125 mM NaCl, 2 mM EDTA) in the presence of 50 nM of Cy3-labelled protein were recorded by time-lapse epifluorescence microscopy at a frame rate of 1 image every 5 s. Data acquisition was performed on an AxioObserver epifluorescence microscope (Zeiss). Epifluorescence images were captured with a 63x oil immersion objective (Plan. Achromat, NA: 1. 4, Zeiss). The laser wavelength for P-Cy3 excitation was 545nm.
Calculation of P and N0-P droplets aspect ratios Quantifications were performed using the image toolbox of MatLab software. Briefly, the grayscale image was converted to a binary image. The connected components (CC) of the output black and white image were then identified. For each CC, its eccentricity e (i. e. , that of the ellipse that has the same second-moments as the CC) was calculated using MatLab software allowing the calculation of the aspect ratio a/b (where a and b are respectively the long and short axes of the ellipse) of each inclusion using the formula: a/b = 1/(1-e 2 ) 1/2.
This work was supported by the « Prix Bettencourt Coups d'elan pour la recherche francaise » attributed to YG, grants from the  Agence Nationale de la Recherche, France , ( ANR CE11 , LiquidFact) to Y. G. (including a post-doctoral grant to Q. N. ) and a grant from the  Fondation pour la Recherche Medicale, France , to Y. G. (including a post-doctoral grant to D. G. ). This work has benefited from the core facilities of  Imagerie-Gif  led by RLB, supported by " France-BioImaging " ( ANR-10-INBS-04-01 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
05) in MS4A1 KO cells (Figure  3C , left and middle panels). CD20 mRNA levels were also 3. 3-fold lower in IRF8 KO than in control cells (13. 6±4. 6 versus 45. 1±12. 2, p<0. 01), which is similar to the decrease in MS4A1 KO cells (20. 1±4. 4, p <0. 05). Therefore, IRF8 controls CD20 transcription and susceptibility to RTX in SU-DHL-4 cells. To extend our observations, we assessed the correlation between MS4A1 and IRF8 mRNA levels using RNA sequencing data from the Cancer Cell Line Encyclopedia (CCLE) (Figure  3D ). We investigated 66 B cell lines originating from Acute Lymphocytic Leukemia (ALL), Chronic Lymphocytic Leukemia (CLL), Burkitt lymphoma, DLBCLs, Hodgkin lymphoma and other non-Hodgkin lymphomas. There was a significant correlation between IRF8 and MS4A1 expression in these cells (Spearman coefficient of 0.
CD20 and IRF8 levels correlate in subsets of diffuse large B-cell lymphomas To further determine how IRF8 may control CD20 expression, we took advantage of a public dataset of patient cancer cell RNA levels and their associated therapeutic outcomes  (47). We examined expression of IRF8 and CD20 in 716 DLBCLs present in this database. The expression of CD20 was slightly but significantly higher in germinal-center B cells (GCB) DLBCLs than in activated B-cell (ABC) and unclassified DLBCLs (median of 10. 6 [95% CI: 10. 4-10. 7] for GCB, 10. 1 [9. 9-10. 2] for ABC and 9. 9 [9. 5-10. 3] for unclassified, p<0. 0001, Figure  6A ). A similar pattern was observed for IRF8 expression with a median of 7. 96 [95% CI: 7. 8-8. 1] for GCB, 7. 5  [7. 4-7. 6]  for ABC and 7. 6  [7. 5-7. 8]  for unclassified (p<0.
0001, p=0. 0002, Figure  6B ). We further observed a significant positive correlation between IRF8 and CD20 expression, with a Spearman coefficient of 0. 27 for GCB cells and of 0. 4 for unclassified lymphomas (Figure  6C ). This was not the case for ABC DLBCLs (Figure  6C ). We also investigated in this dataset the parameters associated with IRF8 expression by performing a multivariate analysis using the partial least regression method. Considering potential cofounder variables such as International Prognostic Indexes, age or treatment response, we observed a strong positive association between IRF8 and CD20 expression (p-value= 1. 2×10 -4 ) (Figure  6D ). GCB DLBCLs were associated with higher IRF8 expression (Figure  6D ). No other variables were independently associated to IRF8 expression in this dataset.
Flow cytometry Cells were stained in MACS Buffer (PBS + 1. 25 g/L Bovine Serum Albumine (BSA) + 2 mM EDTA) for surface staining and in PBS/BSA 1%/Azide for intracellular staining. Cells (1x10 5 ) were incubated with antibodies listed in supplemental methods for 30 min at 4°C. Biotinylated antibodies were incubated for 30 min at 4°C in MACS Buffer. Alexa Fluor TM 647 conjugated streptavidin (#S21374, ThermoFisher Scientific) was then added and incubated for 30 min at 4°C. Cells were permeabilized with 1% triton and incubated with anti-IRF8 antibody. Data were acquired with an Attune NxT instrument (Life Technologies).
Fc-dependent functions assays For flow cytometry CDC assay, cells (1x10 5 ) were incubated with RTX (10 µg/mL unless otherwise stated). Human normal serum was added as a source of complement at the indicated dilutions. Cells were stained with LIVE/DEAD Fixable Aqua Dead Cell marker. Data were acquired on an Attune NxT instrument (Life Technologies) and CDC was calculated as 100 × (% of dead cells with serum -% of dead cells without serum)/(100 -% of dead cells without serum). For high-throughput microscopy, 1x10 5 RAJI cells were seeded in a 96-well flat bottom black plate (#655090, Greiner Bio-One) 12 µg/mL RTX, 25% NHS, 1. 25x10 - 1 µg/mL Hoescht and iodide propidium. ADCC activity was assessed with ADCC Reporter Bioassay cells from Promega (supplemental methods). ADCP activity was assessed as described in 43 and supplemental methods.
Analysis of RNAseq data RNAseq data were obtained from the Cancer Cell Line Encyclopedia 44 available online (https://depmap. org/portal/download/). Gene counts were normalized using upper quartile and logarithm 2. Access to RNA-seq data from DLBCL patients 45 was authorized through an access agreement signed with Pr Sandeep Dave's laboratory and exempted of ethical evaluation by the Institutional Review Board under 45CFR46. 104(d)(4)(ii) legislation. The mapped RNA sequencing reads from 775 samples were downloaded from European Genome-Phenome Archive (EGA: EGAS00001002606).
Statistical analysis Flow cytometry data were analyzed with FlowJo v10 software (TriStar). Calculations were performed using  The dotted line is the standard deviation and the area between the curve and the standard deviation is shaded. To take into account direct apoptosis triggered by RTX, the percentage of CDC was calculated as: (% dead cells in condition -% dead cells in no serum condition)/(100-% dead cells in no serum condition). expression analyzed with the partial least squares (PLS) regression method. The association factor is the estimate effect of each standardized parameter on the standardized CD20 expression taking all parameters into account. Negative and positive factors denote respectively negative and positive associations. Significant associations, as assessed with Jackknife approximate t tests, are in green and ***: p-value<0.
Acknowledgments We thank members of the  Virus & Immunity Unit  and  Maaran Michael RAJAH  for their support. We thank  Nicoletta Casartelli  for critical reading of the manuscript. We thank the staff of  UTechS Cytometry and Biomarkers ,  C2RT  from  Institut Pasteur  for their technical help and Pascal Campagne from The  Center of Bioinformatics, Biostatistics and Integrative Biology of Institut Pasteur  for his statistical expertise. Work in OS lab is funded by  Institut Pasteur ,  Urgence COVID-19 Fundraising Campaign of Institut Pasteur ,  Fondation pour la Recherche Médicale (FRM) ,  ANRS , the  Vaccine Research Institute  ( ANR-10-LABX-77 ),  Labex IBEID  ( ANR-10-LABX-62-IBEID ),  ANR/FRM   Flash Covid PROTEO-SARS-CoV-2, ANR Coronamito , and  IDISCOVR European Health Emergency Preparedness and Response Authority (HERA).