dataesr/scientific-paragraphs-categorization huggingface dataset:
For assessment of lineage output, in vitro-differentiated cells were labeled with the following antibodies: CD45 AF700 (Biolegend, clone HI30), CD34 PB (Biolegend, clone 581), CD7 FITC (Beckman Coulter, clone 8H8. 1), CD123 BV786 (BD Bioscience, clone 7G3), CD115 APC (Biolegend, clone 9-4D2-1E4), CD45RA PE (BD Bioscience, clone HIT100), ITGB7 PC7 (eBioscience, clone FIB504), CD24 PE-CF594 (BD Bioscience, clone ML5), CD19 BV711 (BD bioscience, clone HIB19), CD116 APC-vio770 (Miltenyi, clone REA211), CD127 PC5 (Biolegend, clone A019D5), CD15 BV605 (BD Bioscience, clone HI10a), CD10 BV650 (BD bioscience, clone HI98). Flow cytometry analyses were performed with a BD Fortessa Analyzer and the FlowJo software.
Data were analyzed using the Biomark qPCR Analysis software (Fluidigm). For quantification of gene expression, data were analyzed by the DDCt method. Results were normalized relative to HPRT expression and expressed as mean expression level of R6 biological replicates. Hierarchical clustering was performed on standardized means of gene expression levels based on Euclidean distance using the ''pheatmap'' R package. A complete list of the TaqMan primers is provided as Table  S2.
PCR products were purified by Agencourt AMPure XP (Beckman Coulter). Libraries were prepared using the Illumina DNA Prep kit (Illumina) according to the manufacturer's guidelines and sequenced on a HiSeq 4000 (Illumina) at the Genome East Platform of the IGBMC (Strasbourg, France). Image analysis and base calling were performed using RTA 2. 7. 7 and bcl2fastq 2. 17. 1. 14 software. Adapter dimer reads were removed using Dimer Remover (https://sourceforge. net/projects/dimerremover/).
ACKNOWLEDGMENTS The authors thank  Christelle Doliger  and  Niclas Setterblad  ( Plateforme d'Imagerie et de Tri Cellulaire, IRSL, Paris France ). We are grateful to  Laurent David  ( Inserm 1064, Nantes ) and  Bernard Jost  ( GenomEast platform, IGBMC, Strasbourg, France ). We thank  Justine Poirot  for help in single cell analyses. We acknowledge the contribution of AniRA lentivectors production facility from the  CELPHEDIA Infrastructure  and  SFR Biosciences (UAR3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL) , especially  Gisele Froment ,  Didier Ne  `gre and  Caroline Costa. We also thank  Sophie Ezine  and  Bela Papp  for critical discussions. This work was supported by the  Agence de la Biomedecine ,  Agence National de la Recherche (ANR EpiDev) , the  Institut National Du Cancer (InCA B-REC) , the  Fondation Ramsay Ge  ´nerale  de Sante  ´and by the  Inserm HuDeCA network.
Data quality control and preprocessing Data quality control and pre-processing were performed by SciLicium (Rennes, France). Briefly, the first read contains 16 bases that must have a quality score higher than 10. The first 6 bp correspond to a unique sample-specific barcode and the following 10 bp to a unique molecular identifier (UMI). The second reads were aligned to the human GRCh38/hg38 reference genome from the UCSC website using BWA version 0. 7. 4. 4 with the parameter ''Àl 24''. Reads mapping to several positions in the genome were filtered out from the analysis. The complete pipeline has been previously described in. 53  After quality control and data pre-processing, a gene count matrix was generated by counting the number of unique UMIs associated with each gene (lines) for each sample (columns).
Statistical filtration and clustering analysis The statistical comparisons were performed in the AMEN suite of tools. 55  For each analysis relevant comparisons were selected to identify differentially expressed genes (DEGs). Briefly, genes showing an expression signal higher than 0. 0 and at least a 2. 0-fold-change between the two experimental conditions of each pairwise comparison were selected. The empirical Bayes moderated t-statistics implemented into the LIMMA package (F-value adjusted using the Benjamini & Hochberg (BH) False Discovery Rate approach, p % 0. 05)  [56] [57] [58] [59]  was used to define the set of genes showing significant statistical changes across all comparisons of a given transcriptomic analysis. The resulting set of DEGs were then partitioned into distinct expression clusters with the k-means algorithm and presented as heatmaps by using the pheatmap R package developed by R.
Single cell-RNA-seq analysis Data quality control and preprocessing Demultiplexed raw sequencing reads were processed, mapped to the GRCh38 human reference genome and quantified based on unique molecular identifiers (UMIs) with the Cell Ranger pipeline (v3. 0. 2, 10x Genomics). The resulting raw count matrices were then merged. Subsequently, the Scater R package (v1. 10. 1) was used to remove outlier cells by using several cell features including the proportions of reads mapping mitochondrial and ribosomal genes. In parallel, doublets were filtered out independently in each individual matrix by using the DoubletFinder R package (v. 2. 0. 2). Cells with less than 1000 detected genes and genes detected in less than 10 cells were removed. Cells from all datasets were assigned a cell cycle phase by using Seurat.
Trajectory inference and pseudotime analysis Cell trajectories were inferred the getLineages followed by the getCurves functions implemented into the SlingShot package  41  on the scRNAseq data by indicating the C1 cell cluster (HSC) as starting cells. Trajectory-based differential expression analysis was performed with the tradeSeq package by using the pseudotime values inferred by SlingShot for each lineage. 41, 60.
2% in MSCV-GFP transduced cells), but reached up to 98% GFP positive cells upon treating the cells for 24, 48 and 72 h with ABLki, attesting for the time-dependent stabilization of GFP-R2CT in G0/G1 arrested cells (Fig. S5b ). We interrogated the R2CT interactome after treating the v-abl pro-B cells for 72 h with ABLki, which represents the ideal physiological condition to identify R2CT interacting partners during the V(D)J recombination process. Protein complexes were recovered using nanobodies directed against the GFP moiety covalently linked to magnetic beads  45, 48  and analyzed by SDS-PAGE, which revealed the presence of several specific bands that were absent from the GFP alone pulldown (Fig. 5a , lanes 1, 2). Mass-spectrometry analyses of these complexes identified XRCC6 (Ku70) and XRCC5 (Ku80) among the top 30 hits (ProteomeXchange: PXD045640).
V(D)J recombination assays The pMX-INV V(D)J recombination substrate was introduced in pro-B cell-lines through retroviral infection and cells that had integrated the recombination substrate were enriched based on hCD4 expression  21, 38. For V(D)J recombination assay, v-abl transformed Bcl2/pMX-INV infected pro-B cells (1 million per ml) were treated with 0. 5 uM of the v-abl kinase inhibitor ABLki (PD180970, Sigma) and assayed for rearrangement by FACS analysis of GFP expression at 0, 48, 72 or 96 h. V(D)J recombination efficiency was scored as the percentage of GFP positive cells among hCD4-positive cells ((hCD4-PE antibody; 1/200 dilution). Data were acquired on a BD-LSR Fortessa (BD Biosciences) and analyzed using FlowJo software.
1% formic acid) at a flow rate of 300 nl/min over 100 min. Fullscan MS was acquired using an Orbitrap Analyzer with the resolution set to 120,000, and ions from each full scan were higher-energy C-trap dissociation (HCD) fragmented and analyzed in the linear ion trap. For protein identification, the data were searched against the Mus Musculus UP000000589_10090 database (downloaded 01/2019 containing 22266 entries) using Sequest HT through Proteome Discoverer (v2. 2). Enzyme specificity was set to trypsin and a maximum of two missed cleavages sites were allowed. Oxidized methionine and N-terminal acetylation were set as variable modifications. Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0. 6 Da for MS/MS peaks. The resulting files were further processed using myProMS 61 v3.
05 were considered significantly enriched in sample comparisons. Proteins unique to a condition were also considered if they matched the peptides criteria. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository  64  with the dataset identifier PXD045640 (username: reviewer_pxd045640@ebi. ac. uk, password: BtCe3Rnw).
Calorimetry Interactions between Ku ΔC , Ku full-length, DNA and peptides derived from RAG2 last 39 amino acids were determined by Isothermal Titration Calorimetry (ITC) using a VP-ITC calorimeter (Malvern). Prior to measurements, all solutions were degassed under vacuum. The reaction cell of the ITC (volume 1. 8 mL) was loaded with Ku heterodimers at 10 µM or 20 µM concentration. Proteins were dialyzed against buffer 20 mM Tris pH8, 150 mM NaCl, 5 mM β-mercaptoethanol. The syringe (290 µL) was filled with peptides at concentration 10-fold higher than the samples in the cell (100 µM or 200 µM). The Ku heterodimer present in the cell was titrated by automatic injections of 6-10 μL of the different ligands. All binding experiments were performed at 25 °C. Enthalpy ΔH (in kcal. mol -1 ), stoichiometry of the reaction N, and association constant Ka (in M -1 ) were obtained by nonlinear least-squares fitting of the experimental data using the single set of independent binding sites model of the Origin software provided with the instrument.
Statistics All data were analyzed by one-way or two ANOVA with Dunnett's multiple comparisons test using GraphPad Prism 8 (GraphPad Software Inc. ). All data are reported as the median, arithmetic or geometric mean ± SD as appropriate. Data were considered significant at a P value of less than 0. 05 (*p < 0. 05, **p < 0. 01, ***p < 0. 001, ****p < 0. 0001).
Data availability statement The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository 64 with the dataset identifier PXD045640 (username: reviewer_pxd045640@ebi. ac. uk, password: BtCe3Rnw). b Percentage of relative V(D)J activity in WT, XLF -/-and 2 independent clones each from MRI -/-, MRI -/-/XLF -/-, and MRI -/-/PAXX -/-v-abl pro-B cell clones treated for 0, and 96 h with ABLki were assayed for pMX-INV rearrangement by flow cytometry. c Scheme shows MRI WT , MRI ΔNΔC , XLF WT and XLF ΔC variant retroviral constructs used in the functional complementation assays. The A-KBM (green box) and X-KBM (red box) are indicated. d Flow cytometric analyses relative V(D)J activity in MRI -/-/XLF -/-v-abl pro-B cells retrovirally transduced with empty vector (EV), XLF WT , XLF ΔC , MRI WT , MRI ΔNΔC , and combination of MRI ΔNΔC + XLF ΔC treated with ABLki for 0 and 96 h.
Fig. 6  | In situ PLA analysis to visualize the interaction between RAG2 c-terminal region and Ku complex. a Detection of the proximal location of each protein pair analyzed (visualized as red dots) by in situ PLA conducted on ABLki-treated WT and RAG1 CD vabl pro-B cells (original magnification 63X). b Summary of the PLA results showing the percentage of cells labelled for the indicated protein pairs. Number of labelled cells were calculated relative to those of cells controls (cells expressing GFP alone, as well as when one of the primary Abs were omitted. Data were quantified from three independent experiments where error bars are indicated (± SD). Fig. 7  | AlphaFold2 (AF2) model of R2CT/Ku interaction. a Superimposition of the five AF2 3D structure models of the RAG2 extended KBM (aa 481-527) in complex the vWA domain of human Ku80 (aa 1-245).
Acknowledgments This work was supported by  INCa ,  LNCC ,  AT-Europe , and  "Région Ile-de-France " and  Fondation pour la Recherche Médicale  grants (to D. L. ). SKT was supported by grants from  INCa and AT-Europe. PC and PF were supported by  ANR-20-CE11-0026. PC is a scientist from  INSERM. JBC thanks grants  ANR-20-CE11-0026 ,  ANR-21-CE12-0019 ,  ANR 23-CE11-0033 ,  ANR-23-CE18-Nejin  and  FRISBI national  infrastructure  ANR-10-INBS-0005.
Paramecia were immobilized using the device described in  [22]. Briefly, paramecia are immobilized against a transparent filter (Whatman Cyclopore polycarbonate membranes; diameter 25 mm, pore diameter 12 μm) thanks to a peristaltic pump (Gilson Minipulse 3) that circulates the fluid from below the filter to above the device. Two microelectrodes are then lowered into the cell, and the pump is stopped. The cell is then held in place by the electrodes. Electrophysiology recordings were performed using an amplifier with capacitance neutralization (Axoclamp 2B and Axoclamp 900 A, Molecular Device) and an analog-digital acquisition board operating at a sampling frequency of 40 kHz (USB-6343, National Instruments). Custom Python programs (https://github. com/romainbrette/clampy) were used to control the acquisition board.
Particle image velocimetry To measure the flows induced by cilia beating, the bath was seeded with 1 μm silica or polystyrene particles (~0. 2 mM) after paramecia were immobilized and the pump was stopped. Because of sedimentation, particle density was typically higher at the beginning of the experiment. Images were recorded at 30 Hz with a high-sensitivity CCD camera (Lumenera Infinity 3-6UR) over a 1392×1392 pixels region of interest surrounding the cell (8 bits depth, pixel width 0. 178 μm). Frames were synchronized with electrophysiology recordings using a digital trigger. Frames were preprocessed by removing the background (average image) and band-pass filtering (subtraction of two Gaussian filters with standard deviation 1 μm and 1. 3 μm). Consecutive frames were then analyzed with particle image velocimetry (PIV) using the OpenPIV Python package (https://github.
Electrophysiological modeling In this section, we describe the biophysical models. The parameter values are obtained by fitting the models to the data (section Model fitting). Electrode model. All recordings were done with two electrodes, an injecting electrode and a reading electrode. Because of the capacitance and resistance of the injecting electrode, the current injected in the cell is a low-pass filtered version of the command current  [94]. To estimate this current, we model the injecting electrode as a simple RC circuit and estimate its parameters R e and τ e from responses to small pulses, assuming passive cell responses: C dV dt ¼ À g L V À V 0 ð Þ þ I e ð1Þ t e dV 2 dt ¼ V À V 2 þ R e I þ DV ð2Þ I e ¼ V 2 À V À DV R e ð3Þ where V is the membrane potential, assumed identical to the potential of the reading electrode, V 2 is the potential of the injecting electrode, I e is the current injected in the cell, and ΔV accounts for a difference in tip potentials.
Model optimization Model parameters are estimated with the model fitting toolbox of the Brian simulator  [45, 46]  (https://github. com/brian-team/brian2modelfitting). Briefly, the software performs least square optimization using a combination of global optimization algorithms (we used differential evolution) and gradient descent, where the gradient is calculated symbolically from the model equations. Optimization with multiple objectives is done by adding the errors associated to the different objectives. Compiled code is automatically produced by code generation. Each fitting procedure took up to a few hours, and fitting scripts were run in parallel on different cells, using a small cluster of 3 PC with 8 cores each. After model fitting, cells were discarded if passive properties were abnormal, indicating a bad recording (C > 500 pF or R<30 MO or R>500 MO), or if E K >E L (which is biophysically impossible), indicating a fitting problem.
Over a time dt, the organism rotates by O(dt) = I + [ω]. dt, where ω ½ � ¼ o 0 À cos y ð Þ 0 cos y ð Þ 0 À sin y ð Þ 0 sin y ð Þ 0 2 6 4 3 7 5 is the infinitesimal rotation matrix, such that [ω]y = ω × y. Therefore, the orientation matrix changes as R(t + dt) = RO(dt), giving: _ R ¼ R ω ½ � For numerical reasons, we use quaternions instead of matrices  [108] , implemented with the Python packages quaternion and pyquaternion. Orientation is then represented by a unit quaternion q, and kinematic equations translate to: b x _ ¼ qb v� q where b v is the pure quaternion with imaginary part v, and _ q ¼ 1 2 qb ω Confinement to a plane. We constrain the organism to move in a plane. This is done simply by rotating the orientation vector at each time step so that it lies in the plane. Concretely, we calculate the orientation vector in the observer frame: b p ¼ q d 0; 0; 1 ð Þ � q Then we rotate the orientation vector around the axis u that is orthogonal to both the z axis and p: u = (0, 0, 1) × p, by an angle θ = sin -1 (p z /kpk).
This work was supported by  Agence Nationale de la Recherche  ( ANR-20-CE30-0025-01  to RB, LLP,  AMP  and  ANR-21-CE16-0013-02  to RB),  Programme Investissements d'Avenir IHU FOReSIGHT  (Grant  ANR-18-IAHU-01  to RB),  Fondation Pour l'Audition  (Grant FPA  RD-2017-2  to RB),  CNRS (Defi Mecanobiologie , project  PERCEE , to RB, LLP and AMP), and  Sorbonne Universite (Emergence, project  NEUROSWIM  to RB, LLP and AMP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Code for electrophysiology experiments, model fitting and figures can be found at https://github. com/ romainbrette/Paramecium-model. Electrophysiology data and analyses, including particle image velocimetry can be found at https:// doi. org/10. 5281/zenodo. 6074166. Behavioral data and code can be found at https://doi. org/10. 5281/ zenodo. 6074480.
TAM in the era of single cell RNA-sequencing technology Most of the sc-datasets showed the APOE (apolipoprotein) gene as a TAM marker. Numerous studies, including our, demonstrated the selective APOE expression by TAM from tumor lesions compared with macrophages from normal-tissue (NT) counterparts  (16) (17) (18). Despite tissue resident (TR) or monocytic origin of TAM, they may collectively share a core transcriptomic signature comprising: APOE, complement component genes (i. e. C1QA, C1QB, C1QC), and cathepsin (CTSB, CTSD) across several cancer types  (16, (18) (19) (20) (21).
Collectively, SPP1 mono-derived TAM were associated with protumor and M2-like signatures, proposing a protumor role for these cells. Conversely to the observations above, mono-derived SPP1+ TAM have been recently identified associated to protective CXCL13+ T cell responses and highly correlated with plasma B cells, indicating a protective SPP1+ TAM role in human lung cancer  (30). The large contribution of sc-datasets in identifying several TAM clusters highlighted the importance of having a consensus annotation. A big effort has been done by Mulder et al, in providing a robust online-available platform with the aim to harmonize the annotations of macrophages in healthy and pathological states. The authors have generated a monocyte-macrophage compendium widely distributed across multiple tissues.
Funding This work was supported by the following grants to  ER: Foundation ARC  (grant no. AAP SIGN'IT 2019 ),  Fonds Amgen France pour la Science et l'Humain ;  CIC IGR-Curie 1428 ;  ANR-10-IDEX-0001-02   PSL ; and  ANR-11-LABX-0043. ET was supported by a postdoctoral fellowship abroad from the  AIRC  (2018/2020-number:  20934 ).
We then assessed phosphatase activity of the mutant using a Michaelis-Menten kinetic assay with DiFMUP as the substrate. We found a substantial drop of 50% in the catalytic e ciency (k cat /K m ) for the D71A mutant protein compared to wt NMA1982, demonstrating that D71 is important for catalysis (Table  1 ). Collectively, our data demonstrate that the putative catalytic amino acid residues in NMA1982 are indeed crucial for catalysis. Given the relatively low activity we observed for wt NMA1982, we analyzed the NMA1982 crystal structure for additional insights Interestingly, we found that the side chain of R136 of the α6 helix, a residue that is unique to NMA1982 compared to human PTPs, forms multiple hydrogen bond interactions with the backbone oxygen atoms of A69, R70, and I72 of the WPD-loop (Fig.
As shown in Fig. 4c , NMA1982 exhibited peak activity around 37°C, and activity was substantially reduced at 50°C, and completely absent at 80°C. Thus, NMA1982 was most active at temperatures at which eukaryotic PTPs show peak activity. Finally, we assessed NMA1982 activity in oxidative inactivation and rescue studies. Because PTP activity depends on the catalytic cysteine in the reduced state, and hydrogen peroxide treatment results in thiol oxidation and PTP inactivation 36 , we tested whether NMA1982 activity was similarly sensitive to oxidation. Thus, we preincubated NMA1982 with various concentrations of hydrogen peroxide for 1 or 2 h and subsequently determined catalytic activity compared to a non-treated control (Fig. 4d ). Our data demonstrated that hydrogen peroxide treatment decreased NMA1982 activity, and that this decrease was time-and dose-dependent.
2a ). Quantitative proteomics was used to confirm the levels of TEFM protein in P4 and assess the impact on complex I (Fig. 4d , e, Supplementary Dataset). As can be seen in Fig. 4d , P4 fibroblasts show decreased amounts of TEFM protein (-2. 2 fold-change) relative to controls. It should be noted that TEFM protein was quantified from a single peptide sequenced by MS/MS in all samples (Supplementary Fig. 2d ). Relative complex abundance profiling revealed a 25% reduction in the abundance of complex I subunits in P4 relative to controls. Complex II and complex IV presented a 12% and 7% reduction, respectively, while the mitoribosome presented a 10% increase in relative abundance (Fig. 4e ). Furthermore, the analysis of OXPHOS activities (complexes I and IV) in fibroblasts from P4 showed a 35% reduction in complex I, but no difference in complex IV (Supplementary Fig.
Sequencing biases were modelled via negativebinomial factor analysis to reduce technical artifacts and copy number states and genomic regions of low and high copy number variability were modelled using hierarchical hidden Markov models (HHMM) to impute rare and common copy number variants. WES was performed on genomic DNA (gDNA) extracted from fibroblasts of Individual 3 and from leucocytes of his parents. The exonic regions and flanking splice junctions of the genome were captured using the Twist Human Core Exome Kit (Twist Bioscience, San Francisco, CA, USA). Massively parallel (NextGen) sequencing was done on an Novaseq (Illumina, San Diego, CA, USA; NovaSeq Control Software v1. 8. ) with 75 bp or greater paired-end reads. Reads were aligned to human genome build GRCh38 by using the Burrows-Wheeler Aligner (BWA) tool for Individuals 1 and 2 and were analyzed for sequence variants using a custom-developed analysis tool.
Average coverage was 65X (99. 2% > 25X) for individual 3, 75X (99. 6% > 25X) for his father and 73X (99. 3% > 25X) for his mother. Ensembl's VEP (Variant Effect Predictor, release 92) was used to annotate the variant. Analysis of WES data prioritized variants based on the allele frequency (minor allele frequency [MAF] ≤ 0. 1%) in population database (gnomAD, EVS and 1000 genomes) and no homozygotes in public databases, the presence in databases of medically relevant variants (ClinVar, Human Gene Mutation Database), the predicted impact on coding sequence, the mode of inheritance and the compatibility with the clinical synopsis of the OMIM database when available. After looking for de novo, X-linked or biallelic variants, TEFM was the only candidate gene. For P4 Trio WGS on gDNA from blood was conducted at the Kinghorn Centre for Clinical Genomics (Garvan Institute, Sydney) as described  42.