dataesr/scientific-paragraphs-categorization huggingface dataset:
Oil Red O staining, image processing, and quantification Intracellular lipids were stained by Oil Red O (#O0625; Sigma-Aldrich). Cells were washed with PBS and fixed with 4% PFA in PBS, for 10 min. Fixed cells were incubated with Oil Red O solution for 1 hr at room temperature and then with DAPI (Thermo Fischer Scientific) for 5 min. Fluorescence images were generated with IX83 Olympus microscope, acquired with Cell-Sens V1. 6 and analyzed with FIJI software. Images of 8-10 different areas per condition were visualized by fluorescence microscopy using mCherry filter, followed by computer image analysis using FIJI software. Briefly, analysis was performed by threshold converting the 8-bit Red-Green-Blue image into a binary image, which consists only of pixels representing lipid droplets (i.
Acknowledgements We thank the patients and their families for their participation. The authors would like to thank  Dr. Boris Keren  ( UniteFonctionnelle de Genomique du Developpement, AP-HP, Paris, France ) for analyses of SNP DNA chips,  Beth Hudson  ( GeneDx, Gaithersburg, United States ) for her involvement in searching patients with EPHX1 variants,  Annie Munier  ( Cytometrie et Imagerie Saint-Antoine, Sorbonne Universite, Paris, France ) for cell sorting of transfected 3T3-L1 pre-adipocytes,  Romain Morichon  for image processing ( UMS30 Lumic, Sorbonne Universite, Paris, France ), and  Yves Chretien  ( CRSA, Paris, France ) for expert artwork.
Data availability Plasmids used in this study have been deposited in Addgene under accession number 79368. Exome data from human subjects cannot been made available, since the informed written consents did not permit sharing of the full sequence data. To circumvent this fact and to fit with the Editor and Reviewers' comment, two supplementary files have been provided listing all other rare variants identified in the two families investigated herein, according to the corresponding mode of inheritance. For each variant, we indicated the items arguing against its involvement in the disease phenotype. All data generated or analysed during this study are included in the manuscript and supporting files. Source data have been provided for all western blot experiments.
MS target values of 3e  6  and MS2 target values of 1e  5  were used. The isolation window of precursor ion was set at 0. 7 Da and sequenced peptides were excluded for 40 s. Spectral processing and peptide and protein identification. The acquired raw files from LC-MS/MS were processed using MaxQuant (Cox and Mann) with the integrated Andromeda search engine (v. 1. 6. 6. 0). MS/MS spectra were quantified with reporter ion MS2 from TMT 10plex experiments and searched against Mus musculus, UniProt Fasta database (March19). Carbamidomethylation of cysteines was set as fixed modification, while methionine oxidation, N-terminal acetylation and were set as variable modifications. Protein quantification requirements were set at one unique and razor peptide. In the identification tap, second peptides and match between runs were not selected.
Z-stacks were acquired every hour for 2. 5-3 days using a Leica SP8 confocal microscope. Imaris software (Oxford Instruments) was used for 3D reconstruction, tracking and determination of cell volume. Cell volume values were then normalized to the mean intensity of tdTomato across time (cell volume/mean intensity). For each dataset, comparison of fit was performed with GraphPad Prism. Data were fitted with either a straight line or a circadian damped cosine wave. When a circadian damped cosine wave equation was preferred, only those cells with a period between 20 and 28 h were considered rhythmic. Measurement of the effective diffusion rate of Quantum dots Streptavidin-Quantum Dot synthesis. CdSe/CdS/ZnS core/shell Quantum Dots (QDs) were synthesized using a high-temperature reaction of metal carboxylates and sulfur and selenium precursors in octadecene as described previously  93.
Each acquisition consisted of 1000 frames acquired at 62. 5 Hz (10 ms exposure, 6 ms readout). The system was operated by Metamorph. Estimation of the effective diffusion coefficient of QDs was performed in Fiji 89 and MATLAB 2017b (Mathworks) using custom codes available on request. In brief, for each field of view, QD position was precisely determined by 2D Gaussian fitting using the plugin Thunderstorm  97. Average localization precision across the entire dataset based on photon count was 14 ± 0. 0004 nm (mean ± sem n = 214, 368, 144 QDs). QD trajectories were then tracked using the MATLAB adaptation by Daniel Blair and Eric Dufresne of the IDL particle tracking code originally developed by David Grier, John Crocker, and Eric Weeks in MATLAB 2017b (http://site. physics. georgetown. edu/matlab/index.
MEAs were allowed to equilibrate for 200 s and local field potentials from all electrodes were recorded at 10 kHz for 10 min. Data were recorded and analysed using the Multi-Channel experimenter and Multi-Channel DataManager software. Data from individual MEAs (several replicates per experiment) are shown in the main figures and in supplementary figures. Langendorff heart electrophysiology. Animals were culled and hearts excised between ZT0-1 or ZT11-12. Excess tissue was removed under dissection microscope in ice-cold Krebs-Henseleit Buffer (KHB) solution (118 mM NaCl, 11 mM Glucose, 1. 8 mM CaCl 2 , 4. 7 mM KCl, 25 mM NaHCO 3 , 1. 2 mM MgSO 4 , 1. 2 mM KH 2 PO 4 ). Hearts were cannulated through the aorta and held in place using fine thread tied around the aortic vessel. The cannula was then attached to a jacketed glass coil filled with oxygenated KHB and heart perfused at 37 °C at 4 ml/min.
This whole process took ~3-4 min. Monophasic action potentials were recorded using custom-made silver chloride electrodes from the left atrial appendage. Any hearts that did not stabilize within the first 10 min of recording were excluded from further study. Signals were amplified and digitized using a Powerlab system (4/35) and analysed in LabChart (v8; ADinstruments, UK). After brief electrical stimulation of the right atrial appendage (600 Hz for 15 s), average heart rate was calculated over 30 seconds of stable recording. Mice in the treated group were given rapamycin (dissolved in drinking water) for 2 weeks befre culling. ECG telemetry implantation and analysis. Mice were implanted with telemetry devices (ETA-F20; Data Sciences International, USA) for electrocardiographic recording. Mice were anaesthetized with isoflurane, following which the telemetry remote was inserted into the abdominal cavity.
ECG analysis was carried out as previously described  85  using bespoke ECG software written in MATLAB (R2018a; Mathworks, USA). Briefly, for every 10 s sweep R waves were detected by amplitude thresholding and ECG waveforms compared to the median of the sweep. Any that deviated significantly from this template were removed from subsequent analysis. Any sweeps with low signal/noise ratio or where >20% of detected events were excluded during template matching were excluded. Heart rate was calculated from the median RR interval of each sweep. Mice for Langendorff and telemetry studies. Male, C57BL/6 J mice aged 10-13 weeks at the start of study, purchased from Charles River (UK), were used for ECG and Langendorff studies. Langendorff studies included some Bmal1 f/f animals bred in-house on the same C57BL/6 J background.
Mice were housed under 12:12 LD (~400/0 lux) at 22 ± 2 °C with food and water available ad libitum. Behavioral experiment for NKCC1 KO mice. Sperm from NKCC1 -/-mice (FVB. Cg-Slc12a2tm1Ges/Mmjax, the Jackson Laboratories) was used for in vitro fertilization of PER2::LUC transgenic mice. After two rounds of breeding within the background strain, NKCC1 +/+ -PER2::LUC and NKCC1 -/-PER2::LUC mice (n = 4 males per genotype, 8-12 weeks of age) were individually housed, and locomotor activity monitored via wheel-running. Mice were subjected to 12 h/12 hlight/dark cycles (L/D) for 6 days then released into constant darkness (D/D) for 14 days. Period of locomotor activity was determined using ClockLab software (Actimetrics) (last 6 days in D/D). Osmometry. Stock solutions of 600 mg/ml hemoglobin (MP Biomedicals, 02151234), 400 mg/mL PEG-20K (Sigma, 81300), or 400 mg/mL KCl were prepared in PBS.
Osmolality was measured using OSMOMAT 30 (Gonotec). Three measurements for each dilution were performed. Data were analysed using GraphPad and fitted with either a straight line or a third order polynomial. All cell media were adjusted to be isoosmotic within each experiment, unless otherwise stated. Ratiometric pH measurements. Native Fluc spectra were collected in solution using a Cary Eclipse Fluorescence Spectrophotometer (Agilent) with 10 nM Quantilum (Promega, Madison, WI; E1701) in 30 mM MOPS, 10 mM β-mercaptoethanol, 1 mM ATP pH 7. 4, 1 mM potassium luciferin, 10 mM MgSO 4 , and 1 mg/mL bovine serum albumin, as described previously  101. Cellular pH calibration was performed with stably transfected SV40:Luc U2OS cells at 37 °C in white 96 well plates (Corning™ 3917) on a Tecan Spark 10 M, alternately using 550-575 nm and 610-635 nm filters to calculate 560/620 nm ratio (Supplementary Fig.
Acknowledgements We thank  Alex Harmer ,  Helen Causton , and past and present O'Neill lab members for valuable discussion and contribution, particularly  Priya Crosby  and  Ned Hoyle , visual aids, and the biological services group for assistance with animal work and husbandry. Funding: E. D. was supported by the  Medical Research Council  ( MC_UP_1201/13 ) and the  Human Frontier Science Program  ( CDA00034/2017-C ); RSE by a  Wellcome Trust   Sir Henry Dale Fellowship  ( 208790/Z/17/Z ). This work was supported by the  AstraZeneca Blue Skies Initiative  and the  Medical Research Council  ( MC_UP_1201/4 ).
Data availability The data supporting the findings of this study are available in the main article and supplementary files or from the corresponding author upon reasonable request. Source data are provided with this paper as Source Data File. The original mass spectra and search engine files used in this study have been deposited in the public proteomics repository MassIVE as MSV000087673 [https://massive. ucsd. edu/ProteoSAFe/ dataset. jsp?accession=MSV000087673]. Source data are provided with this paper.
RESULTS Investigating anti-SARS-CoV-2 humoral response in mild and severe COVID-19 patients To characterize the longitudinal evolution of the humoral response against SARS-CoV-2, we set up a prospective cohort of patients with S-CoV requiring oxygen (n = 21, median age: 57 years , corresponding to WHO progression scale >4 and %8) and mild ambulatory forms of COVID-19 (M-CoV, n = 18, median age 35. 5 years , corresponding to WHO progression scale <4;  Marshall et al. , 2020)  (MEMO-COV, NCT04402892, see STAR methods and Table  S1 ). Initial samples were collected from patients in median 18. 8 days (±SD: 8. 8) and 35. 5 days (±SD: 12. 8) after disease onset (M0) for S-CoV and M-CoV respectively. Two additional blood samples were collected at 3 (M3) and 6 months (M6) at a stage when all patients had fully recovered.
SARS-CoV-2-responding B cells harbor phenotypically and transcriptionally an activated phenotype and a timedependent maturation toward the MBC lineage Unsupervised clustering analysis of our scRNA-seq dataset revealed that CD19 + IgD À B cells could be divided in five major clusters according to their gene expression profile  (Figures 2A and 2B; Figures S2A and S2B; Table   S2 ), a clustering which correlated nicely with the expression of barcoded surface markers included in our dataset  (Figures 2C and 2D; Figure S2C). Two of these clusters corresponded to ASCs, with both a shortlived plasmablast (PB) cluster, enriched for expression of cell proliferation-related genes, and non-dividing PCs. Both PB/PC   S1. (legend continued on next page) populations were strongly reduced by 6 months, confirming the resolution with time of the primary extrafollicular response in these severe COVID-19 patients (Figure  2B ; Table  S2 ).
Parallel analysis of barcoded-S reads in our dataset allowed us to identify clear populations of Sspecific ABCs and ASCs at M0 (Figure  2E ; Figure  S2E ; Table  S2 ). By 6 months, S-barcoded-specific cells mainly localized in the resting MBC cluster (Figure  2E ). We performed a second analysis of S-specific cells in our dataset, identified either as barcoded-S cells or as cells clonally related to cells identified as S-specific by single-cell culture, to increase their representation  (Figures S2G and S2F; description in STAR methods). We found limited clonal overlap of S-specific MBCs at M6 with the initial ASC response, suggesting that two distinct, albeit synchronous, responses take place in COVID-19 patients (Figures  S2F-S2H ; Table  S2 ).
S-specific MBCs mature from ABCs and accumulate up to 6 months post-infection in COVID-19 patients To further analyze the dynamics of the overall and SARS-CoV-2 S-specific B cell response at the level of our entire cohort, we next performed multi-parametric FACS analysis using a flow panel that included seven surface markers identified in our scRNA-seq dataset as major markers of ABCs and ASC subsets involved in the initial response (CD19, CD21, CD27, CD38, CD71, CD11c, IgD, as well as His-tagged trimeric S protein). Unsupervised analysis of CD19 + IgD À switched B cell populations (Figures  S3A-S3C ), concatenated from 83 samples collected at M0, M3, and M6 from patients in our cohort, confirmed our initial observation that S-specific B cells were enriched in a cluster of CD27 + CD38 int/+ CD71 + ABCs and in ASCs at M0 (Figures  3A-3C ; Table  S3 ).
(H) Graph showing the frequencies of cells at the M0 time point harboring 0-1 (white), 2-9 (gray), or R10 (black) mutations in their V H genes for all sorted cells according to their cluster of origin (left) as well as for cells stained by the barcoded-SARS-CoV-2 S protein and/or members of the top 10% ASC clones at M0, as defined in (E). Repeated measures (RM) two-way ANOVA and Sidak's multiple comparison tests (B). ****p < 0. 0001, ***p < 0. 001, *p < 0. 05. See also Figure  S2  and Table  S2. S1 ). His-tagged labeled SARS-CoV-2 S-specific cells are overlaid as red dots. (B) Unsupervised clustering (FlowSOM) was performed on the concatenated FACS dataset based on IgD, CD71, CD27, CD38, CD11c, CD19, and CD21 fluorescence intensity. 95% density contours for each identified cluster are overlaid in the UMAP projection.
Aside from severity, none of the clinical parameters or treatment that we could monitor, notably corticosteroids, appeared to have any clear influence on the long-term establishment of these S-specific MBCs (Figures  S3M-S3Q ). To ensure that such stable levels of S-specific CD27 + switched MBCs did not simply reflect a sustained immune response in these convalescent COVID-19 patients, we further used CD19 and CD71 to separate ABCs (CD19 high CD71 + ) and resting MBCs (CD19 + CD71 low ) among S-specific cells over time, as previously described  (Ellebedy et al. , 2016). Confirming our initial observation in the four S-CoV patients included in the scRNA-seq dataset (Figure  2E ) and the clear enrichment of S-specific B cells in cluster 3 of our unsupervised FACS analysis (Figures  3A-3C ), the majority of S-specific MBCs displayed a CD19 high CD71 + ABC phenotype in the days following infection (Figure  3F ).
CDR3 from all sequences in the tree are represented as a frequency plot logo (top left) as well as below the tree, where each amino acid in red indicates a change compared to the top listed CDR3. ANOVA and two-tailed Mann-Whitney tests (B and C). ***p < 0. 01, *p < 0. 05. See also Figure  S4  and Table  S4. relationships with sequences obtained in our scRNA-seq dataset at both M0 and M6. Mutations in V H sequences of SARS-CoV-2 S-specific clones at M0 displayed a bimodal distribution with both near germline and highly mutated sequences. In line with an initial recruitment of cross-reactive MBCs into the anti-SARS-CoV-2 early humoral response, HCoV-HKU1-and HCoV-OC43-specific clones contained sequences that were already highly mutated at M0 (Figure  4C ). In contrast, SARS-CoV-2 RBD-specific clones displayed low mutation numbers in V H sequences, as previously described in numerous studies during the early stages of this pandemic  (Brouwer et al.
, 2020; Kreer et al. , 2020; Liu et al. , 2020; Robbiani et al. , 2020; Seydoux et al. , 2020; Shi et al. , 2020; Wec et al. , 2020; Zost et al. , 2020). Both early SARS-CoV-2 Sand RBD-specific Ig V H sequences showed high levels of convergence between donors in our dataset but also with published SARS-CoV-2 S-and RBD-specific antibody sequences (43/874 sequences in clonal relationship with cells from our dataset) (Figure  S4E ). Although near germline sequences were initially present in S-specific clones, and even predominated in those with SARS-CoV-2 RBD specificity in both our M0 dataset and in the literature, longitudinal tracking of clonal relationships at 3 and 6 months revealed a progressive acquisition of mutations in V H sequences over time  Figures S4G and S4H). Most importantly, over 85% of SARS-CoV-2 RBD-specific culture supernatants at M6 showed intermediate to strong neutralizing potential against SARS-CoV-2 in vitro in S-CoV patients and over 50% in M-CoV patients (Figure  4F ; Figure  S4I ; Table  S4 ).
B cells from four S-CoV patients were further analyzed by scRNaseq, these four patients being selected for the absence of specific treatment (corticosteroids, IL6/IL6R inhibitors) that could interfere with the B cell response. Patients were recruited at Henri Mondor University Hospital (AP-HP), between March and May 2020. Samples from healthy donors were obtained at EFS Henri-Mondor and frozen before 2019. MEMO-COV-2 study (NCT04402892) was approved by the ethical committee Ile-de-France VI (Number: 40-20 HPS), and was performed in accordance with the French law. Written informed consent was obtained from all participants. METHOD DETAILS SARS-CoV-2 viral detection by RT-PCR Nasopharyngeal (NP) swabs in transport media and plasma samples were held at 4 C (or À80 C if > 12 h) prior to testing on the Alinity M SARS CoV-2 Amp kit and/or Cepheid Xpert Xpress SARS-CoV-2 kit.
Samples were acquired using a LSR Fortessa SORP (BD Biosciences). For cell sorting, cells were stained using the same protocol and then sorted in 96 plates using an ultra-purity mode on a MA900 cell sorter (SONY), or Aria III (BD Biosciences). Data were analyzed with FlowJo or Kaluza softwares. Detailed gating strategies for individual markers are depicted in Figure  S1. Control samples included PMBC collected from heathy donors before SARS-CoV-2 pandemic and incubation of S-CoV and M-CoV samples with anti-His antibodies but no His-tagged spike protein to determine the labeling background. For UMAP generation and visualization (Figures  3A-3C ), data from all 83 samples from patients with complete panel acquisition at M0, M3 and M6 (Table  S1 ) in our dataset were individually down-sampled to 3000 cells each using the Downsample (v3.
3) plugin in FlowJO. All samples were subsequently concatenated and FlowJO UMAP (v3. 1) plugin was used to calculate the UMAP coordinates for the resulting 249,000 cells (with 30 neighbors, metric = euclidian and minimum distance = 0. 5 as default parameters) (Figure  3A ). The FlowSOM plugin was used in parallel on the same downsampled dataset to create a self-organizing map (using n = 9 clusters as default parameter) (Figure  3B ). Upsampling was then performed via the FlowSOM plugin by applying this self-organizing map to the initial FCS files from all 83 samples to calculate both total and spike-specific memory B cell repartition in identified clusters on all collected cells for each donor (Figure  3C ; Figure  S3A ; Table  S3 ). Both UMAP and FlowSOM plugin were run taking into account fluorescent intensities from the following parameters: FSC-A, SSC-A, CD19, CD21, CD11c, CD71, CD38, CD27 and IgD.
PCR products were sequenced with the reverse primer CHG-D1 and read on ABI PRISM 3130XL genetic analyzer (Applied Biosystems). Sequence quality was verified with the CodonCode Aligner software (CodonCode Corporation) and data were analyzed with the IMGT/HighV-QUEST web portal (from The International Immunogenetics Information System) or in parallel with the VDJ sequences generated as part of our scRNA-seq dataset (see below).
Single-cell gene expression analysis Paired-end FASTQ reads for all three libraries were demultiplexed and aligned against the GRCh38 human reference genome (GEN-CODE v32/Ensembl 98; July 2020) using 10X Genomics' Cell Ranger v4. 0. 0 pipeline. Outputs of Cell Ranger were directly loaded into Seurat v3. 2. 2  (Stuart et al. , 2019)  for further QC steps and analysis. Following manual inspection of cell quality, only genes detected in at least 10 cells and cells with more than 500 unique genes detected and less than 25% of UMI counts mapped to mitochondrial genes were kept. Reads mapping to the immunoglobulin gene locus were further stored in a separate assay at this step to avoid unwanted clustering based solely on differential isotype expression and cells with exactly one heavy chain sequence were retained for final analysis.
Computational analyses of VDJ sequences Processed FASTA sequences from cultured single-cell heavy chain sequencing, 10X Genomics single-cell RNA sequencing and 874 published SARS-CoV-2 RBD and/or S-specific antibodies  (Brouwer et al. , 2020; Kreer et al. , 2020; Liu et al. , 2020; Robbiani et al. , 2020; Seydoux et al. , 2020; Shi et al. , 2020; Wec et al. , 2020; Zost et al. , 2020)  were annotated using Igblast v1. 16. 0 against the human IMGT reference database. Non-productively rearranged sequences were removed at that step as well as sequences from cells that did not pass the initial QC cut-offs from our scRNA-seq analysis. Cases of 10X Genomics barcodes with two or more consensus heavy chain sequences for which more than ten UMI were detected were generally flagged as potential doublets for removal from our scRNA-seq analysis.
15 were considered as potentially belonging to the same clonal group. Mutation frequencies in V genes were then calculated using the calcObservedMutations() function from Immcantation/SHazaM v1. 0. 2 R package. For the analysis of the initial ASC response in our 10X Genomics dataset (Figures  2E  and 2F ), clonal assignments were further corrected using available light chain information (light_cluster. py script from Immcantation). Further clonal analyses were implemented in R. Based on light chain-corrected clonal affectation, clones were ranked based on the total number of ASC (PC + PB) from the M0 time point of our analysis found in each clone for each individual donor. Clones with equal number of ASC were further ranked based on their total number of Activated and MBC cells from the M0 time point of our analysis.
The top ten percent highest ranked clones were further labeled as Top 10% ASC clones (Figures  2E  and 2F ; Figure  S2D ). Based on heavy-chain only clonal affectation, clones were defined as SARS-CoV-2 S specific (Spike-specific clones) if they contained 1 or more validated single-cell culture sequence or if more than ten percent of the cells from that clone were positively stained by our barcoded His-tagged S protein in our scRNaseq dataset. All Spike-specific clones described in Figure  4  and Figures  S2F  and S2G  were manually curated based on available light chain information (10X Genomics and published antibodies only) and CDR3 sequences. Cut-off for positive staining with barcoded His-tagged S protein in our scRNaseq dataset were set at log normalized count values of 1. 2 for both barcodes.