dataesr/scientific-paragraphs-categorization huggingface dataset:
Phytoplankton groups were manually classified using the CytoClus ® software by generating several two-dimensional cytograms plotting descriptors of the four pulse shapes, such as the area under the curve of the pulse-shape signals  (FWS_cyto, SWS_cyto, Ofluo_cyto, and Rfluo_cyto). Group abundances and cell properties were processed by the software. The sizes of the different phytoplankton cells were estimated based on the relationship between silica beads' real sizes (1. 0, 2. 01, 3. 13, 5. 02, and 7. 27 µm non-functionalised silica microspheres, Bangs Laboratories, Inc. ) and the FWS_cyto signal and were converted to the equivalent spherical diameter (ESD, µm) and biovolume (BV, µm 3 ). A power-law relationship (log(BV) = 0. 912 × log(FWS_cyto) -5. 540; r 2 = 0. 89, n = 7) allowed the conversion of the FWS signal to cell size.
(2012). To do so, the mixed-layer depth was evaluated using a 0. 1 • C criterion on the conservative temperature profiles relative to a reference depth of 10 m  (Houpert et al. , 2015). The relative differences in fluorescence were used as a quantitative proxy for the evolution in the distribution of the Chl-a concentration. The glider fluorescence data have not been calibrated against reference measurements, but they agree well with the surface measurements of the ship's adjusted Chl-a concentrations (Appendix B).
2a ). We performed the association between each Chl_tsg data point and the corresponding Chl satellite data on the same day and for the closest lat/long pixel and then selected those where the Chl_tsg data were between 06:00 and 18:00 UTC (daytime) in order to minimise the effect of night-time extrapolated points. The glider sampling did not follow the ship's route, but a comparison of the 0-5 m signal when the ship-glider distance was smaller than 15 km showed a negligible difference with the ship-adjusted surface Chl-a concentrations (0. 04 ± 0. 13 ng mL -1 ). All of the measurements described above are summarised in Table  1.
Panel (b) shows a linear regression between the Chl-a concentration from in situ discrete sampling (Chl_insitu, ng mL -1 , n = 41) and the sum of all phytoplankton cells' normalised red fluorescence from the CytoSense (nRFluo_cyto, a. u mL -1 ). Two data sets are shown using the same instrument (PIANO, "Réaction fonctionnelle, structurelle et journalière des organismes du pico-au nanophytoplancton", and FUMSECK). The intercept coefficient of the regression was not significant at the 10 % level (t test). Table  1. Summary of the variables measured during the cruise, including their sources, their sampling spatial and temporal resolution, and the vertical range along which they were measured. 5:00 UTC, and finished on 5 May in the evening. Although the conjunction of these two winds is a classic situation in the Ligurian Sea, this event was particularly intense.
Acknowledgements. We thank the captain and the crew of the RV Téthys II for the cruise and their help with the deployment of the instruments. This research was supported by  CNES  ( BioSWOT  project) and by the  French National programme LEFE (Les Enveloppes Fluides et l'Environnement) FUMSECK-vv project  ( PIs Stéphanie Barrillon  and  Anne A. Petrenko ). The flow cytometer was funded by the  CHROME (PI Melilotus Thyssen , financially supported by the  Excellence Initiative of Aix-Marseille University  -A*MIDEX, a  French "Investissements d'Avenir" program ), and  FEDER  funding (PRECYM flow cytometry platform). The authors thank  Nicole Garcia  and  Patrick Raimbault  from the  MIO-PACEM  platform for the chlorophyll-a and nutrient analyses. SPASSO is operated with the support of the  SIP (Service Informatique de Pythéas) , in particular  Christophe Yohia ,  Julien Lecubin ,  Didier Zevaco , and  Cyrille Blanpain  ( Institut Pythéas, Marseille, France ).
6A  (Fig. 6C , Supplementary Fig. 5D  and Supplementary Data 1). Several other populations were identified according to the upregulated expression of specific genes: cycling cells, cells expressing chromaffin markers such as CARTPT  36, 37  , DLK1  38, 39  , and CDKN1C (cluster 5) and cells with a sympathoblast-like identity expressing genes such as TFAP2B  40  , PRPH 41 , GAP43, and NPY 42 (clusters 0, 8, 9, 11, and 12) (Fig. 6C  and Supplementary Data 1). Published signatures from human adrenal development  13  were used to corroborate our cluster identification (Supplementary Fig. 5E ). We noticed that tumor cells with either sympathoblast or chromaffin-like identities co-existed in the same tumors (Supplementary Fig. 5C ). We next investigated our cohort of 15 PDXs. The relevance of these models was assessed by an unsupervised hierarchical clustering which revealed strong similarities between transcriptomes of matched patient tumors and PDXs (Supplementary Fig.
6D ). Of note, this cluster contains cells from IC-pPDX-63 and IC-pPDX-109 (Supplementary Figs. 7C  and 8C ), the two models that gave rise to mesenchymal tumor cells in vitro. This signature was also expressed by the bridge and the mesenchymal tumor cells from the IC-pPDXC-63 cell line (Fig. 6D ). Finally, the three tumor cell identities, NOR-chromaffin, NOR-sympathoblast or NORmesenchymal, did not seem to be influenced by the diagnosis or relapse status of the corresponding patients, nor by their genomic alterations (Supplementary Figs. 8D, E  and 9 ). Finally, to extend our results to other datasets, we reanalyzed the series of 14 cases studied by snRNAseq by Jansky et al. 13  , extracted the noradrenergic tumor cells and applied our signatures. Interestingly, the NOR-mesenchymal signature highlighted a specific cluster of cells (cluster 10) that was mostly composed by cells from one case (Supplementary Fig.
In both models, neuroblastoma plasticity can be orientated from a noradrenergic towards a mesenchymal identity in vitro, especially in response to EGF and TNFα factors. These pathways have previously been linked to a change of tumor identity in the noradrenergic SH-SY5Y cell line  43. Our data also document that the mesenchymal to noradrenergic shift was favored by the in vivo microenvironment. Second, we demonstrate that the spontaneous plasticity between the noradrenergic and mesenchymal states relies on a profound epigenetic reprogramming, as revealed by the analysis of the superenhancer landscape, and the expression of the transcription factors of the CRCs previously characterized for each cell identity  17, 18. Interestingly, we could document that the reprogramming potential associated with plasticity is maintained through several generations in the SK-N-SH cell line.
A transitional state between the noradrenergic and mesenchymal states has been proposed recently using pseudotime trajectory analyses on scRNA-seq data obtained from a series of neuroblastoma, ganglioneuroblastoma and ganglioneuroma  54. Yet, these transitional cells did not appear in a path or in a cluster in between noradrenergic and mesenchymal cells. Moreover, no genomic alterations common to both states were observed in favor of cell plasticity. In our patient tumor dataset, we identified a cluster of noradrenergic tumor cells forming a bridge towards a cluster of normal Schwann and mesenchymal cells and composed of cells of three tumors at diagnosis and one case at relapse. These cells are unambiguously tumor cells exhibiting copy number alterations that co-express markers of a noradrenergic identity and markers of mesenchymal features.
Defining a signature of those noradrenergic tumor cells with mesenchymal features allowed us to highlight a specific cluster in our integration of 15 PDXs and in an independent published dataset of patient biopsies  13. Interestingly, the cluster in PDX was composed of three cases including the IC-pPDX-63 and IC-pPDX-109 models, the two models from which we could obtain bi-phenotypic cell lines with noradrenergic and mesenchymal tumor cells in vitro. The convergence of these observations, based on orthogonal approaches (i. e. analyses of experimental models and of patient's tumors), strongly suggests that cells with such a signature are the ones exhibiting a reprogramming potential towards a full mesenchymal identity in vitro. The observation that CD44 pos -FACS sorted/mesenchymal cells from our heterogeneous and plastic models give rise to full noradrenergic tumors in vivo underlies the powerful pressure towards a noradrenergic state for tumor formation.
Neuroblastoma patients Neuroblastoma samples for single-cell analyses were obtained from patients treated at Institut Curie. Surplus tissues obtained at diagnosis or relapse were processed immediately after receipt at the laboratory for molecular diagnosis (Unité de Génétique Somatique). Written informed consents for this study, including the analysis of surplus tumor tissue were obtained for all patients from parents or guardians. Out of the 18 studied patients (Supplementary Table  1 ), six patients were enrolled in the MICCHADO study (ClinicalTrials. gov identifier NCT03496402) and four patients in the MAPPYACTS trial (ClinicalTrials. gov identifier NCT02613962), with two patients Fig. 4  | The noradrenergic to mesenchymal plasticity is associated with an epigenetic reprogramming. NOR noradrenergic, MES mesenchymal.
Methods details Cell identity. Noradrenergic and mesenchymal tumor cell identities were defined using the transcription factors (TFs) previously identified  17, 18. This common list of 53 TFs is composed of 19 noradrenergic (NOR TFs) and 34 mesenchymal (MES TFs). They have been  used for hierarchical clustering of bulk RNAseq data and as signatures in single-cell RNAseq data. The tumor cell identity score was calculated as the difference between mesenchymal and noradrenergic scores, each score being the mean expression of the transcription factors associated to each identity. Immunofluorescence 100,000 cells for SK-N-SH, SK-N-SHm and IC-pPDXC-63 cell lines were plated in a four-well Lab-Tek chamber (Cat# 177399PK, Thermo Fisher) 48 h before immunostaining. Cells were fixed with 4% PFA buffer, permeabilized with a 0.
100 bp paired-end sequencing was performed with the Illumina NovaSeq 6000 instrument. Reads were aligned to the human reference genome hg38/ GRCh38 using STAR 2. 6. 1a_08-27 (RRID:SCR_015899, https://github. com/alexdobin/STAR) with the following options: out-FilterMismatchNoverLmax 0. 04, alignIntronMin 20, lignIntronMax 1000000, outFilterMultimapNmax 20. Gene expression values (FPKM = fragments per kilobase per million reads) were computed by Cufflinks v2. 2. 1 (RRID:SCR_014597, http://cole-trapnell-lab. github. io/ cufflinks/) and further normalization between samples was done using quantile normalization (R/LIMMA v3. 30. 7 (RRID:SCR_010943)). For scatterplots confronting gene expression and tumor cell identity score, we used the bulk RNAseq of 25 neuroblastoma cell lines previously generated  17.
ChIP-seq reads were mapped to the human reference genome hg19/GRCh37 using Bowtie2 v2. 2. 9 (http://bowtie-bio. sourceforge. net/bowtie2/index. shtml). Low quality reads were filtered using Samtools v1. 9 (Q < 20). Peaks were called with HMCan v1. 40 (RRID:SCR_010858)  67. Super-enhancers were called with LILY software  17. LILY was also used to normalize HMCan density profiles between samples. The H3K27ac signal on super-enhancers shown in the heatmap was computed as the sum of normalized H3K27ac densities divided by the length of the super-enhancers. For transcription factors associated with several super-enhancers, the signal of the associated super-enhancers is summed and divided by their total length. PCA was performed on log 2 values of superenhancers scores of 5975 super-enhancers previously identified in the Supplementary Table  3  of Boeva et al.
Single-cell RNA-sequencing experiments and preprocessing of data Single-cell RNA-seq was performed with the 10x Genomics Chromium Single Cell 3' Kit (v3) according to the standard protocol. Libraries were sequenced on an Illumina HiSeq2500 or NovaSeq 6000 sequencing platform. CellRanger version 3. 1. 0 (10x Genomics, https:// support. 10xgenomics. com/) was used to demultiplex, align and generate UMI count tables from sequencing reads. Two reference genomes were used to align reads: • The human reference genome (hg38/GRCh38) for the 18 patient samples and for in vitro cell lines. • A human-mouse reference genome (GRCh38-mm10) for the 15 neuroblastoma PDX models. In this scenario, we identified the mouse and human cells after inspection of the percentage of coverage from GRCh38 genome. We labeled cells as either human (at least 80%), murine (<30%) or human-murine doublets (between 30 and 80%).
Ambient mRNA correction SoupX R package v1. 4. 5 (https://github. com/constantAmateur/SoupX) was used to estimate and correct for ambient mRNA contaminations in samples aligned to GRCh38 genome. First, depending on the expression profile of empty droplets (defined as droplets with UMI count <100), we picked, as recommended, either HBB/HBA or immunoglobulin genes (IGHA1, IGHA2, IGHG1, IGHG2, IGHG3, IGHG4, IGHD, IGHE, IGHM, IGLC1, IGLC2, GLC3, IGLC4, IGLC5, IGLC6, IGLC7, IGKC) as marker genes to estimate contamination fractions. When these genes were absent from the ambient profile (the top 100 covered genes), we used the automatic mode provided by SoupX to estimate contamination fractions and generate corrected expression matrices. Summary of analyses is shown in Supplementary Table  3.
Single-cell RNA-seq data integrations Patient tumor integration and dissection of tumor heterogeneity. Seurat v4. 3. 0 was used to integrate the 18 neuroblastoma patient samples using 2000 anchor features. The integrated object was subjected to dimension reduction and clustering as described above. Tumor cells and microenvironment were identified based on the expression of specific marker genes. To further dissect tumor heterogeneity, 34,292 tumor cells were extracted from the integration by InferCNV (see "Patient tumor cells extraction"), then Harmony 32 (https://github. com/immunogenomics/harmony) was used for data integration. PDX. Harmony v1. 0 was used to integrated the 58,120 human cells coming from 15 PDX models. First, samples were preprocessed as described above, then Harmony was used for data integration.
Of note, when several single-cell transcriptomes are available for the same model, only one was used for the integration to avoid overrepresentation. C Dot plot graph illustrating cluster-specific gene expression. Three main tumor cell identities could be defined: noradrenergic tumor cells with either sympathoblast, chromaffin or mesenchymal features. D Plot of the signature identifying the noradrenergic cells with mesenchymal features composed of HGF, TIMP3, FOXC1, and ETV1 in the integration of single-cell RNAseq data of biopsies and PDXs, in the IC-pPDXC-63 cell line and in an independent cohort of 14 neuroblastoma cases already published  13. Independent published dataset from Jansky et al  13. Starting from the published integrated and annotated dataset (64. 769 neuroblastoma cells from 14 patients), raw counts were split per patient into individual objects using Seurat.
Neuroblastoma cell lines. The two batches of SK-N-SH (Cat# HTB-11, RRID:CVCL_0531) and SH-SY5Y (Cat# CRL-2266, RRID:CVCL_0019) cell lines have been obtained from the ATCC. The IC-pPDXC-63 cell line was derived from the IC-pPDX-63 model, and the IC-pPDXC-109 from IC-pPDX-109. Cell line authentication was done by STR profiling with PowerPlex® 16 HS System from Promega and Cytoscan HD array (Affymetrix).
Acknowledgements This work was supported by grants from  Institut Curie, Inserm , the  Institut National du Cancer  ( PLBIO18-273  and  PEDIAC program ,  INCA_15670 , I. J. -L. ) and the E. R. C. (ERC-2019-SyG, KILL or DIFFER-ENTIATE, O. D. ). We thank our  ERC partners Igor Adameyko ,  Peter Kharchencko  and  Suzanne Schlisio  for fruitful discussions. We sincerely thank the following associations:  Association Hubert Gouin-Enfance et Cancer ,  Les Bagouz à Manon , les amis de Claire, Courir pour Mathieu, Dans les pas du Géant,  Olivier Chape. The MAPPYACTS protocol is supported by the  Institut National de Cancer  through the  PHRC INCa - DGOS_8519   MERRI  ( RECF2889 , B. G. ), the  Fondation ARC (B. G. ) , the  Association Imagine for Margo (B. G. ) , the  Société Française de lutte contre les Cancers et les leucémies de l'Enfant et l'Adolescent (SFCE , B.
G. ), Fédération Enfants & Santé (B. G. ), the associations AREMIG (B. G. ) and  Thibault BRIET  (B. G. ). The MICCHADO protocol is supported by the  Institut National de Cancer  ( PRT18-035 ,  RECF3275 , G. S. ),  Association Imagine for Margo  (G. S. ), Kickcancer (G. S. ),  Association Hubert Gouin-Enfance et Cancer  (G. S. ), Fédération Enfants & Santé (G. S. ), and funding support by  BMS  and  Roche. High-throughput sequencing was performed by the  ICGex NGS platform of the Institut Curie  supported by the grants  ANR-10-EQPX-03  (Equipex, O. D. ) and  ANR-10-INBS-09-08  ( France Génomique  Consortium, S. B. ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  ITMO-Cancer Aviesan (Plan Cancer III , S. B. ) and by the  SiRIC-Curie program  ( SiRIC Grant INCa-DGOS-4654 , O.
Data availability Source data are provided with this paper as a Source Data file. Alignment of sequencing data were realized using the hg19/GRCh37, the hg38/ GRCh38 and the mm10/GRCh38 public databases of the GRC website (https://www. ncbi. nlm. nih. gov/grc). ChIP-seq (RRID:SCR_001237) data of cell lines are available in Gene Expression Omnibus (GEO, RRID:SCR_005012) under the accession code GSE154907. All single-cell RNA-seq from biopsies and PDXs, RNA-seq and ChIP-seq data on the IC-pPDXC-63 cell line are available in European Genome-Phenome Archive (EGA) under the accession numbers EGAS00001004781 and EGAS00001005322. The study EGAS00001004781 contains three datasets: EGAD00001006557 (4 Fastq files of the ChIPseq done with the IC-pPDXC-63 cell line), EGAD00001007870 (22 BAM files corresponding to the scRNAseq done with PDXs and cell lines) and EGAD00001006558 (72 BAM and Fastq files of corresponding bulk RNAseq).
The study EGAS00001005322 contains one dataset EGAD00001006556 which includes the 19 BAM files of the scRNAseq performed on neuroblastoma patient biopsies. Patient tumor bulk RNAseq have already been published  74  and are accessible in EGA under the accession number EGAS00001007161. Access to EGA archive datasets is obtained by formal application to the Data Access Committee (DAC). Each DAC requires users/applicants to sign a Data Access Agreement (DAA), which details the terms and conditions of use for each dataset. Source data are provided with this paper.
ACKNOWLEDGEMENTS We are grateful to  Jill Henry  at the  Komen Tissue Bank  for her help with the Virtual  Tissue Bank  and tissue procuration. This work was funded by the  Department of Defense Breast Cancer Research Program  ( BC170905  to PAV and  BC190271  to KLC), the  Vera Bradley Foundation and IU Simons Cancer Center  (to PAV and BSH), the  Wake Forest Center for Molecular.
To investigate the functional consequences of this sarcomere maturation defect, we assayed the spontaneous twitching of flight muscles in living intact pupae. As reported before, we found that wild-type flight muscles display spontaneous twitching at 48 h APF, which ceases by 60 h APF when sarcomeres have adapted stretch-activation properties  19. However, we found robust twitches in tono mutant flight muscles at 60 h APF (Figure  2D, E ; Supplemental Movies 1, 2). Together, these data demonstrate a severe sarcomere maturation defect in tono mutants that causes ectopic active forces in flight muscles, identifying abnormal myosin contractility as the likely cause of the muscle atrophy phenotype. In concert with the sarcomeres, the mitochondria network undergoes growth and rearrangement to fuel the intense contractions observed in mature muscle  34.
Mitochondria were quantified from n>4 thoraxes. The total area of mitochondria is identified by Otsu thresholding on Fiji for each z-plane. The multiple quantifications for each z-plane were averaged for each animal (one value per animal)  34. Tono-granularity: To quantify the Tono granularity score, each nucleus was segmented in 2D in one z-plane. For each nucleus (ROI) the mean signal and its standard deviation was retrieved to quantify the granularity score (mean/std) using a Fiji macro (https://github. com/PierreMangeol/Tono). To verify our methodology, we devised a simple simulation in Python to mimic the different images of Tono condensation states we observed. We then quantified these images the same way we analysed Tono distributions in the muscle nuclei. In this simulation, the total amount of Tono is constant, and at a given state of condensation, Tono condensates are spheres of equal radii that cannot overlap each other.
Acknowledgements We thank the  Schnorrer lab  and  Dirk Görlich  for insightful discussions and input on this manuscript. We thank  Johannes Bischof ,  Dirk Görlich ,  Robert J Johnston Jr ,  Mariano Labrador ,  Maria Leptin ,  Jordan Raff  and  Rainer Renkawitz  for generous gifts of antibodies and fly strains. We are grateful to the DSHB for antibodies and the  Bloomington Drosophila Stock Center  for fly strains ( NIH P40OD018537 ) and to Flybase for database organisation. We are grateful to  Christophe Pitaval  for fly embryo injections and fly stock maintenance. We thank  Fabio Marchiano  for generous help in bioinformatics. We are indebted to the  IBDM imaging  and fly facilities for help with image acquisition, maintenance of the microscopes and fly food.
Funding This work was supported by the  Centre National de la Recherche Scientifique  (CNRS, F. S. , B. H. H. ), the  Max Planck Society  (F. S. , B. H. H. ),  Aix-Marseille University  (P. M. ), the  European Research Council  under the European Union's  Horizon 2020 Programme  ( ERC-2019-SyG 856118 F. S. ),  FP/2007-2013 )/ ERC  Grant  310939  (F. S. ), the excellence initiative  Aix-Marseille University A * MIDEX  ( ANR-11-IDEX-0001-02 , F. S. ), the  French National Research Agency  with  ANR-ACHN MUSCLE-FORCES  (F. S. ),  MITO-DYNAMICS  ( ANR-18-CE45-0016-01 , F. S. , B. H. H. ), the  Human Frontier Science Program  ( HFSP ,  RGP0052/2018 , F. S. ), the  Bettencourt Foundation  (F. S. ), the  Fondation pour la Recherche Médicale  ( FDT202106012964 , J. A. ), the  Boehringer Ingelheim Fonds  (S.
These advances are obviously the fruits of pharmacological and molecular studies, but also structural biology, genomics, transcriptomics, proteomics, and metabolomics. They are enabled by novel and specific biological tools, or by dedicated chemical compounds generated by synthetic organic chemists. The various -omics studies have gifted us a great wealth of actionable information on disease aetiology and treatment, but these only bear fruit through the unique contributions of data scientists and bioinformaticians. The modern medicinal chemists and chemical biologists thus need to have some proficiency in handling and understanding large datasets: the ability to speak and interact with data scientists and programmers is a skill set of growing importance, and a trend expected to further gain in importance.
[39] ep biological insights can also be gained by chemotranscriptomics, leading to the discovery of novel modes of actions when small molecular probes or drugs are combined with transcriptome-wide RNA sequencing technologies. For instance, the L1000 approach  [40]  initially allowed for a robust quantification of several hundred transcripts. The throughput and robustness of the sequencing technology was subsequently improved to several thousand transcripts (DRUG-seq)  [41]  and in the meantime reaches, by multiplexing, up to single cell resolution (e. g. , SciPlex). [42]  addition to protein engineering-based tagging and labelling strategies, chemical labelling probes for live cell imaging have become increasingly important for phenotypic drug discovery, high-content imaging and target localization studies.