dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We are very grateful to  Lars Stemmann ,  Olivier Maury ,  Jean-Christophe Poggiale  and  Fabien Lombard  for the insightful comments during the development of this paper and to  Christian Ethé  and  Olivier Torres  for setting up the model configuration. This project used the HPC resources of TGCC under the allocation A13 (project no. gen0040 ) provided by  GENCI  ( Grand Equipement National de Calcul Intensif). This study benefited from the  ESPRI (Ensemble de Services Pour la Recherche  l'IPSL) computing and data center (https://mesocentre. ipsl. fr, last access: 17 November 2022) which is supported by  CNRS ,  Sorbonne Université, Ecole Polytechnique , and  CNES  and through national and international grants. This study has received funding from the  Agence Nationale de la Recherche  (grant no.
ANR-17-CE32-0008 ;  CIGOEF ). Meike Vogt  and  Fabio Benedetti  acknowledge funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 862923. This output reflects only the authors' views, and the  European Union  cannot be held responsible for any use that may be made of the information contained therein. Laurent Bopp  acknowledges support from the  European Union 's  Horizon 2020 research and innovation COMFORT  (grant agreement no. 820989 ),  ESM2025  (grant agreement no. 101003536 ) and from the  Chaire ENS-Chanel. Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-17-CE32-0008 ) and the  Horizon 2020  (grant nos. 862923 ,  820989  and  101003536 ). Review statement. This paper was edited by  Olivier Sulpis  and reviewed by  Rebecca Wright  and one anonymous referee.
Frozen tissues were placed in lysis buffer and homogenized using a cordless Pellet pestle (Sigma-Aldrich) followed by RNA extraction using RNAeasy mini kit (Qiagen). RNA integrity numbers (RIN) of the samples were 9. 8-10. Stranded polyA-enriched libraries were made using the KAPA mRNA HyperPrep kit (Roche) according to the manufacturer's instructions and sequenced on the HiSeq 2500 or HiSeq 4000 (Illumina) with single ended 100 base reads. An average of 24 million reads were generated for the Dp1Tyb and Dp1TybDyrk1a +/+/-and WT samples and an average of 34 million reads for Dp3Tyb, Ts1Rhr and corresponding WT samples. Adaptor trimming was performed with Trimmomatic/0. 36-Java-1. 7   (61). Differential expression analysis was performed with the DESeq2 package within the R programming environment  (62, 63).
The significance threshold for the identification of differentially expressed genes was set as an adjusted P value ≤0. 05. Gene set enrichment analysis was carried out with the GSEA software (version 2. 2. 3)  (10). The software compares ranked lists of genes, in this case, differentially expressed genes ranked by the "stat" value in decreasing order, with the following gene sets from the Molecular Signature Database: "c2. cp. v7. 0. symbols. gmt", "c5. bp. v7. 0. symbols. gmt", "h. all. v7. 0. symbols. gmt" downloaded from the Broad Institute (http://www. broad. mit. edu/gsea/). GSEAPreranked was used with "classic" enrichment analysis not excluding large datasets. For the pharmacological rescue experiment, 5 E13. 5 hearts were analyzed for each of the 4 conditions: 2 genotypes (WT and Dp1Tyb) x 2 treatments (LCTB-21 and iso-LCTB-21).
Single cell RNAseq E13. 5 mouse hearts from Dp1Tyb, Dp1TybDyrk1a +/+/-and WT littermate embryos were dissected and subsequently dissociated into single cells using the Neonatal Heart Dissociation kit and gentleMACS Octo dissociator (Miltenyi Biotec). Cells in suspension were methanol fixed, filtered through a 30 µm cell strainer, spun down and stored at -20ºC. Fixed cells were resuspended in PBS, 0. 04% BSA, assessed for viability using trypan blue staining and quantified using the EVE automated cell counter (Cambridge Bioscience). Cells were processed using the Chromium 3' mRNA-Seq version 2 kit (10x Genomics). Samples were sequenced on the HiSeq 4000 acquiring at least 142,000 reads per cell, achieving a sequencing saturation of 73 -90%. Cell Ranger software (version 2. 1. 1, 10x Genomics) was used to de-multiplex Illumina BCL output, create fastq files and generate single cell feature counts for each library using GRCm38 (v1.
2. 0) as reference. All subsequent analyses were performed in R (v3. 6. 1) using the Seurat (v 3. 1) package  (63, 68). Genes were removed if they were expressed in 3 or fewer cells and cells with < 200 expressed genes detected were also removed. After quality control and filtering 3,384 cells were retained for further analysis. Data was integrated following Seurat's vignette. In brief, for each sample the top 2000 most variable genes were selected for data integration using Canonical correlation Analysis (CCA) with 50 dimensions for dimensional reduction using tSNE and cluster calling using the Louvain algorithm. Clusters were visualized using the Uniform Manifold Approximation and Projection (UMAP). Upon initial examination of the clusters, we detected small clusters corresponding to red blood cells and lung tissue, both of which were removed.
Subsequently data was normalized and integrated, and clustering analysis was repeated with the same parameters as above, with the addition that the effects of cell cycle were regressed out. A cluster resolution of 0. 4 was used to define clusters for further analysis. Cluster markers were identified using "FindConservedMarkers" with default parameters. Differentially expressed genes within each cluster were identified using the DESeq2 test in "FindMarkers" using data in which the effects of cell cycle had not been regressed out. scRNAseq data have been deposited in the Gene Expression Omnibus, accession code: GSE196447.
Zombie Aqua was used to distinguish live and dead cells. To analyze cell cycle phases, dissected E13. 5 mouse hearts were incubated with 10µM EdU at 37°C for 30 min, and then dissociated into single cells using the Neonatal Heart Dissociation kit and gentleMACS Octo dissociator (Miltenyi Biotec). Cells in suspension were stained in PBS with anti-CD106-APC, anti-CD31-BV785 (Biolegend) and Live/dead Near-IR (ThermoFisher) for 30 min, followed by fixation in 4% Paraformaldehyde (PFA) for 20 min. EdU was detected using the Click-it EdU kit (Life Technologies), and DNA was stained with FxCycle violet (ThermoFisher). Data were acquired on an LSR Fortessa X-20 cell analyzer (BD) and analyzed using FlowJo v9.
9. Next, single cardiomyocytes were re-imaged using a ×63/NA 1. 15 waterimmersion lens. Z-stacks from 0 to 3 µm with a step size of 1 µm were acquired using excitation lasers at 405 nm (DAPI), 488 nm (CD106) and 640 nm (MTDR). The mitochondrial network was identified using the MTDR signal (mitochondria) transformed into Ridge texture using the SER (Saddles, Edges, Ridges) feature of Harmony V4. 9 (PerkinElmer). Mitochondrial shape was assessed using aspect ratio (major axis length/minor axis length), a measure of the length to width ratio, and form factor (perimeter 2 /4p[area]), reflecting the complexity and branching of mitochondria  (74). These parameters were determined using Harmony software on maximum projection images (Figure  S3A, B ).
Acknowledgments: We thank  James Turner  for critical reading of this manuscript. We thank  Fabrice Prin  for help with HREM and  Michael Howell  for help with high throughput analysis of mitochondrial morphology. We thank the  Advanced Sequencing, Proteomics, High Throughput Screening, Flow Cytometry, Advanced Light Microscopy, Experimental Histopathology, Genetic Manipulation and Biological Research Facilities of the Francis Crick Institute  for sequencing, proteomics, mitochondrial morphology analysis, flow cytometry, imaging, histology, generation of genetically altered mice and for animal husbandry. We thank  Mariona Arbones  and  Christian Lüscher  for mouse strains.
Data and materials availability All bulk and single-cell RNAseq data have been deposited in the Gene Expression Omnibus, accession code: GSE196447. Mass spectrometry proteomic data have been deposited in the ProteomeXchange via PRIDE, dataset identifier: PXD013053. CC-BY-NC 4. 0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted September 19, 2023. ; https://doi. org/10. 1101/2023. 09. 18. 558244 doi: bioRxiv preprint.
Vitamin B 12 plays a cell-autonomous role in reprogramming In vivo, vitamin B 12 deficiency yields a complex phenotype because it impacts multiple cellular processes and organ functions  26. Therefore, we asked whether the effect of B 12 on reprogramming could be recapitulated in vitro. We first observed, in a previously published bulk RNA-seq dataset of MEFs undergoing in vitro reprogramming  40  , that Mtr and Cd320 were upregulated soon after OSKM induction, remaining high during the early and middle phases of reprogramming, ultimately stabilizing to levels above those measured in MEFs (Extended Data Fig. 6a ). This suggests that during in vitro reprogramming there is also a high demand of B 12 and Met, which we explored further using pharmacological manipulation of the Met cycle. The addition of B 12 significantly increased the efficiency of iPS cell colony formation (Fig.
8g ). The increase in SCA1 and the enhanced recovery promoted by B 12 were blunted by the co-administration of a MAT2A inhibitor (FIDAS-5), suggesting that SAM generation is critical for tissue repair (Fig. 5d, e ). To extend our findings to a comparable human pathology, we found CT was significantly increased in a previously published bulk RNA-seq dataset  63  of paediatric ulcerative colitis patients as compared to healthy controls (Fig. 5g ), suggesting a conserved mechanism in human disease. Thus, echoing our findings during OSKM-driven reprogramming, tissue repair is associated with depletion of B 12 and high levels of CT, and vitamin B 12 supplementation facilitates tissue repair.
Libraries were sequenced (2 × 125 bp) on Illumina's HiSeq 2500. Taxonomic analysis. Reads were aligned to the mm10 genome using STAR 2. 7. 0a with default parameters  80. DNA contaminated reads were filtered out from the analysis. The first and final ten bases of the non-contaminated reads were trimmed using DADA2 1. 10. 1 (ref. 81). Taxonomic assignments were carried out through Kaiju 1. 7. 0 (ref. 82)  using the microbial subset of the NCBI BLAST non-redundant protein database (nr). Resulting sequencing counts were aggregated at genus level. Reads that could not be assigned to any specific genus were classified to the nearest known taxonomic rank (marked by the term _un). The gut microbial compositional plot displays the relative abundances (percentage) at genus level. Only the 17 most abundant taxa are shown, while the rest were moved to the 'others' category.
0 (ref. 86). The abundance of the annotated genes was finally measured by counting aligned reads to them via Bowtie2, version 2. 2. 2, under default parameters  87. Resulting counts data were aggregated at protein level. The treatment effect (finish versus start) was compared between OSKM and control (WT) mice. This was accounted in a model with an interaction term (drug:treatment) using DESeq2 with default options  83. The paired nature of the experimental design was considered in the model as an adjusting factor. The top 500 protein hits from the fitted model (nondirectional set) as well as the top 200 positive hits and the top 200 negative hits (directional sets), in all cases ordered by statistical significance, were used to explore enrichment of functional annotations. In this regard, GO terms for bacteria and archaea were considered using the AmiGO 2 GO annotations database  88  , removing from the analysis gene sets with few genes (less than 8) and too many genes (more than 499).
Data were represented as log 2 fold change (log 2 FC) values to each mouse at day 0 (before doxycycline administration). Metabolic pathway impact was calculated by Global ANOVA pathway enrichment and Out-degree Centrality Topology analysis through the MetaboAnalyst 4. 0 software 91 , using KEGG library (2019) as a reference. The colour gradient from white to red indicates the P value, where red is most significant. Bubble size indicates the relative contribution of the detected metabolites in their respective KEGG pathway. Pathway impact scores the centrality of the detected metabolites in the pathway.
Hybridization was performed with the RNAscope LS 2. 5 Probe -Mm-Lgr5 -Mus musculus leucine rich repeat containing G-protein-coupled receptor 5 (312178, RNAScope, ACD Bio-Techne). Control probe used was the RNAscope 2. 5 LS Probe -Mm-UBC -Mus musculus ubiquitin C (Ubc), as a housekeeping gene (310778, RNAScope -ACD Bio-Techne). The bacterial probe RNAscope 2. 5 LS Negative Control Probe_dapB was used as a negative control (312038, RNAScope -ACD Bio-Techne). Image acquisition. Brightfield images were acquired with a NanoZoomer-2. 0 HT C9600 digital scanner (Hamamatsu) equipped with a ×20 objective. All images were visualized with a gamma correction set at 1. 8 in the image control panel of the NDP. view 2 U12388-01 software (Hamamatsu, Photonics). Image analysis. Brightfield images of immunohistochemistry were quantified using QuPath software  93  with standard detection methods.
Ten cycles of PCR amplification were applied to all libraries. The final libraries were quantified using the Qubit dsDNA HS assay (Invitrogen) and quality controlled with the Bioanalyzer 2100 Article https://doi. org/10. 1038/s42255-023-00916-6 DNA HS assay (Agilent). An equimolar pool was prepared with the 12 libraries and submitted for sequencing at the Centre Nacional d'Anàlisi Genòmica (CRG-CNAG). A final quality control by qPCR was performed by the sequencing provider before paired-end 50-nucleotide sequencing on a NovaSeq 6000 S2 (Illumina). Around 77. 7 Gb of PE50 reads were produced from three NovaSeq 6000 flow cells. A minimum of 55. 7 million reads were obtained for all samples (Extended Data Fig. 7 ). For MEFs 4-5. Total RNA extractions were quantified with a Nanodrop One (Thermo Fisher), and RNA integrity was assessed with the Bioanalyzer 2100 RNA Nano assay (Agilent).
Libraries for RNA-seq were prepared at the IRB Barcelona Functional Genomics Core Facility. Briefly, mRNA was isolated from 1. 2 µg of total RNA and used to generate dual-indexed cDNA libraries with the Illumina Stranded mRNA ligation kit (Illumina) and UD Indexes Set A (Illumina). Ten cycles of PCR amplification were applied to all libraries. Sequencing-ready libraries were quantified using the Qubit dsDNA HS assay (Invitrogen) and quality controlled with the Tapestation HS D5000 assay (Agilent). An equimolar pool was prepared with the 15 libraries for SE75 sequencing on a NextSeq 550 (Illumina). Sequencing output was above 539 million 75-nucleotide single-end reads and a minimum of 28 million reads was obtained for all samples (Extended Data Fig. 7 ).
Bioinformatic analysis RNA-seq data processing. All analyses were performed in the R programming language (version 4. 0. 5)  96  unless otherwise stated. Stranded paired-end reads were aligned to the Mus musculus reference genome version mm10 using STAR  80  with default parameters. STAR indexes were built using the ENSEMBL annotation version GRC138. 97. SAM files were converted to BAM and sorted using sambamba (version 0. 6. 7)  97. Gene counts were obtained with the featureCounts function from the Rsubread package  98  with the gtf file corresponding to ENSEMBL version GRC138. 97 and parameters set to: isPairedEnd = TRUE and strandSpecific = 2. Technical replicates were collapsed by adding the corresponding columns in the count matrix. Reprogramming score. We obtained a reprogramming gene signature from published data  48  and selected genes with false discovery rate (FDR) lower than 0.
A total of 9,365 genes were used to compute the ratio between the intermediate and first exons. Fold changes between untreated and B 12 -treated samples were computed as the ratio between the exon ratios. Comparison of cryptic transcript ratios between conditions. Genes were separated by their expression after transcript length and library size normalization (RPKM). For each sample, we computed the median ratios for genes in each decile. Analysis of CT in DSS time course. Data were accessed from GSE131032. Reads were processed and ratios computed as previously described. log 2 ratios for all transcripts were summarized through the median by sample. Comparisons between days were performed fitting a linear model to the medians using 'cage' as a covariable. The function glht from the multcomp R package was used to find coefficients and P values.
Heat maps of average coverage in gene bodies. For each sample, aligned reads were imported into R using the function scanBam from the Rsamtools package 101. Whole-genome coverage was computed using the coverage function from the IRanges package 102 and binned into 50-bp windows. Gene annotations were imported from Ensembl version GRCm38. The average coverage over gene bodies was computed using the normalizeToMatrix function from the EnrichedHeatmap package 103 with parameters extend = 1,000, mean_mode = w0 and w = 50. Genes were filtered to coincide with those used in the exon ratio calculation from the RNA-seq data. Rows in the heat map were split by the average RNA-seq RPKM values in all samples. Visualization of ChIP tracks. BAM files were transformed to TDF files using the count function from IGVtools (version 2.
12. 2) 104 with parameters --z 7, --w 25 and --e 250. Visualization of TDF files was generated using IGV (version 2. 9. 4)  105. Analysis of human RNA-seq data. Data were accessed from GSE109142. Reads were processed and ratios computed as previously described except using the ENSEMBL GRCm38. 101 human gene annotation and the hg38 genome assembly version. The log 2 ratios for all transcripts were summarized through the median by sample. Comparison between diagnosis status was performed fitting a linear model to the medians with sex and the expression quantiles as covariables. The model was fitted using the lm R function and coefficients and P values with the coeff function.
Statistics and data availability Statistical analysis and figure preparation. Unless otherwise specified, data are presented as the mean ± s. d. Statistical analysis was performed by Student's t-test or one-way analysis of variance (ANOVA) as indicated, using GraphPad Prism v9. 0. 0, and specific statistical tests as indicated for each experiment for bioinformatic analyses. P values of less than 0. 05 were considered as statistically significant. No statistical methods were used to predetermine sample size in the mouse studies, but our sample sizes are similar to those reported in previous publications  3, 9, 16, 17, 19. Animals and data points were not excluded from analysis with the exception of the MEFs that failed to reprogram in the ChIP experiment, which is clearly detailed in the text.
Acknowledgements We are grateful to  M. I. Muñoz  for assistance with the animal protocols, to the  IRB Functional Genomics Unit  and  CRG/CNAG  for library preparation and genomic sequencing, and to the  IRB (Functional Genomics, Biostatistics/Bioinformatics, and Histopathology)  and  PCB (Animal House) core  facilities for general research support. M. K. was funded by the  Barcelona Institute of Science and technology (BIST)  and  Asociación Española Contra el Cáncer (AECC ;  POSTD18020SERR ) and supported by the  European Molecular Biology Organization (EMBO). F. P. is supported by a  Karolinska Institute Starting Grant ,  Starting Grant  from the  Swedish Research Council  ( 2019_02050_3 ). G. K. is supported by the  Ligue contre le Cancer (équipe labellisée) ;  Agence National de la Recherche (ANR)  -Projets blancs;  AMMICa US23/CNRS   UMS3655 ;  Association pour la recherche sur le cancer (ARC) ;  Cancéropôle Ile-de-France ;  Fondation pour la Recherche Médicale (FRM) ; a donation by  Elior ;  Equipex Onco-Pheno-Screen ;  European Joint Programme on Rare Diseases (EJPRD) ;  European   Research Council Advanced Investigator Award  ( ERC-2021-ADG ,  ICD-Cancer , grant no.
101052444 ),  European Union Horizon   2020 Projects Oncobiome, Prevalung  (grant no. 101095604 ) and  Crimson ;  Fondation Carrefour ;  Institut National du Cancer (INCa) ;  Institut Universitaire de France ;  LabEx   Immuno-Oncology  ( ANR-18-IDEX-0001 ); a  Cancer Research ASPIRE Award  from the  Mark Foundation ; the  RHU Immunolife; Seerave Foundation ;  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and  SIRIC Cancer Research and Personalized Medicine (CARPEM). This study contributes to the  IdEx Université de Paris   ANR-18-IDEX-0001. Work in the laboratory of M. S. was funded by the  IRB, 'laCaixa' Foundation , and by grants from the  Milky Way Research Foundation , the  Spanish Ministry of Science  co-funded by the  European Regional Development Fund  (ERDF;  SAF2017-82613-R ), the  European Research Council  ( ERC-2014-AdG/669622 ) and the  Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement of Catalonia (Grup de Recerca consolidat   2017 SGR 282 ).
Data availability All sequencing data are deposited under the following Gene Expression Omnibus accessions: GSE154149, microbial genome analysis from OSKM and WT mice pretreatment and after 7 d of 1 mg ml -1 treatment with doxycycline in the drinking water; GSE200578, ChIP-seq of H3k36me3 samples of OSKM reprogramming MEFs treated with B 12 ; GSE200579, RNA-seq samples of OSKM reprogrammable MEFs treated with or without vitamin B 12 ; GSE232382, RNA-seq samples of OSKM reprogrammable MEFs treated with vitamin B 12 and/or various compounds to modulate SAM or histone methylation. Previously published datasets that were used for analysis in the current study are: GSE131032, RNA-seq of time-course analysis of repairing murine epithelium after DSS injury; GSE109142, RNA-seq of human paediatric ulcerative colitis and normal tissue controls; GSE102518, RNA-seq of murine in vitro reprogramming in MEFs of varying genotypes; GSE77722, RNA-seq of murine in vivo reprogramming in mice of varying genotypes.
P-values by two-tailed Student's t-test. https://doi. org/10. 1038/s42255-023-00916-Recovery from DSS colitis is improved by OSKM or vitamin B12. (a-c) Previously published time course of bulk colonic RNAseq during DSS injury and recovery Data source: GSE131032 63. Mice were treated with 2. 5% DSS for 7 days as indicated. (a) CT analysis for all genes for which a score could be calculated are shown. Each boxplot represents one biologic replicate (that is, mouse), with the cages indicated as described by the authors of the original study. Dashed line represents median CT value at D0. (b) RNAsequencing expression of the indicated genes. Values represent the scaled log2FC as compared to D0 across all samples as computed by the authors of the original study. (c) Gene set enrichment analysis of a published signature of methionine deprivation (msigdb: M13537 ref.
This weak immunostaining of fibrinogen α suggests the possibility of different detection sensitivity between immunostaining and shotgun analysis, suggesting that validation by different methodologies is necessary. Our current shotgun proteomics identified 1057 proteins in the DM of non-FECD subject and 200 proteins in the FECD-DM. One potential explanation for the discrepancies in the numbers of identified proteins might be mostly due to the structural change in FECD-DM, resulting in hampering the effective protein extraction. As we established the pipeline for analyzing flat-mounted DM by shotgun proteomics, we are currently collecting in-depth proteomics data from a larger number of samples to compensate above mentioned limitation. In future experiments, the integration of shotgun proteomics and mass spectrometry imaging will add novel findings for elucidating corneal pathology.