dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Thanks to carefully defined differentiation protocols, hiPSCs and their derivatives can help better understand human embryonic development and investigate the early impact of mutations leading to specific genetic disorders without the use of embryos  13. Using a published protocol recapitulating key developmental steps of human myogenesis, we provide here a temporally resolved dataset spanning ten time points along the differentiation of healthy and DMD hiPSCs from pluripotency to the skeletal muscle lineage. We observed that cells first go through intensive transcriptional changes as they leave pluripotency and adopt an early mesodermal fate. After Day 7, they stabilize as they become somite progenitors and their progenies (i. e. dermomyotome and skeletal muscle cells). The developmental trajectory computed from our pseudotime data showed that fractions of cells reach the end of the myogenic process as early as Day 10, reflecting the fact that cells differentiate in a heterogeneous and asynchronous manner  32, 40, 41.
Data and code availability • The two single-cell RNA-seq data sets have been deposited at GEO and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. This paper also analyzes publicly available data. These accession numbers are listed in the key resources table. Original western blot images and microscopy data reported in this paper have been deposited at Zenodo repository. DOIs are listed in the key resources table. • All original code has been deposited as R Notebook files at Zenodo Repository. DOIs are listed in the key resources table. • Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS Ethical statement: The Healthy, DMD and CRISPR lines have been described, characterized and published previously  34, 37. Participants gave informed consent for the generation of urine-derived hiPSC lines, as required by the Institutional Review Board (IRB). The experiments performed in this manuscript fall into the Category 1A described in the Guidelines for Stem Cell Research and Clinical Translation of the International Society for Stem Cell Research (ISSCR, https://www. isscr. org/guidelines). hiPSC lines: The Healthy UC3-4 line was generated from an adult male participant with no known skeletal muscle disease. The DMD 72039 line was generated from an adult male with a declared DMD pathology and carrying an exon50 deletion in the DMD gene.
Raw data analysis involved base calling (bcl2fastq), demultiplexing based on P5 barcodes (1 mismatched base allowed), adaptor trimming (trim_galore), alignment to the human (hg19) or the mouse (mm10) genome (STAR), removing of UMI duplicates and demultiplexing based on RT barcodes. Percentages of reads mapping uniquely to the human and the mouse genome were quantified and cells with over 90 % of reads assigned to the human genome were kept for subsequent analysis. Secondary analysis of the cell data set object was performed with the Monocle 3 analysis pipeline (Trapnell 2014, Qiu 2017), whose code is freely available on the Cole Trapnell lab Github and in the Zenodo Repository with the DOI listed in the key resources table. • split-pool barcoding: the post hoc analysis of the somite differentiation step at Day 10 was performed with the split-pool barcoding kit commercialized by parse Biosciences, according to manufacturer's instructions.
Cells were collected at Day 10 and counted with a hemocytometer to isolate 500,000 cells for subsequent fixation and permeabilization with the Cell Fixation Kit. Samples were stored at -80°C and thawed immediately prior to library preparation with the Evercode Whole Transcriptome Mini Kit. Primary analysis of the raw data including quality control, alignment to the human genome (hg19), demultiplexing and generation of the matrix, feature annotation and cell annotation files were carried out by the proprietary Parse Biosciences analysis suite. Secondary analysis used the Monocle 3 pipeline as previously indicated. The code has been deposited at Zenodo Repository with the DOI listed in the key resources table.
Acknowledgments This work was funded by the  INSERM   ATIP Avenir program ,  Nantes Université , the  University Hospital of Nantes , the  Association Française contre les Myopathies (AFM) Téléthon ,  Genopole , and by the  Wellstone Muscular Dystrophy Cooperative Research Center  supported by the  National Institute of Health (NIH , grant number  U54 AR065139 ). We thank the  GenoA Genomics Core Facility  and the  BiRD Bioinformatics Core Facility (Nantes Université ,  SFR Bonamy ,  UMS Biocore, Biogenouest) , which is part of the  Institut Français de Bioinformatique  ( ANR-11-INBS-0013 ) for the access to the computing and storage infrastructure and their help with sequencing and processing the single-cell libraries. We acknowledge the  IBISA MicroPICell facility (Nantes Université ,  SFR Bonamy ,  UMS Biocore, Biogenouest) , member of the national infrastructure France-Bioimaging supported by the  French national research agency  ( ANR-10-INBS-04 ).
Software and algorithms Fiji (imageJ) Schindelin et al. 2 https://fiji. sc/ QuPath version 0. 4. 3 Bankhead et al. 3 https://qupath. github. io/ PRISM version 8 Graphpad https://www. graphpa d. com/features bcl2fastq version 2. 16. 0. 10 illumina https://emea. support. illumina. com/seque ncing/sequencing_s oftware/bcl2fastqconversionsoftware. html trim_galore version 0. 4. 5 Felix Krueger https://www. bioinfor matics. babraham. ac. uk/projects/trim_galo re/ STAR version 2. 5. 2b Dobin et al. 4 https://github. com/al exdobin/STAR R version 4. 2. 2 https://cran. rproject. org/ dplyr version 1. 1. 0 Wickham et al. https://dplyr. tidyvers e. org/ monocle version 1. 3. 1 Cao et al. 5,6 https://cole-trapnelllab. github. io/monocl e3/ ggplot2 version 3. 1. 4 Wickham et al. https://ggplot2. tidyve rse.
Legend: * false discovery rate (FDR) < 0. 05; ** FDR < 0. 01; *** FDR < 0. 001; (X/Y): number of genes from the GO category found differentially expressed in the dataset / total number of genes in the GO category. (H-I) Differential expression of cell junction genes and potential implications at the protein level in the DMD (H) and CRISPR (I) hiPSC lines. Fold change estimates from single-cell data are indicated between brackets and color-coded for each protein. CLDN: claudin; OCLN: occludin; TJP: tight junction protein; CDH: cadherin; CTNN: contactin; PCDH: protocadherin; DSG: desmoglein; DSP: desmoplakin; DSC: desmocollin; PKP: plakophilin; JUP: plakoglobin; PERP: p53 apoptosis effector related to PMP22.
KI mice show a severe cortical malformation Global L2/+ brain size at P0 appeared smaller with an architecture not as well defined as WT and L3/+ brains (Fig. 1F ). P0 coronal brain sectioning and area measurements confirmed that the L2/+ hemisphere area was significantly reduced (by 22% WT versus (vs) L2/+ and 15% L3/+ vs L2/+). Brain area (without taking into account the ventricles) also appeared reduced in L2/+ pups (WT vs L2/+ by 22% and L3/+ vs L2/+ by 14%). These data (Fig. 1G-J ) suggest brain development defects. No significant brain or hemisphere area differences were found in L3/+ compared to WT animals. We compared the developing cortices of L2/+ with L3/+ and WT littermate embryos and pups, and we also further assessed the brain phenotype of Loa/+ heterozygote mice for comparison.
Indeed, a previous study of the Loa mouse in the homozygous state reported neuronal migration defects  (Ori-McKenney and Vallee, 2011) , but no major cortical defects were identified in Loa/+ heterozygotes. Cortical wall thickness was assessed at mid-corticogenesis (E14. 5). The cortical wall of L2/+ embryos was significantly thinner compared to WT (20% decrease for WT vs L2/+) (Fig. 1K-L ). The cortical wall thickness of Loa/ + embryonic brains was not different compared to WT littermates, nor was the brain size and architecture at P0 as expected (data not shown)  (Ori-McKenney and Vallee, 2011). Thus, the initial characterization comparing L2/+, L3/+ and Loa/+ mice highlighted the impact of the K3334N mutation (in the absence of the neo cassette) on general, as well as specifically brain development.
Acknowledgements We warmly thank  Dr. J. Chelly  for project initiation and the initial design of the new Dync1h1 KI mouse line. We thank the  MRC Harwell Institute  for the Loa mice. We are grateful to  Dr. A. Baffet ,  Dr. S. Nedelec ,  Dr. A. Alaimo  and members of the lab for comments and discussions. We thank the IFM animal experimentation facility and cellular and tissue imaging platforms at the  Institut du Fer à Moulin , supported also by the  Région Ile de France  and the  FRC Rotary. D. M. R. , D. Z and F. F. were associated with the  BioPsy Labex  project and the  Ecole des Neurosciences de Paris Ile-de-France (ENP)  network. Our salaries and lab were supported by  Inserm , the  Centre national de la recherche scientifique (CNRS)  and  Sorbonne University.
To define the genome-level changes in RNA transcripts that result from Celf1 deficiency, we performed high-throughput RNA-sequencing of Celf1 cKO mouse lenses at postnatal day (P) 0. Celf1 cKO lenses exhibit 987 differentially expressed genes (DEGs) at cut-offs of >1. 0 log2 counts per million (CPM), ≥±0. 58 log2 fold-change and <0. 05 false discovery rate (FDR). Of these, 327 RNAs were reduced while 660 were elevated in Celf1 cKO lenses. The DEGs were subjected to various downstream analyses including iSyTE lens enriched-expression, presence in Cat-map, and gene ontology (GO) and representation of regulatory pathways. Further, a comparative analysis was done with previously generated microarray datasets on Celf1 cKO lenses P0 and P6. Together, these analyses validated and prioritized several key genes mis-expressed in Celf1 cKO lenses that are relevant to lens biology, including known cataract-linked genes (e.
RNA-Sequencing and Analysis Total RNA from the control and Celf1 cKO P0 mouse lens tissue was used for RNAsequencing (strand-specific, paired-end 150 bp-libraries) using the Illumina HiSeq 2500 sequencer at the University of Kansas Medical Center Genomics Core. FastQC was used to evaluate the quality of raw paired-end reads. The RNA-sequencing data reported here is submitted to the NCBI Gene Expression Omnibus (GEO) database under series GSE227293. Raw sequences were trimmed to remove the adaptor sequence. Trimmed sequences were aligned on to the mouse genome (GRCm38. p6) with the STAR software (STAR(2. 7. 8a))  [32] , and only uniquely mapped reads were retained for downstream analysis. Reads were associated to genes by featureCount (v2. 0. 0)  [33] , and only genes with >0. 2 CPM (counts per million) were considered for differential gene expression analysis.
Microarray Analysis Total RNA from Celf1 cKO and control (Celf1 lacZKI/+ ) mouse lenses at stage P6 were isolated as stated above and microarray analysis was performed using the MouseWG-6 v2. 0 BeadChip platform (Illumina, San Diego, CA, USA) following previously described protocols  [9]. The previously unreported microarray data on stage P6 is submitted to NCBI Gene Expression Omnibus (GEO) database under series GSE225303. Previously generated microarray data on Celf1 cKO and control (wild-type) mouse lenses at stage P0 (deposited in GEO, GSE101393) was also used for comparative analysis in the present study. For these datasets, gene expression was estimated based on the fluorescence signal intensity of probes associated to specific genes. In cases where multiple probes were associated to the same gene, the expression of the gene was calculated as the logarithmic average of the signals from all probes assigned to the gene.
Only the probes that had a fluorescence signal intensity significantly higher than background in at least two samples were retained for downstream differential gene expression analysis. The R package edgeR  [34]  was used to identify DEGs, with FC > 1. 5 (|logFC| > 0. 58), FDR <0. 05 and an average expression signal > 2 5 (LogSignal > 4. 6) cut-offs.
Cat-Map: Cataract Associated Genes Celf1 cKO lens DEGs known to be associated with cataracts were identified by comparing individual gene names (mouse gene name) for DEGs to the 454 genes (human gene name) listed in the database CatMap (vOct 21)  [35]. This identified a subset of genes that were differentially expressed in Celf1 cKO lenses and whose deficiency or alterations were also associated with human cataracts.
iSyTE: Gene Expression Enrichment in the Lens To determine the expression enrichment score of the Celf1 cKO lens DEGs in the normal lens-compared to the whole embryonic body (WB)-in different development stages, we used the database iSyTE  [36]. iSyTE contains microarray data from normal mouse lenses at different development stages and the WB reference dataset. The lens-enriched expression scores of the Celf1 cKO lens DEGs were calculated as the maximum expression of the genes at either E10. 5, E12. 5, E14. 5, E16. 5, E17. 5, E19. 5 or P0, normalized by their expression WB. Celf1 cKO lens DEGs with an expression enrichment score >1. 5 (|logFC| > 0. 58) were considered to have enriched expression in normal lens development.
Gene Ontology (GO) Term and Pathways Analysis The R package ClusterProfiler (v3. 18. 0)  [39]  was used to identify Gene Ontology (GO) terms enriched in Celf1 cKO DEGs by GO enrichment analysis as well as gene set enrichment analysis (GSEA), GO biological process (BP), GO cellular component (CC), and molecular functions (MF). GO analysis in turn led to insights into specific pathways that are altered due to Celf1 deficiency in the lens.
Generation of RNA-Seq Datasets from Celf1 cKO and Control Lenses Lenses were micro-dissected, and RNA was isolated from stage P0 Celf1 cKO and control mice as described in detail in the Methods section. An experimental and computational pipeline was developed for RNA-seq analyses (Figure  1 ). Paired-end, 150 bp-long libraries were prepared, sequenced and analyzed using this strategy. For control and Celf1 cKO samples, an average of 55. 1 million reads per replicate were obtained and aligned using STAR software (STAR(2. 7. 8a))  [32]  (Table  S1 ). On average, 77. 3% of the reads were uniquely mapped to the Mus musculus reference genome (GRCm38. p6) (Table  S1 ). munostaining, sections were blocked in a solution of 5% chicken serum, 1% BSA, 0. 1% Tween (prepared in 1× PBS) for 1 hr at room temperature (RT).
Generation of RNA-Seq Datasets from Celf1 cKO and Control Lenses Lenses were micro-dissected, and RNA was isolated from stage P0 Celf1 cKO and control mice as described in detail in the Methods section. An experimental and computational pipeline was developed for RNA-seq analyses (Figure  1 ). Paired-end, 150 bp-long libraries were prepared, sequenced and analyzed using this strategy. For control and Celf1 cKO samples, an average of 55. 1 million reads per replicate were obtained and aligned using STAR software (STAR(2. 7. 8a))  [32]  (Table  S1 ). On average, 77. 3% of the reads were uniquely mapped to the Mus musculus reference genome (GRCm38. p6) (Table  S1 ).
Quality Control of RNA-Seq Datasets We first examined the Celf1 transcript profiles in Celf1 cKO and control lenses by visualization of the RNA-seq mapped reads using the software IGV (2. 8. 10) (mm10)  [41]  and found Celf1 mRNA to be reduced in Celf1 cKO lenses (Figure  2A , Table  S2 ). Since the conditional knockout strategy involves removal of exon 5 (Figure  2B ), we quantified the inclusion of exon 5 in Celf1 mRNA in Celf1 cKO and control lenses. It is expected that exon 5 will be deleted by Cre recombinase driven by the Pax6GFPCre allele only in Celf1 cKO lenses, which in turn will result in a premature stop codon. This analysis shows that while control lenses had normal inclusion of exon 5, on average, Celf1 cKO lenses had 48. 3% reduced Celf1 transcripts that contained exon 5 (Figure  2C, D ).
Together, these data validate Cre-mediated deletion of Celf1 in Celf1 cKO lenses. To assess the quality of the datasets on the global level, principal component analysis (PCA) was performed, which showed that control replicate samples clustered together and separately from Celf1 cKO replicate samples (Figure  3A ). Additionally, hierarchical clustering between samples clearly separated control replicates from Celf1 cKO replicates (Figure  3B ). Together, these analyses validate that Cre-mediated recombination of the Celf1 conditional knockout allele driven by the Pax6GFP Cre-deleter line resulted in global transcriptome changes in the Celf1 cKO lens. This further confirmed that although Cre-deletion did not result in all Celf1 transcripts being devoid of exon 5 in Celf1 cKO lenses, it was sufficient to generate transcriptome changes that result in lens defects.
Together, these data validate Cre-mediated deletion of Celf1 in Celf1 cKO lenses. To assess the quality of the datasets on the global level, principal component analysis (PCA) was performed, which showed that control replicate samples clustered together and separately from Celf1 cKO replicate samples (Figure  3A ). Additionally, hierarchical clustering between samples clearly separated control replicates from Celf1 cKO replicates (Figure  3B ). Together, these analyses validate that Cremediated recombination of the Celf1 conditional knockout allele driven by the Pax6GFP Cre-deleter line resulted in global transcriptome changes in the Celf1 cKO lens. This further confirmed that although Cre-deletion did not result in all Celf1 transcripts being devoid of exon 5 in Celf1 cKO lenses, it was sufficient to generate transcriptome changes that result in lens defects.
Relevance of Celf1 cKO Lens DEGs to Lens Development and Cataracts Next, we sought to prioritize Celf1 cKO lens DEG candidates that are relevant to lens development and are involved in cataract pathology. Toward this goal, we performed comparative analyses with publicly available datasets relevant to lens biology and pathology. For identifying DEGs linked to cataracts, we used the Cat-Map database  [35]. For identifying DEGs exhibiting enriched expression in embryonic lens development, we used the iSyTE database  [36]. For identifying DEGs that are preferentially expressed either in the epithelium or fiber cells, we used transcriptome datasets on isolated epithelial and fiber cells  [37]. Comparison of the 987 DEGs with Cat-Map identified 43 genes (including Celf1) that are linked to cataracts in humans and/or animal models (Table  1 ).
5%) are found to be elevated in iSyTE (Figure  5C ). In this analysis, 98 DEGs were not found in iSyTE lens microarray datasets. This may be due to differences between the two transcriptomics approaches (see Discussion). This analysis suggests that Celf1 may contribute to maintaining normal lens developmental transcriptome by negatively regulating genes not normally enriched in the lens as well as positively regulating genes, likely indirectly, which are normally enriched in the lens. This analysis also identified new potential regulators in lens development (e. g. , Ell2) (Table  S3 ).
Finally, similar to iSyTE data analysis, the upregulated genes in Celf1 cKO lenses appear to not be enriched in either epithelial or fiber cells in normal lens development. Further, the majority of the elevated genes that are not enriched in epithelial or fiber cells (52. 7%, n = 348) are also not enriched in iSyTE, suggesting that in normal lens development, Celf1 is necessary, either directly or indirectly, to repress the expression of these genes. The 987 DEGs were examined for their potential lens-enriched expression in iSyTE at stages E10. 5, E12. 5, E14. 5, E16. 5, E17. 5, E19. 5 and P0. Out of 282 reduced genes in the Celf1 cKO lens, 71. 7% (n = 203) were found to have lens-enriched expression in at least one of the stages examined (Figure  5A ,B; Table  S3 ). In contrast, out of 607 elevated genes in the Celf1 cKO lens, the majority of the genes 65.
9% (n = 400) did not exhibit lens-enriched expression (Figure  5A ,B; Table  S3 ). Furthermore, when only the DEGs that do not exhibit lens-enriched expression are considered, a vast majority (83. 5%) are found to be elevated in iSyTE (Figure  5C ). In this analysis, 98 DEGs were not found in iSyTE lens microarray datasets. This may be due to differences between the two transcriptomics approaches (see Discussion). This analysis suggests that Celf1 may contribute to maintaining normal lens developmental transcriptome by negatively regulating genes not normally enriched in the lens as well as positively regulating genes, likely indirectly, which are normally enriched in the lens. This analysis also identified new potential regulators in lens development (e. g. , Ell2) (Table  S3 ).
6%, n = 182) were preferentially expressed in fiber cells (Table  2 ; Table  S4 ). In contrast, 16. 8% of reduced genes (n = 55) were preferentially expressed in epithelial cells. Next, of the 660  Celf1 encodes a protein containing three RRMs that enable it to bind to its target RNAs and mediate post-transcriptional regulation of gene expression. Previously, CLIP-seq analysis with a CELF1 antibody has been applied to identify the direct-bound RNA targets of CELF1 protein in the human cell line, Hela  [38]. Comparative analysis showed that 32. 2% (n = 318) of the 987 Celf1 cKO lens DEGs are also identified in this CLIP-seq dataset. Further, within these 318 DEGs that are directly bound by CELF1 protein, the majority (83. 0%; n = 264) are found to be significantly elevated in Celf1 cKO lenses.
Gene Ontology and Pathway Analysis of Celf1 cKO Lens DEGs Next, we examined the different pathways that were represented in Celf1 cKO lens DEGs. Toward this goal, we performed pathway enrichment analysis by examining gene ontology (GO) enrichment separately on all DEGs, elevated DEGs and reduced DEGs, compared to all the genes expressed in the RNA-seq data. In parallel, GSEA (gene set enrichment analyses) were performed on all the genes in the RNA-seq data that had an expression of at least 1 logCPM and based on their logFC rank. Further, we performed the GI enrichment analysis on a subset of these DEGs that are found to have enriched expression in the lens by iSyTE described in Section 3. 4. 2. We also performed this analysis on a subset of these DEGs that are preferentially expressed in epithelial or fiber cells described in Section 3.
However, because 327 were found to be reduced in Celf1 cKO lenses, this suggests that Celf1 is also necessary for positive control over these genes that may be important for proper lens development. We aimed to identify both, "established" cataract-linked genes as well as potentially novel candidates that are differentially expressed upon Celf1 deficiency. To address the former, we performed analysis of Celf1 cKO RNA-seq data with respect to the known cataract-linked genes contained in the Cat-Map database. This helped identify the established cataract-linked genes that are significantly impacted because of Celf1 deficiency. On the other hand, the iSyTE database informs on both established cataract-linked genes as well as novel genes that are relevant to lens biology. Therefore, we also performed comparative analysis of Celf1 cKO RNA-seq data with respect to the genes recognized as lens-enriched in the iSyTE database.
Thus, it can be hypothesized that the sub-optimal expression levels of these lens-enriched transcripts could contribute to the cataract pathology. Interestingly, the majority of the DEGs that are elevated in Celf1 cKO lenses are not found to have enriched expression in normal lens development. Thus, this suggests that elevated expression of these transcripts upon Celf1-deficiency may contribute to alterations in lens development. While iSyTE lens-enrichment is helpful, iSyTE data is primarily based on whole lenses. Celf1 is known to be highly expressed in fiber cells, but later in development it is also known to be expressed in epithelial cells, suggesting that it may have a function in both cell types in the lens  [9, 10]. Therefore, Celf1 cKO lens DEGs were examined for their preferential expression in normal isolated lens epithelial or fiber cells.