dataesr/scientific-paragraphs-categorization huggingface dataset:
The outcome of the ROI approach consists of a vector including the list of relevant m/z ROI values (according to the previously mentioned parameters), and a data matrix with the MS intensities at the selected ROIs (for all considered retention times and samples). The graphical interfase allows the visualization of the ROI features. The ROI vectors and MSroi matrix containing information of the features were used for multivariate and univariate analysis in MATLAB environment in order to identify APAP metabolites after APAP, acetaminophen cysteine (APAP-Cys), acetaminophen mercapturate (APAP-NAC), and acetaminophen glutathione (APAP-SG) exposures. Principal component analysis (PCA) was performed using the PLS Toolbox (Eigenvector Research Inc. , Wenatchee, WA, USA) as a first step to examine QC clustering, sample separation and to identify any analytical or biological outliers.
Accurate integration of identified and annotated markers Peak integration of all APAP metabolites was performed manually using the SCIEX OS Analytics tool to ensure accuracy in the study of the excretion rates of APAP metabolites for each individual. The integrated peak area of individual markers was normalized to a corrective factor based on the total peak area measured with MarkerView (this corrective factor was calculated by dividing the total peak area of each sample to that of a reference sample)  (David et al, 2021a). MATLAB (Release 2021b, The Mathworks, Inc. , Natick, MA, USA) was used to generate plots.
Statistical analysis Univariate analyses were performed to compare fold changes, calculate the areas under the curve (AUC) and determine statistical significance. AUCs were calculated using trapezoidal numerical integration (trapz function) from MATLAB. Statistics Toolbox TM from MATLAB (Eigenvector Research Inc. , Wenatchee, WA, USA) was used to assess statistical significance using a one-way ANOVA t-test, considering P < 0. 001 statistically significant, followed by a multiple comparison test (multcompare) to perform pairwise comparisons, based on the Tukey-Kramer critical value.
Acknowledgments We thanks  Thibaut Léger  and  Christine Monfort  for technical assistance. This study was supported by the  MoU  signed between  Inserm  and the  Mailman School of Public Health of Columbia University  on Nov. 12 2019. EG was funded by the  Inserm  and the  Mailman School of Public Health of Columbia University  collaboration project obtained under this MoU. A. D. , J. C. , and R. B acknowledge the research infrastructure France Exposome. The first observational study with PWs was partially funded by the  French Agency for Environmental Health Safety  ( PNREST Anses ,  2018/1/084 ). The  PELAGIE cohort  has been funded by  Inserm , the  French Ministries of Health  ( 2003-2004 ), Labor ( 2002-2003 ), and Research ( ATC 2003-2004 ) and the  French National Institute for Public Health Surveillance (InVS , 2002( InVS, -2006 )).
These "off-target" but developmentally relevant cell-types were then selected against during the 3D neural crest progression phase, where NCCs selectively aggregated with other NCCs until D30, yielding cultures of >75% SOX10+ cells by day 22 (Figure  1C  and S1E ). To characterize the transcriptional changes in our NCCs as they progress through differentiation, we next aimed to analyze the heterogeneity of the SOX10+ NCC clusters across the time points (Figure  1C ). The emerging S1 NCCs were homogenous, remaining as a single cluster, while S2, S3, and S4 NCCs contained two, three, and three subclusters, respectively (Figure  1D ). Stage 5 NCCs also contained three subclusters (Figure  1D ), which were annotated by comparison to our previously published  16  Schwann cell dataset from a similar differentiation stage.
For this comparison we used similarity weighted non-negative embedding (SWNE)  25. We generated the dimensionally reduced SWNE space based on the published dataset and the stage 5 NCCs were subsequently projected into the same SWNE dimensions, such that transcriptionally similar cells were plotted in the same coordinates (Figure  S1F ). All subclusters expressed canonical NC transcription factors (TFs) SOX10, SOX2, FOXD3, TFAP2A/B, and SNAI1 or SNAI2, with expression levels varying between clusters but not correlating directly to SOX10 expression (Figure  1E, top). A new model of NCC fate determination describes the co-activation of competing lineage modules which resolve into fate-biased and then fate-committed NCCs  26. To determine if hPSC-derived NCC populations exhibit differences in fate potential, we examined both the expression of transcription factors involved in early neuronal (sensory and autonomic), melanogenic, gliogenic, and mesenchymal fate specification, as well as their predicted activity using single-cell regulatory network inference and clustering (SCENIC) analysis 27.
In addition to SOX8 expression, NCC2 showed the highest expression of ITGA4 and SERPINE2 among S4 NCCs, while expression of all three markers was homogeneous among S5 NCC subtypes (Figure  S1I ). Interestingly, while S2 NCCs showed moderate to low expression of these markers, we observed a high co-expression of all three markers in a subset of S3 NCCs (Figure  S1I ). Importantly, scoring the dataset published by Adameyko and colleagues  28  for the top 100 markers of S4 NCC2 showed cells in the "hub" and the Schwann cell branch to be the most transcriptionally similar populations (Figure  S1J ). At stage 5, all NCC subtypes, SCPs, Early SCs and SCP derivatives (SCPDs), maintain SOX10, FOXD3, and TFAP2A, as well as gain expression and activity of the key Schwann cell master regulator POU3F1 (OCT6) (Figure  1E-F ).
Finally, at D30, uncommitted S4 NCC1 is predicted to transition to melanogenic S4 NCC3, as well as gliogenic S4 NCC2, suggesting S4 NCCs retain multipotency towards expected lineages. As there was only a single melanogenic cluster at D30, we next asked which one of the two melanogenic cluster at D22 was maintained through D30. To answer this, we employed two different methods to identify the most transcriptionally similar NCC subclusters between the S2, S3, and S4 NCC subclusters. First, we again utilized SWNE to create a dimensionally reduced space based on the S3 NCC dataset. We then projected S2 and S4 NCC populations into the S3 NCC space, such that transcriptionally similar cells would be plotted in the same coordinates (Figure  2C ). Next, we used separate S3 NCC2 and NCC3 transcriptional signatures consisting of the top 100 most significantly differentially expressed genes of each cluster to module score the NCCs of the S2 and S4 samples (Figure  S2B ).
Early and late signatures are expressed in different populations of melanocytes in vivo We next sought to leverage published single cell transcriptomics datasets and embryonic mouse tissue to identify the T1 and T2 molecular signatures in primary melanocytes (Figure  4A ). We first integrated the annotated melanocyte clusters from eight datasets collected from multiple developmental stages and anatomical locations  [34] [35] [36] [37] [38] [39] [40]  (Figure  S4A ). Scoring the primary melanocyte dataset with separate T1 and T2 melanocyte transcriptional signatures consisting of their top 100 significantly differentially expressed genes showed that the majority of primary melanocytes score highly for one signature or the other (Figure  4B ). We annotated the primary melanocytes based on having a higher T1 or T2 melanocyte scoreed and quantified the proportions of T1-like versus T2-like melanocytes in different tissue samples.
Quantification of the relative dorsoventral position of all cutaneous melanocytes across the cranial to hind limb sections similarly showed a significant bias of CD44+ melanocytes to be located more ventral or further from the dorsal neural tube (Figure  4G ). To validate the observation of the anteroposterior bias observed in the human primary scRNAseq datasets (Figure  4C ), we similarly quantified the number of cutaneous CD44+/MITF+ and CD44-/MITF+ melanocytes across each region and observed a similar trend of a higher proportion of CD44-(T1-like) melanocytes in more posterior tissue (Figure  4H ). Taken together, these data provide strong evidence that alternative differentiation trajectories yield distinct melanocyte populations that recapitulate the heterogeneity of melanocytes in vivo.
Signature transcripts of SCP-derived melanocytes promotes metastasis in melanoma As our data confirmed that T1 and T2 markers were expressed in different subsets of adult melanocytes, we next aimed to understand the implication of the T1 or T2 transcriptional states in melanoma pathogenesis. Based on the functional differences between the two types of melanocytes, we hypothesized that melanomas with a T2-like state may be more migratory and exhibit higher rates of metastasis compared to T1-like melanomas. To test this, we gathered the bulk RNAseq datasets from melanoma samples available on the cancer genome atlas (TCGA) and used the T1 and T2 melanocyte transcriptional signatures to perform gene set enrichment analysis (GSEA) and identify melanoma samples with significantly enriched or reduced expression of these signatures (Figure  5A , Figure  S5A ).
We compared the gRNA abundance in the tumors with transduced MeWo cells grown in parallel in vitro, reasoning that any gRNA significantly depleted in the melanoma tumors in vivo is likely to target a gene essential for the metastatic process (Figure  5C ). In total, gRNAs targeting 39 genes were significantly depleted in lung tumors, of which 66% targeted T2 signature genes (Figure  S5C-D ). To prioritize genes for further characterization, we utilized the TCGA melanoma datasets to filter hits in two steps. We performed a multivariate Cox proportional hazard test which first determined whether the hit gene's expression level predicted a significant difference in survival probability. Then, we considered additional variables such as patient age, treatments received and stage at diagnosis to determine if the difference in survival probability based on the gene's expression levels remained significant.
Importantly, tail vein injections of MeWo cell lines with stable knockdown of all three genes showed a significant reduction in formation of lung tumors (Figure  5H-J ). Notably, the effect sizes of the lung colonization assay matched with the effect size of the in vitro migration assay. As the knockdown of the splicing factor SNRPB resulted in the greatest delay in lung colonization in vivo (Figure  5H-J ), we next sought to determine the spliceosome network regulated by SNRPB in melanoma. We performed paired end bulk RNAseq on control and SNRPB knockdown MeWo and A375 cells to identify differentially spliced transcripts. Interestingly, only 13 transcripts were identified as shared differential splicing events in both MeWo and A375 SNRPB knockdown cells (Figure  5K ). Notably, many unique differential splicing events were validated as SNRPB targets in a CLIP dataset generated in HEK293T cells (Figure  5K ).
The physiological implication of this pigmentation difference remains to be determined and possible links between the distribution of T1 and T2 melanocytes in different skin regions and levels of pigment production should be explored further. While T1 and T2 melanocytes expressed the same canonical markers, they were largely transcriptionally unique, with greater than 1500 differentially expressed genes. Importantly, we used these transcriptional signatures to identify T1-like and T2-like melanocytes in published human scRNAseq datasets and mouse fetal tissue, showing that both kinds of melanocytes persisted into adulthood and their markers are conserved. Consistent with their predicted ventrolateral migration path 20 , T2-like melanocytes were more ventrally distributed within the fetal skin.
G. UMAP embeddings and subclustering of the primed stage 2 SOX10+ cluster. H. MITF expression and cluster proportions of stage 2 and primed stage 2 NC subtypes. Table 1. Antibodies and dilutions used for immunofluorescent staining Table  2. scRNAseq dataset quality control filtering metrics Table  3. scRNAseq dataset clustering parameters Table  4. Cluster transcriptional signature used for module scoring Table  5.
Single cell quantification of melanin Melanocytes were prepared for Flow cytometric analysis as described above. Side scatter voltage was set using a 1:1 mixture of T1 and T2 melanocytes. The resulting FCS files were analyzed in R, using the flowCore package (v2. 8) for data importation. Each file, representing a biological replicate (BR) of the respective differentiation, was "gated" to remove debris events by filtering for events with a FSC-A value greater than 190000 and a SSC-A value greater than 50000. Each BR's SSC-A values were then merged into a single event-by-BR matrix using the cbind function. The distribution of each BR's SSC-A values was then calculated by creating 512 equally sized SSC-A value bins, based on the minimum and maximum recorded SSC-A values across all BR's and samples, and recording the number of events greater than or equal to the bin value and less than the next highest bin value for each bin.
Genomic DNA extraction for all samples was performed with QuickDNA (Zymo) kits. Each genomic DNA sample was digested with BstXI, and tagged by UMI-introducing primer extension (Table  5 ) using Klenow polymerase, exo-(NEB). The tagged gRNA sequences were PCR-amplified and sequenced on Illumina HiSeq 4000 sequencer SE50 run at UCSF Center for Advanced Technology. gRNA sequencing data was analyzed using iAnalyzeR (https://github. com/goodarzilab/iAnalyzer). Briefly, count matrices per gRNA were generated for each sample by alignment to the input gRNA protospacer sequences. gRNA abundances per sample were compared using a univariate analysis. Hits were selected as guides significantly decreased in metastatic samples compared to the input samples but not significantly decreased in in vitro samples compared to the input samples.
scRNAseq data analysis Cell quality assessment and filtration Each dataset, corresponding to a unique timepoint or differentiation protocol was analyzed independently using Seurat (v4. 3). First, several per-cell quality control metrics were calculated such as the percentage of mitochondrial gene reads (percent. mito), the percentage of ribosomal gene reads (percent. ribo) and a gene per umi novelty score calculated as log10(nFeatures) divided by the log10(nCount)(novelty. score). Low quality cells were identified using nCount and nFeature ranges specific to each dataset (Table  2 ) and excluded from further analysis. Clustering, dimensionality reduction and annotation Datasets were log normalized using a scale factor of 10,000 using NormalizeData. Variable features were identified using the "vst" method with default parameters of the FindVariableFeatures function.
Cell cycle status was predicted using the CellCycleScoring function based on Seurat provided S phase and G2M Phase gene sets. Feature counts were scaled and the calculated S. Score, G2M. Score, percent. ribo and novelty score variables were regressed out using ScaleData. Principal component analysis was performed using RunPCA with default parameters. Uniform manifold approximation and projection (UMAP) dimensionality reduction was performed using RunUMAP with PCA as the reduction input. Shared nearestneighbor (sNN) graphs were constructed using FindNeighbors. Finally, cell clusters were unbiasedly identified using louvain clustering via the FindClusters function. The number of principal components used in RunUMAP and FindNeighbors as well as the resolution used in FindClusters was specific to each dataset (Table  3 ).
Differentially expressed genes of each cluster were identified using the Wilcoxon Rank Sum test with the FindAllMarkers function. Clusters were annotated using known cell-type markers. Gene-dropout correction via count imputation was performed using adaptively-thresholded low rank approximation (ALRA) using the RunALRA function in the SeuratWrappers package (v. 3). Imputed gene counts were used for all subsequent analysis unless otherwise specified  59, 60. Transcription factor activity analysis SCENIC (v1. 3) was run independently on each dataset. Imputed count matrices and metadata were extracted from the above analyzed Seurat objects. Human RcisTarget databases were downloaded from resources. aertslab. org. Imputed count matrices filtered to remove genes with total counts less than 3% of the number of cells in the dataset or cells with detected genes fewer than 1% of the number of cells in the dataset.
Coexpression networks were identified using runCorrelation and GENIE3 (v1. 18) was used to identify transcription factor modules using runGenie3 from the filtered imputed matrix. Gene regulatory networks were then identified and scored using the SCENIC wrapper functions with default parameters. Briefly, runSCENIC_1_coexNetwork2modules was used to create the co expression modules, runSCENIC_2_createRegulons was used to identify regulons using transcription factor motif analysis and finally, regulon activity was calculated using runSCENIC_3_scoreCells. Pseudotime analysis Monocle3 (v1) was run independently on each dataset. Imputed count matrices and metadata were extracted from the above analyzed Seurat objects and used to create CellDataSets compatible with monocle3 using new_cell_data_set.
The CellDataSet was preprocessed but not normalized, as the imputed counts matrix was already normalized, using preprocess_cds with norm_method = "none". The number of dimensions used for each dataset during pre-processing was specific to each dataset and can be found in (Table  2 ). Cell cycle effects were removed using the align_cds function based on the cell cycle phases determined by Seurat CellCycleScoring. UMAP dimensionality reduction was performed using reduce_dimension. Clustering was not performed as cell cluster labels were transferred from the Seurat object. Finally, the pseudotime trajectories were predicted using the learn_graph function. A root state was chosen using order_cells based on the most central root point in the unbiased NCC cluster. Ligand-receptor interaction analysis CellChat (v1.
4) was run independently on each dataset. Imputed count matrices and metadata were extracted from the above analyzed Seurat objects and used to create CellChat objects using createCellChat. All cell-cell communication types were used from the human CellChat database. The subsetData function was used to filter the imputed counts matrix for signaling related genes. Using the filtered maxtrix, overexpressed ligand and receptors are then identified per each specified cell grouping using the identifyOverExpressedGenes functions followed by over expressed ligand receptor interactions using identifyOverExpressedInteractions. The probability of communication between the specified cell groups is then predicted using the computeCommunProb function. The communication probability of whole signaling pathways were then computed based on the individual receptor-ligand probabilities using computeCommunProbPathway.
Module scoring To identify transcriptionally similar cell populations between datasets, cluster gene signatures were created. First, the differentially expressed genes with a positive log2 fold change for each cluster in the reference dataset were filtered to exclude genes not detected in the query dataset. Of the remaining genes, the top 100 most significantly differentially expressed were used as the cluster's transcriptional signature (Table  4 ). The query dataset's cells were then scored for the expression of the cluster's transcriptional signature using Seurat's AddModuleScore function based on the imputed gene counts. Similarly weighted non-negative embedding (SWNE) analysis For the datasets to be compared, the counts matrices were filtered to only include genes detected in both the query and reference dataset.
First, the similarly weighted nonnegative embeddings (SWNE) are calculated for the reference dataset using the SWNE (v0. 6) package. Briefly, 3000 variable genes are identified from the reference dataset's non-imputed gene counts using FindVariableFeatures and component factors are identified by nonnegative matrix factorization (NMF) using RunNMF from NMF v0. 23. The number of factors to be identified (k) was set equal to the number of PCs used during initial analysis with Seurat) (Table  2 ). sNN graphs calculated previously were extracted from the reference seurat object and pruned using PruneSNN based on the reference dataset's previously calculated kNN matrix, specified cell grouping and a Qvalue cutoff of 0. 001. Finally, Sammon mapping is used for dimensionality reduction of the component factors by calculating factor embeddings using the reference dataset's sNN matrix.
This allows for both cells and genes to be plotted based on their influence by and contribution to the component factors, respectively. To project the query dataset onto the reference SWNE, sNN between the query and reference dataset are first calculated. To do this, the reference dataset's PC loadings are used to calculate PC embeddings of the previously determined 3000 variable gene based on the query dataset's scaled gene counts. The sNN matrix is then calculated from the query dataset's PC embeddings trained on the reference PC embeddings using ProjectSNN. Next, NMF gene loadings for the query dataset are calculated from the reference dataset's factor decomposition results using ProjectSamples. Finally, the query dataset is projected onto the reference SWNE embeddings using ProjectSWNE.
Transcription factor modules identification For Stage 2-4 NCC subset datasets, average regulon activity scores per NC subtype were calculated and the subsequent regulon by NC subtype matrices were merged, excluding S4 NCC3 which is not predicted to follow a melanogenic lineage. Regulon modules were identified using specific filtering criteria to identify regulons exhibiting desired trends in activity along the predicted lineage paths. Module 1 consists of regulons detected to be active in all subtypes, where the activity in S2 NCC2 is greater than S2 NCC1, S3 NCC2 is greater than S3 NCC3 by at least 0. 05, and S3 NCC2 is greater than S3 NCC1 and all S2 and S4 NCC subtypes. Modules 2 consists of regulons detected to be active only in S2 and S3 subtypes, where the activity in S2 NCC2 is greater than S2 NCC1, S3 NCC2 is greater than S3 NCC3 by at least 0.
Ligand-receptor module identification For stage 2-4 NCC subset datasets, receptor-ligand signaling scores for each NC subtype were calculated and the subsequent regulon by NC subtype matrices were merged, excluding S4 NCC3 which is not predicted to follow a melanogenic lineage. Raw signaling scores were scaled by a factor of 10 6 and log transformed for comparison and visualization. Ligand-receptor pair (LRP) modules were identified using specific filtering criteria to identify LRPs exhibiting desired trends in activity along the predicted lineage paths. Module 1 consists of LRPs active in S2 NCC1 and S3 NCC2, and the activity of S3 NCC2 is greater than S3 NCC3 by at least.
Analysis of published datasets Mouse Neural Crest Dataset QC filtered and processed. h5ad files were obtained from GEO (GSE201257). The reticulate package (v1. 28) was used to interface with the python scanpy package to import data into R and was then converted into a Seurat object, preserving the authors original metadata, leiden clustering and UMAP coordinates. Leiden clusters were annotated into cell types using the expression of cell type markers described in the original publication  28. Human Melanocyte Datasets Eight single cell RNA seq datasets containing melanocytes from healthy donors were curated from GEO (GSE151091, GSE162054/GSE153760, GSE147424, GSE150672 GSE130973), the EGA (EGAS00001002927) and the human cell atlas: Developmental (https://developmental. cellatlas. io/) (Developmental cell programs are co-opted in inflammatory skin disease: Human Adult Healthy 10x Data and Human fetal 10x data).