dataesr/scientific-paragraphs-categorization huggingface dataset:
5. ) Mitochondrial content was required to be below a 5% cutoff. The ADU dataset was annotated to 35 cell types according to Hay et al. , 2018  66. Subsequently, the annotated ADU dataset was down-sampled to a maximum of 2500 cells per cell type and served as a label transfer reference to annotate other datasets, including FET, NEU, JUV, and JMML. Label transfer was performed using the "FindTransferAnchors" and "TransferData" functions (dims = 1:30) in Seurat (v. 3. 1. 5)  67. Each cell was assigned one transferring score for each cell type, and the cell type assignment of cells was based on the highest cell type transferring score. In the FET dataset, cells with annotations of endothelial cells, fibroblasts, hepatocytes, Kupffer cells, and VCAM1+ EI macrophages, based on the original paper  68  , retained their original annotations.
scRNA-seq data integration We employed the "anchor" integration method to integrate scRNA-seq datasets from JMML patients and healthy references using Seurat (v. 3. 1. 5). The following steps were taken with default settings: 1. ) Dataset-wise data normalization was performed using the "NormalizeData" function. 2. ) Dataset-wise feature selection was conducted with the "FindVariableFeatures" function. 3. ) Pairwise "anchors" between the datasets were identified using the "FindIntegrationAnchors" function (dims = 1:30). 4. ) Data integration based on these "anchors" was achieved through the "IntegrateData" function (dims = 1:30).
Pseudotemporal analysis of hematopoietic lineages PCA was used to estimate a linear latent pseudotime for each cell across distinct hematopoietic lineages using RNA expression data. RNA expression data was normalized by dividing the feature counts of a given cell by the total counts for that cell, then multiplying the quotient by 10. 000 and finally log1p transforming the product (default settings for Seurat "NormalizeData"). To facilitate PCA in capturing lineage-specific changes, we devised a gene selection procedure consisting of three main steps: 1. ) The dataset was restricted to non-tumor cells, and genes with no observed transcripts across all cells were excluded. 2. ) For each gene, a cumulative link model was constructed to establish a relationship between the position of cells within the lineage and their normalized expression of the respective gene.
MNCs To project the scRNA-seq data of FACS enriched JMML CD34 + CD38 -/lo HSPCs onto the existing MNC UMAP space we used the MNC scRNA-seq set as a reference and identified anchors between both datasets by applying the Seurat "FindTransferAnchor" function with PCA as a specified reference reduction. We mapped the HSPC query data onto the MNC UMAP space using the "MapQuery" function and visualized the combined UMAP using ggplot2  69.
Transcriptional priming and leukemia-related transcription programs of human HSCs Gene expression signatures were extracted from previous publications  57, 70, 71. Transcriptional priming was calculated using the Seurat "AddModuleScore" function for 1000 randomly selected HSCs from each epitype. Statistical significance was determined based on two LMMs as implemented in the lme4 R package followed by ANOVA: LMM1 = signature ~ Subgroup + (1|Donor) LMM2 = signature ~ (1|Donor).
Processing of whole-genome bisulfite sequencing data The Omics IT and Data Management Core Facility (DKFZ, Heidelberg) processed WGBS data as described earlier  76. In short, reads were aligned using an updated pipeline published by Wang et al. 65  , implemented as a Roddy Workflow (https://github. com/DKFZ-ODCF/AlignmentAndQCWorkflows) in the automated One Touch Pipeline  77. In short, adaptor sequences of raw reads were trimmed using Trimmomatic  78. Next, sequencing reads were in silico bisulfite-converted  79  (C>T for the first read in the pair, G>A for the second). BWA-MEM  79  was used with default parameters to align the converted reads to the in silico bisulfiteconverted reference genome hg19, extended with the PhiX and lambda phage sequences. Post alignment, reads were converted back to their original state.
Reads with a mapping quality ≥25 and nucleotides with a Phred-scaled quality score ≥25 were considered for PCR duplicate removal per sequencing library using the software Picar  80. Methylation calling and M-bias QC was performed using MethylDackel 81 v0. 4. 0 and the parameters --OT 6,146,2,144 -- OB 7,146,12,150, according to the M-bias quality control plots. Methylation calls were imported into R 82 v3. 6 using the R package methrix 83 v 1. 0. 05, summarized based on annotated reference indices, and collapsed based on strand information. Furthermore, single nucleotide polymorphisms (SNP) with a minor allele frequency >0. 1 were filtered, using the reference provided in the R package BSgenome. Hsapiens. UCSC. hg19 84 v 1. 4. 0, and initial quality control was performed. For downstream analysis, DNA methylation data were imported into the R package bsseq 85 v1.
Methylation-based cell type classifier For DNA methylation-based cell type classification, a previously published algorithm was applied  24. As normal references, WGBS data of adult Lin -CD34 + CD38 -/lo CD45RA -CD90 + HSCs, Lin -CD34 + CD38 -/lo CD45RA -CD90 -MPPs, Lin -CD34 + CD38 + CD10 -CD45RA -CD135 + CMPs, Lin -CD34 + CD38 + CD10 -CD45RA + CD135 + GMPs, and Lin -CD34 + CD38 + CD10 -CD45RA - CD135 -MEPs was employed, which was generated in course of this study. As input to the classifier, we used WGBS data that was prepared as outlined in the previous section. Briefly, WGBS data was subsetted to regions outlined in Farlik et al. 24  , using the hg38 to hg19 liftover function from the R package rtracklayer  87. The average methylation within each.
Differential methylation analysis To define differentially methylated loci, a linear model was applied, considering the epigenotype (HM and non-HM) and sorting quadrant of the respective sample as a covariate. Therefore, the function "DMLfit. multiFactorR" with the parameter, smoothing = TRUE, from the R package DSS 88 v2. 32. 0 was applied. The epigenotype HM coefficient was extracted using the function "DMLtest. multiFactor" and differentially methylated regions (DMRs) were defined using the function "callDMR", based on regions with >3 CpGs, a length of >50 bps, and a Benjamin-Hochberg corrected P value <0. 05 in at least 50% of all CpGs within that region. Additionally, DMRs were subsetted for an average coverage of ≥5 in at least half of the samples belonging to the HM and non-HM epigenotype respectively and a difference in methylation >0.
2. DMRs were annotated using the R package ChIPseeker 89 v1. 31. 3. 900 and TxDb. Hsapiens. UCSC. hg19. knownGene 90 v3. 2. 2. Hierarchical cluster analysis of DMRs was performed using the R package pheatmap 91 v1. 0. 12, applying "ward. D2" as the clustering method to "Manhattan" distances.
Locus overlap and enrichment analysis of ChromHMM states The R package LOLA  93 (v1. 16. 0) was used to enrich DMRs with ChromHMM states acquired from Encode  94. DMRs were stratified in hypo-and hypermethylated regions and enriched to a background containing a set of random regions with equal size, similar CpG frequency (15% tolerance in the deviation of CpG composition was allowed), and a maximum of 20% N's in the sequence composition.
Locus plots IGV (v2. 12. 2) was used to visualize locus-specific methylation at single CpG resolution of WGBS and DNA methylation array data (450k). Alternatively, the R package gviz 95 v1. 30. 3     was used to generate locus plots. For the visualization of WGBS data, methylation calls were smoothed with the R package bsseq 85 v1. 20. 0, applying the function "BSmooth" with default parameters.
Homeobox gene classes Homeobox genes were taken from Holland et al. 96. We annotated DMRs obtained from comparing HM vs non-HM JMML patients using the "annotatePeak" function from the ChIPseeker package  89  , assuming a tssRegion of +/-3,000 base pairs. Subsequently, we counted how many DMR associated genes were present in either of the previously defined Homeobox gene classes PRD, ANTP and Misc (LIM, POU, HNF, SINCE, TALE, CUT, PROS, ZF, CERS). We divided the size each gene set by 30,000, and the number of DMR associated genes occurring in each gene set by the number of total DMR associated genes. The log2 fold change of those ratios was plotted.
Identifying epigenetic scars of development Epigenetic scars, conceptually, are characterized by marked methylation changes between distinct developmental states, while remaining stable during subsequent development (related to Abdel-Hakeem et al. 98  ). To identify epigenetic scars, DMRs were called between a developmental state of interest and all succeeding states. "callDMR" from the R package DSS  99  (2. 42. 0) was used to identify DMRs across two groups. The "callDMR" function was invoked using the following arguments: delta=. 1, p. threshold=. 05, minlen=50, minCG=3, dis. merge=50, pct. sig=0. 5. Identified DMRs were filtered to suffice an average coverage of five in at least 50% of samples in both groups. We identified 3,332 potential scars from comparing prenatal to postnatal states, and, 8,964 from the comparison between neonatal and post-neonatal states.
To minimize the influence of developmental regions, we excluded DMRs that overlapped with DMRs from all pairwise comparisons of non-tumor neonatal, juvenile, and adult patients. Overlap assessment was performed using the IRanges "findOverlaps" function (version 2. 32. 0) with default arguments. Subsequently, we computed the average methylation per patient per DMR. After defining the sets of DEGs and DMRs, we calculated all DMRs that overlapped with a 100 kb region surrounding the gene body of each DEG. Prior to this, hgLiftOver (https://genome. ucsc. edu/cgi-bin/hgLiftOver) was employed to convert gene coordinates from hg38 to hg19. Utilizing the "glm" function from the stats package in base R, we fitted a generalized linear model with a Gaussian link function to predict the expression of a given DEG through the methylation of associated DMRs.
Reads were aligned to GRCm38 and feature count matrices generated using Cell Ranger version 4. 0. 0 (10X Genomics). Feature counts were further processed using Seurat (version 4. 0) 103 whereby cells with more than 5% of mitochondrial reads or less than 500 and more than 5800 detected genes were removed from the analysis. Moreover, only genes that were expressed in a minimum of 10 cells were retained for further analysis. Doublets were identified and removed using DoubletFinder 104. Cell cycle states were annotated based on the expression of stage specific marker genes (https://github. com/hbc/tinyatlas). Filtered data was subsequently integrated across genotypes using the "SCTransform" method followed by PCA, UMAP calculation and clustering. Clusters were annotated to hematopoietic cell states based on the expression of previously identified state-specific marker genes 105,106.
Differentiation pseudotime was calculated using slingshot for the monocytic differentiation lineage and cells were ordered along this pseudotime based on their rank 107. Cluster contributions were calculated based on 10,000 randomly selected cells from each genotype. P-values were determined using Fisher's exact test followed by Benjamini-Hochberg's false discovery rate (FDR) correction. Transcriptional priming was calculated based on previously published gene sets 108 using the Seurat "AddModuleScore" function. Statistical significance was determined based on two LMMs as implemented in the lme4 R package followed by ANOVA: LMM1 = signature ~ genotype + (1|mouse_id) LMM2 = signature ~ (1|mouse_id) Murine fetal and adult HSC signature genes were previously published  27  and used for GSEA. In short, the Seurat "FindMarkers" function (logfc.
All antibodies were used in 1:100 ratio. Cell acquisition and measurement was done using BD Fortessa. Different cell populations were characterized following gating strategy shown in Extended Data Figure  12i. The expression levels are quantified as total number of events of engrafted CD45+ cells or absolute number of living cells calculated out from two femurs. Statistical analyses were performed using the unpaired Mann-Whitney test in GraphPad Prism 10 software. P values less than 0. 05 were considered statistically significant. Treatment effect of alemtuzumab was calculated as difference of the percentage of engrafted human CD45 cells in treated vs untreated mice (treatment effect=treatedmean -untreatedmean).
Statistical analysis and data visualization Statistical tests are specified in the corresponding figure legends and methods sections. If not differently specified, default statistical tests implemented in Seurat or DSS were used for scRNA-seq or WGBS analyses, respectively. For the analysis of JMML PDX mice, GraphPad Prism was used. Flow cytometry data was analyzed using FlowJo (v. 10. 6. 2, v. 10. 8. 1, BD Biosciences). Flow cytometers and sorters were operated using DIVA v. 8 or v. 9. Visualizations were generated using ggplot2 and pheatmap, Seurat and Nebulosa in R version 4. 0. 3, 4. 0. 5, and 4. 2. 0 82. For Kaplan-Meier analysis, the survminer R package was used to calculate leukemia-free survival (https://github. com/kassambara/survminer).
Acknowledgements We thank the patients and their families and the  EWOG-MDS study group  for supporting our research. We also thank the  NGS Core Facility , the  Omics IT and Data Management Core Facility , the  Flow Cytometry Core Facility  and the  Center for Preclinical Research, German Cancer Research Center (DKFZ) , for providing excellent services. Emely Kleinert  and  Oliver Mücke  provided excellent technical support. The Ptpn11 E76K neo/+ mouse was kindly shared by  Dr. Cheng-Kui Qu ,  Department of Pediatrics, Aflac Cancer and Blood Disorders Center, Emory University, Atlanta, GA, USA. We thank all the members of the  Division of Translational Medical Oncology, the Division of Applied Functional Genomics ,  Oliver Stegle ,  Stephen J. Clark  and  Wolf Reik  for many fruitful discussions.
This work was in part funded by  Wellcome Trust  (personal fellowship to S. B. , institutional grant to the  Wellcome Sanger Institute ; references  220540/Z/20/A  and  223135/Z/21/Z ), by  DFG  ( Emmy Noether   RA 3166/1 ) to S. R. , by the  DKFZ International PhD Program  and the  German-Israeli Helmholtz International   Research School "Cancer-TRAX " to M. Hakobyan, by  NCT Molecular Precision Oncology Program  to S. F. , and by grants from the  "Deutsche José Carreras Leukämie-Stiftung " ( DJCLS 14R/2022 ) to M. M. and D. B. L. and from the  "Wilhelm Sander-Stiftung"  (#  2022. 010. 1 ) to D. B. L. This submission is part of the  2023 HCA Publication Package  ( HCA-105 ).
(v. 3. 1. 5). A total of 255 DEGs with an adjusted p-value < 0. 05 and an absolute log fold-change < 0. 25 were obtained. Developmental stage-specific DEGs were identified in the cells annotated as HSCs in four healthy reference datasets (FET, NEO, JUV, and ADU) using the "FindAllMarkers" function in Seurat (v. 3. 1. 5) for one-vs-rest comparisons. In each stage, the top 50 upregulated DEGs with an adjusted p-value < 0. 05 and an absolute log fold-change < 0. 25 were obtained. Any overlapping DEGs across stages were removed. We identified 44 DEGs for FET HSCs, 41 DEGs for NEO HSCs, 50 DEGs for JUV HSCs, and 47 DEGs for ADU HSCs.
Image annotation For both instruments, the full methodology used is displayed in their respective manual (https://sites. google. com/view/piqv/piqvmanuals/instruments-manuals). Briefly, images generated by the FlowCam and ZooScan were processed using the ZooProcess software  (Gorsky et al. , 2010)  that extract segmented objects as vignettes, with a series of morphometric measurements that were imported into the EcoTaxa web platform  (Picheral et al. , 2017)  for taxonomic classification. Using image recognition algorithms, predicted taxonomic categories were validated or corrected by a trained taxonomist. Overall, 51 406 vignettes were classified into 149 taxa for the ZooScan analyses, and 109 185 vignettes were classified into 124 taxa for the FlowCam analyses. For the majority, the taxonomic classification effort was possible up to the genus and only in rare cases up to the species.
Numerical and statistical analysis Using a custom-made processing tool (https://github. com/ ecotaxa/ecotaxatoolbox), the data were structured in two distinct databases (ZooScan and FlowCam). Major and minor axes of the best ellipsoidal approximation were used to estimate the biovolume (mm 3 m -3 ) of each object according to the recommendation of  Vandromme et al. (2012). Size was expressed as equivalent spherical diameter (ESD, mm). The individual biovolumes of the organisms were arranged in Normalized Biomass Size Spectra (NBSS) as described by  Platt (1978)  along a harmonic range of biovolume so that the minimal and maximal biovolumes of each class be linked by: Bv max = 2 0. 25 Bv min. The NBSS was obtained by dividing the total biovolume of each size class by its biovolume interval (Bv range =Bv max -Bv min ).
The NBSS was representative of the number of organisms (abundance within one factor) per size class. This can provides insight into ecosystem structure and function through the "size spectra" approach, which generalizes Elton's pyramid of numbers  (Elton, 1927). For each database, considering the volumes of water filtered by the nets, plankton concentration in individu per m -3 (ind m -3 ) and biovolume were calculated for each taxonomic annotation and for several levels of Examples of vignettes (FlowCam and ZooScan vignettes) for each category of organisms identified as diazotrophs. All of these categories were grouped into the functional plankton group 'cyanobacteria' for the 3rd analysis (see part 2. 3 and 3. 4).
Plankton adjustment and assembling In order to study the whole community (all planktonic organisms > 20 mm), we merged the two databases (FlowCam and ZooScan) into one single cross-calibrated database. In its lowest size range, each dataset from FlowCam and ZooScan displayed an undersampling which is symptomatic of either incorrect detection of objects due to optical limitation of device or undersampling of net by mesh extrusion of organisms (Figure  3A ;  Lombard et al. , 2019). Therefore, all parts of the NBSS below the maximal abundance of each device were discarded (Figure  3B ). For the highest size range of each dataset, very large organisms were associated with a presence-absence signal rather than a quantitative concentration due to insufficient sampling effort, and were disregarded (Figure  3B ).
We chose as an objective criterion to discard every NBSS size bin separated by more than five empty size bins. A clear difference of intercepts between NBSS spectra of the two instruments was detected (Figure  3B ). Using the ZooScan as a reference, we used overlapping observations between the two instruments (for overlapping observations of size classes 1 to i) at a given station j to produce a cross-size classes average correction coefficient at the station scale such as: NBSS FlowCam adjusted j = NBSS FlowCam j × mean (NBSS FlowCam ij/ NBSS ZooScan ij ). Results of this correction are shown in Figure  3C. Finally, to have a unique dataset in the overlapping size range, an average between the values of NBSS FlowCam adjusted i,j and NBSS ZooScan i,j was performed for each station at the functional and trophic group levels.
Acoustic data: indicator of macrozooplankton biomass and density Continuous acoustic measurements were made with a calibrated  (Foote, 1987)  Simrad EK60 echosounder operating at two frequencies: 38 and 200 kHz. To focus on the acoustic scattering in the photic layer, we only used the 200 kHz frequency, with a maximum acquisition range of 120 m. Power and pulse length were 45 W and 1024 ms, respectively. Data used in this study were acquired with an average ping interval of 7 s when the vessel speed was higher than 2 knots. Acoustic data were scrutinized, corrected and analyzed using the "Movies3D" software developed at the Institut Francais de Recherche pour l'Exploitation de la Mer (Ifremer;  Trenkel et al. , 2009)  combined with the French open-source tool "Matecho"  (Perrot et al. , 2018)  from the Institut de Recherche pour le Developpement (IRD).
Noise from the surface was removed (from 3 m below the transducer, i. e. 8 m depth from the surface), the bottom ghost echoes were excluded, and the bottom line was corrected. Single-ping interferences from electrical noise or other acoustic instruments, and periods with either noise or attenuated signal due to inclement weather were removed using the filters described by  Ryan et al. (2015). Background noise was estimated and subtracted using methods described by de  Robertis and Higginbottom (2007). The nautical area scattering coefficient (NASC in m 2 nmi -2 ), an indicator of marine organism biomass, and the volume backscattering strength (Sv in dB re 1 m -1 ), an indicator of the marine organism density, were calculated. Acoustic symbols and units used here follow  Maclennan (2002). Data were echo-integrated into 1 m high layers over a 0.
Chlorophyll a data Chlorophyll [CHL a] data were obtained from the OCEANCOLOUR_GLO_BGC_L4_MY_009_104-TDS product which was provided by CMEMS platform (product version cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D). This is satellite product of level 4 with 4 km spatial resolution and is provided daily. Daily fields of [CHL a] for the month of November 2019 were averaged together, providing the mean [CHL a] of our region of interest during the campaign period.
Community composition PCA coupled with cluster analysis (Figures  5B, C ) showed a very strong signal on meso-planktonic community change. The maximum of the variance (PC1: 56% of variance; Figure  5B ) showed a clear gradient, with (i) the more diverse communities dominated B C A FIGURE 4 (A) Micro-plankton concentration (ind m -3 ) and composition of the 10 most abundant taxa (representing about 88% of the dataset). The rest of the micro-planktonic organisms (114 taxa) were grouped within the category "others". Values of the Shannon diversity Index are indicated in grey. (B) Principal Component Analysis (PCA) on relative abundances of micro-plankton (Hellinger-transformed data). Only environmental data (in blue) with a contribution greater than 0. 25 and taxonomic data (in black) with a contribution greater than 0.
Quantification To consider both micro-and meso-plankton equally, we considered their biovolume (proxy for biomass) rather than their concentration. An increase in plankton biovolume (Figure  6A ) was observed in the Lau Basin with the highest biovolumes observed at LD 10-T3 and LD 05-T1 (i. e. 100. 15 and 94. 98 mm 3 m -3 , B C A FIGURE 5 (A) Meso-plankton concentration and composition of the 10 most abundant groups (representing about 79% of the dataset). The rest of the planktonic organisms (139 taxa) are grouped within the category "others". Values of the Shannon Index diversity are indicated in grey. (B) Principal Component Analysis (PCA) on relative abundances of meso-plankton (Hellinger-transformed data). Only environmental data (in blue) with a contribution greater than 0. 25 and taxonomic data (in black) with a contribution greater than 0.
Acknowledgments This research is a contribution of the  TONGA  project ( Shallow hydrothermal source of trace elements: potential impacts on biological productivity and the bioloGicAl carbon pump. The authors warmly thank the crew of the  R/V L'Atalante  for outstanding shipboard operations, the  DT-INSU , the  PIQv team of the Villefranche-sur-Mer laboratory  for their help in the analysis of the imaging samples. We also thank the  Belmont Forum  project " World Wibe Web of Plankton Image Curation" (WWWPIC , grant No  ANR-18-BELM-003 ).
Funding TONGA cruise https://doi. org/10. 17600/18000884) funded by the  Agence Nationale de la Recherche  (grant  TONGA ANR-18-CE01-0016  and grant  CINNAMON ANR-17-CE2-0014-01 ), the  LEFE-CyBER program  ( CNRS-INSU) , the  A-Midex foundation , the  Institut de Recherche pour le De ́veloppement (IRD).