dataesr/scientific-paragraphs-categorization huggingface dataset:
Single-cell-derived colonies analysis was performed as described by the Laurenti Lab  44. In brief, colonies were harvested into 96 U-bottom plates using a plate filter to prevent the carryover of MS5 cells. Cells were stained with 50μl/well of antibody cocktail (Supplementary Table  16 -17 for antibody details), incubated for 20 minutes in the dark at room temperature and then washed with 100μl/well of PBS + 3% FBS. The type (lineage composition) and the size of the colonies formed were assessed by high-throughput flow cytometry (BD FACS Symphony). Colony output was determined using the gating strategy shown in Extended Data 9c. A single cell was defined as giving rise to a colony if the sum of cells detected in the CD45 + 5 + and GYPA + gates was ≥ 30 cells. Erythroid colonies were identified as CD45 -GYPA + ≥ 30 cells, Megakaryocyte colonies as CD41 + ≥ 30 cells, Myeloid colonies as [(CD45 + CD14 + ) + (CD45 + CD15 + )] ≥ 30 cells, NK colonies as CD45 + CD56 + ≥ 30 cells.
All high-throughput screening flow cytometry data was recorded in a blinded way, and correlation between the colony phenotype and originating population was only performed at the final stage. Two-tailed Fisher's exact test, performed in Prism (v8. 1. 2, GraphPad Software), were applied to the numbers of colonies of each type by stage to determine statistical significance in lineage differentiation potential with development. For differentiation of B cells from HSC/MPP pool, 10 cells were sorted directly on MS5 stroma in the same conditions as Doulatov et al. 47  (see Supplementary Table  17  for antibody details). An example of gating strategy is shown in Extended Figure  8d. For Ki67 staining, cells were stained using antibody panel in Supplementary Table  18 , CD34 + cells were sorted, fixed/permeabilised using the BD Cytofix/Cytoperm kit according to the manufacturer's instructions and then stained overnight for Ki67-FITC followed by DAPI as a DNA dye.
A minimum of 50,000 cell events were collected per sample. In order to calculate spectral compensation, single stained antibody capture beads were acquired with the bright-field illumination turned off. Spectral compensation and data analysis were performed using the IDEAS analysis software (v6. 2. 64, Luminex Corp). Briefly, dead cells were first excluded based on DAPI positivity. Overt doublets and debris were excluded from the live cell population using the aspect ratio and area of the BF image in channel 1. Single cells had an intermediate area value and an aspect ratio between 1 and 0. 6 and were gated as such. Overt doublets present as having an increased area and a lower aspect ratio value and were also gated as such. In all cases the position and boundaries of a gate was checked for appropriateness using the underlying imagery.
Alignment, quantification and quality control of scRNA-seq data Droplet-based (10x) sequencing data was aligned and quantified using the Cell Ranger Single-Cell Software Suite (version 2. 0. 2, 10x Genomics Inc) using the GRCh38 human reference genome (official Cell Ranger reference, version 1. 2. 0). Smart-seq2 sequencing data was aligned with STAR (version 2. 5. 1b), using the STAR index and annotation from the same reference as the 10x data. Gene-specific read counts were calculated using htseq-count (version 0. 10. 0). Cells with fewer than 200 detected genes and for which the total mitochondrial gene expression exceeded 20% were removed. Genes that were expressed in fewer than 3 cells were also removed. We detected on average ~3,000 genes per cell with the 10x Genomics platform and ~6,000 genes with the Smart-seq2 protocol.
After annotation was completed, a cell type classifier was built by training an SVM on labelled fetal liver scRNA-seq data with grid search for parameter optimization based on training data. 70% of the data was used for training and the other 30% for test. The SVM was previously compared in terms of accuracy and recall with a random forest and logistic regression classifiers trained on the same data. Out of the 3 classifiers the SVM was chosen due to showing a mean accuracy and weighted mean recall of 95%. Random forest showed 89% for both precision and recall (Supplementary Table  11 ). The SVM classifier was used for automatic annotation of the Smart-seq2 and mini bulk RNA sequencing data sets to allow identification of biologically meaningful clusters and DEG computation. Data generated from fetal skin, kidney and yolk sac was pre-processed, normalised, clustered and manually annotated, in parallel with, and using the same pipeline as, the liver data.
Annotation by cell type for skin and kidney, and yolk sac are reported in Supplementary Tables  6  and 7 , respectively. Skin and kidney data were combined using the MergeSeurat function. Clusters characterised by differentially expressed immune gene markers were extracted from the NLT dataset for subsequent comparative analysis with liver-derived immune populations. Human cord blood and adult bone marrow datasets were downloaded from Human Cell Atlas data portal (https://preview. data. humancellatlas. org/). These were processed using the same approach as described above, followed by manual annotation. Decidua and placental data from Vento-Tormo et al. 16  , were downloaded from ArrayExpress record E-MTAB-6701 (https://www. ebi. ac. uk/arrayexpress/experiments/E-MTAB-6701/).
Data integration We used Harmony data integration  58  to correct for batch effect between sample identities. The average kBET rejection rate statistically significantly improved from 0. 735 to 0. 471 (Supplementary Table  13 ) following Harmony data integration (p-value 3. 83e-3 in Kolmogorov-Smirnov test and p-value 8. 8e-6 in Wilcoxon signed-rank test). The manifold was subjected to re-clustering using Harmony adjusted PCs with parameters as mentioned above in "Clustering and annotation". Cell type classifications were then ascertained through re-annotation of the clusters derived from Harmony adjusted PCs to produce the final annotation.
Changes in cell proportions over development Comparison of cell proportions across gestational stages was assessed by modelling cell number data with negative binomial regression based on Poisson-gamma mixture distribution. Cell numbers were corrected for CD45 -/CD45 + FACS sorted ratio (Supplementary Table  1 ) prior to applying negative binomial regression modelling. Modelling was achieved using the glm. nb function in the R MASS package. Modelled cell number data were studied for regression coefficient significance (variable coefficient p-value ≤ 0. 05) to the response variable of gestational age with the corresponding z-score and pvalues taken (Supplementary Table  10 ).
Order of cells in pseudotime was statistically significant using Kruskal-Wallis test (p < 1x10  -7  ). Comparisons of trajectories across stage were performed by subsetting liver dataset by stage using SubsetData function, computing dimensional reduction coordinates, batch correcting by sample using Harmony, and plotting PAGA and FDG by stage. Cell type comparisons across tissue involved subsetting for cell types of interest using SubsetData function, merging cross-tissue datasets using MergeSeurat function, and processing data using the same approach as for the liver and NLT datasets. Harmony batch correction was then performed by tissue type, with results presented as combined UMAPs, FDGs and PAGA score heatmaps.
Dynamically expressed genes across pseudotime Genes that vary across pseudotime were calculated using DifferentialGeneTest function in Monocle in R (version 2. 6. 4) and a cut-off of adjusted p-value < 0. 001 applied. This was applied on the entire pseudotime range and also on the pseudotime intervals specific to each cell type in order to avoid limitation to the genes characterised by monotonic changes across the inferred trajectory. Expression of pseudotime variable genes were min-max normalised prior to visualization and annotated based on each gene's involvement in relevant cellspecific functional modules or hallmark functional pathways from MSigDB v6. 2, a curated molecular signature database  59. Peak expression for each gene over pseudotime was calculated and grouped into 'Early', 'Mid' or 'Late' categories.
For visualisation purposes, the resulting gene lists were minimised by ordering them from those present in the most selected functional pathways to least, as well as ensuring coverage across pseudotime. These genes were manually compared against current literature to determine if they have known functional or cell type associations. The top 20-25 genes in each list were displayed using the ggplot2 package. Transcription factors were marked within the dataset based on AnimalTFDB transcription factor prediction database  60. The full pseudotime gene list is available in the interactive files accompanying diffusion maps. Mouse data generated by Li et al. 19  used for comparison of erythroid-associated genes expressed in mouse Kupffer cells vs EI macrophages shown in Extended Data 4g is available at GEO with accession code GSE127980.
Visualisation by animated force-directed graph representation The FDG animation was created using an in-house modified version of the ForceAtlas2 class in fa2 Python package by saving all the intermediate states (published version only outputs the final state and discards all intermediates). The FDG coordinates at each iteration were plotted and the resulting graphs were assembled in a mp4 video format using VideoWriter in cv2 (version 3. 3. 1) Python package.
Differential gene extraction and validation Differential gene validation was done using a random forest classifier (RandomForestClassifier class in ensemble module of sklearn Python package v0. 19. 1, with 500 estimators, min_sample_split of 5, class weights set to the "balanced" policy and all other parameters set to default). The Random Forest algorithm was chosen as it resembled the FACS gating hierarchy. 70% of the data was used for training and 30% for test. Parameter tuning was performed on training data using grid search. To determine whether tissue-related transcriptome variations were present in equivalent immune populations between liver, skin and kidney, each equivalent population was taken in turn and grouped according to its tissue of origin. Seurat FindMarkers function was then applied in a pair-wise manner between each tissue subset to produce a cell type-specific list of genes marking each tissue subset.
Primary immunodeficiency (PID) gene list curation Disease and genetic deficiency information was extracted from Picard et al. 8  and manually annotated to include HGNC symbol names for each disease-associated genetic defect for subsequent correlation with the liver dataset. Diseases implicated in PID were divided according to the International Union of Immunological Societies (IUIS) major categories and screened across the liver scRNA-seq dataset. 315 unique genes were identified in the dataset from the 354 inborn errors of immunity highlighted in the article. For each disease category a dot plot was generated using Seurat DotPlot function and ordered by highest expression across each gene and across each cell type, highlighting those cell types in each disease category which express the highest number of genes associated with a genetic defect.
Whole genome sequencing and fetal cell identification To identify maternal cells present in our data we combined the information from fetal whole genome DNA sequencing with the single cell RNA-seq data. For each sample we measured the allele frequency in the fetal DNA of SNPs from the 1000 genomes project  62  falling within exons with a population allele frequency in excess of 1%. We then consider only those SNPs which are homozygous in the fetal DNA for follow up in the scRNA-seq data. A SNP was considered to be homozygous if its allele frequency in the fetal DNA was less than 0. 2 or greater than 0. 8 and had an FDR adjusted p-value of less than 0. 01 under a binomial test for the null hypothesis that the allele frequency in the DNA was in the range [0. 3,0. 7]. The allele frequency of each of these SNPs with population allele frequency > 1% that are known to be homozygous in the fetal DNA was then measured in each cell in the scRNA-seq data.
Four lasers (at 488, 561, 647 and 790nm wavelengths) were used to generate light sheets. IMaris (v9. 2, BitPlane) was used for image conversion and processing. Photoshop (Adobe) was used to create panels. All raw files are being made available on demand through our dedicated KeenEye Technologies-hosted Platform (www. transparent-human-embryo. com, ≪request database access≫).
Statistics and reproducibility For all analyses of fetal liver 3' 10x data, n = 14 biologically independent samples were included. This includes Figure  1b , Figure  2a , Figure  3a  For analyses including 10x sequencing data of skin, kidney, yolk sac, decidua and placenta, n = 7, 3, 3, 11 and 5 biologically independent samples were used, respectively. These analyses are shown in Figure  3b , Figure  4b , Figure  5a -b, Figure  6g , Extended Data 1a-b, Extended Data 3c-e and h, Extended Data 5g-i, and Extended Data 6c-e. For analysis including sequencing data of cord blood and adult bone marrow, n = 8 biologically independent samples of each were used. These analyses are shown in Figure  6g  and Extended Data 7h. For all scRNA-seq data shown, all cells of a given label from indicated tissues are shown, no down-sampling or sub-setting was performed.
The following cells numbers generated using scRNA-seq are displayed in each of the listed figures:   liver compared to those in kidney. Expression of genes in ILC precursors in fetal liver, skin and kidney, as shown in Extended Data 5i, was compared between tissues and no significant difference was observed. Extended Data 6e shows comparisons between fetal liver, skin and kidney for macrophages, monocytes, pDCs, DC1, and DC2. The following comparisons were statistically significant (p < 0. 0001) between macrophages from different tissues: CD14 expression in kidney vs liver and vs skin; CD68 expression in liver vs skin and vs kidney; CD163, RNASE1 and F13A1 expression between all tissues; and, VCAM1 expression in liver vs skin and vs kidney. The following comparisons were statistically significant between monocytes from different tissues: CD14 expression in liver vs skin (p < 0.
0162); and FCER1A and CD83 expression between each tissue (p < 0. 0001). Flow cytometric analysis of cell cycle phases, as shown in Figure  6g  and Extended Data 7i was performed on cells from n = 3 7-8 PCW and n = 3 12-16 PCW biologically independent fetal liver samples, and n = 2 biologically independent cord blood samples. One-way ANOVA with Tukey's multiple comparison test was used to determine statistical significance between stages (7-8 PCW and 12-14 PCW) and samples (fetal liver and cord blood). The percent of CD34 + CD38 -cells in G 0 was significantly higher in 12-14 PCW livers compared to 7-8 PCW livers (p = 0. 0136).
Acknowledgements This publication is part of the Human Cell Atlas-www. humancellatlas. org/publications. We thank the  Newcastle University Flow Cytometry Core Facility, Bioimaging Core Facility, Genomics Facility, NUIT  for technical assistance, School of Computing for access to the  High-Performance Computing Cluster, Newcastle Molecular Pathology Node Proximity Lab ,  Alison Farnworth  for clinical liaison,  Sophie Hambleton  for primary immunodeficiency expertise,  Helen Chen  for immunohistochemistry assistance, and  Morgane Belle  and  Stephane Fouquet  for light sheet fluorescence microscopy assistance. The human embryonic and fetal material was provided by the  Joint MRC / Wellcome  ( MR/R006237/1 )  Human Developmental Biology Resource  (www. hdbr. org). Funding We acknowledge funding from the  Wellcome Human Cell Atlas Strategic Science Support  ( WT211276/Z/18/Z ); M.
H. is funded by  Wellcome  ( WT107931/Z/15/Z ), The  Lister Institute for Preventive Medicine and NIHR and Newcastle-Biomedical Research Centre ; S. A. T. is funded by  Wellcome  ( WT206194 ),  ERC Consolidator  and  EU MRG-Grammar  awards and; S. B. is funded by  Wellcome  ( WT110104/Z/15/Z ) and  St. Baldrick's Foundation ; E. L. is funded by a  Wellcome Sir Henry Dale and Royal Society Fellowships, European Haematology Association, Wellcome  and  MRC  to the  Wellcome-MRC Cambridge Stem Cell Institute  and  BBSRC.
Gene constructs. Viral vectors used in this study have been described  (Majer et al. , 2017)  with the following exceptions. A plasmid with recombinant clone TEV-GFP was constructed from plasmid pGTEVa  (Bedoya et al. , 2012)  and used for Arabidopsis Ler plants. The binary viral vector pGTuMV-UK1 was used for Arabidopsis Col plants. Briefly, a cDNA of the UK1 isolate of Turnip mosaic virus (TuMV; GenBank accession number NC_002509. 2) was cloned in vector pG35Z  (Cordero et al. , 2017)  flanked by the Cauliflower mosaic virus 35S promoter and terminator. To generate the different crtB versions, we amplified by PCR the Pantoea ananatis crtB gene  (Majer et al. , 2017). We also amplified cDNA sequences encoding the plastid-targeting peptide of Arabidopsis hydroxymethylbutenyl 4diphosphate synthase  (Gas et al.
Indexed libraries were prepared from 1 μg/ea purified RNA with TruSeq Stranded mRNA Sample Prep Kit (Illumina) according to the manufacturer's instructions. Libraries were quantified using the TapeStation 4200 and pooled such that each index-tagged sample was present in equimolar amounts, with final concentration of the pooled samples of 2 nM. The pooled samples were subject to cluster generation and sequencing using a NextSeq 500 System (Illumina) in a 2x75 paired end format at a final concentration of 1. 8 pmol. The raw sequence files generated (. fastq files) underwent quality control analysis using FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Data analysis was performed with the online platform AIR (www. transcriptomics. cloud) using the SolGenomics Network (https://solgenomics.
Metabolite analyses. Carotenoids were analyzed as previously described  (Llorente et al. , 2016). Phytoene was quantified using a concentration curve with a commercial standard (Sigma). Primary metabolites were extracted as described previously  (Lisec et al. , 2006)  using approximately 20 mg of lyophilized leaf tissue. Derivatization and gas chromatography-time of flightmass spectrometry (GC-TOF-MS) analyses were carried out as described previously  (Lisec et al. , 2006). Metabolites were identified manually following use of TagFinder software in combination with the reference library mass spectra and retention indices from the Golm Metabolome Database, http://gmd. mpimp-golm. mpg. de. The parameters used for the peak annotation of the 52 metabolites can be found in Table  S4 , which follows recommended standards on the report of metabolite data  (Fernie et al.
, 2011). Data were normalized to the mean value of the GFP control samples (i. e. , the value of all metabolites for GFP control samples was set to 1). The means and standard errors of five to six replicates at 96 hpi are presented in Table  S5. Photosynthetic measurements. Photosynthetic efficiencies were assessed by measuring chlorophyll a fluorescence with a MAXI-PAM fluorometer (Heinz Walz GmbH). Leaves were placed under the camera and effective quantum yield (ΔF/Fm') was measured as (Fm'-Fs)/Fm', where Fm' and Fs are, respectively, the maximum and the minimum fluorescence of light exposed plants. The light intensity chosen was 21 PAR (actinic light, AL=2) as the last value able to generate a response in the (p)crtB-infiltrated areas before having a null photosynthetic activity. Each value is the average result of three biological replicates and three different AOI for each replicate.
8 cm 2 each were harvested from leaf sections of two different plants infiltrated with the (p)crtB construct, weighted and placed into the respiration cuvette containing the respiration buffer (30 mM MES pH 6. 2, 0. 2 mM CaCl 2 ). Oxygen uptake rates were measured in darkness using a liquid-phase Clark-type oxygen electrode (Rank Brothers) at a constant temperature of 25ºC. Dry weights from leaf discs were determined after drying for 2 days at 60ºC. Statistical analyses. Differentially expressed genes (DEGs) were identified by comparing crtB and GFP RNAseq datasets with the DESeq2 statistical method in the AIR platform. The resulting crtB/GFP list was filtered using cut-offs of FDR <0. 05 and log2-transformed fold change, log2FC, >0. 585 for upregulated genes and <-0. 599 for downregulated genes.
Gene ontology enrichments were identified by the Parametric Analysis of Gene Set Enrichment (PAGE) function of the AgriGO v2. 0 web-based tool (http://bioinfo. cau. edu. cn/agriGO/) after transforming the gene IDs to the Niben v04. 4 annotation. For the comparison of different biological systems, we selected the significantly enriched gene ontologies from our N. benthamiana RNAseq experiment (p and q values <0. 05) and compared their Z-score values with those obtained from the analysis of published RNAseq data of tomato fruit ripening and Arabidopsis leaf senescence. In particular, we used the RPM values of the total pericarp at mature green (MG), light ripe (LR) and red ripe (RR) stages  (Shinozaki et al. , 2018)  and the FPKM values of the 16D and 30D senescence stages  (Woo et al. , 2016).
DEGs resulting from LR/MG, RR/MG and 30D/16D comparisons were filtered as described above for N. benthamiana crtB/GFP. Student's t-tests were used for the rest of statistical analyses using GraphPad Prism 5. 0a (GraphPad Software). Plant developmental programs create organs with different degrees of photosynthetic capacity and hence chloroplast identity, from strong (e. g. , leaves) to weak (e. g. , green fruits) or absent (e. g. , roots). Loss of chloroplast identity appears to be the first phase (I) in chromoplast differentiation. In a second phase (II), developmental cues promote the expression of genes encoding PSY and other carotenoid biosynthetic enzymes. Enhanced production of carotenoids then reprograms plastid-to-nucleus communication, changes plastidial ultrastructure, and results in the differentiation of chromoplasts, which in turn promotes biosynthesis and improves storage of carotenoids.
Acknowledgements We greatly thank  Jaume F. Martinez-Garcia  for fruitful discussions on the manuscript,  Ralf Welsch  and  Li Li  for providing seeds of the Arabidopsis ccd1 ccd4 and ator atorl mutants, respectively,  Juan José López-Moya  and  Maria Luisa Domingo-Calap  for the gift of the HcProWMV-pGWB702 plasmid, and  M. Rosa Rodriguez-Goberna  for excellent technical support. The help of  Martí Bernardo ,  Fidel Lozano ,  Lídia Jiménez  and members of the CRAG core facilities is also appreciated. This work was funded by the  European Regional Development Fund (FEDER)  and the  Spanish Agencia Estatal de Investigación  (grants  BIO2017-84041-P ,  BIO2017-83184-R ,  BIO2017-90877-REDT , and  BES-2017-080652 ),  Ministry of Education, Culture and Sports  ( AP2012-3751  and  FPU16/04054 ), and  Generalitat de Catalunya  ( 2017SGR-710 ).
We also thank the financial support of the  European Union's Horizon 2020  ( EU-H2020 )  COST Action   CA15136  (EuroCaroten) and  Marie S. Curie Action 753301 (Arcatom) , the  Severo Ochoa Programme for Centres of Excellence  in  R&D 2016-2019  ( SEV-2015-0533 ) and the  Generalitat de Catalunya CERCA Programme  to CRAG. BL is supported by grants from the  CSIRO Synthetic Biology Future Science Platform and Macquarie University. LM is supported by  La Caixa Foundation PhD INPhINIT  fellowship  LCF/BQ/IN18/11660004 , which received funding from the  EU-H2020  ( MSCA grant   713673 ). ARF is supported by the  Deutsche Forschungsgemeinschaft  ( DFG TRR 175 ).