dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability All sequences of Gsp and Gcp proteins analysed in the study are provided in Supplementary Data 1. Gsp and Gcp genes extracted from an unpublished Malawimonas jakobiformis genome assembly have been deposited at GenBank with accession numbers MT460910-MT460938 (etc. ). Raw genome sequencing reads from "Malawimonas californiana" and Neovahlkampfia damariscottae are available from NCBI under the BioProject PRJNA549687. The genome assembly of N. damariscottae has been deposited at GenBank with the accession number JABLTG000000000. The transcriptome assembly of Gefionella okellyi, the genome assembly and predicted proteins of "Malawimonas californiana", and partial genome assemblies of Reclinomonas americana are available from https://megasun. bch. umontreal. ca/papers/T2SS-2020/.
Data For this study we used three postmortem brains from each of the following species: ring-tailed lemurs (L. catta, between 3 and 11 years, 3 males), black-capped squirrel monkeys (S. boliviensis, between 2 and 19 years old, 1 female, 2 males), and rhesus macaques (M. mulatta, between 11 and 15 years old, 1 female, 2 males). The samples were obtained from Copenhagen Zoo (lemurs and squirrel monkeys), the Zoological Society of London (squirrel monkey), and the University of Oxford's Biomedical Sciences (macaques). All brains were extracted and fixed within 24 h after the death of the animal. The brains from the Copenhagen Zoo were obtained after the animals had been euthanized with sodium pentobarbital (intravenous) in line with population management decisions, independent of the current study  (Bertelsen 2019).
Imaging Protocol All brains were rehydrated in a PBS solution 1 week prior to scanning and placed in fomblin or fluorinert for the scanning procedure. The diffusion-weighted MRI data were acquired from the whole brain using a 7 T preclinical MRI scanner (Varian, Oxford UK). The scanner bore diameter is 210 mm, the gradient coil references are the following: 205_120_HD (Varian, Oxford Downloaded from https://academic. oup. com/cercor/advance-article/doi/10. 1093/cercor/bhab285/6369315 by Juridische Bibliotheek Der UU/University Library Utrecht user on 11 November 2021 UK) with a Gmax of 50 G/cm. The radiofrequency coil was made by Rapid Biomedical GmbH (Rimpar, Germany) and is a birdcage transmit receive coil with 72 mm ID. We used a 2D diffusion-weighted spin-echo multi-slice protocol with single line readout (DW-SEMS; TR = 10 s; TE = 26 ms; Matrix size = 128 × 128 with a sufficient number of slices to cover each brain; resolution for lemurs and squirrel monkeys: 0.
5 × 0. 5 × 0. 5 mm 3 , and resolution for macaques: 0. 6 × 0. 6 × 0. 6 mm 3 ). A total of 16 non-diffusion-weighted (b = 0 s/mm 2 ) and 128 diffusionweighted (b = 4000 s/mm 2 ) volumes were acquired with diffusion encoding directions evenly distributed over the whole sphere (single shell protocol). To assess the data quality, we computed the signal-to-noise ratio (SNR) for each individual scans from the diffusion-weighted volumes. The SNR was defined as the ratio between the mean signal in the brain and the mean signal outside the brain. We also computed the mean fractional anisotropy (FA) and mean diffusivity (MD) in the white matter of each individual, as well as in the corpus callosum. Values for these measures are comparable between individuals, species, and brain providers (Fig. 1 ).
Diffusion MRI Data Preprocessing All data were preprocessed using the same protocol implemented in the module phoenix of the MR Comparative Anatomy Toolbox  (Mr Cat; www. neuroecologylab. org). Briefly, the steps are as follows: We first converted the datasets to NIFTI format, then built an image based on the volumes acquired without a diffusion gradient as well as a binary mask of this image. Using tools from FSL (www. fmrib. ox. ac. uk/fsl), we then fitted a diffusion tensor model using FSL's dtifit including saving the tensor image which is used in the subsequent template creation. The principal direction image helped to reorient the data to approximate AC/PC (anterior commissure/posterior commissure) conventional orientation which sets the origin at the middle of the anterior commissure, where a small bundle of fibers decussate.
Template Creation We created templates for each of the species using the method described in  Lange et al. (2020b). This method employs a registration-based, multi-resolution, iterative template creation strategy including spatial unbiasing of both affine and nonlinear shape changes. Registration is performed using the MultiMOdal Registration Framework (MMORF)  (Lange et al. 2020a). MMORF is a multimodal registration tool for simultaneous alignment of datasets with both scalar and tensor MRI images. Multimodal registration offers advantages over traditional registration algorithms, as it is able to exploit the fact that different imaging modalities provide distinct types of information (e. g. , intensity and orientation) and often contain most information at different locations in the brain.
Surface Creation and Labeling Surfaces were generated with the preclinical surface pipeline precon_all (https://github. com/neurabenn/precon_all). precon_all is a minimalist adaptation of Freesurfer's  (Fischl 2012 ) reconall pipeline, aiming to provide broad flexibility to reconstruct cortical surface meshes without a known segmentation or parcellation scheme. This allows precon_all to generate cortical surface meshes in lesser studied animal models. It consists of a modularly designed pipeline and can run brain extraction, tissue segmentation, white matter filling, and surface generation in a continuous workflow on images with a T1-like contrast. To accommodate this, we converted the T2 contrast of our templates to obtain T1-like contrast images, using FSL tools. We inverted the intensities by multiplying the template image by -1 and adding the maximum intensity of the initial image, making sure the cerebrospinal fluid and ventricle intensities remained at zero.
Three additional coronal exclude masks were placed at the same level in the external and internal capsule to avoid invading these fibers. Finally the seeds and targets from the MdLF recipe were added to the exclusion to make sure the SLFc did not invade ventral pathways. Optic radiation (OR): We reconstructed the OR by placing a seed in the lateral geniculate nucleus and a target as a coronal plane just anterior to the lunate sulcus (Fig. 2I ). The exclusion consisted of an axial block of the brainstem, a coronal block directly posterior to the LGN to select only fibers that curl around dorsally, and a coronal plane just anterior to the seed to prevent invading the longitudinal fibers. We used a Matlab in-house routine to produce threedimensional visualization of the averaged log-transformed tractograms for each species, to facilitate the study and the representation of the whole tract anatomy (as seen in subpanel A of tract result figure).
Results In this study, we acquired diffusion MRI data from three primate species representing three distinct lineages: strepsirrhines (ringtailed lemur), platyrrhines (black-capped squirrel monkey), and cercopithecids (rhesus macaque). The dataset is of high quality and three individual brains were used for each of the species, allowing us to establish templates using a recently presented multimodal registration method  (Lange et al. 2020b) , and surfaces using a modified version of previous tools  (Benn et al. 2020). In turn, these allowed us to investigate and compare the cortical, sulcal, and white matter anatomy of the three species.
L. R. is supported by funding from the Biotechnology and Biological Sciences Research Council (BBSRC) UK [BB/M011224/1]. R. A. B. is supported by a fellowship from the FP7-PEOPLE-2013-ITN "Cardionext". J. S. is supported by a Sir Henry Dale Wellcome Trust Fellowship [105651/Z/14/Z], IDEXLYON "IMPULSION 2020 grant (IDEX/IMP/2020/14) and the Labex CORTEX ANR-11-LABX-0042 of Université de Lyon. The work of R. B. M. is supported by the Biotechnology and Biological Sciences Research Council (BBSRC) UK [BB/N019814/1]. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust [203139/Z/16/Z]. Notes We thank Prof. Nicola R. Sibson for her support of the MRI scans. This research was funded in whole, or in part, by the Wellcome Trust [203139/Z/16/Z]. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.
Data Availability Statement Raw data of the lemur and squirrel monkey will be made available on the Digital Brain Bank of the Wellcome Centre for Integrative Neuroimaging (https://open. win. ox. ac. uk/Digita lBrainBank/#/) upon acceptance of the paper. Macaque data are already available within PRIME-DE  (Milham et al. 2018) ; http:// fcon_1000. projects. nitrc. org/indi/indiPRIME. html). The scripts used for preprocessing are available in MrCat (www. neuroeco logylab. org, https://github. com/neuroecology/MrCat). MMORF is available as a Singularity image online (https://git. fmrib. o x. ac. uk/flange/mmorf_beta). The surface pipeline is available online (https://github. com/neurabenn/precon_all). Template and surface images, tractography recipes and results, template and analysis code are available online (https://git.
Funding L. R. is supported by funding from the Biotechnology and Biological Sciences Research Council (BBSRC) UK [BB/M011224/1]. R. A. B. is supported by a fellowship from the FP7-PEOPLE-2013-ITN "Cardionext". J. S. is supported by a Sir Henry Dale Wellcome Trust Fellowship [105651/Z/14/Z], IDEXLYON "IMPULSION 2020 grant (IDEX/IMP/2020/14) and the Labex CORTEX ANR-11-LABX-0042 of Université de Lyon. The work of R. B. M. is supported by the Biotechnology and Biological Sciences Research Council (BBSRC) UK [BB/N019814/1]. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust [203139/Z/16/Z]. Notes We thank Prof. Nicola R. Sibson for her support of the MRI scans. This research was funded in whole, or in part, by the Wellcome Trust [203139/Z/16/Z].
Second, emerging multiplexing techniques present new opportunities for system-level studies of cell types and their changing states at unprecedented resolution in whole organisms. The Clytia medusa offers an appealing platform for pioneering these studies. It is small, transparent, and has simple tissues and organs, stem cell populations actively replenishing many cell types in mature animals, and remarkable regenerative capacity  (19, 22, (24) (25) (26) (27). Furthermore, the 1-cm-diameter adult medusae used in this study contain on the order of 10 5 cells, making it possible to sample cells comprehensively across a whole animal in a cost-effective manner using current scRNA-seq technology (fig. S1  and tables S1 and S2). In this study, we generate a cell atlas for the Clytia medusa while simultaneously performing a whole-organism perturbation study, providing the first medusa single-cell dataset and an examination of changing cell states across the organism.
S4 ). All labeled suspensions were pooled and processed with the 10X Genomics V2. 0 workflow and Illumina sequencing, allowing construction of a combined dataset across organisms and treatments, 1 Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA. 2 Tianqiao and Chrissy Chen Institute for Neuroscience, Pasadena, CA 91125, USA. 3 Howard Hughes Medical Institute, California Institute of Technology, Pasadena, CA 91125, USA. 4 Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. 5 Sorbonne Université, CNRS, Laboratoire de Biologie du Développement de Villefranche-sur-mer (LBDV), 06230, France. without requiring batch correction (Fig. 1 , fig. S4, A  to D, and table  S1 ). A total of 13,673 single-cell profiles derived from 10 individuals (5 control and 5 starved) passed quality control, with high concordance in cell type abundance and gene expression among animals in the same treatment condition (see Materials and Methods and fig.
Our dataset was derived from female medusae so it lacks male germ cells, and late stage oocytes are expected to be too large for capture by the dissociation procedure. The 36 cell types (see Materials and Methods and Fig. 2 , B to D) were concordant between the two separate multiplexed experiments (see "Starvation" and "Stimulation" sections in Materials and Methods) and robust to different transcriptome annotations (figs. S6 and S13). For some of them, cell type identity could be assigned on the basis of published information on gene expression in Clytia and/or of homologous genes in other animals, while for the others we performed in situ hybridization for selected marker genes (Fig. 2C , figs. S11 and S14, and table  S3 ). Previously known cell types apparent in our data included i-cells  (34)  and nematocytes at successive stages of differentiation  (35) (36) (37) , as well as oocytes  (38) , gonad epidermis, manubrium epidermis, and bioluminescent cells in the tentacles that each express specific endogenous green fluorescent proteins (GFPs)  (39).
Nematocilin, a hydrozoan-specific component of the nematocil (ciliary trigger for nematocyte discharge)  (44) , is also expressed in these clusters (fig. S14 , table  S5 , and see below). In situ hybridizations revealed marker expression in morphologically distinguishable nematocytes, notably including two lines along the oral face of each tentacle (Fig. 3E  and fig. S14 ), a notable arrangement overlooked in previous studies. A remarkable feature of the Clytia medusa is that it constantly generates many cell types, notably neural cells and nematocytes from prominent i-cell pools in the tentacle bulb epidermis  (36)  and at other sites  (34). Within our dataset, we thus expected to be able to capture dynamic information relating to the development of i-cellderived cell types, similar to that extracted from Hydra polyp singlecell transcriptome data  (8).
We additionally validated the ability of this method to recapitulate the magnitude of state shifts in response to graded stimuli and state-versus-type distinctions, on two other published, multiperturbation datasets (see Materials and Methods; fig. S19 ). To characterize gene-level responses underlying these starvationinduced shifts, we then asked whether responses are shared or unique across the cell types and compared the extent of the responses, in terms of gene quantity and expression level, across the atlas. For each cell type, we collected genes that were differentially expressed under starvation ("perturbed genes"; Fig. 6A ) (see Materials and Methods). For a high-level view of the general functions and processes affected by starvation and their cell type specificity, we clustered perturbed genes into apparent "gene modules"  (56)  by their patterns of coexpression across cells (see Materials and Methods and Fig.
DISCUSSION The Clytia medusa single-cell atlas presented here is an important addition to the growing number of single-cell atlases across the animal tree of life. It is available for easy interrogation by the community through the UCSC Genome Browser (see Data and materials availability). This provides the first cell-level transcriptomic characterization of a pelagic medusa stage, the most complex of the life cycle forms within the large and diverse phylum Cnidaria. Reflecting this complexity, we found greater cell type diversity in the Clytia medusa than in its polyp-only hydrozoan cousin Hydra  (8). The outer, epidermal body layer could be subdivided into seven clusters encompassing all of the described Clytia muscle types, including two types of fast-contracting striated swimming muscle  (23, 27).
In contrast, our pseudo-time analyses provide transcriptional signatures of the progressive stages of nematogenesis and neurogenesis from i-cells that will guide future studies of their developmental regulation. The large representation of nematogenic stages in this Clytia medusa scRNA dataset allowed us to link two distinct phases of nematocyte formation with extremely different transcriptional profiles. The initial phase covering nematocyst formation has been the focus of many studies  (35) (36) (37) 41) , but the terminal phase has been largely overlooked in previous transcriptomics studies, likely due to the relatively low mRNA content  (8, 35)  and the extremely abrupt degradation of nematocyst-related mRNAs before the terminal phase  (37). We uncovered 14 mature neuronal subtypes in Clytia, which is similar to the number reported in Hydra and Nematostella  (3, 8).
The lack of library-induced batch effects demonstrates how large-scale experiments can be conducted without introduction (or minimizing introduction) of confounding factors from multiple experiments, which can be highly nonlinear and difficult to account for  (13). The second perturbation dataset also demonstrates both how batch effect variability is reduced within multiplexed experiments (see Materials and Methods; fig. S11 ) and the utility of this multiplexed approach in discerning cell typespecific activity markers (see Materials and Methods; fig. S11 ). The fully reproducible framework we have presented, which includes code that can be run on a laptop or for free in the cloud (see "Code availability" in Acknowledgements), will further assist in extending this expression-based analysis to other organisms.
Generation of reference transcriptome Assignments to PANTHER database entries (version 11) were made using the "pantherScore2. 0. pl" script available from the database website  (62). Human-Clytia orthologs were assigned using the OMA (orthologous matrix) program (63) as described in  (21)  and taken from the pairwise human/Clytia orthologs output, rather than the orthologous groups. To ensure highly sensitive transcriptome alignment, a new transcriptome assembly for C. hemisphaerica was generated from bulk RNA-seq data produced from Clytia medusae (organisms at the same life stage as in the single-cell experiments) (www. ncbi. nlm. nih. gov/sra/ERX2868482%5Baccn%5D). We used the Trinity (64) de novo assembler, with default parameters, to generate a transcriptome (http://dx. doi. org/10. 22002/D1.
1824) and the Cufflinks Cuffcompare utility  (55)  to merge the Trinity assembled transcripts with any XLOC annotations from the MARIMBA v. 1 (created on 30 May 2016; http://dx. doi. org/10. 22002/D1. 1830) transcriptome assembly  (21). Then, with the CD-HIT (clustering method), assembled sequences with at least 95% were clustered and only one representative sequence was kept for each cluster. With the published MARIMBA v. 1 genome sequence as a reference (created on 30 May 2016; http://dx. doi. org/10. 22002/D1. 1828) (21) , the GMAP (genomic mapping and alignment program) aligner converted the collapsed Trinity fasta records to gff3 coordinate file (http://dx. doi. org/10. 22002/ D1. 1824). Most differentially expressed genes found in scRNA-seq data from this study were previously identified and annotated (fig.
Preprocessing and clustering of sequencing data Initial cell ranger demultiplexing for ClickTags Initial demultiplexing of ClickTag libraries was done using output from the 10X Cell Ranger pipeline using Cell Ranger 3. 0 count and aggr functions with the MiSeq ClickTag fastqs as input () and combining the counts from the two lanes using the denoted sample IDs. A ClickTag count matrix (cell-by-ClickTag) was generated by counting ClickTag barcodes that had high sequence similarity to the designed sequences using the Python fuzzywuzzy package to identify targets within Levenshtein distance 1. We additionally quantified gene expression with the kallisto-bustools workflow  (66) , which reproduced concordant results described below.
Initial cell ranger demultiplexing and clustering for cDNA Initial processing of starvation cDNA libraries was performed with the 10X Cell Ranger pipeline using Cell Ranger 3. 0 count and aggr functions to align and quantify the HiSeq reads and combining the counts from the two lanes using the denoted sample IDs. This was followed by filtering cells for the high-quality cells chosen during ClickTag analysis, in addition to filtering cells by thresholding the rank-unique molecular identifier (UMI) versus cell barcodes plot. Values were log1p-normalized, mean-centered, and scaled for downstream dimensionality reduction and visualization using Scanpy  (67). We then conducted Louvain clustering  (68)  on the data mapped to a lower dimensional space by applying PCA to the expression data filtered for highly variable genes, initially using Scanpy's filter_ genes_dispersion on only the log-normalized data.
We also followed similar preprocessing to standard cell-by-gene workflows using the inflection point in rank-UMI versus cell barcodes knee plots to filter cell barcodes based on their tag UMI counts. We do find, similar to findings in the original ClickTag multiplexing publication  (14) , that the number of ClickTags per fed cell is higher than for the starved counterparts, possibly supporting the previous observation of ClickTag number per cell increasing with cell size (fig. S4E , shown for oocytes). For the stimulation experiment, we concatenated sequencing data from the MiSeq and HiSeq as input to the previously described kallisto-kITE workflow. With the same clustering procedure, we selected cell barcodes in clusters with strong overlapping expression of both individual and condition ClickTags.
S10 ). To ensure that we additionally detected low expression marker genes, we extracted low expression markers in table  S5 , which consist of genes with at least 10 counts over all cells and with 90% of those counts deriving from the same cell type. We used the PAGA partition-based graph abstraction method (31) to generate an underlying graph representation of the connectivity between cells (determining connectivity by the number of inter-edges between cell groups compared to the number of interedges under random assignment). We then generated a two-dimensional UMAP  (32)  embedding initialized with the PAGA graph structure for cell atlas visualization (Fig. 2A ) using Scanpy. For Fig. 2B , 100 cells were randomly subsampled from each cell type to generate the heatmap. The stimulation cDNA data were processed with the same kallistobustools workflow and commands as the starvation experiment data.
S19, A  and B ). We additionally compared all pairwise L 1 distances between perturbed and control cells within the monocyte population or "T cell" PBMC populations in (71) using the oNMF space. We then compared these measurements against the magnitude of cell type distances (between control monocyte and T cell populations) to highlight "state"-versus-"type"-level transcriptional differences (fig. S19C ). To then extract the most perturbed monocyte populations, we calculated centroid distances between the centroid of each perturbation condition and the CTRL1 (control) condition to rank populations by perturbation "distance" (fig. S19D ). We then used the stimulation dataset to assess the validity of the clusters/cell types generated with the starvation data. We created a joint representation of the two datasets by using a concatenated cell-by-gene matrix including only the genes highly variable in both the control and starved datasets and used 70% of the starvation data to train a k-nearest neighbor (KNN) classifier (using sklearn's KNNClassifier with k = 15) to assign cluster labels to the remaining starvation cells and to the stimulation dataset.