dataesr/scientific-paragraphs-categorization huggingface dataset:
INTRODUCTION As imaging methods for thick biological samples improve and become more widespread in various fields of life sciences, the volume of image data keeps growing and their analysis becomes even more complex. Biologists are therefore facing a rising need for computational tools to analyze large bio-image datasets and extract reproducible and meaningful biological information. The fish ovary is a complex organ that shows important structural and functional changes during reproductive cycles. It contains different types of cells, including oocytes (i. e. , female gametes) and numerous surrounding somatic supporting cells that form, together with each oocyte, the functional units known as ovarian follicles  (Lubzens et al. , 2010; Nakamura et al. , 2009). During oogenesis, each follicle grows and differentiates until finally giving rise to eggs that are ultimately released during spawning.
Various algorithms based on deep learning (DL) have emerged and provide many applications in microscopy allowing to overcome classical limitations such as image segmentation. They allow to increase object recognition accuracy, segmentation reproducibility and enable to save a considerable amount of time for the analysis of large datasets by limiting manual interventions of users  (Moen et al. , 2019). Some specific methods have thus been proposed to automatically segment follicles in the mammalian ovary from histological 2D sections using a convolutional neural network (CNN)  (İnik et al. , 2019; Sonigo et al. , 2018). Other more generalist tools have recently emerged to democratize the use of DL technology with few prerequisites in computed coding, by providing either DL trained models accessible from public databases (https://bioimage.
Samples mounting and imaging Image acquisitions were performed with a Leica TCS SP8 laser scanning confocal microscope equipped with a 16x/0. 6 IMM CORR VISIR  HC FLUOTAR objective (ref. 15506533, Leica, Wetzlar, Germany). For larvae ovaries, samples embedded in agarose blocks were glued on a coverslip and placed in a glass Petri dish filled with ECi. Adult ovaries were successively placed with ventral side up or down for complete imaging despite the objective working distance limitation, and mounted as described previously  (Lesage et al. , 2020). Mosaic z-stack tiles were stitched in Leica software using 11,72% overlap. Larvae ovaries were acquired in 1024x1024 pixels, 400Hz (unidirectional) with an optical zoom of 1. 3 and a z-step of 1. 63 µm (voxel size 0. 52 x 0. 52 x 1. 6264 µm). PH3 fluorescent signal was acquired using 552 nm laser excitation slightly above optimal intensity (3-4%), and frame average was set to 2.
Image contrast was then enhanced by applying Contrast Limited Adaptive Histogram Equalization (CLAHE) with following parameters: block size 128, bins 256, slope 3 and fast mode, for larva; block size 512, bins 256, slope 30 and fast mode, for adult. A Fiji macro was used to apply this function on Z-stacks by batches, which is available on our GitHub page: https://github. com/INRAE-LPGP/ImageAnalysis_CombineLabels.
Signal-to-noise ratios enhancement Three-dimensional images were denoised using Noise2Void (N2V) deep-learning based tool available on Fiji, using a model trained on a few selected 3D stacks snippets. A 2D model was Denoising prediction duration was estimated to ~12 min for 1GB of data with our stated parameters (batch size 2). For adult, similar strategy was used for training, using 10 z-stack snippets (256x256, 100-200 Z-steps), patch shape 64x64 pixels. Training was performed with 300 epochs, 200 steps/epoch, batch size 128, for a total of ~9 hrs of training. Denoising prediction duration was estimated to ~8 min for 1GB of data with our hardware specifications and stated parameters (batch size 2). For image edges enhancement, stacks were subjected to a 3D median filter (x,y,z radius 1,1,1 for larvae and 2,2,2 for adult).
Filtered image was then subjected to external morphological gradient computation (shape: ball; x,y,z radius 3,3,3 for larvae and 2,2,2 for adult) with Morphological filters (3D) function of MorpholibJ plugin. External gradient image was then subtracted from original pre-treated stack (without median filtering). An internal morphological gradient was also computed on larvae stacks (element shape: ball, x,y,z radius 4,4,4) and added to image data. For 3D visualization of data, volume reconstructions were performed on the Amira software using Volren rendering (Fig 2A  , E ) or  F and 4A, F).
Post-processing and data extraction For post-processing of segmented follicles, data were first slightly narrowed. For that operation, label boundaries were computed with MorpholibJ plugin and subtracted from the original Cellpose results. For larvae ovary images, labels were then post-processed on AMIRA software. Labels were subjected to an opening morphological operator (3 pixels, precise) and then filtered based on their size (Equivalent Diameter >= 1. 5e-5m) and shape (ShapeVAa3d <=3. 5). Few remaining errors were manually corrected. For adult ovary images, label shrinkage, filtration and combination were performed automatically or semi-automatically using a Fiji macro. Labels were filtered based on volume and sphericity parameters. When necessary, segmentation images were rescaled to match 3D registered image size (1324 x 1108 x 713px).
The combination strategy consisted in adding largest segmented labels from downscaled images (using Cellpose diameter 30) on original scale label segmentation (Cellpose diameter 30) where largest follicles were over-segmented (Supplemental Fig. 1 ). Briefly, labels >650µm volumeequivalent diameter (EqDiameter) were filtered from the downscaled image and added to the original scale label image after selection and deletion (using morphological reconstruction operation) of wrong labels resulting from over-segmentation. For combination of missing largest labels, a similar strategy was used with downscaled label image obtained with Cellpose diameter 60, but with semi-automatic method. Missing labels were manually selected with multi-point tool and then processed as presented before. Macro "CombineLabels" was developed in IJ1 Macro language and can be downloaded from the Github page: https://github.
com/INRAE-LPGP/ImageAnalysis_CombineLabels. The volumes of all segmented follicles were exported and equivalent diameters were calculated. For adults, EqDiameter were subjected to a correction factor of 1. 12 to compensate the volume shrinkage due to sample clearing, as described in Lesage et al. (Lesage et al. , 2020). Data analysis was performed on labels above 25 and 50 µm in diameter, for larvae and adult samples respectively. Label 3D reconstructions were generated on Amira using volume-rendering object.
Hardware and software Data were analyzed on a 64-bit Windows 10 Pro computer equipped with a 2x Intel Xeon Silver 4110 (8 Cores, 3. 0GHz) processor, a Nvidia Geforce GTX 1080 graphic card, and 384 Go of RAM. We used the Amira 2020. 2 software with the XLVolume extension (Thermo Fisher Scientific, Waltham, Massachusetts, United States), Anaconda3-2021. 11 python distribution, Python 3. 7. 9, CUDA toolkit 10. 0, PyTorch 1. 6. 0 and Cellpose v0. 6. 1  (Stringer et al. , 2021). We also used FIJI  (Schindelin et al. , 2012)  and the following plugins: CLAHE  (Pizer et al. , 1987; Zuiderveld, 1994) , Progressive intensity and gamma correction  (Murtin, 2016) , Fijiyama (fijiyama-4. 0. 0)  (Fernandez and Moisy, 2021) , CLIJ2 (clij2-2. 5. 3. 0, Haase et al. , 2020) ,  MorpholibJ (morpholibJ-1. 4. 3, Legland et al.
Oocyte content analyses To assess the ovarian oocyte content at both larvae and adult stages, ovaries were imaged at each of these stages and 3D computational analyses were performed following our deep learning-based pipeline. Three-dimensional reconstructions after data pre-processing revealed the thin oval-shape of larvae ovaries oriented along the anteroposterior axis, which then evolves into a thicker rounded shape at the adult stage (Fig. 3F , 4F, 7A and 7D). Ventrally, larvae ovaries exhibited lateral folds and a marked central depression, likewise adult ovaries displayed two lateral folds as well as a ventro-median bulge, giving the ovary a wheat grain appearance. Diameters of segmented oocytes or follicles were computed, classified into different size classes and merged to the 3D ovary reconstructions (Fig.
Enhancement and homogenization of input dataset The first part of our pipeline was dedicated to signal quality improvement in depth of raw image stacks. Such image pre-processing steps allowed improving segmentation efficiency by Cellpose. To some extent, the decrease in fluorescence level in depth on raw images should not be a major issue for predicting feature boundaries with Cellpose as it uses vector gradients representation of objects to accurately predict complex cell outlines with non-homogenous cell marker distribution  (Stringer et al. , 2021). However, our result indicates that the SNR is an important prerequisite for image analysis with Cellpose, in line with previous observations  (Kar et al. , 2021). Along with an enhanced visualization of the structures of interest across the sample, the pre-processing of 3D images therefore allows for homogenization of the data set and much more efficient 3D segmentation with Cellpose, thus increasing the reproducibility and quality of analysis.
It is however worth noting that there are still few inaccuracies that could not be fixed. Under-segmentations or unsegmented objects were sometimes detected mostly with larvae image datasets. Albeit minor, these errors occur in highly oocyte-dense regions or with non-optimal signal levels. Such observation is in agreement with some studies that do not recommend Cellpose for highly overlapping masks or that describe lower accuracy with over-or underexposed images  (Kar et al. , 2021). This could be attributed to the 2D averaging process for the 3D Cellpose extension that may have lower accuracy than a model trained with 3D data, especially for highly dense regions  (Lalit et al. ,2022; Stringer et al. , 2021). Obviously, one can assume that better accuracy could be achieved by using a dedicated specialized DL model, and in particular with 3D trained model on our data, as shown by D.
Funding This work was funded by The  DYNAMO  project ( Agence Nationale de la Recherche ,  ANR-18-CE20-0004  to V. T. ). This work has also been supported by the  IMMO  project (grants from the  INRAE Metaprogramme DIGIT-BIO  to V. T. ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
5HT-secreting and 5HT-sensitive cells in chromaffin organs. To address the role of 5HT signaling in adrenal gland development, we re-analyzed the expression of related enzymes and receptors using previously published single-cell transcriptomics dataset of chromaffin and sympathetic development at E12. 5 and E13. 5 stages 6 (Supplementary Fig. 1 ). As shown earlier by Furlan et al. , chromaffin cells originate from SCPs and the differentiation progresses through the transitory "bridge" population. Thus "bridge" cells are immediate progenitors of chromaffin cells (Supplementary Fig. 1a ). According to our analysis at E12. 5, Htr3a/3b (encoding 5HT-receptor 3A/3B) are strongly expressed in the population of "bridge" cells and are only sporadically expressed in cells from other clusters. At E13.
The microCT scan was carried out at 60 kV acceleration voltage and with 200 μA X-ray tube current. The beam was filtered by a 0. 2 mm-thick aluminum filter. The voxel size of obtained volumes was 2. 2 μm for all samples. The tomographic reconstructions were performed using GE phoenix datos|x 2. 0 3D computed tomography software. Segmentation of structures was performed manually by a combination of software Avizo (Thermo Fisher Scientific, USA) and VG Studio MAX 3. 2 (Volume Graphics GmbH, Germany). Thymidine analogues labeling during S-phase combined with 5HTP treatment. Double thymidine analogues labeling of cells in S-phase was based on the methods described previously  87. 5-ethynyl-2′-deoxyuridine (EdU, Life Technologies, E10415) and 5-Chloro-2′-deoxyuridine (CldU, Sigma-Aldrich, C6891) were dissolved in PBS at stock concentrations 5 mg/ml and 5.
1. 0/1. 53c) software. The area of adrenal gland section was calculated by surrounding the area based on DAPI signal. The area of medulla was calculated based on TH + signal within adrenal gland. The area of cortex was calculated by subtraction of adrenal medulla area from the area of the whole gland per individual section. Three section per gland and 2 glands per embryo were evaluated. Venn diagrams were built with https://www. meta-chart. com/venn#/your-charts free on-line platform and redrawn in Adobe Illustrator 25. 2. 1. The percentage of Sox10 + Htr3a EGFP+ cells was calculated as a fraction of Sox10 + Htr3a EGFP+ cells from the sum of all Sox10 + and all Htr3a EGFP+ at E13. 5 and E14. 5 in adrenal medulla. Measurements of relative medulla size in postnatal animals. The dissected adrenals of P30 BalbC mice and collected red-backed voles Clethrionomys rutilus were processed for whole-mount Benzyl alcohol/Benzyl benzoate tissue optical clearing method.
The preparations were scanned using LSM 880 confocal microscope (Carl Zeiss, Germany) with a green channel determining the tissue autofluorescence. The optical section with maximal external volume was selected for relative medulla size analysis. The area of central medulla and the whole adrenal were measured using ImageJ software. Relative medulla size represented by the ratio: (medulla area/total area) × 100%. Re-analysis of single-cell transcriptomic data published by Furlan et al. , 2017. We re-analyzed single-cell RNA-seq data of mouse adrenal gland from Furlan et al. 6. Gene counts were obtained from GEO database (GSE99933). Gene count matrix was analyzed with a standard Seurat (version 3. 0. 2) workflow  88. We used the original embeddings and clustering from ref. 6  (Figs. 5B  and 5G ), downloaded from the published pagoda apps: http://pklab.
The single-cell transcriptome data were generated at the Eukaryotic Single-cell Genomics facility at Science for Life Laboratory in Stockholm, Sweden. The samples were analyzed by first demultiplexing the. fastq files using deindexer (https://github. com/ws6/deindexer) using the NextEra index adapters and the 384-well plate layout. Individual. fastq files were then mapped to mm10_ERCC genome (https://www. ncbi. nlm. nih. gov/ assembly/GCF_000001635. 20/) using the STAR aligner version 2. 7. 5c using 2-pass alignment. Reads were filtered for only uniquely mapped and were saved in BAM file format; count matrices were subsequently produced. Estimated count matrices were gathered and converted to an anndata object. Cells with the following quantities were kept as high quality: having more than 10 5 and less than 5 × 10 5 transcripts, containing more than 4000 and less than 8000 detected genes and or less than 15% of ERCC reads.
Cells with low number of reads, potential doublets, cells with high fraction of ribosomal and mitochondrial genes were removed from the analysis. The resulting filtered count matrix contained 1361 high-quality cells (out of 1528 total cells sequenced). The preprocessing and initial analysis of the count matrix was performed without any batch correction, using scanpy python package v1. 7. 2, scFates python package v0. 2. 4, and scvelo python package v0. 2. 3. Highly variable genes were detected using pagoda2 approach (scFates. pp. find_overdispersed, default parameters). Cell cycle genes were removed from the list of high variable genes to remove their effect. PCA was performed on the scaled count matrix using the high variable genes (scanpy. pp. pca, default parameters). kNN graph (scanpy.
Sequencing was done on Illumina NovaSeq 6000 Sequencing System (NovaSeq 6000 S1 Reagent Kit or NovaSeq 6000 S2 Reagent Kit were used) with the standard recommended read set up for 10X Genomics libraries: Read 1: 28 cycles (Cell barcode and UMI), i7 index: 8 cycles (Sample index), Read 2: 91 cycles. The 10X single-cell transcriptome data were generated at the Eukaryotic Single-cell Genomics facility at Science for Life Laboratory in Stockholm, Sweden. Human single-cell transcriptomics data analysis with 10x Genomics. The count matrix for each sample was produced by Cell Ranger version 3. 1. 0 that processed, mapped, and counted raw sequencing data to the Cell Ranger GRCh38-3. 0. 0 genome and its corresponding annotation. Seurat package pipeline (v. 4. 0. 2)  93  was used for the downstream analysis.
Genes expressed in fewer than ten cells in each dataset were removed from further analysis. To keep only high-quality cells, the cells with less than 2000 or more than 25,000 transcripts and the cells with less than 1500 detected genes per cell were omitted. The cells with high mitochondrial content (more than 10%) were also excluded from the analysis. The putative doublets were predicted by Scrublet  94. The filtered datasets first were analyzed separately to extract the cells belonging to the neural crest-derived sympathoadrenal lineage and then integrated with Seurat function IntegrateData (2000 integration anchor features, 20 dims). The resulting integrated filtered dataset consisted of 3503 high-quality cells. The effects of cell cycle heterogeneity in gene expression were mitigated by regressing out the difference between G2M and S phase signatures by Seurat functions CellCycleScoring and ScaleData (vars.
to. regress = "CC. Difference"). Then to perform a graph-based clustering and visualization by UMAP, the first 30 principal components and 30 nearest neighbors were used. Louvain clustering algorithm with resolution equal to 0. 2 resulted into finding 13 clusters, two of them were removed due to containing the high doublet scores. The remaining clusters were re-analyzed using the same parameters except the resolution (30 PCs, 30 kNN, resolution = 0. 1) and the resulting seven clusters were merged based on the expression of classical cell type-specific markers into four biologically meaningful clusters. To check whether the expression of HTR3A gene in the "bridge" cells is not noise-derived, the exact Fisher test was applied after removal of the sympathoblast clusters from the dataset. Behavioral studies.
5 and 9. 5. Ethyl Ester (1500 μL) was added to extract the analytes and vortex-mixed (10 min), and the sample was then centrifuged for 10 min at 3000 × g. The Ethyl Ester layer was removed (800 μL) and placed into a recovery vial (Waters Corp. , Elstree, UK). The vial solution was then evaporated to dryness using a centrifugal vacuum evaporator (Eppendorf, USA). The samples were reconstituted in 200 μL (0. 1% formic acid in water), and 2 μL was injected onto the column. Quantification was performed using Shimadzu LabSolution software, version 5. 3 (Japan) using the integration peak area ratio of analyte and labeled IS. 5HT measurement by high-performance liquid chromatography with electrochemical detection (HPLC-ED). 5HT concentration was determined after the following experiments: (1) Pregnant BalbC mice were subjected to mild stress based on a published method  52  for 1 h daily from E10.
1 M citrate-phosphate buffer containing 0. 3 mM sodium octanesulfonate, 0. 1 mM EDTA and 7% acetonitrile (all reagents purchased from Sigma) (pH 2. 56). Decade II electrochemical detector (Antec Leyden, Netherlands) was equipped with a glassy carbon working electrode (+0. 85 V) and an Ag/AgCl reference electrode. Collected tissues were homogenized in 10 volumes of 0. 1 N HClO 4 containing 3,4dihydroxybenzylamine hydrobromide (internal standard, Sigma, St. Louis, USA) (25-250 pmol/mL) by an ultrasonic homogenizer (Sonopuls HD 2000. 2, Bandelin, Berlin, Germany), centrifuged at 16,500 × g for 20 min, and 5HT in the supernatant were measured. Peaks corresponding to 5HT were identified according to elution time of the standard solution, and the contents of substances were estimated as a ratio of the peak area of the internal standard solution to that of the specimen using software LabSolutions (Shimadzu, Japan).
The next day, the membranes were incubated for 1 h with HRP-linked secondary antibodies: goat anti-rabbit IgG (1:5000, CST, #7074) or horse anti-mouse IgG (1:5000, CST, #7076). Chemiluminescent detection was performed using Amersham ECL Prime (Cytiva, Marlborough, MA, USA) and either Azure 600 imaging system (Azure Biosystems, Dublin, CA, USA) or photosensitive film. GAPDH or α-tubulin served as loading controls. Protein band densities were quantified using ImageJ (Fiji) software (NIH, Bethesda, MD, USA), version 2. 1. 0/1. 53c. After a week, for each cell density, the fraction of wells containing neurospheres ≥50 µm in diameter was determined using an Olympus CKX41 light microscope with Lumenera Infinity 2 CCD camera and QuickPhoto Camera 2. 3 system (PROMICRA, Prague, Czech Republic). Frequencies of sphere-forming cells among experimental groups were calculated and compared using ELDA software  101.
Statistical analysis. Statistical analysis was done using GraphPad Prism 8. 1. 1 software. All datasets were checked for normality with Shapiro-Wilk test, as this test reliably works for small datasets. For datasets with normal distribution double-sided unpaired t-test was applied. For datasets which failed a test for normal distribution, Mann-Whitney test was applied. For comparisons between multiple groups for one variable the one-way ANOVA test was applied (Fig. 3d , Fig. 6a, c , l, Supplementary Fig. 7d, e, f ). In case one-way ANOVA test was showing significant differences, Tukey's multiple comparison test was used for a robust pairwise comparison of groups with unequal size (every mean to every other mean) or Dunnet's multiple comparison test for a pairwise comparison of groups to a control group.
Acknowledgements This work of J. S. , P. C. , L. C. , L. S. , and J. N. was supported by the  European Regional Development Fund -Project ENOCH  (No. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000868 ). J. S. acknowledges support from the  Czech Science Foundation  (No. GJ20-00987Y ). Experiments of V. I. M. , E. E. V. , and A. K. on mice and rats describing the effects of 5HT on the developing AM, long-term effects on CAs and CA-mediated behavior, AM size, as well as experiments with wild population of red-backed voles were supported by the  Russian Science Foundation  grant #  17-14-01353. M. E. K. was supported by the  Novo Nordisk Foundation  ( Postdoc fellowship in Endocrinology and Metabolism at International Elite Environments ,  NNF17OC0026874 ) and  Stiftelsen Riksbankens Jubileumsfond  ( Erik Rönnbergs  fond stipend).
M. T. , T. Z. , and J. K. acknowledge  CzechNanoLab Research Infrastructure  supported by  MEYS CR  ( LM2018110 ). M. T. and L. C. were financially supported by the  Brno City Municipality as Brno Ph. D. Talent Scholarship Holders. M. T. acknowledges  Martina Roeselova Memorial  fellowship. P. G. , M. P. , N. A. , and M. B. were supported by the  EU H2020 MSCA ITN projects "Serotonin and Beyond"  ( N 953327 ). N. A. and M. B. were supported by  German Federal Ministry of Education and Research  under the framework of the  EU-ERA-NET   NEURON  project; N. A. was supported by the project  73022475  of the  St. Petersburg State University  and the  Volkswagen Foundation. I. A. was supported by  Knut and Alice Wallenberg Research Foundation, Bertil Hallsten Research Foundation ,  Paradifference Foundation, Swedish Research Council, Austrian Science Fund (FWF) ,  ERC   Synergy grant  " KILL-OR-DIFFERENTIAT ",  EMBO Young Investigator Program ,  Cancer Fonden.
Data availability The raw and processed data of single-cell transcriptomic experiments generated in this study have been deposited in the GEO database under accession codes: GSE180861 (mouse), GSE195929 (human). The single-cell RNA-seq data of mouse adrenal gland from  Furlan et al. (2017)  used in this study are available in the GEO database under accession code GSE99933, mm10_ERCC genome used in this study is available in the RefSeq database under accession code GCF_000001635. 20. The data other than RNA-seq data generated in this study are provided in the Source data file.