Find research datasets worth reusing

openodc-odblOct 2021View details →

Porous single crystal unit-cell simulation database for ductile fracture by void growth and coalescence

Ductile fracture through void growth to coalescence occurs at the grain scale in numerous metallic alloys encountered in engineering applications. In order to perform mechanical homogenization of porous single crystals, a database of porous single crystal unit-cell simulation results has been gathered through Finite Element Modeling and Fast-Fourrier Transform simulations, respectively performed on Z-set and Amitex_FFTP. In these simulations, a cubic unit-cell with a unique central spherical void undergo axisymmetric mechanical loading. Mechanical simulations are performed within finite strain theory. Input parameters of interest are stress triaxiality, crystallographic orientation, initial porosity and strain hardening law type; results include macroscopic stress, macroscopic deformation gradient, porosity, void aspect ratio, ligament size and cell aspect ratio.

opencc-by-4.0Apr 2024View details →

A single-cell atlas of multiple myeloma disease evolution

A collection of computationally integrated (batch corrected) scRNA-seq and scTCR-seq data from a large cohort of bone marrow and peripheral blood samples from patients with multiple myeloma, myeloma precursor disease (MGUS and SMM) and non-cancer controls. <h3>Datasets</h3> <ul> <li>dataDescription.xlsx - Definition of dataset components.</li> <li>panImmune.h5ad - All cells types integrated gene expression object.</li> <li>Tcell.h5ad - T cells integrated object.</li> <li>Tcell-scTCR.csv - Single-cell TCR data.</li> <li>Botta_2023-predicted.h5ad - Single-cell gene expression data from Botta et al. analysed by label transfer.</li> <li>metadata.donor.csv - Donor metadata.</li> </ul> <h3>Published data acquisition</h3> Data shared through the gene expression omnibus (GEO) can be accessed for Maura et al. under accession GSE161195, Bailur et al. GSE163278, Oetjen et al. GSE120221, Granja et al. GSE139369, Zavidij et al. GSE124310, Kfoury et al. GSE143791, Botta et al. GSE205393, and Zheng et al. GSE156728. Data shared via dbGaP for Sklavenitis-Pistofidis et al. can be accessed under accession phs002476.v1.p1. Data shared online can be accessed for Stephenson et al. (via <a href="https://covid19cellatlas.org/">https://covid19cellatlas.org/</a>), Conde et al. (via <a href="https://www.tissueimmunecellatlas.org/">https://www.tissueimmunecellatlas.org/</a>), and Liu et al. (via <a href="https://explore.data.humancellatlas.org/projects/2ad191cd-bd7a-409b-9bd1-e72b5e4cce81">https://explore.data.humancellatlas.org/projects/2ad191cd-bd7a-409b-9bd1-e72b5e4cce81</a>). Single-cell data from <a href="https://doi.org/10.1016/j.ccell.2022.10.017" target="_blank" rel="noopener">Sklavenitis-Pistofidis et al.</a> and extended clinical data from <a href="https://doi.org/10.1038/s43018-023-00657-1">Maura et al.</a> (i.e. paraprotein) are omitted and must be acquired through direct contact with these authors. <h3>Contact information </h3> <ul> <li>Kane Foster - kane.foster.web@gmail.com</li> <li>Kwee Yong - kwee.yong@ucl.ac.uk</li> </ul> Please cite <a href="https://www.medrxiv.org/content/10.1101/2024.06.22.24309250v1">our pre-print</a> if you use this resource in your research.

opencc-by-4.0Oct 2022View details →

Data for - Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics

Large-scale Corynebacterium glutamicum data set with Segmentation and Tracking Annotation We provide five time-lapse sequences with manually corrected segmentation and tracking annotations of growing C. glutamicum cultivations. The dataset contains more than 1.4 million cell observations in 29k cell tracks and 14k cell divisions. We provide videos of the annotations (videos.zip) and the dataset in <a href="http://celltrackingchallenge.net/datasets/">Cell Tracking Challenge</a> format (ctc_format.zip). In the videos, cell contours are rendered in yellow, cell links between frames are colored red and cell divisions, and their links are colored in blue. Data Acquisition Corynebacterium glutamicum ATCC 13032 was cultivated in BHI-medium at 30°C in this study. From and overnight preculture, the main culture was inoculated the next day with a starting OD600 of 0.05 and grown at 120 rpm to a OD600 of 0.25. A chip was fabricated, according to <a href="https://doi.org/10.1039/D0LC00711K">(Täuber et al., 2020)</a>, and fixed to the microscope’s holder. The main culture cells were transferred to monolayer growth chambers (height = 720 nm) on the microfluidic chip. Flow through the microfluidic device was mediated by pressure driven pumps with a pressure of 100 mbar on the medium reservoir. The time-lapse phase contrast images of five monolayer growth chambers were taken every minute using an inverted microscope (Nikon Eclipse Ti2) with a 100x oil emersion objective and a DS-QI2 camera (Nikon) at 15 % relative DIA-illumination intensity and 100 ms exposure time. The spatial image resolution is 0.072 μm/px.

opencc-zeroNov 2022View details →

Processed single cell data from CODEX multiplexed imaging of the human intestine

We performed CODEX (co-detection by indexing) multiplexed imaging on 64 sections of the human intestine (~16 mm2) from 8 donors (B004, B005, B006, B008, B009, B010, B011, and B012) using a panel of 57 oligonucleotide-barcoded antibodies. Subsequently, images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), single cell segmentation, and column marker z-normalization by tissue. The outputs of this process were data frames of 2.6 million cells with 57 antibody fluorescence values quantified from each marker. Each cell has its cell type, cellular neighborhood, community of neighborhooods, and tissue unit defined with x, y coordinates representing pixel location in the original image. This is from a total of 25 cell types, 20 multicellular neighborhoods, 10 communities of neighborhoods, and 3 tissue segments that could be used to understand the cellular interactions, composition, and structure of the human intestine from the duodenum to the sigmoid colon and understand differences between different areas of the intestine. This data could be used as a healthy baseline to compare other single-cell datasets of the human intestine, particularly multiplexed imaging ones. The overall structure of the datasets is individual cells segmented out in each row. Columns MUC2 through CD161 are the markers used for clustering the cell types. These are the columns that are the values of the antibody staining the target protein within the tissue quantified at the single-cell level. This value is the per cell/area averaged fluorescent intensity that has subsequently been z normalized along each column as described above. OLFM4 through MUC6 were captured in the quantification but not used within the clustering of cell types. Other columns are explained in the table in the Usage Notes section below. Along with this main data table, there is also a donor metadata table that links the donor ids to clinical metadata such as: age, sex, race, BMI, history of diabetes, history of cancer, history of hypertension, and history of gastorintestinal disease. The raw imaging data can be found at (<a href="https://portal.hubmapconsortium.org/">https://portal.hubmapconsortium.org/</a>). We have created a landing page with links to all the raw dataset IDs and the HuBMAP ID for this Collection is HBM692.JRZB.356 and the DOI is:10.35079/HBM692.JRZB.356. This can be used to also pair it with the matched snRNAseq and snATACseq for each section of tissue.

opencc-by-4.0Nov 2022View details →

single-cell RNAseq data (data set 1) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data

The present dataset (dataset1) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CRC samples downloaded from the GEO website  (GSE81861). see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data and github: https://github.com/sysbiolux/scFASTCORMICS For more information, version updates of the scFASTCORMICS.

opencc-by-4.0Oct 2022View details →

A single-cell transcriptional gradient in human cutaneous memory T cells restricts Th17/Tc17 identity

In our manuscript, we utilized scRNA-seq libraries we generated from: -8 human psoriatic skin samples and 7 healthy control skin samples (ZIST.rds) -3 human psoriatic skin samples before and after tildrakizumab treatment (three_tildra_Trm1.rds)  These *rds files are the Seurat objects for these data sets post-filtering and integration. For raw sequencing data corresponding to these samples, access can be found under accession number EGA: S00001005271. We also utilized bulk RNAseq data generated from CRISPR-Cas9 knockout of ZFP36L2 in human CD4 T cells from 3 different donors. The count matrices for each of these individual samples is uploaded here alongside a key explaining what each of the samples are with filenames that also correspond to the raw fastq files submitted at the European Genome-Phenome Archive (EGA), under accession number EGA: S00001005271. There are two replicates for each sample. All methods underlying the generation and analysis of these datasets can be found in the original manuscript:  Cook CP, Taylor M, Liu Y, et al. A single-cell transcriptional gradient in human cutaneous memory T cells restricts Th17/Tc17 identity. Cell Rep Med. 2022;3(8):100715. doi:10.1016/j.xcrm.2022.100715 Any additional questions or information requests can be addressed to Jeffrey.cheng@ucsf.edu or cook.675@berkeley.edu

opencc-by-4.0Jan 2022View details →

The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data

In our work, we implemented the ABC-model and could show that one assay for measuring the openness of enhancers is sufficient. Further, we propose a generalised calculation of the ABC-score, which describes enhancer activity in a gene-specific manner, and which includes all TSS, without requiring any additional data. We combined our implementation of the ABC-score with an approach to quantify TF binding affinity into STARE: a framework to derive TF affinities to genes. STARE was also designed for potential application on single-cell data. You can find the code in our <a href="https://github.com/schulzlab/stare">GitHub repository</a> and more details in our <a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtad062">publication</a>. We provide the data for the validation of our ABC-implementation on two CRISPR-screens. We also provide the results of our analysis of single-cell data of the human heart with STARE. All data is in hg19. Content: <ul> <li>CRISPRi_screens: One file for each CRISPRi-screen with interactions that were used to plot precision-recall curves, containing columns for different ABC scoring versions.</li> <li>Enformer: Similar to the CRISPRi_screens, but containing columns for different calculations for Enformer's predicted expression change upon in silico mutagenesis of the enhancer region.</li> <li>K562_CandidateEnhancer: K562 enhancer with the 4th column for enhancer activity, one file for each activity representation that was measured.</li> <li>K562_ABC_Predictions: Regular ABC-scores and generalised ABC-scores for each activity measurement. The files contain all scored interactions for a 10MB window, without any cut-off. We also included the results of the implementation of the ABC-score of Fulco et al. (2019).</li> <li>STARE_Hocker_*: Whole STARE output for human heart single-cell data, one for regular ABC, generalised ABC, generalised ABC with average Hi-C matrix and one based on co-accessibility analysis. All approaches were run with a 5 MB window (except for GeneralisedABC500kb), the ABC-based runs with a score cut-off of 0.02. Each folder contains two subdirectories, one for the ABC-scoring and one for the Gene-TF affinity matrices. The 'ABC_output' also contains a GeneInfo file for each cell type, summarising different attributes per gene.</li> <li>INVOKE_Hocker_*: Folder with the input and output of INVOKE (see https://github.com/schulzlab/tepic), based on the STARE runs. CS genes stands for cell type-specific genes, defined as genes with a z-score across cell types of ≥ 2 and TPM ≥ 0.5. The INVOKE commands were as follows: <ul> <li>Rscript INVOKE.R --dataDir=<TF-Gene matrix> --outDir=<out_path> --response=Expression --regularization=E --performance=TRUE --outerCV=10 --seed=1234</li> </ul> </li> </ul> Importantly, the results are based on data from the following publications: <ul> <li>CRISPRi-screens: <ul> <li>Gasperini, Molly, Andrew J. Hill, José L. McFaline-Figueroa, Beth Martin, Seungsoo Kim, Melissa D. Zhang, Dana Jackson, et al. “A Genome-Wide Framework for Mapping Gene Regulation via Cellular Genetic Screens.” Cell 176, no. 1–2 (January 2019): 377-390.e19. https://doi.org/10.1016/j.cell.2018.11.029.</li> <li> Schraivogel, Daniel, Andreas R. Gschwind, Jennifer H. Milbank, Daniel R. Leonce, Petra Jakob, Lukas Mathur, Jan O. Korbel, Christoph A. Merten, Lars Velten, and Lars M. Steinmetz. “Targeted Perturb-Seq Enables Genome-Scale Genetic Screens in Single Cells.” Nature Methods 17, no. 6 (June 2020): 629–35. https://doi.org/10.1038/s41592-020-0837-5. </li> <li> Fulco, Charles P., Joseph Nasser, Thouis R. Jones, Glen Munson, Drew T. Bergman, Vidya Subramanian, Sharon R. Grossman, et al. “Activity-by-Contact Model of Enhancer–Promoter Regulation from Thousands of CRISPR Perturbations.” Nature Genetics 51, no. 12 (December 2019): 1664–69. https://doi.org/10.1038/s41588-019-0538-0. </li> </ul> </li> <li>Enformer model: Avsec, Žiga, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, and David R. Kelley. “Effective Gene Expression Prediction from Sequence by Integrating Long-Range Interactions.” Nature Methods 18, no. 10 (October 2021): 1196–1203. https://doi.org/10.1038/s41592-021-01252-x.</li> <li>K562 predictions and average Hi-C matrix: Fulco, Charles P., Joseph Nasser, Thouis R. Jones, Glen Munson, Drew T. Bergman, Vidya Subramanian, Sharon R. Grossman, et al. “Activity-by-Contact Model of Enhancer–Promoter Regulation from Thousands of CRISPR Perturbations.” Nature Genetics 51, no. 12 (December 2019): 1664–69. https://doi.org/10.1038/s41588-019-0538-0.</li> <li>Hi-C matrix for K562 predictions: Rao, S. et al. (2014). A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell, 159(7), 1665–1680</li> <li>STARE and INVOKE runs: Hocker, J. D. et al. (2021). Cardiac cell type–specific gene regulatory programs and disease risk association. Science Advances, 7(20), eabf1444</li> <li>H3K27ac HiChIP for STARE runs: Anene-Nzelu, C. G. et al. (2020). Assigning Distal Genomic Enhancers to Cardiac Disease–Causing Genes. Circulation, 142(9), 910–912</li> <li>INVOKE software: Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction Schmidt et al., Nucleic Acids Research 2016; doi: 10.1093/nar/gkw1061</li> </ul>

opencc-by-4.0Sep 2022View details →

Additional files of scTensor paper "scTensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data"

Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression. However, the analytical methods are still not mature; such methods cannot detect CCIs and the related L-R pairs simultaneously or also are not appropriate to detect many-to-many CCIs. In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. Through extensive studies with simulated and empirical datasets, we have shown that scTensor could detect some hypergraphs, which cannot be detected by conventional methods, especially when those CCIs are many-to-many relationships.

Single-cell naïve IgM VH:VL sequence data from 22 Kymice

Single-cell VH:VL sequencing data derived from naïve B-cells isolated from 22 Kymice. This dataset is published as part of the review process for the following preprint: https://www.biorxiv.org/content/10.1101/2022.06.27.497709v1.

pSCoPE: Prioritized Single-Cell Proteomics (data for generating publication figures)

Major aims of single-cell proteomics include increasing the consistency, sensitivity, and depth of protein quantification, especially for proteins and modifications of biological interest. To simultaneously advance all these aims, we developed prioritized Single Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while analyzing identifiable peptides at full duty-cycle, thus increasing proteome depth. These strategies increased the sensitivity, data completeness, and proteome coverage over 2-fold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport. This protein covariation within a treatment condition was similar across the treatment conditions and coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at: <a href="http://scp.slavovlab.net/pSCoPE">scp.slavovlab.net/pSCoPE</a>   The files contained in this .zip directory are necessary for replicating the analysis and figures associated with the pSCoPE manuscript.

opencc-zeroJan 2023View details →

Single-cell profiling identifies ACE+ granuloma macrophages as a non-permissive niche for intracellular bacteria during persistent Salmonella infection

Macrophages mediate key antimicrobial responses against intracellular bacterial pathogens, such as Salmonella enterica. Yet, they can also act as a permissive niche for these pathogens to persist in infected tissues within granulomas, which are immunological structures comprised of macrophages and other immune cells. We apply single-cell transcriptomics to investigate macrophage functional diversity during persistent Salmonella enterica serovar Typhimurium (STm) infection in mice. We identify determinants of macrophage heterogeneity in infected spleens and describe populations of distinct phenotypes, functional programming, and spatial localization. Using a STm mutant with impaired ability to polarize macrophage phenotypes, we find that angiotensin converting enzyme (ACE) defines a granuloma macrophage population that is non-permissive for intracellular bacteria and their abundance anticorrelates with tissue bacterial burden. Disruption of pathogen control by neutralizing TNF is linked to preferential depletion of ACE+ macrophages in infected tissues. Thus ACE+ macrophages have limited capacity to serve as cellular niche for intracellular bacteria to establish persistent infection.

Text-fig. 48. Scanning electron microscope (SEM, a, b, d–i) and synchrotron radiation X-ray tomographic microscopy (SRXTM, c) images of "One-seeded fruit sp. 2" (a–c), "Unassigned, unnamed fruits" (d–f), Pazliopsis sp. (g, h) and "Follicular fruit with exotestal seeds" (i); Catefica locality, Portugal. a) Lateral view of fruit showing remains of tepals (te) and a single stamen (st); b) Detail of fruit surface showing short, scale-like, peltate trichomes (arrows); c) Transverse section (orthoslice xy0475) of fruit containing a single seed showing tepals (te) and fruit surface with peltate trichomes (arrow); note partial preservation of internal nutritive tissue; d) Fruit in lateral view showing the almost smooth epidermis with scattered openings; e) Detail of fruit surface from (d) showing the scattered openings in the epidermis interpreted as burst secretory cells (arrows); f) Dorsi-ventral view of tiny fruit with an irregular surface; g) Lateral view of exotestal seed assigned to cf. Pazliopsis sp.; h) Detail of fruit surface of seed in (g) showing faint facets of outer palisade layer with fine jigsaw-puzzle outlines of the anticlinal walls; i) Lateral view of fragmentary follicular fruit showing two exposed exotestal seeds. Specimens, Catefica 153-S174314 (a–c), Catefica 50-S170420 (d, e), Catefica 152-S174300 (f), Catefica 49-S172319 (g, h), Catefica MM158-P0272 (i). Scale bars = 300 Μm (a, c, d, f, g, i), 100 Μm (e), 50 Μm (b, h). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms

Text-fig. 48. Scanning electron microscope (SEM, a, b, d–i) and synchrotron radiation X-ray tomographic microscopy (SRXTM, c) images of "One-seeded fruit sp. 2" (a–c), "Unassigned, unnamed fruits" (d–f), Pazliopsis sp. (g, h) and "Follicular fruit with exotestal seeds" (i); Catefica locality, Portugal. a) Lateral view of fruit showing remains of tepals (te) and a single stamen (st); b) Detail of fruit surface showing short, scale-like, peltate trichomes (arrows); c) Transverse section (orthoslice xy0475) of fruit containing a single seed showing tepals (te) and fruit surface with peltate trichomes (arrow); note partial preservation of internal nutritive tissue; d) Fruit in lateral view showing the almost smooth epidermis with scattered openings; e) Detail of fruit surface from (d) showing the scattered openings in the epidermis interpreted as burst secretory cells (arrows); f) Dorsi-ventral view of tiny fruit with an irregular surface; g) Lateral view of exotestal seed assigned to cf. Pazliopsis sp.; h) Detail of fruit surface of seed in (g) showing faint facets of outer palisade layer with fine jigsaw-puzzle outlines of the anticlinal walls; i) Lateral view of fragmentary follicular fruit showing two exposed exotestal seeds. Specimens, Catefica 153-S174314 (a–c), Catefica 50-S170420 (d, e), Catefica 152-S174300 (f), Catefica 49-S172319 (g, h), Catefica MM158-P0272 (i). Scale bars = 300 Μm (a, c, d, f, g, i), 100 Μm (e), 50 Μm (b, h).

Text-fig. 47. Synchrotron radiation X-ray tomographic microscopy (SRXTM) images of "One-seeded fruit sp. 1"; Catefica locality, Portugal. a, b) Lateral view of fruits showing slightly sinuous ventral margin and the curved stalk; c, d) Longitudinal sections perpendicular to each other through the median part of fruit and its single seed (c, orthoslice yz0652, d, xz0739) showing the bitegmic seed closely adhering to the fruit wall (fw); the several cell layer thick outer integument (oi) and the membranous inner integument (ii); note the vascular bundle (vb) branching into a dorsal and lateral bundle near the base of the fruit; e) Transverse section (orthoslice xy0600) showing fruit wall (fw) and outer (oi) and inner (ii) integuments of the seed; f) Longitudinal section (orthoslice yz0871) through the micropylar region showing micropyle (mi) formed from membranous inner integument (ii). Specimens, Catefica 49-S174927 (a), Catefica 49-S174923 (b, f), Catefica 49-S174769 (c–e). Scale bars = 300 Μm (a–d), 100 Μm (e, f). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms

Text-fig. 47. Synchrotron radiation X-ray tomographic microscopy (SRXTM) images of "One-seeded fruit sp. 1"; Catefica locality, Portugal. a, b) Lateral view of fruits showing slightly sinuous ventral margin and the curved stalk; c, d) Longitudinal sections perpendicular to each other through the median part of fruit and its single seed (c, orthoslice yz0652, d, xz0739) showing the bitegmic seed closely adhering to the fruit wall (fw); the several cell layer thick outer integument (oi) and the membranous inner integument (ii); note the vascular bundle (vb) branching into a dorsal and lateral bundle near the base of the fruit; e) Transverse section (orthoslice xy0600) showing fruit wall (fw) and outer (oi) and inner (ii) integuments of the seed; f) Longitudinal section (orthoslice yz0871) through the micropylar region showing micropyle (mi) formed from membranous inner integument (ii). Specimens, Catefica 49-S174927 (a), Catefica 49-S174923 (b, f), Catefica 49-S174769 (c–e). Scale bars = 300 Μm (a–d), 100 Μm (e, f).

Text-fig. 2. Scanning electron microscope (SEM, a, c–f) and synchrotron radiation X-ray tomographic microscopy (SRXTM, b, g) images of the flower of Mugideiriflora portugallica (a, b) and fruits, seeds and pollen of Canrightia resinifera (c–g); Catefica locality, Portugal. a) Oblique, apical view of flower showing multiparted organization with numerous laminar tepals, stamens that are rhomboidal and flattened in transverse section and carpels that are borne on the short conical apex of the receptacle; b) Transverse section (orthoslice xy0800) through basal part of flower showing the elongate bases of the laminar tepals and the flattened rhomboidal bases of the stamens; c) Fruit in lateral view showing irregular surface resulting from the abundant resin bodies in the fruit and hypanthium wall, scars from stamens on the rim of the hypanthium (arrowheads) and the lobed apical stigmatic region (st); d) Broken fruit with one or two seeds missing but showing three pendant, orthotropous seeds with pointed micropylar regions (mi) and a finely pitted crystalliferous endotesta; note the remains of the apical vascular bundles (vb); e) Single seed isolated from a fruit showing two distinct bundles (vb) still attached apically to the chalazal region of the seed, the pointed micropyle (mi) and the finely pitted surface of the crystalliferous endotesta; f) Monocolpate pollen from stigmatic region of fruit showing the long colpus and coarse reticulum; g) Transverse section (orthoslice xy0705) through a fruit showing four seeds all with radially elongated endothelium cells formed from the inner epidermis of the tegmen (asterisks). Specimens, Catefica 150-S174254 (a, b), Catefica 49-S170377 (c), Catefica 49-S170372 (d), Catefica 50-S170401 (e), Catefica 50-S170404 (f), Catefica 50-S174906 (g). Scale bars = 300 Μm (a–e, g), 6 Μm (f). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms

Text-fig. 2. Scanning electron microscope (SEM, a, c–f) and synchrotron radiation X-ray tomographic microscopy (SRXTM, b, g) images of the flower of Mugideiriflora portugallica (a, b) and fruits, seeds and pollen of Canrightia resinifera (c–g); Catefica locality, Portugal. a) Oblique, apical view of flower showing multiparted organization with numerous laminar tepals, stamens that are rhomboidal and flattened in transverse section and carpels that are borne on the short conical apex of the receptacle; b) Transverse section (orthoslice xy0800) through basal part of flower showing the elongate bases of the laminar tepals and the flattened rhomboidal bases of the stamens; c) Fruit in lateral view showing irregular surface resulting from the abundant resin bodies in the fruit and hypanthium wall, scars from stamens on the rim of the hypanthium (arrowheads) and the lobed apical stigmatic region (st); d) Broken fruit with one or two seeds missing but showing three pendant, orthotropous seeds with pointed micropylar regions (mi) and a finely pitted crystalliferous endotesta; note the remains of the apical vascular bundles (vb); e) Single seed isolated from a fruit showing two distinct bundles (vb) still attached apically to the chalazal region of the seed, the pointed micropyle (mi) and the finely pitted surface of the crystalliferous endotesta; f) Monocolpate pollen from stigmatic region of fruit showing the long colpus and coarse reticulum; g) Transverse section (orthoslice xy0705) through a fruit showing four seeds all with radially elongated endothelium cells formed from the inner epidermis of the tegmen (asterisks). Specimens, Catefica 150-S174254 (a, b), Catefica 49-S170377 (c), Catefica 49-S170372 (d), Catefica 50-S170401 (e), Catefica 50-S170404 (f), Catefica 50-S174906 (g). Scale bars = 300 Μm (a–e, g), 6 Μm (f).

opencc-zeroJan 2023View details →

Data for: Emergent dynamics of adult stem cell lineages from single nucleus and single cell RNA-Seq of Drosophila testes

Proper differentiation of sperm from germline stem cells, essential for production of the next generation, requires dramatic changes in gene expression that drive remodeling of almost all cellular components, from chromatin to organelles to cell shape itself. Here we provide a single nucleus and single cell RNA-seq resource covering all of spermatogenesis in Drosophila starting from in-depth analysis of adult testis single nucleus RNA-seq (snRNA-seq) data from the Fly Cell Atlas (FCA) study (Li et al., 2022). With over 44,000 nuclei and 6,000 cells analyzed, the data provide identification of rare cell types, mapping of intermediate steps in differentiation, and the potential to identify new factors impacting fertility or controlling differentiation of germline and supporting somatic cells. We justify assignment of key germline and somatic cell types using combinations of known markers, in situ hybridization, and analysis of extant protein traps. Comparison of single cell and single nucleus datasets proved particularly revealing of dynamic developmental transitions in germline differentiation. To complement the web-based portals for data analysis hosted by the FCA, we provide datasets compatible with commonly used software such as Seurat and Monocle. The foundation provided here will enable communities studying spermatogenesis to interrogate the datasets to identify candidate genes to test for function in vivo.

opencc-by-4.0Jan 2023View details →

Dual-modality imaging of immunofluorescence and imaging mass cytometry for high-resolution whole slide imaging with accurate single-cell segmentation

Imaging mass cytometry (IMC) is a powerful multiplexed tissue imaging technology that allows simultaneous detection of more than 30 makers on a single slide. It has been increasingly used for single-cell based spatial phenotyping in a wide range of samples. However, it only acquires a small, rectangle field of view (FOV) with a low image resolution that hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dementional IMC on the same tissue slide. Our computational pipeline uses the whole slide image (WSI) of IF as spatial reference,  integrates small FOV IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images and demonstrated the advantage of the dual-modality imaging strategy.

opencc-zeroFeb 2023View details →

scRNA data from: Organization of the human Intestine at single cell resolution

The human adult intestinal system is a complex organ that is approximately 9 meters long and performs a variety of complex functions including digestion, nutrient absorption, and immune surveillance. We performed snRNA-seq on 8 regions of of the human intestine (duodenum, proximal-jejunum, mid-jejunum, ileum, ascending colon, transverse colon, descending colon, and sigmoid colon) from 9 donors (B001, B004, B005, B006, B008, B009, B010, B011, and B012). In the corresponding paper, we find cell compositions differ dramatically across regions of the intestine and demonstrate the complexity of epithelial subtypes. We map gene regulatory differences in these cells suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation, and organization in the human intestine, and serve as an important reference map for understanding human biology and disease.

opencc-by-4.0Aug 2022View details →

Single cell annotations in 3D bacterial biofilms of Vibrio cholerae

This repository contains the data to reproduce the study `Single-cell segmentation in bacterial biofilms with optimized convolutional neural networks enables tracking of cell lineages and measurements of growth rates` by Jelli, Ohmura, Netter, et al. It contains the following four subfolders:   - `training-data-from-experimentally-acquired-images`: the dataset that was created to train segmentation models - `trained-models`: contains five models trained on the trainind dataset to be used for predictions - `segmentation-predictions-for-different-species`: segmentations based on a trained model on biofilms of different species - `training-data-synthetic`: Synthetic microscope images and their corresponding label images together with scripts to create the data

opencc-by-4.0Mar 2022View details →

Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease

This dataset repository corresponds to the project Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease.