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  • Species: human
  • Number of cells: 82843
  • Number of downloads: 67
  • Study size: 164MB
  • Uploaded at: Jul 18, 2022

spatialffpeCancer

CosMx SMI FFPE Dataset of non-small-cell lung cancer tissue (Lung13)

NanoString

This is an open-source dataset generated by NanoString CosMx SMI technology. Transcripts are quantified per cells that are segmented from the images. We included all available metadata of downstream analysis from NanoString. For more information, please visit: http://nanostring-public-share.s3.us-west-2.amazonaws.com/SMI-Compressed/SMI-ReadMe.html

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  • Species: human
  • Number of cells: 73997
  • Number of downloads: 32
  • Study size: 139MB
  • Uploaded at: Jul 15, 2022

Cancerspatialffpe

CosMx SMI FFPE Dataset of non-small-cell lung cancer tissue (Lung12)

NanoString

This is an open-source dataset generated by NanoString CosMx SMI technology. Transcripts are quantified per cells that are segmented from the images. We included all available metadata of downstream analysis from NanoString. For more information, please visit: http://nanostring-public-share.s3.us-west-2.amazonaws.com/SMI-Compressed/SMI-ReadMe.html

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  • Species: human
  • Number of cells: 252
  • Number of downloads: 49
  • Study size: 55MB
  • Uploaded at: Jul 8, 2022

AtlasspatialBrain

Spatial Organ Atlas (SOA) Human Brain Demonstration (beta)

NanoString

This cohort contains 5 samples: Sex: Male, Race: White, Age Range: 70 years to 90 years, BMI: 22 to 29; Whole Transcriptome Atlas Profiling​: >18,000 genes​, 5μm FFPE tissue section​; Area of Illumination (AOI) Profiling Strategy:​252 AOIs total, Subregions of cortex and hippocampus,​Cell type enriched and geometric areas of illumination; Morphology Markers:​NeuN: Neurons, Iba1: ​Microglia, GFAP: Astrocytes, DNA: Nuclei.

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  • Species: human
  • Number of cells: 7434
  • Number of downloads: 33
  • Study size: 288MB
  • Uploaded at: Jun 18, 2022

The Mammalian Spermatogenesis Single-Cell Transcriptome, from Spermatogonial Stem Cells to Spermatids (StaPut spermatids)

Brian P Hermann, Keren Cheng, Anukriti Singh, Lorena Roa-De La Cruz, Kazadi N Mutoji, I-Chung Chen, Heidi Gildersleeve, Jake D Lehle, Max Mayo, Birgit Westernströer, Nathan C Law, Melissa J Oatley, Ellen K Velte, Bryan A Niedenberger, Danielle Fritze, Sherman Silber, Christopher B Geyer, Jon M Oatley, John R McCarrey

Spermatogenesis is a complex and dynamic cellular differentiation process critical to male reproduction and sustained by spermatogonial stem cells (SSCs). Although patterns of gene expression have been described for aggregates of certain spermatogenic cell types, the full continuum of gene expression patterns underlying ongoing spermatogenesis in steady state was previously unclear. Here, we catalog single-cell transcriptomes for >62,000 individual spermatogenic cells from immature (postnatal day 6) and adult male mice and adult men. This allowed us to resolve SSC and progenitor spermatogonia, elucidate the full range of gene expression changes during male meiosis and spermiogenesis, and derive unique gene expression signatures for multiple mouse and human spermatogenic cell types and/or subtypes. These transcriptome datasets provide an information-rich resource for studies of SSCs, male meiosis, testicular cancer, male infertility, or contraceptive development, as well as a gene expression roadmap to be emulated in efforts to achieve spermatogenesis in vitro.

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  • Species: human
  • Number of cells: 8158
  • Number of downloads: 53
  • Study size: 63MB
  • Uploaded at: Jun 18, 2022

Spatially discrete signalling niches regulate fibroblast heterogeneity in human lung cancer (Target lung)

CJ Hanley, S Waise, R Parker, MA Lopez, J Taylor, LM Kimbley, J West, CH Ottensmeier, MJJ Rose-Zerilli, GJ Thomas

Fibroblasts are functionally heterogeneous cells, capable of promoting and suppressing tumour progression. Across cancer types, the extent and cause of this phenotypic diversity remains unknown. We used single-cell RNA sequencing and multiplexed immunohistochemistry to examine fibroblast heterogeneity in human lung and non-small cell lung cancer (NSCLC) samples. This identified seven fibroblast subpopulations: including inflammatory fibroblasts and myofibroblasts (representing terminal differentiation states), quiescent fibroblasts, proto-myofibroblasts (x2) and proto-inflammatory fibroblasts (x2). Fibroblast subpopulations were variably distributed throughout tissues but accumulated at discrete niches associated with differentiation status. Bioinformatics analyses suggested TGF-β1 and IL-1 as key regulators of myofibroblastic and inflammatory differentiation respectively. However, in vitro analyses showed that whilst TGF-β1 stimulation in combination with increased tissue tension could induce myofibroblast marker expression, it failed to fully re-capitulate ex-vivo phenotypes. Similarly, IL-1β treatment only induced upregulation of a subset of inflammatory fibroblast marker genes. In silico modelling of ligand-receptor signalling identified additional pathways and cell interactions likely to be involved in fibroblast activation, which can be examined using publicly available R shiny applications (at the following links: myofibroblast activation and inflammatory fibroblast activation). This highlighted a potential role for IL-11 and IL-6 (among other ligands) in myofibroblast and inflammatory fibroblast activation respectively. This analysis provides valuable insight into fibroblast subtypes and differentiation mechanisms in NSCLC.

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  • Species: human
  • Number of cells: 2147
  • Number of downloads: 29
  • Study size: 25MB
  • Uploaded at: Jun 18, 2022

Spatially discrete signalling niches regulate fibroblast heterogeneity in human lung cancer (Merged stromal cell)

CJ Hanley, S Waise, R Parker, MA Lopez, J Taylor, LM Kimbley, J West, CH Ottensmeier, MJJ Rose-Zerilli, GJ Thomas

Fibroblasts are functionally heterogeneous cells, capable of promoting and suppressing tumour progression. Across cancer types, the extent and cause of this phenotypic diversity remains unknown. We used single-cell RNA sequencing and multiplexed immunohistochemistry to examine fibroblast heterogeneity in human lung and non-small cell lung cancer (NSCLC) samples. This identified seven fibroblast subpopulations: including inflammatory fibroblasts and myofibroblasts (representing terminal differentiation states), quiescent fibroblasts, proto-myofibroblasts (x2) and proto-inflammatory fibroblasts (x2). Fibroblast subpopulations were variably distributed throughout tissues but accumulated at discrete niches associated with differentiation status. Bioinformatics analyses suggested TGF-β1 and IL-1 as key regulators of myofibroblastic and inflammatory differentiation respectively. However, in vitro analyses showed that whilst TGF-β1 stimulation in combination with increased tissue tension could induce myofibroblast marker expression, it failed to fully re-capitulate ex-vivo phenotypes. Similarly, IL-1β treatment only induced upregulation of a subset of inflammatory fibroblast marker genes. In silico modelling of ligand-receptor signalling identified additional pathways and cell interactions likely to be involved in fibroblast activation, which can be examined using publicly available R shiny applications (at the following links: myofibroblast activation and inflammatory fibroblast activation). This highlighted a potential role for IL-11 and IL-6 (among other ligands) in myofibroblast and inflammatory fibroblast activation respectively. This analysis provides valuable insight into fibroblast subtypes and differentiation mechanisms in NSCLC.

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  • Species: human
  • Number of cells: 68557
  • Number of downloads: 13
  • Study size: 2GB
  • Uploaded at: Jun 18, 2022

Single-nucleus transcriptome analysis of human brain immune response in patients with severe COVID-19

John F. Fullard, Hao-Chih Lee, Georgios Voloudakis, Shengbao Suo, , Zhiping Shao, Cyril Peter, Wen Zhang, Shan Jiang, André Corvelo, Heather Wargnier, Emma Woodoff-Leith, Dushyant P. Purohit, Sadhna Ahuja, Nadejda M. Tsankova Nathalie Jette, Gabriel E. Hoffman, Schahram Akbarian, Mary Fowkes, John F. Crary, Guo-Cheng Yuan, and Panos Roussos

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has been associated with neurological and neuropsychiatric illness in many individuals. We sought to further our understanding of the relationship between brain tropism, neuro-inflammation, and host immune response in acute COVID-19 cases.

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  • Species: human
  • Number of cells: 14156
  • Number of downloads: 19
  • Study size: 121MB
  • Uploaded at: Jun 18, 2022

Single-nucleus cross-tissue molecular reference maps to decipher disease gene function (Immune)

Gokcen Eraslan, Eugene Drokhlyansky, Shankara Anand, Ayshwarya Subramanian, Evgenij Fiskin, Michal Slyper, Jiali Wang, Nicholas Van Wittenberghe, John M. Rouhana, Julia Waldman, Orr Ashenberg, Danielle Dionne, Thet Su Win, Michael S. Cuoco, Olena Kuksenko, Philip A. Branton, Jamie L. Marshall, Anna Greka, Gad Getz, Ayellet V. Segrè, François Aguet, Orit Rozenblatt-Rosen, Kristin G. Ardlie and Aviv Regev

Understanding the function of genes and their regulation in tissue homeostasis and disease requires knowing the cellular context in which genes are expressed in tissues across the body. Single cell genomics allows the generation of detailed cellular atlases in human tissues, but most efforts are focused on single tissue types. Here, we establish a framework for profiling multiple tissues across the human body at single-cell resolution using single nucleus RNA-Seq (snRNA-seq), and apply it to 8 diverse, archived, frozen tissue types (three donors per tissue). We apply four snRNA-seq methods to each of 25 samples from 16 donors, generating a cross-tissue atlas of 209,126 nuclei profiles, and benchmark them vs. scRNA-seq of comparable fresh tissues. We use a conditional variational autoencoder (cVAE) to integrate an atlas across tissues, donors, and laboratory methods. We highlight shared and tissue-specific features of tissue-resident immune cells, identifying tissue-restricted and non-restricted resident myeloid populations. These include a cross-tissue conserved dichotomy between LYVE1- and HLA class II-expressing macrophages, and the broad presence of LAM-like macrophages across healthy tissues that is also observed in disease. For rare, monogenic muscle diseases, we identify cell types that likely underlie the neuromuscular, metabolic, and immune components of these diseases, and biological processes involved in their pathology. For common complex diseases and traits analyzed by GWAS, we identify the cell types and gene modules that potentially underlie disease mechanisms. The experimental and analytical frameworks we describe will enable the generation of large-scale studies of how cellular and molecular processes vary across individuals and populations.

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  • Species: human
  • Number of cells: 209126
  • Number of downloads: 15
  • Study size: 2GB
  • Uploaded at: Jun 18, 2022

Single-nucleus cross-tissue molecular reference maps to decipher disease gene function (Full atlas)

Gokcen Eraslan, Eugene Drokhlyansky, Shankara Anand, Ayshwarya Subramanian, Evgenij Fiskin, Michal Slyper, Jiali Wang, Nicholas Van Wittenberghe, John M. Rouhana, Julia Waldman, Orr Ashenberg, Danielle Dionne, Thet Su Win, Michael S. Cuoco, Olena Kuksenko, Philip A. Branton, Jamie L. Marshall, Anna Greka, Gad Getz, Ayellet V. Segrè, François Aguet, Orit Rozenblatt-Rosen, Kristin G. Ardlie and Aviv Regev

Understanding the function of genes and their regulation in tissue homeostasis and disease requires knowing the cellular context in which genes are expressed in tissues across the body. Single cell genomics allows the generation of detailed cellular atlases in human tissues, but most efforts are focused on single tissue types. Here, we establish a framework for profiling multiple tissues across the human body at single-cell resolution using single nucleus RNA-Seq (snRNA-seq), and apply it to 8 diverse, archived, frozen tissue types (three donors per tissue). We apply four snRNA-seq methods to each of 25 samples from 16 donors, generating a cross-tissue atlas of 209,126 nuclei profiles, and benchmark them vs. scRNA-seq of comparable fresh tissues. We use a conditional variational autoencoder (cVAE) to integrate an atlas across tissues, donors, and laboratory methods. We highlight shared and tissue-specific features of tissue-resident immune cells, identifying tissue-restricted and non-restricted resident myeloid populations. These include a cross-tissue conserved dichotomy between LYVE1- and HLA class II-expressing macrophages, and the broad presence of LAM-like macrophages across healthy tissues that is also observed in disease. For rare, monogenic muscle diseases, we identify cell types that likely underlie the neuromuscular, metabolic, and immune components of these diseases, and biological processes involved in their pathology. For common complex diseases and traits analyzed by GWAS, we identify the cell types and gene modules that potentially underlie disease mechanisms. The experimental and analytical frameworks we describe will enable the generation of large-scale studies of how cellular and molecular processes vary across individuals and populations.

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  • Species: human
  • Number of cells: 98366
  • Number of downloads: 6
  • Study size: 776MB
  • Uploaded at: Jun 18, 2022

Single-Cell Transcriptomic Comparison of Human Fetal Retina, hPSC-Derived Retinal Organoids, and Long-Term Retinal Cultures

Akshayalakshmi Sridhar, Akina Hoshino, Connor R Finkbeiner, Alex Chitsazan, Li Dai, Alexandra K Haugan, Kayla M Eschenbacher, Dana L Jackson, Cole Trapnell, Olivia Bermingham-McDonogh, Ian Glass, Thomas A Reh

To study the development of the human retina, we use single-cell RNA sequencing (RNA-seq) at key fetal stages and follow the development of the major cell types as well as populations of transitional cells. We also analyze stem cell (hPSC)-derived retinal organoids; although organoids have a very similar cellular composition at equivalent ages as the fetal retina, there are some differences in gene expression of particular cell types. Moreover, the inner retinal lamination is disrupted at more advanced stages of organoids compared with fetal retina. To determine whether the disorganization in the inner retina is due to the culture conditions, we analyze retinal development in fetal retina maintained under similar conditions. These retinospheres develop for at least 6 months, displaying better inner retinal lamination than retinal organoids. Our single-cell RNA sequencing (scRNA-seq) comparisons of fetal retina, retinal organoids, and retinospheres provide a resource for developing better in vitro models for retinal disease.

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  • Species: human
  • Number of cells: 957
  • Number of downloads: 9
  • Study size: 16MB
  • Uploaded at: Jun 18, 2022

Single-cell transcriptomic analyses provide insights into the developmental origins of neuroblastoma (8 post-conceptional weeks)

Selina Jansky, Ashwini Kumar Sharma, Verena Körber, Andrés Quintero, Umut H. Toprak, Elisa M. Wecht, Moritz Gartlgruber, Alessandro Greco, Elad Chomsky, Thomas G. P. Grünewald, Kai-Oliver Henrich, Amos Tanay, Carl Herrmann, Thomas Höfer, Frank Westermann

Neuroblastoma is a pediatric tumor of the developing sympathetic nervous system. However, the cellular origin of neuroblastoma has yet to be defined. Here we studied the single-cell transcriptomes of neuroblastomas and normal human developing adrenal glands at various stages of embryonic and fetal development. We defined normal differentiation trajectories from Schwann cell precursors over intermediate states to neuroblasts or chromaffin cells and showed that neuroblastomas transcriptionally resemble normal fetal adrenal neuroblasts. Importantly, neuroblastomas with varying clinical phenotypes matched different temporal states along normal neuroblast differentiation trajectories, with the degree of differentiation corresponding to clinical prognosis. Our work highlights the roles of oncogenic MYCN and loss of TFAP2B in blocking differentiation and may provide the basis for designing therapeutic interventions to overcome differentiation blocks.

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  • Species: human
  • Number of cells: 730223
  • Number of downloads: 18
  • Study size: 4GB
  • Uploaded at: Jun 18, 2022

Single-cell RNA-seq reveals cell type–specific molecular and genetic associations to lupus (T/NK)

Richard K Perez, M Grace Gordon, Meena Subramaniam, Min Cheol Kim, George C Hartoularos, Sasha Targ, Yang Sun, Anton Ogorodnikov, Raymund Bueno, Andrew Lu, Mike Thompson, Nadav Rappoport, Andrew Dahl, Cristina M Lanata, Mehrdad Matloubian, Lenka Maliskova, Serena S Kwek, Tony Li, Michal Slyper, Julia Waldman, Danielle Dionne, Orit Rozenblatt-Rosen, Lawrence Fong, Maria Dall'Era, Brunilda Balliu, Aviv Regev, Jinoos Yazdany, Lindsey A Criswell, Noah Zaitlen, Chun Jimmie Ye

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Knowledge of circulating immune cell types and states associated with SLE remains incomplete. We profiled more than 1.2 million peripheral blood mononuclear cells (162 cases, 99 controls) with multiplexed single-cell RNA sequencing (mux-seq). Cases exhibited elevated expression of type 1 interferon-stimulated genes (ISGs) in monocytes, reduction of naïve CD4+ T cells that correlated with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH+ CD8+ T cells. Cell type-specific expression features predicted case-control status and stratified patients into two molecular subtypes. We integrated dense genotyping data to map cell type-specific cis-expression quantitative trait loci and to link SLE-associated variants to cell type-specific expression. These results demonstrate mux-seq as a systematic approach to characterize cellular composition, identify transcriptional signatures, and annotate genetic variants associated with SLE.

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