scholarly journals Cell transcriptomic atlas of the non-human primate Macaca fascicularis

2021 ◽  
Author(s):  
Lei Han ◽  
Xiaoyu Wei ◽  
Chuanyu Liu ◽  
Giacomo Volpe ◽  
Zhenkun Zhuang ◽  
...  
Keyword(s):  
Single Cell ◽  
Macaca Fascicularis ◽  
Large Scale ◽  
Cell Interaction ◽  
The Body ◽  
Tissue Composition ◽  
Human Genetic Disease ◽  
Single Nucleus ◽  
And Function ◽  
Human Primate

Studying tissue composition and function in non-human primates (NHP) is crucial to understand the nature of our own species. Here, we present a large-scale single-cell and single-nucleus transcriptomic atlas encompassing over one million cells from 43 tissues from the adult NHP Macaca fascicularis. This dataset provides a vast, carefully annotated, resource to study a species phylogenetically close to humans. As proof of principle, we have reconstructed the cell-cell interaction networks driving Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases and intersected our data with human genetic disease orthologous coordinates to identify both expected and unexpected associations. Our Macaca fascicularis cell atlas constitutes an essential reference for future single-cell studies in human and NHP.

scholarly journals Single-nucleus cross-tissue molecular reference maps to decipher disease gene function

2021 ◽  
Author(s):  
Gokcen Eraslan ◽  
Eugene Drokhlyansky ◽  
Shankara Anand ◽  
Ayshwarya Subramanian ◽  
Evgenij Fiskin ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Cell Types ◽  
The Body ◽  
Laboratory Methods ◽  
Variational Autoencoder ◽  
Cellular Context ◽  
Single Nucleus ◽  
Gene Modules ◽  
Analytical Frameworks

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.

scholarly journals FlsnRNA-seq: protoplasting-free full-length single-nucleus RNA profiling in plants

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanping Long ◽  
Zhijian Liu ◽  
Jinbu Jia ◽  
Weipeng Mo ◽  
Liang Fang ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Walls ◽  
Large Scale ◽  
Full Length ◽  
Cell Level ◽  
Root Cells ◽  
Rna Profiling ◽  
Different Types ◽  
Long Read ◽  
Single Nucleus

AbstractThe broad application of single-cell RNA profiling in plants has been hindered by the prerequisite of protoplasting that requires digesting the cell walls from different types of plant tissues. Here, we present a protoplasting-free approach, flsnRNA-seq, for large-scale full-length RNA profiling at a single-nucleus level in plants using isolated nuclei. Combined with 10x Genomics and Nanopore long-read sequencing, we validate the robustness of this approach in Arabidopsis root cells and the developing endosperm. Sequencing results demonstrate that it allows for uncovering alternative splicing and polyadenylation-related RNA isoform information at the single-cell level, which facilitates characterizing cell identities.

scholarly journals SCISSOR™: a single-cell inferred site-specific omics resource for tumor microenvironment association study

NAR Cancer ◽  
2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Xiang Cui ◽  
Fei Qin ◽  
Xuanxuan Yu ◽  
Feifei Xiao ◽  
Guoshuai Cai
Keyword(s):  
Tumor Microenvironment ◽  
Single Cell ◽  
Clinical Outcomes ◽  
Large Scale ◽  
Cell Types ◽  
Cell Interaction ◽  
Specific Cell ◽  
Dynamic Visualization ◽  
Tissue Specific ◽  
Cell Composition

Abstract Tumor tissues are heterogeneous with different cell types in tumor microenvironment, which play an important role in tumorigenesis and tumor progression. Several computational algorithms and tools have been developed to infer the cell composition from bulk transcriptome profiles. However, they ignore the tissue specificity and thus a new resource for tissue-specific cell transcriptomic reference is needed for inferring cell composition in tumor microenvironment and exploring their association with clinical outcomes and tumor omics. In this study, we developed SCISSOR™ (https://thecailab.com/scissor/), an online open resource to fulfill that demand by integrating five orthogonal omics data of >6031 large-scale bulk samples, patient clinical outcomes and 451 917 high-granularity tissue-specific single-cell transcriptomic profiles of 16 cancer types. SCISSOR™ provides five major analysis modules that enable flexible modeling with adjustable parameters and dynamic visualization approaches. SCISSOR™ is valuable as a new resource for promoting tumor heterogeneity and tumor–tumor microenvironment cell interaction research, by delineating cells in the tissue-specific tumor microenvironment and characterizing their associations with tumor omics and clinical outcomes.

scholarly journals From single nuclei to whole genome assemblies

2019 ◽  
Author(s):  
Merce Montoliu-Nerin ◽  
Marisol Sánchez-García ◽  
Claudia Bergin ◽  
Manfred Grabherr ◽  
Barbara Ellis ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Genomic Data ◽  
Life Cycles ◽  
Genomic Research ◽  
Metagenomic Data ◽  
Model Organisms ◽  
Genomic Study ◽  
And Function ◽  
Genome Assemblies

SummaryA large proportion of Earth's biodiversity constitutes organisms that cannot be cultured, have cryptic life-cycles and/or live submerged within their substrates1–4. Genomic data are key to unravel both their identity and function5. The development of metagenomic methods6,7 and the advent of single cell sequencing8–10 have revolutionized the study of life and function of cryptic organisms by upending the need for large and pure biological material, and allowing generation of genomic data from complex or limited environmental samples. Genome assemblies from metagenomic data have so far been restricted to organisms with small genomes, such as bacteria11, archaea12 and certain eukaryotes13. On the other hand, single cell technologies have allowed the targeting of unicellular organisms, attaining a better resolution than metagenomics8,9,14–16, moreover, it has allowed the genomic study of cells from complex organisms one cell at a time17,18. However, single cell genomics are not easily applied to multicellular organisms formed by consortia of diverse taxa, and the generation of specific workflows for sequencing and data analysis is needed to expand genomic research to the entire tree of life, including sponges19, lichens3,20, intracellular parasites21,22, and plant endophytes23,24. Among the most important plant endophytes are the obligate mutualistic symbionts, arbuscular mycorrhizal (AM) fungi, that pose an additional challenge with their multinucleate coenocytic mycelia25. Here, the development of a novel single nuclei sequencing and assembly workflow is reported. This workflow allows, for the first time, the generation of reference genome assemblies from large scale, unbiased sorted, and sequenced AM fungal nuclei circumventing tedious, and often impossible, culturing efforts. This method opens infinite possibilities for studies of evolution and adaptation in these important plant symbionts and demonstrates that reference genomes can be generated from complex non-model organisms by isolating only a handful of their nuclei.

scholarly journals Adversarial domain translation networks enable fast and accurate large-scale atlas-level single-cell data integration

2021 ◽  
Author(s):  
Jia Zhao ◽  
Gefei Wang ◽  
Jingsi Ming ◽  
Zhixiang Lin ◽  
Yang Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Superior Performance ◽  
Brain Atlas ◽  
Unified Framework ◽  
Fine Grained ◽  
Wide Range ◽  
Shared Information ◽  
Single Nucleus

The rapid emergence of large-scale atlas-level single-cell RNA-sequencing (scRNA-seq) datasets from various sources presents remarkable opportunities for broad and deep biological investigations through integrative analyses. However, harmonizing such datasets requires integration approaches to be not only computationally scalable, but also capable of preserving a wide range of fine-grained cell populations. We created Portal, a unified framework of adversarial domain translation to learn harmonized representations of datasets. With innovation in model and algorithm designs, Portal achieves superior performance in preserving biological variation during integration, while having significantly reduced running time and memory compared to existing approaches, achieving integration of millions of cells in minutes with low memory consumption. We demonstrate the efficiency and accuracy of Portal using diverse datasets ranging from mouse brain atlas projects, the Tabula Muris project, and the Tabula Microcebus project. Portal has broad applicability and in addition to integrating multiple scRNA-seq datasets, it can also integrate scRNA-seq with single-nucleus RNA-sequencing (snRNA-seq) data. Finally, we demonstrate the utility of Portal by applying it to the integration of cross-species datasets with limited shared-information between them, and are able to elucidate biological insights into the similarities and divergences in the spermatogenesis process between mouse, macaque, and human.

scholarly journals Single-nucleus full-length RNA profiling in plants incorporates isoform information to facilitate cell type identification

2020 ◽  
Author(s):  
Yanping Long ◽  
Zhijian Liu ◽  
Jinbu Jia ◽  
Weipeng Mo ◽  
Liang Fang ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Molecule ◽  
Large Scale ◽  
Alternative Polyadenylation ◽  
Full Length ◽  
Cell Type ◽  
Arabidopsis Root ◽  
Rna Profiling ◽  
Long Read ◽  
Single Nucleus

AbstractThe broad application of large-scale single-cell RNA profiling in plants has been restricted by the prerequisite of protoplasting. We recently found that the Arabidopsis nucleus contains abundant polyadenylated mRNAs, many of which are incompletely spliced. To capture the isoform information, we combined 10x Genomics and Nanopore long-read sequencing to develop a protoplasting-free full-length single-nucleus RNA profiling method in plants. Our results demonstrated using Arabidopsis root that nuclear mRNAs faithfully retain cell identity information, and single-molecule full-length RNA sequencing could further improve cell type identification by revealing splicing status and alternative polyadenylation at single-cell level.

scholarly journals Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq

2020 ◽  
Vol 17 (8) ◽  
pp. 793-798 ◽  
Author(s):  
Bo Li ◽  
Joshua Gould ◽  
Yiming Yang ◽  
Siranush Sarkizova ◽  
Marcin Tabaka ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Large Scale ◽  
Rna Seq ◽  
Single Nucleus

scholarly journals Cumulus: a cloud-based data analysis framework for large-scale single-cell and single-nucleus RNA-seq

2019 ◽  
Author(s):  
Bo Li ◽  
Joshua Gould ◽  
Yiming Yang ◽  
Siranush Sarkizova ◽  
Marcin Tabaka ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Bone Marrow Cells ◽  
Low Cost ◽  
Data Generation ◽  
Rna Seq ◽  
User Friendliness ◽  
Health And Disease ◽  
Single Nucleus

AbstractMassively parallel single-cell and single-nucleus RNA-seq (sc/snRNA-seq) have opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so does the need for computational pipelines for scaled analysis. Here, we developed Cumulus, a cloud-based framework for analyzing large scale sc/snRNA-seq datasets. Cumulus combines the power of cloud computing with improvements in algorithm implementations to achieve high scalability, low cost, user-friendliness, and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies.

scholarly journals CDCP: a visualization and analyzing platform for single-cell datasets

2021 ◽  
Author(s):  
Yuejiao Li ◽  
Tao Yang ◽  
Tingting Lai ◽  
Lijin You ◽  
Fan Yang ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Rna Seq ◽  
Rapid Accumulation ◽  
Unique Approach ◽  
Functional States ◽  
User Friendly ◽  
Human Primate

Advances in single-cell sequencing technology provide a unique approach to characterize the heterogeneity and distinctive functional states at single-cell resolution, leading to rapid accumulation of large-scale single-cell datasets. A big challenge undertaken by research community especially bench scientists is how to simplify the way of retrieving, processing and analyzing the huge number of datasets. Towards this end, we developed Cell-omics Data Coordinate Platform (CDCP),a platform that aims to share and integrate comprehensive single-cell datasets, and to provide a network analysis toolkit for personalized analysis. CDCP contains single-cell RNA-seq and ATAC-seq datasets of 474,572 cells from 6,459 samples in species covering humans, non-human primate models and other animals. It allows querying and visualization of interested datasets and the expression profile of distinct genes in different cell clusters and cell types. Besides, this platform provides an analysis pipeline for non-bioinformatician experimental scientists to address questions not focused by the submitters of the datasets. In summary, CDCP provides a user-friendly interface for researchers to explore, visualize, analyze, download and submit published single-cell datasets and it will be a valuable resource for investigators to explore the global transcriptome profiling at single-cell level.

scholarly journals Single cell atlas for 11 non-model mammals, reptiles and birds

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dongsheng Chen ◽  
Jian Sun ◽  
Jiacheng Zhu ◽  
Xiangning Ding ◽  
Tianming Lan ◽  
...  
Keyword(s):  
Single Cell ◽  
Viral Entry ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Target Cells ◽  
Animal Cells ◽  
Regulatory Circuits ◽  
Single Nucleus

AbstractThe availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs.

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