scholarly journals Single-cell proteo-genomic reference maps of the hematopoietic system enable the purification and massive profiling of precisely defined cell states

2021 ◽  
Author(s):  
Sergio Triana ◽  
Dominik Vonficht ◽  
Lea Jopp-Saile ◽  
Simon Raffel ◽  
Raphael Lutz ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Healthy Aging ◽  
Functional Characterization ◽  
Cost Effective ◽  
Cell Types ◽  
Cellular Systems ◽  
Biological Processes ◽  
Cell Level ◽  
Functional Capacities

AbstractSingle-cell genomics technology has transformed our understanding of complex cellular systems. However, excessive cost and a lack of strategies for the purification of newly identified cell types impede their functional characterization and large-scale profiling. Here, we have generated high-content single-cell proteo-genomic reference maps of human blood and bone marrow that quantitatively link the expression of up to 197 surface markers to cellular identities and biological processes across all main hematopoietic cell types in healthy aging and leukemia. These reference maps enable the automatic design of cost-effective high-throughput cytometry schemes that outperform state-of-the-art approaches, accurately reflect complex topologies of cellular systems and permit the purification of precisely defined cell states. The systematic integration of cytometry and proteo-genomic data enables the functional capacities of precisely mapped cell states to be measured at the single-cell level. Our study serves as an accessible resource and paves the way for a data-driven era in cytometry.

scholarly journals Single-cell proteo-genomic reference maps of the hematopoietic system enable the purification and massive profiling of precisely defined cell states

2021 ◽  
Author(s):  
Sergio H. Triana ◽  
Dominik Vonficht ◽  
Lea Jopp-Saile ◽  
Simon Raffel ◽  
Raphael Lutz ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Healthy Aging ◽  
Functional Characterization ◽  
Cost Effective ◽  
Cell Types ◽  
Cellular Systems ◽  
Biological Processes ◽  
Cell Level ◽  
Functional Capacities

AbstractSingle-cell genomics has transformed our understanding of complex cellular systems. However, excessive costs and a lack of strategies for the purification of newly identified cell types impede their functional characterization and large-scale profiling. Here, we have generated high content single-cell proteo-genomic reference maps of human blood and bone marrow that quantitatively link the expression of up to 197 surface markers to cellular identities and biological processes across all hematopoietic cell types in healthy aging and leukemia. These reference maps enable the automatic design of cost-effective high-throughput cytometry schemes that outperform state-of-the-art approaches, accurately reflect complex topologies of cellular systems, and permit the purification of precisely defined cell states. The systematic integration of cytometry and proteo-genomic data enables the interpretation of functional capacities of such precisely mapped cell states at the single-cell level. Our study serves as an accessible resource and paves the way for a data-driven era in cytometry.

scholarly journals Saturating Single-Cell atlas Datasets

2017 ◽  
Author(s):  
Aparna Bhaduri ◽  
Tomasz J. Nowakowski ◽  
Alex A. Pollen ◽  
Arnold R. Kriegstein
Keyword(s):  
Population Structure ◽  
Single Cell ◽  
Mouse Brain ◽  
Large Scale ◽  
Single Cells ◽  
Cost Effective ◽  
Cell Types ◽  
Cell Number ◽  
Cell Type ◽  
The Relationship

AbstractHigh throughput methods for profiling the transcriptomes of single cells have recently emerged as transformative approaches for large-scale population surveys of cellular diversity in heterogeneous primary tissues. Efficient generation of such an atlas will depend on sufficient sampling of the diverse cell types while remaining cost-effective to enable a comprehensive examination of organs, developmental stages, and individuals. To examine the relationship between cell number and transcriptional heterogeneity in the context of unbiased cell type classification, we explicitly explored the population structure of a publically available 1.3 million cell dataset from the E18.5 mouse brain. We propose a computational framework for inferring the saturation point of cluster discovery in a single cell mRNA-seq experiment, centered around cluster preservation in downsampled datasets. In addition, we introduce a “complexity index”, which characterizes the heterogeneity of cells in a given dataset. Using Cajal-Retzius cells as an example of a limited complexity dataset, we explored whether biological distinctions relate to technical clustering. Surprisingly, we found that clustering distinctions carrying biologically interpretable meaning are achieved with far fewer cells (20,000). Together, these findings suggest that most of the biologically interpretable insights from the 1.3 million cells can be recapitulated by analyzing 50,000 randomly selected cells, indicating that instead of profiling few individuals at high “cellular coverage”, the much anticipated cell atlasing studies may instead benefit from profiling more individuals, or many time points at lower cellular coverage.Recent efforts seek to create a comprehensive cell atlas of the human body1,2 Current technology, however, makes it precipitously expensive to perform analysis of every cell. Therefore, designing effective sampling strategies be critical to generate a working atlas in an efficient, cost-effective, and streamlined manner. The advent of single cell and single nucleus mRNA sequencing (RNAseq) in droplet format3,4 now enables large scale sampling of cells from any tissue, and a recently released publicly available dataset of 1.3 million single cells from the E18.5 mouse brain generated with the 10X Chromium5 provides an opportunity to explore the relationship between population structure and the number of sampled cells necessary to reveal the underlying diversity of cell types. Here, we present a framework for how researchers can evaluate whether a dataset has reached saturation, and we estimate how many cells would be required to generate an atlas of the sample analyzed here. This framework can be applied to any organ or cell type specific atlas for any organism.

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 Single-cell transcriptomic analysis elucidates APOE genotype specific changes across cell types in two brain regions in Alzheimer’s disease

2021 ◽  
Author(s):  
Stella Belonwu ◽  
Yaqiao Li ◽  
Daniel Bunis ◽  
Arjun Arkal Rao ◽  
Caroline Warly Solsberg ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Single Cell ◽  
Molecular Mechanisms ◽  
Apoe Genotype ◽  
Cell Types ◽  
Brain Regions ◽  
Single Cell Level ◽  
Cell Level ◽  
Single Nucleus

Abstract Alzheimer’s Disease (AD) is a complex neurodegenerative disease that gravely affects patients and imposes an immense burden on caregivers. Apolipoprotein E4 (APOE4) has been identified as the most common genetic risk factor for AD, yet the molecular mechanisms connecting APOE4 to AD are not well understood. Past transcriptomic analyses in AD have revealed APOE genotype-specific transcriptomic differences; however, these differences have not been explored at a single-cell level. Here, we leverage the first two single-nucleus RNA sequencing AD datasets from human brain samples, including nearly 55,000 cells from the prefrontal and entorhinal cortices. We observed more global transcriptomic changes in APOE4 positive AD cells and identified differences across APOE genotypes primarily in glial cell types. Our findings highlight the differential transcriptomic perturbations of APOE isoforms at a single-cell level in AD pathogenesis and have implications for precision medicine development in the diagnosis and treatment of AD.

scholarly journals Single-cell RNA counting at allele- and isoform-resolution using Smart-seq3

2019 ◽  
Author(s):  
Michael Hagemann-Jensen ◽  
Christoph Ziegenhain ◽  
Ping Chen ◽  
Daniel Ramsköld ◽  
Gert-Jan Hendriks ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Cell Types ◽  
Mouse Strains ◽  
Rna Molecules ◽  
Counting Strategy ◽  
Long Read ◽  
Sequencing Strategy ◽  
Transcriptome Coverage ◽  
Scale Characterization

AbstractLarge-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states1. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells2,3. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5’ unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.

scholarly journals scAIDE: clustering of large-scale single-cell RNA-seq data reveals putative and rare cell types

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Kaikun Xie ◽  
Yu Huang ◽  
Feng Zeng ◽  
Zehua Liu ◽  
Ting Chen
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Random Projection ◽  
Good Representation ◽  
Rna Seq ◽  
Unsupervised Deep Learning ◽  
High Level ◽  
Computational Resources

Abstract Recent advancements in both single-cell RNA-sequencing technology and computational resources facilitate the study of cell types on global populations. Up to millions of cells can now be sequenced in one experiment; thus, accurate and efficient computational methods are needed to provide clustering and post-analysis of assigning putative and rare cell types. Here, we present a novel unsupervised deep learning clustering framework that is robust and highly scalable. To overcome the high level of noise, scAIDE first incorporates an autoencoder-imputation network with a distance-preserved embedding network (AIDE) to learn a good representation of data, and then applies a random projection hashing based k-means algorithm to accommodate the detection of rare cell types. We analyzed a 1.3 million neural cell dataset within 30 min, obtaining 64 clusters which were mapped to 19 putative cell types. In particular, we further identified three different neural stem cell developmental trajectories in these clusters. We also classified two subpopulations of malignant cells in a small glioblastoma dataset using scAIDE. We anticipate that scAIDE would provide a more in-depth understanding of cell development and diseases.

scholarly journals Integrated profiling of single cell epigenomic and transcriptomic landscape of Parkinson’s disease mouse brain

2020 ◽  
Author(s):  
Jixing Zhong ◽  
Gen Tang ◽  
Jiacheng Zhu ◽  
Xin Qiu ◽  
Weiying Wu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Single Cell ◽  
Early Stage ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Cell Level ◽  
Distinct Cell ◽  
Single Nucleus

AbstractParkinson’s disease (PD) is a neurodegenerative disease leading to the impairment of execution of movement. PD pathogenesis has been largely investigated, but either restricted in bulk level or at certain cell types, which failed to capture cellular heterogeneity and intrinsic interplays among distinct cell types. To overcome this, we applied single-nucleus RNA-seq and single cell ATAC-seq on cerebellum, midbrain and striatum of PD mouse and matched control. With 74,493 cells in total, we comprehensively depicted the dysfunctions under PD pathology covering proteostasis, neuroinflammation, calcium homeostasis and extracellular neurotransmitter homeostasis. Besides, by multi-omics approach, we identified putative biomarkers for early stage of PD, based on the relationships between transcriptomic and epigenetic profiles. We located certain cell types that primarily contribute to PD early pathology, narrowing the gap between genotypes and phenotypes. Taken together, our study provides a valuable resource to dissect the molecular mechanism of PD pathogenesis at single cell level, which could facilitate the development of novel methods regarding diagnosis, monitoring and practical therapies against PD at early stage.

scholarly journals scAPAdb: a comprehensive database of alternative polyadenylation at single-cell resolution

2021 ◽  
Author(s):  
Sheng Zhu ◽  
Qiwei Lian ◽  
Wenbin Ye ◽  
Wei Qin ◽  
Zhe Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
Alternative Polyadenylation ◽  
Cell Types ◽  
Single Cell Level ◽  
Cell Heterogeneity ◽  
Rna Seq ◽  
Cell Level ◽  
Eukaryotic Gene ◽  
User Friendly ◽  
Different Cell Types

Abstract Alternative polyadenylation (APA) is a widespread regulatory mechanism of transcript diversification in eukaryotes, which is increasingly recognized as an important layer for eukaryotic gene expression. Recent studies based on single-cell RNA-seq (scRNA-seq) have revealed cell-to-cell heterogeneity in APA usage and APA dynamics across different cell types in various tissues, biological processes and diseases. However, currently available APA databases were all collected from bulk 3′-seq and/or RNA-seq data, and no existing database has provided APA information at single-cell resolution. Here, we present a user-friendly database called scAPAdb (http://www.bmibig.cn/scAPAdb), which provides a comprehensive and manually curated atlas of poly(A) sites, APA events and poly(A) signals at the single-cell level. Currently, scAPAdb collects APA information from > 360 scRNA-seq experiments, covering six species including human, mouse and several other plant species. scAPAdb also provides batch download of data, and users can query the database through a variety of keywords such as gene identifier, gene function and accession number. scAPAdb would be a valuable and extendable resource for the study of cell-to-cell heterogeneity in APA isoform usages and APA-mediated gene regulation at the single-cell level under diverse cell types, tissues and species.

scholarly journals Linear-time cluster ensembles of large-scale single-cell RNA-seq and multimodal data

2020 ◽  
Author(s):  
Van Hoan Do ◽  
Francisca Rojas Ringeling ◽  
Stefan Canzar
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Linear Time ◽  
Cell Types ◽  
Substantial Improvement ◽  
Rna Seq ◽  
Sampling Step ◽  
Protein Marker ◽  
Cluster Ensembles ◽  
Sequencing Technologies

AbstractA fundamental task in single-cell RNA-seq (scRNA-seq) analysis is the identification of transcriptionally distinct groups of cells. Numerous methods have been proposed for this problem, with a recent focus on methods for the cluster analysis of ultra-large scRNA-seq data sets produced by droplet-based sequencing technologies. Most existing methods rely on a sampling step to bridge the gap between algorithm scalability and volume of the data. Ignoring large parts of the data, however, often yields inaccurate groupings of cells and risks overlooking rare cell types. We propose method Specter that adopts and extends recent algorithmic advances in (fast) spectral clustering. In contrast to methods that cluster a (random) subsample of the data, we adopt the idea of landmarks that are used to create a sparse representation of the full data from which a spectral embedding can then be computed in linear time. We exploit Specter’s speed in a cluster ensemble scheme that achieves a substantial improvement in accuracy over existing methods and that is sensitive to rare cell types. Its linear time complexity allows Specter to scale to millions of cells and leads to fast computation times in practice. Furthermore, on CITE-seq data that simultaneously measures gene and protein marker expression we demonstrate that Specter is able to utilize multimodal omics measurements to resolve subtle transcriptomic differences between subpopulations of cells. Specter is open source and available at https://github.com/canzarlab/Specter.

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.

Sign in / Sign up

Export Citation Format

Share Document