scholarly journals SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections

2020 ◽  
Author(s):  
Alexandros Sountoulidis ◽  
Andreas Liontos ◽  
Hong Phuong Nguyen ◽  
Alexandra B. Firsova ◽  
Athanasios Fysikopoulos ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rolling Circle Amplification ◽  
Marker Gene ◽  
Cell Types ◽  
Experimental Conditions ◽  
Rolling Circle ◽  
Padlock Probes ◽  
Tissue Sections ◽  
Specificity And Sensitivity

AbstractChanges in cell identities and positions underlie tissue development and disease progression. Although, single-cell mRNA sequencing (scRNA-Seq) methods rapidly generate extensive lists of cell-states, spatially resolved single-cell mapping presents a challenging task. We developed SCRINSHOT (Single Cell Resolution INSitu Hybridization On Tissues), a sensitive, multiplex RNA mapping approach. Direct hybridization of padlock probes on mRNA is followed by circularization with SplintR ligase and rolling circle amplification (RCA) of the hybridized padlock probes. Sequential detection of RCA-products using fluorophore-labeled oligonucleotides profiles thousands of cells in tissue sections. We evaluated SCRINSHOT specificity and sensitivity on murine and human organs. SCRINSHOT quantification of marker gene expression shows high correlation with published scRNA-Seq data over a broad range of gene expression levels. We demonstrate the utility of SCRISHOT by mapping the locations of abundant and rare cell types along the murine airways. The amenability, multiplexity and quantitative qualities of SCRINSHOT facilitate single cell mRNA profiling of cell-state alterations in tissues under a variety of native and experimental conditions.

scholarly journals SCRINSHOT enables spatial mapping of cell states in tissue sections with single-cell resolution

PLoS Biology ◽  
2020 ◽  
Vol 18 (11) ◽  
pp. e3000675
Author(s):  
Alexandros Sountoulidis ◽  
Andreas Liontos ◽  
Hong Phuong Nguyen ◽  
Alexandra B. Firsova ◽  
Athanasios Fysikopoulos ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rolling Circle Amplification ◽  
Marker Gene ◽  
Cell Types ◽  
Experimental Conditions ◽  
Rolling Circle ◽  
Padlock Probes ◽  
Tissue Sections ◽  
Specificity And Sensitivity

Changes in cell identities and positions underlie tissue development and disease progression. Although single-cell mRNA sequencing (scRNA-Seq) methods rapidly generate extensive lists of cell states, spatially resolved single-cell mapping presents a challenging task. We developed SCRINSHOT (Single-Cell Resolution IN Situ Hybridization On Tissues), a sensitive, multiplex RNA mapping approach. Direct hybridization of padlock probes on mRNA is followed by circularization with SplintR ligase and rolling circle amplification (RCA) of the hybridized padlock probes. Sequential detection of RCA-products using fluorophore-labeled oligonucleotides profiles thousands of cells in tissue sections. We evaluated SCRINSHOT specificity and sensitivity on murine and human organs. SCRINSHOT quantification of marker gene expression shows high correlation with published scRNA-Seq data over a broad range of gene expression levels. We demonstrate the utility of SCRINSHOT by mapping the locations of abundant and rare cell types along the murine airways. The amenability, multiplexity, and quantitative qualities of SCRINSHOT facilitate single-cell mRNA profiling of cell-state alterations in tissues under a variety of native and experimental conditions.

scholarly journals Simultaneous Single-Cell In Situ Analysis of Human Adenovirus Type 5 DNA and mRNA Expression Patterns in Lytic and Persistent Infection

2017 ◽  
Vol 91 (11) ◽  
Author(s):  
Tomasz Krzywkowski ◽  
Sibel Ciftci ◽  
Farzaneh Assadian ◽  
Mats Nilsson ◽  
Tanel Punga
Keyword(s):  
Gene Expression ◽  
Mrna Expression ◽  
Single Cell ◽  
Cell Population ◽  
Rolling Circle Amplification ◽  
Expression Patterns ◽  
Human Adenovirus ◽  
Viral Dna ◽  
Rolling Circle ◽  
Infected Cells

ABSTRACT An efficient adenovirus infection results in high-level accumulation of viral DNA and mRNAs in the infected cell population. However, the average viral DNA and mRNA content in a heterogeneous cell population does not necessarily reflect the same abundance in individual cells. Here, we describe a novel padlock probe-based rolling-circle amplification technique that enables simultaneous detection and analysis of human adenovirus type 5 (HAdV-5) genomic DNA and virus-encoded mRNAs in individual infected cells. We demonstrate that the method is applicable for detection and quantification of HAdV-5 DNA and mRNAs in short-term infections in human epithelial cells and in long-term infections in human B lymphocytes. Single-cell evaluation of these infections revealed high heterogeneity and unique cell subpopulations defined by differential viral DNA content and mRNA expression. Further, our single-cell analysis shows that the specific expression pattern of viral E1A 13S and 12S mRNA splice variants is linked to HAdV-5 DNA content in the individual cells. Furthermore, we show that expression of a mature form of the HAdV-5 histone-like protein VII affects virus genome detection in HAdV-5-infected cells. Collectively, padlock probes combined with rolling-circle amplification should be a welcome addition to the method repertoire for the characterization of the molecular details of the HAdV life cycle in individual infected cells. IMPORTANCE Human adenoviruses (HAdVs) have been extensively used as model systems to study various aspects of eukaryotic gene expression and genome organization. The vast majority of the HAdV studies are based on standard experimental procedures carried out using heterogeneous cell populations, where data averaging often masks biological differences. As every cell is unique, characteristics and efficiency of an HAdV infection can vary from cell to cell. Therefore, the analysis of HAdV gene expression and genome organization would benefit from a method that permits analysis of individual infected cells in the heterogeneous cell population. Here, we show that the padlock probe-based rolling-circle amplification method can be used to study concurrent viral DNA accumulation and mRNA expression patterns in individual HAdV-5-infected cells. Hence, this versatile method can be applied to detect the extent of infection and virus gene expression changes in different HAdV-5 infections.

scholarly journals Glyoxal as alternative fixative for single cell RNA sequencing

2021 ◽  
Author(s):  
Josephine Bageritz ◽  
Niklas Krausse ◽  
Schayan Yousefian ◽  
Svenja Leible ◽  
Erica Valentini ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Cell Isolation ◽  
Sample Collection ◽  
Experimental Conditions ◽  
Single Cell Rna Sequencing ◽  
Highly Expressed Genes ◽  
Single Cell Isolation

Single cell RNA sequencing (scRNA-seq) has become an important method to identify cell types, delineate the trajectories of cell differentiation in whole organisms and understand the heterogeneity in cellular responses. Nevertheless, sample collection and processing remain a severe bottleneck for scRNA-seq experiments. Cell isolation protocols often lead to significant changes in the transcriptomes of cells, requiring novel methods to preserve cell states. Here, we developed and benchmarked protocols using glyoxal as a fixative for scRNA-seq application. Using Drop-seq methodology, we detected high numbers of transcripts and genes from glyoxal-fixed Drosophila cells after scRNA-seq. The effective glyoxal fixation of transcriptomes in Drosophila and human cells was further supported by a high correlation of gene expression data between glyoxal-fixed and unfixed samples. Accordingly, we also found highly expressed genes overlapping to a large extent between experimental conditions. These results indicated that our fixation protocol did not induce considerable changes in gene expression and conserved the transcriptome for subsequent single cell isolation procedures. In conclusion, we present glyoxal as a suitable fixative for Drosophila cells and potentially cells of other species that allows high-quality scRNA-seq applications.

scholarly journals Expression Atlas update: from tissues to single cells

2019 ◽  
Author(s):  
Irene Papatheodorou ◽  
Pablo Moreno ◽  
Jonathan Manning ◽  
Alfonso Muñoz-Pomer Fuentes ◽  
Nancy George ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Marker Gene ◽  
Cell Types ◽  
Added Value ◽  
Rna Seq ◽  
Gene And Protein Expression ◽  
Expression Atlas ◽  
Public Archives

Abstract Expression Atlas is EMBL-EBI’s resource for gene and protein expression. It sources and compiles data on the abundance and localisation of RNA and proteins in various biological systems and contexts and provides open access to this data for the research community. With the increased availability of single cell RNA-Seq datasets in the public archives, we have now extended Expression Atlas with a new added-value service to display gene expression in single cells. Single Cell Expression Atlas was launched in 2018 and currently includes 123 single cell RNA-Seq studies from 12 species. The website can be searched by genes within or across species to reveal experiments, tissues and cell types where this gene is expressed or under which conditions it is a marker gene. Within each study, cells can be visualized using a pre-calculated t-SNE plot and can be coloured by different features or by cell clusters based on gene expression. Within each experiment, there are links to downloadable files, such as RNA quantification matrices, clustering results, reports on protocols and associated metadata, such as assigned cell types.

scholarly journals Single-cell study of the extracellular matrix effect on cell growth by in situ imaging of gene expression

2017 ◽  
Vol 8 (12) ◽  
pp. 8019-8024 ◽  
Author(s):  
Yupeng Sun ◽  
Ruijie Deng ◽  
Kaixiang Zhang ◽  
Xiaojun Ren ◽  
Ling Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Cell Growth ◽  
Single Cell ◽  
Rolling Circle Amplification ◽  
Matrix Stiffness ◽  
Rolling Circle ◽  
Single Cell Imaging ◽  
Extracellular Matrix Stiffness

The effect of extracellular matrix stiffness on cell growth and the underlying molecular mechanism was investigated using an in situ single-cell imaging of gene expression method based on rolling circle amplification.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals Optimizing expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anna S. E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

scholarly journals A practical solution to pseudoreplication bias in single-cell studies

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kip D. Zimmerman ◽  
Mark A. Espeland ◽  
Carl D. Langefeld
Keyword(s):  
Single Cell ◽  
Mixed Models ◽  
Random Effect ◽  
Cell Types ◽  
Error Rates ◽  
Specific Cell ◽  
Experimental Conditions ◽  
Type 1 Error ◽  
Sample Correlation ◽  
Inflated Type

AbstractCells from the same individual share common genetic and environmental backgrounds and are not statistically independent; therefore, they are subsamples or pseudoreplicates. Thus, single-cell data have a hierarchical structure that many current single-cell methods do not address, leading to biased inference, highly inflated type 1 error rates, and reduced robustness and reproducibility. This includes methods that use a batch effect correction for individual as a means of accounting for within-sample correlation. Here, we document this dependence across a range of cell types and show that pseudo-bulk aggregation methods are conservative and underpowered relative to mixed models. To compute differential expression within a specific cell type across treatment groups, we propose applying generalized linear mixed models with a random effect for individual, to properly account for both zero inflation and the correlation structure among measures from cells within an individual. Finally, we provide power estimates across a range of experimental conditions to assist researchers in designing appropriately powered studies.

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