scholarly journals In situelectro-sequencing in three-dimensional tissues

2021 ◽  
Author(s):  
Qiang Li ◽  
Zuwan Lin ◽  
Ren Liu ◽  
Xin Tang ◽  
Jiahao Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Three Dimensional ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Single Cell Level ◽  
Cell Level ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractPairwise mapping of single-cell gene expression and electrophysiology in intact three-dimensional (3D) tissues is crucial for studying electrogenic organs (e.g., brain and heart)1–5. Here, we introducein situelectro-sequencing (electro-seq), combining soft bioelectronics within situRNA sequencing to stably map millisecond-timescale cellular electrophysiology and simultaneously profile a large number of genes at single-cell level across 3D tissues. We appliedin situelectro-seq to 3D human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) patches, precisely registering the CM gene expression with electrophysiology at single-cell level, enabling multimodalin situanalysis. Such multimodal data integration substantially improved the dissection of cell types and the reconstruction of developmental trajectory from spatially heterogeneous tissues. Using machine learning (ML)-based cross-modal analysis,in situelectro-seq identified the gene-to-electrophysiology relationship over the time course of cardiac maturation. Further leveraging such a relationship to train a coupled autoencoder, we demonstrated the prediction of single-cell gene expression profile evolution using long-term electrical measurement from the same cardiac patch or 3D millimeter-scale cardiac organoids. As exemplified by cardiac tissue maturation,in situelectro-seq will be broadly applicable to create spatiotemporal multimodal maps and predictive models in electrogenic organs, allowing discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.

scholarly journals Prediction of single-cell gene expression for transcription factor analysis

GigaScience ◽  
2020 ◽  
Vol 9 (11) ◽  
Author(s):  
Fatemeh Behjati Ardakani ◽  
Kathrin Kattler ◽  
Tobias Heinen ◽  
Florian Schmidt ◽  
David Feuerborn ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Single Cell ◽  
Cell Types ◽  
Single Cell Level ◽  
Cell Level ◽  
Novel Approach ◽  
Cell Gene Expression ◽  
Specific Regulation ◽  
Cell Gene

Abstract Background Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data. Results Here, we propose a novel approach for predicting gene expression at the single-cell level using cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature. Conclusion Our proposed method allows us to identify distinct TFs that show cell type–specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via https://github.com/SchulzLab/Triangulate.

scholarly journals A bead-based microfluidic approach to integrated single-cell gene expression analysis by quantitative RT-PCR

RSC Advances ◽  
2015 ◽  
Vol 5 (7) ◽  
pp. 4886-4893 ◽  
Author(s):  
Hao Sun ◽  
Tim Olsen ◽  
Jing Zhu ◽  
Jianguo Tao ◽  
Brian Ponnaiya ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Single Cell Level ◽  
Rt Pcr ◽  
Cell Level ◽  
P Gene ◽  
Cell Gene Expression ◽  
Cell Gene

Gene expression analysis at the single-cell level is critical to understanding variations among cells in heterogeneous populations.

scholarly journals Gene expression-based identification of antigen-responsive CD8+ T cells on a single-cell level

2019 ◽  
Author(s):  
Yannick F. Fuchs ◽  
Virag Sharma ◽  
Anne Eugster ◽  
Gloria Kraus ◽  
Robert Morgenstern ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
T Cells ◽  
Single Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
Large Gene ◽  
Gene Sets ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractCD8+ T cells are important effectors of adaptive immunity against pathogens, tumors and self antigens. Here, we asked how human cognate antigen-responsive CD8+ T cells and their receptors could be identified in unselected single-cell gene expression data. Single-cell RNA sequencing and qPCR of dye-labelled antigen-specific cells identified large gene sets that were congruently up- or downregulated in virus-responsive CD8+ T cells under different antigen presentation conditions. Combined expression of TNFRSF9, XCL1, XCL2, and CRTAM was the most distinct marker of virus-responsive cells on a single-cell level. Using transcriptomic data, we developed a machine learning-based classifier that provides sensitive and specific detection of virus-responsive CD8+ T cells from unselected populations. Gene response profiles of CD8+ T cells specific for the autoantigen islet-specific glucose-6-phosphatase catalytic subunit-related protein differed markedly from virus-specific cells. These findings provide single-cell gene expression parameters for comprehensive identification of rare antigen-responsive cells and T cell receptors.One-sentence summaryIdentification of genes, gene sets, and development of a machine learning-based classifier that distinguishes antigen-responsive CD8+ T cells on a single-cell level.

From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture

2018 ◽  
Vol 1 (1) ◽  
pp. 29-51 ◽  
Author(s):  
Xi Chen ◽  
Sarah A. Teichmann ◽  
Kerstin B. Meyer
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Three Dimensional ◽  
Cell Types ◽  
Biological Knowledge ◽  
Tissue Architecture ◽  
Cell Gene Expression ◽  
Cell Gene

With the recent transformative developments in single-cell genomics and, in particular, single-cell gene expression analysis, it is now possible to study tissues at the single-cell level, rather than having to rely on data from bulk measurements. Here we review the rapid developments in single-cell RNA sequencing (scRNA-seq) protocols that have the potential for unbiased identification and profiling of all cell types within a tissue or organism. In addition, novel approaches for spatial profiling of gene expression allow us to map individual cells and cell types back into the three-dimensional context of organs. The combination of in-depth single-cell and spatial gene expression data will reveal tissue architecture in unprecedented detail, generating a wealth of biological knowledge and a better understanding of many diseases.

scholarly journals Inferring Relevant Cell Types for Complex Traits by Using Single-Cell Gene Expression

2017 ◽  
Vol 101 (5) ◽  
pp. 686-699 ◽  
Author(s):  
Diego Calderon ◽  
Anand Bhaskar ◽  
David A. Knowles ◽  
David Golan ◽  
Towfique Raj ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Complex Traits ◽  
Cell Types ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals Single Cell Gene Transcriptome Analysis of Ovarian Mature Teratomas

2021 ◽  
Vol 27 ◽  
Author(s):  
Sun Shin ◽  
Youn Jin Choi ◽  
Seung-Hyun Jung ◽  
Yeun-Jun Chung ◽  
Sug Hyung Lee
Keyword(s):  
Single Cell ◽  
Germ Cells ◽  
Expression Profiles ◽  
Cell Types ◽  
Cell Tumor ◽  
Single Cell Level ◽  
Cell Level ◽  
Normal Cells ◽  
Single Cell Rna Sequencing ◽  
Cell Gene

Teratoma is a type of germ cell tumor that originates from totipotential germ cells that are present in gonads, which can differentiate into any of the cell types found in adult tissues. Ovarian teratomas are usually mature cystic teratomas (OMCTs, also known as dermoid cysts). Chromosome studies in OMCTs show that the chromosomes are uniformly homozygous with karyotype of 46, XX, indicating that they may be parthenogenic tumors that arise from a single ovum after thefirst meiotic division. However, the tissues in OMCTs have been known to be morphologically and immunophenotypically identical to the orthotopic tissues. Currently, expression profiles of tissue components in OMCTs are not known. To identify whether OMCT tissues are expressionally similar to or different from the orthotopic tissues, we adopted single-cell RNA-sequencing (scRNA-seq), and analyzed transcriptomes of individual cells in heterogenous tissues of two OMCTs. We found that transcriptome profiles of the OMCTs at single cell level were not significantly different from those of normal cells in orthotopic locations. The present data suggest that parthenogeneticlly altered OMCTs may not alter expression profiles of inrivirual tissue components in OMCTs.

Microfluidic single-cell transcriptomics: moving towards multimodal and spatiotemporal omics

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Shichao Lin ◽  
Yilong Liu ◽  
Mingxia Zhang ◽  
Xing Xu ◽  
Yingwen Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Cell Gene Expression ◽  
Cell Gene

Cells are the basic units of life with vast heterogeneity. Single-cell transcriptomics unveils cell-to-cell gene expression variabilities, discovers novel cell types, and uncovers the critical roles of cellular heterogeneity in...

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