Dataset Information

Single-cell mRNA cytometry via sequence-specific nanoparticle clustering and trapping.

ABSTRACT: Cell-to-cell variation in gene expression creates a need for techniques that can characterize expression at the level of individual cells. This is particularly true for rare circulating tumour cells, in which subtyping and drug resistance are of intense interest. Here we describe a method for cell analysis-single-cell mRNA cytometry-that enables the isolation of rare cells from whole blood as a function of target mRNA sequences. This approach uses two classes of magnetic particles that are labelled to selectively hybridize with different regions of the target mRNA. Hybridization leads to the formation of large magnetic clusters that remain localized within the cells of interest, thereby enabling the cells to be magnetically separated. Targeting specific intracellular mRNAs enablescirculating tumour cells to be distinguished from normal haematopoietic cells. No polymerase chain reaction amplification is required to determine RNA expression levels and genotype at the single-cell level, and minimal cell manipulation is required. To demonstrate this approach we use single-cell mRNA cytometry to detect clinically important sequences in prostate cancer specimens.

SUBMITTER: Labib M

PROVIDER: S-EPMC5910253 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Single-cell mRNA cytometry via sequence-specific nanoparticle clustering and trapping.

Labib Mahmoud M Mohamadi Reza M RM Poudineh Mahla M Ahmed Sharif U SU Ivanov Ivaylo I Huang Ching-Lung CL Moosavi Maral M Sargent Edward H EH Kelley Shana O SO

Nature chemistry 20180402 5

Cell-to-cell variation in gene expression creates a need for techniques that can characterize expression at the level of individual cells. This is particularly true for rare circulating tumour cells, in which subtyping and drug resistance are of intense interest. Here we describe a method for cell analysis-single-cell mRNA cytometry-that enables the isolation of rare cells from whole blood as a function of target mRNA sequences. This approach uses two classes of magnetic particles that are label ...[more]

PMID: 29610463

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Project description:The cell-derived vesicles (CDVs) obtained using a proprietary extrusion process are the foundation of BioDrone platform technology. With superior productivity and versatility, this technology has garnered increasing attention in broad applications, particularly as a drug delivery vehicle. Previously, we showed that CDVs exhibited varying levels of expression for tetraspanin and organelle membrane markers while revealing no discernible differences in physical characteristics compared to naturally produced extracellular vesicles (EVs). To further understand and utilize the therapeutic potentials of CDVs, a more comprehensive study of membrane protein profiles is necessary. In addition, it is crucial to validate that the CDVs produced from extrusion are indeed intact lipid vesicles rather than other impurities. Here, we produced multiple batches of CDVs and EVs from HEK293 cells. CDVs and EVs were subjected to the same purification processes for subsequent proteome and particle analyses. The proteome analyses revealed unique proteome signatures between CDVs, EVs, and parental cells. Extensive proteome analyses identified the nine most prominent membrane markers that are abundant in CDVs compared to cells and EVs. Subsequent western blotting and nanoparticle flow cytometry analyses confirmed that CD63, lysosome-associated membrane glycoprotein 1 (LAMP1), and nicastrin (NCSTN) are highly enriched in CDVs, whereas CD81, CD9, and prostaglandin F2 receptor negative regulator (PTGFRN) are more abundant in EVs. This highlights the unique membrane composition and marker signature of CDVs that are distinct from EVs. Lastly, we demonstrated that more than 90% of the CDVs are genuine lipid vesicles by combining two different classes of vesicle labeling dyes and detergents to disrupt lipid membranes. This indicates that our proprietary extrusion technology is highly compatible with other well-characterized EV production methods. The robust CDV markers identified in this study will also facilitate the engineering of CDVs to achieve enhanced therapeutic effects or tissue-selective cargo delivery.

Project description:MotivationSingle-cell RNA sequencing (scRNA-seq) is an increasingly popular technique for transcriptomic analysis of gene expression at the single-cell level. Cell-type clustering is the first crucial task in the analysis of scRNA-seq data that facilitates accurate identification of cell types and the study of the characteristics of their transcripts. Recently, several computational models based on a deep autoencoder and the ensemble clustering have been developed to analyze scRNA-seq data. However, current deep autoencoders are not sufficient to learn the latent representations of scRNA-seq data, and obtaining consensus partitions from these feature representations remains under-explored.ResultsTo address this challenge, we propose a single-cell deep clustering model via a dual denoising autoencoder with bipartite graph ensemble clustering called scBGEDA, to identify specific cell populations in single-cell transcriptome profiles. First, a single-cell dual denoising autoencoder network is proposed to project the data into a compressed low-dimensional space and that can learn feature representation via explicit modeling of synergistic optimization of the zero-inflated negative binomial reconstruction loss and denoising reconstruction loss. Then, a bipartite graph ensemble clustering algorithm is designed to exploit the relationships between cells and the learned latent embedded space by means of a graph-based consensus function. Multiple comparison experiments were conducted on 20 scRNA-seq datasets from different sequencing platforms using a variety of clustering metrics. The experimental results indicated that scBGEDA outperforms other state-of-the-art methods on these datasets, and also demonstrated its scalability to large-scale scRNA-seq datasets. Moreover, scBGEDA was able to identify cell-type specific marker genes and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into identifying cell types and characterizing the scRNA-seq data from different perspectives.Availability and implementationThe source code of scBGEDA is available at https://github.com/wangyh082/scBGEDA. The software and the supporting data can be downloaded from https://figshare.com/articles/software/scBGEDA/19657911.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Single-cell mRNA cytometry via sequence-specific nanoparticle clustering and trapping.

Publications

Single-cell mRNA cytometry via sequence-specific nanoparticle clustering and trapping.

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