Preparation of single-cell suspension from mouse breast cancer focusing on preservation of original cell state information and cell type composition

SUMMARYSingle-cell analysis of gene expression has become a very popular method during the last decade. Unfortunately, appropriate standardization and workflow optimization remain elusive. The first step of the single cell analysis requires that the solid tissue be disassociated into a suspension of individual cells. However, during this step several technical bias can arise which can later result in the misinterpretation of the data. The goal of this study was to identify and quantify the effect of these technical factors on the quality of the single-cell suspension and the subsequent interpretation of the produced expression data. We tested the effects of various enzymes used for dissociation, several centrifugation forces, dissociation temperatures and the addition of Actinomycin D, a gene expression inhibitor. RT-qPCR was used to assess the effect from each parameter alteration, while a single-cell RNA sequencing experiment was used to confirm the optimized factors. Our concluding results provide a complete protocol for the tissue dissociation of mouse mammary tumour from 4T1 cells that preserves the original cell state and is suitable for any single-cell RNA sequencing analysis. Furthermore, our workflow may serve as a guide for the optimization of the dissociation procedure of any other tissue of interest, which would ultimately improve the reproducibility of the reported data.

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Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽

10.1126/science.aba5257 ◽

2020 ◽

Vol 371 (6531) ◽

pp. eaba5257 ◽

Cited By ~ 2

Author(s):

Anna Kuchina ◽

Leandra M. Brettner ◽

Luana Paleologu ◽

Charles M. Roco ◽

Alexander B. Rosenberg ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Single Cell Analysis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Growth Stages ◽

High Throughput Analysis ◽

Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

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SAVER: Gene expression recovery for UMI-based single cell RNA sequencing

10.1101/138677 ◽

2017 ◽

Cited By ~ 15

Author(s):

Mo Huang ◽

Jingshu Wang ◽

Eduardo Torre ◽

Hannah Dueck ◽

Sydney Shaffer ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Single Cell Analysis ◽

Specific Gene ◽

Recovery Method ◽

Single Cell Rna Sequencing ◽

Cell Gene Expression ◽

Single Cell Profiling ◽

Downstream Analysis

AbstractRapid advances in massively parallel single cell RNA sequencing (scRNA-seq) is paving the way for high-resolution single cell profiling of biological samples. In most scRNA-seq studies, only a small fraction of the transcripts present in each cell are sequenced. The efficiency, that is, the proportion of transcripts in the cell that are sequenced, can be especially low in highly parallelized experiments where the number of reads allocated for each cell is small. This leads to unreliable quantification of lowly and moderately expressed genes, resulting in extremely sparse data and hindering downstream analysis. To address this challenge, we introduce SAVER (Single-cell Analysis Via Expression Recovery), an expression recovery method for scRNA-seq that borrows information across genes and cells to impute the zeros as well as to improve the expression estimates for all genes. We show, by comparison to RNA fluorescence in situ hybridization (FISH) and by data down-sampling experiments, that SAVER reliably recovers cell-specific gene expression concentrations, cross-cell gene expression distributions, and gene-to-gene and cell-to-cell correlations. This improves the power and accuracy of any downstream analysis involving genes with low to moderate expression.

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Single-cell RNA-sequencing of peripheral neuroblastic tumors reveals an aggressive transitional cell state at the junction of an adrenergic-mesenchymal transdifferentiation trajectory

10.1101/2020.05.15.097469 ◽

2020 ◽

Cited By ~ 1

Author(s):

Xiaojun Yuan ◽

Janith A. Seneviratne ◽

Shibei Du ◽

Ying Xu ◽

Yijun Chen ◽

...

Keyword(s):

Gene Expression ◽

Neural Crest ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Transitional Cell ◽

Malignant Cell ◽

Cell State ◽

Single Cell Rna Sequencing ◽

Neuroblastic Tumors

AbstractPeripheral neuroblastic tumors (PNTs) are the most common extracranial solid tumors in early childhood. They represent a spectrum of neural crest derived tumors including neuroblastoma, ganglioneuroblastoma and ganglioneuroma. PNTs exhibit heterogeneity due to interconverting malignant cell states described as adrenergic/nor-adrenergic or mesenchymal/neural crest cell in origin. The factors determining individual patient levels of tumor heterogeneity, their impact on the malignant phenotype, and the presence of other cell states are unknown. Here, single-cell RNA-sequencing analysis of 4267 cells from 7 PNTs demonstrated extensive transcriptomic heterogeneity. Trajectory modelling showed that malignant neuroblasts move between adrenergic and mesenchymal cell states via a novel state that we termed a “transitional” phenotype. Transitional cells are characterized by gene expression programs linked to a sympathoadrenal development, and aggressive tumor phenotypes such as rapid proliferation and tumor dissemination. Among primary bulk tumor patient cohorts, high expression of the transitional gene signature was highly predictive of poor prognosis when compared to adrenergic and mesenchymal expression patterns. High transitional gene expression in neuroblastoma cell lines identified a similar transitional H3K27-acetylation super-enhancer landscape, supporting the concept that PNTs have phenotypic plasticity and transdifferentiation capacity. Additionally, examination of PNT microenvironments, found that neuroblastomas contained low immune cell infiltration, high levels of non-inflammatory macrophages, and low cytotoxic T lymphocyte levels compared with more benign PNT subtypes. Modeling of cell-cell signaling in the tumor microenvironment predicted specific paracrine effects toward the various subtypes of malignant cells, suggesting further cell-extrinsic influences on malignant cell phenotype. Collectively, our study reveals the presence of a previously unrecognized transitional cell state with high malignant potential and an immune cell architecture which serve both as potential biomarkers and therapeutic targets.

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Allele-specific single-cell RNA sequencing reveals different architectures of intrinsic and extrinsic gene expression noises

10.1101/667840 ◽

2019 ◽

Author(s):

Mengyi Sun ◽

Jianzhi Zhang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Intrinsic Noise ◽

Mouse Fibroblast ◽

Extrinsic Noise ◽

Gene Expression Noise ◽

Cell State ◽

Single Cell Rna Sequencing ◽

Allele Specific

ABSTRACTGene expression noise refers to the variation of the expression level of a gene among isogenic cells in the same environment, and has two sources: extrinsic noise arising from the disparity of the cell state and intrinsic noise arising from the stochastic process of gene expression in the same cell state. Due to the low throughput of the existing method for measuring the two noise components, the architectures of intrinsic and extrinsic expression noises remain elusive. Using allele-specific single-cell RNA sequencing, we here estimate the two noise components of 3975 genes in mouse fibroblast cells. Our analyses verify predicted influences of several factors such as the TATA-box and microRNA targeting on intrinsic and extrinsic noises and reveal gene function-associated noise trends implicating the action of natural selection. These findings unravel differential regulations, optimizations, and biological consequences of intrinsic and extrinsic noises and can aid the construction of desired synthetic circuits.

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Detecting differential alternative splicing events in scRNA-seq with or without UMIs

10.1101/738997 ◽

2019 ◽

Author(s):

Yu Hu ◽

Kai Wang ◽

Mingyao Li

Keyword(s):

Alternative Splicing ◽

Single Cell ◽

Rna Sequencing ◽

Single Cell Analysis ◽

High Sensitivity ◽

Sequencing Depth ◽

Cell Analysis ◽

Technical Noise ◽

Qrt Pcr ◽

Single Cell Rna Sequencing

Analysis of alternative splicing in single-cell RNA sequencing (scRNA-seq) is challenging due to its inherent technical noise and generally low sequencing depth. We present SCATS (Single-Cell Analysis of Transcript Splicing) for differential alternative splicing (DAS) analysis for scRNA-seq data with or without unique molecular identifiers (UMIs). By modeling technical noise and grouping exons that originate from the same isoform(s), SCATS achieves high sensitivity to detect DAS events compared to Census, DEXSeq and MISO, and these events were confirmed by qRT-PCR experiment.

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Allele-specific single-cell RNA sequencing reveals different architectures of intrinsic and extrinsic gene expression noises

Nucleic Acids Research ◽

10.1093/nar/gkz1134 ◽

2019 ◽

Vol 48 (2) ◽

pp. 533-547 ◽

Cited By ~ 2

Author(s):

Mengyi Sun ◽

Jianzhi Zhang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Intrinsic Noise ◽

Mouse Fibroblast ◽

Extrinsic Noise ◽

Gene Expression Noise ◽

Cell State ◽

Single Cell Rna Sequencing ◽

Allele Specific

Abstract Gene expression noise refers to the variation of the expression level of a gene among isogenic cells in the same environment, and has two sources: extrinsic noise arising from the disparity of the cell state and intrinsic noise arising from the stochastic process of gene expression in the same cell state. Due to the low throughput of the existing method for measuring the two noise components, the architectures of intrinsic and extrinsic expression noises remain elusive. Using allele-specific single-cell RNA sequencing, we here estimate the two noise components of 3975 genes in mouse fibroblast cells. Our analyses verify predicted influences of several factors such as the TATA-box and microRNA targeting on intrinsic or extrinsic noises and reveal gene function-associated noise trends implicating the action of natural selection. These findings unravel differential regulations, optimizations, and biological consequences of intrinsic and extrinsic noises and can aid the construction of desired synthetic circuits.

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Microfluidic platform accelerates tissue processing into single cells for molecular analysis and primary culture models

Nature Communications ◽

10.1038/s41467-021-23238-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Jeremy A. Lombardo ◽

Marzieh Aliaghaei ◽

Quy H. Nguyen ◽

Kai Kessenbrock ◽

Jered B. Haun

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Cell Analysis ◽

Processing Technologies ◽

Microfluidic Platform ◽

Tissue Processing ◽

Single Cell Rna Sequencing ◽

Culture Models ◽

Tissue Digestion

AbstractTissues are complex mixtures of different cell subtypes, and this diversity is increasingly characterized using high-throughput single cell analysis methods. However, these efforts are hindered, as tissues must first be dissociated into single cell suspensions using methods that are often inefficient, labor-intensive, highly variable, and potentially biased towards certain cell subtypes. Here, we present a microfluidic platform consisting of three tissue processing technologies that combine tissue digestion, disaggregation, and filtration. The platform is evaluated using a diverse array of tissues. For kidney and mammary tumor, microfluidic processing produces 2.5-fold more single cells. Single cell RNA sequencing further reveals that endothelial cells, fibroblasts, and basal epithelium are enriched without affecting stress response. For liver and heart, processing time is dramatically reduced. We also demonstrate that recovery of cells from the system at periodic intervals during processing increases hepatocyte and cardiomyocyte numbers, as well as increases reproducibility from batch-to-batch for all tissues.

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