Nuclear extraction from endometrial tumors for single nuclei sequencing v1

ABSTRACTMultiplexing of samples in single-cell RNA-seq studies allows significant reduction of experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or - lipids allow barcoding sample-specific cells, a process called ‘hashing’. Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines. Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects.

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Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

10.1101/786285 ◽

2019 ◽

Cited By ~ 4

Author(s):

Marcus Alvarez ◽

Elior Rahmani ◽

Brandon Jew ◽

Kristina M. Garske ◽

Zong Miao ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Types ◽

Supervised Machine Learning ◽

Data Sets ◽

Rna Seq ◽

Novel Approach ◽

Single Nucleus ◽

Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

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CellMap: Characterizing the types and composition of iPSC-derived cells from RNA-seq data

10.1101/2021.05.24.445360 ◽

2021 ◽

Author(s):

Zhengyu Ouyang ◽

Nathanael Bourgeois ◽

Eugenia Lyashenko ◽

Paige Cundiff ◽

Patrick F Cullen ◽

...

Keyword(s):

Single Cell ◽

Induced Pluripotent Stem Cell ◽

Cell Types ◽

Model Systems ◽

Rna Seq ◽

Cell Type ◽

Fine Grained ◽

Single Nucleus ◽

Induced Pluripotent

Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.

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Single Cell and Single Nucleus RNA-Seq Reveal Cellular Heterogeneity and Homeostatic Regulatory Networks in Adult Mouse Stria Vascularis

Frontiers in Molecular Neuroscience ◽

10.3389/fnmol.2019.00316 ◽

2019 ◽

Vol 12 ◽

Cited By ~ 7

Author(s):

Soumya Korrapati ◽

Ian Taukulis ◽

Rafal Olszewski ◽

Madeline Pyle ◽

Shoujun Gu ◽

...

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Adult Mouse ◽

Stria Vascularis ◽

Cellular Heterogeneity ◽

Rna Seq ◽

Single Nucleus

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

10.1101/2020.02.04.933259 ◽

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.

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SHERRY2: A method for rapid and sensitive single cell RNA-seq

10.1101/2021.12.25.474161 ◽

2021 ◽

Author(s):

Lin Di ◽

Bo Liu ◽

Yuzhu Lyu ◽

Shihui Zhao ◽

Yuhong Pang ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Dynamic Range ◽

Single Cells ◽

Rna Seq ◽

Wide Dynamic Range ◽

Uniform Coverage ◽

Optimized Protocol ◽

Tn5 Transposase ◽

Higher Sensitivity

Many single cell RNA-seq applications aim to probe a wide dynamic range of gene expression, but most of them are still challenging to accurately quantify low-aboundance transcripts. Based on our previous finding that Tn5 transposase can directly cut-and-tag DNA/RNA hetero-duplexes, we present SHERRY2, an optimized protocol for sequencing transcriptomes of single cells or single nuclei. SHERRY2 is robust and scalable, and it has higher sensitivity and more uniform coverage in comparison with prevalent scRNA-seq methods. With throughput of a few thousand cells per batch, SHERRY2 can reveal the subtle transcriptomic differences between cells and facilitate important biological discoveries.

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Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

10.1101/807552 ◽

2019 ◽

Cited By ~ 4

Author(s):

Gabriela S. Kinker ◽

Alissa C. Greenwald ◽

Rotem Tal ◽

Zhanna Orlova ◽

Michael S. Cuoco ◽

...

Keyword(s):

Single Cell ◽

Cell Lines ◽

Cancer Cell ◽

Cancer Cell Lines ◽

Human Tumors ◽

Model Systems ◽

Cellular Heterogeneity ◽

Specific Cell ◽

Rna Seq ◽

Mesenchymal Transition

AbstractCultured cell lines are the workhorse of cancer research, but it is unclear to what extent they recapitulate the cellular heterogeneity observed among malignant cells in tumors, given the absence of a native tumor microenvironment. Here, we used multiplexed single cell RNA-seq to profile ~200 cancer cell lines. We uncovered expression programs that are recurrently heterogeneous within many cancer cell lines and are largely independent of observed genetic diversity. These programs of heterogeneity are associated with diverse biological processes, including cell cycle, senescence, stress and interferon responses, epithelial-to-mesenchymal transition, and protein maturation and degradation. Notably, some of these recurrent programs recapitulate those seen in human tumors, suggesting a prominent role of intrinsic plasticity in generating intra-tumoral heterogeneity. Moreover, the data allowed us to prioritize specific cell lines as model systems of cellular plasticity. We used two such models to demonstrate the dynamics, regulation and drug sensitivities associated with a cancer senescence program also observed in human tumors. Our work describes the landscape of cellular heterogeneity in diverse cancer cell lines, and identifies recurrent patterns of expression heterogeneity that are shared between tumors and specific cell lines and can thus be further explored in follow up studies.

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sNucDrop-Seq: Dissecting cell-type composition and neuronal activity state in mammalian brains by massively parallel single-nucleus RNA-Seq

10.1101/154476 ◽

2017 ◽

Author(s):

Peng Hu ◽

Emily Fabyanic ◽

Zhaolan Zhou ◽

Hao Wu

Keyword(s):

Single Cell ◽

Neuronal Activity ◽

Low Cost ◽

Single Cells ◽

High Sensitivity ◽

Droplet Microfluidics ◽

Massively Parallel ◽

Rna Seq ◽

Single Nucleus

Massively parallel single-cell RNA sequencing can precisely resolve cellular diversity in a high-throughput manner at low cost, but unbiased isolation of intact single cells from complex tissues, such as adult mammalian brains, is challenging. Here, we integrate sucrose-gradient assisted nuclear purification with droplet microfluidics to develop a highly scalable single-nucleus RNA-Seq approach (sNucDrop-Seq), which is free of enzymatic dissociation and nucleus sorting. By profiling ~11,000 nuclei isolated from adult mouse cerebral cortex, we demonstrate that sNucDrop-Seq not only accurately reveals neuronal and non-neuronal subtype composition with high sensitivity, but also enables analysis of long non-coding RNAs and transient states such as neuronal activity-dependent transcription at single-cell resolution in vivo.

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