scholarly journals A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors

2020 ◽  
Vol 26 (5) ◽  
pp. 792-802 ◽  
Author(s):  
Michal Slyper ◽  
Caroline B. M. Porter ◽  
Orr Ashenberg ◽  
Julia Waldman ◽  
Eugene Drokhlyansky ◽  
...  
2020 ◽  
Vol 26 (8) ◽  
pp. 1307-1307
Author(s):  
Michal Slyper ◽  
Caroline B. M. Porter ◽  
Orr Ashenberg ◽  
Julia Waldman ◽  
Eugene Drokhlyansky ◽  
...  

2022 ◽  
Author(s):  
gracefoley not provided

Modified from: Slyper, M. et al. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat. Med. 2020 26526, 792–802 (2020). A optimized protocol for nuclear extraction from endometrial tumors. Performed with endometrial adenocarcinoma, endometrioid type, FIGO grade 1.


2020 ◽  
Author(s):  
Viacheslav Mylka ◽  
Jeroen Aerts ◽  
Irina Matetovici ◽  
Suresh Poovathingal ◽  
Niels Vandamme ◽  
...  

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.


2019 ◽  
Author(s):  
Marcus Alvarez ◽  
Elior Rahmani ◽  
Brandon Jew ◽  
Kristina M. Garske ◽  
Zong Miao ◽  
...  

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.


2021 ◽  
Author(s):  
Zhengyu Ouyang ◽  
Nathanael Bourgeois ◽  
Eugenia Lyashenko ◽  
Paige Cundiff ◽  
Patrick F Cullen ◽  
...  

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.


2020 ◽  
Author(s):  
Jixing Zhong ◽  
Gen Tang ◽  
Jiacheng Zhu ◽  
Xin Qiu ◽  
Weiying Wu ◽  
...  

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.


2019 ◽  
Author(s):  
Gabriela S. Kinker ◽  
Alissa C. Greenwald ◽  
Rotem Tal ◽  
Zhanna Orlova ◽  
Michael S. Cuoco ◽  
...  

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.


2017 ◽  
Author(s):  
Peng Hu ◽  
Emily Fabyanic ◽  
Zhaolan Zhou ◽  
Hao Wu

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.


2018 ◽  
Author(s):  
Shan Jiang ◽  
Katherine Williams ◽  
Xiangduo Kong ◽  
Weihua Zeng ◽  
Xinyi Ma ◽  
...  

AbstractFSHD is characterized by the misexpression of DUX4 in skeletal muscle. However, DUX4 is lowly expressed in patient samples and analysis of the consequences of DUX4 expression has largely relied on artificial overexpression. To better understand the native expression profile of DUX4 and its targets, we performed pooled RNA-seq differentiation time-course in FSHD2 patient-derived primary myoblasts and identified early-and late-induced sets of FSHD-associated genes. Using single-cell and single-nucleus RNA-seq on FSHD2 myoblasts and myotubes respectively, we captured DUX4 expression in single-nuclei and found that only some DUX4 targets are coexpressed. We identified two populations of FSHD myotube nuclei with distinct transcriptional profiles. One population is highly enriched with DUX4 and FSHD related genes, including the DUX4 paralog DUXA (“FSHD-Hi”). The other population has no expression of DUX4 and expresses low amounts of FSHD related genes (“FSHD-Lo”), but is marked by the expression of CYTL1 and CHI3L1. “FSHD-Hi” myotube nuclei upregulated a set of transcription factors (TFs) that may form a self-sustaining network of gene dysregulation, which perpetuates this disease after DUX4 is no longer expressed.


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