Extraction of nuclei from archived post-mortem tissues for single-nucleus sequencing applications

Abstract Single-cell and single-nucleus sequencing techniques are a burgeoning field with various biological, biomedical, and clinical applications. Numerous high and low-throughput methods have been developed for sequencing the RNA and DNA content of single cells. However, for all these methods the key requirement is high quality input of a single-cell or single-nucleus suspension. Preparing such a suspension is the limiting step when working with fragile, archived tissues of variable quality. This hurdle can prevent such tissues from being extensively investigated with single-cell technologies. We describe a protocol for preparing single-nucleus suspensions within the span of a few hours that reliably works for multiple post-mortem and archived tissues types using standard lab equipment. Moreover, these preparations are compatible with single-nucleus RNA-seq and ATAC-seq using the 10X Genomics’ Chromium system.

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Extraction of nuclei from archived post-mortem tissues for single-nucleus sequencing applications

10.21203/rs.3.pex-905/v3 ◽

2020 ◽

Author(s):

Malosree Maitra ◽

Corina Nagy ◽

Yu Chang Wang ◽

Camila Nascimento ◽

Matthew Suderman ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Clinical Applications ◽

Post Mortem ◽

Rna Seq ◽

High Quality ◽

Limiting Step ◽

Single Nucleus ◽

Rna And Dna ◽

Variable Quality

Abstract Single-cell and single-nucleus sequencing techniques are a burgeoning field with various biological, biomedical, and clinical applications. Numerous high and low-throughput methods have been developed for sequencing the RNA and DNA content of single cells. However, for all these methods the key requirement is high quality input of a single-cell or single-nucleus suspension. Preparing such a suspension is the limiting step when working with fragile, archived tissues of variable quality. This hurdle can prevent such tissues from being extensively investigated with single-cell technologies. We describe a protocol for preparing single-nucleus suspensions within the span of a few hours that reliably works for multiple post-mortem and archived tissues types using standard lab equipment. Moreover, these preparations are compatible with single-nucleus RNA-seq and ATAC-seq using the 10X Genomics’ Chromium system.

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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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Single nucleus sequencing fails to detect microglial activation in human tissue

10.1101/2020.04.13.035386 ◽

2020 ◽

Cited By ~ 4

Author(s):

N. Thrupp ◽

C. Sala Frigerio ◽

L. Wolfs ◽

N. G. Skene ◽

S. Poovathingal ◽

...

Keyword(s):

Single Cell ◽

Human Tissue ◽

Microglial Activation ◽

Human Subjects ◽

Single Cells ◽

Small Population ◽

Rna Seq ◽

Whole Cells ◽

Single Nucleus ◽

Low Sensitivity

AbstractSingle nucleus RNA-Seq (snRNA-Seq) methods are used as an alternative to single cell RNA-Seq methods, as they allow transcriptomic profiling of frozen tissue. However, it is unclear whether snRNA-Seq is able to detect cellular state in human tissue. Indeed, snRNA-Seq analyses of human brain samples have failed to detect a consistent microglial activation signature in Alzheimer’s Disease. A comparison of microglia from single cells and single nuclei of four human subjects reveals that ~1% of genes is depleted in nuclei compared to whole cells. This small population contains 18% of genes previously implicated in microglial activation, including APOE, CST3, FTL, SPP1, and CD74. We confirm our findings across multiple previous single nucleus and single cell studies. Given the low sensitivity of snRNA-Seq to this population of activation genes, we conclude that snRNA-Seq is not suited to detecting cellular activation in microglia in human disease.

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SUGAR-seq enables simultaneous detection of glycans, epitopes, and the transcriptome in single cells

Science Advances ◽

10.1126/sciadv.abe3610 ◽

2021 ◽

Vol 7 (8) ◽

pp. eabe3610

Author(s):

Conor J. Kearney ◽

Stephin J. Vervoort ◽

Kelly M. Ramsbottom ◽

Izabela Todorovski ◽

Emily J. Lelliott ◽

...

Keyword(s):

Single Cell ◽

Posttranslational Modification ◽

Cellular Differentiation ◽

Single Cells ◽

Simultaneous Detection ◽

Surface Protein ◽

Rna Seq ◽

Cell Level ◽

Simultaneous Integration ◽

Unique Surface

Multimodal single-cell RNA sequencing enables the precise mapping of transcriptional and phenotypic features of cellular differentiation states but does not allow for simultaneous integration of critical posttranslational modification data. Here, we describe SUrface-protein Glycan And RNA-seq (SUGAR-seq), a method that enables detection and analysis of N-linked glycosylation, extracellular epitopes, and the transcriptome at the single-cell level. Integrated SUGAR-seq and glycoproteome analysis identified tumor-infiltrating T cells with unique surface glycan properties that report their epigenetic and functional state.

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Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq

10.1101/2020.11.16.384222 ◽

2020 ◽

Author(s):

Viacheslav Mylka ◽

Jeroen Aerts ◽

Irina Matetovici ◽

Suresh Poovathingal ◽

Niels Vandamme ◽

...

Keyword(s):

Genetic Variation ◽

Comparative Analysis ◽

Single Cell ◽

Cell Lines ◽

Clinical Studies ◽

Clinical Samples ◽

Rna Seq ◽

Batch Effects ◽

Single Cell Sequencing ◽

Single Nucleus

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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Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

10.1101/553040 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ning Wang ◽

Andrew E. Teschendorff

Keyword(s):

Transcription Factors ◽

Single Cell ◽

Cell Fate ◽

Regulatory Networks ◽

Large Scale ◽

Single Cells ◽

Differential Expression Analysis ◽

Dropout Rate ◽

Rna Seq ◽

Regulatory Activity

AbstractInferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

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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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Characterizing Intercellular Communication of Pan-Cancer Reveals SPP1+ Tumor-Associated Macrophage Expanded in Hypoxia and Promoting Cancer Malignancy Through Single-Cell RNA-Seq Data

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.749210 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jinfen Wei ◽

Zixi Chen ◽

Meiling Hu ◽

Ziqing He ◽

Dawei Jiang ◽

...

Keyword(s):

Single Cell ◽

Cancer Cells ◽

Cancer Progression ◽

Epithelial Mesenchymal Transition ◽

Single Cells ◽

Matrix Remodeling ◽

Rna Seq ◽

Mesenchymal Transition ◽

Tumor Associated Macrophage ◽

Cancer Types

Hypoxia is a characteristic of tumor microenvironment (TME) and is a major contributor to tumor progression. Yet, subtype identification of tumor-associated non-malignant cells at single-cell resolution and how they influence cancer progression under hypoxia TME remain largely unexplored. Here, we used RNA-seq data of 424,194 single cells from 108 patients to identify the subtypes of cancer cells, stromal cells, and immune cells; to evaluate their hypoxia score; and also to uncover potential interaction signals between these cells in vivo across six cancer types. We identified SPP1+ tumor-associated macrophage (TAM) subpopulation potentially enhanced epithelial–mesenchymal transition (EMT) by interaction with cancer cells through paracrine pattern. We prioritized SPP1 as a TAM-secreted factor to act on cancer cells and found a significant enhanced migration phenotype and invasion ability in A549 lung cancer cells induced by recombinant protein SPP1. Besides, prognostic analysis indicated that a higher expression of SPP1 was found to be related to worse clinical outcome in six cancer types. SPP1 expression was higher in hypoxia-high macrophages based on single-cell data, which was further validated by an in vitro experiment that SPP1 was upregulated in macrophages under hypoxia-cultured compared with normoxic conditions. Additionally, a differential analysis demonstrated that hypoxia potentially influences extracellular matrix remodeling, glycolysis, and interleukin-10 signal activation in various cancer types. Our work illuminates the clearer underlying mechanism in the intricate interaction between different cell subtypes within hypoxia TME and proposes the guidelines for the development of therapeutic targets specifically for patients with high proportion of SPP1+ TAMs in hypoxic lesions.

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DEsingle for detecting three types of differential expression in single-cell RNA-seq data

10.1101/173997 ◽

2017 ◽

Cited By ~ 1

Author(s):

Zhun Miao ◽

Ke Deng ◽

Xiaowo Wang ◽

Xuegong Zhang

Keyword(s):

Single Cell ◽

Differential Expression ◽

Negative Binomial ◽

Single Cells ◽

R Package ◽

Supplementary Information ◽

Binomial Model ◽

Supplementary Data ◽

Rna Seq ◽

Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

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