scholarly journals Tn5Prime, a Tn5 based 5’ Capture Method for Single Cell RNA-seq

2017 ◽  
Author(s):  
Charles Cole ◽  
Ashley Byrne ◽  
Anna E. Beaudin ◽  
E. Camilla Forsberg ◽  
Christopher Vollmers
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Adaptive Immune System ◽  
Cost Effective ◽  
Rna Seq ◽  
Transcription Start ◽  
Capture Process ◽  
Transcription Start Sites ◽  
Large Numbers ◽  
Basic And Applied Research

AbstractRNA-seq is a powerful technique to investigate and quantify entire transcriptomes. Recent advances in the field have made it possible to explore the transcriptomes of single cells. However, most widely used RNA-seq protocols fail to provide crucial information regarding transcription start sites. Here we present a protocol, Tn5Prime, that takes advantage of the Tn5 transposase based Smartseq2 protocol to create RNA-seq libraries that capture the 5’ end of transcripts. The Tn5Prime method dramatically streamlines the 5’ capture process and is both cost effective and reliable. By applying Tn5Prime to bulk RNA and single cell samples we were able to define transcription start sites as well as quantify transcriptomes at high accuracy and reproducibility. Additionally, similar to 3’ end based high-throughput methods like Drop-Seq and 10X Genomics Chromium, the 5’ capture Tn5Prime method allows the introduction of cellular identifiers during reverse transcription, simplifying the analysis of large numbers of single cells. In contrast to 3’ end based methods, Tn5Prime also enables the assembly of the variable 5’ ends of antibody sequences present in single B-cell data. Therefore, Tn5Prime presents a robust tool for both basic and applied research into the adaptive immune system and beyond.

scholarly journals Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

2017 ◽  
Author(s):  
Junyue Cao ◽  
Jonathan S. Packer ◽  
Vijay Ramani ◽  
Darren A. Cusanovich ◽  
Chau Huynh ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Transcriptional Profiling ◽  
Cost Effective ◽  
Cell Types ◽  
Cell Capture ◽  
Rna Seq ◽  
Major Step ◽  
C Elegans

AbstractConventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold “shotgun cellular coverage” of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major step towards a comprehensive, single-cell molecular atlas of a whole animal.

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

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.

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

2019 ◽  
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.

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

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.

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

2017 ◽  
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.

scholarly journals Single-cell RNA cap and tail sequencing (scRCAT-seq) reveals subtype-specific isoforms differing in transcript demarcation

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Youjin Hu ◽  
Jiawei Zhong ◽  
Yuhua Xiao ◽  
Zheng Xing ◽  
Katherine Sheu ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Single Gene ◽  
Cell Types ◽  
Machine Learning Algorithms ◽  
Translation Efficiency ◽  
Transcription Start Sites ◽  
Long Read ◽  
Mrna Gene ◽  
Gene Isoforms

Abstract The differences in transcription start sites (TSS) and transcription end sites (TES) among gene isoforms can affect the stability, localization, and translation efficiency of mRNA. Gene isoforms allow a single gene diverse functions across different cell types, and isoform dynamics allow different functions over time. However, methods to efficiently identify and quantify RNA isoforms genome-wide in single cells are still lacking. Here, we introduce single cell RNA Cap And Tail sequencing (scRCAT-seq), a method to demarcate the boundaries of isoforms based on short-read sequencing, with higher efficiency and lower cost than existing long-read sequencing methods. In conjunction with machine learning algorithms, scRCAT-seq demarcates RNA transcripts with unprecedented accuracy. We identified hundreds of previously uncharacterized transcripts and thousands of alternative transcripts for known genes, revealed cell-type specific isoforms for various cell types across different species, and generated a cell atlas of isoform dynamics during the development of retinal cones.

scholarly journals DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data

2019 ◽  
Vol 35 (24) ◽  
pp. 5155-5162 ◽  
Author(s):  
Chengzhong Ye ◽  
Terence P Speed ◽  
Agus Salim
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Type I Error ◽  
R Package ◽  
Supplementary Information ◽  
Type I ◽  
Common Phenomenon ◽  
Rna Seq ◽  
Capture Process ◽  
Technological Platforms

Abstract Motivation Dropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed it affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the process that gives rise to the dropout events. We develop DECENT, a method for DE analysis of scRNA-seq data that explicitly and accurately models the molecule capture process in scRNA-seq experiments. Results We show that DECENT demonstrates improved DE performance over existing DE methods that do not explicitly model dropout. This improvement is consistently observed across several public scRNA-seq datasets generated using different technological platforms. The gain in improvement is especially large when the capture process is overdispersed. DECENT maintains type I error well while achieving better sensitivity. Its performance without spike-ins is almost as good as when spike-ins are used to calibrate the capture model. Availability and implementation The method is implemented as a publicly available R package available from https://github.com/cz-ye/DECENT. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Single cell census of human kidney organoids shows reproducibility and diminished off-target cells after transplantation

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Ayshwarya Subramanian ◽  
Eriene-Heidi Sidhom ◽  
Maheswarareddy Emani ◽  
Katherine Vernon ◽  
Nareh Sahakian ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Human Kidney ◽  
Mouse Kidney ◽  
Target Cells ◽  
Rna Seq ◽  
Kidney Capsule ◽  
Time Points ◽  
Human Ipsc ◽  
Kidney Organoids

AbstractHuman iPSC-derived kidney organoids have the potential to revolutionize discovery, but assessing their consistency and reproducibility across iPSC lines, and reducing the generation of off-target cells remain an open challenge. Here, we profile four human iPSC lines for a total of 450,118 single cells to show how organoid composition and development are comparable to human fetal and adult kidneys. Although cell classes are largely reproducible across time points, protocols, and replicates, we detect variability in cell proportions between different iPSC lines, largely due to off-target cells. To address this, we analyze organoids transplanted under the mouse kidney capsule and find diminished off-target cells. Our work shows how single cell RNA-seq (scRNA-seq) can score organoids for reproducibility, faithfulness and quality, that kidney organoids derived from different iPSC lines are comparable surrogates for human kidney, and that transplantation enhances their formation by diminishing off-target cells.

scholarly journals Observing Growth and Division of Large Numbers of Individual Bacteria by Image Analysis

2004 ◽  
Vol 70 (2) ◽  
pp. 675-678 ◽  
Author(s):  
A. Elfwing ◽  
Y. LeMarc ◽  
J. Baranyi ◽  
A. Ballagi
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Digital Camera ◽  
Flow Chamber ◽  
Bacterial Cells ◽  
Daughter Cells ◽  
Large Numbers ◽  
History Effect ◽  
Lag Times ◽  
Cell Growth And Proliferation

ABSTRACT We describe a method that enabled us to observe large numbers of individual bacterial cells during a long period of cell growth and proliferation. We designed a flow chamber in which the cells attached to a transparent solid surface. The flow chamber was mounted on a microscope equipped with a digital camera. The shear force of the flow removed the daughter cells, making it possible to monitor the consecutive divisions of a single cell. In this way, kinetic parameters and their distributions, as well as some physiological characteristics of the bacteria, could be analyzed based on more than 1,000 single-cell observations. The method which we developed enabled us to study the history effect on the distribution of the lag times of single cells.

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