scholarly journals Highly Multiplexed Single-Cell RNA-seq for Defining Cell Population and Transcriptional Spaces

2018 ◽  
Author(s):  
Jase Gehring ◽  
Jong Hwee Park ◽  
Sisi Chen ◽  
Matthew Thomson ◽  
Lior Pachter
Keyword(s):  
Population Structure ◽  
Single Cell ◽  
Cell Population ◽  
Effective Means ◽  
Cost Effective ◽  
Clinical Samples ◽  
Cell Populations ◽  
Rna Seq ◽  
Dna Oligonucleotides ◽  
Perturbation Experiment

AbstractWe describe a universal sample multiplexing method for single-cell RNA-seq in which cells are chemically labeled with identifying DNA oligonucleotides. Analysis of a 96-plex perturbation experiment revealed changes in cell population structure and transcriptional states that cannot be discerned from bulk measurements, establishing a cost effective means to survey cell populations from large experiments and clinical samples with the depth and resolution of single-cell RNA-seq.

scholarly journals ILoReg enables high-resolution cell population identification from single-cell RNA-seq data

2020 ◽  
Author(s):  
Johannes Smolander ◽  
Sini Junttila ◽  
Mikko S Venäläinen ◽  
Laura L Elo
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Single Cell ◽  
Cell Population ◽  
R Package ◽  
High Dimensionality ◽  
Cell Populations ◽  
Rna Seq ◽  
Extraction Step ◽  
Resolution Cell

AbstractSingle-cell RNA-seq allows researchers to identify cell populations based on unsupervised clustering of the transcriptome. However, subpopulations can have only subtle transcriptomic differences and the high dimensionality of the data makes their identification challenging. We introduce ILoReg (https://github.com/elolab/iloreg), an R package implementing a new cell population identification method that achieves high differentiation resolution through a probabilistic feature extraction step that is applied before clustering and visualization.

scholarly journals An interpretable meta-clustering framework for single-cell RNA-Seq data integration and evaluation

2021 ◽  
Author(s):  
Zhiyuan Hu ◽  
Ahmed Ashour Ahmed ◽  
Christopher Yau
Keyword(s):  
Data Integration ◽  
Single Cell ◽  
Similarity Measures ◽  
Clinical Samples ◽  
Cell Populations ◽  
Rna Seq ◽  
Batch Effects ◽  
Clustering Methods ◽  
Single Cell Rna Sequencing ◽  
Group Similarity

Single-cell RNA sequencing (scRNA-Seq) datasets that are produced from clinical samples are often confounded by batch effects and inter-patient variability. Existing batch effect removal methods typically require strong assumptions on the composition of cell populations being near identical across patients. Here we present a novel meta-clustering workflow, CIDER, based on inter-group similarity measures. We demonstrate that CIDER outperforms other scRNA-Seq clustering methods and integration approaches in both simulated and real datasets. Moreover, we show that CIDER can be used to assess the biological correctness of integration in real datasets, while it does not require the existence of prior cellular annotations.

scholarly journals Development of a Serial Dilution Technique for Obtaining Monoclonal Cell Populations

2021 ◽  
Author(s):  
Maryam Ranjbar ◽  
Marjan Nourigorji ◽  
Farshid Amiri ◽  
Hossein Jafari Khamirani ◽  
Sina Zoghi ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Genetic Information ◽  
Genetically Modified ◽  
Cost Effective ◽  
Serial Dilution ◽  
Cell Populations ◽  
Dilution Technique ◽  
Cell Clones ◽  
Limiting Dilution

Abstract Single cell-based techniques have drawn the attention of researchers, because they provide invaluable information of various domains ranging from genomics to epigenetics, transcriptomics, and proteomics. Single cell-derived clones provide a reliable and sustainable source of genetic information due to the homogeneity of the cell population. Aiming to obtain single-cell clones, several approaches were engineered, among which, the Limiting dilution approach stands out as a cost-effective and unsophisticated, and easy-to-apply method. Here, we demonstrate how to acquire single cell-derived clones through a simple 1:10 diluting from genetically modified heterogeneous cell populations.

scholarly journals ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq data

2020 ◽  
Author(s):  
Johannes Smolander ◽  
Sini Junttila ◽  
Mikko S Venäläinen ◽  
Laura L Elo
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Single Cell ◽  
Cell Population ◽  
Learning Algorithm ◽  
R Package ◽  
Supplementary Information ◽  
Cell Populations ◽  
Rna Seq ◽  
Pancreatic Cell

Abstract Motivation Single-cell RNA-seq allows researchers to identify cell populations based on unsupervised clustering of the transcriptome. However, subpopulations can have only subtle transcriptomic differences and the high dimensionality of the data makes their identification challenging. Results We introduce ILoReg, an R package implementing a new cell population identification method that improves identification of cell populations with subtle differences through a probabilistic feature extraction step that is applied before clustering and visualization. The feature extraction is performed using a novel machine learning algorithm, called iterative clustering projection (ICP), that uses logistic regression and clustering similarity comparison to iteratively cluster data. Remarkably, ICP also manages to integrate feature selection with the clustering through L1-regularization, enabling the identification of genes that are differentially expressed between cell populations. By combining solutions of multiple ICP runs into a single consensus solution, ILoReg creates a representation that enables investigating cell populations with a high resolution. In particular, we show that the visualization of ILoReg allows segregation of immune and pancreatic cell populations in a more pronounced manner compared with current state-of-the-art methods. Availability and implementation ILoReg is available as an R package at https://bioconductor.org/packages/ILoReg. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Single-Cell Transcriptomics Reveals the Expression of Aging- and Senescence-Associated Genes in Distinct Cancer Cell Populations

Cells ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 3126
Author(s):  
Dominik Saul ◽  
Robyn Laura Kosinsky
Keyword(s):  
Single Cell ◽  
Myelogenous Leukemia ◽  
Ductal Adenocarcinoma ◽  
Cell Populations ◽  
Rna Seq ◽  
Healthy Control ◽  
Transcriptional Changes ◽  
The Relationship ◽  
Senescence Associated Genes

The human aging process is associated with molecular changes and cellular degeneration, resulting in a significant increase in cancer incidence with age. Despite their potential correlation, the relationship between cancer- and ageing-related transcriptional changes is largely unknown. In this study, we aimed to analyze aging-associated transcriptional patterns in publicly available bulk mRNA-seq and single-cell RNA-seq (scRNA-seq) datasets for chronic myelogenous leukemia (CML), colorectal cancer (CRC), hepatocellular carcinoma (HCC), lung cancer (LC), and pancreatic ductal adenocarcinoma (PDAC). Indeed, we detected that various aging/senescence-induced genes (ASIGs) were upregulated in malignant diseases compared to healthy control samples. To elucidate the importance of ASIGs during cell development, pseudotime analyses were performed, which revealed a late enrichment of distinct cancer-specific ASIG signatures. Notably, we were able to demonstrate that all cancer entities analyzed in this study comprised cell populations expressing ASIGs. While only minor correlations were detected between ASIGs and transcriptome-wide changes in PDAC, a high proportion of ASIGs was induced in CML, CRC, HCC, and LC samples. These unique cellular subpopulations could serve as a basis for future studies on the role of aging and senescence in human malignancies.

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

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.

scholarly journals CellSIUS provides sensitive and specific detection of rare cell populations from complex single-cell RNA-seq data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Rebekka Wegmann ◽  
Marilisa Neri ◽  
Sven Schuierer ◽  
Bilada Bilican ◽  
Huyen Hartkopf ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Populations ◽  
Rna Seq ◽  
Specific Detection

scholarly journals Full-Length Transcriptome: A Reliable Alternative for Single-Cell RNA-Seq Analysis in the Spleen of Teleost Without Reference Genome

2021 ◽  
Vol 12 ◽  
Author(s):  
Lixing Huang ◽  
Ying Qiao ◽  
Wei Xu ◽  
Linfeng Gong ◽  
Rongchao He ◽  
...  
Keyword(s):  
Data Analysis ◽  
B Cells ◽  
Single Cell ◽  
Reference Genome ◽  
Immune Cell ◽  
Cell Types ◽  
Full Length ◽  
Cell Populations ◽  
Epinephelus Coioides ◽  
Rna Seq

Fish is considered as a supreme model for clarifying the evolution and regulatory mechanism of vertebrate immunity. However, the knowledge of distinct immune cell populations in fish is still limited, and further development of techniques advancing the identification of fish immune cell populations and their functions are required. Single cell RNA-seq (scRNA-seq) has provided a new approach for effective in-depth identification and characterization of cell subpopulations. Current approaches for scRNA-seq data analysis usually rely on comparison with a reference genome and hence are not suited for samples without any reference genome, which is currently very common in fish research. Here, we present an alternative, i.e. scRNA-seq data analysis with a full-length transcriptome as a reference, and evaluate this approach on samples from Epinephelus coioides-a teleost without any published genome. We show that it reconstructs well most of the present transcripts in the scRNA-seq data achieving a sensitivity equivalent to approaches relying on genome alignments of related species. Based on cell heterogeneity and known markers, we characterized four cell types: T cells, B cells, monocytes/macrophages (Mo/MΦ) and NCC (non-specific cytotoxic cells). Further analysis indicated the presence of two subsets of Mo/MΦ including M1 and M2 type, as well as four subsets in B cells, i.e. mature B cells, immature B cells, pre B cells and early-pre B cells. Our research will provide new clues for understanding biological characteristics, development and function of immune cell populations of teleost. Furthermore, our approach provides a reliable alternative for scRNA-seq data analysis in teleost for which no reference genome is currently available.

scholarly journals RNA-seq: primary cells, cell lines and heat stress

2015 ◽  
Author(s):  
Carl J Schmdt ◽  
Elizabeth M Pritchett ◽  
Liang Sun ◽  
Richard V.N. Davis ◽  
Allen Hubbard ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heat Stress ◽  
Sequence Data ◽  
Expression Patterns ◽  
Effective Means ◽  
Cost Effective ◽  
Rna Seq ◽  
Individual Gene ◽  
Examine Gene Expression ◽  
A Cell

Transcriptome analysis by RNA-seq has emerged as a high-throughput, cost-effective means to evaluate the expression pattern of genes in organisms. Unlike other methods, such as microarrays or quantitative PCR, RNA-seq is a target free method that permits analysis of essentially any RNA that can be amplified from a cell or tissue. At its most basic, RNA-seq can determine individual gene expression levels by counting the number of times a particular transcript was found in the sequence data. Transcript levels can be compared across multiple samples to identify differentially expressed genes and infer differences in biological states between the samples. We have used this approach to examine gene expression patterns in chicken and human cells, with particular interest in determining response to heat stress.

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