Simultaneous multiplexed amplicon sequencing and transcriptome profiling in single cells

Saikia, Mridusmita; Burnham, Philip; Keshavjee, S.; Wang, Michael F. Z.; Heyang, Michael; Moral-Lopez, Pablo; Hinchman, Meleana M.; Danko, Charles G.; Parker, John S. L.; Vlaminck, Iwijn De

doi:10.1038/s41592-018-0259-9

Cited by 75 publications

(48 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, with the availability of high throughput single-cell platforms [2][3][4][5][6][7][8][9][10] , expression profiling across an ever-growing variety of cell types from dissociated tissues without prior purification has become common. To understand how individual cells and cell types within these complex samples communicate, multiple groups have since used our ligand-receptor pairs from 2015 1 to infer cell-to-cell communication in developing heart 11 , kidney 12 , liver 13 , lung 14 , cancer 15,16 , and cortex 17 .…”

mentioning

confidence: 99%

Predicting cell-to-cell communication networks using NATMI

et al. 2020

View full text Add to dashboard Cite

Development of high throughput single-cell sequencing technologies has made it cost-effective to profile thousands of cells from diverse samples containing multiple cell types. To study how these different cell types work together, here we develop NATMI (Network Analysis Toolkit for Multicellular Interactions). NATMI uses connectomeDB2020 (a database of 2293 manually curated ligand-receptor pairs with literature support) to predict and visualise cell-to-cell communication networks from single-cell (or bulk) expression data. Using multiple published single-cell datasets we demonstrate how NATMI can be used to identify (i) the cell-type pairs that are communicating the most (or most specifically) within a network, (ii) the most active (or specific) ligand-receptor pairs active within a network, (iii) putative highly-communicating cellular communities and (iv) differences in intercellular communication when profiling given cell types under different conditions. Furthermore, analysis of the Tabula Muris (organism-wide) atlas confirms our previous prediction that autocrine signalling is a major feature of cell-to-cell communication networks, while also revealing that hundreds of ligands and their cognate receptors are co-expressed in individual cells suggesting a substantial potential for self-signalling.

show abstract

mentioning

confidence: 99%

Predicting cell-to-cell communication networks using NATMI

et al. 2020

View full text Add to dashboard Cite

show abstract

“…By synthesizing target sequence-linked beads, targeted sequences could be captured directly without laborious PCR amplification of each targeted sequence 45 . Alternatively, ligating target sequences to conventional beads could also be used 46 . Another application involves coupling of methods targeting individual cells using barcoding 47 .…”

Section: Discussionmentioning

confidence: 99%

Single-cell analysis of a mutant library generated using CRISPR-guided deaminase in human melanoma cells

et al. 2020

View full text Add to dashboard Cite

CRISPR-based screening methods using single-cell RNA sequencing (scRNA-seq) technology enable comprehensive profiling of gene perturbations from knockout mutations. However, evaluating substitution mutations using scRNA-seq is currently limited. We combined CRISPR RNA-guided deaminase and scRNA-seq technology to develop a platform for introducing mutations in multiple genes and assessing the mutation-associated signatures. Using this platform, we generated a library consisting of 420 sgRNAs, performed sgRNA tracking analysis, and assessed the effect size of the response to vemurafenib in the human melanoma cell line, which has been well-studied via knockout-based drop-out screens. However, a substitution mutation library screen has not been applied and transcriptional information for mechanisms of action was not assessed. Our platform permits discrimination of several candidate mutations that function differently from other mutations by integrating sgRNA candidates and gene expression readout. We anticipate that our platform will enable highthroughput analyses of the mechanisms related to a variety of biological events.

show abstract

“…Takara Bio also sells reagent kits for the ICELL8, however, protocols on the instrument are customizable allowing for greater flexibility. Drop-seq is also an open system that is fully customizable and custom reagents such as target-capture oligonucleotide beads [29,30] can be easily integrated into the protocol. The fluorescent imaging capabilities of the ICELL8 cx enable the pairing of sequencing and imaging data, in downstream analysis.…”

Section: Discussionmentioning

confidence: 99%

Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

Yamawaki

Ellwanger

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results: Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluate methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5-end v1 and 3-end v3 methods. We demonstrate that these methods have fewer drop-out events which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion: Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

show abstract

Simultaneous multiplexed amplicon sequencing and transcriptome profiling in single cells

Cited by 75 publications

References 29 publications

Predicting cell-to-cell communication networks using NATMI

Predicting cell-to-cell communication networks using NATMI

Single-cell analysis of a mutant library generated using CRISPR-guided deaminase in human melanoma cells

Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

Contact Info

Product

Resources

About