scholarly journals Massively parallel, time-resolved single-cell RNA sequencing with scNT-Seq

2019 ◽  
Author(s):  
Qi Qiu ◽  
Peng Hu ◽  
Kiya W. Govek ◽  
Pablo G. Camara ◽  
Hao Wu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Temporal Dynamics ◽  
Embryonic Stem ◽  
Droplet Microfluidics ◽  
Massively Parallel ◽  
Specific Gene ◽  
Time Resolved ◽  
Single Cell Rna Sequencing ◽  
Rna Biogenesis

ABSTRACTSingle-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal dynamics of RNA biogenesis and decay. Here we present single-cell new transcript tagging sequencing (scNT-Seq), a method for massively parallel analysis of newly-transcribed and pre-existing RNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking metabolically labeled newly-transcribed RNAs with T-to-C substitutions. By simultaneously measuring new and old transcriptomes, scNT-Seq reveals neuronal subtype-specific gene regulatory networks and time-resolved RNA trajectories in response to brief (minutes) versus sustained (hours) neuronal activation. Integrating scNT-Seq with genetic perturbation reveals that DNA methylcytosine dioxygenases may inhibit stepwise transition from pluripotent embryonic stem cell state to intermediate and totipotent two-cell-embryo-like (2C-like) states by promoting global RNA biogenesis. Furthermore, pulse-chase scNT-Seq enables transcriptome-wide measurements of RNA stability in rare 2C-like cells. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms.

scholarly journals Massively parallel and time-resolved RNA sequencing in single cells with scNT-Seq

2021 ◽  
Author(s):  
Qi Qiu ◽  
Peng Hu ◽  
Hao Wu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Temporal Dynamics ◽  
Single Cells ◽  
Chemical Conversion ◽  
Droplet Microfluidics ◽  
Massively Parallel ◽  
One Pot ◽  
Time Resolved ◽  
Rna Biogenesis

Abstract Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal dynamics of RNA biogenesis and decay. Here we present single-cell metabolically labeled new RNA tagging sequencing (scNT-Seq), a method for massively parallel analysis of newly-transcribed and pre-existing RNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking newly-transcribed RNAs with T-to-C substitutions. The steps of the protocol are (1) metabolically labeling of cells with 4sU, (2) co-encapsulating individual cell with a barcoded oligo-dT primer coated bead in a nanoliter-scale droplet, (3) performing one-pot 4sU chemical conversion on pooled barcoded beads, and (4) reverse transcription, cDNA amplification, tagmentation, indexing PCR, and sequencing. scNT-Seq provides a broadly applicable strategy to investigate dynamic biological systems at single-cell resolution.

Inertial-ordering-assisted droplet microfluidics for high-throughput single-cell RNA-sequencing

Lab on a Chip ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 775-784 ◽  
Author(s):  
Hui-Sung Moon ◽  
Kwanghwi Je ◽  
Jae-Woong Min ◽  
Donghyun Park ◽  
Kyung-Yeon Han ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Droplet Microfluidics ◽  
Single Cell Rna Sequencing ◽  
Spiral Microchannel

We developed a modified high-throughput droplet barcoding technique for single-cell Drop-Seq via introduction of hydrodynamic ordering in a spiral microchannel.

scholarly journals Single-Cell RNA Sequencing in Human Retinal Degeneration Reveals Distinct Glial Cell Populations

Cells ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 438 ◽  
Author(s):  
Andrew P. Voigt ◽  
Elaine Binkley ◽  
Miles J. Flamme-Wiese ◽  
Shemin Zeng ◽  
Adam P. DeLuca ◽  
...  
Keyword(s):  
Single Cell ◽  
Retinal Degeneration ◽  
Rna Sequencing ◽  
Glial Cell ◽  
Glial Cells ◽  
Peripheral Retina ◽  
Cell Populations ◽  
Specific Gene ◽  
Autoimmune Retinopathy ◽  
Single Cell Rna Sequencing

Degenerative diseases affecting retinal photoreceptor cells have numerous etiologies and clinical presentations. We clinically and molecularly studied the retina of a 70-year-old patient with retinal degeneration attributed to autoimmune retinopathy. The patient was followed for 19 years for progressive peripheral visual field loss and pigmentary changes. Single-cell RNA sequencing was performed on foveal and peripheral retina from this patient and four control patients, and cell-specific gene expression differences were identified between healthy and degenerating retina. Distinct populations of glial cells, including astrocytes and Müller cells, were identified in the tissue from the retinal degeneration patient. The glial cell populations demonstrated an expression profile consistent with reactive gliosis. This report provides evidence that glial cells have a distinct transcriptome in the setting of human retinal degeneration and represents a complementary clinical and molecular investigation of a case of progressive retinal disease.

scholarly journals Pancreatic cancer is marked by complement-high blood monocytes and tumor-associated macrophages

2021 ◽  
Vol 4 (6) ◽  
pp. e202000935
Author(s):  
Samantha B Kemp ◽  
Nina G Steele ◽  
Eileen S Carpenter ◽  
Katelyn L Donahue ◽  
Grace G Bushnell ◽  
...  
Keyword(s):  
Single Cell ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Rna Sequencing ◽  
Gene Expression Signature ◽  
Ductal Adenocarcinoma ◽  
Specific Gene ◽  
Sequencing Analysis ◽  
Tumor Associated Macrophages ◽  
Primary Tumors ◽  
Single Cell Rna Sequencing

Pancreatic ductal adenocarcinoma (PDA) is accompanied by reprogramming of the local microenvironment, but changes at distal sites are poorly understood. We implanted biomaterial scaffolds, which act as an artificial premetastatic niche, into immunocompetent tumor-bearing and control mice, and identified a unique tumor-specific gene expression signature that includes high expression of C1qa, C1qb, Trem2, and Chil3. Single-cell RNA sequencing mapped these genes to two distinct macrophage populations in the scaffolds, one marked by elevated C1qa, C1qb, and Trem2, the other with high Chil3, Ly6c2 and Plac8. In mice, expression of these genes in the corresponding populations was elevated in tumor-associated macrophages compared with macrophages in the normal pancreas. We then analyzed single-cell RNA sequencing from patient samples, and determined expression of C1QA, C1QB, and TREM2 is elevated in human macrophages in primary tumors and liver metastases. Single-cell sequencing analysis of patient blood revealed a substantial enrichment of the same gene signature in monocytes. Taken together, our study identifies two distinct tumor-associated macrophage and monocyte populations that reflects systemic immune changes in pancreatic ductal adenocarcinoma patients.

scholarly journals Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing

2019 ◽  
Author(s):  
Paul Datlinger ◽  
André F Rendeiro ◽  
Thorina Boenke ◽  
Thomas Krausgruber ◽  
Daniele Barreca ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Population Genomics ◽  
Cost Effective ◽  
Mouse Cell ◽  
Droplet Microfluidics ◽  
Rna Seq ◽  
Single Cell Rna Sequencing ◽  
Massive Scale ◽  
Tcr Activation

AbstractCell atlas projects and single-cell CRISPR screens hit the limits of current technology, as they require cost-effective profiling for millions of individual cells. To satisfy these enormous throughput requirements, we developed “single-cell combinatorial fluidic indexing” (scifi) and applied it to single-cell RNA sequencing. The resulting scifi-RNA-seq assay combines one-step combinatorial pre-indexing of single-cell transcriptomes with subsequent single-cell RNA-seq using widely available droplet microfluidics. Pre-indexing allows us to load multiple cells per droplet, which increases the throughput of droplet-based single-cell RNA-seq up to 15-fold, and it provides a straightforward way of multiplexing hundreds of samples in a single scifi-RNA-seq experiment. Compared to multi-round combinatorial indexing, scifi-RNA-seq provides an easier, faster, and more efficient workflow, thereby enabling massive-scale scRNA-seq experiments for a broad range of applications ranging from population genomics to drug screens with scRNA-seq readout. We benchmarked scifi-RNA-seq on various human and mouse cell lines, and we demonstrated its feasibility for human primary material by profiling TCR activation in T cells.

scholarly journals Gene length and detection bias in single cell RNA sequencing protocols

2017 ◽  
Author(s):  
Belinda Phipson ◽  
Luke Zappia ◽  
Alicia Oshlack
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Full Length ◽  
Specific Gene ◽  
Gene Length ◽  
Length Bias ◽  
Single Cell Rna Sequencing ◽  
Full Length Transcript ◽  
Gene Length Bias

AbstractSingle cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include Unique Molecular Identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene and PCR duplicates resulting in copies of the UMI are not included in expression estimates. Here we investigate the effect of gene length bias in scRNA-Seq across a variety of datasets differing in terms of capture technology, library preparation, cell types and species. We find that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protocols that include UMIs do not exhibit gene length bias, and have a mostly uniform rate of dropout across genes of varying length. Across four different scRNA-Seq datasets profiling mouse embryonic stem cells (mESCs), we found the subset of genes that are only detected in the UMI datasets tended to be shorter, while the subset of genes detected only in the full-length datasets tended to be longer. We briefly discuss the role of these genes in the context of differential expression testing and GO analysis. In addition, despite clear differences between UMI and full-length transcript data, we illustrate that full-length and UMI data can be combined to reveal underlying biology influencing expression of mESCs.

scholarly journals DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors

2018 ◽  
Author(s):  
Christopher S. McGinnis ◽  
Lyndsay M. Murrow ◽  
Zev J. Gartner
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
False Negative ◽  
Droplet Microfluidics ◽  
Cell Capture ◽  
Sequencing Data ◽  
Putative Gene ◽  
Detection Tool ◽  
Single Cell Rna Sequencing

SUMMARYSingle-cell RNA sequencing (scRNA-seq) using droplet microfluidics occasionally produces transcriptome data representing more than one cell. These technical artifacts are caused by cell doublets formed during cell capture and occur at a frequency proportional to the total number of sequenced cells. The presence of doublets can lead to spurious biological conclusions, which justifies the practice of sequencing fewer cells to limit doublet formation rates. Here, we present a computational doublet detection tool – DoubletFinder – that identifies doublets based solely on gene expression features. DoubletFinder infers the putative gene expression profile of real doublets by generating artificial doublets from existing scRNA-seq data. Neighborhood detection in gene expression space then identifies sequenced cells with increased probability of being doublets based on their proximity to artificial doublets. DoubletFinder robustly identifies doublets across scRNA-seq datasets with variable numbers of cells and sequencing depth, and predicts false-negative and false-positive doublets defined using conventional barcoding approaches. We anticipate that DoubletFinder will aid in scRNA-seq data analysis and will increase the throughput and accuracy of scRNA-seq experiments.

scholarly journals Single-Cell RNA Sequencing Elucidates the Structure and Organization of Microbial Communities

2021 ◽  
Vol 12 ◽  
Author(s):  
Melanie A. Brennan ◽  
Adam Z. Rosenthal
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Expression Profiles ◽  
Human Life ◽  
Bacterial Species ◽  
Population Heterogeneity ◽  
Specific Gene ◽  
Valuable Insight ◽  
Single Cell Rna Sequencing

Clonal bacterial populations exhibit various forms of heterogeneity, including co-occurrence of cells with different morphological traits, biochemical properties, and gene expression profiles. This heterogeneity is prevalent in a variety of environments. For example, the productivity of large-scale industrial fermentations and virulence of infectious diseases are shaped by cell population heterogeneity and have a direct impact on human life. Due to the need and importance to better understand this heterogeneity, multiple methods of examining single-cell heterogeneity have been developed. Traditionally, fluorescent reporters or probes are used to examine a specific gene of interest, providing a useful but inherently biased approach. In contrast, single-cell RNA sequencing (scRNA-seq) is an agnostic approach to examine heterogeneity and has been successfully applied to eukaryotic cells. Unfortunately, current extensively utilized methods of eukaryotic scRNA-seq present difficulties when applied to bacteria. Specifically, bacteria have a cell wall which makes eukaryotic lysis methods incompatible, bacterial mRNA has a shorter half-life and lower copy numbers, and isolating an individual bacterial species from a mixed community is difficult. Recent work has demonstrated that these technical hurdles can be overcome, providing valuable insight into factors influencing microbial heterogeneity. This perspective describes the emerging microbial scRNA-seq toolkit. We outline the benefit of these new tools in elucidating numerous scientific questions in microbiological studies and offer insight about the possible rules that govern the segregation of traits in individual microbial cells.

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