scholarly journals Impact of sequencing depth and read length on single cell RNA sequencing data: lessons from T cells

2017 ◽  
Author(s):  
Simone Rizzetto ◽  
Auda A. Eltahla ◽  
Peijie Lin ◽  
Rowena Bull ◽  
Andrew R. Lloyd ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sequencing Depth ◽  
Read Length ◽  
Single Cell Rna Sequencing

ABSTRACTSingle cell RNA sequencing (scRNA-seq) has shown great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant sub-populations of T cells, and notably the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, such as RNA library capture, cell quality, and sequencing output have been suggested to affect the quality of scRNA-seq data, but these factors have not been systematically examined.We studied the effect of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (<50 bp), but these featured higher technical variability compared to profiles from longer reads. TCRαβ were detected in 1,027 cells (79%), with a success rate between 81% and 100% for datasets with at least 250,000 (PE) reads of length >50 bp.Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCRαβ and gene expression profiles from scRNA-seq data of T cells.

scholarly journals Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽  
2020 ◽  
Vol 371 (6531) ◽  
pp. eaba5257 ◽  
Author(s):  
Anna Kuchina ◽  
Leandra M. Brettner ◽  
Luana Paleologu ◽  
Charles M. Roco ◽  
Alexander B. Rosenberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Growth Stages ◽  
High Throughput Analysis ◽  
Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

scholarly journals G2S3: a gene graph-based imputation method for single-cell RNA sequencing data

2020 ◽  
Author(s):  
Weimiao Wu ◽  
Qile Dai ◽  
Yunqing Liu ◽  
Xiting Yan ◽  
Zuoheng Wang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sequencing Data ◽  
High Data ◽  
Study Gene Expression ◽  
Single Cell Rna Sequencing ◽  
Novel Method

AbstractSingle-cell RNA sequencing provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses. We propose a novel method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and other existing methods to seven single-cell datasets to compare their performance. Our results demonstrated that G2S3 is superior in recovering true expression levels, identifying cell subtypes, improving differential expression analyses, and recovering gene regulatory relationships, especially for mildly expressed genes.

scholarly journals SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

2020 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

Abstract 119: Donor Macrophages Modulate Cellular Rejection After Heart Transplantation

2021 ◽  
Vol 129 (Suppl_1) ◽  
Author(s):  
Benjamin Kopecky ◽  
Junedh Amrute ◽  
Hao Dun ◽  
C. Corbin Frye ◽  
DANIEL KREISEL ◽  
...  
Keyword(s):  
T Cells ◽  
Heart Transplantation ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Transplant Rejection ◽  
Gene Expression Profiles ◽  
Single Cell Rna Sequencing ◽  
Monocytes And Macrophages ◽  
Antigen Presenting

Heart transplant rejection is common and is associated with significant morbidity and mortality. Current immunosuppressive therapies primarily target recipient T-cells and have a multitude of untoward effects including infections, malignancies, and end-organ damage. Recent studies implicate the roles of antigen presenting cells towards pathogenesis of allograft rejection through recruitment and activation of T-cells. The importance of antigen presenting cell origin, identity, and functional importance remains unknown. Using complimentary imaging and single cell RNA sequencing techniques, we show that donor and recipient monocytes and macrophages co-exist after heart transplantation. These myeloid populations have diverse transcriptional signatures that evolve throughout ongoing rejection. Donor macrophages can be defined ontologically and based on their expression of C-C chemokine receptor 2 (CCR2) and expression of MHC-II. Donor CCR2+ and CCR2- populations can be further defined based on their gene expression profiles, highlighting the marked heterogeneity in the donor macrophage population. Selective depletion of CCR2+ macrophages result in prolonged allograft survival. We use longitudinal single cell RNA sequencing to show that donor CCR2+ and CCR2- macrophages have distinct activation mechanisms such that donor CCR2+ macrophages signal through MyD88/NF-kB. Conditional depletion of MyD88 in donor macrophages recapitulates the donor CCR2+ depletion phenotype. Further interrogation of MyD88 conditionally depleted allografts shows reduced T-cell alloreactivity, holding promise for a potential therapeutic target pathway. Together, we show the molecular identity, diversity, and evolution of donor and recipient monocytes and macrophages as well as the functional relevance and activation pathways of donor macrophages in cardiac allografts.

scholarly journals SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

2019 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M. Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

AbstractRecent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

scholarly journals Impact of sequencing depth and read length on single cell RNA sequencing data of T cells

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Simone Rizzetto ◽  
Auda A. Eltahla ◽  
Peijie Lin ◽  
Rowena Bull ◽  
Andrew R. Lloyd ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Sequencing Depth ◽  
Read Length ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

scholarly journals G2S3: A gene graph-based imputation method for single-cell RNA sequencing data

2021 ◽  
Vol 17 (5) ◽  
pp. e1009029
Author(s):  
Weimiao Wu ◽  
Yunqing Liu ◽  
Qile Dai ◽  
Xiting Yan ◽  
Zuoheng Wang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sequencing Data ◽  
High Data ◽  
Study Gene Expression ◽  
Single Cell Rna Sequencing

Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing imputation methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for imputation in large-scale single-cell transcriptomic datasets.

scholarly journals Single-Cell RNA Sequencing Analysis of Chicken Anterior Pituitary: A Bird’s-Eye View on Vertebrate Pituitary

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiannan Zhang ◽  
Can Lv ◽  
Chunheng Mo ◽  
Meng Liu ◽  
Yiping Wan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Population ◽  
Endocrine Cell ◽  
Anterior Pituitary ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Cluster ◽  
Single Cell Rna Sequencing

It is well-established that anterior pituitary contains multiple endocrine cell populations, and each of them can secrete one/two hormone(s) to regulate vital physiological processes of vertebrates. However, the gene expression profiles of each pituitary cell population remains poorly characterized in most vertebrate groups. Here we analyzed the transcriptome of each cell population in adult chicken anterior pituitaries using single-cell RNA sequencing technology. The results showed that: (1) four out of five known endocrine cell clusters have been identified and designated as the lactotrophs, thyrotrophs, corticotrophs, and gonadotrophs, respectively. Somatotrophs were not analyzed in the current study. Each cell cluster can express at least one known endocrine hormone, and novel marker genes (e.g., CD24 and HSPB1 in lactotrophs, NPBWR2 and NDRG1 in corticotrophs; DIO2 and SOUL in thyrotrophs, C5H11ORF96 and HPGDS in gonadotrophs) are identified. Interestingly, gonadotrophs were shown to abundantly express five peptide hormones: FSH, LH, GRP, CART and RLN3; (2) four non-endocrine/secretory cell types, including endothelial cells (expressing IGFBP7 and CFD) and folliculo-stellate cells (FS-cells, expressing S100A6 and S100A10), were identified in chicken anterior pituitaries. Among them, FS-cells can express many growth factors, peptides (e.g., WNT5A, HBEGF, Activins, VEGFC, NPY, and BMP4), and progenitor/stem cell-associated genes (e.g., Notch signaling components, CDH1), implying that the FS-cell cluster may act as a paracrine/autocrine signaling center and enrich pituitary progenitor/stem cells; (3) sexually dimorphic expression of many genes were identified in most cell clusters, including gonadotrophs and lactotrophs. Taken together, our data provides a bird’s-eye view on the diverse aspects of anterior pituitaries, including cell composition, heterogeneity, cell-to-cell communication, and gene expression profiles, which facilitates our comprehensive understanding of vertebrate pituitary biology.

scholarly journals Microbial single-cell RNA sequencing by split-pool barcoding

2019 ◽  
Author(s):  
Anna Kuchina ◽  
Leandra M. Brettner ◽  
Luana Paleologu ◽  
Charles M. Roco ◽  
Alexander B. Rosenberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Population Level ◽  
Growth Stages ◽  
High Throughput Analysis ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA-sequencing (scRNA-seq) has become an essential tool for characterizing multi-celled eukaryotic systems but current methods are not compatible with bacteria. Here, we introduce microSPLiT, a low cost and high-throughput scRNA-seq method that works for gram-negative and gram-positive bacteria and can resolve transcriptional states that remain hidden at a population level. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled from different growth stages, creating a detailed atlas of changes in metabolism and lifestyle. We not only retrieve detailed gene expression profiles associated with known but rare states such as competence and PBSX prophage induction, but also identify novel and unexpected gene expression states including heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. microSPLiT empowers high-throughput analysis of gene expression in complex bacterial communities.

scholarly journals Heterogeneity of hypothalamic Pro-opiomelanocortin-expressing neurons revealed by single-cell RNA sequencing

2017 ◽  
Author(s):  
Brian Y. H. Lam ◽  
Irene Cimino ◽  
Joseph Polex-Wolf ◽  
Sara Nicole Kohnke ◽  
Debra Rimmington ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Leptin Receptor ◽  
Expression Profiles ◽  
Receptor Gene ◽  
Gene Expression Profiles ◽  
Cell Surface Receptor ◽  
Similar Frequency ◽  
Single Cell Rna Sequencing

SummaryArcuate proopiomelanocortin (POMC) neurons are critical nodes in the control of body weight. Often characterised simply as direct targets for leptin, recent data suggest a more complex architecture. Using single cell RNA sequencing, we have generated an atlas of gene expression in murine POMC neurons. Of 163 neurons, 118 expressed high levels of Pomc with little/no Agrp expression and were considered “canonical” POMC neurons (P+). The other 45/163 expressed low levels of Pomc and high levels of Agrp (A+P+). Unbiased clustering analysis of P+ neurons revealed four different classes, each with distinct cell surface receptor gene expression profiles. Further, only 12% (14/118) of P+ neurons expressed the leptin receptor (Lepr) compared with 58% (26/45) of A+P+ neurons. In contrast, the insulin receptor (Insr) was expressed at similar frequency on P+ and A+P+ neurons (64% and 55%, respectively). These data reveal arcuate POMC neurons to be a highly heterogeneous population.

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