Single-cell RNA sequencing reveals developmental heterogeneity among Plasmodium berghei sporozoites

AbstractIn the malaria-causing parasite’s life cycle, Plasmodium sporozoites must travel from the midgut of a mosquito to the salivary glands before they can infect a mammalian host. However, only a fraction of sporozoites complete the journey. Since salivary gland invasion is required for transmission of sporozoites, insights at the molecular level can contribute to strategies for malaria prevention. Recent advances in single-cell RNA sequencing provide an opportunity to assess sporozoite heterogeneity at a resolution unattainable by bulk RNA sequencing methods. In this study, we use a droplet-based single-cell RNA sequencing workflow to analyze the transcriptomes of over 8000 Plasmodium berghei sporozoites derived from the midguts and salivary glands of Anopheles stephensi mosquitoes. The detection of known marker genes confirms the successful capture and sequencing of samples composed of a mixed population of sporozoites. Using data integration, clustering, and trajectory analyses, we reveal differences in gene expression profiles of individual sporozoites, and identify both annotated and unannotated markers associated with sporozoite development. Our work highlights the utility of a high-throughput workflow for the transcriptomic profiling of Plasmodium sporozoites, and provides new insights into gene usage during the parasite’s development in the mosquito.

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Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽

10.1126/science.aba5257 ◽

2020 ◽

Vol 371 (6531) ◽

pp. eaba5257 ◽

Cited By ~ 2

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.

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A map of tumor–host interactions in glioma at single-cell resolution

GigaScience ◽

10.1093/gigascience/giaa109 ◽

2020 ◽

Vol 9 (10) ◽

Cited By ~ 3

Author(s):

Francesca Pia Caruso ◽

Luciano Garofano ◽

Fulvio D'Angelo ◽

Kai Yu ◽

Fuchou Tang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cross Talk ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Sequencing Data ◽

Host Interaction ◽

Receptor Interactions ◽

Single Cell Rna Sequencing

ABSTRACT Background Single-cell RNA sequencing is the reference technique for characterizing the heterogeneity of the tumor microenvironment. The composition of the various cell types making up the microenvironment can significantly affect the way in which the immune system activates cancer rejection mechanisms. Understanding the cross-talk signals between immune cells and cancer cells is of fundamental importance for the identification of immuno-oncology therapeutic targets. Results We present a novel method, single-cell Tumor–Host Interaction tool (scTHI), to identify significantly activated ligand–receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand–receptor interactions in glioma using 6 publicly available human glioma datasets encompassing 57,060 gene expression profiles from 71 patients. By leveraging this large-scale collection we show that unexpected cross-talk partners are highly conserved across different datasets in the majority of the tumor samples. This suggests that shared cross-talk mechanisms exist in glioma. Conclusions Our results provide a complete map of the active tumor–host interaction pairs in glioma that can be therapeutically exploited to reduce the immunosuppressive action of the microenvironment in brain tumor.

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Reclassification of classic T cell subsets and identification of novel cell types by single-cell RNA sequencing

10.21203/rs.3.rs-79282/v1 ◽

2020 ◽

Author(s):

Xiangru Shen ◽

Xuefei Wang ◽

Shan Chen ◽

Hongyi Liu ◽

Ni Hong ◽

...

Keyword(s):

T Cell ◽

Single Cell ◽

Rna Sequencing ◽

T Cell Activation ◽

Cell Activation ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Types ◽

T Cell Subsets ◽

Single Cell Rna Sequencing

Abstract Single cell RNA sequencing (scRNA-seq) clusters cells using genome-wide gene expression profiles. The relationship between scRNA-seq Clustered-Populations (scCPops) and cell surface marker-defined classic T cell subsets is unclear. Here, we interrogated 6 bead-enriched T cell subsets with 62,235 single cell transcriptomes and re-grouped them into 9 scCPops. Bead-enriched CD4 Naïve, CD8 Naïve and CD4 memory were mainly clustered into their scCPop counterparts, while the other T cell subsets were clustered into multiple scCPops including unexpected mucosal-associated invariant T cell and natural killer T cell. Most interestingly, we discovered a new T cell type that highly expressed Interferon Signaling Associated Genes (ISAGs), namely IFNhi T. We further enriched IFNhi T for scRNA-seq analyses. IFNhi T cluster disappeared on tSNE after removing ISAGs, and IFNhi T cluster showed up by tSNE analyses of ISAGs alone, indicating ISAGs are the major contributor of IFNhi T cluster. BST2+ cells and BST2- cells showing different efficiencies of T cell activation indicates high ISAGs may contribute to quick immune responses.

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

10.1101/2020.04.01.020586 ◽

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.

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

Briefings in Bioinformatics ◽

10.1093/bib/bbz166 ◽

2020 ◽

Cited By ~ 13

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.

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Abstract 119: Donor Macrophages Modulate Cellular Rejection After Heart Transplantation

Circulation Research ◽

10.1161/res.129.suppl_1.119 ◽

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.

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Single-cell RNA sequencing of Trypanosoma brucei from tsetse salivary glands unveils metacyclogenesis and identifies potential transmission blocking antigens

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1914423117 ◽

2020 ◽

Vol 117 (5) ◽

pp. 2613-2621 ◽

Cited By ~ 5

Author(s):

Aurélien Vigneron ◽

Michelle B. O’Neill ◽

Brian L. Weiss ◽

Amy F. Savage ◽

Olivia C. Campbell ◽

...

Keyword(s):

Single Cell ◽

Salivary Glands ◽

Rna Sequencing ◽

Molecular Mechanisms ◽

Developmental Process ◽

Surface Glycoprotein ◽

Mammalian Host ◽

Transmission Blocking ◽

African Trypanosomes ◽

Single Cell Rna Sequencing

Tsetse-transmitted African trypanosomes must develop into mammalian-infectious metacyclic cells in the fly’s salivary glands (SGs) before transmission to a new host. The molecular mechanisms that underlie this developmental process, known as metacyclogenesis, are poorly understood. Blocking the few metacyclic parasites deposited in saliva from further development in the mammal could prevent disease. To obtain an in-depth perspective of metacyclogenesis, we performed single-cell RNA sequencing (scRNA-seq) from a pool of 2,045 parasites collected from infected tsetse SGs. Our data revealed three major cell clusters that represent the epimastigote, and pre- and mature metacyclic trypanosome developmental stages. Individual cell level data also confirm that the metacyclic pool is diverse, and that each parasite expresses only one of the unique metacyclic variant surface glycoprotein (mVSG) coat protein transcripts identified. Further clustering of cells revealed a dynamic transcriptomic and metabolic landscape reflective of a developmental program leading to infectious metacyclic forms preadapted to survive in the mammalian host environment. We describe the expression profile of proteins that regulate gene expression and that potentially play a role in metacyclogenesis. We also report on a family of nonvariant surface proteins (Fam10) and demonstrate surface localization of one member (named SGM1.7) on mature metacyclic parasites. Vaccination of mice with recombinant SGM1.7 reduced parasitemia early in the infection. Future studies are warranted to investigate Fam10 family proteins as potential trypanosome transmission blocking vaccine antigens. Our experimental approach is translationally relevant for developing strategies to prevent other insect saliva-transmitted parasites from infecting and causing disease in mammalian hosts.

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Identification of silkworm hemocyte subsets and analysis of their response to BmNPV infection based on single-cell RNA sequencing

10.1101/2020.10.18.344127 ◽

2020 ◽

Author(s):

Min Feng ◽

Junming Xia ◽

Shigang Fei ◽

Xiong Wang ◽

Yaohong Zhou ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Current Knowledge ◽

Early Stage ◽

Expression Profiles ◽

Marker Genes ◽

Potential Marker ◽

Wide Range ◽

Single Cell Rna Sequencing

AbstractA wide range of hemocyte types exist in insects but a full definition of the different subclasses is not yet established. The current knowledge of the classification of silkworm hemocytes mainly comes from morphology rather than specific markers, so our understanding of the detailed classification, hemocyte lineage and functions of silkworm hemocytes is very incomplete. Bombyx mori nucleopolyhedrovirus (BmNPV) is a representative member of the baculoviruses, which are a major pathogens that specifically infects silkworms and cause serious loss in sericulture industry. Here, we performed single-cell RNA sequencing (scRNA-seq) of silkworm hemocytes in BmNPV and mock-infected larvae to comprehensively identify silkworm hemocyte subsets and determined specific molecular and cellular characteristics in each hemocyte subset before and after viral infection. A total of 19 cell clusters and their potential marker genes were identified in silkworm hemocytes. Among these hemocyte clusters, clusters 0, 1, 2, 5 and 9 might be granulocytes (GR); clusters 14 and 17 were predicted as plasmatocytes (PL); cluster 18 was tentatively identified as spherulocytes (SP); and clusters 7 and 11 could possibly correspond to oenocytoids (OE). In addition, all of the hemocyte clusters were infected by BmNPV and some infected cells carried high viral-load in silkworm larvae at 3 day post infection (dpi). Interestingly, BmNPV infection can cause severe and diverse changes in gene expression in hemocytes. Cells belonging to the infection group mainly located at the early stage of the pseudotime trajectories. Furthermore, we found that BmNPV infection suppresses the immune response in the major hemocyte types. In summary, our scRNA-seq analysis revealed the diversity of silkworm hemocytes and provided a rich resource of gene expression profiles for a systems-level understanding of their functions in the uninfected condition and as a response to BmNPV.

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

10.1101/134130 ◽

2017 ◽

Cited By ~ 2

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.

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Transcriptional heterogeneity and tightly regulated changes in gene expression during Plasmodium berghei sporozoite development

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2023438118 ◽

2021 ◽

Vol 118 (10) ◽

pp. e2023438118

Author(s):

Haikel N. Bogale ◽

Tales V. Pascini ◽

Sachie Kanatani ◽

Juliana M. Sá ◽

Thomas E. Wellems ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Salivary Glands ◽

Plasmodium Berghei ◽

Expression Profiles ◽

Critical Role ◽

Gene Expression Profiles ◽

Anatomical Site ◽

Quiescent State

Despite the critical role of Plasmodium sporozoites in malaria transmission, we still know little about the mechanisms underlying their development in mosquitoes. Here, we use single-cell RNA sequencing to characterize the gene expression profiles of 16,038 Plasmodium berghei sporozoites isolated throughout their development from midgut oocysts to salivary glands, and from forced salivation experiments. Our results reveal a succession of tightly regulated changes in gene expression occurring during the maturation of sporozoites and highlight candidate genes that could play important roles in oocyst egress, sporozoite motility, and the mechanisms underlying the invasion of mosquito salivary glands and mammalian hepatocytes. In addition, the single-cell data reveal extensive transcriptional heterogeneity among parasites isolated from the same anatomical site, suggesting that Plasmodium development in mosquitoes is asynchronous and regulated by intrinsic as well as environmental factors. Finally, our analyses show a decrease in transcriptional activity preceding the translational repression observed in mature sporozoites and associated with their quiescent state in salivary glands, followed by a rapid reactivation of the transcriptional machinery immediately upon salivation.

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