Droplet-based single cell RNA sequencing of bacteria identifies known and previously unseen cellular states

Abstract Clonal bacterial populations rely on transcriptional variation to differentiate into specialized cell states that increase the community’s fitness. Such heterogeneous gene expression is implicated in many fundamental microbial processes including sporulation, cell communication, detoxification, substrate utilization, competence, biofilm formation, motility, pathogenicity, and antibiotic resistance1. To identify these specialized cell states and determine the processes by which they develop, we need to study isogenic bacterial populations at the single cell level2,3. Here, we develop a method that uses DNA probes and leverages an existing commercial microfluidic platform (10X Chromium) to conduct bacterial single cell RNA sequencing. We sequenced the transcriptome of over 15,000 individual bacterial cells, detecting on average 365 transcripts mapping to 265 genes per cell in B. subtilis and 329 transcripts mapping to 149 genes per cell in E. coli. Our findings correctly identify known cell states and uncover previously unreported cell states. Interestingly, we find that some metabolic pathways segregate into distinct subpopulations across different bacteria and growth conditions, suggesting that some cellular processes may be more prone to differentiation than others. Our high throughput, highly resolved single cell transcriptomic platform can be broadly used for understanding heterogeneity in microbial populations.

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Droplet-based single cell RNA sequencing of bacteria identifies known and previously unseen cellular states

10.1101/2021.03.10.434868 ◽

2021 ◽

Author(s):

Ryan McNulty ◽

Duluxan Sritharan ◽

Shichen Liu ◽

Sahand Hormoz ◽

Adam Z. Rosenthal

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Communication ◽

Growth Conditions ◽

Bacterial Cells ◽

Bacterial Populations ◽

Specialized Cell ◽

E Coli ◽

Cellular Processes ◽

Single Cell Rna Sequencing

AbstractClonal bacterial populations rely on transcriptional variation to differentiate into specialized cell states that increase the community’s fitness. Such heterogeneous gene expression is implicated in many fundamental microbial processes including sporulation, cell communication, detoxification, substrate utilization, competence, biofilm formation, motility, pathogenicity, and antibiotic resistance1. To identify these specialized cell states and determine the processes by which they develop, we need to study isogenic bacterial populations at the single cell level2,3. Here, we develop a method that uses DNA probes and leverages an existing commercial microfluidic platform (10X Chromium) to conduct bacterial single cell RNA sequencing. We sequenced the transcriptome of over 15,000 individual bacterial cells, detecting on average 365 transcripts mapping to 265 genes per cell in B. subtilis and 329 transcripts mapping to 149 genes per cell in E. coli. Our findings correctly identify known cell states and uncover previously unreported cell states. Interestingly, we find that some metabolic pathways segregate into distinct subpopulations across different bacteria and growth conditions, suggesting that some cellular processes may be more prone to differentiation than others. Our high throughput, highly resolved single cell transcriptomic platform can be broadly used for understanding heterogeneity in microbial populations.

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Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

10.1101/866244 ◽

2019 ◽

Cited By ~ 3

Author(s):

Sydney B. Blattman ◽

Wenyan Jiang ◽

Panos Oikonomou ◽

Saeed Tavazoie

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Low Cost ◽

Bacterial Cells ◽

Single Experiment ◽

Bacterial Populations ◽

Single Cell Rna Sequencing ◽

Gene Expression Heterogeneity ◽

Mrna Polyadenylation

AbstractDespite longstanding appreciation of gene expression heterogeneity in isogenic bacterial populations, affordable and scalable technologies for studying single bacterial cells have been limited. While single-cell RNA sequencing (scRNA-seq) has revolutionized studies of transcriptional heterogeneity in diverse eukaryotic systems, application of scRNA-seq to prokaryotes has been hindered by their extremely low mRNA abundance, lack of mRNA polyadenylation, and thick cell walls. Here, we present Prokaryotic Expression-profiling by Tagging RNA In Situ and sequencing (PETRI-seq), a low-cost, high-throughput, prokaryotic scRNA-seq pipeline that overcomes these technical obstacles. PETRI-seq uses in situ combinatorial indexing to barcode transcripts from tens of thousands of cells in a single experiment. PETRI-seq captures single cell transcriptomes of Gram-negative and Gram-positive bacteria with high purity and low bias, with median capture rates >200 mRNAs/cell for exponentially growing E. coli. These characteristics enable robust discrimination of cell-states corresponding to different phases of growth. When applied to wild-type S. aureus, PETRI-seq revealed a rare sub-population of cells undergoing prophage induction. We anticipate broad utility of PETRI-seq in defining single-cell states and their dynamics in complex microbial communities.

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Applications and analytical tools of cell communication based on ligand-receptor interactions at single cell level

Cell & Bioscience ◽

10.1186/s13578-021-00635-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Fen Ma ◽

Siwei Zhang ◽

Lianhao Song ◽

Bozhi Wang ◽

Lanlan Wei ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Communication ◽

Cellular Communication ◽

Specific Cell ◽

Single Cell Level ◽

Cell Level ◽

Advantages And Disadvantages ◽

Receptor Interactions ◽

Single Cell Rna Sequencing

Abstract Background Cellular communication is an essential feature of multicellular organisms. Binding of ligands to their homologous receptors, which activate specific cell signaling pathways, is a basic type of cellular communication and intimately linked to many degeneration processes leading to diseases. Main body This study reviewed the history of ligand-receptor and presents the databases which store ligand-receptor pairs. The recently applications and research tools of ligand-receptor interactions for cell communication at single cell level by using single cell RNA sequencing have been sorted out. Conclusion The summary of the advantages and disadvantages of analysis tools will greatly help researchers analyze cell communication at the single cell level. Learning cell communication based on ligand-receptor interactions by single cell RNA sequencing gives way to developing new target drugs and personalizing treatment.

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Single-cell RNA sequencing identifies aberrant transcriptional profiles of cellular populations and altered alveolar niche signalling networks in Chronic Obstructive Pulmonary Disease (COPD)

10.1101/2020.09.13.20193417 ◽

2020 ◽

Cited By ~ 2

Author(s):

Maor Sauler ◽

John E McDonough ◽

Taylor S Adams ◽

Neeha Kothapalli ◽

Jonas S Schupp ◽

...

Keyword(s):

Chronic Obstructive Pulmonary Disease ◽

Single Cell ◽

Pulmonary Disease ◽

Rna Sequencing ◽

Cell Communication ◽

Chronic Obstructive ◽

Alveolar Type ◽

Obstructive Pulmonary Disease ◽

Transcriptional Profiles ◽

Single Cell Rna Sequencing

ABSTRACT Chronic Obstructive Pulmonary Disease (COPD) pathogenesis involves a failure to maintain alveolar homeostasis due to repetitive injury and inflammation. In order to improve our understanding of cell-specific mechanisms contributing to COPD pathogenesis, we analysed single-cell RNA sequencing (scRNAseq) profiles of explanted parenchymal lung tissue from 17 subjects with advanced COPD requiring transplant and 15 control donor lungs. We identified a subpopulation of alveolar type II epithelial cells that uniquely express HHIP and have aberrant stress tolerance profiles in COPD. Amongst endothelial cells, we identified overlapping and unique shifts in transcriptional profiles of endothelial subtypes that may contribute to vascular inflammation and susceptibility to injury. We also identified population composition changes amongst alveolar macrophages. Finally, application of integrative analyses to our scRNAseq data identified cell-specific contributions to COPD heritability and dysfunctional cell-cell communication pathways that occur within the COPD alveolar niche. These findings provide cell type-specific resolution of transcriptional changes associated with advanced COPD that may underlie disease pathogenesis.

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Investigating immune and non-immune cell interactions in head and neck tumors by single-cell RNA sequencing

Nature Communications ◽

10.1038/s41467-021-27619-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Cornelius H. L. Kürten ◽

Aditi Kulkarni ◽

Anthony R. Cillo ◽

Patricia M. Santos ◽

Anna K. Roble ◽

...

Keyword(s):

Head And Neck ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Cell Communication ◽

Cell Interactions ◽

Cell Populations ◽

Hpv Status ◽

Single Cell Rna Sequencing ◽

Cell Cell

AbstractHead and neck squamous cell carcinoma (HNSCC) is characterized by complex relations between stromal, epithelial, and immune cells within the tumor microenvironment (TME). To enable the development of more efficacious therapies, we aim to study the heterogeneity, signatures of unique cell populations, and cell-cell interactions of non-immune and immune cell populations in 6 human papillomavirus (HPV)+ and 12 HPV– HNSCC patient tumor and matched peripheral blood specimens using single-cell RNA sequencing. Using this dataset of 134,606 cells, we show cell type-specific signatures associated with inflammation and HPV status, describe the negative prognostic value of fibroblasts with elastic differentiation specifically in the HPV+ TME, predict therapeutically targetable checkpoint receptor-ligand interactions, and show that tumor-associated macrophages are dominant contributors of PD-L1 and other immune checkpoint ligands in the TME. We present a comprehensive single-cell view of cell-intrinsic mechanisms and cell-cell communication shaping the HNSCC microenvironment.

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Estimating allele-specific expression of SNVs from 10x Genomics Single-Cell RNA-Sequencing Data

10.1101/2019.12.22.886119 ◽

2019 ◽

Cited By ~ 2

Author(s):

N M Prashant ◽

Hongyu Liu ◽

Pavlos Bousounis ◽

Liam Spurr ◽

Nawaf Alomran ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Single Cells ◽

Monoallelic Expression ◽

Sequencing Data ◽

Specific Expression ◽

Cellular Processes ◽

Healthy Donors ◽

Allele Expression ◽

Single Cell Rna Sequencing

AbstractWith the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10x Genomics platform. We include in the analysis 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), with an average sequencing reads over 120K/cell (more than 4 billion scRNA-seq reads total). High quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimate the expressed Variant Allele Fraction (VAFRNA) from SNV-aware alignments and analyze its variance and distribution (mono- and bi-allelic) at different cutoffs for required minimal number of sequencing reads. Our analysis shows that when assessing SNV loci covered by a minimum of 3 unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at minimum of 10 reads, nearly 90% of the SNVs are bi-allelic. Consistent with single cell studies on RNA velocity and models of transcriptional burst kinetics, we observe a substantially higher rate of monoallelic expression among intronic SNVs, signifying the usefulness of scVAFRNA to assess dynamic cellular processes. Our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3’-based library generation protocol of 10x Genomics scRNA-seq data can be highly informative in SNV-based analyses.

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