scholarly journals Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

2019 ◽  
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.

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

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.

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

2021 ◽  
Author(s):  
Adam Rosenthal ◽  
Ryan McNulty ◽  
Duluxan Sritha ◽  
Shichen Liu ◽  
Sahand Hormoz
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Communication ◽  
Growth Conditions ◽  
Bacterial Cells ◽  
Bacterial Populations ◽  
Specialized Cell ◽  
E Coli ◽  
Cellular Processes ◽  
Single Cell Rna Sequencing

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.

scholarly journals Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing

Cells ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1751 ◽  
Author(s):  
Rishikesh Kumar Gupta ◽  
Jacek Kuznicki
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Future Directions ◽  
Strong Basis ◽  
Bodily Fluids ◽  
Single Cell Rna Sequencing

The present review discusses recent progress in single-cell RNA sequencing (scRNA-seq), which can describe cellular heterogeneity in various organs, bodily fluids, and pathologies (e.g., cancer and Alzheimer’s disease). We outline scRNA-seq techniques that are suitable for investigating cellular heterogeneity that is present in cell populations with very high resolution of the transcriptomic landscape. We summarize scRNA-seq findings and applications of this technology to identify cell types, activity, and other features that are important for the function of different bodily organs. We discuss future directions for scRNA-seq techniques that can link gene expression, protein expression, cellular function, and their roles in pathology. We speculate on how the field could develop beyond its present limitations (e.g., performing scRNA-seq in situ and in vivo). Finally, we discuss the integration of machine learning and artificial intelligence with cutting-edge scRNA-seq technology, which could provide a strong basis for designing precision medicine and targeted therapy in the future.

scholarly journals Comparison of high-throughput single-cell RNA sequencing data processing pipelines

2020 ◽  
Author(s):  
Mingxuan Gao ◽  
Mingyi Ling ◽  
Xinwei Tang ◽  
Shun Wang ◽  
Xu Xiao ◽  
...  
Keyword(s):  
Data Processing ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Large Scale ◽  
Evaluation Framework ◽  
Integrated Analysis ◽  
Sequencing Data ◽  
Single Experiment ◽  
Single Cell Rna Sequencing

Abstract With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

scholarly journals Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing

2020 ◽  
Vol 5 (10) ◽  
pp. 1192-1201 ◽  
Author(s):  
Sydney B. Blattman ◽  
Wenyan Jiang ◽  
Panos Oikonomou ◽  
Saeed Tavazoie
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Cell Rna Sequencing

scholarly journals Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines

2020 ◽  
Author(s):  
Mingxuan Gao ◽  
Mingyi Ling ◽  
Xinwei Tang ◽  
Shun Wang ◽  
Xu Xiao ◽  
...  
Keyword(s):  
Data Processing ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Large Scale ◽  
Evaluation Framework ◽  
Integrated Analysis ◽  
Sequencing Data ◽  
Single Experiment ◽  
Single Cell Rna Sequencing

AbstractWith the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. How-ever, it remains unclear whether such integrated analysis would be biased if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performances in terms of running time, computational resource consumption, and data processing consistency using nine public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performances on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

scholarly journals Single Cell RNA Sequencing of Serial Tumor and Blood Biopsies from Lymphoma Patients on an in Situ Vaccination Clinical Trial

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4107-4107
Author(s):  
Tanaya Shree ◽  
Anuja Sathe ◽  
Debra K. Czerwinski ◽  
Steven R. Long ◽  
Hanlee Ji ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Peripheral Blood ◽  
Fine Needle ◽  
Fine Needle Aspirates ◽  
Single Cell Rna Sequencing ◽  
Cell Gene Expression ◽  
Cell Gene

Abstract The critical determinants of effective antitumor immune responses, whether native or induced by therapy, remain poorly understood due to the complexity and plasticity of the immune system. To better profile and track these responses, we have employed the novel approach of performing single cell RNA sequencing for paired gene expression and immune repertoire analysis on tumor fine needle aspirates and peripheral blood of lymphoma patients undergoing immunotherapy on a clinical trial (NCT02927964). In this in situ vaccination study, patients with low-grade lymphoma received local low-dose radiation and intratumoral SD-101 (a TLR9 agonist) to one site of disease, with systemic ibrutinib (a BTK inhibitor) added in the second week of treatment. Tumor fine needle aspirates and peripheral blood samples were obtained prior to treatment, at one week (prior to ibrutinib initiation) and at six weeks after treatment start. Single cell preparations were processed using 10X Genomics' single cell RNA transcription and library preparation protocol, followed by sequencing on the Illumina platform. Cells were sequenced to an average depth of 50,000 reads/cell for gene expression libraries and 5000 reads/cell for TCR sequencing. Identification of variable genes, principal component and/or canonical correlation analysis, graph-based clustering and differential expression analysis of single-cell gene expression data was performed using the Seurat algorithm. Single-cell TCR repertoires were analyzed using TCR-specific analysis software. This data is being integrated with data from multiparameter flow cytometry and functional immune assays for these same patients, as well as with their clinical outcomes. Sequencing libraries have been prepared from 37 samples from 4 patients thus far. We have successfully generated single cell gene expression and TCR libraries from 3,000-10,000 cells from tumor fine needle aspirates and peripheral blood, with excellent sequencing quality metrics obtained. From detailed analyses of one patient's samples thus far, we have identified distinct immune populations in blood and tumor (Figure 1), including light-chain restricted B-cells, with good concordance with flow cytometry. Preliminary results show changes occurring in immune cell frequencies and phenotypes at the treated tumor site, at distant tumor sites and in the peripheral blood when samples from before and after treatment are compared. Sample collection, sequencing, and analysis are ongoing. Deep profiling of serial biopsies during immunotherapy using single cell RNA sequencing promises to illuminate underlying cellular dynamics, and paired with clinical outcome data, determinants of response. Ultimately, this may provide a roadmap for successful translation of single-cell genomics into the clinic for treatment monitoring and response prediction. Disclosures No relevant conflicts of interest to declare.

scholarly journals Single-cell RNA sequencing-based characterization of resident lung mesenchymal stromal cells in bronchopulmonary dysplasia

2021 ◽  
Author(s):  
Ivana Mizikova ◽  
Flore Lesage ◽  
Chanele Cyr-Depauw ◽  
David Parker Cook ◽  
Maria Hurskainen ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Bronchopulmonary Dysplasia ◽  
Mesenchymal Stromal Cells ◽  
Stromal Cells ◽  
Lung Development ◽  
Expression Profiles ◽  
Communication Analysis ◽  
Single Cell Rna Sequencing

Late lung development is a period of alveolar and microvascular formation, which is pivotal in ensuring sufficient and effective gas exchange. Defects in late lung development manifest in premature infants as a chronic lung disease named bronchopulmonary dysplasia (BPD). Numerous studies demonstrated the therapeutic properties of exogenous bone marrow and umbilical cord-derived mesenchymal stromal cells (MSCs) in experimental BPD. However, very little is known regarding the regenerative capacity of resident lung MSCs (L-MSCs) during normal development and in BPD. In this study we aimed to characterize the L-MSC population in homeostasis and upon injury. We used single-cell RNA sequencing (scRNA-seq) to profile in situ Ly6a+ L-MSCs in the lungs of normal and O2-exposed neonatal mice (a well-established model to mimic BPD) at three developmental timepoints (postnatal days 3, 7 and 14). Hyperoxia exposure increased the number, and altered the expression profile of L-MSCs, particularly by increasing the expression of multiple pro-inflammatory, pro-fibrotic, and anti-angiogenic genes. In order to identify potential changes induced in the L-MSCs transcriptome by storage and culture, we profiled 15,000 Ly6a+ L-MSCs after in vitro culture. We observed great differences in expression profiles of in situ and cultured L-MSCs, particularly those derived from healthy lungs. Additionally, we have identified the location of L-MSCs in the developing lung and propose Serpinf1 as a novel, culture-stable marker of L-MSCs. Finally, cell communication analysis suggests inflammatory signals from immune and endothelial cells as main drivers of hyperoxia-induced changes in L-MSCs transcriptome.

scholarly journals Single-cell molecular and cellular architecture of the mouse neurohypophysis

2019 ◽  
Author(s):  
Qiyu Chen ◽  
Dena Leshkowitz ◽  
Janna Blechman ◽  
Gil Levkowitz
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Adult Male ◽  
Spatial Organization ◽  
Cell Types ◽  
Intermediate Lobe ◽  
Posterior Lobe ◽  
Single Cell Rna Sequencing ◽  
The Brain

AbstractThe neurohypophysis (NH), located at the posterior lobe of the pituitary, is a major neuroendocrine tissue, which mediates osmotic balance, blood pressure, reproduction, and lactation by means of releasing the neurohormones oxytocin and arginine-vasopressin from the brain into the peripheral blood circulation. The major cellular components of the NH are hypothalamic axonal termini, fenestrated endothelia and pituicytes, the resident astroglia. However, despite the physiological importance of the NH, the exact molecular signature defining neurohypophyseal cell types and in particular the pituicytes, remains unclear. Using single cell RNA sequencing, we captured seven distinct cell types in the NH and intermediate lobe (IL) of adult male mouse. We revealed novel pituicyte markers showing higher specificity than previously reported. Single molecule in situ hybridization revealed spatial organization of the major cell types implying intercellular communications. We present a comprehensive molecular and cellular characterization of neurohypophyseal cell-types serving as a valuable resource for further functional research.Significance StatementThe neurohypophysis (NH) is a major neuroendocrine interface, which allows the brain to regulate the function of peripheral organs in response to specific physiological demands. Despite its importance, a comprehensive molecular description of cell identities in the NH is still lacking. Utilizing single cell RNA sequencing technology, we identified the transcriptomes of five major neurohypophyseal cell types in the adult male mice and mapped the spatial distribution of selected cell types in situ. We revealed an unexpected cellular heterogeneity of the neurohypophysis and provide novel molecular markers for neurohypophyseal cell types with higher specificity than previously reported.

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