scholarly journals Isolation of persisters enabled by ß-lactam-induced filamentation reveals their single-cell awakening characteristics

2019 ◽  
Author(s):  
Etthel M. Windels ◽  
Zacchari Ben Meriem ◽  
Taiyeb Zahir ◽  
Kevin J. Verstrepen ◽  
Pascal Hersen ◽  
...  
Keyword(s):  
Single Cell ◽  
Low Frequency ◽  
Cell Types ◽  
Single Cell Level ◽  
Isolation Method ◽  
Cell Level ◽  
Isolated Cells ◽  
Bacterial Populations ◽  
Novel Approach ◽  
Major Pathogens

AbstractWhen exposed to lethal doses of antibiotics, bacterial populations are most often not completely eradicated. A small number of phenotypic variants, defined as ‘persisters’, are refractory to antibiotics and survive treatment. Despite their involvement in relapsing infections caused by major pathogens, processes determining phenotypic switches from and to the persister state largely remain elusive. This is mainly due to the low frequency of persisters in a population and the lack of reliable persistence markers, both hampering studies of persistence at the single-cell level. Problematically, existing methods to enrich for persisters result in samples with very low persister densities and/or a too high abundance of other cell types. Here we present a novel and highly effective persister isolation method involving cephalexin, an antibiotic that induces extensive filamentation of susceptible cells. We show that antibiotic-tolerant cells can easily be separated by size after a short cephalexin treatment, and that the isolated cells are genuine persisters. We used our isolation method to monitor persister outgrowth at the single-cell level in a microfluidic device, thereby conclusively demonstrating that awakening is a stochastic phenomenon. We anticipate that our novel approach can have far-reaching consequences in the persistence field, by allowing single-cell studies at a much higher throughput than previously reported.

scholarly journals Prediction of single-cell gene expression for transcription factor analysis

GigaScience ◽  
2020 ◽  
Vol 9 (11) ◽  
Author(s):  
Fatemeh Behjati Ardakani ◽  
Kathrin Kattler ◽  
Tobias Heinen ◽  
Florian Schmidt ◽  
David Feuerborn ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Single Cell ◽  
Cell Types ◽  
Single Cell Level ◽  
Cell Level ◽  
Novel Approach ◽  
Cell Gene Expression ◽  
Specific Regulation ◽  
Cell Gene

Abstract Background Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data. Results Here, we propose a novel approach for predicting gene expression at the single-cell level using cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature. Conclusion Our proposed method allows us to identify distinct TFs that show cell type–specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via https://github.com/SchulzLab/Triangulate.

scholarly journals Enrichment of persisters enabled by a ß-lactam-induced filamentation method reveals their stochastic single-cell awakening

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Etthel M. Windels ◽  
Zacchari Ben Meriem ◽  
Taiyeb Zahir ◽  
Kevin J. Verstrepen ◽  
Pascal Hersen ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Single Cell ◽  
Low Frequency ◽  
Single Cell Level ◽  
Cell Level ◽  
Enrichment Method ◽  
Bacterial Populations ◽  
Highly Effective ◽  
Lethal Doses

AbstractWhen exposed to lethal doses of antibiotics, bacterial populations are most often not completely eradicated. A small number of phenotypic variants, defined as ‘persisters’, are refractory to antibiotics and survive treatment. Despite their involvement in relapsing infections, processes determining phenotypic switches from and to the persister state largely remain elusive. This is mainly due to the low frequency of persisters and the lack of reliable persistence markers, both hampering studies of persistence at the single-cell level. Here we present a highly effective persister enrichment method involving cephalexin, an antibiotic that induces extensive filamentation of susceptible cells. We used our enrichment method to monitor outgrowth of Escherichia coli persisters at the single-cell level, thereby conclusively demonstrating that persister awakening is a stochastic phenomenon. We anticipate that our approach can have far-reaching consequences in the persistence field, by allowing single-cell studies at a much higher throughput than previously reported.

scholarly journals Single-cell transcriptomic analysis elucidates APOE genotype specific changes across cell types in two brain regions in Alzheimer’s disease

2021 ◽  
Author(s):  
Stella Belonwu ◽  
Yaqiao Li ◽  
Daniel Bunis ◽  
Arjun Arkal Rao ◽  
Caroline Warly Solsberg ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Single Cell ◽  
Molecular Mechanisms ◽  
Apoe Genotype ◽  
Cell Types ◽  
Brain Regions ◽  
Single Cell Level ◽  
Cell Level ◽  
Single Nucleus

Abstract Alzheimer’s Disease (AD) is a complex neurodegenerative disease that gravely affects patients and imposes an immense burden on caregivers. Apolipoprotein E4 (APOE4) has been identified as the most common genetic risk factor for AD, yet the molecular mechanisms connecting APOE4 to AD are not well understood. Past transcriptomic analyses in AD have revealed APOE genotype-specific transcriptomic differences; however, these differences have not been explored at a single-cell level. Here, we leverage the first two single-nucleus RNA sequencing AD datasets from human brain samples, including nearly 55,000 cells from the prefrontal and entorhinal cortices. We observed more global transcriptomic changes in APOE4 positive AD cells and identified differences across APOE genotypes primarily in glial cell types. Our findings highlight the differential transcriptomic perturbations of APOE isoforms at a single-cell level in AD pathogenesis and have implications for precision medicine development in the diagnosis and treatment of AD.

scholarly journals Kinetics of HIV-1 Latency Reversal Quantified on the Single-Cell Level Using a Novel Flow-Based Technique

2016 ◽  
Vol 90 (20) ◽  
pp. 9018-9028 ◽  
Author(s):  
G. Martrus ◽  
A. Niehrs ◽  
R. Cornelis ◽  
A. Rechtien ◽  
W. García-Beltran ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
Gag Protein ◽  
Novel Approach ◽  
Latently Infected ◽  
Infected Cells ◽  
First Time ◽  
Kinetics Of ◽  
Hiv 1

ABSTRACTHIV-1 establishes a pool of latently infected cells early following infection. New therapeutic approaches aiming at diminishing this persisting reservoir by reactivation of latently infected cells are currently being developed and tested. However, the reactivation kinetics of viral mRNA and viral protein production, and their respective consequences for phenotypical changes in infected cells that might enable immune recognition, remain poorly understood. We adapted a novel approach to assess the dynamics of HIV-1 mRNA and protein expression in latently and newly infected cells on the single-cell level by flow cytometry. This technique allowed the simultaneous detection ofgagpolmRNA, intracellular p24 Gag protein, and cell surface markers. Following stimulation of latently HIV-1-infected J89 cells with human tumor necrosis factor alpha (hTNF-α)/romidepsin (RMD) or HIV-1 infection of primary CD4+T cells, four cell populations were detected according to their expression levels of viral mRNA and protein.gagpolmRNA in J89 cells was quantifiable for the first time 3 h after stimulation with hTNF-α and 12 h after stimulation with RMD, while p24 Gag protein was detected for the first time after 18 h poststimulation. HIV-1-infected primary CD4+T cells downregulated CD4, BST-2, and HLA class I expression at early stages of infection, proceeding Gag protein detection. In conclusion, here we describe a novel approach allowing quantification of the kinetics of HIV-1 mRNA and protein synthesis on the single-cell level and phenotypic characterization of HIV-1-infected cells at different stages of the viral life cycle.IMPORTANCEEarly after infection, HIV-1 establishes a pool of latently infected cells, which hide from the immune system. Latency reversal and immune-mediated elimination of these latently infected cells are some of the goals of current HIV-1 cure approaches; however, little is known about the HIV-1 reactivation kinetics following stimulation with latency-reversing agents. Here we describe a novel approach allowing for the first time quantification of the kinetics of HIV-1 mRNA and protein synthesis after latency reactivation orde novoinfection on the single-cell level using flow cytometry. This new technique furthermore enabled the phenotypic characterization of latently infected andde novo-infected cells dependent on the presence of viral RNA or protein.

scholarly journals scAPAdb: a comprehensive database of alternative polyadenylation at single-cell resolution

2021 ◽  
Author(s):  
Sheng Zhu ◽  
Qiwei Lian ◽  
Wenbin Ye ◽  
Wei Qin ◽  
Zhe Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
Alternative Polyadenylation ◽  
Cell Types ◽  
Single Cell Level ◽  
Cell Heterogeneity ◽  
Rna Seq ◽  
Cell Level ◽  
Eukaryotic Gene ◽  
User Friendly ◽  
Different Cell Types

Abstract Alternative polyadenylation (APA) is a widespread regulatory mechanism of transcript diversification in eukaryotes, which is increasingly recognized as an important layer for eukaryotic gene expression. Recent studies based on single-cell RNA-seq (scRNA-seq) have revealed cell-to-cell heterogeneity in APA usage and APA dynamics across different cell types in various tissues, biological processes and diseases. However, currently available APA databases were all collected from bulk 3′-seq and/or RNA-seq data, and no existing database has provided APA information at single-cell resolution. Here, we present a user-friendly database called scAPAdb (http://www.bmibig.cn/scAPAdb), which provides a comprehensive and manually curated atlas of poly(A) sites, APA events and poly(A) signals at the single-cell level. Currently, scAPAdb collects APA information from > 360 scRNA-seq experiments, covering six species including human, mouse and several other plant species. scAPAdb also provides batch download of data, and users can query the database through a variety of keywords such as gene identifier, gene function and accession number. scAPAdb would be a valuable and extendable resource for the study of cell-to-cell heterogeneity in APA isoform usages and APA-mediated gene regulation at the single-cell level under diverse cell types, tissues and species.

scholarly journals In situelectro-sequencing in three-dimensional tissues

2021 ◽  
Author(s):  
Qiang Li ◽  
Zuwan Lin ◽  
Ren Liu ◽  
Xin Tang ◽  
Jiahao Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Three Dimensional ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Single Cell Level ◽  
Cell Level ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractPairwise mapping of single-cell gene expression and electrophysiology in intact three-dimensional (3D) tissues is crucial for studying electrogenic organs (e.g., brain and heart)1–5. Here, we introducein situelectro-sequencing (electro-seq), combining soft bioelectronics within situRNA sequencing to stably map millisecond-timescale cellular electrophysiology and simultaneously profile a large number of genes at single-cell level across 3D tissues. We appliedin situelectro-seq to 3D human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) patches, precisely registering the CM gene expression with electrophysiology at single-cell level, enabling multimodalin situanalysis. Such multimodal data integration substantially improved the dissection of cell types and the reconstruction of developmental trajectory from spatially heterogeneous tissues. Using machine learning (ML)-based cross-modal analysis,in situelectro-seq identified the gene-to-electrophysiology relationship over the time course of cardiac maturation. Further leveraging such a relationship to train a coupled autoencoder, we demonstrated the prediction of single-cell gene expression profile evolution using long-term electrical measurement from the same cardiac patch or 3D millimeter-scale cardiac organoids. As exemplified by cardiac tissue maturation,in situelectro-seq will be broadly applicable to create spatiotemporal multimodal maps and predictive models in electrogenic organs, allowing discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.

scholarly journals Single Cell Gene Transcriptome Analysis of Ovarian Mature Teratomas

2021 ◽  
Vol 27 ◽  
Author(s):  
Sun Shin ◽  
Youn Jin Choi ◽  
Seung-Hyun Jung ◽  
Yeun-Jun Chung ◽  
Sug Hyung Lee
Keyword(s):  
Single Cell ◽  
Germ Cells ◽  
Expression Profiles ◽  
Cell Types ◽  
Cell Tumor ◽  
Single Cell Level ◽  
Cell Level ◽  
Normal Cells ◽  
Single Cell Rna Sequencing ◽  
Cell Gene

Teratoma is a type of germ cell tumor that originates from totipotential germ cells that are present in gonads, which can differentiate into any of the cell types found in adult tissues. Ovarian teratomas are usually mature cystic teratomas (OMCTs, also known as dermoid cysts). Chromosome studies in OMCTs show that the chromosomes are uniformly homozygous with karyotype of 46, XX, indicating that they may be parthenogenic tumors that arise from a single ovum after thefirst meiotic division. However, the tissues in OMCTs have been known to be morphologically and immunophenotypically identical to the orthotopic tissues. Currently, expression profiles of tissue components in OMCTs are not known. To identify whether OMCT tissues are expressionally similar to or different from the orthotopic tissues, we adopted single-cell RNA-sequencing (scRNA-seq), and analyzed transcriptomes of individual cells in heterogenous tissues of two OMCTs. We found that transcriptome profiles of the OMCTs at single cell level were not significantly different from those of normal cells in orthotopic locations. The present data suggest that parthenogeneticlly altered OMCTs may not alter expression profiles of inrivirual tissue components in OMCTs.

scholarly journals Deep Learning for Non-Invasive Determination of the Differentiation Status of Human Neuronal Cells by Using Phase-Contrast Photomicrographs

2019 ◽  
Vol 9 (24) ◽  
pp. 5503
Author(s):  
Maya Ooka ◽  
Yuta Tokuoka ◽  
Shori Nishimoto ◽  
Noriko F. Hiroi ◽  
Takahiro G. Yamada ◽  
...  
Keyword(s):  
Single Cell ◽  
Phase Contrast ◽  
Spinal Cord Injuries ◽  
Cell Types ◽  
Single Cell Level ◽  
Cell Level ◽  
Biologically Relevant ◽  
Non Invasive ◽  
Differentiation Status ◽  
Human Neuronal Cells

Regenerative medicine using neural stem cells (NSCs), which self-renew and have pluripotency, has recently attracted a lot of interest. Much research has focused on the transplantation of differentiated NSCs to damaged tissues for the treatment of various neurodegenerative diseases and spinal cord injuries. However, current approaches for distinguishing differentiated from non-differentiated NSCs at the single-cell level have low reproducibility or are invasive to the cells. Here, we developed a fully automated, non-invasive convolutional neural network-based model to determine the differentiation status of human NSCs at the single-cell level from phase-contrast photomicrographs; after training, our model showed an accuracy of identification greater than 94%. To understand how our model distinguished between differentiated and non-differentiated NSCs, we evaluated the informative features it learned for the two cell types and found that it had learned several biologically relevant features related to NSC shape during differentiation. We also used our model to examine the differentiation of NSCs over time; the findings confirmed our model’s ability to distinguish between non-differentiated and differentiated NSCs. Thus, our model was able to non-invasively and quantitatively identify differentiated NSCs with high accuracy and reproducibility, and, therefore, could be an ideal means of identifying differentiated NSCs in the clinic.

scholarly journals Measuring phenotypic heterogeneity in isogenic bacterial populations using flow cytometry and Raman spectroscopy

2019 ◽  
Author(s):  
Cristina García-Timermans ◽  
Peter Rubbens ◽  
Jasmine Heyse ◽  
Frederiek-Maarten Kerckhof ◽  
Ruben Props ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Raman Spectroscopy ◽  
Data Analysis ◽  
Single Cell ◽  
Population Level ◽  
Phenotypic Heterogeneity ◽  
Single Cell Level ◽  
Analysis Framework ◽  
Cell Level ◽  
Bacterial Populations

AbstractInvestigating phenotypic heterogeneity can help to better understand and manage microbial communities. However, characterizing phenotypic heterogeneity remains a challenge, as there is no standardized analysis framework. Several optical tools are available, which often describe properties of the individual cell. In this work, we compare Raman spectroscopy and flow cytometry to study phenotypic heterogeneity in bacterial populations. The growth phase of E. coli populations was characterized using both technologies. Our findings show that flow cytometry detects and quantifies shifts in phenotypic heterogeneity at the population level due to its high-throughput nature. Raman spectroscopy, on the other hand, offers a much higher resolution at the single-cell level (i.e. more biochemical information is recorded). Therefore, it is capable of identifying distinct phenotypic populations when coupled with standardized data analysis. In addition, it provides information about biomolecules that are present, which can be linked to cell functionality. We propose an automated workflow to distinguish between bacterial phenotypic populations using Raman spectroscopy and validated this approach with an external dataset. We recommend to apply flow cytometry to characterize phenotypic heterogeneity at the population level, and Raman spectroscopy to perform a more in-depth analysis of heterogeneity at the single-cell level.ImportanceSingle-cell techniques are frequently applied tools to study phenotypic characteristics of bacterial populations. As flow cytometry and Raman spectroscopy gain popularity in the field, there is a need to understand their advantages and limitations, as well as to create a more standardized data analysis framework. Our work shows that flow cytometry allows to study and quantify shifts at the bacterial population level, but since its resolution is limited for microbial purposes, distinct phenotypic populations cannot be distinguished at the single-cell level. Raman spectroscopy, combined with appropriate data analysis, has sufficient resolving power at the single-cell level, enabling the identification of distinct phenotypic populations. As regions in a Raman spectrum are associated with specific (bio)molecules, it is possible to link these to the cell state and/or its function.

scholarly journals Single cell transcriptomics view of cell lineage, cell fate and cellular differentiation

2017 ◽  
Author(s):  
Wenfa Ng
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Cellular Differentiation ◽  
Cell Types ◽  
Rna Degradation ◽  
Single Cell Level ◽  
Cell Level ◽  
Single Cell Rna Sequencing ◽  
A Cell

Single cell studies increasing reveal myriad cellular subtypes beyond those postulated or observed through optical and fluorescence microscopy as well as DNA sequencing studies. While gene sequencing at the single cell level offer a path towards illuminating, in totality, the different subtypes of cells present, the technique nevertheless does not offer answers concerning the functional repertoire of the cell, which is defined by the collection of RNA transcribed from the genome. Known as the transcriptome, transcribed RNA defines the function of the cell as proteins or effector RNA molecules, while the genome is the collection of all information endowed in the cell type, expressed or not. Thus, a particular cell state, lineage, cell fate or cellular differentiation is more fully depicted by transcriptomic analysis compared to delineating the genomic context at the single cell level. While conceptually sound and could be analysed by contemporary single cell RNA sequencing technology and data analysis pipelines, the relative instability of RNA in view of RNase in the environment would make sample preparation particularly challenging, where degradation of cellular RNA by extraneous factors could provide a misinterpretation of specific functions available to a cell type. Hence, RNA as the de facto functional molecule of the cell defining the proteomics landscape as well as effector RNA repertoire, meant that RNA transcriptomics at the single cell level is the way forward if the goal is to understand all available cell types, lineage, cell fate and cellular differentiation. Given that a cell state is defined by the functions encoded by functional molecules such as proteins and RNA, single cell RNA sequencing offers a larger contextual basis for understanding cellular decision making and functions, for example, proteins are increasingly known to work in concert with RNA effector molecules in enabling a function. Hence, providing a view of the diverse cell types and lineages present in a body, single cell RNA sequencing is only hampered by the high sensitivity required to analyse the small amount of RNA available in single cells, as well as the perennial problem of RNA studies: how to prevent or reduce RNA degradation by environmental RNase enzymes. Ability to reduce RNA degradation would provide the cell biologist a unique view of the functional landscape of different cells in the body through the language of RNA.

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