scholarly journals Single cell infection with influenza A virus using drop-based microfluidics

2021 ◽  
Author(s):  
Emma Kate Loveday ◽  
Humberto S. Sanchez ◽  
Mallory M. Thomas ◽  
Connie B. Chang
Keyword(s):  
Single Cell ◽  
Influenza A Virus ◽  
Influenza A ◽  
Single Cell Analysis ◽  
Rna Virus ◽  
Host Cells ◽  
Single Cell Level ◽  
Cell Infection ◽  
Cell Level ◽  
High Genetic Diversity

SummaryInfluenza A virus (IAV) is an RNA virus with high genetic diversity which necessitates the development of new vaccines targeting emerging mutations each year. As IAV exists in genetically heterogeneous populations, current studies focus on understanding population dynamics at the single cell level. These studies include novel methodology that can be used for probing populations at the single cell level, such as single cell sequencing and microfluidics. Here, we introduce a drop-based microfluidics method to study IAV infection at a single cell level by isolating infected host cells in microscale drops. Single human alveolar basal epithelial (A549), Madin-Darby Canine Kidney cells (MDCK) and MDCK + human siat7e gene (Siat7e) cells infected with the pandemic A/California/07/2009 (H1N1) strain were encapsulated within 50 μm radii drops and incubated at 37°C. We demonstrate that drops remain stable over 24 hours, that 75% of cells remain viable, and that IAV virus can propagate within the drops. Drop-based microfluidics therefore enables single cell analysis of viral populations produced from individually infected cells.

scholarly journals Single-Cell Transcriptome Analysis as a Promising Tool to Study Pluripotent Stem Cell Reprogramming

2021 ◽  
Vol 22 (11) ◽  
pp. 5988
Author(s):  
Hyun Kyu Kim ◽  
Tae Won Ha ◽  
Man Ryul Lee
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Cell Fate ◽  
Human Cell ◽  
Transcriptome Analysis ◽  
Single Cell Analysis ◽  
Embryonic Stem ◽  
Single Cell Level ◽  
Cell Analysis ◽  
Cell Level

Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.

scholarly journals Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

2021 ◽  
Author(s):  
Yuefan Wang ◽  
Tung-Shing Mamie Lih ◽  
Lijun Chen ◽  
Yuanwei Xu ◽  
Morgan D. Kuczler ◽  
...  
Keyword(s):  
Single Cell ◽  
Proteomic Analysis ◽  
Single Cell Analysis ◽  
Cancer Cell Line ◽  
Cellular Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Data Independent Acquisition ◽  
Peptide Precursors ◽  
Proteomic Study

Abstract Background: Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Methods: We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. Results: We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Conclusions: Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

scholarly journals Single-Cell Analysis Uncovers a Vast Diversity in Intracellular Viral Defective Interfering RNA Content Affecting the Large Cell-to-Cell Heterogeneity in Influenza A Virus Replication

Viruses ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 71 ◽  
Author(s):  
Sascha Young Kupke ◽  
Lam-Ha Ly ◽  
Stefan Thomas Börno ◽  
Alexander Ruff ◽  
Bernd Timmermann ◽  
...  
Keyword(s):  
Single Cell ◽  
Influenza A Virus ◽  
Virus Replication ◽  
Influenza A ◽  
Single Cell Analysis ◽  
Virus Production ◽  
Large Cell ◽  
Cell Heterogeneity ◽  
Viral Mrnas ◽  
Cell To Cell Variability

Virus replication displays a large cell-to-cell heterogeneity; yet, not all sources of this variability are known. Here, we study the effect of defective interfering (DI) particle (DIP) co-infection on cell-to-cell variability in influenza A virus (IAV) replication. DIPs contain a large internal deletion in one of their eight viral RNAs (vRNA) and are, thus, defective in virus replication. Moreover, they interfere with virus replication. Using single-cell isolation and reverse transcription polymerase chain reaction, we uncovered a large between-cell heterogeneity in the DI vRNA content of infected cells, which was confirmed for DI mRNAs by single-cell RNA sequencing. A high load of intracellular DI vRNAs and DI mRNAs was found in low-productive cells, indicating their contribution to the large cell-to-cell variability in virus release. Furthermore, we show that the magnitude of host cell mRNA expression (some factors may inhibit virus replication), but not the ribosome content, may further affect the strength of single-cell virus replication. Finally, we show that the load of viral mRNAs (facilitating viral protein production) and the DI mRNA content are, independently from one another, connected with single-cell virus production. Together, these insights advance single-cell virology research toward the elucidation of the complex multi-parametric origin of the large cell-to-cell heterogeneity in virus infections.

scholarly journals Innate Immune Response to Influenza Virus at Single-Cell Resolution in Human Epithelial Cells Revealed Paracrine Induction of Interferon Lambda 1

2019 ◽  
Vol 93 (20) ◽  
Author(s):  
Irene Ramos ◽  
Gregory Smith ◽  
Frederique Ruf-Zamojski ◽  
Carles Martínez-Romero ◽  
Miguel Fribourg ◽  
...  
Keyword(s):  
Public Health ◽  
Epithelial Cells ◽  
Immune Responses ◽  
Single Cell ◽  
Influenza A ◽  
Respiratory Epithelium ◽  
Innate Immune ◽  
Single Cell Level ◽  
Innate Immune Responses ◽  
Cell Level

ABSTRACT Early interactions of influenza A virus (IAV) with respiratory epithelium might determine the outcome of infection. The study of global cellular innate immune responses often masks multiple aspects of the mechanisms by which populations of cells work as organized and heterogeneous systems to defeat virus infection, and how the virus counteracts these systems. In this study, we experimentally dissected the dynamics of IAV and human epithelial respiratory cell interaction during early infection at the single-cell level. We found that the number of viruses infecting a cell (multiplicity of infection [MOI]) influences the magnitude of virus antagonism of the host innate antiviral response. Infections performed at high MOIs resulted in increased viral gene expression per cell and stronger antagonist effect than infections at low MOIs. In addition, single-cell patterns of expression of interferons (IFN) and IFN-stimulated genes (ISGs) provided important insights into the contributions of the infected and bystander cells to the innate immune responses during infection. Specifically, the expression of multiple ISGs was lower in infected than in bystander cells. In contrast with other IFNs, IFN lambda 1 (IFNL1) showed a widespread pattern of expression, suggesting a different cell-to-cell propagation mechanism more reliant on paracrine signaling. Finally, we measured the dynamics of the antiviral response in primary human epithelial cells, which highlighted the importance of early innate immune responses at inhibiting virus spread. IMPORTANCE Influenza A virus (IAV) is a respiratory pathogen of high importance to public health. Annual epidemics of seasonal IAV infections in humans are a significant public health and economic burden. IAV also causes sporadic pandemics, which can have devastating effects. The main target cells for IAV replication are epithelial cells in the respiratory epithelium. The cellular innate immune responses induced in these cells upon infection are critical for defense against the virus, and therefore, it is important to understand the complex interactions between the virus and the host cells. In this study, we investigated the innate immune response to IAV in the respiratory epithelium at the single-cell level, providing a better understanding on how a population of epithelial cells functions as a complex system to orchestrate the response to virus infection and how the virus counteracts this system.

scholarly journals Roles of the Non-Structural Proteins of Influenza A Virus

Pathogens ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 812
Author(s):  
Wenzhuo Hao ◽  
Lingyan Wang ◽  
Shitao Li
Keyword(s):  
Influenza A Virus ◽  
Influenza A ◽  
Rna Virus ◽  
Critical Role ◽  
Viral Proteins ◽  
Host Cells ◽  
Structural Proteins ◽  
Host Interactions ◽  
New Findings ◽  
Seasonal Epidemics

Influenza A virus (IAV) is a segmented, negative single-stranded RNA virus that causes seasonal epidemics and has a potential for pandemics. Several viral proteins are not packed in the IAV viral particle and only expressed in the infected host cells. These proteins are named non-structural proteins (NSPs), including NS1, PB1-F2 and PA-X. They play a versatile role in the viral life cycle by modulating viral replication and transcription. More importantly, they also play a critical role in the evasion of the surveillance of host defense and viral pathogenicity by inducing apoptosis, perturbing innate immunity, and exacerbating inflammation. Here, we review the recent advances of these NSPs and how the new findings deepen our understanding of IAV–host interactions and viral pathogenesis.

scholarly journals Identifying tumor cells at the single cell level

2021 ◽  
Author(s):  
Jan Dohmen ◽  
Artem Baranovskii ◽  
Bora Uyar ◽  
Jonathan Ronen ◽  
Vedran Franke ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Biomarker Discovery ◽  
Single Cell Analysis ◽  
High Sensitivity ◽  
Simple Problem ◽  
Cancer Therapies ◽  
Single Cell Level ◽  
Cell Level

Tumors are highly complex tissues composed of cancerous cells, surrounded by a heterogeneous cellular microenvironment. Tumor response to treatments is governed by an interaction of cancer cell intrinsic factors with external influences of the tumor microenvironment. Disentangling the heterogeneity within a tumor is a crucial step in developing and utilization of effective cancer therapies. Single cell sequencing has the potential to revolutionize personalized medicine. In cancer therapy it enables an effective characterization of the complete heterogeneity within the tumor. A governing challenge in cancer single cell analysis is cell annotation, the assignment of a particular cell type or a cell state to each sequenced cell. We propose Ikarus, a machine learning pipeline aimed at solving a perceived simple problem, distinguishing tumor cells from normal cells at the single cell level. Automatic characterization of tumor cells is a critical limiting step for a multitude of research, clinical, and commercial applications. Automatic characterization of tumor cells would expedite neoantigen prediction, automatic characterization of tumor cell states, it would greatly facilitate cancer biomarker discovery. Such a tool can be used for automatic annotation of histopathological data, profiled using multichannel immunofluorescence or spatial sequencing. We have tested ikarus on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.

scholarly journals Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

2021 ◽  
Author(s):  
Yuefan Wang ◽  
Tung-Shing Mamie Lih ◽  
Lijun Chen ◽  
Yuanwei Xu ◽  
Morgan D Kuczler ◽  
...  
Keyword(s):  
Single Cell ◽  
Proteomic Analysis ◽  
Single Cell Analysis ◽  
Cancer Cell Line ◽  
Cellular Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Data Independent Acquisition ◽  
Peptide Precursors ◽  
Proteomic Study

Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Herein, we report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Our results demonstrates that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

scholarly journals Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

2021 ◽  
Author(s):  
Yuefan Wang ◽  
T. Mamie Lih ◽  
Lijun Chen ◽  
Yuanwei Xu ◽  
Morgan Kuczler ◽  
...  
Keyword(s):  
Single Cell ◽  
Proteomic Analysis ◽  
Single Cell Analysis ◽  
Cancer Cell Line ◽  
Cellular Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Data Independent Acquisition ◽  
Peptide Precursors ◽  
Proteomic Study

Abstract Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Herein, we report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

scholarly journals Microfluidic Single-Cell Manipulation and Analysis: Methods and Applications

Micromachines ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 104 ◽  
Author(s):  
Tao Luo ◽  
Lei Fan ◽  
Rong Zhu ◽  
Dong Sun
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Petri Dish ◽  
Cell Manipulation ◽  
Single Cell Level ◽  
Cell Analysis ◽  
Cell Level ◽  
Advantages And Disadvantages ◽  
Single Cell Manipulation

In a forest of a hundred thousand trees, no two leaves are alike. Similarly, no two cells in a genetically identical group are the same. This heterogeneity at the single-cell level has been recognized to be vital for the correct interpretation of diagnostic and therapeutic results of diseases, but has been masked for a long time by studying average responses from a population. To comprehensively understand cell heterogeneity, diverse manipulation and comprehensive analysis of cells at the single-cell level are demanded. However, using traditional biological tools, such as petri-dishes and well-plates, is technically challengeable for manipulating and analyzing single-cells with small size and low concentration of target biomolecules. With the development of microfluidics, which is a technology of manipulating and controlling fluids in the range of micro- to pico-liters in networks of channels with dimensions from tens to hundreds of microns, single-cell study has been blooming for almost two decades. Comparing to conventional petri-dish or well-plate experiments, microfluidic single-cell analysis offers advantages of higher throughput, smaller sample volume, automatic sample processing, and lower contamination risk, etc., which made microfluidics an ideal technology for conducting statically meaningful single-cell research. In this review, we will summarize the advances of microfluidics for single-cell manipulation and analysis from the aspects of methods and applications. First, various methods, such as hydrodynamic and electrical approaches, for microfluidic single-cell manipulation will be summarized. Second, single-cell analysis ranging from cellular to genetic level by using microfluidic technology is summarized. Last, we will also discuss the advantages and disadvantages of various microfluidic methods for single-cell manipulation, and then outlook the trend of microfluidic single-cell analysis.

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