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Plaque2.0-A High-Throughput Analysis Framework to Score Virus-Cell Transmission and Clonal Cell Expansion.

Yakimovich A, Andriasyan V, Witte R, Wang IH, Prasad V, Suomalainen M, Greber UF - PLoS ONE (2015)

Bottom Line: Plaque2.0 is an open source framework to extract information from chemically fixed cells by immuno-histochemistry or RNA in situ hybridization, or from live cells expressing GFP transgene.Plaque2.0 also analyzes clonal growth of cancer cells, which is relevant for cell migration and metastatic invasion studies.Plaque2.0 is suitable to quantitatively analyze virus infections, vector properties, or cancer cell phenotypes.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.

ABSTRACT
Classical plaque assay measures the propagation of infectious agents across a monolayer of cells. It is dependent on cell lysis, and limited by user-specific settings and low throughput. Here, we developed Plaque2.0, a broadly applicable, fluorescence microscopy-based high-throughput method to mine patho-biological clonal cell features. Plaque2.0 is an open source framework to extract information from chemically fixed cells by immuno-histochemistry or RNA in situ hybridization, or from live cells expressing GFP transgene. Multi-parametric measurements include infection density, intensity, area, shape or location information at single plaque or population levels. Plaque2.0 distinguishes lytic and non-lytic spread of a variety of DNA and RNA viruses, including vaccinia virus, adenovirus and rhinovirus, and can be used to visualize simultaneous plaque formation from co-infecting viruses. Plaque2.0 also analyzes clonal growth of cancer cells, which is relevant for cell migration and metastatic invasion studies. Plaque2.0 is suitable to quantitatively analyze virus infections, vector properties, or cancer cell phenotypes.

No MeSH data available.


Related in: MedlinePlus

Fluorescence in situ hybridization scores HRV co-infections.(A) HRV-A1A infected HeLa cells were immunostained with anti-VP1 antibodies (white signal) 72 h pi, and processed by Plaque2.0 analyses. Yellow lines designate plaque borders and red signals local intensity maxima. (B) HRV-A1A and HRV-A16 co-infected HeLa cells were detected by RNA FISH probes stained at 488 nm (HRV-A1A, green signal) and 550 nm (HRV-A16, red signal) followed by Plaque2.0 analyses. Single infections with HRV-A1A and HRV-A16 are shown in the left and middle micrograph, respectively. (C) Bar graph of HRV-A1A and HRV-A16 plaque analyses by the Plaque2.0 software. Results from individual infections and co-infections are mean values from 3 replicas, and error bars represent the standard deviations of the respective means. (D) Nearest neighbor analyses of plaque centers from HRV-A1A and HRV-A16 infections. The nearest neighbor distances between HRV-A1A and HRV-A16 plaque centroids were not different in single infections (random) or co-infections (i.e. random). Note that self co-localization control was close to zero, as expected. Results are mean values from 3 replicas containing at least 18 plaques per condition, and error bars represent the standard deviations of the respective means.
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pone.0138760.g006: Fluorescence in situ hybridization scores HRV co-infections.(A) HRV-A1A infected HeLa cells were immunostained with anti-VP1 antibodies (white signal) 72 h pi, and processed by Plaque2.0 analyses. Yellow lines designate plaque borders and red signals local intensity maxima. (B) HRV-A1A and HRV-A16 co-infected HeLa cells were detected by RNA FISH probes stained at 488 nm (HRV-A1A, green signal) and 550 nm (HRV-A16, red signal) followed by Plaque2.0 analyses. Single infections with HRV-A1A and HRV-A16 are shown in the left and middle micrograph, respectively. (C) Bar graph of HRV-A1A and HRV-A16 plaque analyses by the Plaque2.0 software. Results from individual infections and co-infections are mean values from 3 replicas, and error bars represent the standard deviations of the respective means. (D) Nearest neighbor analyses of plaque centers from HRV-A1A and HRV-A16 infections. The nearest neighbor distances between HRV-A1A and HRV-A16 plaque centroids were not different in single infections (random) or co-infections (i.e. random). Note that self co-localization control was close to zero, as expected. Results are mean values from 3 replicas containing at least 18 plaques per condition, and error bars represent the standard deviations of the respective means.

Mentions: So far, we have shown that Plaque2.0 scores infection phenotypes from GFP-expressing reporter viruses. We next analyzed plaques from wild type viruses by immuno-staining newly synthesized viral protein 1 (VP1) of human rhinovirus A1A (HRV-A1A) 72 h pi (Fig 6A). In humans, HRVs cause the common cold, frequently as a result of co-infections by different HRV types [54]. We analyzed if Plaque2.0 scores plaques in co-infections of HeLa cells by HRV-A1A and HRV-A16 serotypes at very low MOI yielding about 10 plaques from each HRV type, and visualized the infected cells by genotype specific RNA FISH (Fig 6B and 6C). Similar to the GFP-expressing VACV and HAdV, we scored several hundred VP1-positive cells per plaque. To determine the extent of plaque overlap between the two genotypes we measured the distance between a plaque of one genotype and its nearest neighbor plaque of another genotype using location readout from Plaque2.0 (Fig 6D). We introduced two internal controls, one mimicking perfect co-localization (complete overlap of the signals), and the other one perfect random co-localization. In the first case, co-localization of the plaques of single genotypes to themselves was measured (“HRV-A1A Self” and “HRV-A16 Self”). As expected the nearest neighbor distances were equal to zero. In the second case, nearest neighbor distances between plaques of two genotypes obtained from separate wells of single virus infected cells were measured. This case is designated “Random”. We compared the results of these controls to the co-localization of plaques from mixed infections. Results showed that Plaque2.0 unequivocally detected HRV type-specific plaques with an average distance of about 200 μm (Fig 6D). The data indicate that spreading infections with HRV-A1A or HRV-A16 do not overlap. This implies that a single infectious event can result in a plaque without the contribution of other infectious agents in the inoculum.


Plaque2.0-A High-Throughput Analysis Framework to Score Virus-Cell Transmission and Clonal Cell Expansion.

Yakimovich A, Andriasyan V, Witte R, Wang IH, Prasad V, Suomalainen M, Greber UF - PLoS ONE (2015)

Fluorescence in situ hybridization scores HRV co-infections.(A) HRV-A1A infected HeLa cells were immunostained with anti-VP1 antibodies (white signal) 72 h pi, and processed by Plaque2.0 analyses. Yellow lines designate plaque borders and red signals local intensity maxima. (B) HRV-A1A and HRV-A16 co-infected HeLa cells were detected by RNA FISH probes stained at 488 nm (HRV-A1A, green signal) and 550 nm (HRV-A16, red signal) followed by Plaque2.0 analyses. Single infections with HRV-A1A and HRV-A16 are shown in the left and middle micrograph, respectively. (C) Bar graph of HRV-A1A and HRV-A16 plaque analyses by the Plaque2.0 software. Results from individual infections and co-infections are mean values from 3 replicas, and error bars represent the standard deviations of the respective means. (D) Nearest neighbor analyses of plaque centers from HRV-A1A and HRV-A16 infections. The nearest neighbor distances between HRV-A1A and HRV-A16 plaque centroids were not different in single infections (random) or co-infections (i.e. random). Note that self co-localization control was close to zero, as expected. Results are mean values from 3 replicas containing at least 18 plaques per condition, and error bars represent the standard deviations of the respective means.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4587671&req=5

pone.0138760.g006: Fluorescence in situ hybridization scores HRV co-infections.(A) HRV-A1A infected HeLa cells were immunostained with anti-VP1 antibodies (white signal) 72 h pi, and processed by Plaque2.0 analyses. Yellow lines designate plaque borders and red signals local intensity maxima. (B) HRV-A1A and HRV-A16 co-infected HeLa cells were detected by RNA FISH probes stained at 488 nm (HRV-A1A, green signal) and 550 nm (HRV-A16, red signal) followed by Plaque2.0 analyses. Single infections with HRV-A1A and HRV-A16 are shown in the left and middle micrograph, respectively. (C) Bar graph of HRV-A1A and HRV-A16 plaque analyses by the Plaque2.0 software. Results from individual infections and co-infections are mean values from 3 replicas, and error bars represent the standard deviations of the respective means. (D) Nearest neighbor analyses of plaque centers from HRV-A1A and HRV-A16 infections. The nearest neighbor distances between HRV-A1A and HRV-A16 plaque centroids were not different in single infections (random) or co-infections (i.e. random). Note that self co-localization control was close to zero, as expected. Results are mean values from 3 replicas containing at least 18 plaques per condition, and error bars represent the standard deviations of the respective means.
Mentions: So far, we have shown that Plaque2.0 scores infection phenotypes from GFP-expressing reporter viruses. We next analyzed plaques from wild type viruses by immuno-staining newly synthesized viral protein 1 (VP1) of human rhinovirus A1A (HRV-A1A) 72 h pi (Fig 6A). In humans, HRVs cause the common cold, frequently as a result of co-infections by different HRV types [54]. We analyzed if Plaque2.0 scores plaques in co-infections of HeLa cells by HRV-A1A and HRV-A16 serotypes at very low MOI yielding about 10 plaques from each HRV type, and visualized the infected cells by genotype specific RNA FISH (Fig 6B and 6C). Similar to the GFP-expressing VACV and HAdV, we scored several hundred VP1-positive cells per plaque. To determine the extent of plaque overlap between the two genotypes we measured the distance between a plaque of one genotype and its nearest neighbor plaque of another genotype using location readout from Plaque2.0 (Fig 6D). We introduced two internal controls, one mimicking perfect co-localization (complete overlap of the signals), and the other one perfect random co-localization. In the first case, co-localization of the plaques of single genotypes to themselves was measured (“HRV-A1A Self” and “HRV-A16 Self”). As expected the nearest neighbor distances were equal to zero. In the second case, nearest neighbor distances between plaques of two genotypes obtained from separate wells of single virus infected cells were measured. This case is designated “Random”. We compared the results of these controls to the co-localization of plaques from mixed infections. Results showed that Plaque2.0 unequivocally detected HRV type-specific plaques with an average distance of about 200 μm (Fig 6D). The data indicate that spreading infections with HRV-A1A or HRV-A16 do not overlap. This implies that a single infectious event can result in a plaque without the contribution of other infectious agents in the inoculum.

Bottom Line: Plaque2.0 is an open source framework to extract information from chemically fixed cells by immuno-histochemistry or RNA in situ hybridization, or from live cells expressing GFP transgene.Plaque2.0 also analyzes clonal growth of cancer cells, which is relevant for cell migration and metastatic invasion studies.Plaque2.0 is suitable to quantitatively analyze virus infections, vector properties, or cancer cell phenotypes.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.

ABSTRACT
Classical plaque assay measures the propagation of infectious agents across a monolayer of cells. It is dependent on cell lysis, and limited by user-specific settings and low throughput. Here, we developed Plaque2.0, a broadly applicable, fluorescence microscopy-based high-throughput method to mine patho-biological clonal cell features. Plaque2.0 is an open source framework to extract information from chemically fixed cells by immuno-histochemistry or RNA in situ hybridization, or from live cells expressing GFP transgene. Multi-parametric measurements include infection density, intensity, area, shape or location information at single plaque or population levels. Plaque2.0 distinguishes lytic and non-lytic spread of a variety of DNA and RNA viruses, including vaccinia virus, adenovirus and rhinovirus, and can be used to visualize simultaneous plaque formation from co-infecting viruses. Plaque2.0 also analyzes clonal growth of cancer cells, which is relevant for cell migration and metastatic invasion studies. Plaque2.0 is suitable to quantitatively analyze virus infections, vector properties, or cancer cell phenotypes.

No MeSH data available.


Related in: MedlinePlus