Single-cell and population level viral infection dynamics revealed by phageFISH, a method to visualize intracellular and free viruses
Summary Microbes drive the biogeochemical cycles that fuel planet Earth, and their viruses (phages) alter microbial population structure, genome repertoire, and metabolic capacity. However, our ability to understand and quantify phage–host interactions is technique‐limited. Here, we introduce phageF...
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| Veröffentlicht in: | Environmental microbiology Jg. 15; H. 8; S. 2306 - 2318 |
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| Hauptverfasser: | , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Oxford
Blackwell Publishing Ltd
01.08.2013
Blackwell Wiley Subscription Services, Inc John Wiley & Sons Ltd |
| Schlagworte: | |
| ISSN: | 1462-2912, 1462-2920, 1462-2920 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Summary
Microbes drive the biogeochemical cycles that fuel planet Earth, and their viruses (phages) alter microbial population structure, genome repertoire, and metabolic capacity. However, our ability to understand and quantify phage–host interactions is technique‐limited. Here, we introduce phageFISH – a markedly improved geneFISH protocol that increases gene detection efficiency from 40% to > 92% and is optimized for detection and visualization of intra‐ and extracellular phage DNA. The application of phageFISH to characterize infection dynamics in a marine podovirus–gammaproteobacterial host model system corroborated classical metrics (qPCR, plaque assay, FVIC, DAPI) and outperformed most of them to reveal new biology. PhageFISH detected both replicating and encapsidated (intracellular and extracellular) phage DNA, while simultaneously identifying and quantifying host cells during all stages of infection. Additionally, phageFISH allowed per‐cell relative measurements of phage DNA, enabling single‐cell documentation of infection status (e.g. early vs late stage infections). Further, it discriminated between two waves of infection, which no other measurement could due to population‐averaged signals. Together, these findings richly characterize the infection dynamics of a novel model phage–host system, and debut phageFISH as a much‐needed tool for studying phage–host interactions in the laboratory, with great promise for environmental surveys and lineage‐specific population ecology of free phages. |
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| Bibliographie: | BIO5 istex:BAB283F7DFE189E3D5A98145742BDBE02BFF1D6C SI Text. Table S1. Polynucleotide probes targeting a region spanning a phage gene of unknown function (abbreviated here as unk) in the Pseudoalteromonas phage PSA-HP1 genome (genome position 8564-13 387 bp). Table S2. Q-PCR primer and amplicon sequences. Table S3. Calculated and measured Tm for the polynucleotides forming the unk probe mix. Fig. S1. geneFISH protocol optimization. A. Genome map of phage PSA-HP1. The six (300 bp each) probe-target regions are indicated in orange. Probes target unknown phage gene, unk (grey). B. Variation of the gene detection efficiency with increasing number of polynuclotide probes. Escherichia coli low target copy clones (3-8 copies per cell) were hybridized with an increasing number of polynucleotide probes targeting the unk gene. As negative control (no unk gene), E. coli strain B/R cells were used. The detection efficiency is defined as the fraction of all cells showing a gene positive signal. C. Detection of unk gene in high target copy cells using three polynucleotide probes - all the cells have a gene signal, resulting in 100% detection efficiency. Top image - overlay image between 16S rRNA signal and gene signal. Bottom image - gene signal. Scale bar = 5 μm. Exposure time (ms, milliseconds) is described for the gene image. D. Appearance of gene signals for different dextran sulfate concentrations (10%, 20% and 30%). All pictures were taken using the same exposure time. Concentrations of 20% and 30% dextran sulfate resulted in a much sharper signal as compared with 10%. Scale bar = 5 μm. E. Gene detection efficiency for different dextran sulfate concentrations (10%, 20% and 30%). Blue bars = low target gene copy cells, red bars = negative control cells. While the detection efficiency was high for all concentrations, the background level (% of false positives in the negative control) increased with the dextran sulfate concentration. F. Variation of the gene (unk) signal intensity and spread through the cell with variation of the gene probe and target copy number. Scale bar = 5 μm. Exposure times (ms, milliseconds) are described for the gene images. The signal intensity increases with the increasing number of probes (higher exposure time was necessary when hybridizing with one probe). The signal spread and intensity increases with the increase in the target number, from dot-like for low target copy cells to whole cell signal for high target copy cells. For high target copy number cells, starting with ∼ 6 probes, the signal does not increase anymore with the probe number, most likely due to a saturation of tyramide binding sites. Fig. S2. Pseudoalteromonas sp. H100 growth curves based on triplicate measurements. The bacterial host was physiologically acclimated for three generations resulting in 0.72 doublings per hour (± 0.06 doublings per hour, n = 3) during exponential growth. Error bars indicate standard deviation. Fig. S3. Virus assays including controls. A. Extracellular phage DNA as measured by quantitative PCR in infected (black circles) and control (white circles) cultures. B. Extracellular phage number as measured by PFU in infected (black circles) and control (white circles) cultures. Control data are zero unless plotted otherwise. Fig. S4. Determination of phage signal size classes and segregation of the two waves of infection. A. Plot of signal size versus signal intensity for T0-T81. B. Plots of signal size versus signal intensity for each of the individual time points (from T0 to T81). Blue lines delimitate signal size classes. Class I (< 0.4 μm2): most probably new infections; Class II (0.4-1.4 μm2): most probably replicating infections; Class III (1.4-7.0 μm2): most probably advanced infections. To establish the upper and lower limits of the smallest, first size class, we assumed that T0 signals represented new infections - these signals have both a small area and a low intensity (panel B). To establish the bounds of the largest, third size class, the first time point where both signal area and intensity were maximum (T36) was considered to represent advanced infections, i.e. late replication and assembly. All signals between those two size classes were considered as size class II, that is replicating infections - for examples, compare T0 with T21 and T36. While at T36 there were almost no class I signals, at T51 they reappeared and were abundant at T66 and T81. Furthermore, the class III signals decreased in abundance at T66 and T81. The re-appearance of class I signals in T51-T81 was assumed to represent new infection events by newly released mature phage particles and thus, a second wave of infection. All other T51-T81 signals were considered old infections from the first wave, in the process of phage maturation and release. Fig. S5. Localization of encapsidated phage and host cell ribosomes in TEM image of phage-infected Pseudoalteromonas cells from T66. Magnification 40 000×, scale bar = 500 nm. Fig. S6. PhageFISH with the negative control gene probe (NonPoly350Pr) on infected cells from T81. The false positive events (white arrows) are all in the smallest signal size class and they amount to a background of ∼ 2% from the cells. No false positives similar to the signals in the higher size class categories or to the cell bursts releasing phage particles are visible. Fig. S7. Reconstruction of the Alexa594 image T21 from Fig. A by using the High Dynamic Range Imaging protocol. A-C. Exposure time series. D. Reconstructed image. Fig. S8. Reconstruction of the Alexa594 image T36 from Fig. A by using the High Dynamic Range Imaging protocol. A-C. Exposure time series. D. Reconstructed image Fig. S9. Reconstruction of the Alexa594 image T51 from Fig. A by using the High Dynamic Range Imaging protocol. A-B. Exposure time series. C. Reconstructed image. Fig. S10. Reconstruction of the Alexa594 image T66 from Fig. B by using the High Dynamic Range Imaging protocol. A-D. Exposure time series. E. Reconstructed image. Fig. S11. Reconstruction of the Alexa594 image T81 from Fig. B by using the High Dynamic Range Imaging protocol. A-E. Exposure time series. F. Reconstructed image. Fig. S12. Reconstruction of the Alexa594 image T96 from Fig. B by using the High Dynamic Range Imaging protocol. A-D. Exposure time series. E. Reconstructed image. Fig. S13. Reconstruction of the Alexa594 image T111 from Fig. C by using the High Dynamic Range Imaging protocol. A-D. Exposure time series. E. Reconstructed image. Fig. S14. Reconstruction of the Alexa594 image T126 from Fig. C by using the High Dynamic Range Imaging protocol. A-E. Exposure time series. F. Reconstructed image. Fig. S15. Reconstruction of the Alexa594 image T141 from Fig. C by using the High Dynamic Range Imaging protocol. A-D. Exposure time series. E. Reconstructed image. Deutsche Forschungsgemeinschaft ark:/67375/WNG-07K4RS7G-T ArticleID:EMI12100 Gordon and Betty Moore Foundation Biosphere2 Max Planck Society ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ISSN: | 1462-2912 1462-2920 1462-2920 |
| DOI: | 10.1111/1462-2920.12100 |