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In plaque-mass spectrometry imaging of a bloom-forming alga during viral infection reveals a metabolic shift towards odd-chain fatty acid lipids

Tapping into the metabolic cross-talk between a host and its virus can reveal unique strategies employed during infection. Viral infection is a dynamic process that generates an evolving metabolic landscape. Gaining a continuous view into the infection process is highly challenging and is limited by current metabolomics approaches, which typically measure the average of the entire population at various stages of infection. Here, we took an innovative approach to study the metabolic basis of host-virus interactions between the bloom-forming alga Emiliania huxleyi and its specific virus. We combined a classical method in virology, the plaque assay, with advanced mass spectrometry imaging (MSI), an approach we termed ‘in plaque-MSI’. Taking advantage of the spatial characteristics of the plaque, we mapped the metabolic landscape induced during infection in a high spatiotemporal resolution, unfolding the infection process in a continuous manner. Further unsupervised spatially-aware clustering, combined with known lipid biomarkers, revealed a systematic metabolic shift during infection towards lipids containing the odd-chain fatty acid pentadecanoic acid (C15:0). Applying ‘in plaque-MSI’ might facilitate the discovery of bioactive compounds that mediate the chemical arms race of host-virus interactions in diverse model systems.


Introduction
Microbial interactions, such as those between a host and its pathogen, are shaped by a dynamic metabolic cross-talk and chemical communication1,2. Metabolomics tools allow simultaneous identification of thousands of molecules in a given sample3, and have been used to monitor the metabolic fluctuations in diverse host-symbiont and host-pathogen model systems4,5.
Mapping the metabolic landscape of dynamic processes like viral infection requires high temporal resolution of the different stages of infection5, 6. Most metabolic studies of viral infection are performed in bulk liquid cultures, measuring the average of the entire population at various time points during infection7,8. However, infection is not always a synchronous process, where all cells are infected at the same time. Asynchronous infection can lead to a mixed population of cells at different stages of infection. Consequently, bulk analysis might overlook rare subpopulations (e.g. resistant cells)9 and mask metabolic alterations that originate within them10.
Rewiring of host metabolic pathways by the virus during infection generates multiple metabolic states which may affect the infection outcome (i.e. cell death or resistance)9. Mapping these metabolic states into infection states might uncover unique strategies employed by the virus, which are essential for an optimal infection process1. Furthermore, occurrence of specialized metabolic capabilities encoded by viruses (i.e. auxiliary metabolic genes), common in the marine environment, provides an opportunity to reveal metabolic innovation during viral infection11. Recent advances in mass spectrometry imaging (MSI) technologies allow to localize specific metabolites of various dynamic processes and biotic interactions at the microscale level, and consequently to monitor metabolic changes in high spatiotemporal resolution12,13. MSI-based approaches were used to study the metabolic footprints of host-associated bacteria14, bacterial population dynamics15 and to detect human pathogens16.
An attractive host-virus model system is the cosmopolitan alga Emiliania huxleyi and its specific virus, E. huxleyi virus (EhV), which plays a key role in regulating the fate of carbon and sulfur in contemporary oceans17. E. huxleyi forms massive annual blooms that cover vast oceanic areas and are terminated following infection by EhV18. EhV infection causes profound remodeling of E. huxleyi's transcriptome, which leads to massive changes in the metabolome and lipidome of infected cells. For example, changes in glycolysis, de novo fatty acid (FA) synthesis19, induction of triacylglycerols (TAGs) and rewiring of sphingolipid (SL) biosynthesis8,20,21. EhV is a large dsDNA virus with a genome harboring 472 predicted protein coding sequences, and it encodes almost a full biosynthetic pathway for SLs, a pathway that was not detected in any other viral genome to date22-24. Virus-derived glycosphingolipids (vGSLs), products of this virus-encoded pathway, were found to be central components of the EhV membranes and to trigger host programmed cell death (PCD) 19,23,24. Hallmarks of PCD include the production of reactive oxygen species25, induction of caspase activity, metacaspase expression and compromised membrane integrity26, 27. vGSLs are produced exclusively during viral infection and are used as an effective metabolic biomarker to detect viral infection during natural blooms of E. huxleyi in the ocean23, 28. In this study, we sought to gain higher spatiotemporal resolution of metabolites that are associated with viral infection, and in addition, to discover metabolites that are induced or reduced throughout this dynamic process. We combined a classical method in virology, the plaque assay29,30, with advanced MSI technologies, a unique approach we termed 'in plaque-MSI'. This approach increased the spatiotemporal resolution by which we could study the metabolic profile of viral infection, taking advantage of the characteristics of the plaque. MSI data were used to visualize the changes of known lipid biomarkers for viral infection across the plaque. In parallel, spatially-aware unsupervised clustering and colocalization allowed for identification of lipids that were previously undescribed in this hostvirus model system in addition to discovery of an unknown class of lipids. This approach uncovered a profound metabolic shift towards C15:0-based lipids during infection.

Results
To gain high spatiotemporal resolution of the metabolic alterations during host-virus interactions, we mapped the metabolic profile of viral plaques (Fig. 1). As opposed to infection in liquid medium, a plaque originates in a single infected cell30. The plaque expands via concentric rings, and each ring corresponds to the plaque circumference at a different time point, keeping a metabolic record of the infection31. Thus, a snapshot of a plaque unfolds the infection process in a continuous manner, from the center of the plaque to the uninfected area in its periphery. We used the virus-susceptible E. huxleyi CCMP2090 strain and the lytic virus EhV201 to perform plaque assays (Fig. 1a). Accurate identification of a plaque and its center prior to MSI was based on the algal host chlorophyll autofluorescence, as detected by epi-fluorescence microscopy32. The microscopic image served as a reference for the following MSI analysis. Two MSI techniques were used to analyze the plaques: matrix assisted laser desorption ionization-MS (MALDI-MS, 100 µm resolution) and Flow-probe in situ micro-extraction33 (25 × 500 µm resolution on the x and y axes, respectively) (Fig. 1b). While MALDI-MS allows identification of spatial patterns of metabolites at a higher resolution, the Flow-probe-MS system has the advantage of faster sample analysis (~30 min vs. ~10 hours per sample) and a matrix-free preparation. The data collected by means of MSI was used to visualize the intensity of different metabolites along the plaque and to identify different spatial patterns (Fig. 1c). The spatial dimension of the plaque revealed the potential link between changes of specific metabolites and infection dynamics.

Targeted MSI analysis of known lipid biomarkers across a viral plaque
We focused on known lipid biomarkers for viral infection, which were shown to be remodelled during infection in previous liquid chromatography-MS (LC-MS) based lipidomics experiments performed on liquid cultures8,20,21. These lipid biomarkers are usually classified based on their chemical category, such as: glycerolipids (GLs), glycerophospholipids (GPs) and sphingolipids (SLs). Since the viral genome also encodes the biosynthetic pathway for SLs22,23, the lipids can also be classified based on their biosynthetic origin (i.e. produced by alga-or virus-encoded enzymes). Additionally, these lipid biomarkers can be classified based on their relative abundance during infection (i.e. induction or reduction). Therefore, we grouped the lipids into three biological categories: virus-derived (encoded by the virus and induced during infection), host-induced and hostreduced (encoded by the host and induced or reduced during infection). vGSLs are the only virus-derived lipids known to date, with the majority of enzymes in their biosynthetic pathway encoded by the viral genome. Remodelled host lipids, on the other hand, include diverse classes: GLs such as BLL 36:6, TAG 48:1 and DGCC 40:7, and GPs such as PDPT 40:7 and PC 32:1, were all found to be induced during infection (i.e. 'host-induced'), while others, such as BLL 38:6, were found to be reduced (i.e. 'host-reduced')8,20,21.
MS images of these lipid biomarkers across a plaque (Fig. 2) allowed us to follow their induction and reduction patterns during infection in a continuous manner based on a snapshot view of the plaque. Despite the differences in resolution and sample preparation, in most cases both MALDI-MSI (Fig. 2a) and Flow-probe-MSI (Fig. 2b) presented comparable spatial patterns (correlation > 0.8 for most lipids, see Supplementary Table 1) and highlighted the differences in the intensity of various lipids across the plaque. For example, vGSL (t17:0/h22:0) showed high relative intensity across the plaque area, while BLL 36:6 showed a different pattern, with lower intensity at the center of the plaque. Both lipids were detected only inside the plaque, whereas others, such as DGCC 40:7, PDPT 40:7 and TAG 48:1 were detected also outside the plaque area, albeit at a lower intensity, indicating a basal production in uninfected cells. Cross-section profiles ( Fig. 2a and b) were used to present the relative abundance of each lipid along the plaque and revealed even finer differences, such as distance and degree of induction, between lipids with similar MS images. Interestingly, TAG 48:1 presented a unique ring-like pattern inside the plaque (visible only in the MALDI-MS, due to its higher spatial resolution), not identified in other known lipid species.
Taken together, the targeted analysis indicates the existence of chemotypic heterogeneity during infection, as visible from the diverse intensity patterns of specific lipids across the plaque. This continuous metabolic view of the infection process would not have been possible in bulk LC-MS-based lipidomics.

Spatially-aware clustering of Flow-probe-MS data
We applied an unsupervised spatially-aware clustering approach on Flow-probe-MS data generated from a plaque at 6 days post infection (dpi) in order to discover metabolites that are altered during infection34,35. First, m/z-images were generated for each m/z-value recorded, depicting the intensity of the m/z-values in each of the pixels analyzed in the sample. The m/z-images were clustered based on correlations between their spatial intensity profiles (Fig. 3a). A representative image was then generated for each cluster in the dendrogram, averaging the m/z-images of the cluster members. Based on these clusterrepresentative images, we selected clusters that presented distinct induction or reduction patterns across the plaque (Supplementary Fig. 2 and 3). This allowed us to focus on clusters with biological relevance to the infection dynamics. Next, we matched the m/z-values in each cluster with the known annotations of lipid biomarkers and used them as markers for the cluster category (i.e. virus-derived, host-induced or host-reduced). For example, cluster two (CL2) included several vGSL species (Supplementary Fig. 4) as well as other hostinduced lipids, and therefore was classified as a 'virus-derived and host-induced' cluster. We then focused on four clusters which presented more pronounced changes across the plaque (Fig. 3b). Three clusters (CL2, 4 and 5) were induced in the plaque and one (CL7) was reduced. In total, the four clusters contained 129 mass features (see Methods section and Supplementary Table 2). Nineteen mass features were putatively annotated as lipids previously undescribed in this host-virus model system (eighteen induced and one reduced, see Table 1 and Supplementary Fig. 5), one of which was putatively identified as vGSL-like t16:0/h22:0 (as previously suggested by long-chain base analysis24, see Supplementary  Table 3). Five features that shared the same head group were not found in known repositories and were termed sulfonioglycerolipids (all decreased, see Table 1 Table 7). These OC-FA lipids were also detected in LC-MS-based analysis of the plaques, as well as in purified virions and in extracellular vesicles that originated from infected cultures (Supplementary Table 7). In order to explore the global distribution of C15:0-based lipids, we compared the abundance of C15:0 FA fragments in the lipidome of infected and uninfected cultures using LC-MS-based lipidomics of liquid cultures (Fig. 4d). This comparison revealed massive induction of C15:0-based lipids during lytic infection in diverse lipid classes, suggesting a systemic-level metabolic switch towards a C15:0-based lipidome. Following a targeted search, we were able to putatively identify seven more lipids containing a C15:0 FA (Supplementary Data 3). One of these lipids was TAG 48:1, which presented a unique ring-like intensity pattern across the plaque (Fig. 2) and has been shown to be highly induced during viral infection8, however, its FA composition was never explored. Our LC-MS/MS analyses indicated the existence of two structural isomers differing in their FA composition. While one isomer contained only even-chain FAs (C14:0, C16:0, C18:1) and was found both in uninfected and infected cells, the second isomer contained two OC-FAs (C15:0, C15:0, C18:1) and was found only in infected cells (Supplementary Fig. 11 and Supplementary Data 1).
A parallel analysis of MALDI-MS data by a computational co-localization approach was applied to three known lipid biomarkers: vGSL (t17:0/h22:0), BLL 36:6 and BLL 38:6 (Supplementary Tables 8-10). Most of the lipids co-localized with vGSL and BLL 36:6 were also found in the two clusters that originated from the Flow-probe-MS data (CL2 and CL4, see Fig. 3), including many of the C15:0-based lipids (Table 1). BLL 38:6, which is reduced during infection, was co-localized with all the sulfonioglycerolipids found in the hostreduced cluster in the Flow-probe-MS dataset (CL7, see Fig. 3 and Supplementary Table  10). Co-localization of TAG 48:1, which presented a unique ring-like intensity pattern, revealed seven additional mass features harboring a similar pattern, one of which was identified as TAG 46:1, and the remaining six features are currently unknown (Supplementary Fig. 12 and Supplementary Table 11).

Discussion
Tapping into the metabolic cross-talk between a host and its infecting virus can provide a unique way for the identification of metabolic pathways that are essential for viral infection or host defence strategies. E. huxleyi-EhV is an important host-pathogen model system with great ecological significance in the marine environment17,37. Viral infection leads to substantial rewiring of the lipidome of infected cells19, including a profound induction in saturated TAGs8 and specific vGSLs20,21,23. The production of both TAGs and vGSLs is essential for the assembly of virions8,24. TAGs are also enriched in extracellular vesicles, which are produced during infection and were shown to expedite infection38. A major obstacle in studying host-virus interactions is to track this highly dynamic process and to map the metabolic landscape produced to specific infection states. Gaining a continuous view into the infection process is limited by current metabolomics approaches (e.g. LC-MS), which typically measure the average of the entire population at various stages of infection. A viral plaque, on the other hand, captures the entire dynamics of the infection in one snapshot and resolves cells at different stages of infection in a spatiotemporal manner31. The spatial structure of the plaque might allow us to distinguish between the different metabolic states11 along the plaque and use them to predict the infection outcome (i.e. cell death or resistance)9. Segmentation of MALDI-MS data, having higher spatial resolution, could be instrumental to define these metabolic states that evolve during infection.
By applying our MSI-based analyses to viral plaques, we were able to gain a high spatiotemporal resolution view of the metabolic continuum during infection. This approach enabled us to detect differences in the induction and reduction patterns of known lipid biomarkers of viral infection. These lipid biomarkers were shown to be remodelled during viral infection in previous LC-MS-based lipidomics experiments performed on liquid cultures and represent diverse lipid classes8,20,21. We could identify differences between lipids that previously presented similar trends during infection. vGSL and BLL 36:6, which are used as lipid biomarkers for E. huxleyi infection in the natural environment23,28,39, were both induced in the plaque. However, BLL 36:6 was reduced at the center of the plaque and was induced at a greater distance from the center than vGSL (Fig. 2). Lower intensity of BLL 36:6 at the center might suggest modification of this lipid or its consumption and recycling for the synthesis of other metabolites at later stages of infection. The different localization of induction might be explained by production of BLL 36:6 in cells at earlier stages of infection than vGSL, or even in uninfected cells due to a chemical signal diffusing from the infected cells. It is also possible that BLL diffuses at a faster rate than vGSL in the plaque. It is important to note that since the center of the plaque is consisting of mainly lysed cells32, the detected lipids in this area and their signal might originate not only from the lysed cells and virions, but also from different biodegradation processes that can occur in the agarose. The chemical stability of lipids, on the other hand, does not seem to play a significant role in the relative abundance measured across the plaque ( Supplementary Fig.  13).
The structure of the plaque and the identification of distinct induction and reduction patterns across the plaque allowed us to apply unsupervised spatially-aware clustering on Flowprobe-MS data in order to discover metabolites that are induced or reduced during infection. The representative average image of each cluster was used to reduce the complexity of the untargeted data, allowing us to select clusters with biological relevance to the infection dynamics. A selected number of clusters, all of them with distinct patterns, enabled us to concentrate our identification efforts on a smaller number of infection-related mass features (Fig. 3). Thus, we were able to detect a group of functionally relevant metabolites from a plethora of unknown mass features based on their spatial intensity patterns. Such a dedicated identification might help to uncover metabolites that are masked in untargeted LC-MS-based metabolomics analyses. Applying 'in plaque-MSI' to other host-virus interactions might allow the discovery of bioactive molecules and potential anti-viral compounds.
We putatively annotated nineteen lipid species that were previously undescribed in this hostvirus model system and five of an unknown class of lipids, termed sulfonioglycerolipids. The sulfonioglycerolipids share the same head group and were highly reduced along the plaque and in infected liquid cultures (Supplementary Table 7). More than half of the lipids previously undescribed in this model system were putatively identified as C15:0-based lipids and were highly increased across the plaque and in infected liquid cultures (Fig. 4c). They were also detected in purified virions and in extracellular vesicles that originated from infected cultures (Supplementary Table 7) and represent a fraction of a systematic induction of C15:0-based lipids during infection (Fig. 4d). Interestingly, C15:0 FAs were incorporated in lipids usually found in the cytoplasmic membrane (e.g. SLs, DGCCs, DGTSs and PDPTs) or in lipid droplets (e.g. TAGs), but not in lipids found in the chloroplast (e.g. glycosyldiradylglycerols, see Fig. 5). While the biological function of OC-FA lipids is still unknown, C15:0 FA has been previously shown to be less preferred for β-oxidation than even-numbered FAs40. Thus, selective incorporation of OC-FA lipids in the viral membrane might increase the durability of the viral particles, and consequently attenuate their decay rate due to UV stress in the marine environment41. Induction of C15:0-based lipids during viral infection is also consistent with a previous report that indicated an increase in the levels of C15:0 FA during viral infection, although the specific lipids were not investigated42. Interestingly, C15:0 FA is also involved in the production of vGSLs during viral infection of E. huxleyi by EhV. vGSLs are products of a virus-encoded biosynthetic pathway and were found to be central components of the EhV membrane and to trigger E. huxleyi PCD19,23,24. EhV causes a shift in the substrate specificity of the virus-encoded enzyme pyridoxal 5′-phosphate (PLP)-dependent serine palmitoyl transferase (SPT; EC 2.3.1.50), the rate limiting enzyme of the entire SL biosynthetic pathway. In infected cells, the virus-derived SPT enzyme was shown to use C15:0-FA-CoA, while in uninfected cells the hostderived SPT typically utilizes the canonical C16:0-FA-CoA24. The C15:0-FA-CoA used by virus-derived SPT serves as a substrate for the unusual hydroxylated C17 long-chain base found in vGSLs24.
Recent years have witnessed a growing interest in lipids containing OC-FA and their role as markers for metabolic disorders in humans43. OC-FA lipids, including C15:0-based lipids, have been reported in humans, animals, microorganisms and plants44-46. Nevertheless, understanding of their biological function is still elusive. Several biosynthetic pathways have been proposed for OC-FAs, either de novo or by chain-shortening of longer FA chains (

Culture growth and viral infection dynamics
E. huxleyi strain CCMP2090 was used for this study. Cells were cultured in K/2 medium51 in artificial seawater (ASW)52 supplemented with ampicillin (100 µg mL -1 ) and kanamycin (50 µg mL -1 ), and incubated at 18 °C with a 16:8 h light:dark illumination cycle. A light intensity of 100 μmol photons m -2 s -1 was provided by cool white light-emitting diode lights. The virus used in this study was the lytic E. huxleyi virus EhV20153. Growing liquid cultures in the presence of antibiotics maintained a low basal abundance of bacteria throughout the experiments in both control and infected samples (Supplementary Fig. 14a). The lipid profile of the samples did not change significantly upon adding antibiotics, suggesting that the contribution of bacteria to the production of these specific lipids was insignificant ( Supplementary Fig. 14b). The bacterial abundance was measured also in agarose samples, and no significant difference was detected between control and plaque samples ( Supplementary Fig. 14c).

Plaque assay
150 mL of cells at 2-3×10 6 cells mL -1 were concentrated (2000 ×g, 5 min, 18 °C) to 2.7 mL. 300 μL of virions at a concentration of 10 4 virions mL -1 were added to the cells. After 2 h of incubation under normal growth conditions, the host-virus mixture was mixed with 9 mL of K/2 medium in ASW that contained 0.2% agarose (SeaKem LE agarose, Rockland, ME, USA) and then poured onto an agarose plate (12×12 cm) containing K/2 medium in ASW solidified by a 1.5%, supplemented with ampicillin (100 µg mL -1 ) and kanamycin (50 µg mL -1 ). Plaques were visible to the naked eye usually at 2 dpi. Plaque samples were subjected to MSI analysis at 5-6 dpi, when they were at a size large enough for spatial analysis (diameter of 4-8 mm) yet were still active and growing. Control samples were prepared following the same procedure, adding 300 µL ASW instead of virions. Blank samples were prepared following the same procedure, using K/2 medium only (i.e. without the host-virus mixture).

Enumeration of cells, viruses and bacteria
Cells were quantified using a Multisizer 4 Coulter Counter (Beckman Coulter, version 4.01) and an Eclipse (iCyt) flow cytometer (Sony Biotechnology, Champaign, IL, USA, using ec800 version 1.3.7 software), equipped with 405 and 488 nm solid state-air cooled lasers (both 25 mW on the flowCell) and standard filter setup. Algae were identified by plotting chlorophyll autofluorescence in the red channel (737 to 663 nm) versus green fluorescence (500 to 550 nm) or side scatter (Supplementary Fig. 15a). Extracellular viral particles were quantified as described previously (Supplementary Fig. 15b)24.
Quantification of E. huxleyi and bacterial cells from agarose samples was performed by suspending agarose samples in ASW. A sterile biopsy punch (8 mm diameter) was used for sampling. Each punched sample was added to 500 µL ASW, vortexed, pipetted and placed at 18 °C for 15 min to allow transfer of cells from the agarose to the ASW. Samples were then filtered through a 35 µm cell strainer. Plaques were smaller than 8 mm in diameter, and hence contained also uninfected areas. E. huxleyi cells were quantified as described above.

Sample preparation for epi-fluorescence microscopy and MSI
Plaque samples were cut using a metal square punch and transferred to a glass slide. The upper layer of 0.2% agarose was blotted onto a 0.22 µm PVDF filter (Merck Millipore, Cork, Ireland). The filter containing the plaque was then dried for 10 min at 30 °C. The filter was immobilized on a microscope slide with double-sided adhesive tape for further analyses using epi-fluorescence microscopy and MSI.55-58 (Hamamatsu Photonics KK, Hamamatsu City, Japan, for Flow-probe-MS samples) or Coolsnap HQ2 CCD (Photometrics, Tuscon, AZ, USA, for MALDI-MS samples) cameras and processed using MetaMorph (version 7.8.7, Molecular Devices) and CellSens Dimension (version 1.11, Olympus) software packages. Image analysis and processing was done using Fiji (Fiji Is Just ImageJ, version 1.51w). To correct for uneven illumination and remove background, a background image was generated by projecting the median of all frames and performing Gaussian blur (30 pixels diameter). Then, all frames were divided by the background image. Finally, the frames were stitched with 10% tile overlap. The resulting microscopy image allowed accurate identification of the plaque and its center prior to MSI. The center of the plaque can be detected by a reduced chlorophyll signal and is mainly consisting of lysed cells (Fig. 2)32,59. The infected area surrounding the center, on the other hand, is populated by living cells, as indicated by the chlorophyll signal.

Flow-probe-MS imaging
The analysis was performed at ambient conditions using a 'Flow-probe' in situ microextraction system33 (Prosolia, Indianapolis, IN, USA), connected to a Thermo Scientific 'Q Exactive' mass spectrometer. The 'Flow-probe' was operated using Flowprobe nMotion software (version 1.0.0.58, Prosolia). The instrument was operated in positive ion mode and the spray voltage set to 3 kV with a capillary temperature of 150 °C. A mixture of methanol:acetonitrile:toluene 50:35:15 + 0.1% TFA60, and a flow-rate of 10 μL min -1 was used. Samples were analyzed by measuring several parallel lines (pixel size: 25 × 500 µm, with spacing of one mm between lines) using a probe-speed of 100 μm sec -1 . Chemical entities were monitored in a mass range between 125 and 1100 m/z at a resolution of 70,000 at 400 m/z for MS1 scans. Data-dependent fragmentation was performed on the ten most intense precursor ions per MS1 scan at a resolution of 17,500 at 400 m/z. Simultaneous Flow-probe imaging and data-dependent acquisitions were performed either using an inclusion list of known lipids, allowing ions not included in the inclusion list to be fragmented in the absence of listed ions, or without an inclusion list and using a dynamic exclusion window of three seconds. The full list of the lipids annotated using Flow-probe-MS/MS and their fragments can be found in Supplementary Data 1.

Processing of Flow-probe-MSI data
Raw Flow-probe-MSI data generated from a plaque at 6 dpi was converted from the vendor's raw format to the open format mzML using the 'msconvert' software (part of ProteoWizard version 3.0)61 and from the mzML format to the MSI compatible data format imzML62 using the 'imzMLConverter' software (version 1.3)63. Next, imzML files corresponding with each experimental run were imported into the R open source programming environment (www.r-project.org) using the R package 'MALDIquantForeign'64. Initially, 75 heading and tailing scans were removed from each horizontal acquisition line due to high levels of noise and measurement calibration issues, and in addition, the first horizontal acquisition line (out of eleven), which was detected in manual inspection of the raw data as an outlier, showing irregular visual pattern due to poor instrumental performance, was completely removed from analysis. This particular acquisition line was spatially located away from the center of infection, and we assume that no meaningful data was removed and that any subsequent data processing was not affected  Fig. 16). Next, scans with a total ion current (TIC) lower than the 5% quantile of the overall per scan TIC (203 scans in total) were also removed immediately after raw data import and before any subsequent processing steps, to minimize unwanted shifts in mass and intensity measurement values ( Supplementary Fig. 16a). Next, software parameters for mass feature detection, smoothing, alignment, binning and filtering were set according to the instrument's mass measurement specifications and detailed manual inspection of known features in the raw data, as suggested by the software guidelines (Supplementary Table 12). The initial preprocessing step after the filtering procedures resulted in a matrix of 7035 mass features measured across 385.6 vertical scans per each of the ten horizontal acquisition lines (i.e. a matrix of 3856 scans by 7035 mass features). Next, matrix intensity values were log transformed and then scaled using a series of 25 specific mass features detected in a 'blank' sample (i.e. Flow-probe-MS analysis of a culture-free sample derived from K/2 medium in ASW containing 0.2% agarose). These 25 blankspecific features were used as reference values for the baseline analytical measurement variance, based on the assumption that they are independent of any biological factor. The median intensity value of these mass features was subtracted from the measured intensity values of all mass features across all scans, resulting in a feature matrix of log transformed and analytically-scaled intensity values in the arbitrary range of -6.46 to 4.91. The feature matrix was finally reduced column-wise by removing mass features which contained more than 99% missing values (corresponding with 5295 mass features or 75.26% of all mass features). The remaining matrix of 1740 mass features was then used to compute an intensity profile correlation matrix. Spearman's rho measure of association between all complete (i.e. non-NA) pairs of intensity values was calculated using the 'cor.test' function in R, resulting in a 1740 by 1740 correlation matrix. Agglomerative hierarchical clustering was then performed by transforming the correlation matrix into a distance matrix (i.e. scaling the correlation values to the range of zero to one) using the 'agnes' function in the R package 'cluster'65. The 'Ward' clustering method was used after comparing the agglomerative coefficient (ac) scores between several methods (namely: 'single', 'complete', 'average' and 'ward') and using the method which resulted in the highest score ('ac score' = 0.98). Further cluster analysis was performed by converting the hierarchical cluster object into a dendrogram object and traversing the dendrogram structure to detect meaningful subclusters, focusing on sub-clusters containing mass features of previously identified, biologically meaningful lipids (i.e. 'guilt-by-association') or on clusters presenting meaningful spatial morphologies. Traversing the clustering tree and generating average spatial representations corresponding with different clusters was done using dedicated inhouse R scripts based on the R function 'dendrapply'. For plotting the average spatial representations, a simple moving average smoothing was applied using a rectangular box of 3 horizontal lines by 11 vertical scans. The detection of meaningful clusters was supported by tagging each cluster by its height in the dendrogram tree and the number of its members, using a minimum height of four and minimum member number of five ( Supplementary Fig.  3). Plotting the average spatial representations was performed using the 'YlGnBu' in the R package 'RColorBrewer'66. The uniqueness in each of the nine detected clusters was evaluated by generating a silhouette index as score of 'goodness' of clustering67,68, and are presented in Supplementary Fig. 17.
Annotation of mass features to masses of known lipids was done using a dedicated in-house R script which performs mass-to-mass comparisons of observed mass features with the theoretical accurate mass of the protonated ([M+H] + ), sodium ([M+Na] + ) and ammonium ([M+NH 4 ] + ) ions of each known lipid, using a mass error tolerance of 6 parts per million (ppm). The annotation script further extracted the natural heavy isotope pattern of each putatively annotated parent ion and performed chemical formula decomposition using the 'decomposeMass' in the R package 'Rdisop'69. Putative annotations of mass features to known lipids were then assigned using the combined criteria of: mass-to-mass, chemical formula decomposition and manual inspection of the corresponding mass peak in the raw data.
In total, the four clusters we focused on contained 129 mass features (Supplementary Table  2

MALDI-MS imaging and data processing
MALDI imaging experiments were performed using a 7T Solarix FT-ICR (Fourier transform ion cyclotron resonance) mass spectrometer (Bruker Daltonics, Bremen, Germany). Datasets were recorded in positive ion mode using lock mass calibration (DHB matrix peak: [3DHB +H-3H 2 O] + , m/z 409.055408) at a frequency of 1 kHz and a laser power of 18%, with 100 laser shots per pixel and 100 µm pixel size. Each mass spectrum was recorded in the range of 150-3000 m/z in broadband mode with a Time Domain for Acquisition of 1M, providing an estimated resolving power of 115,000 at 400 m/z. The acquired spectra were processed using FlexImaging software (version 4.0, Bruker Daltonics, Bremen, Germany) and SCiLS Lab 2015b (SCiLS GmbH, Bremen, Germany). Data sets were normalized to Root Mean Square (RMS) intensity and MALDI images were plotted at the theoretical m/z ± 0.001% (FlexImaging) and m/z ± 5 ppm (SCiLS Lab), with pixel interpolation on. MS images were produced using SCiLS Lab 2015b using 'Viridis' colormap. Co-localization was carried out using SCiLS Lab based on Pearson's correlation analysis (p = 0.05), using RMS normalization and an m/z interval of 5 ppm. Pixel smoothing was not applied to the images or to the co-localization analysis.

Cross-section graphs
Cross-section graphs were plotted by averaging the intensity of each lipid or chlorophyll signal across several lines from the outer part of the plaque to its center. The center was selected to be the minimum of chlorophyll signal. Ten lines were used in epi-fluorescence microscopy images and MALDI-MS and two in Flow-Probe-MS images. Intensity values for each line were extracted from the images using Fiji. Graphs were plotted using Matlab R2016b.

Chemical stability of lipid standards in MALDI-MS
A lipid standard solution containing 12.5 µM of Ceramide/Sphingoid internal standard mixture (Avanti Polar Lipids, Alabaster, AL, USA, LM6002) was mixed with freshly prepared DHB matrix solution (10 mg/mL in 100% MeOH and 0.1% TFA) in a 1:1 ratio (v/v). One µL of the mix was spotted onto a MALDI AnchorChip target (Bruker Daltonics) with six technical repeats per day, and the spotted target was allowed to air dry before MALDI-MS analysis. Each MALDI spot was analyzed with the same instrumental method as described for MALDI-MSI, and the spectrum was obtained from the sum of 5 scans in 'smart walk' mode with a 500 µm grid width and 10% grid increment.

Chemicals and internal standards for LC-MS analyses
Liquid chromatography-grade solvents were purchased from Merck (Darmstadt, Germany) and Bio-Lab (Jerusalem, Israel). Ammonium acetate was purchased from Sigma-Aldrich (St Louis, MO, USA). Internal standards for lipidomics analysis were purchased from Avanti Polar Lipids (Alabaster, AL, USA). The lipid internal standards were added to the extraction solution employed in the initial extraction step.

Lipidomics Analyses
Lipids were extracted from liquid cultures of E. huxleyi cells infected with EhV201 and from non-infected cells harvested at 4, 24, 48 and 72 hpi in three biological replicates. Infection was performed on exponential phase cultures (5×10 5 to 10 6 cells mL -1 ), which were infected 2-3 h after the onset of the light period, with a 1:100 volumetric ratio of viral lysate to culture (multiplicity of infection of 1:1 viral particles per cell). The samples (30-150 mL of each culture, equivalent to ~5×10 7 cells per sample) were collected on GF/C filters (pre-combusted at 400 °C for 8 h), plunged into liquid nitrogen, and stored at -80 °C until analysis23.
Lipid extraction and analysis were performed as previously described72 with some modifications: filters containing algae were placed in a 15 mL glass tube and extracted with 3 mL of a pre-cooled (-20 °C) homogenous methanol: methyl-tert-butyl-ether (MTBE) 1:3 (v/v) mixture containing 0.1 μg mL -1 of glucosylceramide (d18:1/12:0) which was used as an internal standard. The tubes were shaken for 30 min at 4 °C and then sonicated for 30 min. Ultra-performance liquid chromatography (UPLC) grade water:methanol (3:1, v/v) solution (1.5 mL) was added to the tubes followed by centrifugation. The upper organic phase (1.2 mL) was transferred into 2 mL Eppendorf tube. The polar phase containing algae debris and filter pieces was re-extracted with 0.5 mL of MTBE. Organic phases were combined and dried under N 2 flow and then stored at -80 °C until analysis.
Extraction of lipids from plaque samples was performed by suspending the upper soft layer containing the plaque in the lipid extraction solution, following the same procedure described above (using third of the mentioned volumes). As a blank, soft layer without E. huxleyi cells (i.e. ASW + K/2 containing 0.2% agarose only) was extracted as well. Samples were extracted in three replicates. Isolation, concentration and lipid extraction of vesicles and virions was performed as previously described38. All dried lipid extracts were resuspended in 300 μL buffer B (see below) and centrifuged again at 18,000 ×g and 4 °C for 5 min for LC-MS and LC-MS/MS analyses.

UPLC-q-TOF MS for lipidomics analysis
Post extraction, the supernatant was transferred to an autosampler vial and an aliquot of 1 µL was subjected to UPLC-MS analysis. Lipid extracts were analyzed using a Waters ACQUITY UPLC I-Class system coupled to a SYNAPT G2 HDMS mass spectrometer (Waters Corp., Milford, MA, USA

LC-MS and LC-MS/MS data analysis, lipid identification and quantification
LC-MS data were analyzed and processed with MassLynx and QuanLynx (version 4.1, Waters Corp., Milford, MA, USA). The putative identification of the various lipid species was performed according to the lipid accurate mass and LC-MS/MS fragmentation pattern, as described previously20,21,24. The putative identification of lipid species that were previously undescribed in this host-virus model system (listed in Table 1) was based on the Lipid Maps computationally-generated database of lipid classes and structure database (LMSD)36 (and carried out following the Metabolomics Standards Initiative level 2 annotation73. See Fig. 4a and Supplementary Fig. 8, 9 and 11 for MS/MS spectra and the list of fragments of representative species of each class). Five unidentified mass features, which we termed sulfonioglycerolipids (listed in Table 1), did not exist in our in-house repository or in public repositories such as Lipid Maps36 and METLIN74. Putative structures of these sulfonioglycerolipids, which belong to the same putatively characterized compound class, was based on accurate mass and LC-MS/MS fragmentation pattern (corresponding with level 3 annotation), as elaborated in Supplementary Fig. 7 and Supplementary Table 4

GC-MS for FAME analysis
Post extraction, the supernatant was transferred to an autosampler vial and an aliquot of 1 µL was subjected to GC-MS analysis. The GC-MS system comprised of an Agilent 7890A gas chromatograph equipped with split/splitless injector and a LECO Pegasus HT Time-of-Flight Mass Spectrometer (TOF-MS, LECO Corp., St Joseph, MI, USA). GC was performed on a 30 m × 0.25 mm × 0.25 μm Rxi-5Sil MS column (Restek). Samples were analyzed in the splitless mode; injector temperature was set at 280 °C. Analytes by 1 μL injected were separated using the following chromatographic conditions: helium was used as carrier gas at a flow rate of 1.0 mL min -1 . The thermal gradient started at 80 °C and was held at this temperature for 2 min, ramped to 330 °C at 15 °C min -1 and then held at 330 °C for 6.0 min. Eluents were fragmented in the electron impact mode with an ionization voltage of 70 eV. The MS mass range was 45-800 m/z with an acquisition rate of 20 spectra per second. The ion source chamber was set to 250 °C and the transfer line to 250 °C. LECO ChromaTOF (version 4.50.8.0, LECO Corp.) was used for acquisition control and data processing. FAMEs were identified by comparison of their mass spectra and retention times to the corresponding analytical standards, injected in the same GC-MS conditions. Two standards were used: methyl pentadecanoate (methyl-C15:0 FA, Sigma, St. Louis, MO, USA) and methyl 12-methylmyristate (methyl-12-methyl-C14:0 FA, Santa Cruz Biotechnology, CA, USA), at a concentration of 10 µg mL -1 in hexane ( Fig. 4b and Supplementary Fig. 10).

Data availability
Data supporting the findings of this study are available within the paper (and supplementary information files

Supplementary Material
Refer to Web version on PubMed Central for supplementary material. a, Viral plaques were prepared by mixing infectious EhV virions with E. huxleyi host cells in agarose and pouring the mixture into a petri dish, which was then incubated until the formation of plaques. b, A plaque sample was blotted onto a filter, which was imaged by epi-fluorescence microscopy prior to MSI in order to accurately identify the plaque morphology. Two MSI techniques were used to analyze plaque samples: MALDI-MS and Flow-probe-MS, each technique applied to a different biological sample. c, The collected spectra were used for visualization of specific metabolic markers for viral infection, as well as for untargeted data analysis using spatially-aware unsupervised clustering (Flow-probe-MS) and co-localization (MALDI-MS), aiming at discovering metabolites associated with viral infection.   Table 7). The metabolic shift towards C15:0 FA42 during infection provides building blocks for C15:0-based lipids and is also required for the biosynthesis of vGSLs24, which were found to be central components of the EhV membranes and to trigger host PCD23. Several biosynthetic pathways have been proposed for C15:0 FA in other systems, Nineteen lipids were previously undescribed in the E. huxleyi-EhV model system and five belong to an unknown class of lipids (termed sulfonioglycerolipids). The lipids were putatively annotated based on MS/MS spectra (Metabolomics Standards Initiative level 2 annotation73), except the sulfonioglycerolipids (marked with an asterisk), which belong to the same putatively characterized compound class based on MS/MS spectra (level 3 annotation). 'CL', cluster (Fig. 3). 'Change', as visible in the Flow-probe-MS images ( Supplementary Fig. 5).