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High-throughput phenotyping of plant resistance to aphids by automated video tracking.

Kloth KJ, Ten Broeke CJ, Thoen MP, Hanhart-van den Brink M, Wiegers GL, Krips OE, Noldus LP, Dicke M, Jongsma MA - Plant Methods (2015)

Bottom Line: Functional genomics of plant resistance to these insects would greatly benefit from the availability of high-throughput, quantitative phenotyping methods.The use of leaf discs instead of intact plants reduced the intensity of the resistance effect in video tracking, but sufficiently replicated experiments resulted in similar conclusions as EPG recordings and aphid population assays.One video tracking platform could screen 100 samples in parallel.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Entomology, Wageningen University, P.O. Box 16, 6700 AA Wageningen, The Netherlands ; Laboratory of Plant Physiology, Wageningen University, P.O. Box 16, 6700 AA Wageningen, The Netherlands ; Plant Research International, Wageningen University and Research Center, P.O. Box 16, 6700 AA Wageningen, The Netherlands.

ABSTRACT

Background: Piercing-sucking insects are major vectors of plant viruses causing significant yield losses in crops. Functional genomics of plant resistance to these insects would greatly benefit from the availability of high-throughput, quantitative phenotyping methods.

Results: We have developed an automated video tracking platform that quantifies aphid feeding behaviour on leaf discs to assess the level of plant resistance. Through the analysis of aphid movement, the start and duration of plant penetrations by aphids were estimated. As a case study, video tracking confirmed the near-complete resistance of lettuce cultivar 'Corbana' against Nasonovia ribisnigri (Mosely), biotype Nr:0, and revealed quantitative resistance in Arabidopsis accession Co-2 against Myzus persicae (Sulzer). The video tracking platform was benchmarked against Electrical Penetration Graph (EPG) recordings and aphid population development assays. The use of leaf discs instead of intact plants reduced the intensity of the resistance effect in video tracking, but sufficiently replicated experiments resulted in similar conclusions as EPG recordings and aphid population assays. One video tracking platform could screen 100 samples in parallel.

Conclusions: Automated video tracking can be used to screen large plant populations for resistance to aphids and other piercing-sucking insects.

No MeSH data available.


Related in: MedlinePlus

Correlation between automated video tracking and human observations.M. persicae behaviour was measured by automated video tracking (x-axes) and human observations simultaneously (y-axes). Three categories of probes were distinguished: All probes, Long probes (>15 min), and Short probes (<3 min). The duration (min) and number of probes measured by human observations were compared to: (a,d,g,j,m) the duration (min) and number of probes (all, long, and short probes) measured by video tracking, (b,e,h,k,n) the total time not moving (min), and (c,f,i,l,o) the distance moved by the aphids (cm) (*P < 0.05; **P < 0.01; ***P < 0.001, Pe = Pearson correlation test, Pl = Pearson correlation test on log transformed data, S = Spearman correlation test, dashed lines represent a hypothetical r2 = 1, n = 16 recordings of 1 aphid for 55 min, 275 pixels per mm2).
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Fig4: Correlation between automated video tracking and human observations.M. persicae behaviour was measured by automated video tracking (x-axes) and human observations simultaneously (y-axes). Three categories of probes were distinguished: All probes, Long probes (>15 min), and Short probes (<3 min). The duration (min) and number of probes measured by human observations were compared to: (a,d,g,j,m) the duration (min) and number of probes (all, long, and short probes) measured by video tracking, (b,e,h,k,n) the total time not moving (min), and (c,f,i,l,o) the distance moved by the aphids (cm) (*P < 0.05; **P < 0.01; ***P < 0.001, Pe = Pearson correlation test, Pl = Pearson correlation test on log transformed data, S = Spearman correlation test, dashed lines represent a hypothetical r2 = 1, n = 16 recordings of 1 aphid for 55 min, 275 pixels per mm2).

Mentions: To test the accuracy of the platform, we performed automated video tracking and human observations simultaneously. A camera was attached to a stereo microscope to deliver a side-view on the arena for manual scoring of probes (Additional file 2). Among a total of 139 probes of 16 different M. persicae aphids scored by hand, 88% was detected with video tracking (Figure 3a). Undetected and false positive probes involved only short events (<3 min). Of the detected probes, 19% was either underrated (multiple ‘true’ probes were considered as one probe), or overrated (one ‘true’ probe was translated into multiple probes by the software). Underrated samples were caused by undetected probe stops due to slow movements below the velocity threshold. Overrated samples were caused by false probe stops when, for example, the aphid was immobile on the edge of the leaf disc and the assigned position continuously switched between an “on the leaf disc” and “off the leaf disc” status (Figure 3b). Three times this incident occurred, leading to 17 redundant probes of which 10 were filtered out automatically (see Methods, section Software settings). Other reasons for premature probe stops were abdominal movements during probing related to e.g. reproduction or honeydew excretion. The longer probes lasted, the higher the risk was of encountering such incidents. Indeed automatically tracked probes were in general biased to end 73 to 12 seconds too early (Figure 3c), and the total duration of probing was underestimated (on average 46 min ± 2.5 min standard error, versus 50 min ± 1.9, P = 0.01, Mann–Whitney U test, total observation duration: 55 min). Nevertheless, the recorded number and duration of probes were highly correlated to human observations (Figure 4, average r2 = 0.7 with 275 pixels per mm2). Other parameters, such as distance moved, were also highly correlated with feeding behaviour in general, but were less informative with regard to long probes (Figure 4l). Although automated video tracking did not achieve a precision as high as manual scoring, it enabled observing multiple arenas simultaneously. In the above described tests, we used 275 pixels per mm2, equal to a coverage of 20 arenas with our 768 × 576 pixels camera. To determine whether the capacity could be increased, we repeated the experiment with only 155 pixels per mm2, equal to a coverage of 35 arenas, but found that reduced resolution resulted in decreased correlations with human observations (average r2 < 0.5).Figure 3


High-throughput phenotyping of plant resistance to aphids by automated video tracking.

Kloth KJ, Ten Broeke CJ, Thoen MP, Hanhart-van den Brink M, Wiegers GL, Krips OE, Noldus LP, Dicke M, Jongsma MA - Plant Methods (2015)

Correlation between automated video tracking and human observations.M. persicae behaviour was measured by automated video tracking (x-axes) and human observations simultaneously (y-axes). Three categories of probes were distinguished: All probes, Long probes (>15 min), and Short probes (<3 min). The duration (min) and number of probes measured by human observations were compared to: (a,d,g,j,m) the duration (min) and number of probes (all, long, and short probes) measured by video tracking, (b,e,h,k,n) the total time not moving (min), and (c,f,i,l,o) the distance moved by the aphids (cm) (*P < 0.05; **P < 0.01; ***P < 0.001, Pe = Pearson correlation test, Pl = Pearson correlation test on log transformed data, S = Spearman correlation test, dashed lines represent a hypothetical r2 = 1, n = 16 recordings of 1 aphid for 55 min, 275 pixels per mm2).
© Copyright Policy - open-access
Related In: Results  -  Collection

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Fig4: Correlation between automated video tracking and human observations.M. persicae behaviour was measured by automated video tracking (x-axes) and human observations simultaneously (y-axes). Three categories of probes were distinguished: All probes, Long probes (>15 min), and Short probes (<3 min). The duration (min) and number of probes measured by human observations were compared to: (a,d,g,j,m) the duration (min) and number of probes (all, long, and short probes) measured by video tracking, (b,e,h,k,n) the total time not moving (min), and (c,f,i,l,o) the distance moved by the aphids (cm) (*P < 0.05; **P < 0.01; ***P < 0.001, Pe = Pearson correlation test, Pl = Pearson correlation test on log transformed data, S = Spearman correlation test, dashed lines represent a hypothetical r2 = 1, n = 16 recordings of 1 aphid for 55 min, 275 pixels per mm2).
Mentions: To test the accuracy of the platform, we performed automated video tracking and human observations simultaneously. A camera was attached to a stereo microscope to deliver a side-view on the arena for manual scoring of probes (Additional file 2). Among a total of 139 probes of 16 different M. persicae aphids scored by hand, 88% was detected with video tracking (Figure 3a). Undetected and false positive probes involved only short events (<3 min). Of the detected probes, 19% was either underrated (multiple ‘true’ probes were considered as one probe), or overrated (one ‘true’ probe was translated into multiple probes by the software). Underrated samples were caused by undetected probe stops due to slow movements below the velocity threshold. Overrated samples were caused by false probe stops when, for example, the aphid was immobile on the edge of the leaf disc and the assigned position continuously switched between an “on the leaf disc” and “off the leaf disc” status (Figure 3b). Three times this incident occurred, leading to 17 redundant probes of which 10 were filtered out automatically (see Methods, section Software settings). Other reasons for premature probe stops were abdominal movements during probing related to e.g. reproduction or honeydew excretion. The longer probes lasted, the higher the risk was of encountering such incidents. Indeed automatically tracked probes were in general biased to end 73 to 12 seconds too early (Figure 3c), and the total duration of probing was underestimated (on average 46 min ± 2.5 min standard error, versus 50 min ± 1.9, P = 0.01, Mann–Whitney U test, total observation duration: 55 min). Nevertheless, the recorded number and duration of probes were highly correlated to human observations (Figure 4, average r2 = 0.7 with 275 pixels per mm2). Other parameters, such as distance moved, were also highly correlated with feeding behaviour in general, but were less informative with regard to long probes (Figure 4l). Although automated video tracking did not achieve a precision as high as manual scoring, it enabled observing multiple arenas simultaneously. In the above described tests, we used 275 pixels per mm2, equal to a coverage of 20 arenas with our 768 × 576 pixels camera. To determine whether the capacity could be increased, we repeated the experiment with only 155 pixels per mm2, equal to a coverage of 35 arenas, but found that reduced resolution resulted in decreased correlations with human observations (average r2 < 0.5).Figure 3

Bottom Line: Functional genomics of plant resistance to these insects would greatly benefit from the availability of high-throughput, quantitative phenotyping methods.The use of leaf discs instead of intact plants reduced the intensity of the resistance effect in video tracking, but sufficiently replicated experiments resulted in similar conclusions as EPG recordings and aphid population assays.One video tracking platform could screen 100 samples in parallel.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Entomology, Wageningen University, P.O. Box 16, 6700 AA Wageningen, The Netherlands ; Laboratory of Plant Physiology, Wageningen University, P.O. Box 16, 6700 AA Wageningen, The Netherlands ; Plant Research International, Wageningen University and Research Center, P.O. Box 16, 6700 AA Wageningen, The Netherlands.

ABSTRACT

Background: Piercing-sucking insects are major vectors of plant viruses causing significant yield losses in crops. Functional genomics of plant resistance to these insects would greatly benefit from the availability of high-throughput, quantitative phenotyping methods.

Results: We have developed an automated video tracking platform that quantifies aphid feeding behaviour on leaf discs to assess the level of plant resistance. Through the analysis of aphid movement, the start and duration of plant penetrations by aphids were estimated. As a case study, video tracking confirmed the near-complete resistance of lettuce cultivar 'Corbana' against Nasonovia ribisnigri (Mosely), biotype Nr:0, and revealed quantitative resistance in Arabidopsis accession Co-2 against Myzus persicae (Sulzer). The video tracking platform was benchmarked against Electrical Penetration Graph (EPG) recordings and aphid population development assays. The use of leaf discs instead of intact plants reduced the intensity of the resistance effect in video tracking, but sufficiently replicated experiments resulted in similar conclusions as EPG recordings and aphid population assays. One video tracking platform could screen 100 samples in parallel.

Conclusions: Automated video tracking can be used to screen large plant populations for resistance to aphids and other piercing-sucking insects.

No MeSH data available.


Related in: MedlinePlus