Attachment 1 - Linking Seed Respiration and Seed Vigo.pdf

VIM: Linking Seed Respiration and Seed Vigor

Pedro Bello and Kent J. Bradford Seed Biotechnology Center, Department of Plant Sciences, University of California, Davis, CA 95616

Introduction

Seed germination is responsive to diverse environmental, hormonal and chemical signals.

Germination rates (i.e., speed and distribution in time) reveal information about timing, uniformity and extent of germination in seed populations and are sensitive indicators of seed vigor and stress tolerance. Population-based threshold (PBT) models can describe germination responses to temperature, water potential, hormones, priming, aging, oxygen and other factors while providing quantitative parameters for comparing seed lots.

However, obtaining detailed data on germination rates of seed populations requires repeated observations at frequent times to construct germination time courses, which is labor intensive and often impractical. Seed respiration is known to be related to seed vigor and quality, but other than being indirectly assessed via the tetrazolium test, has not been widely utilized as a seed quality test. Recently, a novel fluorescence-based method of measuring oxygen levels inside closed spaces enables the measurement and analysis of the respiratory patterns of individual seeds.

The Q2 instrument, developed on this principle by ASTEC Global (www.astec-global.com), can measure and analyze the respiratory activity of populations of individual seeds in a relatively high-throughput manner. The basic goal of the technology has been to associate these respiratory measurements with seed germination and vigor as indicators of seed quality. Most publications using this technology relied on the software provided with the instrument for data analysis, which is based on fitting equations to the oxygen consumption time courses. Some of the indices (termed ASTEC values) derived from the respiratory patterns are correlated to other seed quality parameters. In particular, seed and seedling respiratory rates following imbibition are closely associated with timing of radicle emergence, which is a sensitive indicator of seed vigor.

Although this initial methodology attempted to analyze every seed respiration curve and extracted parameters for individual seeds, only averages and standard errors were actually used for comparison between seed lots. Additionally, this approach was limited when dealing with unexpected shapes of respiratory patterns generated by more complex seed species/lots or increasing stress conditions for tests such as varying temperature and water availability. Adjusting the fit of the expected equations to these respiration curves increased the labor and time required for the analysis as well introducing bias into the analyses when fractions of the seed lot tested were excluded from the analysis due to lack conformity to the expected pattern (Bradford et al., 2013; Van Asbrouck and Taridno, 2009; Zhao and Zhong, 2012; Zhao et al., 2009; Zhao et al., 2013). A number of seed companies in Europe and research institutes worldwide purchased the Q2 instrument after its release, but mostly due to these issues, it was not widely used. Although large amounts of data were generated on every test, relatively little useful information was extracted due to the issues with the data analysis and mixed results.

Nonetheless, the Q2 user’s community recognized the potential of the technology and founded a Q2 research group, led by the University of California, Davis, to conduct basic research and further explore the technology. The latest outcome of this research was the development of simpler but more relevant analysis approaches. A new method was recently introduced to better use the capability of individual seed measurements generated by the Q2 instrument by constructing cumulative curves based on the time required for each seed to consume a specific fraction of the oxygen available to it (Bello and Bradford, 2016).

These population oxygen deletion (POD) curves resemble germination time course curves and are not dependent upon the shapes of the individual curves. Furthermore, POD curves can be directly analyzed using existing PBT models that had been applied to germination time course curves, allowing derivation of quantitative parameters relating to seed quality and predictions of seed performance under a wide range of conditions. This close correspondence between seed respiration and germination time courses enables the use of semi-automated respiratory measurements to assess seed vigor and quality parameters. It also raises intriguing questions about the fundamental relationship between the respiratory capacities of seeds and the rates at which they proceed toward completion of germination.

Another long-standing goal of the manufacturer was to have the Q2 recognized as a standard seed testing tool recognized by seed testing authorities. The International Seed Testing Association (ISTA) recently recognized the merit of the technology for seed science research but did not endorse it as a seed testing tool to predict germination and vigor of commercial seed lots (Powell, 2016). This report also included an addendum section mentioning that information about the new approach for data analysis (mentioned above) was received after completion of that review.

Germination and Respiration: Relationships to Seed Vigor

Respiration is an integrative overall indication of plant metabolism and is essential to generate the metabolic energy required for biological functions. Respiration is initiated very quickly following imbibition of dry seeds, generally within the first minutes or hours after tissue hydration (Hourmant and Pradet, 1981), and most seeds require oxidative respiration to complete germination (Al-Ani et al., 1985; Corbineau and Come, 1995).

Depending upon the seed structure, type of enclosing tissues and other factors, seed respiration rates increase as imbibition proceeds and reach a plateau level prior to radicle emergence, then increase again after radicle emergence (Dahal et al., 1996; Ibrahim et al., 1983; Woodstock and Grabe, 1967). Species differ in their requirements for oxygen during imbibition and germination, and these requirements can change, for example due to dormancy, after-ripening or priming (Bradford et al., 2008; Bradford et al., 2007; Patane et al., 2006). In some cases, the ability of oxygen to penetrate the tissues covering the embryo or the absorption of oxygen by the enclosing tissues has been implicated in controlling germination (Benech-Arnold et al., 2006; Edelstein and Welbaum, 2011;

Lenoir et al., 1986).

Both direct and indirect (e.g. tetrazolium tests) measures of seed respiratory activity have confirmed a relationship between respiration and seed quality (Baalbaki et al., 2009;

Woodstock and Grabe, 1967), although results across species have been variable (Elias et al., 2012). A delay in the initiation of respiration or an inability of critical seed tissues to commence respiratory activity is associated with poor seed quality. The ability of a dry seed to repair its respiratory systems following imbibition may also be associated with seed quality (Ferguson et al., 1990). On the other hand, seed priming treatments that enhance germination rates can result in higher respiration rates following imbibition (Cantliffe et al., 1984; Chojnowski et al., 1997; Li et al., 2010). However, it has been difficult to develop this relationship between respiration and vigor into a simple and efficient seed quality test.

Tetrazolium (TZ) tests are labor-intensive and require specialized training to interpret.

Fermentative metabolism and increased ethanol production is associated with poor seed quality, and can be used as an indicator of vigor (Buckley and Huang, 2011; Kodde et al., 2012; Rutzke et al., 2008). However, measurements of overall respiration or fermentation rates of populations of seeds are not well adapted to high-throughput tests and can only give a mean value for all seeds in the sample (Baker et al., 2004). Unless respiration measurements can be made on individual seeds, it is not possible to determine which seeds were responsible for a change in respiratory characteristics. While useful on a seed lot basis, respiration measurements on seed populations are also limited in their capacity to connect different respiratory characteristics with specific aspects of seed quality. The ability to accurately and non-destructively measure respiration rates of individual seeds in a high-throughput format would allow more specific tests of the relationships between respiration and seed quality.

The development of the Q2 technology enabled measurement of oxygen consumption of individual seeds at frequent intervals over time. The instrument measures oxygen consumption of relatively large numbers of individual seeds (up to 1536 in the current version) automatically with repeated sampling over time after imbibition. Correlations observed between these respiration-related parameters and seed germination characteristics indicate that Q2 tests could enable the use of oxygen consumption rates as indirect indicators of seed vigor and quality (Bradford et al., 2013; Van Asbrouck and Taridno, 2009; Zhao and Zhong, 2012; Zhao et al., 2009). Additionally, using a new approach for data analysis, a close relationship was reported between the effects on seed respiration and germination of temperature (T), water potential (Ψ), their combination, abscisic acid (ABA) or gibberellin (GA), respiratory inhibitors, aging and priming (Bello and Bradford, 2016).

How the Q2 technology works

The Q2 instrument consists of a measuring system and associated computer and software for data acquisition and analysis. Individual seeds are sealed in wells of microtiter plates with membranes having a dot in the inner side containing a metal organic dye that changes its fluorescence properties in proportion to the oxygen concentration (Draaijer et al., 1999).

The wells contain agar or other substrates to provide moisture for seed imbibition. An individual seed is placed into each well of the 96-well microtiter plates, the time of initiation of imbibition is noted, and a sealing membrane with the attached dye dots is then placed over the plate and is heat-sealed using a hot-plate and pressure apparatus to form an oxygen-impermeable barrier. Alternatively, vials of needed sizes can be used and sealed with caps having the dye dot underneath them. The plates or vials are then placed in the Q2 and the instrument is programmed to scan the plates at a frequency specified by the user. Initially, an empty well in each plate allowed calibration of the sensor at 100% relative oxygen content (actually 21% O2 in air); subsequently, two vials outside of the plates containing only air or 0% oxygen were utilized for two-point calibration of the sensor.

As the seed respires, it depletes the oxygen in the sealed well, which increases the fluorescence of the dye. This change is detected by a light source that shines blue light on the dye dot and a sensor that measures the fluorescence intensity. A robotic arm sequentially moves the light source and sensor over each well, measuring the oxygen concentration inside the well at desired time intervals and developing oxygen consumption time courses for individual seeds. Up to 16 plates can be positioned in the apparatus at a time and measured automatically by the robotic sensor. A critical Q2 instrument modification was the addition of a custom-designed temperature control system using Peltier heating/cooling units and fans. In this configuration, the temperatures of groups of four plates can be independently controlled between 10 and 35°C (± 0.5°C). The measurements can be repeated as often as desired (generally at 30-minute to 2-hour intervals) to obtain time courses of respiratory activity (oxygen depletion in the sealed wells). The time course data from each well are collected in a database that is accessible to the Q2 analysis software or can be output to a spreadsheet file.

Several methods can be used for providing water to the seeds in the microtiter plate wells or vials. For example, filter paper disks can be placed in the bottom of each well and wetted with water. Instead, our preferred approach is to use agar (superpure agarose, 0.4 to 1% w/v) as the hydration medium for the seeds, which has several advantages over filter paper:

1) it can be efficiently pipetted into the wells when warm using multichannel pipettes, rather than requiring placement of paper disks in each well; 2) it allows the volume of the airspace in the well to be adjusted easily for different species or to change the rate of oxygen depletion in the well; and 3) it is transparent, so that radicle emergence can be observed through the bottom of the wells. Experiments with different volumes of agar confirmed that, as expected, varying the headspace volume had a directly proportional effect on the measured rate of oxygen depletion in the wells. Thus, by varying the agar volume, the headspace volume appropriate to the seed size and respiration rate can be adjusted for different species to give oxygen depletion time courses of convenient duration.

The total volume of each well of a 96-well plate is approximately 400 µL. For small-seeded vegetables and flowers, an agar volume of 150 to 250 µL has worked well for 96-well plates (Bradford et al., 2013). For larger seeds (e.g., corn, soybean, cotton and melon) screw-cap vials (volume of 800 to 2000 µl) can be utilized with varying volumes of agar depending on seed species size (Bello and Bradford, 2016).

Output measures from the Q2 instrument

In general, oxygen consumption time courses during seed imbibition and germination exhibit a characteristic shape with a slow linear initial rate, followed by a transition to a more rapid rate, and then a decrease again at low oxygen levels, resulting in an overall sigmoid pattern (Fig.1). However, oxygen consumption time courses of individual seeds can vary widely in both timing and shape, even within a single seed lot.

In the provided Q2 analysis software, the analyst can categorize each seed according to the pattern it exhibits. Dead seeds, for example, do not show significant changes in oxygen concentration over time, and can be excluded from further analysis. Respiratory patterns for wells subsequently exhibiting fungal contamination were often characterized by an abrupt change to a very rapid respiration rate. Any wells exhibiting this pattern or with visible fungal or bacterial contamination at the end of the test were excluded from analysis.

The analysis software fits equations to each curve in the categorized data, averages the time courses of oxygen depletion across seeds and plots the fitted and mean curves and standard deviation range.

The Q2 analysis software calculates a number of parameters, termed ASTEC values, which characterize components of the respiratory patterns of each seed and subsequently averages these values across the test population. In most cases, an initial slow rate of oxygen consumption is followed by a transition to a more rapid respiratory rate. The duration of the initial phase can vary, as can the rate of oxygen consumption. The Q2 analysis software calculates the initial slope (Starting Metabolism Rate or SMR) and identifies the time when the initial respiration rate increases (termed Increased Metabolism Time or IMT) (Fig.1).

Together, these values give information about the respiratory capacity of seeds during the early phase of imbibition when seeds are hydrating, activating metabolic processes, repairing damage accumulated during storage and preparing for embryo growth. From the fitted curves, the Q2 software also identifies the maximal respiration rate or the inflection point in the sigmoid portion of the oxygen depletion curve (Fig.1) and calculates the slope of the curve at that point. This Oxygen Metabolism Rate (OMR) value indicates the maximum rate of respiration when not limited by oxygen availability. The slope of the curve at this point is also extrapolated to intercept the time (x) axis and the intercept is designated as the Relative Germination Time (RGT). Although this value does not actually match the time of radicle emergence, which generally occurs earlier in the time course, it provides a relative measure of respiratory activity, as it incorporates both the lag phase prior to the IMT and the subsequent rate of respiration (compare RGT values from curves of slow and fast germinating seeds in Figure 1). In order to have an index of the variation among seeds in this parameter, the software also calculates the Homogeneity of RGT (HOM), which is the variance (sum of squares divided by n-1) in RGT values among all seeds in the test population (n). Higher HOM values indicate more variation (less uniformity) in RGT among the individual seeds. As respiration proceeds and oxygen is depleted in the wells, the rate of respiration eventually begins to decrease due to the limitation of oxygen availability. This is identified by the Critical Oxygen Pressure (COP), or the point at which the oxygen depletion rate begins to decrease after reaching its maximum (Fig.1). Initially it was observed that seeds differed in the final minimum level to which oxygen could be lowered in the wells. Differences in COP could reflect the permeability of seed coverings or the efficiency of oxidative (mitochondrial) respiration.

However, this variation was also partly due to the original calibration method and/or baseline drift. A new calibration method was subsequently introduced using both 0 and 100% (21% O2) calibration points that has improved the accuracy of these low oxygen measurements, and oxygen in the wells is generally depleted to very low levels. Thus, COP values reported are likely to have correct overall relative relationships among treatments or seed lots, but the absolute values reported may not be quantitatively accurate in some cases when the original calibration method was in use.

Scientific Reports Using ASTEC Values from the Q2

The Q2 instrument was anticipated to be a practical and automatic tool for seed quality and vigor assessment. Initial approaches for data analysis were successful when all seeds tested displayed similar sigmoid curves. In such cases, as for a study done with tomato seeds, good correlations were reported between certain respiratory parameters (OMR and RGT) and official seed test parameters (early count and mean germination time) (Van Asbrouck and Taridno, 2009).

Another early study with sweet corn (Zhao et al., 2009) reported OMR (maximum oxygen consumption rate) and COP as relevant parameters when compared to seed vigor tests.

Findings in this study could have been affected by the initial oxygen calibration issue that resulted in elimination of varying amounts of data. The value of the COP parameter was questionable after the improved calibration method was introduced. When using a proper protocol, virtually all viable seeds tested eventually deplete the oxygen to very low levels.

Q2 tests in coniferous (Cunninghamia lanceolata) and pine (Pinus massoniana) tree seeds also displayed correlations between OMR, RGT and laboratory germination tests and field emergence (Zhao and Zhong, 2012). Additionally, mixed results on tests on 40 conventional rice (Oryza sativa L.) seed lots (indica and japonica) have been reported (Zhao et al., 2013). OMR was the only value with somewhat significant correlation with germination parameters and field indices (emergence rate and plant height). Parameters correlated to varying degrees with other measures of seed vigor across Brassica and onion seed lots. Germination rates of tomato and lettuce seeds that varied in response to seed priming and/or aging were highly correlated with some measures of respiratory activity in the Q2 (Bradford et al., 2013).

It is often the case that individual seeds in a lot exhibit different shapes of oxygen consumption patterns (see Fig. 3 below). This can be problematic for determining the ASTEC values for comparison across all seeds, which are based upon the various components of the typical oxygen consumption time course (Fig. 1). In some of the above publications, a fraction of seeds is simply excluded from the analysis if they did not display the expected S-shaped curve. As described by Zhao (2013), “For the Q2 data, non-S-shaped curves were manually rejected and a new database with accepted curves produced.” The fraction of seeds excluded in this way is not disclosed in the publications, and can vary among treatments of the same seed lot (see Fig. 3). An attempt to address the issue was made by calculating the ratio of seeds exhibiting S-shaped curves (Zhao et al., 2013), which displayed some mixed correlation results with vigor and field indices for rice seeds.

Ultimately the problem was categorically addressed with the development of two additional indices ((Bradford et al., 2013)) that do not rely on the shapes of the oxygen consumption curves: 1) the time required for the seed to reduce the initial oxygen level by 50% (R50); and 2) the area under the curve from time zero to 50% oxygen depletion (AUC50) (Fig. 1). The oxygen depletion level is arbitrary for these indices and other percentages could also be used. These two parameters are generally highly correlated with each other and with RGT, which is also an integrative measure of the overall respiratory rate. R50 and AUC50 are not currently calculated by the Q2 software, but can be easily calculated from a spreadsheet provided by the authors.

In summary, the approach for Q2 data analysis using the software provided with the instrument, focused on: 1) fitting equations to the original oxygen depletion time courses;

2) deriving parameters from the shape and timing of features in those time courses; and 3) averaging those values across the seed population (Bradford et al., 2013; Van Asbrouck and Taridno, 2009; Zhao and Zhong, 2012; Zhao et al., 2009; Zhao et al., 2013). Although the information provided by this analysis was useful to compare the vigor of seed lots, this approach had some limitations. First, the analyst had to inspect the data for each seed and assure that it was properly fit by the software’s equation. The analyst often had to adjust the parameters manually to achieve acceptable fits, making the data analysis process inefficient and time consuming. Second, in many cases when different conditions were applied, the oxygen depletion curves no longer had the expected shape, which resulted in loss of data for those parameters that relied on a particular component of those curves. To resolve this problem, some researchers simply removed data for those seeds from their analyses (Zhao et al., 2013), but this strongly biases the results by restricting the analyzed dataset to a variable subfraction of the total seed population. In the case of lettuce seeds, which generally exhibit a range of oxygen depletion patterns (see Fig. 3E-I), this approach would consistently censor data for a significant fraction of the seed population. Thus, while recognizing the potential for the Q2 to acquire precise single-seed data robotically and at frequent time intervals, we sought alternative approaches to analyze the resulting data.

Population-based analyses of seed quality

While focusing on the shapes of the individual oxygen depletion time courses may be useful for some investigations, a new approach was developed to visualize the overall variation in respiratory capacity among seeds (Bello and Bradford, 2016). This method converts the oxygen depletion time courses of the individual seeds in the measured population into a cumulative curve based on the time required for each seed to reduce the oxygen in the Q2 well to a specific percentage of the initial value. This initial oxygen concentration (21% in the air) is normalized to 100% based on the first measurement for each individual well or vial. The time at which each seed reduces the oxygen concentration to that percentage can be plotted as a cumulative percentage curve versus the time of imbibition (Fig. 2). The resulting time course of respiratory activity is analogous to a germination time course, which is a cumulative plot over time of when each seed achieves the end point of radicle emergence. These population oxygen depletion (POD) curves provide a simple and convenient way to compare the effects of various treatments on the distribution of respiration rates in the measured seed population. A spreadsheet accepting data exported from the Q2 instrument and converting it to POD curves is publicly available (www.seed.technology). This approach takes advantage of the ability of the Q2 instrument to collect data for each individual seed and utilizes all of the data for analyses, rather than only means or medians of the population (see Fig. 4). Unlike other derived parameters based on the shapes of the original oxygen depletion time courses (Fig. 1), the POD curves are visually similar to germination time courses and are therefore familiar to seed biologists and technologists.

Varying the percentage of the oxygen depletion selected results in somewhat different shapes of these cumulative curves (Fig. 2). For convenience and illustrative purposes, 75% (R75), 50% (R50) and 25% (R25) of the initial oxygen remaining were utilized, but the percentage of oxygen depletion used can be varied as long as it is applied consistently across all seeds. For example, use of 16%, 50% and 84% would indicate the median (50%) and one standard deviation below (16%) and above (84%) the median. Of course, greater oxygen depletions mean lower oxygen percentages remaining in the vials, which itself can reduce respiration rates and delay or prevent germination. As this is a closed-system test, essentially all of the oxygen is eventually consumed. However, for many species, germination was not affected greatly until the percentage of oxygen in air was less than ∼5% (Bradford et al., 2007), corresponding to approximately 25% of the initial oxygen concentration (21%) remaining. In general, the R50 level of oxygen depletion provides the median response of the population, while other depletion levels can be used to reduce the duration of the test (R75) or to better match germination time courses in some cases. By selecting the appropriate oxygen depletion percentage value, time courses and treatment differences that closely resemble those among germination time courses can be generated (see Fig. 5).

The ability of the Q2 instrument to collect repeated, closely spaced measurements of individual seed oxygen consumption rates provided an opportunity to test whether such data could be analyzed by the PBT models that have been applied to germination time courses. The resulting POD curves provide immediate visual information related to seed respiratory performance, directly analogous to seed germination time courses. More importantly, these POD curves can be analyzed using the same models that have been applied to germination time-course data (Alvarado and Bradford, 2002). In fact, the models generally fit better to the POD curves than to germination time courses, as respiratory information is available for every seed at frequent time intervals, in contrast to the more sparse germination data that are generally snapshots of germination progress at selected time points, and vary in density according to the speed of germination and the observation frequency (Fig. 5). Having fit the appropriate PBT models to the full dataset, unbiased median values can be derived as well as measures of the population variances (σ values).

These values are highly correlated with median times to germination observed in the same experiments (Fig. 6). While there has been considerable discussion in the seed literature about a need for alternative statistical methods and use of non-normal distributions for applying PBT models to germination data (Bloomberg et al., 2009; Hardegree et al., 2015;

Hay et al., 2014; Mesgaran et al., 2013; Watt et al., 2011), the majority of our results with POD curve data fit the assumptions of the normal distribution and probit analysis extremely well.

Using the POD curve approach for data analysis, a close relationship was reported between the effects of temperature (T), water potential (Ψ), their combination, abscisic acid (ABA) or gibberellin (GA), respiratory inhibitors, aging and priming on lettuce, tomato and radish seed respiration and germination (Bello and Bradford, 2016). Such broad parallel behavior across all of these conditions suggests the possibilities that all of these factors act on germination through their effects on respiration or that sensitive feedback relationships operate to adjust metabolic rates to developmental progress.

This was explored by evaluating the effects of respiratory inhibitors on germination (Bello and Bradford, 2016). The effects of the respiratory inhibitors potassium cyanide (KCN) and salicylhydroxamic acid (SHAM) showed that the rate of progress toward germination is closely associated with respiration rates (Figs. 4, 5, 6). Either inhibitor alone reduced respiration rates, but germination was capable of recovering. However, in the presence of both inhibitors, blocking both the cytochrome c and the alternative respiratory pathways, respiration rates were low and germination was prevented. The effects on germination rates of limiting oxygen availability, which would also reduce respiration rates, are also consistent with PBT models (Bradford et al., 2007). This implies that a quantity of ‘respiration-time’ (in analogy to thermal time) must be accomplished or accumulated by a seed before radicle emergence can be completed. Part of this presumably supports repair activities, as seed aging initially slows germination rates before viability is lost (Fig. 7), but normal seedlings resulting from aged but viable seeds can grow at the same rates as seedlings from unaged seeds after germination occurs, indicating that they have not sustained permanent damage (Baker, 1995; Tarquis and Bradford, 1992). Thus, aging may initially affect principally respiratory capacity, and slower rates of respiration take longer to accomplish the metabolic events required to prepare for radicle emergence.

Mitochondria have been proposed to be the primary targets for aging damage during accelerated aging (Amable and Obendorf, 1986; Ferguson et al., 1990; Fu et al., 2015), possibly as a result of the action of free radicals and reactive oxygen species (El-Maarouf- Bouteau and Bailly, 2008; McDonald, 1999). Furthermore, the oxidative properties of purified pea seed mitochondria improved during imbibition and due to priming, indicating a correlation between seed quality and mitochondrial function (Benamar et al., 2003).

Consistent with this, mitochondrial membrane structure was damaged, the matrix became less dense and mitochondrial biogenesis during seed imbibition was inhibited in aged soybean seeds compared to control seeds (Xin et al., 2014). Thus, it is not surprising that there are close relationships between respiration rates and germination rates in response to inhibitors, oxygen and aging (Figs. 5-7).

Seed progress toward germination is closely attuned to Ψ and makes adjustments in response to incubation at low Ψ (Dahal and Bradford, 1994) (Fig. 3). Water availability is critical for germination and for the success of the resulting seedling, and germination is even more sensitive to reduced Ψ than is seedling growth (Ross and Hegarty, 1979). Thus, the high sensitivity of seed respiration to reduced Ψ could serve to delay or prevent completion of germination to enable the seed to make the determination of whether the conditions are adequate for subsequent seedling growth (Finch-Savage and Bassel, 2016).

The phenomenon of seed priming demonstrates that physiological progress toward germination can occur at Ψ levels below those permitting radicle emergence (Bradford and Haigh, 1994), but further reducing seed water contents slightly below those levels stops progress toward germination and initiates processes associated with desiccation tolerance and seed longevity (Bruggink et al., 1999). Seeds carry finite amounts of reserves, so reducing metabolic rates when Ψ is sensed as being too low to support germination, or as a harbinger of future desiccation, would make evolutionary sense in terms of seed survival.

It is unknown how such changes in Ψ are perceived in the seed, and whether the initial response is to reduce respiration rates or metabolism slows as a secondary response to delayed developmental progress (Dahal et al., 1996). Seeds lacking GA, or exposed to high concentrations of ABA that will prevent germination, also rapidly reduce their respiration rates to very low levels following imbibition (Fig. 9). Dormant rice seeds also exhibited low but constant rates of respiration (Footitt and Cohn, 1995), and extreme reductions in respiration rates were observed in the embryos of some fish species during diapause

(animal dormancy) (Podrabsky and Hand, 2015). Thus, seeds appear to have feedback mechanisms that reduce respiration rates when the probability of successful completion of germination and subsequent seedling growth is low due to environmental conditions or dormancy.

In cases where a single normal distribution of seed response thresholds does not describe the data as well as expected (e.g., Fig. 7F–H), it is likely to be due to the existence of subpopulations of seeds within the seed lot having distinct germination and respiration characteristics. Such subpopulation structure within a seed lot may not be evident in the more sparse germination data (Fig. 7E), demonstrating the value of having data for each individual seed in a population. Using the higher-resolution POD data, we have extended our analyses to show that seed lots can be partitioned into separate subpopulations by applying multiple PBT models to the same dataset (Fig. 8). In this case, direct multivariate fitting of the data and minimizing the residual error was used, rather than probit analysis, to define the parameters of the two subpopulations (see the legend to Fig. 8). Summing the predicted time courses for the two (or more) overlapping subpopulations reproduces the complete complex time course of the total seed population, in a biological analogy to deconvoluting a complex absorbance spectrum into the sum of subspectra using Fourier transform methods (Stein and Shakarchi, 2003). We believe that this is a more biologically relevant approach to analyzing complex germination patterns of specific seed lots, rather than seeking to fit a specific mathematical distribution empirically to describe the entire population (Watt et al., 2011). Only two normally distributed subpopulations can reproduce a wide range of non-normal distributions in the total population, depending upon their means and standard deviations relative to each other (Fig. 8E–H), and additional sub-populations can be added, if needed, to fully account for the characteristics of the seed lot.

It is a biological and commercial fact that many seed lots are mixtures of multiple subpopulations, whether it is due to different genotypes, locations of seeds in the fruit or on the mother plant, maturity dates, dormancy mechanisms or purposeful blending of commercial seed lots (Bewley et al., 2013; Egli, 1998; Roach and Wulff, 1987).

Regardless of the source, variations in the performance of seed lots ultimately trace back to the characteristics of the individual seeds present in the populations, so methods that reveal, quantify and characterize the subpopulations present will ultimately be more useful (e.g., for cleaning and conditioning seed lots as well as for understanding natural variation) than fitting an arbitrary overall distribution to each unique seed population. This may be particularly useful for non-cultivated species, where seed lots collected in the wild could be expected to contain multiple subcomponents (Bloomberg et al., 2009; Finch-Savage and Bassel, 2016). An exception to this could be where the primary interest is in the average population behavior in an ecological context, as, for example, in modelling the seasonal timing of maximum weed emergence, where greater precision may be superfluous, or subpopulations may be multiple and undefined (Boddy et al., 2012; Forcella et al., 2000;

Grundy, 2003; Meyer and Allen, 2009).

Why VIM is Relevant to the Seed Industry

Results indicate that with respect to many conditions influencing seed germination, POD curves can substitute for germination time courses. As germination rates are among the most sensitive indicators of seed vigor or its loss (Matthews and Powell, 2011), this should enable the use of respiration rates for vigor ratings of seed lots.

The VIM system is being developed to expand the current technology and address the known limitations of the current setup. VIM, which refers to “VIM and Vigor”, is aimed to provide an additional powerful tool for seed testing and vigor assessment. The proposed system will combine the basic science of the Q2 instrument integrated with the latest data analysis and database management approaches and a complete re-engineering of the equipment. The current throughput capacity is sufficient for research purposes but not satisfactory for commercial applications, where thousands of tests are performed monthly.

The main constraint is the temperature control, which is critical for reproducibility. The current system handles up to 4 distinct temperatures for a total of 16 fixed plates where the robotic arm moves the sensor for measurements. These 16 plates are the maximum output of the instrument during the duration of the test (usually 2-7 days). The VIM system will essentially rethink that approach to drastically increase testing capacity. The system will have the potential to efficiently provide untapped and valuable information regarding seed quality and vigor on several areas as described below.

Seed lot performance ranking under stress. Seed lots vary in how their germination/respiration rates change in response to varying temperature, salt concentration, water stress and other factors. VIM tests on seed lots at optimum conditions and increasing stress levels reveal corresponding limits for respiration and germination completion. These shifts in POD curves can also be characterized using PBT models, providing meaningful scores for each lot regarding its tolerance to the respective stresses as well as its sensitivity distribution.

Seed enhancement treatments. For seed enhancement treatments such as priming, extensive suitability tests are often conducted to enable the design of safe and effective treatments. After treatment, additional tests are required to assess the effects of the treatments. A matrix of seed treatment conditions (e.g., temperature and water potential) can be easily tested in the VIM system. Data derived from such assays can also be modeled and values for basal, optimum and ceiling treatment concentration limits can be generated, assisting definition of application rates and treatment surveying.

Seed treatment toxicity. Pesticidal chemicals are often coated on seeds. VIM tests could be used to assess potential adverse effects of such treatments on seed germination performance.

Replacement of existing seed tests. Tetrazolium tests can provide an estimate of seed viability in a short amount of time (24 hours). However, it requires analyst expertise and is somewhat subjective. Respiration measurements could potentially give rapid results that would correlate with the results from tetrazolium tests. Other tests such as sand, cold and saturated cold germination tests are also labor-intensive, time-consuming and varying with conditions. It is likely that respiration-based tests can be developed that would give similar results. For example, damage due to an initial period of cold might be revealed more conveniently in subsequent respiration tests than in a growth period and normal seedling analysis. Further VIM tests need to be performed for validation and comparison with these official tests.

Seed longevity: Respiration tests after controlled aging could indicate the potential longevity of seed lots for germplasm conservation. Seed populations generally exhibit an extended period of high viability in storage, followed by a relatively rapid loss of viability (Walters, 1998). The length of the initial period of high viability varies among seed lots and is largely unpredictable, resulting in frequent testing, using up stored seed samples and resulting in the need for expensive regeneration, even if sample viability still remained high (Walters et al., 2005). Multiple lines of research are being pursued to better understand the mechanisms of seed aging, to identify molecular and biochemical markers that could assist in the prediction of viability loss over storage time, but to date these have not resulted in practical tools to anticipate potential seed longevity (Fu et al., 2015). However, periodic VIM tests of seed lots during an initial period of their storage under constant temperature and moisture conditions would reveal shifts in POD curves that can be used in the PBT aging model to predict when viability would fall to a prescribed percentage (Fig. 7). While requiring experimental verification, this would be an efficient empirical method to estimate the potential length of the initial plateau storage period for specific seed lots, and to enable adjustment of testing schedules and storage conditions accordingly.

Conclusion

The Q2 technology provides a convenient method to assay the respiratory activity of individual seeds during imbibition and germination. It can be used with a wide variety of seed types. The volume of air space within the wells and the duration of the tests can be adjusted depending upon the respiratory rates of the seeds.

Results confirm that seed oxygen consumption rates on an individual seed and population basis are highly correlated with germination timing across a wide range of conditions that affect seed germination rates. The automated Q2 measurements could substitute for labor-intensive germination time-course observations, making this sensitive measure of seed quality more feasible and practical for seed vigor assessments. These results also provide insight into how seed metabolic rates, as indicated by oxygen consumption rates, relate to the timing of the physiological processes required for the completion of germination and initiation of seedling growth.

Improved population-based analyses (POD curves) take full advantage of the ability to measure the oxygen consumption of individual seeds and extract population-based parameters from that information. It might be thought that such individual variation would make the results difficult to interpret without averaging, but as Eric Roberts (1973) insightfully noted, “It has been suggested that seeds are unpredictable things and consequently it would be a waste of time to try to perceive laws of behavior among such erratic individuals ... But other disciplines have shown us quite clearly that, although the behavior of any individual in a population may be quite unpredictable, the behavior of populations of individuals can often be defined very accurately.” This was confirmed by subsequent development of population-based models of seed viability loss during storage (Ellis and Roberts, 1981). Population-based threshold (PBT) models analyzing the timing of radicle emergence in seed populations have proven to be very powerful in describing and quantifying seed behavior in response to a wide range of environmental and hormonal conditions.

In summary, the Q2 instrument and its improved VIM successor measures respiration rates of populations of individual seeds that can reveal seed quality variation between or within lots or after priming or aging treatments. It can therefore be used as a relatively rapid seed vigor assay, and since it is automated and can repeatedly sample individual seeds over time, it could reduce lab requirements for determining germination timing and enable broader use of germination rate as a seed quality index.

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