GLO-Roots: an imaging platform enabling multidimensional characterization of soil-grown root systems
Plant roots are three-dimensional assemblies of cells that coordinately monitor and acclimate to soil environmental change by altering physiological and developmental processes through cell-type and organ-specific regulatory mechanisms ( Dinneny et al . , 2008; Duan et al . , 2013 ) . Soil comprises a complex distribution of particles of different size , composition and physical properties , airspaces , variation in nutrient availability and microbial diversity ( Brady and Weil , 2009; Lynch and Wojciechowski , 2015 ) . These physical , chemical , and biological properties of soil can vary on spatial scales of meters to microns , and on temporal scales ranging from seasonal change to seconds . Root tips monitor this environment through locally and systemically acting sensory mechanisms ( Bao et al . , 2014; Tabata et al . , 2014 ) . The architecture of the root system determines the volume of soil where resources can be accessed by the plant ( rhizosphere ) and is under both environmental and genetic control . Plasticity in growth parameters allows the plant to adjust its form to suit a particular soil . Lateral roots , which usually make up the majority of the total root system , often grow at an angle divergent from the gravity vector . This gravity set-point angle is controlled by auxin biosynthesis and signaling and can be regulated by developmental age and root type ( Rosquete et al . , 2013 ) . Recent cloning of the DRO1 quantitative trait locus demonstrates that natural genetic variation is a powerful tool for uncovering such control mechanisms ( Uga et al . , 2013 ) . Specific root ideotypes ( idealized phenotypes ) have been proposed to be optimal for acquisition of water and nitrogen , which are distinct from ideotypes for low phosphorus . Based on computational modeling and field studies , the ‘steep , deep , and cheap’ ideotype proposed by Lynch and colleagues may provide advantages to the plant for capturing water and elements like nitrogen that are water soluble and therefore tend to move in the soil column with water . This ideotype consists of highly gravitropic , vertically oriented roots that grow deep in the soil column and develop large amounts of aerenchyma , which reduces the overall metabolic cost of the root system ( Lynch and Wojciechowski , 2015 ) . Other nutrients , like phosphorus , which have limited water solubility and are tightly bound to soil particles , usually accumulate in the top layers of soil and favor root systems that are more highly branched and shallow . The low-phosphorus ideotype effectively increases root exploration at the top layers of soil ( Lynch and Wojciechowski , 2015 ) . Modeling of root system variables shows that optimum architecture for nitrogen and phosphorus uptake is not the same ( Postma and Lynch , 2014 ) and suggests tradeoffs that may affect the evolution of root architecture as a population adapts to a particular environmental niche ( Laliberté et al . , 2013 ) . Clearly , understanding the architecture of root systems and how environmental conditions alter root developmental programs is important for understanding adaptive mechanisms of plants and for identifying the molecular-genetic basis for different response programs . In addition , root systems have complexity beyond their architecture that needs to be incorporated into our understanding of plant–environment interactions . Primary and lateral roots exhibit different stress response programs in Arabidopsis ( Duan et al . , 2013; Tian et al . , 2014 ) and may play specialized roles in water and nutrient uptake . Thus , it is important to develop methods that allow for a multidimensional characterization of the root system that includes growth , signaling , and interactions with other organisms . Furthermore , physiological parameters that affect whole-plant responses to the environment , such as transpiration , are likely integrated into such processes , thus , requiring a more holistic approach to studies of root function . Based on these considerations , we have developed a new root imaging platform , Growth and Luminescence Observatory for Roots ( GLO-Roots ) , which allows root architecture and gene expression to be studied in soil-grown plants . GLO-Roots is an integrated system composed of custom growth vessels , luminescent reporters , and imaging systems . We use rhizotrons that have soil volumes equivalent to small pots and support growth of Arabidopsis from germination to senescence . To visualize roots , we designed plant–codon-optimized luciferase reporters that emit light of different wavelengths . To visualize reporter expression , plants are watered with a dilute luciferin solution and imaged afterwards . We have built a custom luminescence-imaging system that automatically captures images of rhizotrons held vertically . The signal from each reporter is distinguished using band-pass filters held in a motorized filter wheel , which enables automated acquisition of images from plants expressing both structural and environmentally or developmentally responsive reporters . We have also developed GLO-RIA ( Growth and Luminescence Observatory Root Image Analysis ) , an ImageJ ( Schneider et al . , 2012 ) plugin that allows for automated determination of ( among other traits ) root system area , convex hull , depth , width , and directionality , a metric which quantifies the angle of root segments with respect to gravity . GLO-RIA is also able to relate root system parameters to local root-associated variables such as reporter expression intensity and soil-moisture content . Overall GLO-Roots has great utility in presenting environmental stimuli to roots in physiologically relevant ways and provides tools for characterizing responses to such stimuli at the molecular level in whole-adult root systems over broad time scales .
GLO-Roots is comprised of four parts: ( i ) growth vessels called rhizotrons that allow plant growth in soil and root imaging; ( ii ) luminescent reporters that allow various aspects of root biology to be tracked in living plants; ( iii ) GLO1 ( Growth and Luminescence Observatory 1 ) luminescence-imaging system designed to automatically image rhizotrons; ( iv ) GLO-RIA , an image analysis suite designed to quantify root systems imaged using GLO-Roots . The size of our rhizotrons enables undisturbed root system development ( before roots reach the sides or the bottom of the rhizotron ) for about 21–23 days for the Col-0 accession growing under long-day conditions ( Figure 2 , Figure 2—source data 1 ) ; however , root traits such as directionality can be observed through later stages of plant development . See 35 DAS root system and directionality in Figure 2A , B . An example of a time series spanning 11 to 21 days after sowing ( DAS ) of Col-0 roots expressing ProUBQ10:LUC2o is shown in Figure 2A and Video 1 with a color-coded time projection shown in Figure 2C . Directionality analysis ( Figure 2B ) shows a progressive change in root system angles from 0° ( vertical ) to 55° as lateral roots take over as the predominant root type . Figure 2D shows the evolution over time of several root traits that can be automatically captured by GLO-RIA ( depth , width , area ) and others that were manually quantified ( primary root growth rate or number of lateral roots per primary root ) . 10 . 7554/eLife . 07597 . 017Figure 2 . Time-lapse imaging of Arabidopsis root systems and quantification using GLO-RIA . ( A ) Typical daily time-lapse image series from 11 to 35 DAS of a ProUBQ10:LUC2o Col-0 plant . ( B ) Average directionality of three root systems imaged in time series as in panel A calculated using the directionality plugin implemented in GLO-RIA . See the GLO-RIA ‘Materials and methods’ section for information of how the directionality is calculated . ( C ) Color-coded projection of root growth using the images in panel A . ( D ) Root system depth , width , root system area are automatically calculated from the convex hull , which is semi-automatically determined with GLO-RIA ( n = 3 ) . Primary root length , lateral root number and number of lateral roots divided by the primary root length were quantified manually . A local polynomial regression fitting with 95% confidence interval ( gray ) was used to represent the directionality distribution curve . 0° is the direction of the gravity vector . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 01710 . 7554/eLife . 07597 . 018Figure 2—source data 1 . Directionality and whole root system architecture trait values from the time series . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 01810 . 7554/eLife . 07597 . 019Video 1 . Time lapse from 11 to 21 DAS of a Col-0 plant expressing ProUBQ10:LUC2o grown in control conditions . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 019 As a proof of concept to estimate the utility of our root-imaging system to phenotype adult root system traits , we transformed a small set of accessions ( Bay-0 , Col-0 , and Sha ) with the ProUBQ10:LUC2o reporter and quantified root system architecture at 22 DAS ( Figure 3A–C , Figure 3—source data 1 ) . GLO-RIA analysis of these root systems identified several root traits that distinguish Col-0 , Bay-0 , and Sha . Directionality analysis revealed an abundance of steep-angle regions in the root system of Bay-0 , while Sha showed an abundance of shallow-angled regions and Col-0 was intermediate ( Figure 3D ) . Bay-0 shows the deepest and narrowest root system leading to the highest depth/width ratio , while Sha has the widest root system ( Figure 3E ) . Other root traits such as root system area and the vertical center of mass also showed significant differences ( Figure 3—figure supplement 1B ) . Broad-sense heritability values for depth ( 96 . 3 ) , area ( 92 . 0 ) , depth/width ( 97 . 8 ) , width ( 95 . 7 ) , and vertical center of mass ( 95 . 0 ) were all higher than 90% . 10 . 7554/eLife . 07597 . 020Figure 3 . Variation in root architecture between accessions of Arabidopsis . Representative root and shoot images of ( A ) Bay-0 , ( B ) Col-0 , and ( C ) Sha accessions transformed with ProUBQ10:LUC2o and imaged after 22 DAS . ( D ) Directionality of the root systems , ( E ) depth/width ratio , ( F ) pseudo-landmarks describing shape variation in root system architecture . Eigenvalues derived from the analysis of 9–12 plants per accession are shown . The first two principal components explaining 38% ( PC1 ) and 22% ( PC2 ) of the shape variation are plotted . PC1 captures homogeneity of root system width along the vertical axis and PC2 a combination of depth and width in top parts of the root system . Red and green lines indicate −3SD and +3SD ( Standard Deviations ) , respectively . ( G ) PC separation of the different ecotypes using the PCs described in ( F ) . A local polynomial regression fitting with 95% confidence interval ( gray ) was used to represent the directionality distribution curve . 0° is the direction of the gravity vector . Kolmogorov-Smirnov test at p < 0 . 001 showed significant differences in directionality distributions between all three accessions . Wilcoxon test analysis with p < 0 . 01 was used to test significant differences between the different accessions ( n = 9–12 plants ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 02010 . 7554/eLife . 07597 . 021Figure 3—source data 1 . Directionality , whole root system architectural trait values and shape predictors from Bay-0 , Col-0 and Sha . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 02110 . 7554/eLife . 07597 . 022Figure 3—source data 2 . Shape predictor values ( TPS format ) from Bay-0 , Col-0 and Sha used to perform PCA . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 02210 . 7554/eLife . 07597 . 023Figure 3—source data 3 . Whole root system architecture trait values from Bay-0 , Col-0 and Sha . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 02310 . 7554/eLife . 07597 . 024Figure 3—figure supplement 1 . ( A ) Root area , ( B ) vertical center of mass of Bay-0 , Col-0 , and Sha accessions . Wilcoxon test analysis with p < 0 . 01 was used to test significant differences between the accessions ( n = 9-12 plants ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 024 To capture the richness of root architecture shape , we used GLO-RIA to extract pseudo-landmarks describing the shape of the root system ( see ‘Materials and methods’ for more details ) and performed PCA analysis ( Figure 3—source data 2 ) . The first principal component captures differences in the distribution of widths along the vertical axis and separates Col-0 and Sha from Bay-0 root systems ( Figure 3F ) . Bay-0 shows a homogenous distribution of widths along the vertical axis , while Sha and Col-0 are much wider at the top than bottom . PC2 seems to be capturing a relationship between width at the top and total depth and separates Sha root systems , which are wide at the top and deep from Col-0 root systems , which are wide but not as deep as Sha . Shape information extracted from pseudo-landmarks can distinguish the three different accession using PCA analysis ( Figure 3G , Figure 3—source data 3 ) . We tested whether spectrally distinct luciferase reporters would enable additional information besides root architecture to be captured from root systems . Luciferase reporters have been commonly used to study gene expression and these resources can potentially be utilized to study such regulatory events in soil-grown roots . We transformed ProACT2:PpyRE8o into two well-studied LUC reporter lines: the reactive oxygen species response reporter ProZAT12:LUC ( Miller et al . , 2009 ) ( Figure 4A , B ) and the auxin response reporter line ProDR5:LUC+ ( Moreno-Risueno et al . , 2010 ) ( Figure 4C , D ) . We implemented in GLO-RIA an algorithm that semi-automatically identifies gene reporter signal and associates this object to the corresponding root structure segment . A graphical representation of the results obtained with RootReporter can be observed in Figure 4—figure supplement 1 ( Figure 4—source data 1 ) . Reporter intensity values along the first 5 mm of root tips can also be observed in Figure 4—figure supplement 2 ( Figure 4—source data 2 ) . 10 . 7554/eLife . 07597 . 025Figure 4 . Dual-color reporter visualization of root structure and gene expression . Images of whole-root systems ( A , D ) or magnified portion of roots ( C , F ) at 22 DAS expressing ProACT2:PPYRE8o and ProZAT12:LUC ( green , A , B , C ) or ProDR5rev:LUC+ ( green , D , E , F ) . Luminescence from PPyRE8 and LUC reporters visualized together using an open filter setting ( visualized in grey-scale ) while LUC signal is distinguished using a band-pass filter ( 517 to 567 nm , visualized as green ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 02510 . 7554/eLife . 07597 . 026Figure 4—source data 1 . Data for ProZAT12:LUC reporter gene expression in root segments extracted from a whole root system . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 02610 . 7554/eLife . 07597 . 027Figure 4—source data 2 . Luciferase intensity values from the root tip to maturation zone of ProUBQ10:LUC2o , ProZAT12:LUC and ProDR5:LUC+ . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 02710 . 7554/eLife . 07597 . 028Figure 4—source data 3 . Distances to boundary between plants . Shoot FW , root system architecture and shoot area of single and pairs of plants grown in the same rhizotron . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 02810 . 7554/eLife . 07597 . 029Figure 4—figure supplement 1 . ProZAT12:LUC intensity and root segments automatically identified with GLO-RIA . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 02910 . 7554/eLife . 07597 . 030Figure 4—figure supplement 2 . ProDR5rev:LUC+ , ProUBQ10:LUC2o , and ProZAT12:LUC intensity values along the root tip . Data were manually obtained by measuring the intensity profile of the first 0 . 5 cm from the root tip of individual lateral roots . 10 lateral roots for each reporter were measured . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 03010 . 7554/eLife . 07597 . 031Figure 4—figure supplement 3 . Images of plants at 22 DAS growing in the same rhizotron and expressing different luciferases . ( A ) Two Col-0 plants expressing ProUBQ10:LUC2o and ProACT2:PPyRE8o ( B ) Col-0 plant expressing ProACT2:PPyRE8o and Sha plant expressing ProUBQ10:LUC2o . Wilcoxon test analysis with p < 0 . 01 showed no significant differences between treatments ( n = 7-8 plants ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 03110 . 7554/eLife . 07597 . 032Figure 4—figure supplement 4 . Three reporter-based analysis of root–root–microbe interactions . ( A ) Image showing a 22 DAS ProUBQ10:LUC2o plant ( magenta ) grown in the same rhizotron with ProACT2:PpyRE8o plants ( gray ) . Plants were inoculated with Pseudomonas fluorescens CH267 ( green ) . Magnified portion of root systems colonized by P . fluorescens showing P . fluorescences ( B ) only or all three reporters together ( C ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 032 We then took advantage of our ability to constitutively express two spectrally different luciferases and imaged the overlapping root systems ( one expressing ProUBQ10:LUC2o and the other ProACT2:PPyRE8o ) . While two root systems were distinguishable using this system ( Figure 4—figure supplement 3 ) ; measurements of root system area did not reveal a significant effect on root growth when two plants were grown in the same rhizotron , compared to one; however , further studies are warranted ( Figure 4—figure supplement 3 , Figure 4—source data 3 ) . The GLO-Roots system uses non-sterile growth conditions , which allows complex biotic interactions that may affect responses to the environment . Bacteria themselves can be engineered to express luminescent reporters through integration of the LUX operon , which results in luminescence in the blue region of the spectrum and is thus compatible with the plant-expressed luciferase isoforms we have tested . Pseudomonas fluorescens CH267 ( Haney et al . , 2015 ) , a natural Arabidopsis root commensal , was transformed with the bacterial LUX operon and used to inoculate plants . 13 days after inoculation , we were able to observe bacterial luminescence colocalizing with plant roots . P . fluorescens did not show an obvious pattern of colonization at the root system scale level ( Figure 4—figure supplement 4 ) . As a proof-of-principle test of the multi-dimensional capabilities of the GLO-Roots system , we visualized both LUC2o and PPyRE8o reporters in plants and the LUX reporter in bacteria in the same rhizotron ( Figure 4—figure supplement 4 ) . To examine the general applicability of the GLO-Roots system for other species , we introduced LUC2o-expressing reporters into the model grass Brachypodium distachyon and the crop plant Lycopersicon esculentum ( tomato ) . Brachypodium is well suited to the GLO-Root system because , like Arabidopsis , its small size allows mature root systems to be studied in relatively small soil volumes ( Watt et al . , 2009; Pacheco-Villalobos and Hardtke , 2012 ) . LUC2o driven by the ZmUb1 promoter was introduced into Brachypodium using the pANIC vector ( Mann et al . , 2012 ) . Brachypodium roots showed a distinct architecture from Arabidopsis marked by prolific development of secondary and tertiary lateral roots ( Figure 8A ) . This is consistent with other studies that show that Brachypodium has a typical grass root system ( Watt et al . , 2009 ) . Comparison of root system development in rhizotrons with gel-based media showed that root growth is higher in soil than in plates ( Figure 8—figure supplement 1 , Figure 8—source data 1 ) . Previous work has suggested that auxin levels in Brachypodium roots are sub-optimal for growth ( Pacheco-Villalobos et al . , 2013 ) . Pacheco-Villalobos and colleagues suggest that , in Brachypodium , and contrary to what happens in Arabidopsis , ethylene represses YUCCA reducing the synthesis of auxin . The reduced growth that we observe in plates and the high levels of ethylene that build up in sealed plates ( Buer et al . , 2003 ) would support this mechanism . 10 . 7554/eLife . 07597 . 051Figure 8 . Roots of Brachypodium distachyon transformed with ProZmUB1:LUC2o and imaged at 15 ( A ) and 24 ( B ) DAS grown in control conditions . ( C ) Open channel of 17 DAS tomato plant transformed with ProeDR5rev:LUC2o and Pro35S:PPyRE8o . ( D ) Green channel showing only ProeDR5rev:LUC2o . ( E ) Amplification of the open and green channel showing increased expression of ProeDR5rev:LUC2o reporter in early-stage lateral roots . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 05110 . 7554/eLife . 07597 . 052Figure 8—source data 1 . Depth of Brachypodium primary roots grown in petri plates and rhizotrons . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 05210 . 7554/eLife . 07597 . 053Figure 8—figure supplement 1 . Depth of the primary root of Brachypodium plants grown in rhizotrons or on gel-based media ( n = 8–11 ) . Red dots indicate mean values . DOI: http://dx . doi . org/10 . 7554/eLife . 07597 . 053 Tomato plants were transformed with Pro35S:PPyRE8o and ProeDR5rev:LUC2 reporters . The plants showed more rapid growth than Arabidopsis or Brachypodium and required fertilizer to prevent obvious signs of stress ( reduced growth , anthocyanin accumulation ) . Root systems were imaged from 17 DAS plants . Roots showed presumptive lateral root primordia marked by DR5-expression ( Figure 8C , D ) . These results show that the GLO-Roots method can be applied to study root systems of different plant species and will likely be useful for studying root systems of other small- to medium-sized model plants and for early stages of larger crop plants .
Recent studies of root systems has emphasized structural attributes as important contributors of root system function . Indeed , studies examining the role of genetic variants in tolerating abiotic stress have demonstrated the importance of such characteristics ( Uga et al . , 2013 ) . Roots , however , are highly diverse in the biology they perform and a multi-dimensional understanding of root systems , which incorporates differences in signaling , metabolism , and microbial association as well as structure , may provide a clearer understanding of the degree to which sub-functionalization of the root system plays a role in important processes such as acclimation and efficient resource acquisition . We have developed tools in GLO-Roots that allow for tracking multiple aspects of soil physicochemical properties and root biology simultaneously . Using GLO-Roots , we are able to map in 2D coordinates the soil physical properties such as soil moisture together with root architecture traits such as directionality , growth rates , and gene expression levels . All this information is aggregated in layers for each x , y coordinate . Using GLO-RIA , we integrate this multilayer information , leveraging our ability to simultaneously and seamlessly investigate root responses to environmental stimuli such as soil-moisture content . Luciferases that emit light at different wavelengths allow for constitutive and regulated promoters to be studied together . Introduction of luciferase reporters into microbes provides an additional layer of information that is a readout of the association between organisms and how this might be affected by environmental conditions . The flexibility of the GLO-Roots system may enable additional dimensionality to our understanding of root biology . Other physical properties such as CO2 or pH mapping in rhizotrons have already been enabled by using planar optodes ( Blossfeld et al . , 2013 ) . It may be possible to engineer LUX-based reporters in microbes that are responsive to extracellular metabolites , creating microbial biosensors , and integration of such tools may enable root-exudation and nutrition to be analyzed in soil . Split-luciferase reporters have been engineered that allow bi-molecular interactions to be studied . Finally , molecular sensors analogous to Förster resonance energy transfer ( FRET ) sensors , termed bioluminescence resonance energy transfer ( BRET ) -sensors ( Shaw and Ehrhardt , 2013 ) , may allow metabolite tracking dynamically through the root system . With additional innovation in the development of luciferase reporters , the GLO-Roots system will likely expand the repertoire of biological processes that can be studied over an expanded range of developmental time points and environmental conditions . It has been proposed that plants with steep root systems will be better able to tap into deep water resources and thus perform better under water deficit . For example in rice , the IR64 paddy cultivar shows shallow root systems in upland fields , whereas Kinandang Patong , an upland cultivar , is deeper rooting ( Uga et al . , 2013 ) . Plants maintain a number of regulatory pathways that mediate changes in physiology during WD . Enhanced growth of root systems has been well characterized in field-grown plants; however , this has not been recapitulated in studies of gel-grown Arabidopsis plants . Thus , it has been unclear whether Arabidopsis simply responds to WD differently . Our results here show that Arabidopsis does indeed maintain a classical WD response that expands the root system and directs growth downward . Interestingly , under our stress regime , we did not observe a significant decrease in the relative water content of shoot tissues ( Figure 6—figure supplement 5 , Figure 6—source data 5 ) , suggesting that the changes in root architecture were sufficient to provide access to deep water and prevent dehydration . Such changes in root growth are likely regulated through systemic and local signaling that involve auxin signaling but acts independently of known pathways that control moisture-directed root growth . Understanding plant biology requires a sophisticated understanding of how environmental stimuli affect the form and function of plants as well as an understanding of how physiological context informs such responses . Environmental conditions are at least as complex as the plants they affect . Plant roots are exposed to a variety of environmental signals that change in time and space at very different scales that are integrated at the whole-plant system . It is an important challenge in biology to develop methods of growing and studying plants that present such stimuli in a manner that the plant is likely to encounter in nature . After all , the plants we study have evolved to survive through mechanisms that have been selected , over evolutionary time , in nature . It will be interesting for future studies to determine how other environmental stimuli affect root growth using GLO-Roots and whether these responses differ between accessions of Arabidopsis . Identification of the genetic loci responsible for phenotypic variation in adult root phenotypes may identify the molecular basis for adaptive variation that exists in this species and potentially identify loci that are useful for breeding efforts needed for the next green revolution .
Seeds were surface sterilized as described before ( Duan et al . , 2013 ) and grown in rhizotrons , 100 cm3 pots , or on two types of 1% agar ( Difco , Becton , Dickinson and Company , Franklin Lakes , NJ ) media containing either 1× MS nutrients ( Caisson Labs , Smithfield , UT ) and 1% Sucrose , ( termed ms media ) or ¼× MS nutrients only ( termed ms25 media ) . Both media were buffered using 0 . 5 g/l MES and pH was adjusted to 5 . 7 with KOH . All plants were grown together in a growth cabinet with LED lights under long-day conditions ( 16-hr day/8-hr night ) . Root and shoot tissue was collected separately from individual plants at the end of the day ( 1 hr before the lights shut off ) and at the end of the night ( 1 hr before lights came on ) . Three biological replicates were collected for each condition . RNA was extracted using the Plant RNA MiniPrepTM kit ( ZYMO Research , Irvine , CA ) according to manufacturer's instructions with on-column DNase treatment ( Qiagen ) . cDNA was made using the iScript Advanced cDNA Synthesis for RT-qPCR kit ( Bio-Rad ) from 200 ng of total RNA . qRT-PCR was performed using a Fluidigm BioMarkTM 96 . 96 Dynamic Array IFC with the EvaGreen ( Bio-Rad ) fluorescence probe according to the Fluidigm Advanced Development Protocol number 37 . For the analysis , all the reactions with no amplification ( Ct = 999 ) were set to the maximal Ct for that assay type . The two technical replicates were then averaged and dCt values calculated using AT3G07480 , AT4G37830 , At1g13320 , and At1g13440 as reference internal controls . PCA plots were generated with Devium Web ( Dmitry Grapov , 2014 ) using dCt values . dCT values were calculated as dCT = CT∼gene interest∼ − mean ( CT∼reference gene∼ ) . Primers used are listed in Supplementary file 2 .
Root systems develop different root types that individually sense cues from their local environment and integrate this information with systemic signals . This complex multi-dimensional amalgam of inputs enables continuous adjustment of root growth rates , direction , and metabolic activity that define a dynamic physical network . Current methods for analyzing root biology balance physiological relevance with imaging capability . To bridge this divide , we developed an integrated-imaging system called Growth and Luminescence Observatory for Roots ( GLO-Roots ) that uses luminescence-based reporters to enable studies of root architecture and gene expression patterns in soil-grown , light-shielded roots . We have developed image analysis algorithms that allow the spatial integration of soil properties , gene expression , and root system architecture traits . We propose GLO-Roots as a system that has great utility in presenting environmental stimuli to roots in ways that evoke natural adaptive responses and in providing tools for studying the multi-dimensional nature of such processes .
Most plants absorb water and nutrients from the soil via structures called roots . The shape , size , and structure of a plant's root system can change over its lifetime as the plant responds to changes in their local environment . For example , if water is scarce , a plant may develop a very deep root system that is more efficient at capturing water . Understanding how root systems respond to environmental cues may help us to identify the genes and processes involved . In this study , Rellán-Álvarez et al . report a new live-imaging platform for analyzing root architecture and its regulation . This platform is called Growth and Luminescence Observatory for Roots ( or GLO-Roots for short ) and uses ‘luminescent’ markers that allow growing roots to be visualized when plants are grown in thin , soil-filled , transparent pots . GLO-Roots can track the growth of the plant roots as well as the activity of genes that respond to environmental stress . Rellán-Álvarez et al . developed a software tool called GLO-RIA ( GLO-Roots Image Analysis ) to analyze the resulting images . GLO-RIA performs several different types of image analysis , including one that detects the position , length , and direction of roots , as well as their shape and depth . Rellán-Álvarez et al . tested the GLO-Roots techniques in various ways , for example , by analyzing the effects that different conditions have on the growth of the roots of the model plant known as Arabidopsis thaliana . Depriving the plants of a nutrient called phosphorous caused the roots to grow more horizontally than when phosphorus is plentiful , presumably to allow the plants to expand their search for phosphate in the upper layers of the soil , where this nutrient is usually more abundant . On the other hand , a shortage of water caused the roots to grow more vertically to access water stored deeper in the soil . GLO-Roots can also be used to measure the water content of soil at different depths and how this influences the architecture of the root . Further experiments on tomato plants and a grass species called Brachypodium distachyon revealed the different architectures of their root systems . Rellán-Álvarez et al . propose that this system could be used to study the roots of other plant species in a variety of environmental conditions . This will provide a more detailed understanding of the ways that different plants adapt in response to changes in their environment .