Schiedea PTR-MS Experiments
John Powers, Celia Faiola, Roger Seco, Alex Guenther, Ann Sakai, Steve Weller, Diane Campbell
2018-02-06
Overview
Here we investigate temporal patterns in floral volatile emissions in Schiedea hookeri, Schiedea kaalae, and their hybrid. Prior GC-MS analysis and the human nose suggest differences in total volatiles output and composition between broad day and evening sampling periods. PTR-MS allows quantification of these volatile emission rates in real time. This report includes:
- photosynthetic parameters inferred from carbon dioxide and water fluxes using an infrared gas analyzer (IRGA)
- timeseries of individual ion peaks: small molecules, known top compounds from each species, other ions of interest
- a clustering analysis of timeseries for all peaks to group ions by their emission patterns
The PTR-MS produces ions via proton transfer from hydronium ions (H3O+). The ions are transfered from the source into a drift tube and a time-of-flight mass spectrometer. Analytes undergo soft chemical ionization and do not intiailly fragment, but due to the electric field, ions may fragment in the drift tube. Mono- and sesquiterpenes are known to fragment depending on the strength of this field (Faiola et al., in prep).
A PTR-Quadrupole (actual instrument used is a PTR-TOF)
First moth visits
vst <- read.table("firstvisits.csv", header=T, sep="\t", colClasses=c(firstmoth="POSIXct"))
library(maptools)
ekahanui <- matrix(c(-158.088214, 21.440004), nrow=1)
vst$sunsets <- do.call("c", lapply(lapply(vst$firstmoth, sunriset, crds=ekahanui, direction="sunset", POSIXct.out=T), function(x) x$time))
attr(vst$sunsets, "tz") <- Sys.timezone()
vst$sunsets <- as.POSIXct(format(vst$sunsets, tz="US/Hawaii", usetz=TRUE))
vst$firstmothsunset <-vst$firstmoth - vst$sunsets
vst$solarnoons <- do.call("c", lapply(lapply(vst$firstmoth, solarnoon, crds=ekahanui, POSIXct.out=T), function(x) x$time))
attr(vst$solarnoons, "tz") <- Sys.timezone()
vst$solarnoons <- as.POSIXct(format(vst$solarnoons, tz="US/Hawaii", usetz=TRUE))
vst$firstmothnoon <- vst$firstmoth - vst$solarnoons
kable(vst)| firstmoth | sunsets | firstmothsunset | solarnoons | firstmothnoon |
|---|---|---|---|---|
| 2014-03-03 17:48:00 | 2014-03-03 18:37:31 | -49.51667 mins | 2014-03-03 12:44:11 | 5.063611 hours |
| 2014-03-04 18:10:00 | 2014-03-04 18:37:54 | -27.90000 mins | 2014-03-04 12:43:58 | 5.433889 hours |
| 2014-03-05 18:00:00 | 2014-03-05 18:38:17 | -38.28333 mins | 2014-03-05 12:43:44 | 5.271111 hours |
| 2014-07-03 17:58:00 | 2014-07-03 19:18:48 | -80.80000 mins | 2014-07-03 12:36:39 | 5.355833 hours |
| 2015-02-16 17:51:00 | 2015-02-16 18:30:45 | -39.75000 mins | 2015-02-16 12:46:25 | 5.076389 hours |
| 2015-02-17 17:50:00 | 2015-02-17 18:31:15 | -41.25000 mins | 2015-02-17 12:46:21 | 5.060833 hours |
| 2015-02-18 17:56:00 | 2015-02-18 18:31:44 | -35.73333 mins | 2015-02-18 12:46:16 | 5.162222 hours |
| 2015-02-19 17:48:00 | 2015-02-19 18:32:13 | -44.21667 mins | 2015-02-19 12:46:10 | 5.030556 hours |
| 2015-02-21 17:52:00 | 2015-02-21 18:33:09 | -41.15000 mins | 2015-02-21 12:45:57 | 5.100833 hours |
| 2016-02-09 17:47:00 | 2016-02-09 18:26:54 | -39.90000 mins | 2016-02-09 12:46:34 | 5.007222 hours |
qplot(as.numeric(vst$firstmothsunset), xlim=c(-100,0), binwidth=10, xlab="Minutes until sunset",ylab="Count (n=10)", main="First Pseudoschankia brevipalpis visits to S. kaalae flowers at ‘Ēkahanui Gulch") +theme_bw()
Experimental
sp <- read.delim("Schiedea_Fluxtron_20171106_sync.csv")
rownames(sp) <- paste0(sp$Species, sp$Experiment, sp$Cuvette)
nexp <- length(unique(sp$Experiment))
exper <- !duplicated(sp$Experiment)
spcol <- setNames(c("grey20",brewer.pal(8, "Set1"), brewer.pal(3, "Dark2"))[c(1,3,2,6,5,8,9,4,10)], rownames(sp))
cscale <- scale_color_manual("Species",values=spcol)
cguide <- guides(color = guide_legend(override.aes = list(size=3)))
tzone <- "Etc/GMT+8"
tfrmt <- "%Y-%m-%d %H:%M:%S"
sp$start <- as.POSIXct(strptime(sp$start, format=tfrmt, tz=tzone))
sp$lightoff <- as.POSIXct(strptime(sp$lightoff, format=tfrmt, tz=tzone))
tp <- mapply(seq.POSIXt, from=sp$start, length.out=sp$steps, MoreArgs=list(by="sec")) # changed starts in file to be the same date!
lights <- mapply(seq.POSIXt, from=sp$lightoff[exper], length.out=sp$changes[exper], MoreArgs=list(by=12 * 60 * 60))
tscale <- lapply(lights, scale_x_datetime, name="Time", date_minor_breaks="1 hour", date_labels="%H:%M",expand = c(0, 0))
rects <- lapply(1:nexp, function(i) {data.frame(xstart = c(sp$start[exper][i], lights[[i]]), xend = c(lights[[i]], tail(tp[exper][[i]], 1)))})
rects <- lapply(rects, function(x) cbind(x,Period=rep_len(c("day","night"), nrow(x)),stringsAsFactors=F))
grect <- lapply(rects, function(x) geom_rect(data=x, mapping=aes(xmin = xstart, xmax = xend, ymin = -Inf, ymax = Inf, fill = Period), alpha = 0.4, inherit.aes = FALSE))
greenhouse <- matrix(c(-117.8475746, 33.6472914), nrow=1)
sp$sunrise <- sapply(tp, function(x) {sunriset(crds=greenhouse, dateTime=x[1]-24*60*60, direction="sunrise", POSIXct.out=T)$time} )
sp$sunset <- sapply(tp, function(x) {sunriset(crds=greenhouse, dateTime=x[1]-24*60*60, direction="sunset", POSIXct.out=T)$time} )
attributes(sp$sunrise) <- attributes(sp$sunset) <- attributes(tp[[1]])Three plants with open flowers were selected from greenhouse (one of each type). Plants were all watered prior to sampling and placed in a photochamber set to 23 C and with 60% relative humidity. During the photoperiod, plants received about 450 µmol/(m2 s) PAR (see table) at the top of the inflorescence from LED lights (c.f. direct sunlight = 2,000 µmol/(m2 s) PAR).
Flowering inflorescences from each plant were supported with a stake and placed in “cuvettes” made of nylon-6 oven bags (dimensions 40.6 cm x 44.4 cm). An empty bag was used as a blank (cuvette #2). Each cuvette recieved dry zero air at the bottom of the bag from a zero-air generator (critical orifice Reynolds number 33, 25 psi, 12 L/min of air combined to the 4 cuvettes). Air was sampled from the top of the bag. Each cuvette contailed a thermocouple to measure temperature in the bag. The bag was sealed with a twist-tie around the pedicel, input and sampling lines, and thermocouple.
All times are PST (UTC-8). The experiment spanned hours, from mbient sunrise and sunset times the day prior were
The Fluxtron switched gas flow to the PTR-MS between each cuvette every 4 min, and flow to the IRGA between the blank cuvette and sampled cuvette every 20 s. The IRGA measured CO2 concentration and temperature. The Ionicon PTR-TOF held the settings below for volatiles analysis.
Plants
kable(sp[,1:14], row.names=F)| Batch | Experiment | Cuvette | Species | Population | ID | Sex | Leaves | Inflor | Mphase | Fphase | Closed | Buds | Flrs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2017-08-09 | 0 | 1 | KH | 3587-7 x 879-10-1 | 189-3 | H | 2 | 1 | 6 | 29 | 29 | 50 | 35 |
| 2017-08-09 | 0 | 3 | hookeri | 891 | 3-11 | H | 8 | 8 | 4 | 24 | 153 | 30 | 28 |
| 2017-08-09 | 0 | 4 | kaalae | 3587-A x 3587-14 | 229-3 | H | 0 | 3 | 15 | 39 | 114 | 83 | 54 |
| 2017-11-06 | 1 | 1 | kaalae | 904-2 x 904-5 | 158-3 | H | 0 | 1 | 2 | 3 | 203 | 8 | 5 |
| 2017-11-06 | 1 | 3 | kaalae | 904-2 x 904-5 | 158-5 | H | 0 | 1 | 1 | 51 | 136 | 89 | 52 |
| 2017-11-06 | 1 | 4 | kaalae | 904-5 x 904-3 | 184-18 | H | 0 | 2 | 17 | 46 | 377 | 220 | 63 |
| 2017-11-06 | 2 | 1 | kaalae | 3587 | 6-1 | H | 0 | 1 | 0 | 18 | 290 | 8 | 18 |
| 2017-11-06 | 2 | 3 | hookeri | 866 | 2 | H | 12 | 4 | 3 | 16 | 51 | 17 | 19 |
| 2017-11-06 | 2 | 4 | hookeri | 879 | 3-8 | H | 0 | 1 | 0 | 9 | 32 | 11 | 9 |
PTR-MS parameters
| Parameter | Value |
|---|---|
| MCP | 2100 V |
| inlet temperature | 60 C |
| inlet flow | 140 sccm* |
| H2O flow | 5 sccm |
| ion source current | 3 mA |
| drift tube temperature | 60 C |
| drift tube pressure | 2.8 mbar |
| Us | 150 V |
| Uso | 80 V |
| Udrift | 600 V |
| Ufunnel | 30 V |
| Uout | 7 V |
| Urf | 16 V |
Photosynthesis parameters
| Variable | Units | Description |
|---|---|---|
| Anet | (mol CO2)/(m2 s) | net CO2 assimilation rate |
| E | (mol H2O)/(m2 s) | water transpiration rate |
| Gsw | (mol H2O)/(m2 s) | stomatal conductance to water vapor |
| Gbw | (mol H2O)//(m2 s) | boundary layer conductance to water vapor |
| Gtw | (mol H2O)/(m2 s) | total conductance to water vapor |
| Ci | (mol CO2)/(mol air) | intercellular CO2 concentration |
| Gtc | (mol CO2)/(m2 s) | total conductance to CO2 |
| Ue | mol/s | standardized flow of air entering the cuvette |
| SVP | kPa | saturated water vapor pressure at Tleaf |
| Wl | (mol H2O)/(mol air) | molar concentration of water vapor within the leaf |
Environmental
Other data recorded by the fluxtron: * Flow to each cuvette (SLPM) * PAR light intensity (umol/m2/s) * CO2 concentration (ppm) * temperature in each cuvette (K)
flow.files = setNames(lapply(levels(sp$name), function(x) { all <- paste0(x, "/", dir(paste0(path, x))); paste0(path,all[grepl("Fluxtron2",all)]) }), levels(sp$name))
flow.cols <- colnames(fread(flow.files[[1]][1], header=T, sep=",", nrows=0))
read.flow <- function(file) {
fread(file, header=F, sep=",", skip=2, colClasses="numeric", col.names=flow.cols)
}
flow <- lapply(flow.files, function(x) { rbindlist(lapply(x,read.flow)) })
makeblocks <- function(x) { cbind(x, block=rep(1:nrow(x), each = 60, len=nrow(x))) }
flow <- lapply(flow, makeblocks)
flow <- lapply(flow, avgblocks)
conv.tzone <- function(x) { x$tp <- as.POSIXct(x$TS_Labview_UTC, origin = "1904-01-01", tz = "GMT")+1; x }
flow <- lapply(flow, conv.tzone)
flow <- lapply(flow, fixtz)
aligndays <- lapply(flow, function(x) { floor(difftime(x$tp[1], flow[[1]]$tp[1], "days")) })#assumes that the first file is the start day of the experiment
aligndates <- function(x, i) { x$tp <- x$tp - as.numeric(aligndays[i])*24*60*60; x}
flow <- mapply(aligndates, x=flow, i=1:3, SIMPLIFY=FALSE)flow.plot <- function(fc) {
flow.plotvars <- setNames(flow.cols[fc], flow.cols[fc])
mflow <- lapply(flow, melt, id.vars="tp")
mmflow <- do.call(rbind, lapply(names(mflow), function(x){data.frame(id=x, mflow[[x]])}))
mmflow <- mmflow[mmflow$variable %in% names(flow.plotvars),]
ggplot(mmflow, aes(x=tp, y=value, color=id)) + facet_wrap(~ variable, scales="free_y", ncol=1, labeller=labeller(variable=flow.plotvars)) + cguide + scale_fill_manual(values=c("day"="grey60", "night"="grey40")) + geom_line(size=0.5, alpha=1) + tscale[[3]] + grect[[3]]
}
flow.plot(32:39) + ylim(1.4,2.8)#flows
flow.plot(28:31) + ylim(293,300) #temps
flow.plot(40:41)
Results
IRGA
Photosynthesis plots
irga.files = setNames(paste0(path, sp$name, "/FTirgaFluxc", sp$Cuvette, ".txt"), rownames(sp))
read.irga <- function(file, timepoints) {
fixtz(medianblocks(blockjumps(na.omit(cbind(fread(file, header=T, sep="\t", skip=2, colClasses="numeric"), tp=timepoints)))))
}
irga <- mapply(read.irga, file=irga.files, timepoints=tp, SIMPLIFY=F, USE.NAMES=T)
#normailze by number of flowers
irga.normvars <- c("Anet","E","Gsw","Gbw","Gtw","Gtc")
irga.normalize <- function(x,flrs){x[,irga.normvars] <- x[,irga.normvars]/flrs; return(x)}
irgan <- mapply(irga.normalize, irga, sp$Flrs, SIMPLIFY=F)
irga.plotvars <- c("Anet"="Anet: net CO2 assimilation rate",
"E"="E: water transpiration rate",
"Ue"="Ue: standardized flow of air entering the cuvette")
mirga <- lapply(irgan, melt, id.vars="tp")
mmirga <- do.call(rbind, lapply(names(mirga), function(x){data.frame(id=x, mirga[[x]])}))
mmirga <- mmirga[mmirga$variable %in% names(irga.plotvars),]Calculated photosynthetic parameters Anet and E normalized by the number of flowers, and the flow of air entering the cuvette Ue.
irgaplot <- ggplot(mmirga, aes(x=tp, y=value, color=id)) + facet_wrap(~ variable, scales="free_y", ncol=1, labeller=labeller(variable=irga.plotvars)) + cscale + cguide + scale_fill_manual(values=c("day"="grey60", "night"="grey40"))
irgaplot + tscale[[3]] + grect[[3]] + geom_line(size=0.5, alpha=1)
#for(i in 1:nexp) {
#ip <- irgaplot %+% subset(mmirga, id %in% rownames(sp[sp$Experiment==i-1,])) + tscale[[i]] + grect[[i]] + geom_line(size=0.5, alpha=1)
#plot(ip)
#}Photosynthesis discussion
Exp 0. Increase in noise at 4 am PST (Why 3 am in figure?) 17/08/10 . The CO2 concentration started to oscillate, with a markedly serrated pattern (compare the left-hand side of the graph to the right-hand side (black line is the signal from all cuvettes, the blue line is the blank coming from c2, and the red is the signal from c4).
Anet : net CO2 assimilation rate
- Infloresences were net photosynthetic during the day, emitted CO<sub2~ through respiration at night
- After normalizing by number of open flowers, Anet is similar
- Responses <30 min
E: water transpiration rate
- Water transpiration was higher during the day than night - opened stomata?
- Responses <30 min
Ue: standardized flow of air entering the cuvette
- S. kaalae flow is 30% higher than other two cuvettes - is this accounted for?
PTR-MS ion timecourses
ncps.files = setNames(paste0(path, sp$name, "/FTncpsFluxc", sp$Cuvette, ".txt"), rownames(sp))
read.ncps <- function(file, timepoints) {
fixtz(medianblocks(blockjumps(na.omit(cbind(Filter(function(x) !all(is.na(x)), fread(file, header=T, sep="\t", skip=2, colClasses="numeric")), tp=timepoints)))))
}
#ncps <- mapply(read.ncps, file=ncps.files, timepoints=tp, SIMPLIFY=F, USE.NAMES=T)
load("ncps.Rdata")
library(lubridate)
remove.pib <- function(x) {
dec <- hour(x$tp)+minute(x$tp)/60
x$pib <- (dec > 16.9 & dec < 17.25) | (dec > 4.9 & dec < 5.2)
x
}
ncps <- lapply(ncps, remove.pib)
#Weird blips
ncps[["kaalae04"]]$pib[ncps[["kaalae04"]]$tp == as.POSIXct("2017-08-09 22:20:32.5", tz=tzone)] <- TRUE
ncps[["kaalae11"]]$pib[ncps[["kaalae11"]]$tp == as.POSIXct("2017-08-12 04:47:46.5", tz=tzone)] <- TRUE
ncps[["kaalae13"]]$pib[ncps[["kaalae13"]]$tp == as.POSIXct("2017-08-11 06:31:49.5", tz=tzone)] <- TRUE
ncps[["kaalae14"]]$pib[ncps[["kaalae14"]]$tp == as.POSIXct("2017-08-09 15:55:53.5", tz=tzone)] <- TRUE
ncps[["kaalae14"]]$pib[ncps[["kaalae14"]]$tp == as.POSIXct("2017-08-12 04:43:46.5", tz=tzone)] <- TRUE
noncount <- c("tp","block", "Group.1", "pib")
ncps.normalize <- function(x,flrs){x[,!colnames(x) %in% noncount] <- x[,!colnames(x) %in% noncount]/flrs; return(x)}
nncps <- mapply(ncps.normalize, ncps, sp$Closed+sp$Flrs, SIMPLIFY=F) #Norm by closed and open flowers - makes a difference for scales and thresholds
mncps <- lapply(nncps, melt, id.vars=c("tp","pib"))
mncps <- do.call(rbind, lapply(rownames(sp), function(x){data.frame(id=x, mncps[[x]])}))
#mncps$id <- factor(mncps$id,levels(mncps$id)[c(2,3,1)])
mdncps <- mncps
mdncps$day <- format(mncps$tp,"%d")
mdncps$tp <- as.POSIXct(paste("2017-08-10 ", format(mncps$tp,"%H:%M:%S")), tz=tzone)frmla <- read.table("formulas.csv", sep="\t", header=T, stringsAsFactors=F, colClasses=c("factor"="factor", "cycle"="factor"))Top volatiles
lineplot <- function(plotpeaks) {
ggplot(mncps[mncps$variable %in% names(plotpeaks) & !mncps$pib,], mapping=aes(x=tp, y=value, color=id)) + facet_wrap(~ variable, scales="free_y", ncol=1, labeller=labeller(variable=plotpeaks)) + tscale[[3]] + cscale + cguide + scale_fill_manual(values=c("day"="grey90", "night"="grey80")) + grect[[3]] + geom_line(size=0.5, alpha=1) }
dailyplot <- function(plotpeaks) {
ggplot(mdncps[mdncps$variable %in% names(plotpeaks) & !mncps$pib,], mapping=aes(x=tp, y=value, color=id, linetype=day)) + scale_linetype_manual(name="Day", values = c("longdash", "solid", "twodash","dotted", "dotdash")) + facet_wrap(~ variable, scales="free_y", ncol=1, labeller=labeller(variable=plotpeaks)) + tscale[[1]] + cscale + cguide + scale_fill_manual(values=c("day"="grey60", "night"="grey40")) + grect[[1]] + geom_line(size=0.5, alpha=1) }
HOOKpeaks <- c("129.127"="129: 1-octen-3-ol / 3-octanone / 2,3-heptanedione : HOOK 29.3%, KAAL 13.8%, KAHO 34.7%",
"101.096"="101: 3-hexen-1-ol : HOOK 27.9%, KAAL 0.2%, KAHO 6.3%",
"163.123"="163: nearC11H14O unknown : HOOK 15.3%, KAAL 0.0%, KAHO 0.7%",
"107.049"="107: benzaldehyde : HOOK 5.2%, KAAL 0.9%, KAHO 0.7%")
KAALpeaks <- c("169.122"="169: linalool ketone (pyranoid): HOOK 1.3%, KAAL 39.3%, KAHO 37.8%",
"171.138"="171: linalool oxide (pyranoid): HOOK 0.2%, KAAL 17.4%, KAHO 3.6%",
"243.159"="243: Linalool oxide (furanoid) ethyl carbonate: HOOK 0.2%, KAAL 13.9%, KAHO 3.0%",
"121.065"="121: phenylacetaldehyde : HOOK 0.4%, KAAL 5.9%, KAHO 0.2%",
"137.132"="137: α-phellandrene / monoterpenes : HOOK 0.1%, KAAL 2.6%, KAHO 0.4%")
MYSTpeaks <- c("163.123"="163: Unknown benzenoid","92.062"="92: Unknown benzenoid fragment 1","120.081"="120 Unknown benzenoid fragment 2")
lineplot(HOOKpeaks)
lineplot(KAALpeaks)
lineplot(MYSTpeaks)
Stacked plots
dailyplot(HOOKpeaks)
dailyplot(KAALpeaks)
All ions
ncpsp <- lapply(ncps, function(x) {x[,!names(x) %in% noncount]})
frmlas <- setNames(trimws(substr(paste(frmla$mass, frmla$factor, frmla$cycle, frmla$compound, frmla$common), 1, 40)), colnames(ncpsp[[1]]))
rownames(frmla) <- colnames(ncpsp[[1]])
ncps_plot <- function(pl) {
trange <- !ncps[[pl]]$pib
prange <- 31:ncol(ncpsp[[pl]])
ncpscut <- ncpsp[[pl]][trange,prange]
ncpscut <- ncpscut[,colMax(ncpscut)>threshold]
ncpscut[ncpscut<0] <- 0
ncpscut <- log(ncpscut+1)
ncpscut.s <- divmax(ncpscut)
ncpscut.st <- t(as.matrix(ncpscut.s))
ncpscut.st3 <- ncpscut.st
ncpscut.tp <- ncps[[pl]]$tp[trange]
ncpscut.frmlas <- frmlas[colnames(ncpscut)]
ncpscut.frmlas <- aggregate(ncpscut.frmlas~round(mass), paste, collapse="/", data=frmla[colnames(ncpscut),])
wmass <- factor(round(frmla[colnames(ncpscut),"mass"]))
ncpscut.st <- as.data.frame(t(ncpscut.s))
ncpscut.st <- aggregate(. ~ wmass, mean, data=ncpscut.st)
rownames(ncpscut.st) <- ncpscut.st$wmass
ncpscut.st$wmass <- NULL
ncpscut.t <- as.data.frame(t(ncpscut))
ncpscut.t <- aggregate(. ~ wmass, mean, data=ncpscut.t)
rownames(ncpscut.t) <- ncpscut.t$wmass
ncpscut.t$wmass <- NULL
colside_max <- range01(rowMax(ncpscut.t)^(1/16))
sidecols <- colorRampPalette(c("white", "purple"))(512)[round(colside_max*511)+1]
xhclust <- function(data) { hclust(data, method="mcquitty") }
xdist <- function(data) { dist(data, method="DTW") }
#heatmap(as.matrix(ncpscut.st), Colv=NA, col=topo.colors(512), hclustfun=xhclust, distfun=xdist, labRow=rownames(ncpscut.st), labCol=NA, RowSideColors=sidecols, margins=c(5,18))
#rownames(ncpscut.st)<-ncpscut.frmlas[,2]
colnames(ncpscut.st)<-ncpscut.tp
hm <- heatmaply(ncpscut.st, k_row = 7, Colv=NA, hclustfun=xhclust, distfun=xdist, RowSideColors=sidecols, row_dend_left=F, cexRow=0.6, showticklabels = c(F,T), margins=c(1,50,NA,0), label_names=c("ion","time","fracmax"), main=with(sp[pl,],paste("Exp",Experiment," Cuv",Cuvette,":",Species,Population,":",Closed, "closed,",Flrs,"open flowers")))
layout(hm)
}
threshold <- 1e-4
#TODO: time markersHybrid
ncps_plot(1)S. hookeri
ncps_plot(2)ncps_plot(8)ncps_plot(9)S. kaalae
ncps_plot(3)ncps_plot(4)