JIP-test和主成分分析(PCA)在植物光合作用研究中的應(yīng)用

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1.快速葉綠素?zé)晒庹T導(dǎo)動力學(xué)分析(JIP-test)

近二十年來，基于“生物膜能量通量理論”的活體快速葉綠素 a 熒光誘導(dǎo)動力學(xué)OJIP曲線和JIP-test分析，由于其無損、精確、快速等特性，已被廣泛而成功地用做研究植物生理狀態(tài)的有力工具(Strasser et al.,1995, 2004)。植物快速葉綠素?zé)晒庹T導(dǎo)曲線（OJIP曲線）中包含著大量關(guān)于PSⅡ反應(yīng)中心原初光化學(xué)反應(yīng)的信息，植物在不同脅迫處理后OJIP曲線會發(fā)生特異性變化(Strasser et al., 2004)。
OJIP曲線對不同的環(huán)境變化極為敏感，例如光脅迫、化學(xué)物質(zhì)影響、熱脅迫、低溫或凍害、干旱脅迫、重金屬或鹽脅迫、營養(yǎng)不良、大氣CO₂或臭氧升高和病害。通過對曲線熒光參數(shù)的分析，可以知道在環(huán)境因子影響下植物光合機(jī)構(gòu)的變化。

表1.JIP-test在各種植物脅迫研究中的舉例

• 不同環(huán)境脅迫JIP-test應(yīng)用文獻(xiàn)目錄請移步至“漢莎科技集團(tuán)”微信公眾號底部“技術(shù)支持” → “文獻(xiàn)目錄” → “植物效率”

從動力學(xué)曲線上可以得到大量的原始數(shù)據(jù)，為了能更好地反映動力學(xué)曲線和被測樣品的關(guān)系，Strasser RJ(1995)以生物膜能量流動為基礎(chǔ)，通過計(jì)算能量流和能量比率來衡量在給定物理狀態(tài)下樣品材料內(nèi)部變化，建立了高度簡化的能量流動模型圖。

 

圖1. 高度簡化的能量在光合器官中的流動模型圖(Strasser BJ, Strasser RJ, 1995)

依照能量流動模型，天線色素(Chl)吸收的能量(Absorption, ABS)的一部分以熱能和熒光(F)的形式耗散掉，另一部分則被反應(yīng)中心(Reaction Centre, RC，在JIP-test中RC指有活性的反應(yīng)中心)所捕獲(Trapping, TR)，在反應(yīng)中心激發(fā)能被轉(zhuǎn)化為還原能，將Q_A還原為Q_A^-，后者又可以被重新氧化，從而產(chǎn)生電子傳遞(electron transport,ET)，把傳遞的電子用于固定CO₂或其它途徑。

在此基礎(chǔ)上發(fā)展起來的數(shù)據(jù)處理稱為“JIP-test”(Strasser etal. 1995; Krüger et al. 1997; Strasser et al. 2000, 2004)。JIP-test為我們提供了被測樣品的大量信息，如光合器官在不同環(huán)境條件下的結(jié)構(gòu)和功能的變化(Strivastava & Strasser1996; Jiang et al. 2003; Hermans et al. 2003; van Heerden et al. 2003, 2004)。

圖2. 葉綠素?zé)晒庀嚓P(guān)聯(lián)合作者網(wǎng)絡(luò)（注意R.Strasser和R.J.Strasser是同一個人）。從黃色到紅色，協(xié)作性更強(qiáng)，中心性更高(K. HU et al, 2020)
學(xué)術(shù)界對JIP-test方法的研究和應(yīng)用熱度在不斷增加，而對脈沖調(diào)制式(PAM)方法的興趣在逐漸減弱。這是什么意思？乍一看，一個可能的解釋是源于對OJIP動力學(xué)實(shí)驗(yàn)測量可用性的增加，主要是因?yàn)椋?）研究者有新的熒光檢測方法可用，2）JIP-test已明顯證明是基于半經(jīng)驗(yàn)合理假設(shè)的穩(wěn)健分析工具(robust analysis tool based on semi-empiricalreasonable assumptions)。

圖3：Strasser教授和Hansatech初代PEA植物效率分析儀（Rodriguez, 2000年)

由Reto J.Strasser教授發(fā)明授權(quán)英國Hansatech公司生產(chǎn)的PEA植物效率分析儀系列產(chǎn)品(Handy PEA、M-PEA...)是目前世界上可以真實(shí)測定OJIP曲線的成熟商品化設(shè)備。近20年來，JIP-test方法的不斷發(fā)展及其在野外應(yīng)用和實(shí)驗(yàn)室研究中的應(yīng)用呈現(xiàn)出顯著的增長趨勢。
近期發(fā)表文章《能量流理論慶祝40年：走向系統(tǒng)生物學(xué)概念？》(The energy flux theory celebrates 40 years: toward a systems biology concept?" Photosynthetica, April 2019, 57(2):521-522.)詳細(xì)闡述了這一研究熱點(diǎn)趨勢。
2019年末國際光合作用研究雜志(Photosynthetica)推出榮耀特刊，刊發(fā)30余篇榮耀文章以表彰紀(jì)念Strasser教授在JIP-test理論方向做出的卓越貢獻(xiàn)。

榮耀特刊文獻(xiàn)預(yù)覽及下載請點(diǎn)擊以下鏈接文章：

• 國際光合作用研究Photosynthetica推出特別刊以表彰Reto J. Strasser教授在快速葉綠素?zé)晒庋芯康淖吭截暙I(xiàn)

2.主成分分析(PCA)簡介

主成分分析(Principal Components Analysis)也稱主分量分析，旨在利用“降維”的思想，把多指標(biāo)轉(zhuǎn)化為少數(shù)幾個綜合指標(biāo)。在許多研究領(lǐng)域中，通常需要對含有多個變量的數(shù)據(jù)進(jìn)行觀測，收集大量數(shù)據(jù)后進(jìn)行分析尋找規(guī)律。多變量大數(shù)據(jù)集為研究提供了豐富的信息，而在多數(shù)情況下，許多變量之間可能存在相關(guān)性，從而增加了問題分析的復(fù)雜性。
如果分別對每個指標(biāo)進(jìn)行分析，分析往往是孤立的，不能完全利用數(shù)據(jù)中的信息，因此盲目減少指標(biāo)會損失很多有用的信息，從而產(chǎn)生錯誤的結(jié)論。鑒于各變量之間存在一定的相關(guān)關(guān)系，因此可以考慮將關(guān)系緊密的變量變成盡可能少的新變量，使這些新變量是兩兩不相關(guān)的，那么就可以用較少的綜合指標(biāo)分別代表存在于各個變量中的各類信息。
主成分分析PCA就屬于這類降維算法，將高維度的數(shù)據(jù)保留下最重要的一些特征，去除噪聲和不重要的特征，從而實(shí)現(xiàn)提升數(shù)據(jù)處理速度的目的。

圖4a. 數(shù)據(jù)點(diǎn)降維的信息損失與矯正：X軸投影

如何降維？我們以簡單的二維轉(zhuǎn)一維為例，如圖4中就是把二維平面上不同位置上的點(diǎn)投影到同一條直線上（X軸或Y軸）。但是仔細(xì)觀察前兩個圖，我們就會發(fā)現(xiàn)，有些點(diǎn)在投影過后，位置是重合的，也就是說，存在不同的點(diǎn)在壓縮過后表示的信息是完全一樣的，投影到x軸，有兩個點(diǎn)重合，投影到y(tǒng)軸，有三個點(diǎn)重合。

 

圖4b. 數(shù)據(jù)點(diǎn)降維的信息損失與矯正：Y軸投影

這就是當(dāng)所有點(diǎn)集中至一條軸上時，另一維度或另一軸上的信息就會丟失，這是不可逆的過程，這一信息的損失也是必然的。這不是我們想要的結(jié)果，最終我們還是希望點(diǎn)與點(diǎn)之間間隔盡可能的遠(yuǎn)，保留的信息盡可能的多，讓所有的點(diǎn)能夠盡可能的進(jìn)行區(qū)分。

 

圖4c. 數(shù)據(jù)點(diǎn)降維的信息損失與矯正：X/Y軸矯正

最好的結(jié)果應(yīng)該是我們依然選擇了某個直線，并把點(diǎn)投影到這條直線上，但是點(diǎn)之間沒有重合，點(diǎn)與點(diǎn)的間隔也比較遠(yuǎn)�？吹竭@里，我們就知道PCA到底要做什么了，沒錯，就是找到這條直線，并求出投影到這條直線的點(diǎn)的坐標(biāo)（當(dāng)然二維降一維是直線，三維降二維就是平面了，更多維度也是類似的）。

3.主成分分析在JIP-test中的應(yīng)用

主成分分析（PCA）是深度分析JIP-test眾多熒光參數(shù)的有效方法。通過PCA對JIP-test熒光參數(shù)進(jìn)行二次處理，對其數(shù)量、精度和復(fù)雜性進(jìn)行分析，可以識別熒光參數(shù)大數(shù)據(jù)中內(nèi)的隱藏信息，而傳統(tǒng)方法則是無法有效進(jìn)行的(Samborska et al.2014)。

使用PEA系列植物效率分析儀，每個樣品僅需2秒鐘，即可獲得完整OJIP曲線和50多個熒光參數(shù)，包括(i)OJIP曲線特征位點(diǎn)F_J、F_I、Area等，(ii)比活性參數(shù)ABS/RC、TR_M/RC等，(iii)性能指數(shù)PI_ABS、PI_total等和(iiiii)推動力DF_ABS等。
JIP-test每個熒光參數(shù)并不是完全獨(dú)立的，因?yàn)镴IP-test熒光參數(shù)是根據(jù)熒光瞬態(tài)曲線點(diǎn)計(jì)算的，其中一些參數(shù)由于其數(shù)學(xué)表達(dá)式（如φD_o和φP_o）而具有很高的相關(guān)性。
通過主成分分析PCA評估植物在不同環(huán)境下的生理或脅迫效應(yīng)，以確定對植物光合生理反應(yīng)最敏感的參數(shù)，這種方法允許將一組測量參數(shù)轉(zhuǎn)換成較少的變量，以確定植物生理狀態(tài)的變化(Jolliffe，2002; Legendre and Legendre 2012; Goltsev etal. 2012)。       

       圖5：羽狀短柄草(Brachypodium pinnatum)不同林分密度對54個JIP-test熒光參數(shù)的PCA分析(Baba，未發(fā)表)

如圖5中JIP-test熒光數(shù)據(jù)來自于不同生長年齡短柄草(隨著生長年齡的增大，其林分密度隨之增大)。首先第一PCA軸(Dim1)向上，兩個極值分別為：V_I和單位PSⅡ活性反應(yīng)中心比通量參數(shù)(TRo/RC、ETo/RC、REo/RC)。

同時第二PCA軸(Dim2)向上，可以看到參數(shù)Fv/Fo和PSⅡ原初最大量子產(chǎn)率(Φ_Po)的增大。

通過這種方法，我們發(fā)現(xiàn)了四個最重要的參數(shù)(而不是最初的54個)來描述光合機(jī)構(gòu)的狀態(tài)，它們與短柄草的林分密度的增加顯著相關(guān)。

 

圖6. 缺肥條件下玉米葉片JIP-test參數(shù)變異性的主成分分析(Kalaji,2014)

圖6中對不同施肥處理的玉米JIP-test熒光數(shù)據(jù)進(jìn)行PCA分析，使其分為了5個分離簇。第一類為對照組和缺磷植株。此簇位于Comp1和Comp2均為正值的第一象限，結(jié)果表明與對照組相比，缺磷處理對玉米光合機(jī)構(gòu)的影響不顯著。
第二類是均勻分布在坐標(biāo)系原點(diǎn)附近的缺氮、缺鎂和缺硫樣品。缺氮、缺硫植株的參數(shù)點(diǎn)略有向正方向移動，缺鎂植株的參數(shù)點(diǎn)向負(fù)方向移動。這意味著盡管JIP-test熒光參數(shù)變化具有相似性，但仍有足夠的特征可用作區(qū)分組內(nèi)樣本的熒光表型標(biāo)記。
第三類主要由植物缺鉀樣品組成，位于Comp1和Comp2的負(fù)區(qū)。這意味著玉米中鉀的缺乏可以通過JIP-test來很容易地確定。第四和第五個簇是由缺鐵和缺鈣植株形成的，即當(dāng)玉米缺鐵或缺鈣時，具有相似的JIP-test參數(shù)，并且它們與其他缺肥處理有很好的分離。

圖7. 不同環(huán)境條件下5個玉米雜交種葉片JIP試驗(yàn)參數(shù)變異性的主成分分析：對照（C）、弱光（LL）、田間（F）、冷（Co）、熱（H）和高溫（SH）(Frani M et al. 2020)

圖7為不同環(huán)境條件下5個玉米雜交種葉片JIP試驗(yàn)參數(shù)變異性的主成分分析：前三主成分占總方差的95.9%，選擇的14個參數(shù)對環(huán)境效應(yīng)的敏感性不同，因而對主成分形成的貢獻(xiàn)也不同(數(shù)據(jù)見原文)。

所有五種處理都是獨(dú)立的簇，并位于坐標(biāo)系的不同區(qū)域。SH處理對玉米植株的熱脅迫最為分散，通過JIP-test熒光參數(shù)的變化可以看出熱脅迫對玉米植株的嚴(yán)重性。

PC1與DIo/RC(0.98)和RC/ABS(–0.96)的相關(guān)性最強(qiáng)，因此可以認(rèn)為PC1是一個功能反應(yīng)中心的量度，其兩端極值處理組為C和SH。與PC2兩極相關(guān)性最強(qiáng)的參數(shù)為(V_J，-0.90)和Ψ_Eo(0.87)。

在第二主成分兩端的是F、Co和LL處理組，其中LL和Co的主要特征參數(shù)是V_J和V_I，F(xiàn)處理組的特征是解釋電子傳遞通量的Ψ_Eo和ETo/RC。在最近對幾種植物的環(huán)境影響分類的研究中，也顯示了相似的JIP參數(shù)分組(Bussotti et al. 2020)。

此例中PI_ABS似乎只提供了一個軸向的分類，而其他JIP-test熒光參數(shù)可用于檢測各個環(huán)境條件下對玉米的特定影響。例如，第一主成分的相對側(cè)顯示了玉米植株受到的兩個環(huán)境極值：冷脅迫處理組(Co)-主要由VJ和VI參數(shù)表征，而高溫脅迫處理組(SH)-主要由K、M_o、RE_o/RC和DI_o/RC表征。

Stirbet(Stirbet et al. 2018)等人也證實(shí)了這一點(diǎn)，同時建議設(shè)計(jì)新參數(shù)以表征已知特定條件反應(yīng)的JIP-test參數(shù)。同時Galic等人(Galic et al. 2019)表明，PI_ABS可以有效地用于熱脅迫環(huán)境下的糧食產(chǎn)量選擇。

總的來說通過PCA我們可以分類植物對各種環(huán)境因素的不同反應(yīng)：
(i)找到特定處理下植物樣品OJIP曲線發(fā)生的特異性變化
(ii)篩選出發(fā)生顯著變化的JIP-test熒光參數(shù)及其變化特征，可更好對植物樣品光合機(jī)構(gòu)發(fā)生的變化（傷害）進(jìn)行定位分析，如PSⅡ供體側(cè)/受體測或PSⅡ活性中心等。
(iii)我們還可以將JIP-test熒光數(shù)據(jù)與其他環(huán)境數(shù)據(jù)或生理參數(shù)進(jìn)行聚類結(jié)合(Goltsev et al. 2012)。
(iv)此外Tyystjärvi等人應(yīng)用PCA等人工智能方法分析不同類型光照(低光強(qiáng)、飽和脈沖、遠(yuǎn)紅色等)激發(fā)的JIP-test熒光數(shù)據(jù)，可識別植物物種(Tyystjärvi et al. 1999; Keränen et al. 2003; Codrea et al. 2003;Kirova et al. 2009)。
(v)Kalaji等人利用JIP-test、主成分分析(PCA)和一種新的機(jī)器學(xué)習(xí)方法建立了一種無創(chuàng)檢測和監(jiān)測大田條件下油菜籽微量和大量營養(yǎng)素缺乏的方法(Kalaji et al. 2017)。
鑒于篇幅限制，我們將在下期文章中篩選數(shù)篇應(yīng)用PCA方法分析JIP-test熒光數(shù)據(jù)具有代表性的文章進(jìn)行詳細(xì)介紹，期待您的關(guān)注，謝謝！

 

4.引用文獻(xiàn)

[1] Appenroth, K.J., Stöckel, J., Srivastava, A.,Strasser, R.J., 2001. Multiple effects of chromate on the photosyntheticapparatus of Spirodela polyrhiza as probed by OJIP chlorophyll a fluorescencemeasurements. Environ. Pollut. 115, 49–64.
[2] Bussotti F, Gerosa G, Digrado A, Pollastrini M, 2020.Selection of chlorophyll fluorescence parameters as indicators of photosyntheticefficiency in large scale plant ecological studies. Ecol Indic 108: 105686.
[3] Bussotti, F., Strasser, R.J., Schaub, M., 2007.Photosynthetic behavior of woody species under high ozone exposure probed withthe JIP-test: a review. Environ. Pollut. 147, 430–437.
[4] Ceppi, M.G., Oukarroum, A., Cicek, N., Strasser,R.J., Schansker, G., 2012. The IP amplitude of the fluorescence rise OJIP issensitive to changes in the photosystem I content of leaves: a study on plantsexposed to magnesium and sulfate deficiencies, drought stress and salt stress. Physiol.Plant 144, 277–288.
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