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.快速葉綠素熒光誘導(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)。植物快速葉綠素熒光誘導(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)用文獻目錄請移步至“漢莎科技集團”微信公眾號底部“技術(shù)支持” → “文獻目錄” → “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. 葉綠素熒光相關(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，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)實驗測量可用性的增加，主要是因為：1）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)用和實驗室研究中的應(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.)詳細闡述了這一研究熱點趨勢。
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念Strasser教授在JIP-test理論方向做出的卓越貢獻。

榮耀特刊文獻預(yù)覽及下載請點擊以下鏈接文章：

• 國際光合作用研究Photosynthetica推出特別刊以表彰Reto J. Strasser教授在快速葉綠素熒光研究的卓越貢獻

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，把多指標轉(zhuǎn)化為少數(shù)幾個綜合指標。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)進行觀測，收集大量數(shù)據(jù)后進行分析尋找規(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進行分析，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，從而實現(xiàn)提升數(shù)據(jù)處理速度的目的。

圖4a. 數(shù)據(jù)點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投影到同一條直線上（X軸或Y軸）。但是仔細觀察前兩個圖，我們就會發(fā)現(xiàn)，有些點在投影過后，位置是重合的，也就是說，存在不同的點在壓縮過后表示的信息是完全一樣的，投影到x軸，有兩個點重合，投影到y(tǒng)軸，有三個點重合。

 

圖4b. 數(shù)據(jù)點降維的信息損失與矯正：Y軸投影

這就是當所有點集中至一條軸上時，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與點之間間隔盡可能的遠，保留的信息盡可能的多，讓所有的點能夠盡可能的進行區(qū)分。

 

圖4c. 數(shù)據(jù)點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，并把點投影到這條直線上，但是點之間沒有重合，點與點的間隔也比較遠�？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的坐標（當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)進行二次處理，對其數(shù)量、精度和復(fù)雜性進行分析，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)并不是完全獨立的，因為JIP-test熒光參數(shù)是根據(jù)熒光瞬態(tài)曲線點計算的，其中一些參數(shù)由于其數(shù)學(xué)表達式（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構(gòu)的影響不顯著。
第二類是均勻分布在坐標系原點附近的缺氮、缺鎂和缺硫樣品。缺氮、缺硫植株的參數(shù)點略有向正方向移動，缺鎂植株的參數(shù)點向負方向移動。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)樣本的熒光表型標記。
第三類主要由植物缺鉀樣品組成，位于Comp1和Comp2的負區(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也不同(數(shù)據(jù)見原文)。

所有五種處理都是獨立的簇，并位于坐標系的不同區(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重性。

PC1與DIo/RC(0.98)和RC/ABS(–0.96)的相關(guān)性最強，因此可以認為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)等人也證實了這一點，同時建議設(shè)計新參數(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構(gòu)發(fā)生的變化（傷害）進行定位分析，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、飽和脈沖、遠紅色等)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行詳細介紹，期待您的關(guān)注，謝謝！

 

4.引用文獻

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