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JIP-test和主成分分析(PCA)在植物光合作用研究中的應用
歡迎關(guān)注「漢莎科技集團」微信公眾號！ 1.快速葉綠素熒光誘導動(dòng)力學(xué)分析(JIP-test) 近二十年來(lái)，基于“生物膜能量通量理論”的活體快速葉綠素 a 熒光誘導動(dòng)力學(xué)OJIP曲線(xiàn)和JIP-test分析，由于其無(wú)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曲線(xiàn)（OJIP曲線(xiàn)）中包含著(zhù)大量關(guān)于PSⅡ反應中心原初光化學(xué)反應的信息，植物在不同脅迫處理后OJIP曲線(xiàn)會(huì )發(fā)生特異性變化(Strasser et al., 2004)。OJIP曲線(xiàn)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(yíng)養不良、大氣CO2或臭氧升高和病害。通過(guò)對曲線(xiàn)熒光參數的分析，可以知道在環(huán)境因子影響下植物光合機構的變化。
從動(dòng)力學(xué)曲線(xiàn)上可以得到大量的原始數據，為了能更好地反映動(dòng)力學(xué)曲線(xiàn)和被測樣品的關(guān)系，Strasser RJ(1995)以生物膜能量流動(dòng)為基礎，通過(guò)計算能量流和能量比率來(lái)衡量在給定物理狀態(tài)下樣品材料內部變化，建立了高度簡(jiǎn)化的能量流動(dòng)模型圖。 
圖1. 高度簡(jiǎn)化的能量在光合器官中的流動(dòng)模型圖(Strasser BJ, Strasser RJ, 1995)依照能量流動(dòng)模型，天線(xiàn)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，另一部分則被反應中心(Reaction Centre, RC，在JIP-test中RC指有活性的反應中心)所捕獲(Trapping, TR)，在反應中心激發(fā)能被轉化為還原能，將QA還原為QA-，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。 在此基礎上發(fā)展起來(lái)的數據處理稱(chēng)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)境條件下的結構和功能的變化(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(gè)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方法的研究和應用熱度在不斷增加，而對脈沖調制式(PAM)方法的興趣在逐漸減弱。這是什么意思？乍一看，一個(gè)可能的解釋是源于對OJIP動(dòng)力學(xué)實(shí)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)驗合理假設的穩健分析工具(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...)是目前世界上可以完美真實(shí)測定OJIP曲線(xiàn)的成熟商品化設備。近20年來(lái)，JIP-test方法的不斷發(fā)展及其在野外應用和實(shí)驗室研究中的應用呈現出顯著(zhù)的增長(cháng)趨勢。近期發(fā)表文章《能量流理論慶祝40年：走向系統生物學(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(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。榮耀特刊文獻預覽及下載請點(diǎn)擊以下鏈接文章：
2.主成分分析(PCA)簡(jiǎn)介 主成分分析(Principal Components Analysis)也稱(chēng)主分量分析，旨在利用“降維”的思想，把多指標轉化為少數幾個(gè)綜合指標。在許多研究領(lǐng)域中，通常需要對含有多個(gè)變量的數據進(jìn)行觀(guān)測，收集大量數據后進(jìn)行分析尋找規律。多變量大數據集為研究提供了豐富的信息，而在多數情況下，許多變量之間可能存在相關(guān)性，從而增加了問(wèn)題分析的復雜性。如果分別對每個(gè)指標進(jìn)行分析，分析往往是孤立的，不能完全利用數據中的信息，因此盲目減少指標會(huì )損失很多有用的信息，從而產(chǎn)生錯誤的結論。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(gè)變量中的各類(lèi)信息。主成分分析PCA就屬于這類(lèi)降維算法，將高維度的數據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)現提升數據處理速度的目的。圖4a. 數據點(diǎn)降維的信息損失與矯正：X軸投影 如何降維？我們以最簡(jiǎn)單的二維轉一維為例，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(xiàn)上（X軸或Y軸）。但是仔細觀(guān)察前兩個(gè)圖，我們就會(huì )發(fā)現，有些點(diǎn)在投影過(guò)后，位置是重合的，也就是說(shuō)，存在不同的點(diǎn)在壓縮過(guò)后表示的信息是完全一樣的，投影到x軸，有兩個(gè)點(diǎn)重合，投影到y軸，有三個(gè)點(diǎn)重合。 
圖4b. 數據點(diǎn)降維的信息損失與矯正：Y軸投影這就是當所有點(diǎn)集中至一條軸上時(shí)，另一維度或另一軸上的信息就會(huì )丟失，這是不可逆的過(guò)程，這一信息的損失也是必然的。這不是我們想要的結果，最終我們還是希望點(diǎn)與點(diǎn)之間間隔盡可能的遠，保留的信息盡可能的多，讓所有的點(diǎn)能夠盡可能的進(jìn)行區分。 
圖4c. 數據點(diǎn)降維的信息損失與矯正：X/Y軸矯正最好的結果應該是我們依然選擇了某個(gè)直線(xiàn)，并把點(diǎn)投影到這條直線(xiàn)上，但是點(diǎn)之間沒(méi)有重合，點(diǎn)與點(diǎn)的間隔也比較遠。看到這里，我們就知道PCA到底要做什么了，沒(méi)錯，就是找到這條直線(xiàn)，并求出投影到這條直線(xiàn)的點(diǎn)的坐標（當然二維降一維是直線(xiàn)，三維降二維就是平面了，更多維度也是類(lèi)似的）。
3.主成分分析在JIP-test中的應用 主成分分析（PCA）是深度分析JIP-test眾多熒光參數的有效方法。通過(guò)PCA對JIP-test熒光參數進(jìn)行二次處理，對其數量、精度和復雜性進(jìn)行分析，可以識別熒光參數大數據中內的隱藏信息，而傳統方法則是無(wú)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(gè)樣品僅需2秒鐘，即可獲得完整OJIP曲線(xiàn)和50多個(gè)熒光參數，包括(i)OJIP曲線(xiàn)特征位點(diǎn)FJ、FI、Area等，(ii)比活性參數ABS/RC、TRM/RC等，(iii)性能指數PIABS、PItotal等和(iiiii)推動(dòng)力DFABS等。JIP-test每個(gè)熒光參數并不是完全獨立的，因為JIP-test熒光參數是根據熒光瞬態(tài)曲線(xiàn)點(diǎn)計算的，其中一些參數由于其數學(xué)表達式（如φDo和φPo）而具有很高的相關(guān)性。通過(guò)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，以確定對植物光合生理反應最敏感的參數，這種方法允許將一組測量參數轉換成較少的變量，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(gè)JIP-test熒光參數的PCA分析(Baba，未發(fā)表) 如圖5中JIP-test熒光數據來(lái)自于不同生長(cháng)年齡短柄草(隨著(zhù)生長(cháng)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(gè)極值分別為：VI和單位PSⅡ活性反應中心比通量參數(TRo/RC、ETo/RC、REo/RC)。 同時(shí)第二PCA軸(Dim2)向上，可以看到參數Fv/Fo和PSⅡ原初最大量子產(chǎn)率(ΦPo)的增大。 通過(guò)這種方法，我們發(fā)現了四個(gè)最重要的參數(而不是最初的54個(gè))來(lái)描述光合機構的狀態(tài)，它們與短柄草的林分密度的增加顯著(zhù)相關(guān)。 
圖6. 缺肥條件下玉米葉片JIP-test參數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熒光數據進(jìn)行PCA分析，使其分為了5個(gè)分離簇。第一類(lèi)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，結果表明與對照組相比，缺磷處理對玉米光合機構的影響不顯著(zhù)。第二類(lèi)是均勻分布在坐標系原點(diǎn)附近的缺氮、缺鎂和缺硫樣品。缺氮、缺硫植株的參數點(diǎn)略有向正方向移動(dòng)，缺鎂植株的參數點(diǎn)向負方向移動(dòng)。這意味著(zhù)盡管JIP-test熒光參數變化具有相似性，但仍有足夠的特征可用作區分組內樣本的熒光表型標記。第三類(lèi)主要由植物缺鉀樣品組成，位于Comp1和Comp2的負區。這意味著(zhù)玉米中鉀的缺乏可以通過(guò)JIP-test來(lái)很容易地確定。第四和第五個(gè)簇是由缺鐵和缺鈣植株形成的，即當玉米缺鐵或缺鈣時(shí)，具有相似的JIP-test參數，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(gè)玉米雜交種葉片JIP試驗參數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(gè)玉米雜交種葉片JIP試驗參數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(gè)參數對環(huán)境效應的敏感性不同，因而對主成分形成的貢獻也不同(數據見(jiàn)原文)。 所有五種處理都是獨立的簇，并位于坐標系的不同區域。SH處理對玉米植株的熱脅迫最為分散，通過(guò)JIP-test熒光參數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(gè)功能反應中心的量度，其兩端極值處理組為C和SH。與PC2兩極相關(guān)性最強的參數為(VJ，-0.90)和ΨEo(0.87)。 在第二主成分兩端的是F、Co和LL處理組，其中LL和Co的主要特征參數是VJ和VI，F處理組的特征是解釋電子傳遞通量的ΨEo和ETo/RC。在最近對幾種植物的環(huán)境影響分類(lèi)的研究中，也顯示了相似的JIP參數分組(Bussotti et al. 2020)。 此例中PIABS似乎只提供了一個(gè)軸向的分類(lèi)，而其他JIP-test熒光參數可用于檢測各個(gè)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顯示了玉米植株受到的兩個(gè)環(huán)境極值：冷脅迫處理組(Co)-主要由VJ和VI參數表征，而高溫脅迫處理組(SH)-主要由K、Mo、REo/RC和DIo/RC表征。 Stirbet(Stirbet et al. 2018)等人也證實(shí)了這一點(diǎn)，同時(shí)建議設計新參數以表征已知特定條件反應的JIP-test參數。同時(shí)Galic等人(Galic et al. 2019)表明，PIABS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(lái)說(shuō)通過(guò)PCA我們可以分類(lèi)植物對各種環(huán)境因素的不同反應：(i)找到特定處理下植物樣品OJIP曲線(xiàn)發(fā)生的特異性變化(ii)篩選出發(fā)生顯著(zhù)變化的JIP-test熒光參數及其變化特征，可更好對植物樣品光合機構發(fā)生的變化（傷害）進(jìn)行定位分析，如PSⅡ供體側/受體測或PSⅡ活性中心等。(iii)我們還可以將JIP-test熒光數據與其他環(huán)境數據或生理參數進(jìn)行聚類(lèi)結合(Goltsev et al. 2012)。(iv)此外Tyystjärvi等人應用PCA等人工智能方法分析不同類(lèi)型光照(低光強、飽和脈沖、遠紅色等)激發(fā)的JIP-test熒光數據，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)習方法建立了一種無(wú)創(chuàng )檢測和監測大田條件下油菜籽微量和大量營(yíng)養素缺乏的方法(Kalaji et al. 2017)。鑒于篇幅限制，我們將在下期文章中篩選數篇應用PCA方法分析JIP-test熒光數據具有代表性的文章進(jìn)行詳細介紹，期待您的關(guān)注，謝謝！
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CIRAS4第四代便攜式光合作用測定系統
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