2012年11月5日 訊 /生物谷BIOON/ --近日,，來自國家標(biāo)準(zhǔn)技術(shù)局（NIST）的研究者開發(fā)出了一種新型的模型，用于對一組細(xì)胞對給定環(huán)境或刺激做出反應(yīng)和改變的情況進(jìn)行量化預(yù)測,。新型的研究模型可以對一系列細(xì)胞復(fù)雜的進(jìn)化過程分配可靠的數(shù)字,，而且為高效的生物工業(yè)化操作和基于干細(xì)胞的療法提供更為精確的能力。相關(guān)研究成果刊登于10月30日的國際雜志PNAS上,。
細(xì)胞的行為和命運(yùn)在部分上是由DNA來決定的,，活細(xì)胞對于內(nèi)外環(huán)境的反應(yīng)，比如說是其內(nèi)部特殊蛋白質(zhì)的濃度或者其外部的化學(xué)環(huán)境,，都是具有固有的概率的,，我們并不能預(yù)測任何細(xì)胞未來的狀況到底如何。
研究者表示,，這種固有的不確定性最終都會出現(xiàn)一種結(jié)果,，尤其是干細(xì)胞使用過程中，最終都會發(fā)生安全效用事件,，因?yàn)楹茈y在培養(yǎng)基中得到100%完全意義上的干細(xì)胞最終分化的狀態(tài),。
這項(xiàng)研究中，研究者并沒有使用干細(xì)胞,，而是使用了成纖維細(xì)胞,，一種常見的細(xì)胞模型。研究者運(yùn)用標(biāo)準(zhǔn)的追蹤技術(shù),，修改了基因的特性,。研究者給編碼構(gòu)建胞外支架蛋白的基因中摻入了編碼熒光分子的小片段，當(dāng)細(xì)胞基因的表達(dá)量越高,，那么熒光分子的發(fā)光越亮,，以亮度作為基因表達(dá)量的多少。隨后研究者用顯微鏡來監(jiān)控細(xì)胞培養(yǎng)基,，每隔15分鐘進(jìn)行照相,，持續(xù)40小時，來記錄細(xì)胞的行為波動情況,。
研究者使用軟件來分析所獲得的圖像,，來自個體細(xì)胞的時間流失數(shù)據(jù)以及整個細(xì)胞群的時間依賴數(shù)據(jù)都進(jìn)行統(tǒng)計(jì)模型分析，結(jié)果用算術(shù)方法描述為細(xì)胞效應(yīng)的范圍，同時也揭示了細(xì)胞如何表現(xiàn)出這種反應(yīng),。
研究結(jié)果為預(yù)測細(xì)胞改變特性的百分比提供了可能性,，對于生物工業(yè)來說，可以更好地來控制細(xì)胞的生物過程,。如果適用于干細(xì)胞,，那么這種技術(shù)就會被用于預(yù)測細(xì)胞分化的速度以及某一時間點(diǎn)未分化細(xì)胞存在的可能性。（生物谷Bioon.com）
編譯自：Cellular landscaping: Predicting how, and how fast, cells will change
doi:10.1073/pnas.1207544109
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PMID:
Predicting rates of cell state change caused by stochastic fluctuations using a data-driven landscape model
Daniel R. Sisan, Michael Halter, Joseph B. Hubbard, and Anne L. Plant1
We develop a potential landscape approach to quantitatively describe experimental data from a fibroblast cell line that exhibits a wide range of GFP expression levels under the control of the promoter for tenascin-C. Time-lapse live-cell microscopy provides data about short-term fluctuations in promoter activity, and flow cytometry measurements provide data about the long-term kinetics, because isolated subpopulations of cells relax from a relatively narrow distribution of GFP expression back to the original broad distribution of responses. The landscape is obtained from the steady state distribution of GFP expression and connected to a potential-like function using a stochastic differential equation description (Langevin/Fokker–Planck). The range of cell states is constrained by a force that is proportional to the gradient of the potential, and biochemical noise causes movement of cells within the landscape. Analyzing the mean square displacement of GFP intensity changes in live cells indicates that these fluctuations are described by a single diffusion constant in log GFP space. This finding allows application of the Kramers’ model to calculate rates of switching between two attractor states and enables an accurate simulation of the dynamics of relaxation back to the steady state with no adjustable parameters. With this approach, it is possible to use the steady state distribution of phenotypes and a quantitative description of the short-term fluctuations in individual cells to accurately predict the rates at which different phenotypes will arise from an isolated subpopulation of cells.
