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画像データに基づく上皮組織力学のモデル構築およびパラメータ推定法

荻田, 豪士 京都大学 DOI:10.14989/doctor.k24269

2022.09.26

概要

個体発生において組織が変形し体が形作られる形態形成の制御機構を解明することは、発生生物学における中心課題の一つである。形態形成の力学による理解はメカノバイオロジーの目標であり、その達成のためには組織や細胞の変形と力に加えて、細胞の体積弾性などの機械特性の把握が必須である。細胞の機械特性の生体計測は極めて高度な技術を要するため、細胞の機械特性を表す数理モデル(力学モデル)を用いて理論的・数値的な解析が行われてきた。しかしながら、多くの先行研究では、分子の機能など定性的な情報から力学モデルを構築し、数値計算の結果と実験データとを要約統計量を用いて間接的に比較することで力学モデルのパラメータ(力学パラメータ)を推定していた。以上の技術的な問題を解決するために、本研究では、定量的な情報に基づいて力学モデルを構築し、力学パラメータを画像データから直接抽出する手法の確立を目指した。

本研究において申請者が開発した手法は、モデル構築、パラメータ推定、モデル評価からなり、上皮組織の細胞接着面を蛍光標識した画像データを入力として、上皮組織の最適力学モデルと力学パラメータを出力する。この手法のモデル構築では、力のベイズ推定法によって画像データから推定した細胞の力と形態特徴量の相関を基に、力学モデルの関数を決定する。次に、力学モデルの関数を細胞の頂点における力のつり合い方程式に代入し、この方程式を解くことで、力学パラメータを推定する。加えて、統計学において広く用いられるモデル評価指標である赤池情報量規準(Akaike information criterion; AIC)を用いて、複数の候補モデルの中から最も適した力学モデルを選択する。

申請者が開発した手法の妥当性を、数値計算により生成した人工データを用いて検証した結果、力学パラメータを高い精度で推定できること、および最適な力学モデルを選択できることが示された。人工データおよびショウジョウバエ上皮組織画像データに観測誤差を加えてパラメータ推定を行った結果、パラメータ推定は画像解析による誤差に対して十分な頑健性を示した。さらに、ショウジョウバエ上皮組織画像データに申請者が開発した手法を適用し、推定した力学パラメータを用いて計算した細胞接着面の張力が、先行研究において報告された体軸に対する方向依存性(異方性)を再現することを示した。

次に、申請者が開発した手法用いてショウジョウバエ上皮細胞の機械特性を解析した。まず、先行研究において提案された力学モデルと申請者が開発した手法によって構築した力学モデルを、AICを用いて比較した結果、すべてのサンプルにおいて後者の力学モデルが選択された。次に、推定した力学パラメータの発生過程に伴う変化を解析したところ、細胞接着面の張力を制御するパラメータの異方性が、方向性のある細胞配置換えを担うことを見出した。さらに、申請者が開発した手法により、組織の外科的・遺伝的な撹乱による力学パラメータの変化を検出できることが示された。

以上の結果から、申請者が開発した手法によって画像データに基づいた定量的なモデル構築と迅速かつ正確なパラメータ推定が可能になり、さらに生体組織においても有効に機能することが示された。今後、この手法を用いた細胞の機械特性の簡便な定量が、個体発生の力学制御の理解に寄与することが期待される。

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