美国斯坦福大学Karl Deisseroth、Emily L. Sylwestrak等研究人员合作揭示不同奖励计算的细胞类型特异性群体动力学。2022年9月15日出版的《细胞》杂志发表了这项成果。
研究人员表示，细胞活动的计算分析在很大程度上独立于现代转录组细胞类型学而发展，但整合这些方法对于全面了解大脑功能和功能障碍的细胞水平机制可能是至关重要的。
将这一方法应用于松果体缰（一个具有不同的、相互混合的分子、解剖和计算特征的结构），研究人员在不同的基因定义的神经群体中，包括TH+细胞和Tac1+细胞，确定了奖励预测线索的编码和奖励结果。来自基因定向记录的数据被用来训练一个优化的非线性动力系统模型，并揭示了与线吸引子一致的活动动力学。高密度、特定细胞类型的电生理记录和光遗传扰动为该模型提供了支持性证据。反向工程预测了Tac1+细胞如何整合奖赏历史，这得到了体内实验的补充。
这种综合方法描述了一个过程，通过这个过程，数据驱动的群体活动计算模型可以为生物系统中特定细胞类型的调查产生和构建可操作的假设。
附：英文原文
Title: Cell-type-specific population dynamics of diverse reward computations
Author: Emily L. Sylwestrak, YoungJu Jo, Sam Vesuna, Xiao Wang, Blake Holcomb, Rebecca H. Tien, Doo Kyung Kim, Lief Fenno, Charu Ramakrishnan, William E. Allen, Ritchie Chen, Krishna V. Shenoy, David Sussillo, Karl Deisseroth
Issue&Volume: 2022/09/15
Abstract: Computational analysis of cellular activity has developed largely independently of modern transcriptomic cell typology, but integrating these approaches may be essential for full insight into cellular-level mechanisms underlying brain function and dysfunction. Applying this approach to the habenula (a structure with diverse, intermingled molecular, anatomical, and computational features), we identified encoding of reward-predictive cues and reward outcomes in distinct genetically defined neural populations, including TH+ cells and Tac1+ cells. Data from genetically targeted recordings were used to train an optimized nonlinear dynamical systems model and revealed activity dynamics consistent with a line attractor. High-density, cell-type-specific electrophysiological recordings and optogenetic perturbation provided supporting evidence for this model. Reverse-engineering predicted how Tac1+ cells might integrate reward history, which was complemented by in vivo experimentation. This integrated approach describes a process by which data-driven computational models of population activity can generate and frame actionable hypotheses for cell-type-specific investigation in biological systems.
DOI: 10.1016/j.cell.2022.08.019
Source: https://www.cell.com/cell/fulltext/S0092-8674(22)01113-8