Estrogen modulates reward prediction errors and reinforcement learning

This data was used and described in the following paper: Golden C.E.M., Martin, A.C., Kaur, D., Mah, A., Levy, D.H., Yamaguchi, T., Lasek, A.W., Lin, D., Aoki, C., Constantinople, C.M. (2025). Estrogen modulates reward prediction errors and reinforcement learning. Nature Neuroscience. The dataset comprises raw data used to generate figures in the publication: RawData_Figure1: Contains behavioral data from the rat temporal wagering task and estradiol levels from serum measured with an ELISA. Used, at least in part, to generate Figure 1, Figure 2, Figure 6, Extended Data 1, Extended Data 2, Extended Data 3, and Extended Data 10. RawData_Figure5: Contains mass spectrometry fold change results comparing nucleus accumbens core (NAcc) proteomic expression in proestrus compared to diestrus and estrus compared to diestrus, immunohistochemistry quantification of SERT and DAT expression in the NAcc, and localization of DAT in the NAcc with electron microscopy. Used to generate Figure 5. RawData_Figure6: Contains RNAscope data to validate the knockdown of Esr1 with two versions of shRNA, behavioral data after Esr1 knockdown, and measurement of estradiol and osmolality from serum. Used to generate Figure 6. All files are Matlab data (.mat) files. The code to analyze this data and generate all figures in Golden et al., 2025 is available at {https://github.com/constantinoplelab/published/tree/main/EstrousRPEPaper}. Data was analyzed using Matlab 2024a with the following additional toolboxes: Curve Fitting, Optimization, Signal Processing, and Statistics and Machine Learning. Funding: This work was supported by a K99/R00 Pathway to Independence Award (R00MH11-1926), a Klingenstein-Simons Fellowship in Neuroscience, and an NIH Director’s New Innovator Award (DP2MH126376) to C.M.C. C.G. was supported by a grant from the Simons Foundation (855332), F32MH125448, and 5T32MH019524. The mass spectrometric experiments were supported in part by NYU Langone Health, the Laura and Isaac Perlmutter Cancer Center support grant P30CA016087 from the National Cancer Institute, and the NIH Shared Instrumentation Grant 1S10OD010582-01A1 for the purchase of an Orbitrap Fusion™ Lumos™ Tribrid™ mass spectrometer.本数据集曾在以下论文中使用并被详细说明：Golden C.E.M.、Martin A.C.、Kaur D.、Mah A.、Levy D.H.、Yamaguchi T.、Lasek A.W.、Lin D.、Aoki C.、Constantinople C.M.（2025）。《雌激素调节奖赏预测误差与强化学习》，发表于《自然-神经科学》（Nature Neuroscience）。本数据集包含用于生成该论文各图表的原始数据： RawData_Figure1：包含大鼠时序赌注任务的行为学数据，以及采用酶联免疫吸附试验（ELISA）测得的血清雌二醇水平。该数据集部分或全部用于生成图1、图2、图6、扩展数据1、扩展数据2、扩展数据3及扩展数据10。 RawData_Figure5：包含伏隔核核心区（NAcc）蛋白质组表达的质谱倍数变化结果，对比了动情前期与动情间期、动情期与动情间期的差异；还包含伏隔核中5-羟色胺转运体（SERT）和多巴胺转运体（DAT）表达的免疫组化定量结果，以及伏隔核内多巴胺转运体的电镜定位结果。该数据集用于生成图5。 RawData_Figure6：包含采用两种短发夹RNA（shRNA）验证Esr1基因敲低效果的RNAscope数据、Esr1基因敲低后的行为学数据，以及血清雌二醇和渗透压的测量结果。该数据集用于生成图6。 所有文件均为Matlab数据格式（.mat）文件。用于分析本数据集并生成Golden等人2025年论文中所有图表的代码已公开于{https://github.com/constantinoplelab/published/tree/main/EstrousRPEPaper}。本数据集采用Matlab 2024a进行分析，额外使用了以下工具箱：曲线拟合工具箱（Curve Fitting）、优化工具箱（Optimization）、信号处理工具箱（Signal Processing）以及统计与机器学习工具箱（Statistics and Machine Learning）。 资助说明：本研究获得的资助包括：授予C.M.C.的K99/R00独立生涯发展奖（R00MH11-1926）、克林根斯坦-西蒙斯神经科学奖学金，以及美国国立卫生研究院（NIH）主任创新新奖（DP2MH126376）。C.G.的研究得到了西蒙斯基金会（Grant #855332）、F32MH125448以及5T32MH019524的资助。质谱实验得到了纽约大学朗格尼健康中心、劳拉与艾萨克·珀尔马特癌症中心的支持项目P30CA016087（由美国国家癌症研究所资助），以及用于购置Orbitrap Fusion™ Lumos™ Tribrid™质谱仪的NIH共享仪器 grant 1S10OD010582-01A1的支持。