Chloroplasts are eukaryotic photosynthetic organelles that drive the global carbon cycle. Despite their importance, our understanding of their protein composition, function, and spatial organization remains limited. Here, we determined the localizations of 1,034 candidate chloroplast proteins using fluorescent protein tagging in the model alga Chlamydomonas reinhardtii. The localizations provide insights into the functions of poorly characterized proteins; identify novel components of nucleoids, plastoglobules, and the pyrenoid; and reveal widespread protein targeting to multiple compartments. We discovered and further characterized cellular organizational features, including eleven chloroplast punctate structures, cytosolic crescent structures, and unexpected spatial distributions of enzymes within the chloroplast. We also used machine learning to predict the localizations of other nuclear-encoded Chlamydomonas proteins. The strains and localization atlas developed here will serve as a resource to accelerate studies of chloroplast architecture and functions.

叶绿体是驱动全球碳循环的真核光合细胞器。尽管它们很重要，但我们对其蛋白质组成、功能和空间组织的了解仍然有限。在这里，我们使用荧光蛋白标记在模型藻莱茵衣藻中确定了 1,034 个候选叶绿体蛋白的定位。这些定位提供了对特征较差的蛋白质功能的深入了解；鉴定类核、质体球和类核的新成分；并揭示了针对多个区室的广泛蛋白质靶向。我们发现并进一步表征了细胞组织特征，包括十一个叶绿体点状结构、胞质新月结构以及叶绿体内酶的意外空间分布。我们还使用机器学习来预测其他核编码衣藻蛋白的定位。这里开发的菌株和定位图谱将作为加速叶绿体结构和功能研究的资源。