MINFLUX (Minimal Photon Flux) is a single-molecule imaging technique capable of resolving fluorophores at a precision of <5 nm. Interpretation of the point patterns generated by this technique presents challenges due to variable emitter density, incomplete bio-labelling of target molecules and their detection, error prone measurement processes, and the presence of spurious (non-structure associated) fluorescent detections. Together, these challenges ensure structural inferences from single-molecule imaging datasets are non-trivial in the absence of strong a priori information, for all but the smallest of point patterns. In addition, current methods often require subjective parameter tuning and presuppose known structural templates, limiting reference-free discovery. We present a statistically grounded, end-to-end analysis framework. Focusing on MINFLUX derived datasets and leveraging Bayesian and spatial statistical methods, a pipeline is presented that demonstrates 1) uncertainty aware clustering of measurements into emitter groups that performs better than current gold standards, 2) rapid identification of molecular structure supergroups, and 3) reconstruction of repeating structures within the dataset without substantial prior knowledge. This pipeline is demonstrated using simulated and real MINFLUX datasets, where emitter clustering and centre detection maintain high performance (emitter subset assignment accuracy > 0.75) across all conditions evaluated, while structural inference achieves reliable discrimination (F1 approx. 0.9) at high labelling efficiency. Template-free reconstruction of Nup96 and DNA-Origami 3x3 grids are achieved.

翻译：MINFLUX（最小光子通量）是一种能够以<5 nm精度解析荧光团的单分子成像技术。由于发射体密度可变、目标分子及其检测的生物标记不完整、测量过程易产生误差以及存在虚假（非结构相关）荧光检测，对该技术生成的点模式进行解析面临挑战。这些挑战共同导致，除非点模式规模极小，否则在缺乏强先验信息的情况下，从单分子成像数据集中进行结构推断并非易事。此外，现有方法通常需要主观参数调整并预设已知结构模板，限制了无参考结构的发现。我们提出了一种基于统计学的端到端分析框架。该框架聚焦于MINFLUX衍生数据集，利用贝叶斯和空间统计方法，构建了一个分析流程，其优势在于：1）将测量值聚类为发射体组时具备不确定性感知能力，性能优于当前金标准；2）能够快速识别分子结构超群；3）无需大量先验知识即可重建数据集内的重复结构。该流程在模拟和真实MINFLUX数据集上进行了验证，其中发射体聚类和中心检测在所有评估条件下均保持高性能（发射体子集分配准确率>0.75），而结构推断在高标记效率下实现了可靠的区分能力（F1值约0.9）。最终成功实现了Nup96和DNA-Origami 3x3网格的无模板重建。