意大利那不勒斯费德里克二世大学Mario Nicodemi小组使用染色质聚合物模型对Hi-C、GAM和SPRITE进行了比较。这一研究成果发表在2021年5月7日出版的国际学术期刊《自然—方法学》上。

据研究人员介绍，Hi-C、SPRITE（split-pool recognition of interactions by tag extension）和GAM（genome architecture mapping）是用于探测全基因组染色质相互作用的强大技术，但是它们如何忠实地捕获三维（3D）接触以及他们彼此之间的表现如何相对不清楚，因为没有统一的基准。

研究人员在简化但可控制的框架内，通过计算机对这些方法进行了比较，并与鼠和人类位点的聚合物模型的已知3D结构进行了比较，这些模型可以概括Hi-C、GAM和SPRITE实验以及多重荧光原位杂交（FISH）单分子构象。研究人员发现，计算机模拟Hi-C、GAM和SPRITE批量数据忠实于参考3D结构，而单细胞数据则反映了单个分子之间的强烈变异性。在重复实验中，重现统计上相似联系所需的最小单元数在不同技术之间是不同的，在相同条件下，SPRITE最低，GAM最高。三种方法中，信噪比的水平遵循逆幂定律，具有检测效率，并且随着基因组距离的增长而不同，对于大于1 Mb的基因组分离，GAM最低。 

附：英文原文

Title: Comparison of the Hi-C, GAM and SPRITE methods using polymer models of chromatin

Author: Luca Fiorillo, Francesco Musella, Mattia Conte, Rieke Kempfer, Andrea M. Chiariello, Simona Bianco, Alexander Kukalev, Ibai Irastorza-Azcarate, Andrea Esposito, Alex Abraham, Antonella Prisco, Ana Pombo, Mario Nicodemi

Issue&Volume: 2021-05-07

Abstract: Hi-C, split-pool recognition of interactions by tag extension (SPRITE) and genome architecture mapping (GAM) are powerful technologies utilized to probe chromatin interactions genome wide, but how faithfully they capture three-dimensional (3D) contacts and how they perform relative to each other is unclear, as no benchmark exists. Here, we compare these methods in silico in a simplified, yet controlled, framework against known 3D structures of polymer models of murine and human loci, which can recapitulate Hi-C, GAM and SPRITE experiments and multiplexed fluorescence in situ hybridization (FISH) single-molecule conformations. We find that in silico Hi-C, GAM and SPRITE bulk data are faithful to the reference 3D structures whereas single-cell data reflect strong variability among single molecules. The minimal number of cells required in replicate experiments to return statistically similar contacts is different across the technologies, being lowest in SPRITE and highest in GAM under the same conditions. Noise-to-signal levels follow an inverse power law with detection efficiency and grow with genomic distance differently among the three methods, being lowest in GAM for genomic separations >1Mb.

DOI: 10.1038/s41592-021-01135-1

Source: https://www.nature.com/articles/s41592-021-01135-1