Boström Johan, Zapaɫa Michaɫ, Adameyko Igor
Department of Neuroimmunology, Center for Brain Research, Medical University Vienna, Vienna, Austria.
Maria Grzegorzewska University, Warsaw, Poland.
Sci Adv. 2025 May 2;11(18):eadr4026. doi: 10.1126/sciadv.adr4026.
In the last decades, image-based transcriptomic and proteomic experiments have moved from single-target probes to multiplexed experiments, allowing researchers to study hundreds or even thousands of mRNA and protein targets simultaneously. This large increase in scope necessitates methods in either increased specificity or in error correction, such as the Hamming codes used in the imaging-based spatial transcriptomic method MERFISH. For some experimental conditions, Hamming codes are efficient in encoding the highest possible number of genes for spatial analysis. However, for most experimental parameters, the optimal generation of error-robust codebooks is an unsolved mathematical problem. Here, we present a method to generate highly optimized extended Hamming codebooks compatible with established error-correctable methodologies such as MERFISH. Our method uses an iterative set-exchange approach and generally reaches over 90% of the theoretical maximum limit of gene set complexity. We also provide ready-to-use codebooks and discuss the advantages and disadvantages of changing probe density.
在过去几十年中,基于图像的转录组学和蛋白质组学实验已从单靶点探针转向多重实验,使研究人员能够同时研究数百甚至数千个mRNA和蛋白质靶点。研究范围的大幅扩大需要提高特异性或纠错的方法,例如基于成像的空间转录组学方法MERFISH中使用的汉明码。在某些实验条件下,汉明码能有效地对尽可能多的基因进行编码以用于空间分析。然而,对于大多数实验参数而言,生成抗错码本的最佳方法仍是一个未解决的数学问题。在此,我们提出一种方法,可生成与MERFISH等既定纠错方法兼容的高度优化的扩展汉明码本。我们的方法采用迭代集交换方法,通常能达到基因集复杂性理论最大极限的90%以上。我们还提供了现成可用的码本,并讨论了改变探针密度的优缺点。