Automation of Spatial Transcriptomics library preparation to enable rapid and robust insights into spatial organization of tissues.

Berglund E, Saarenpää S, Jemt A, Gruselius J, Larsson L, Bergenstråhle L, Lundeberg J, Giacomello S

BMC Genomics 21 (1) 298 [2020-04-15; online 2020-04-15]

Interest in studying the spatial distribution of gene expression in tissues is rapidly increasing. Spatial Transcriptomics is a novel sequencing-based technology that generates high-throughput information on the distribution, heterogeneity and co-expression of cells in tissues. Unfortunately, manual preparation of high-quality sequencing libraries is time-consuming and subject to technical variability due to human error during manual pipetting, which results in sample swapping and the accidental introduction of batch effects. All these factors complicate the production and interpretation of biological datasets. We have integrated an Agilent Bravo Automated Liquid Handling Platform into the Spatial Transcriptomics workflow. Compared to the previously reported Magnatrix 8000+ automated protocol, this approach increases the number of samples processed per run, reduces sample preparation time by 35%, and minimizes batch effects between samples. The new approach is also shown to be highly accurate and almost completely free from technical variability between prepared samples. The new automated Spatial Transcriptomics protocol using the Agilent Bravo Automated Liquid Handling Platform rapidly generates high-quality Spatial Transcriptomics libraries. Given the wide use of the Agilent Bravo Automated Liquid Handling Platform in research laboratories and facilities, this will allow many researchers to quickly create robust Spatial Transcriptomics libraries.

Bioinformatics Support for Computational Resources [Service]

NGI Stockholm (Genomics Applications) [Service]

NGI Stockholm (Genomics Production) [Service]

National Genomics Infrastructure [Service]

PubMed 32293264

DOI 10.1186/s12864-020-6631-z

Crossref 10.1186/s12864-020-6631-z

pii: 10.1186/s12864-020-6631-z
pmc: PMC7158132

Publications 9.5.0