MerQuaCo: a computational tool for quality control in image-based spatial transcriptomics
Abstract Image-based spatial transcriptomics platforms are powerful tools often used to identify cell populations and describe gene expression in intact tissue. Spatial experiments return large, high-dimensional datasets and several open-source software packages are available to facilitate analysis and visualization. The outputs of spatial transcriptomics platforms are typically imperfect. For example, local variations in transcript detection probability are common. Software tools to characterize imperfections and their impact on downstream analyses are lacking so the data quality is assessed manually, a laborious and often a subjective process. Here we describe imperfections in a dataset of 641 fresh-frozen adult mouse brain sections collected using the Vizgen MERSCOPE. Common imperfections included the local loss of tissue from the section, tissue outside the imaging volume due to detachment from the coverslip, transcripts missing due to dropped images, varying detection probability through space, and differences in transcript detection probability between experiments. We describe the incidence of each imperfection and the likely impact on the accuracy of cell type labels. We develop MerQuaCo, open-source code that detects and quantifies imperfections without user input, facilitating the selection of sections for further analysis with existing packages. Together, our results and MerQuaCo facilitate rigorous, objective assessment of the quality of spatial transcriptomics results.
Authors
- Delissa McMillen (ORCID: https://orcid.org/0000-0002-3413-4424)
- Chelsea M. Pagan (ORCID: https://orcid.org/0000-0002-3932-9992)
- Michael Kunst (ORCID: https://orcid.org/0000-0003-4159-8056)
- Amanda Gary (ORCID: https://orcid.org/0000-0002-6397-1520)
- Jennie Close (ORCID: https://orcid.org/0000-0001-9234-5855)
- Hongkui Zeng (ORCID: https://orcid.org/0000-0002-0326-5878)
- Naomi Martin (ORCID: https://orcid.org/0000-0002-0034-1628)
- Nasmil Valera Cuevas (ORCID: https://orcid.org/0000-0002-9846-3796)
- Elizabeth Liang (ORCID: https://orcid.org/0000-0001-6139-9228)
- Patrick Latimer
- Jazmin Campos (ORCID: https://orcid.org/0000-0002-9384-9913)
- Cliff Slaughterbeck (ORCID: https://orcid.org/0000-0003-2789-9071)
- Brian Long (ORCID: https://orcid.org/0000-0002-7793-5969)
- Jason Alexander (ORCID: https://orcid.org/0000-0001-6093-6116)
- Krissy Brouner
- Raymond Sanchez (ORCID: https://orcid.org/0000-0001-9141-090X)
- Jack Waters (ORCID: https://orcid.org/0000-0002-2312-4183)
- Josh Nagra
- Christina Alice Pom (ORCID: https://orcid.org/0000-0001-9057-040X)
- Jocelin Malone (ORCID: https://orcid.org/0000-0003-0794-4250)
- Emily Gelfand (ORCID: https://orcid.org/0000-0003-2175-5894)
- Lyudmila Shulga (ORCID: https://orcid.org/0009-0002-9785-328X)
- Jeanelle Ariza (ORCID: https://orcid.org/0000-0002-0829-7887)
- Cindy Huang (ORCID: https://orcid.org/0009-0002-5386-6345)
- Augustin Ruiz (ORCID: https://orcid.org/0000-0003-2633-2495)
- Paul Olsen (ORCID: https://orcid.org/0000-0001-8132-3722)
- Jose Melchor (ORCID: https://orcid.org/0009-0008-9759-0583)
- Alana Oyama (ORCID: https://orcid.org/0000-0002-4492-0533)
- Zoe Maltzer (ORCID: https://orcid.org/0000-0002-0993-7604)
- Angela Alaya
- Melissa Reding (ORCID: https://orcid.org/0000-0001-9845-1827)
- Ed S. Lein (ORCID: https://orcid.org/0000-0001-9012-6552)
- Tom Egdorf
- Cameron Bielstein (ORCID: https://orcid.org/0009-0006-0852-7255)
- Stuard Barta (ORCID: https://orcid.org/0000-0001-7180-3466)
- Yimin Wang (ORCID: https://orcid.org/0000-0002-4123-6269)
- Kaity Colbert
- Zachary Madigan (ORCID: https://orcid.org/0009-0001-4949-9637)
- Cade Reynoldson (ORCID: https://orcid.org/0009-0000-8658-0293)
- Jacob Quon
- Marshall VanNess
Institutions
- Allen Institute for Brain Science (US)
- Allen Institute (US)
Publication Details
- Journal
- eLife
- Published
- 2025-10-14
- DOI
- https://doi.org/10.7554/elife.105149.2
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00