On technical biases in imaging spatial transcriptomics
- 1University of Padova
Abstract
Imaging spatial transcriptomics (iST) has revolutionized our ability to contextualize gene expression within intact tissue architectures at single-cell and subcellular resolution. However, these platforms introduce complex technical biases that span the entire measurement pipeline -- from sample preparation to data acquisition to quantification. Here, we categorize these biases into experimental (e.g., sample preservation and spatial sampling) and computational sources (e.g., z-stack registration and field-of-view stitching). Using public datasets, we demonstrate how different biases can compound and manifest, and propose a unified count modeling framework to formalize these sources of variation. Finally, we call for a more integrated understanding of iST data origins and greater knowledge sharing within and across sectors to support the prospective development of more robust experimental design and reliable downstream analysis.
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BibTeX
@misc{chen2026technical,
title = {{On technical biases in imaging spatial transcriptomics}},
author = {C. Chen and L. E. Borm and A. Pascual-Reguant and I. Ruano and D. Risso and H. L. Crowell},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.16.759529},
url = {https://www.biorxiv.org/content/10.64898/2026.10.16.759529v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - On technical biases in imaging spatial transcriptomics
AU - C. Chen
AU - L. E. Borm
AU - A. Pascual-Reguant
AU - I. Ruano
AU - D. Risso
AU - H. L. Crowell
PY - 2026
DA - 2026/10/16
PB - bioRxiv
DO - 10.64898/2026.10.16.759529
UR - https://www.biorxiv.org/content/10.64898/2026.10.16.759529v1
AB - Imaging spatial transcriptomics (iST) has revolutionized our ability to contextualize gene expression within intact tissue architectures at single-cell and subcellular resolution. However, these platforms introduce complex technical biases that span the entire measurement pipeline -- from sample preparation to data acquisition to quantification. Here, we categorize these biases into experimental (e.g., sample preservation and spatial sampling) and computational sources (e.g., z-stack registration and field-of-view stitching). Using public datasets, we demonstrate how different biases can compound and manifest, and propose a unified count modeling framework to formalize these sources of variation. Finally, we call for a more integrated understanding of iST data origins and greater knowledge sharing within and across sectors to support the prospective development of more robust experimental design and reliable downstream analysis.
ER -