Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.

Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial transcriptomics that combines deep-learning models for cell segmentation and spot detection with a probabilistic gene decoder to quantify single-cell gene expression accurately. Polaris offers a unifying, turnkey solution for analyzing spatial transcriptomics data from multiplexed error-robust FISH (MERFISH), sequential fluorescence in situ hybridization (seqFISH), or in situ RNA sequencing (ISS) experiments. Polaris is available through the DeepCell software library (https://github.com/vanvalenlab/deepcell-spots) and https://www.deepcell.org.

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Copyright © 2024. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

Declaration of interests D.V.V. is a co-founder of Barrier Biosciences and holds equity in the company. D.V.V., E.L., and N.R. filed a patent for weakly supervised deep learning for spot detection. J.R.M. is co-founder and scientific advisor to Vizgen and holds equity in the company. J.R.M. is an inventor of patents related to MERFISH filed on his behalf by Harvard University and Boston Children’s Hospital.