Spotiflow: accurate and efficient spot detection for imaging-based spatial transcriptomics with stereographic flow regression

Abstract

The identification of spot-like structures in large and noisy microscopy images is an important task in many life science techniques, and it is essential to their quantitative performance. For example, imaging-based spatial transcriptomics (iST) methods rely critically on the accurate detection of millions of transcripts in low signal-to-noise ratio (SNR) images. While recent developments in computer vision have revolutionized many bioimage tasks, currently adopted spot detection approaches for iST still rely on classical signal processing methods that are fragile and require manually tuning. In this work we introduce Spotiflow, a deep-learning method that casts the spot-detection problem as a multiscale stereographic flow regression problem that yields subpixel-accurate localizations. Spotiflow is robust to different noise conditions and generalizes across different chemistries while being up to an order-of-magnitude more time and memory efficient than commonly used methods. We show the efficacy of Spotiflow by comprehensive quantitative comparisons against other methods on a variety of datasets and demonstrate the impact of its increased accuracy on the biological conclusions drawn from iST and live imaging experiments. Spotiflow is available as an easy-to-use Python library as well as a napari plugin at http://www.github.com/weigertlab/spotiflow.