STARCall integrates image stitching, alignment, and read calling to enable scalable analysis of in situ sequencing data

Abstract Fluorescent in situ sequencing involves imaging-based sequencing by synthesis in intact cells or tissues to reveal target nucleotide sequences inside each cell. Often, the target sequences are barcodes that indicate a perturbation (e.g. CRISPR guide or genetic variant) delivered to the cell. However, processing in situ sequencing data presents a considerable challenge, requiring stitching and aligning tens of thousands of images with millions of cells, detecting small amplicon colonies across sequencing cycles, and calling reads. To address these challenges, we introduce STARCall: STitching, Alignment and Read Calling for in situ sequencing, a software package that analyzes raw in situ sequencing images to produce a genotype-to-phenotype mapping for each cell. STAR-Call improves upon previous solutions by combining stitching and alignment of images into a single step that minimizes both inter-cycle and intra-cycle alignment error. STARCall also improves detection and extraction of sequencing reads, incorporating filters and normalization to combat background fluorophore signal. We compare STARCall to other methods using a diverse set of images that include commonly encountered imaging problems such as variable intensity across channels and cycles and high levels of background. Specifically, this comprises ∼250,000 images from a pooled screen of ∼3,500 barcoded LMNA variants expressed in U2OS cells and ∼1,200 bar-coded PTEN variants in induced pluripotent stem cells (iPSC) and iPSC-derived neurons. Overall, STARCall aligned more than 50% of tiles with