MAPPER: An Open-Source, High-Dimensional Image Analysis Pipeline Unmasks Differential Regulation of Drosophila Wing Features

Abstract

Phenomics requires quantification of large volumes of image data, necessitating high throughput image processing approaches. Existing image processing pipelines for Drosophila wings, a powerful genetic model for studying the underlying genetics for a broad range of cellular and developmental processes, are limited in speed, precision, and functional versatility. To expand on the utility of the wing as a phenotypic screening system, we developed MAPPER, an automated machine learning-based pipeline that quantifies high-dimensional phenotypic signatures, with each dimension quantifying a unique morphological feature of the Drosophila wing. MAPPER magnifies the power of Drosophila phenomics by rapidly quantifying subtle phenotypic differences in sample populations. We benchmarked MAPPER’s accuracy and precision in replicating manual measurements to demonstrate its widespread utility. The morphological features extracted using MAPPER reveal variable sexual dimorphism across Drosophila species and unique underlying sex-specific differences in morphogen signaling in male and female wings. Moreover, the length of the proximal-distal axis across the species and sexes shows a conserved scaling relationship with respect to the wing size. In sum, MAPPER is an open-source tool for rapid, high-dimensional analysis of large imaging datasets. These high-content phenomic capabilities enable rigorous and systematic identification of genotype-to-phenotype relationships in a broad range of screening and drug testing applications and amplify the potential power of multimodal genomic approaches.

Acis-regulatory sequence of the wing selector gene,vestigial, drives the evolution of scaling relationships inDrosophilaspecies

“…We then choose two species within the melanogaster subgroup ( D. melanogaster and D. simulans ), another species outside of this subgroup but still belonging to the melanogaster group ( D. ananassae ), and another species from an outside group ( D. virilis ). Wings in these species are similar in shape and pattern (Kumar et al, 2022), but animals across these species vary considerably in whole-body size (Morin et al, 1996). This suggests that the evolution of wing-to-body allometries may be investigated by comparing these species.…”

G protein-coupled Receptor Contributions to Wing Growth and Morphogenesis inDrosophila melanogaster

“…Here, we report a systematic RNAi-based investigation into the phenotypes associated with inhibition of 111 GPCRs along with 8 Gα, 3 Gβ, and 2 Gγ proteins during Drosophila wing development (Figure 1B). For quantitative analysis, we employed our comprehensive pipeline, MAPPER (Kumar et al 2022), to perform highcontent genetic wing screening via deep learning for image segmentation and machine learning for feature classification (Figure 1C). For qualitative analysis, the ResNet-50 convolutional neural network (He et al 2015;LeCun et al 2015) was used in tandem with a support-vector machine (Wang 2005) for classification of severe (which was not certified by peer review) is the author/funder.…”

“…These advantages enable rapid phenotypic screening of genes in Drosophila with results that are directly relevant to human biology (Belacortu and Paricio 2011; Perrimon et al . 2016; Kumar et al . 2022).…”

“…2014; Narciso and Zartman 2018; Restrepo et al . 2014; Strigini and Cohen 1999; Kumar et al . 2022).…”

“…2022). More specifically, targeted genetic perturbations induced by RNA interference (RNAi) can be used to uncover novel biological insights and future research directions in developmental biology using Drosophila (Kumar et al . 2022; George et al .…”