PAMP: an automated high-resolution image analysis pipeline for life cycle quantitative morphological profiling in filamentous fungi

Morphological development in filamentous fungi critically impacts nutrient and oxygen transfer, broth rheology, and metabolite production during submerged fermentation. However, life-cycle-resolved quantitative analysis remains technically challenging because fungal morphology dynamically spans multiple spatial scales from germinating spores and branching hyphae to mycelial pellets. Here, we developed PAMP, Python-based Automated Morphology analysis Pipeline, integrating skeletonization with quantitative image analysis for high-resolution morphological profiling throughout the fungal life cycle. Using two Aspergillus niger strains, MA70.15 and D353.8, PAMP accurately captures distinct morphogenetic trajectories associated with protein secretion and citric acid production. Application to pkaC conditional-expression mutant further reveals that graded PkaC activity modulates hyphal extension, branching frequency and pellet porosity, thereby improving citric acid production. Correlation analysis identifies peripheral hyphae and pellet porosity as major morphology determinants of productivity, which were not detectable by conventional pellet-analysis tools due to their limited resolution of peripheral hyphae. PAMP aslo demonstrates robust performance across multiple filamentous fungi, including Fusarium venenatum and Acremonium chrysogenum. Overall, PAMP provides a robust platform for morphology-guided strain engineering and fermentation optimization in fungal biotechnology.