Automated Bacterial Identification and Morphological Feature Analysis in Low-Dose Cryo-EM Using YOLOv11

Abstract

Bacteria rapidly adapt to environmental cues through morphological and ultrastructural changes that correlate with physiology and behavior. Cryogenic transmission electron microscopy (cryo-TEM) can capture these phenotypic changes in near-native, vitrified states, but manual analysis of low-dose micrographs is labor-intensive and limits throughput. Here, we present an end-to-end workflow that combines low-dose cryo-TEM imaging with a YOLOv11-based instance-segmentation model to automatically identify bacteria and quantify key structural features directly from the micrographs. This workflow enables (i) robust bacterial localization and counting from low-magnification atlas/montage images, (ii) automated measurements of cell-envelope (outer–inner membrane) thickness and anisotropy from higher-magnification views, and (iii) detection and quantification of bacteria–flagella interactions, including overlap length and curvature metrics for interacting versus non-interacting flagella. Using Pantoea sp. YR343 grown under distinct media conditions, we show that the automated measurements agree with manual annotations while substantially reducing analysis time. Together, these tools provide a practical framework for scalable bacterial identification and quantitative phenotyping in low-dose cryo-TEM datasets, and establish a foundation for extending cryo-TEM image analysis toward higher-throughput studies of microbial heterogeneity and biointerfaces.