Machine Learning Algorithms Applied to Identify Microbial Species by Their Motility

Abstract

(1) Background: Future missions to potentially habitable places in the Solar System require biochemistry-independent methods for detecting potential alien life forms. The technology was not advanced enough for onboard machine analysis of microscopic observations to be performed in past missions, but recent increases in computational power make the use of automated in-situ analyses feasible. (2) Methods: Here, we present a semi-automated experimental setup, capable of distinguishing the movement of abiotic particles due to Brownian motion from the motility behavior of the bacteria Pseudoalteromonas haloplanktis, Planococcus halocryophilus, Bacillus subtilis, and Escherichia coli. Supervised machine learning algorithms were also used to specifically identify these species based on their characteristic motility behavior. (3) Results: While we were able to distinguish microbial motility from the abiotic movements due to Brownian motion with an accuracy exceeding 99%, the accuracy of the automated identification rates for the selected species does not exceed 82%. (4) Conclusions: Motility is an excellent biosignature, which can be used as a tool for upcoming life-detection missions. This study serves as the basis for the further development of a microscopic life recognition system for upcoming missions to Mars or the ocean worlds of the outer Solar System.

Digital holographic microscopy for bacterial species classification and motility characterization

“…Many of these works rely on deep learning the quantitative phase images of cells, which are produced by off-axis DHM or similar techniques and are typically not accessible by inline DHM. There are also reports on machine learning bacteria's motility characteristics to identify bacterial cells, especially in studies aimed at searching for extraterrestrial life 12,13 . However, using motility characteristics to classify species may not be suitable for investigating the interactions of bacteria, as their swimming behavior could be varied in the mixture, different from when they are the sole species in the environment.…”

The Hypothesis of a “Living Pulse” in Cells

“…In fact, for most microbes, we do not even know how they move. Nevertheless, paths taken by microbes can be tracked with machine learning methods and the type of organism can be identified, in some cases down to the species level [3]. We do know, however, that motility is an early trait of the evolution of life that is present in all kingdoms of life [4][5][6].…”

Quantification of Motility in Bacillus subtilis at Temperatures Up to 84°C Using a Submersible Volumetric Microscope and Automated Tracking

“…The persistence of the motility phenotype under conditions of heat stress has been little explored, largely due to technical difficulties of imaging and image analysis as temperatures rise. One study reported that convection currents made tracking difficult above 40°C ( Riekeles et al, 2021 ). In addition, most heated stages can only achieve temperatures of 55–60°C.…”