Application of quantitative phase microscopy in microbiology for label-free imaging of bacteria

Abstract

Bacteria are the planet’s oldest and the most common life forms. Bacteria have developed alongside humans and are good and harmful to our health. Our bodies contain nearly ten times the number of bacteria as human cells, and this natural microbiota is critical for appropriate development, nutrition, and disease resistance. Unfortunately, we live in an environment rich with bacteria that may cause a wide range of human diseases.[1] Antimicrobial resistance (AMR) occurs when bacteria no longer remain vulnerable to the antimicrobial for which it was responsive in the past. Around 33,000 Europeans die each year from infections caused by (AMR) bacteria [2]. However, this number will be ten times the current number of deaths by 2050 if AMR develops rapidly [2]. Furthermore, the lack of new antibiotics in the development or trial phases is causing concern, particularly for multidrug resistant bacteria that manufacture extended-spectrum beta-lactamase (ESBLs) and carbapenems. Enterobacteriaceae (E. coli and Klebsiella pneumonia) is a family of bacteria that belongs to the WHO’s priority one pathogen list.[3] In this thesis, we tried to see if it is possible to see the potential difference between an AMR and a nonAMR bacterium. Also, we want to explore if it is possible to visualize any difference between different AMR bacteria cells. And to see the bacteria, we need an imaging technique suitable for imaging at high speed without the need for labels. The next important fact is if the method is quantitative, we might be able to see the difference in the quantitative parameters of the two types of bacteria cells. We had one such option in our laboratory as Quantitative phase microscopy (QPM). QPM is a noncontact, noninvasive, and label-free methodology that can quantify various morphological and statistical parameters such as refractive index, height, dry mass, surface area, volume, sphericity, mean associated with biological specimens.[4] This thesis aims to obtain QPM images of three different bacteria species: E. coli, Klebsiella pneumonia (K. pneumonia), and Staphylococcus aureus (S. aureus). The E. coli bacteria have two different strains: E. coli(CCUG17620 and NCTC13441). One of them is the wild type without an antimicrobial resistance gene, and the other is the nonwild type with an AMR gene and, in this case, will be an extended-spectrum beta-lactamase ESBLs. Except for one bacteria sample, all others were with AMRgene. The primary hypothesis was to investigate any difference in the morphology and quantitative parameters obtained by the QPM images of four different bacteria. The longterm aim was to examine if QPM can be used to image and classify bacteria. First, a systematic characterization of the QPM system is performed in terms of spatial phase sensitivity, temporal stability, spatial resolution, and defocus correction is done after phase recovery. Next, QPM imaging of four different bacteria sampled is done to investigate morphological parameter changes at a single wavelength. Further, the work is extended with multispectral QPM of these bacteria samples to develop new biomarkers related to them. In the future, the result can be fueled with the power of machine learning for the classification of these bacteria samples based on the quantitative parameters extracted from their QPM images.