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Evaluating surface bacterial growth at buried interfaces can be problematic due to the difficulties associated with obtaining samples. In this work, we present a new method to detect signals from microorganisms at buried interfaces that is nondestructive and can be conducted continuously. Inspired by vascular systems in nature that permit chemical communication between the surface and underlying tissues of an organism, we created a system in which an inert carrier fluid could be introduced into an empty vascular network embedded in a polymer matrix. When a microorganism layer was grown on top, small molecules produced by the growth process would diffuse down into the carrier fluid, which could then be collected and analyzed. We used this system to nondestructively detect signals from a surface layer of Escherichia coli using conductivity, ultraviolet–visible (UV–vis) absorbance spectroscopy, and high-performance liquid chromatography (HPLC) for organic acids, methods that ranged in sensitivity, time-to-result, and cost. Carrier fluid from sample vascularized polymers with surface bacterial growth recorded significantly higher values in both conductivity and absorbance at 350 nm compared to controls with no bacteria after 24 h. HPLC analysis showed three clear peaks that varied between the samples with bacteria and the controls without. Tests tracking the change in signals over 48 h showed clear trends that matched the bacterial growth curves, demonstrating the system’s ability to monitor changes over time. A 2D finite element model of the system closely matched the experimental results, confirming the predictability of the system. Finally, tests using clinically relevant Staphylococcus aureus and Pseudomonas aeruginosa yielded differences in conductivity, absorbance, and HPLC peak areas unique to each species. This work lays the foundation for the use of vascularized polymers as an adaptive system for the continuous, nondestructive detection of surface microorganisms at buried interfaces in both industry and medicine.
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hplc-py: A Python Utility For Rapid Quantification ofComplex Chemical Chromatograms
High-Performance Liquid Chromatography (HPLC) and Gas Chromatography are analytical techniques which allow for the quantitative characterization of the chemical components of mixtures. Technological advancements in sample preparation and mechanical automation have allowed HPLC to become a high-throughput tool which poses new challenges for reproducible and rapid analysis of the resulting chromatograms. Here we present hplc-py, a Python package that permits rapid and reliable quantitation of component signals within a chromatogram for pipelined workflows. This is achieved by a signal detection and quantitation algorithm which i) identifies windows of time which contain peaks and ii) infers the parameters of a mixture of amplitude-weighted skew-normal distributions which sum to reconstruct the observed signal. This approach is particularly effective at deconvolving highly overlapping signals, allowing for precise absolute quantitation of chemical constituents with similar chromatographic retention times.
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- Award ID(s):
- 2010807
- PAR ID:
- 10493112
- Publisher / Repository:
- Journal of Open Source Software
- Date Published:
- Journal Name:
- Journal of Open Source Software
- Volume:
- 9
- Issue:
- 94
- ISSN:
- 2475-9066
- Page Range / eLocation ID:
- 6270
- Subject(s) / Keyword(s):
- Analytical chemistry, High-Performance Liquid Chromatography, Software
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.21105/joss.06270
