Hyperspectral Visualization-Based Mass Spectrometry Imaging by LMJ-SSP: A Novel Strategy for Rapid Natural Product Profiling in Bacteria.

Mass spectrometry imaging (MSI) has been widely used to discover natural products (NPs) from underexplored microbiological sources. However, the technique is limited by incompatibility with complicated/uneven surface topography and labor-intensive sample preparation, as well as lengthy compound profiling procedures. Here, liquid micro-junction surface sampling probe (LMJ-SSP)-based MSI is used for rapid profiling of natural products from Gram-negative marine bacteria Pseudoalteromonas on nutrient agar media without any sample preparation. A conductance-based autosampling platform with 1 mm spatial resolution and an innovative multivariant analysis-driven method was used to create one hyperspectral image for the sampling area. NP discovery requires general spatial correlation between m/z and colony location but not highly precise spatial resolution. The hyperspectral image was used to annotate different m/z by straightforward color differences without the need to directly interrogate the spectra. To demonstrate the utility of our approach, the rapid analysis of Pseudoalteromonas rubra DSM6842, Pseudoalteromonas tunicata DSM14096, Pseudoalteromonas piscicida JCM20779, and Pseudoalteromonas elyakovii ATCC700519 cultures was directly performed on Agar. Various natural products, including prodiginine and tambjamine analogues, were quickly identified from the hyperspectral image, and the dynamic extracellular environment was shown with compound heatmaps. Hyperspectral visualization-based MSI is an efficient and sensitive strategy for direct and rapid natural product profiling from different Pseudoalteromonas strains.

3D printer platform and conductance feedback loop for automated imaging of uneven surfaces by liquid microjunction-surface sampling probe mass spectrometry.

RATIONALE: Molecular imaging of samples using mass spectrometric techniques, such as matrix-assisted laser desorption ionization or desorption electrospray ionization, requires the sample surface to be even/flat and sliced into thin sections (c. 10 µm). Furthermore, sample preparation steps can alter the analyte composition of the sample. The liquid microjunction-surface sampling probe (LMJ-SSP) is a robust sampling interface that enables surface profiling with minimal sample preparation. In conjunction with a conductance feedback system, the LMJ-SSP can be used to automatically sample uneven specimens. METHODS: A sampling stage was built with a modified 3D printer where the LMJ-SSP is attached to the printing head. This setup can scan across flat and even surfaces in a predefined pattern ("static sampling mode"). Uneven samples are automatically probed in "conductance sampling mode" where an electric potential is applied and measured at the probe. When the probe contacts the electrically grounded sample, the potential at the probe drops, which is used as a feedback signal to determine the optimal position of the probe for sampling each location. RESULTS: The applicability of the probe/sensing system was demonstrated by first examining the strawberry tissue using the "static sampling mode." Second, porcine tissue samples were profiled using the "conductance sampling mode." With minimal sample preparation, an area of 11 × 15 mm was profiled in less than 2 h. From the obtained results, adipose areas could be distinguished from non-adipose parts. The versatility of the approach was further demonstrated by directly sampling the bacteria colonies on agar and resected human kidney (intratumoral hemorrhage) specimens with thicknesses ranging from 1 to 4 mm. CONCLUSION: The LMJ-SSP in conjunction with a conductive feedback system is a powerful tool that allows for fast, reproducible, and automated assessment of uneven surfaces with minimal sample preparation. This setup could be used for perioperative assessment of tissue samples, food screening, and natural product discovery, among others.

Advances in microbial culturing conditions to activate silent biosynthetic gene clusters for novel metabolite production.

Natural products (NPs) produced by bacteria and fungi are often used as therapeutic agents due to their complex structures and wide range of bioactivities. Enzymes that build NPs are encoded by co-localized biosynthetic gene clusters (BGCs), and genome sequencing has recently revealed that many BGCs are "silent" under standard laboratory conditions. There are numerous methods used to activate "silent" BGCs that rely either upon altering culture conditions or genetic modification. In this review, we discuss several recent microbial cultivation methods that have been used to expand the scope of NPs accessible in the laboratory. These approaches are divided into three categories: addition of a physical scaffold, addition of small molecule elicitors, and co-cultivation with another microbe.

SpectraX: A Straightforward Tool for Principal Component Analysis-Based Spectral Analysis.

The analysis of complex spectra is an important component of direct/ambient mass spectrometry (MS) applications such as natural product screening. Unlike chromatography-based metabolomics or proteomics approaches, which rely on software and algorithms, the work of spectral screening is mostly performed manually in the initial stages of research and relies heavily on the experience of the analyst. As a result, throughput and spectral screening reliability are problematic when dealing with large amounts of data. Here, we present SpectraX, a MATLAB-based application, which can analyze MS spectra and quickly locate m/z features from them. Principal component analysis (PCA) is used to analyze the data set, and scoring plots are presented to help in understanding the clustering of data. The algorithm uses mass to charge (m/z) features to produce a list of potential natural products.