Spatiotemporal Analysis of Secondary Metabolites Produced by S.coelicolor Using Automated Sampling Mass Spectrometry
Bibliographic record
Abstract
Natural products (NPs) are organic compounds that are synthesized by living organisms such as bacteria. NPs and their derivatives are very useful in the medical field to develop new drugs, particularly new antibiotics which are crucial to combat developing antibiotic resistance. The current research on the secondary metabolites (SMs) produced throughout the growth cycle of bacteria is limited, as the standard procedure is to examine the SM production at the end of the bacteria’s growth cycle. Therefore, there may be many undiscovered SMs which are produced and consumed prior to analysis. A mass spectrometer (MS) can be used to track analytes of interest, such as SMs. When the MS is paired with an ambient ionization technique like the Liquid Micro-Junction Surface Sampling Probe (LMJ-SSP) it works as a minimally invasive approach to track the SMs in real time produced throughout the bacterial growth cycle. This allows for the SMs to be tracked both temporally and spatially without killing the bacteria. The process is automated using the LMJ-SSP mounted to a 3D printer, allowing for a grid-like sampling process to map NPs production. Herein, A 3D-printed growth chamber dubbed the “Washing And Sampling Agar Bacterial Inoculation” (WASABI) Box was designed to resemble a sampling grid for simpler analysis. Another key feature of the WASABI Box are washing ports, containing 70% ethanol, to sterilize the LMJ-SSP and minimize contamination between rows. The SM production of S. coelicolor in the WASABI Box was analyzed from day 4 to 7 of the bacterial growth cycle, where the data was used to generate spatial-temporal heat maps tracking metabolites including Albaflavenone (m/z 219.2), Desferrioxamine B (m/z 561.4), Germicidin B (m/z 183.1), Kalafungin (m/z 301.1), and Streptorubin B (m/z 392.3).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".