MiL-FISH: Multilabeled Oligonucleotides for Fluorescence <i>In Situ</i> Hybridization Improve Visualization of Bacterial Cells
Bibliographic record
Abstract
Fluorescence in situ hybridization (FISH) has become a vital tool for environmental and medical microbiology and is commonly used for the identification, localization, and isolation of defined microbial taxa. However, fluorescence signal strength is often a limiting factor for targeting all members in a microbial community. Here, we present the application of a multilabeled FISH approach (MiL-FISH) that (i) enables the simultaneous targeting of up to seven microbial groups using combinatorial labeling of a single oligonucleotide probe, (ii) is applicable for the isolation of unfixed environmental microorganisms via fluorescence-activated cell sorting (FACS), and (iii) improves signal and imaging quality of tissue sections in acrylic resin for precise localization of individual microbial cells. We show the ability of MiL-FISH to distinguish between seven microbial groups using a mock community of marine organisms and its applicability for the localization of bacteria associated with animal tissue and their isolation from host tissues using FACS. To further increase the number of potential target organisms, a streamlined combinatorial labeling and spectral imaging-FISH (CLASI-FISH) concept with MiL-FISH probes is presented here. Through the combination of increased probe signal, the possibility of targeting hard-to-detect taxa and isolating these from an environmental sample, the identification and precise localization of microbiota in host tissues, and the simultaneous multilabeling of up to seven microbial groups, we show here that MiL-FISH is a multifaceted alternative to standard monolabeled FISH that can be used for a wide range of biological and medical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".