Microencapsulation of intestinal bacteria
Bibliographic record
Abstract
The human gut microbiota plays an important role in human health; however, our understanding of the bacteria which make up the microbiota remains limited. The gaps in current knowledge can be attributed to the difficulties in culturing a major part of this diverse community using conventional in vitro techniques. The lack or absence of cell-to-cell communication and the inability to accurately replicate optimal growth conditions and nutrient requirements are some of the key limitations apparent in standard culture. Additionally, current genomic techniques struggle to reveal bacterial physiology and function, as 16S rRNA and shotgun sequencing often miss crucial microbial roles and are affected by host DNA contamination. To address this challenge, we propose an innovative approach to support the growth of difficult-to-culture bacteria by employing droplet-based microfluidics. Microfluidics techniques have shown promise in human gut microbiota, research, but the role of microencapsulation on bacteria enrichment has yet to be explored. Microencapsulation has been explored for probiotic formulation and delivery, but no studies have attempted to harness the benefits of microencapsulation to promote viability and increase biomass of difficult-to-culture gut bacteria. This study explores the use of four-arm poly(ethylene glycol) maleimide (PEG4MAL), a synthetic, non-digestible, and biocompatible material, for the microencapsulation of highly sensitive anaerobic bacteria. PEG4MAL is functionalized with 0.8 mM of RGD, and crosslinking is achieved through a rapid thiol-based reaction with 20mM dithiothreitol (DTT). Encapsulation is performed using a 70 µm junction microfluidic chip for droplet generation with syringe pump driven flow. Encapsulated Escherichia coli and Akkermansia muciniphila are fluorescently imaged after staining with a Live Dead Assay. We have demonstrated that single-cell isolation can be achieved through microbead production, which will enable the isolation of species for community-based culture. It was observed that cell behavior can be influenced by manipulating the concentration of PEG4MAL, with increased concentrations resulting in higher cell motility. Further modification of PEG4MAL through functionalization with arginylglycylaspartic acid (RGD) led to enhanced cell density within the microbeads due to peptide-cell interactions. By employing this approach, we achieved high cell viability and colony formation within the microbeads of the difficult-to-culture Akkermansia muciniphila. This increased aggregation facilitates the capture and analysis of a broader range of bacterial species, including those that are considered ‘unculturable’.This work opens the door to in situ cultivation, allowing researchers to better characterize the human gut microbiota composition and its complex interactions with human health and disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".