A programmable droplet-based microfluidic device applied to multiparameter analysis of single microbes and microbial communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present a programmable droplet-based microfluidic device that combines the reconfigurable flow-routing capabilities of integrated microvalve technology with the sample compartmentalization and dispersion-free transport that is inherent to droplets. The device allows for the execution of user-defined multistep reaction protocols in 95 individually addressable nanoliter-volume storage chambers by consecutively merging programmable sequences of picoliter-volume droplets containing reagents or cells. This functionality is enabled by "flow-controlled wetting," a droplet docking and merging mechanism that exploits the physics of droplet flow through a channel to control the precise location of droplet wetting. The device also allows for automated cross-contamination-free recovery of reaction products from individual chambers into standard microfuge tubes for downstream analysis. The combined features of programmability, addressability, and selective recovery provide a general hardware platform that can be reprogrammed for multiple applications. We demonstrate this versatility by implementing multiple single-cell experiment types with this device: bacterial cell sorting and cultivation, taxonomic gene identification, and high-throughput single-cell whole genome amplification and sequencing using common laboratory strains. Finally, we apply the device to genome analysis of single cells and microbial consortia from diverse environmental samples including a marine enrichment culture, deep-sea sediments, and the human oral cavity. The resulting datasets capture genotypic properties of individual cells and illuminate known and potentially unique partnerships between microbial community members.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it