PiP‐Plex: A Particle‐in‐Particle System for Multiplexed Quantification of Proteins Secreted by Single Cells
Bibliographic record
Abstract
Abstract Cell signaling is modulated by the secretion of various proteins, which can be used to infer a cell's phenotype. However, these proteins cannot be readily detected in multiplex by commonly used methods at the single‐cell level. Here we present PiP‐plex a particles‐in‐particle (PiPs) system for multiplex protein secretion analysis by confocal microscopy. PiP‐plex‐comprises (i) fluorescence intensity barcoded microparticles (BMPs) co‐entrapped with (ii) a single cell inside an alginate hydrogel particle. We found that PiPs maintained >90% cellular viability and allowed live cells retrieval. A seven‐plex fluorescent barcoding and concomitant sandwich immunoassay in PiPs was developed with limits of detection ranging from 0.8 pg mL −1 to 2 ng mL −1 depending on the protein. PiP‐plex assays were benchmarked with bulk immunoassays and found to rival or outperform them. Proteins secreted by single THP‐1 cells upon exposure to lipopolysaccharide were measured by PiP‐plex and varying cell responses detected, including a significant increase in MIP‐1α, TNF‐α, and IL‐17A; MIP‐1α and IL‐17A were the most frequently secreted cytokines, while other cytokines were typically co‐secreted. Using PiP‐plex, we analyzed ≈750 THP‐1 cells, showcasing its potential for characterizing cells and cell‐based therapeutics for e.g. cancer immunotherapies.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".