The DOI matches its Crossref record.
Multifunctional Encoded Particles for High-Throughput Biomolecule Analysis
Abstract
High-throughput screening for genetic analysis, combinatorial chemistry, and clinical diagnostics benefits from multiplexing, which allows for the simultaneous assay of several analytes but necessitates an encoding scheme for molecular identification. Current approaches for multiplexed analysis involve complicated or expensive processes for encoding, functionalizing, or decoding active substrates (particles or surfaces) and often yield a very limited number of analyte-specific codes. We present a method based on continuous-flow lithography that combines particle synthesis and encoding and probe incorporation into a single process to generate multifunctional particles bearing over a million unique codes. By using such particles, we demonstrate a multiplexed, single-fluorescence detection of DNA oligomers with encoded particle libraries that can be scanned rapidly in a flow-through microfluidic channel. Furthermore, we demonstrate with high specificity the same multiplexed detection using individual multiprobe particles.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Janus Particles: Synthesis, Self-Assembly, Physical Properties, and Applications
Walther, A., & Müller, A. H. E. (2013) · Chemical Reviews · doi:10.1021/cr300089t · Cited by 1,769
Physical approaches to biomaterial design
Mitragotri, S., & Lahann, J. (2009) · Nature Materials · doi:10.1038/nmat2344 · Cited by 1,445
Diverse Applications of Nanomedicine
Pelaz, B., Alexiou, C., Alvarez-Puebla, R. A., Alves, F., Andrews, A. M., Ashraf, S., Balogh, L. P., Ballerini, L., Bestetti, A., Brendel, C., et al. (2017) · ACS Nano · doi:10.1021/acsnano.6b06040 · Cited by 1,350
Synthesis of micro and nanostructures in microfluidic systems
Marre, S., & Jensen, K. F. (2010) · Chemical Society Reviews · doi:10.1039/b821324k · Cited by 734
Amini, H., Lee, W., & Di Carlo, D. (2014) · Lab on a Chip · doi:10.1039/c4lc00128a · Cited by 722
Metadata
- Authors
- Pregibon, D. C., Toner, M., & Doyle, P. S.
- Venue
- Science
- Published
- 2007-03-09
- Volume
- 315
- Issue
- 5817
- Pages
- 1393-1396
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2027571016
- Cited by
- 726
- References
- 0
- FWCI
- 21.91
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Multifunctional Encoded Particles for High-Throughput Biomolecule Analysis. https://app.cochat.ai/papers/10.1126/science.1134929
- The paper
- Pregibon, D. C., Toner, M., & Doyle, P. S. (2007). Multifunctional Encoded Particles for High-Throughput Biomolecule Analysis. Science. https://doi.org/10.1126/science.1134929
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.