Evidence map›Paper›PMID 39487108›Full record

ArticleNature communications2024

NOVAsort for error-free droplet microfluidics.

Han Zhang, Rohit Gupte, Yuwen Li, Can Huang, Adrian R Guzman, Jeong Jae Han, Haemin Jung, Rushant Sabnis, Paul de Figueiredo, Arum Han

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Han Zhang *Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843, USA.ORCID 0000-0002-9991-9409
Rohit Gupte *Department of Biomedical Engineering, Texas A&M University, College Station, TX, 77843, USA.ORCID 0000-0003-3875-8442
Yuwen LiDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843, USA.
Can HuangDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843, USA.ORCID 0000-0002-3675-6585
Adrian R GuzmanDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843, USA.ORCID 0000-0002-6559-8758
Jeong Jae HanDepartment of Multidisciplinary Engineering, Texas A&M University, College Station, TX, 77843, USA.
Haemin JungDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843, USA.ORCID 0000-0001-5441-1663
Rushant SabnisDepartment of Chemical Engineering, Texas A&M University, College Station, TX, 77843, USA.
Paul de FigueiredoChristopher S. Bond Life Science Center, University of Missouri, Columbia, MO, 65211, USA.
Arum HanDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843, USA. arum.han@ece.tamu.edu.ORCID 0000-0002-9223-8301

Funding

Development of a High-Throughput Microfluidics-Enabled Functional Assay for Rapidly Identifying Neutralizing AntibodiesR01AI141607 · NIAID · UNIVERSITY OF MISSOURI-COLUMBIA · PI DE FIGUEIREDO, PAUL, HAN, ARUM · 2019 to 2023
$3.4M
GUARD: A global unbiased antimicrobial discovery platformR01AI168685 · NIAID · TEXAS ENGINEERING EXPERIMENT STATION · PI ARUL JAYARAMAN · 2023 to 2026
$3.0M
Accelerating discovery of neutralizing paratopes with Functional Antibody Screening TechnologyR21AI139738 · NIAID · TEXAS A&M UNIVERSITY · PI HAN, ARUM · 2020 to 2021
$415k
NIAID NIH HHS R01 AI141607NIAID NIH HHS R01 AI168685NIAID NIH HHS R21 AI139738United States Department of Defense | Defense Advanced Research Projects Agency (DARPA) W911NF1920013United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Laboratory (U.S. Army Research Laboratory) W911NF-19-1-0290U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID) R01AI141607U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID) R01AI168685
6 · The paper itself

Abstract

High-throughput screening techniques are pivotal to unlocking the mysteries of biology. Yet, the promise of droplet microfluidics in enabling single-cell resolution, ultra-high-throughput screening remains largely unfulfilled. Droplet sorting errors caused by polydisperse droplet sizes that are often inevitable in multi-step assays have severely limited the effectiveness and utility of this technique, especially when screening large libraries. Even a relatively low 1% sorting error results in 10,000 false calls in a 1,000,000 droplet screen, imposing an unreasonably large burden on downstream validation. Here, we present NOVAsort (Next-generation Opto-Volume-based Accurate droplet sorter), a device capable of discerning droplets based on both size and fluorescence intensity. With a 1000- and 10,000-fold reduction in false positives and false negatives, respectively. NOVAsort addresses the challenges of conventional droplet sorting approaches and sets standards for accuracy and throughput in droplet microfluidic assays.

Indexed as

High-Throughput Screening AssaysMicrofluidic Analytical TechniquesMicrofluidicsHumansSingle-Cell Analysis

Identifiers

PMID39487108
PMCPMC11530522

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.