Evidence map›Paper›PMID 37090549›Full record

ArticlebioRxiv : the preprint server for biology2023

NULISA: a novel proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing.

Wei Feng, Joanne Beer, Qinyu Hao, Ishara S Ariyapala, Aparna Sahajan, Andrei Komarov, Katie Cha, Mason Moua, Xiaolei Qiu, Xiaomei Xu and 27 more

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 7 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

37 authors at 1 institution in 2 countries.

Wei Feng
Joanne Beer
Qinyu Hao
Ishara S Ariyapala
Aparna Sahajan
Andrei Komarov
Katie Cha
Mason Moua
Xiaolei Qiu
Xiaomei Xu
Shweta Iyengar
Thu Yoshimura
Rajini Nagaraj
Li Wang
Ming Yu
Kate Engel
Lucas Zhen
Wen Xue
Chen-Jung Lee
Chan Ho Park
Cheng Peng
Kaiyuan Zhang
Adrian Grzybowski
Johnnie Hahm
Susanne V Schmidt
Alexandru Odainic
Bonn Consortium COVIMMUNE
Kasun Buddika
Dwight Kuo
Lei Fang
Bingqing Zhang
Steve Chen
Eicke Latz
Yiyuan Yin
Yuling Luo
University of Bonn · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The blood proteome holds great promise for precision medicine but poses substantial challenges due to the low abundance of most plasma proteins and the vast dynamic range across the proteome. We report a novel proteomic technology - NUcleic acid Linked Immuno-Sandwich Assay (NULISA™) - that incorporates a dual capture and release mechanism to suppress the assay background and improves the sensitivity of the proximity ligation assay by over 10,000-fold to the attomolar level. It utilizes pairs of antibodies conjugated to DNA oligonucleotides that enable immunocomplex purification and generate reporter DNA containing target- and sample-specific barcodes for a next-generation sequencing-based, highly multiplexed readout. A 200-plex NULISA targeting 124 cytokines and chemokines and 80 other immune response-related proteins demonstrated superior sensitivity for detecting low-abundance proteins and high concordance with other immunoassays. The ultrahigh sensitivity allowed the detection of previously difficult-to-detect, but biologically important, low-abundance biomarkers in patients with autoimmune diseases and COVID-19. Fully automated NULISA addresses longstanding challenges in proteomic analysis of liquid biopsies and makes broad and in-depth proteomic analysis accessible to the general research community and future diagnostic applications.

Identifiers

PMID37090549
PMCPMC10120728
OpenAlexW4363624497

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.