Evidence map›Paper›PMID 36608325›Full record

ArticleAnalytical chemistry2023

Microfluidic Platform for Profiling of Extracellular Vesicles from Single Breast Cancer Cells.

Jonas M Nikoloff, Mario A Saucedo-Espinosa, Petra S Dittrich

Open access · hybridAbstract read
In one paragraph

Article in Analytical chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
3.4field-weighted citation impact, top 8% of its field
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

17 citing papers in PubMed, 22 citations in OpenAlex.

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  6. An integrated microfluidic system for automatic and self-validated analysis of cervical extracellular vesicle markers PD-L1 and ERBB3.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026
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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

3 authors at 1 institution in 1 country.

Jonas M NikoloffDepartment of Biosystems Science and Engineering, ETH Zurich, CH-4058 Basel, Switzerland.
Mario A Saucedo-EspinosaDepartment of Biosystems Science and Engineering, ETH Zurich, CH-4058 Basel, Switzerland.
Petra S DittrichDepartment of Biosystems Science and Engineering, ETH Zurich, CH-4058 Basel, Switzerland.ORCID 0000-0001-5359-8403
ETH Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extracellular vesicles (EVs) are considered as valuable biomarkers to discriminate healthy from diseased cells such as cancer. Passing cytosolic and plasma membranes before their release, EVs inherit the biochemical properties of the cell. Here, we determine protein profiles of single EVs to understand how much they represent their cell of origin. We use a microfluidic platform which allows to immobilize EVs from completely isolated single cells, reducing heterogeneity of EVs as strongly seen in cell populations. After immunostaining, we employ four-color total internal reflection fluorescence microscopy to enumerate EVs and determine their biochemical fingerprint encoded in membranous or cytosolic proteins. Analyzing single cells derived from pleural effusions of two different human adenocarcinoma as well as from human embryonic kidney (SkBr3, MCF-7 and HEK293, respectively), we observed that a single cell secretes enough EVs to extract the respective tissue fingerprint. We show that overexpressed integral plasma membrane proteins are also found in EV membranes, which together with populations of colocalized proteins, provide a cell-specific, characteristic pattern. Our method highlights the potential of EVs as a diagnostic marker and can be directly employed for fundamental studies of EV biogenesis.

Identifiers

PMID36608325
PMCPMC9878503
OpenAlexW4313612966

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.