Evidence map›Paper›PMID 30179236›Full record

ArticleLab on a chip2018

A single-cell translocation and secretion assay (TransSeA).

Wei Cai, Yu-Jui Chiu, Valya Ramakrishnan, Yihuan Tsai, Clark Chen, Yu-Hwa Lo

Abstract read
In one paragraph

Article in Lab on a chip, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Article
  6. The Role of microRNAs in Inflammation.International journal of molecular sciences · 2022
    Review
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

6 authors.

Wei CaiMaterials Science and Engineering Program, University of California at San Diego, La Jolla, California, USA. ylo@ucsd.edu.
Yu-Jui Chiu
Valya Ramakrishnan
Yihuan Tsai
Clark Chen
Yu-Hwa Lo

Funding

High-throughput single-cell sorting and kinetic analysis of secreted particlesR44GM128223 · NIGMS · NANOCELLECT BIOMEDICAL, INC. · PI CHEN, CLARK CHIN-CHUNG, LO, YU-HWA · 2017 to 2018
$1.6M
3D-FACS: 3D image-based fluorescence activated cell sortingR44DA045460 · NIDA · NANOCELLECT BIOMEDICAL, INC. · PI CHO, SUNG HWAN, LO, YU-HWA · 2019 to 2020
$1.5M
High-throughput single-cell sorting and kinetic analysis of secreted particlesR43EB021129 · NIBIB · NANOCELLECT BIOMEDICAL, INC. · PI MORACHIS, JOSE MANUEL · 2015 to 2015
$225k
NIBIB NIH HHS R43 EB021129NIDA NIH HHS R44 DA045460NIGMS NIH HHS R44 GM128223
6 · The paper itself

Abstract

Understanding biological heterogeneity at the single cell level is required for advancing insights into the complexity of human physiology and diseases. While advances in technological and analytical methods have afforded unprecedented glimpses of this heterogeneity, the information captured to date largely represents one-time "snap" shots of single cell physiology. To address the limits of existing methods and to accelerate discoveries from single cell studies, we developed a single-cell translocation and secretion assay (TransSeA) that supports time lapse analysis, enables molecular cargo analysis of secretions such as extracellular vesicles (EVs) from single cells, allows massively parallel single cell transfer according to user-defined cell selection criteria, and supports tracking of phenotypes between parental and progeny cells derived from single cells. To demonstrate the unique capabilities and efficiencies of the assay, we present unprecedented single cell studies related to cell secretions, EV cargos and cell intrinsic properties. Although used as examples to demonstrate the feasibility and versatility of the technology, the studies already provided insights into key unanswered questions such as the microRNAs carried by EVs, the relationships between EV secretion rates and gene expressions, and the spontaneous, trans-generational phenotypic changes in EV secretion between parental and progeny cells.

Indexed as

Cell Line, TumorExosomesHumansMicroRNAsPhenotypeSingle-Cell AnalysisMicroRNAs

Identifiers

PMID30179236
PMCPMC6177299

What OpenQuestion holds

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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.