Evidence map›Paper›PMID 31250984›Full record

ArticleFEBS open bio2019

Multidimensional profiling of drug-treated cells by Imaging Mass Cytometry.

Alexandre Bouzekri, Amanda Esch, Olga Ornatsky

Open access · goldAbstract read
In one paragraph

Article in FEBS open bio, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
7.4field-weighted citation impact, top 3% 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

20 citing papers in PubMed, 45 citations in OpenAlex.

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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.

Alexandre BouzekriProteomics R&D Department, Fluidigm Canada Inc., Markham, Canada.
Amanda EschProteomics R&D Department, Fluidigm Canada Inc., Markham, Canada.
Olga OrnatskyProteomics R&D Department, Fluidigm Canada Inc., Markham, Canada.
Fluidigm (Canada) · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In pharmaceutical research, high-content screening is an integral part of lead candidate development. Measuring drug response in vitro by examining over 40 parameters, including biomarkers, signaling molecules, cell morphological changes, proliferation indices, and toxicity in a single sample, could significantly enhance discovery of new therapeutics. As a proof of concept, we present here a workflow for multidimensional Imaging Mass Cytometry™ (IMC™) and data processing with open source computational tools. CellProfiler was used to identify single cells through establishing cellular boundaries, followed by histoCAT™ (histology topography cytometry analysis toolbox) for extracting single-cell quantitative information visualized as t-SNE plots and heatmaps. Human breast cancer-derived cell lines SKBR3, HCC1143, and MCF-7 were screened for expression of cellular markers to generate digital images with a resolution comparable to conventional fluorescence microscopy. Predicted pharmacodynamic effects were measured in MCF-7 cells dosed with three target-specific compounds: growth stimulatory EGF, microtubule depolymerization agent nocodazole, and genotoxic chemotherapeutic drug etoposide. We show strong pairwise correlation between nuclear markers pHistone3

Indexed as

Image CytometryBiomarkers, TumorCell ProliferationEpidermal Growth FactorEtoposideHumansMCF-7 CellsMicroscopy, FluorescenceNocodazolePhenotypeSoftwareTumor Cells, CulturedBiomarkers, TumorEpidermal Growth FactorEtoposideNocodazoleanticancer drug treatmentcell-based screeningimage-based computational analysisImaging Mass Cytometrymultiparametric similarities measurements

Identifiers

PMID31250984
PMCPMC6722888
OpenAlexW2955333422

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.