Evidence map›Paper›PMID 34939076›Full record

ArticleACS measurement science au2021

Programmed Cell-Death Mechanism Analysis Using Same-Cell, Multimode DNA and Proteoform Electrophoresis.

Ana E Gomez Martinez, Amy E Herr

Open access · goldAbstract read
In one paragraph

Article in ACS measurement science au, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 46% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. 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

2 authors at 1 institution in 1 country.

Ana E Gomez MartinezDepartment of Bioengineering, University of California Berkeley, Berkeley, California 94720, United States.ORCID https://orcid.org/0000-0001-8759-4400
Amy E HerrDepartment of Bioengineering, University of California Berkeley, Berkeley, California 94720, United States.ORCID https://orcid.org/0000-0002-6906-2985
University of California, San Francisco · US

Funding

MULTIPLEXED ISOFORM QUANTIFICATION IN HER2-POSITIVE BREAST CANCERR01CA203018 · NCI · UNIVERSITY OF CALIFORNIA BERKELEY · PI HERR, AMY ELIZABETH · 2016 to 2024
$2.8M
Advanced Cancer Classification via Single-Cell Electrophoretic CytopathologyR33CA225296 · NCI · UNIVERSITY OF CALIFORNIA BERKELEY · PI HERR, AMY ELIZABETH · 2017 to 2017
$1.5M
NCI NIH HHS R01 CA203018NCI NIH HHS R33 CA225296
6 · The paper itself

Abstract

Gaining insight into the timing of cell apoptosis events requires single-cell-resolution measurements of cell viability. We explore the supposition that mechanism-based scrutiny of programmed cell death would benefit from same-cell analysis of both the DNA state (intact vs fragmented) and the protein states, specifically the full-length vs cleaved state of the DNA-repair protein PARP1, which is cleaved by caspase-3 during caspase-dependent apoptosis. To make this same-cell, multimode measurement, we introduce the single-cell electrophoresis-based viability and protein (SEVAP) assay. Using SEVAP, we (1) isolate human breast cancer SKBR3 cells in microwells molded in thin polyacrylamide gels, (2) electrophoretically separate protein molecular states and DNA molecular states-using differences in electrophoretic mobility-from each single-cell lysate, and (3) perform in-gel DNA staining and PARP1 immunoprobing. Performed in an open microfluidic device, SEVAP scrutinized hundreds to thousands of individual SKBR3 cells. In each single-cell lysate separation, SEVAP baseline-resolved fragmented DNA from intact DNA (

Identifiers

PMID34939076
PMCPMC8679084
OpenAlexW3193668499

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.