Evidence map›Paper›PMID 41769252›Full record

ArticleBio-protocol2026

Time-Lapse Into Immunofluorescence Imaging Using a Gridded Dish.

Nick Lang, Catherine G Chu, Andrew D Stephens

Abstract read
In one paragraph

Article in Bio-protocol, 2026. 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.

No citing paper in PubMed yet.

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.

Nick LangBiology, University of Massachusetts Amherst, Amherst, MA, USA.
Catherine G ChuBiology, University of Massachusetts Amherst, Amherst, MA, USA.
Andrew D StephensBiology, University of Massachusetts Amherst, Amherst, MA, USA.

Funding

Role of chromatin mechanics in nuclear shape and integrityR35GM154928 · NIGMS · UNIVERSITY OF MASSACHUSETTS AMHERST · PI Andrew Daniel Stephens · 2024 to 2026
$1.2M
NIGMS NIH HHS R35 GM154928
6 · The paper itself

Abstract

Time-lapse into immunofluorescence (TL into IF) imaging combines the wealth of information acquired during live-cell imaging with ease of access for static immunofluorescence markers. In the field of mechanobiology, connecting live and static imaging to visualize cell biology dynamics is often troublesome. For instance, nuclear blebs are deformations of the nucleus that often rupture spontaneously, leading to changes in the molecular composition of the nucleus and the nuclear bleb. Current techniques to connect cellular dynamics and their downstream effects via live-cell imaging, followed by immunofluorescence, often require third-party analysis programs or stage position measurements to accurately track cells. This protocol simplifies the connection between live and static imaging by utilizing a gridded imaging dish. In our protocol, cells are plated on a dish with an engraved coordinate plane. Individual cells are then matched from when the time-lapse ends to the immunofluorescence images simply by their known coordinate location. Overall, TL into IF offers a straightforward method for connecting dynamic live-cell with static immunofluorescence imaging, in an easy and accessible tool for cell biologists. Key features • This protocol directly links live-cell imaging to immunofluorescence imaging. • The only special equipment required for this protocol is gridded imaging dishes. • This protocol does not require third-party applications.

Indexed as

Cell biology dynamicsImmunofluorescenceLive-cell imagingMechanobiologyNuclear dynamicsNuclear ruptureStatic imagingTime-lapse imaging

Identifiers

PMID41769252
PMCPMC12945560

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.