Evidence map›Paper›PMID 40757639›Full record

ReviewThe journal of physical chemistry. B2025

Liquid-Liquid Phase Separation from the Viewpoint of Molecular Crowding Environment: A Raman Imaging Study.

Takakazu Nakabayashi, Shinya Tahara, Shinji Kajimoto

Abstract readReview
In one paragraph

Review in The journal of physical chemistry. B, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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.

Takakazu NakabayashiGraduate School of Pharmaceutical Sciences, Tohoku University, Sendai 980-8578, Japan.ORCID 0000-0003-2942-2189
Shinya TaharaGraduate School of Pharmaceutical Sciences, Tohoku University, Sendai 980-8578, Japan.ORCID 0000-0003-3979-3135
Shinji KajimotoGraduate School of Pharmaceutical Sciences, Tohoku University, Sendai 980-8578, Japan.ORCID 0000-0001-7432-8090

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present the results of Raman imaging of molecular crowding environments in a living cell and a liquid droplet formed by liquid-liquid phase separation. Using the Raman band of water as an internal intensity standard, we propose an in situ quantification method for evaluating biomolecular concentrations. Based on these concentration measurements, intracellular crowding environments can be quantitatively assessed. A single liquid droplet in a buffer solution is found to have a very high biomolecular concentration, exceeding a few millimolar concentrations, and the concentration within a droplet varies depending on the surrounding environment. The biomolecular concentration in a droplet increases as conditions facilitate droplet formation, and we propose a model in which changes in the surrounding environment lead to the formation of dense droplets, which subsequently transform into aggregates. Concentration quantification in a single droplet was also performed in a living cell, demonstrating that the biomolecular concentration in a droplet is not much different from that in the surrounding intracellular environment. This result indicates that within a droplet in a cell, certain molecules are highly concentrated, while other molecules are excluded, and the overall concentration is comparable to that of the surrounding intracellular environment. The droplet formation within a cell can be regarded as a redistribution of biomolecules constituting a molecular crowding environment.

Indexed as

Spectrum Analysis, RamanPhase SeparationWaterWater

Identifiers

PMID40757639
PMCPMC12359118

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.