Evidence map›Paper›PMID 41927734›Full record

ArticleCommunications biology2026

Spectral demixing enables reliable dual color pair correlation function analysis of viral and cellular proteins in live cells.

Natalia Philipp, Melina Magalnik, Berta Pozzi, Anabella Srebrow, Laura C Estrada

Abstract read
In one paragraph

Article in Communications biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Natalia Philipp *Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Buenos Aires, Argentina.ORCID http://orcid.org/0000-0003-2001-4656
Melina Magalnik *Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Fisiología, Biología Molecular y Celular, Buenos Aires, Argentina.
Berta PozziUniversidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Fisiología, Biología Molecular y Celular, Buenos Aires, Argentina.
Anabella SrebrowUniversidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Fisiología, Biología Molecular y Celular, Buenos Aires, Argentina. asrebrow@fbmc.fcen.uba.ar.ORCID http://orcid.org/0000-0002-1468-4736
Laura C EstradaUniversidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Buenos Aires, Argentina. lestrada@df.uba.ar.ORCID http://orcid.org/0000-0001-9881-0201

Funding

Ministerio de Ciencia, Tecnología e Innovación Productiva (Ministry of Science, Technology and Productive Innovation, Argentina) Red Federal de Alto Impacto- CONVE-2023-100766162-APN-MCTMinistry of Science, Technology and Productive Innovation, Argentina | Agencia Nacional de Promoción Científica y Tecnológica (National Agency for Science and Technology, Argentina) PICT 2019-00263Ministry of Science, Technology and Productive Innovation, Argentina | Agencia Nacional de Promoción Científica y Tecnológica (National Agency for Science and Technology, Argentina) PICT 2020-00198Ministry of Science, Technology and Productive Innovation, Argentina | Agencia Nacional de Promoción Científica y Tecnológica (National Agency for Science and Technology, Argentina) PICT 2020-02718Universidad de Buenos Aires (University of Buenos Aires) PIDAE 2020-3980Universidad de Buenos Aires (University of Buenos Aires) PIDAE 2025-5201Universidad de Buenos Aires (University of Buenos Aires) UBACyT 20020170100045BAUniversidad de Buenos Aires (University of Buenos Aires) UBACyT 20020220100088BAUniversidad de Buenos Aires (University of Buenos Aires) UBACyT 20020220200041BA
6 · The paper itself

Abstract

Pair correlation function (pCF) is a powerful approach that has gained increasing popularity in recent years for studying protein dynamics in living cells; however, its application remains limited by methodological constraints and the absence of standardized protocols. In this work, we optimize key aspects of pCF and dual color-pCF analysis, showing that reliable results can be achieved with shorter acquisition times, reducing motion artifacts. We demonstrate that spectral bleed-through as low as 1% produces spurious cross-correlations, underscoring the need for rigorous correction. To address this, we introduce a robust signal demixing strategy that removes false-positive correlations while preserving genuine molecular interactions. This advance expands the reliability of correlation analyses in complex live-cell environments. Applying this approach, we investigate the nucleo-cytoplasmic shuttling of the splicing factor RBM10 in living cells expressing dengue virus (DENV) NS5 polymerase under poly(I:C)-triggered innate immune response. Our findings reveal that RBM10 and NS5 not only interact but also co-shuttle between the nuclear and cytoplasmic compartments. Furthermore, RBM10 transport is differentially modulated by both NS5 and innate immune induction, reinforcing the role of DENV in altering host nuclear transport. Overall, this work establishes signal demixing as a key advance for live-cell correlation techniques and demonstrates its potential for uncovering complex host-virus interactions.

Indexed as

Dengue VirusViral Nonstructural ProteinsAnimalsCell NucleusCytoplasmHumansRNA-Binding ProteinsRNA-Binding ProteinsViral Nonstructural Proteins

Identifiers

PMID41927734
PMCPMC13216581

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.