Evidence map›Paper›PMID 40798881›Full record

ArticleBiophysical journal2025

Combinatorial sample- and back-focal-plane imaging. Pt. I: Instrument and acquisition parameters affecting BFP images and their analysis.

Omer Shavit, Hervé Suaudeau, Carine Julien, Hodaya Klimovsky, Natalia Mañas-Chavernas, Adi Salomon, Martin Oheim

Abstract read
In one paragraph

Article in Biophysical journal, 2025. 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

7 authors.

Omer ShavitUniversité Paris Cité, CNRS, Saints Pères Paris Institute for the Neurosciences, Paris, France; Institute of Nanotechnology and Advanced Materials (BINA), Department of Chemistry, Bar-Ilan University, Ramat-Gan, Israel.
Hervé SuaudeauUniversité Paris Cité, CNRS, Saints Pères Paris Institute for the Neurosciences, Paris, France.
Carine JulienUniversité Paris Cité, CNRS, Saints Pères Paris Institute for the Neurosciences, Paris, France; Université Paris Saclay, Ecole Normale Supérieure Paris-Saclay, CNRS, PPSM, Gif-sur-Yvette, France.
Hodaya KlimovskyInstitute of Nanotechnology and Advanced Materials (BINA), Department of Chemistry, Bar-Ilan University, Ramat-Gan, Israel.
Natalia Mañas-ChavernasUniversité Paris Cité, CNRS, Saints Pères Paris Institute for the Neurosciences, Paris, France.
Adi SalomonUniversité Paris Cité, CNRS, Saints Pères Paris Institute for the Neurosciences, Paris, France; Institute of Nanotechnology and Advanced Materials (BINA), Department of Chemistry, Bar-Ilan University, Ramat-Gan, Israel.
Martin OheimUniversité Paris Cité, CNRS, Saints Pères Paris Institute for the Neurosciences, Paris, France. Electronic address: martin.oheim@u-paris.fr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The back-focal plane (BFP) of a high-numerical aperture objective contains the fluorophore radiation pattern, which encodes information about the axial fluorophore position, molecular orientation and the local refractive index of the embedding medium. BFP image acquisition and analysis are common to conoscopy, k-space imaging, supercritical-angle fluorescence, and single-molecule detection, but they are rarely being used in biological fluorescence. This work addresses a critical gap in quantitative microscopy by enabling reliable, real-time BFP imaging under low-light conditions and/or short exposure times, typical of biological experiments. By systematically analyzing how key parameters-such as Bertrand lens position, defocus, pixel size, and binning-affect BFP image quality and supercritical-angle fluorescence/undercritical-angle fluorescence ratios, we provide a robust framework for accurate axial fluorophore localization and near-membrane refractive index measurements. The described hardware and software integration allows for multidimensional image series and online quality control, reducing experimental error and enhancing reproducibility. Our contributions lay the foundation for standardized BFP imaging across laboratories, expanding its application to dynamic biological systems, and opening the door to machine-learning-based analysis pipelines. Ultimately, this work transforms BFP imaging from an expert-dependent technique into a reproducible and scalable tool for surface-sensitive fluorescence microscopy.

Indexed as

Image Processing, Computer-AssistedHumansMicroscopy, Fluorescence

Identifiers

PMID40798881
PMCPMC12709242

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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.