Combinatorial Sample-and Back-Focal-Plane (BFP) Imaging. Pt. I: Instrument and acquisition parameters affecting BFP images and their analysis

TL;DR

This paper develops a robust, real-time back-focal-plane imaging method for biological fluorescence microscopy, enabling accurate axial localization and refractive index measurements under low-light conditions.

Contribution

It systematically analyzes key parameters affecting BFP image quality and introduces a hardware-software framework for standardized, reproducible BFP imaging in biological research.

Findings

Optimized BFP imaging parameters for low-light conditions

Enhanced accuracy in axial fluorophore localization

Framework for reproducible and scalable BFP microscopy

Abstract

The back-focal plane (BFP) of a high-numerical aperture objective contains the fluoro-phore 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 (SAF) 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 SAF/UAF 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 multi-dimensional 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.