FLINO: a new method for immunofluorescence bioimage normalization

Abstract

Motivation Multiplexed immunofluorescence bioimaging of single-cells and their spatial organization in tissue holds great promise to the development of future precision diagnostics and therapeutics. Current multiplexing pipelines typically involve multiple rounds of immunofluorescence staining across multiple tissue slides. This introduces experimental batch effects that can hide underlying biological signal. It is important to have robust algorithms that can correct for the batch effects while not introducing biases into the data. Performance of data normalization methods can vary among different assay pipelines. To evaluate differences, it is critical to have a ground truth dataset that is representative of the assay. Results A new immunoFLuorescence Image NOrmalization (FLINO) method is presented and evaluated against alternative methods and workflows. Multi-round immunofluorescence staining of the same tissue with the nuclear dye DAPI was used to represent virtual slides and a ground truth. DAPI was re-stained on a given tissue slide producing multiple images of the same underlying structure but undergoing multiple representative tissue handling steps. This ground truth dataset was used to evaluate and compare multiple normalization methods including median, quantile, smooth quantile, median ratio normalization (MRN) and trimmed mean of the M-values (TMM). These methods were applied in both an unbiased grid object and segmented cell object workflow to 24 multiplexed biomarkers. An upper quartile normalization of grid objects in log space was found to obtain almost equivalent performance to directly normalizing segmented cell objects by the middle quantile. The developed grid-based technique was then applied with on-slide controls for evaluation. Using five or fewer controls per slide can introduce biases into the data. Ten or more on-slide controls were able to robustly correct for batch effects. Supplementary information Supplementary data are available at Bioinformatics online.

Consensus tissue domain detection in spatial multi-omics data using MILWRM

“…Here, we implemented the mean of non-zero pixels to accommodate channels with sparse signal intensities (27). The downsampling performed on images prior to this normalization step also aligns with the unbiased grid-based normalization framework described by Graf et al, 2022 (24). In order to incorporate broader spatial information within each pixel, after normalization, we applied gaussian smoothing.…”

Dual-modality imaging of immunofluorescence and imaging mass cytometry for whole slide imaging with accurate single-cell segmentation

“…Nuclear imaging of IF DAPI most effectively revealed the nuclear structure at high resolution, delineating nuclear contour and nucleus texture even in densely packed cells, intermixed immune cells, and membrane imaging of IF E-Cadherin delineated stratified normal squamous epithelium without a nucleus (Figure 5A). IF DAPI is considered the empirical ground truth as the most widely used marker in nuclear segmentation and image registration 9 . We were able to generate accurate nuclei and cell masks using high-resolution IF DAPI and E-Cadherin images and register them with IMC images, which enabled us to analyze high-dimensional IMC images using accurate IF-based cell masks.…”

“…It results in grave difficulties in downstream image registration and introduces significant errors. Moreover, autofluorescence and experimental batch effects also introduce biases into the data and may mask the true underlying biological signals [6][7][8][9][10] .…”

mxnorm: An R Package to Normalize Multiplexed Imaging Data

“…Recent developments in multiplexed imaging seek to address the broad lack of standardized tools – the MCMICRO pipeline seeks to provide a set of open-source, reproducible analyses to transform whole-slide images into single-cell data ( Schapiro et al, 2021 ). Researchers in the field have also developed a ground truth dataset to evaluate differences in batch effects and normalization methods ( Graf et al, 2022 ), while other open issues in the field that may produce open-source solutions include tissue segmentation, end-to-end image processing, and removal of image artifacts. With this diversity of open issues in multiplexed imaging, our work focuses specifically on normalization methods and evaluating these results in multiplexed imaging data.…”