Biology subjects

Meijering, E.

Publications and source records attributed to Meijering, E..

7 recordsLinked to original sources

bioRxiv2025

Generative semantic multiplexing (SemaPlex) for accessible and scalable multiplexed fluorescence imaging

Multiplexed fluorescence imaging enhances spatially-resolved interrogation of complex, multi-molecular cell processes that are insufficiently sampled using standard 4-5 plex imaging. To improve accessibility and scalability for multiplexed imaging, we demonstrate generative Semantic Multiplexing (SemaPlex); a simple experimental and deep learning strategy for amplifying marker plexity several-fold by semantically unmixing multiple markers combined per imaging channel. We first characterise key determinants of SemaPlex performance, achieving precise computational multiplexing of 2-to-8 markers synthetically mixed in one channel, facilitating enhanced cell phenotype classification. We then demonstrate practical SemaPlex application, acquiring 10 markers over 4 channels (3*3-plex+1) to efficiently emulate real multiplexed labelling. This permitted accurate reconstruction of quantitative single-cell phenotypic manifolds delineating cell-cycle and mitotic dynamics, with internally validated error-detection. Finally, we exemplify use of semantic guides; additional input channels that significantly enhance multiplexing fidelity. SemaPlex makes multiple-fold increases in fluorescence imaging-plexity accessible, scalable and customisable; democratising multiplexed imaging-based interrogation of complex cell biology.

systems biology↗

bioRxiv2025

Image quality metrics fail to accurately represent biological information in fluorescence microscopy

Image processing methods offer the potential to improve the quality of fluorescence microscopy data, allowing for image acquisition at lower, less phototoxic illumination doses. The training and evaluation of such methods is informed and driven by full-reference image quality metrics (IQMs); however, these metrics derive from applications to natural scene images, not fluorescence microscopy images. Here we investigate the response of IQMs to common properties of fluorescence microscopy data and whether IQMs are capable of reporting the biological information content of images. We find that IQM scores are biased by image content for both raw and processed microscopy data, and that improvements in IQM values reported after processing are not reliably correlated with performance in downstream analysis tasks. As common IQMs are unreliable proxies for guiding image processing developments in biological fluorescence microscopy, image processing performance should be benchmarked according to downstream analysis success.

bioinformatics↗

bioRxiv2022

Imbalanced classification for protein subcellularlocalisation with multilabel oversampling

MotivationSubcellular localisation of human proteins is essential to comprehend their functions and roles in physiological processes, which in turn helps in diagnostic and prognostic studies of pathological conditions and impacts clinical decision making. Since proteins reside at multiple locations at the same time and few subcellular locations host far more proteins than other locations, the computational task for their subcellular localisation is to train a multilabel classifier while handling data imbalance. In imbalanced data, minority classes are underrepresented, thus leading to a heavy bias towards the majority classes and the degradation of predictive capability for the minority classes. Furthermore, data imbalance in multilabel settings is an even more complex problem due to the coexistence of majority and minority classes. ResultsOur studies reveal that based on the extent of concurrence of majority and minority classes, oversampling of minority samples through appropriate data augmentation techniques holds promising scope for boosting the classification performance for the minority classes. We measured the magnitude of data imbalance per class and the concurrence of majority and minority classes in the dataset. Based on the obtained values, we identified minority and medium classes, and a new oversampling method is proposed that includes nonlinear mixup, geometric and colour transformations for data augmentation and a sampling approach to prepare minibatches. Performance evaluation on the Human Protein Atlas Kaggle challenge dataset shows that the proposed method is capable of achieving better predictions for minority classes than existing methods. AvailabilityData used in this study is available at https://www.kaggle.com/competitions/human-protein-atlas-image-classification/data. Contactyang.song1@unsw.edu.au Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

Data augmentation for imbalanced blood cell image classification

Due to progression in cell-cycle or duration of storage, classification of morphological changes in human blood cells is important for correct and effective clinical decisions. Automated classification systems help avoid subjective outcomes and are more efficient. Deep learning and more specifically Convolutional Neural Networks have achieved state-of-the-art performance on various biomedical image classification problems. However, real-world data often suffers from the data imbalance problem, owing to which the trained classifier is biased towards the majority classes and does not perform well on the minority classes. This study presents an imbalanced blood cells classification method that utilises Wasserstein divergence GAN, mixup and novel nonlinear mixup for data augmentation to achieve oversampling of the minority classes. We also present a minority class focussed sampling strategy, which allows effective representation of minority class samples produced by all three data augmentation techniques and contributes to the classification performance. The method was evaluated on two publicly available datasets of immortalised human T-lymphocyte cells and Red Blood Cells. Classification performance evaluated using F1-score shows that our proposed approach outperforms existing methods on the same datasets.

bioinformatics↗

bioRxiv2020

Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation

Classification and characterisation of cellular morphological states are vital for understanding cell differentiation, development, proliferation and diverse pathological conditions. As the onset of morphological changes transpires following genetic alterations in the chromatin configuration inside the nucleus, the nuclear texture as one of the low-level properties if detected and quantified accurately has the potential to provide insights on nuclear organisation and enable early diagnosis and prognosis. This study presents a three dimensional (3D) nuclear texture description method for cell nucleus classification and variation measurement in chromatin patterns on the transition to another phenotypic state. The proposed approach includes third plane information using hyperplanes into the design of the Sorted Random Projections (SRP) texture feature. The significance of including third plane information for low-resolution volumetric images is investigated by comparing the performance of 3D texture descriptor with its respective pseudo 3D form that ignores the interslice intensity correlations. Following classification, changes in chromatin pattern are estimated by computing the ratio of heterochromatin and euchromatin corresponding to their respective intensities and image gradient obtained by 3D SRP. The proposed method is evaluated on two publicly available 3D image datasets of human fibroblast and human prostate cancer cell lines in two phenotypic states obtained from the public Statistics Online Computational Resource. Experimental results show that 3D SRP and 3D Local Binary Pattern provide better results than other utilised handcrafted descriptors and deep learning features extracted using a pre-trained model. The results also show the advantage of utilising 3D feature descriptor for classification over its corresponding pseudo version. In addition, the proposed method validates that as the cell passes to another phenotypic state, there is a change in intensity and aggregation of heterochromatin. Author SummaryAutomated classification and measurement of cellular phenotypic traits can significantly impact clinical decision making. Early detection of diseases requires an accurate description of low-level cellular features to detect small-scale abnormalities in the few abnormal cells in the tissue microenvironment. The challenge is the development of a computational approach for 3D textural feature description that can capture the heterogeneous information in multiple dimensions and characterise the cells in their ultimate and intermediate phenotypic states effectively. Our work has proposed the method and metrics to measure chromatin condensation pattern and classify the phenotypic states. Experimental evaluation on the 3D image set of human fibroblast and human prostate cancer cell collections validates the proposed method for the classification of cell states. Results also signify the credibility of proposed metrics to characterise the cellular phenotypic states and contributes to studies related to early diagnosis, prognosis and drug resistance.

bioinformatics↗

bioRxiv2018

Reverse genetic screen reveals that Il34 facilitates yolk sac macrophage distribution and seeding of the brain

Microglia are brain resident macrophages, which have specialized functions important in brain development and in disease. They colonize the brain in early embryonic stages, but few factors that drive the migration of yolk sac macrophages into the embryonic brain, or regulate their acquisition of specialized properties are currently known.\n\nHere, we present a CRISPR/Cas9-based in vivo reverse genetic screening pipeline to identify new regulators important for microglia development using zebrafish. Zebrafish larvae are particularly suitable due to their external development, transparency, high fecundity and conserved microglia features. We targeted putative microglia regulators, including signature genes and non-cell autonomous factors, by Cas9/gRNA-complex injections, followed by neutral red-based visualization of microglia. Microglia were quantified automatically in 3-day-old larvae using a software tool we called SpotNGlia. We identified that loss of function of the zebrafish homolog of the colony stimulating factor 1 receptor (CSF1R) ligand IL34, caused strongly reduced microglia numbers in early development. Previous studies on the role of the IL34 on microglia development in vivo were ambiguous. Our data, and a concurrent paper, show that in zebrafish, il34 is required during the earliest seeding of the brain by microglia progenitors. Our data also indicate that Il34 is required for distribution to other organs.\n\nPreviously, we showed that csf1ra and csf1rb double mutant zebrafish have no microglia. As there is a moderate effect of il34 on microglia development, relative to the effect csf1r, additional Csf1r-dependent signalling may be needed for establishment of the microglia network. In all, we identified il34 as a modifier of microglia colonization, by affecting distribution of yolk sac macrophages to target organs, validating our reverse genetic screening pipeline in zebrafish which can be used for the identification of additional regulators of microglia development.

developmental biology↗

bioRxiv2016

Automatic Tracing of Ultra-Volume of Neuronal Images

Despite substantial advancement in the automatic tracing of neurons' morphology in recent years, it is challenging to apply the existing algorithms to very large image datasets containing billions or more voxels. We introduce UltraTracer, a solution designed to extend any base neuron-tracing algorithm to be able to trace virtually unlimited data volumes. We applied this approach to neuron-tracing algorithms with completely different design principles and tested on challenging human and mouse neuron datasets that have hundreds of billions of voxels. Results indicate that UltraTracer is scalable, accurate, and about 3 to 6 times more efficient compared to other state-of-the-art approaches.

bioinformatics↗