De Beuckeleer, S. | Search bioRxiv

bioRxiv2024

PAC-MAP: Proximity Adjusted Centroid Mapping for Accurate Detection of Nuclei in Dense 3D Cell Systems

MotivationIn the past decade, deep learning algorithms have surpassed the performance of many conventional image segmentation pipelines. Powerful models are now available for segmenting cells and nuclei in diverse 2D image types, but segmentation in 3D cell systems remains challenging due to the high cell density, the heterogenous resolution and contrast across the image volume, and the difficulty in generating reliable and sufficient ground truth data for model training. Reasoning that most image processing applications rely on nuclear segmentation but do not necessarily require an accurate delineation of their shapes, we implemented PAC-MAP, a 3D U-net based method that predicts the position of nuclei centroids and their proximity to other nuclei. ResultsWe show that our model outperforms existing methods, predominantly by boosting recall, especially in conditions of high cell density. When trained from scratch PAC-MAP attained an average F1 score of 0.793 in dense spheroids. When pretraining using weakly supervised bulk data input and finetuning with few expert annotations the average F1 score could be significantly improved up to 0.817. We demonstrate the utility of our method for quantifying the cell content of spheroids and mapping the degree of glioblastoma multiforme infiltration in cerebral organoids. Availability and implementationThe code is available on GitHub, at https://github.com/DeVosLab/PAC-MAP. ContactWinnok H. De Vos (winnok.devos@uantwerpen.be)

bioinformatics↗

bioRxiv2024

Unbiased identification of cell identity in dense mixed neural cultures

Induced pluripotent stem cell (iPSC) technology is revolutionizing cell biology. However, the variability between individual iPSC lines and the lack of efficient technology to comprehensively characterize iPSC-derived cell types hinder its adoption in routine preclinical screening settings. To facilitate the validation of iPSC-derived cell culture composition, we have implemented an imaging assay based on cell painting and convolutional neural networks to recognize cell types in dense and mixed cultures with high fidelity. We have benchmarked our approach using pure and mixed cultures of neuroblastoma and astrocytoma cell lines and attained a classification accuracy above 96%. Through iterative data erosion we found that inputs containing the nuclear region of interest and its close environment, allow achieving equally high classification accuracy as inputs containing the whole cell for semi-confluent cultures and preserved prediction accuracy even in very dense cultures. We then applied this regionally restricted cell profiling approach to evaluate the differentiation status of iPSC-derived neural cultures, by determining the ratio of postmitotic neurons and neural progenitors. We found that the cell-based prediction significantly outperformed an approach in which the time in culture was used as classification criterion (96% vs. 86%, resp.). In mixed iPSC-derived neuronal cultures, microglia could be unequivocally discriminated from neurons, regardless of their reactivity state. A tiered strategy, allowed for discriminating microglial cell states as well, albeit with lower accuracy. Thus, morphological single cell profiling provides a means to quantify cell composition in complex mixed neural cultures and holds promise for use in quality control of iPSC-derived cell culture models.

cell biology↗