CLANet: A Comprehensive Framework for Cross-Batch Cell Line Identification Using Brightfield Images

TL;DR

CLANet is a novel deep learning framework that effectively addresses batch effects in cross-batch cell line identification from brightfield images, improving accuracy and reliability in biomedical research.

Contribution

The paper introduces CLANet, combining cell cluster selection, self-supervised learning, multiple instance learning, and time-series sampling to handle batch effects in cell line identification.

Findings

Outperforms existing methods like domain adaptation and MIL.

Successfully identifies 32 cell lines across 93 batches.

Demonstrates robustness against batch-induced data shifts.

Abstract

Cell line authentication plays a crucial role in the biomedical field, ensuring researchers work with accurately identified cells. Supervised deep learning has made remarkable strides in cell line identification by studying cell morphological features through cell imaging. However, batch effects, a significant issue stemming from the different times at which data is generated, lead to substantial shifts in the underlying data distribution, thus complicating reliable differentiation between cell lines from distinct batch cultures. To address this challenge, we introduce CLANet, a pioneering framework for cross-batch cell line identification using brightfield images, specifically designed to tackle three distinct batch effects. We propose a cell cluster-level selection method to efficiently capture cell density variations, and a self-supervised learning strategy to manage image quality variations, thus producing reliable patch representations. Additionally, we adopt multiple instance learning(MIL) for effective aggregation of instance-level features for cell line identification. Our innovative time-series segment sampling module further enhances MIL's feature-learning capabilities, mitigating biases from varying incubation times across batches. We validate CLANet using data from 32 cell lines across 93 experimental batches from the AstraZeneca Global Cell Bank. Our results show that CLANet outperforms related approaches (e.g. domain adaptation, MIL), demonstrating its effectiveness in addressing batch effects in cell line identification.