Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation

TL;DR

This paper presents a novel deep learning approach for analyzing microfluidic single-cell cultivation data, enabling accurate cell growth analysis with minimal labeled data by training a generative model on natural and synthetic images.

Contribution

The authors introduce a new machine learning architecture and training method that reduces the need for extensive labeled data in cell growth analysis from microfluidic experiments.

Findings

High-performing regression model for cell count estimation

Effective training with minimal labeled data

Shared representation learned from natural and synthetic data

Abstract

Motivation: Innovative microfluidic systems carry the promise to greatly facilitate spatio-temporal analysis of single cells under well-defined environmental conditions, allowing novel insights into population heterogeneity and opening new opportunities for fundamental and applied biotechnology. Microfluidics experiments, however, are accompanied by vast amounts of data, such as time series of microscopic images, for which manual evaluation is infeasible due to the sheer number of samples. While classical image processing technologies do not lead to satisfactory results in this domain, modern deep learning technologies such as convolutional networks can be sufficiently versatile for diverse tasks, including automatic cell tracking and counting as well as the extraction of critical parameters, such as growth rate. However, for successful training, current supervised deep learning requires label information, such as the number or positions of cells for each image in a series; obtaining these annotations is very costly in this setting. Results: We propose a novel Machine Learning architecture together with a specialized training procedure, which allows us to infuse a deep neural network with human-powered abstraction on the level of data, leading to a high-performing regression model that requires only a very small amount of labeled data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.