Automated tracking of label-free cells with enhanced recognition of whole tracks

Anna Medyukhina,Alessandra Marolda,Oliver Kurzai,Marc Thilo Figge,Naim Al-Zaben,Kerstin Hünniger,Stefanie Dietrich

doi:10.1038/s41598-019-39725-x

Abstract

Migration and interactions of immune cells are routinely studied by time-lapse microscopy of in vitro migration and confrontation assays. To objectively quantify the dynamic behavior of cells, software tools for automated cell tracking can be applied. However, many existing tracking algorithms recognize only rather short fragments of a whole cell track and rely on cell staining to enhance cell segmentation. While our previously developed segmentation approach enables tracking of label-free cells, it still suffers from frequently recognizing only short track fragments. In this study, we identify sources of track fragmentation and provide solutions to obtain longer cell tracks. This is achieved by improving the detection of low-contrast cells and by optimizing the value of the gap size parameter, which defines the number of missing cell positions between track fragments that is accepted for still connecting them into one track. We find that the enhanced track recognition increases the average length of cell tracks up to 2.2-fold. Recognizing cell tracks as a whole will enable studying and quantifying more complex patterns of cell behavior, e.g. switches in migration mode or dependence of the phagocytosis efficiency on the number and type of preceding interactions. Such quantitative analyses will improve our understanding of how immune cells interact and function in health and disease.

Highlights

Proper functioning of the immune system relies on adequate behavior of individual immune cells
We visually examined the AMIT tracking results and identified three possible reasons why cell tracks become fragmented: (1) tracklets are mismatched when resolving clusters of interacting cells, (2) tracks are interrupted on cells leaving the focal plane by transient spreading[12,13], and (3) automated tracking employs too low values of the gap size – a parameter that defines the number of allowed missing time steps between tracklets when connecting them to longer tracks
The second source of track fragmentation is inherently associated with the non-rigid nature of immune cells, which dynamically change their shape while migrating and interacting with other cells

Summary

Introduction

Proper functioning of the immune system relies on adequate behavior of individual immune cells. To get the most of this powerful method, in vitro assays should be combined with automated image analysis and tracking: To objectively characterize cell behavior, the assays must be repeated many times, which inevitably generates large amounts of data. This is especially relevant when analyzing rare events that only occur in a few percent of all cell interactions. To scrutinize the details of this “dumping” process and its implications for antigen presenting cells, we have to analyze large amounts of video data Such analysis is too tedious to be performed manually and requires automated image segmentation and tracking.

Objectives

Methods

Results

Conclusion