The influence of spatial and temporal resolutions on the analysis of cell-cell interaction: a systematic study for time-lapse microscopy applications

Abstract

Cell-cell interactions are an observable manifestation of underlying complex biological processes occurring in response to diversified biochemical stimuli. Recent experiments with microfluidic devices and live cell imaging show that it is possible to characterize cell kinematics via computerized algorithms and unravel the effects of targeted therapies. We study the influence of spatial and temporal resolutions of time-lapse videos on motility and interaction descriptors with computational models that mimic the interaction dynamics among cells. We show that the experimental set-up of time-lapse microscopy has a direct impact on the cell tracking algorithm and on the derived numerical descriptors. We also show that, when comparing kinematic descriptors in two diverse experimental conditions, too low resolutions may alter the descriptors’ discriminative power, and so the statistical significance of the difference between the two compared distributions. The conclusions derived from the computational models were experimentally confirmed by a series of video-microscopy acquisitions of co-cultures of unlabelled human cancer and immune cells embedded in 3D collagen gels within microfluidic devices. We argue that the experimental protocol of acquisition should be adapted to the specific kind of analysis involved and to the chosen descriptors in order to derive reliable conclusions and avoid biasing the interpretation of results.

Accelerating the experimental responses on cell behaviors: a long-term prediction of cell trajectories using Social Generative Adversarial Network

“…The motion of the tumor cell was modelled as a pure random walk. Please refer to Comes et al 13 for mathematical equations. Geometrical constraints of the models were chosen by inspiring to real experiments 25 , as expressed in Comes et al 13 .…”

“…Please refer to Comes et al 13 for mathematical equations. Geometrical constraints of the models were chosen by inspiring to real experiments 25 , as expressed in Comes et al 13 . Immune theoretical trajectories extracted from such two sets of movies formed two distinct test sets for the predictive validation of the social method.…”

“…Cell occlusion and overlapping problems were solved through the geometrical mechanism behind. Please refer to Comes et al 13 for a detailed explanation of the method.…”

“…Typically, cell motility experiments use time-lapse microscopy imaging techniques that allow to acquire high-frame rate video sequences and reconstitute the position of many cells simultaneously in a label free modality 12 . To date, the image-based system approach has steadily provided great contribution to decipher cellular motion pattern thanks to accurate single-cell segmentation and tracking tools 2 , 13 . If on one hand, standard quantification of cell motility relies upon the extraction of handcrafted kinematic descriptors 14 , 15 , on the other hand, deep learning methodologies have recently been developed to discover the “motility style” by learning features directly from cell trajectories 2 , 16 .…”

A Camera Sensors-Based System to Study Drug Effects on In Vitro Motility: The Case of PC-3 Prostate Cancer Cells

“…Thanks to the incremented uptake provided by time-lapse microscopy (TLM) and computer simulations for data analysis, it is possible to simultaneously analyze the motion of many cells, and to track their morphological as well as motion changes during cell life. This is possible now also, thanks to the increased spatial and temporal resolution of the camera sensors and related electronic interfaces [10]. Such aspects are going to be ever more relevant, since, as well known, they can be related to alterations in the chemical compounds in the culture (drug administering), cell signaling (cell-cell interaction), DNA damage, cell metabolism, etc.…”