Abstract

Cellular micromotion—a tiny movement of cell membranes on the nm-µm scale—has been proposed as a pathway for inter-cellular signal transduction and as a label-free proxy signal to neural activity. Here we harness several recent approaches of signal processing to detect such micromotion in video recordings of unlabeled cells. Our survey includes spectral filtering of the video signal, matched filtering, as well as 1D and 3D convolutional neural networks acting on pixel-wise time-domain data and a whole recording respectively.

Highlights

  • Cellular micromotion—a tiny movement of cell membranes on the nm-μm scale—has been proposed as a pathway for inter-cellular signal transduction and as a label-free proxy signal to neural activity

  • We focus on “convolutional neural networks” (CNNs), a widely employed subclass of networks that can be understood as an extension of matched filtering

  • Recent schemes of signal processing can effectively detect and amplify small fluctuations in video recordings, revealing tiny visual cues such as micromotion of cells correlating with Calcium activity

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Summary

Introduction

Cellular micromotion—a tiny movement of cell membranes on the nm-μm scale—has been proposed as a pathway for inter-cellular signal transduction and as a label-free proxy signal to neural activity. There is considerable evidence that the intrinsic mechanical and optical properties of cells change slightly upon firing of an action potential. Signatures of such changes have been reported as early as 1­ 9491 and have since been studied in a variety of channels. Nanometer-scale motion of the cell membrane in response to an action potential has been observed by fiber-optical and piezoelectric s­ ensors[17], atomic force m­ icroscopy[18] and optical ­interferometry[19,20,21,22,23,24] Such motion has been detected in non-interferometric microscope videos by image ­processing[25,26], which is the technique we intend to advance with the present work. It should be able to detect micromotion of cells

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