Tracking longitudinal population dynamics of single neuronal calcium signal using SCOUT

Kevin G Johnston; Steven F Grieco; Hai Zhang; Suoqin Jin; Xiangmin Xu; Qing Nie

doi:10.1016/j.crmeth.2022.100207

Tracking longitudinal population dynamics of single neuronal calcium signal using SCOUT

Cell Rep Methods. 2022 Apr 29;2(5):100207. doi: 10.1016/j.crmeth.2022.100207. eCollection 2022 May 23.

Authors

Kevin G Johnston¹, Steven F Grieco², Hai Zhang², Suoqin Jin¹, Xiangmin Xu^{2

3

4

5

6}, Qing Nie^{1

7

3

6}

Affiliations

¹ Department of Mathematics and the NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697, USA.
² Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697, USA.
³ Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA.
⁴ Department of Computer Science, University of California, Irvine, CA 92697, USA.
⁵ The Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA 92697, USA.
⁶ The Center for Neural Circuit Mapping, University of California, Irvine, CA 92697, USA.
⁷ Department of Developmental and Cell Biology, University of California, Irvine, CA 92697, USA.

Abstract

In vivo calcium imaging enables simultaneous recording of large neuronal ensembles engaged in complex operations. Many experiments require monitoring and identification of cell populations across multiple sessions. Population cell tracking across multiple sessions is complicated by non-rigid transformations induced by cell movement and imaging field shifts. We introduce SCOUT (Single-Cell spatiOtemporal longitUdinal Tracking), a fast, robust cell-tracking method utilizing multiple cell-cell similarity metrics, probabilistic inference, and an adaptive clustering methodology, to perform cell identification across multiple sessions. By comparing SCOUT with earlier cell-tracking algorithms on simulated, 1-photon, and 2-photon recordings, we show that our approach significantly improves cell-tracking quality, particularly when recordings exhibit spatial footprint movement between sessions or sub-optimal neural extraction quality.

Keywords: brain imaging; calcium imaging; cell tracking; consensus clustering; machine learning; probabilistic modeling; single cell; spatiotemporal integration.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms
Calcium, Dietary*
Movement
Neurons* / physiology

Substances

Calcium, Dietary