Inferring cell communication using single-cell calcium spatiotemporal dynamics - PubMed (original) (raw)
Inferring cell communication using single-cell calcium spatiotemporal dynamics
Nika Taghdiri et al. STAR Protoc. 2022.
Abstract
There are a limited number of experimental tools for non-destructively discovering cell communication events in vitro and in vivo. Here, using tissue-specific genetically encoded calcium indicator (GECI) mice, we describe a protocol for preprocessing GECI fluorescence time-series measured by live cell imaging or intravital microscopy, detecting peaks of single-cell calcium fluorescence transients, and inferring putative cell communication events from peak synchrony. For complete details on the use and execution of this protocol, please refer to Taghdiri et al. (2021).
Keywords: Bioinformatics; Cell biology; Microscopy; Model organisms; Molecular biology; Signal transduction.
© 2022 The Author(s).
Conflict of interest statement
K.R.K. is Founder, Director, on Board of Directors, and holds equity in Nightingale Labs, which has no scientific overlap with the material in this protocol.
Figures
Graphical abstract
Figure 1
Example of undersampling and oversampling single-cell calcium transients (A) Undersampling at 0.4 Hz (1 sample per 2.5 s) (top) compared to more optimal sampling at 2 Hz (bottom) sacrifices detail about the onset of the calcium transient. (B) Oversampling at 15 Hz (top) compared to 2 Hz (bottom) does not obviously improve the signal.
Figure 2
Example of a single-cell calcium transient with and without background fluorescence correction afu = arbitrary fluorescence units.
Figure 3
Example of fluorescence time-series preprocessing (A) Remove background. Specifically, Image > Stacks > Z Project > Projection type (Median), Process > Image Calculator > Image 1 (Raw fluorescence) > Subtract > Image 2 (MedIP). (B) Load pre-saved ROIs. Specifically, Analyze > tools > ROI manager > open. (C) Select quantitative features (e.g., mean gray value, centroid). Specifically, Analyze > set measurement. (D) Record quantitative features for all ROIs. Specifically, Analyze > tools > ROI manager > More > multi measure.
Figure 4
Generation of a single-cell binary impulse train from the normalized differential fluorescence time-series of a single ROI Normalized differential fluorescence time series’ are low pass filtered, subjected to peak-finding, and converted to single cell binary impulse trains. Figure reprinted with permission from Taghdiri et al. (2021).
Figure 5
Diagram of cell communication pipeline inference steps (A–C) (A) “Real” experimental binary impulse trains are (B) modeled based on their impulse train statistics to create (C) “generated” binary impulse trains. (D–F) Real and generated impulse trains are independently used to calculate synchrony, S (w, τ), specifically (E/F) Sreal (w, τ) and Sgen (w, τ). (G) A threshold called Sth is defined based on generated synchrony and a user-generated z score. (H) Excess synchrony, ΔS/w. (I and J) Cell communication events are identified at time τcomm’s and spatial locations (Xcomm’s, Ycomm’s). Figure reprinted with permission from Taghdiri et al. (2021).
Figure 6
Modeling experimental impulse train statistics to generate synthetic cell impulse trains
Figure 7
Determination of real and generated synchrony from single-cell impulse trains Synchrony is defined as the sum of impulses within a window of length w beginning at time τ. “Real” synchrony Sreal derives from real experimental single impulse trains. “Gen” synchrony Sgen derives from generated single impulse trains. Excess synchrony is shown without normalization ΔS. Figure reprinted with permission from Taghdiri et al. (2021).
Figure 8
Example of how varying Sth affects predicted cell communication events Sth corresponding to the 50th, 80th, and 100th percentiles of Sgen are illustrated. Communication is predicted where Sreal exceeds Sth, (e.g., where excess synchrony is greater than zero). The initiation of each putative communication event where Sreal sustainably exceeds Sth is deemed a putative communication event assigned to the start time τcomm. Figure reprinted with permission from Taghdiri et al. (2021).
Figure 9
Finding high spatiotemporal synchronous cells Plot of cluster density vs cluster number for a single communication event (left). Cells with impulses in the time interval between τcomm and τcomm + w are subjected to k-Means clustering (right). The centroid of the cluster with the maximum density is interpreted as the location of putative communication (right).
Figure 10
Example of the output folder contents at analysis completion The folder includes an output file, FinalResults.csv, and associated plots.
Figure 11
Example of peak finding optimization (A) Peaks identified using findpeaks with default parameters suggested by MATLAB. (B) Peaks identified using findpeaks with optimized parameters suggested by inferring cell communication pipeline. Please refer to
findpeaks
function documentation in MATLAB if you want to adjust the parameters.
Figure 12
Algorithmic determination of the optimal window size, w The optimal window size is taken to be the global maximum excess synchrony. However, we reject window sizes shorter than the average single cell calcium transient.
References
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