Cargando…

Improved Workflow for Analysis of Vascular Myocyte Time-Series and Line-Scan Ca(2+) Imaging Datasets

Intracellular Ca(2+) signals are key for the regulation of cellular processes ranging from myocyte contraction, hormonal secretion, neural transmission, cellular metabolism, transcriptional regulation, and cell proliferation. Measurement of cellular Ca(2+) is routinely performed using fluorescence m...

Descripción completa

Detalles Bibliográficos
Autores principales: Boskind, Madison, Nelapudi, Nikitha, Williamson, Grace, Mendez, Bobby, Juarez, Rucha, Zhang, Lubo, Blood, Arlin B., Wilson, Christopher G., Puglisi, Jose Luis, Wilson, Sean M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10253939/
https://www.ncbi.nlm.nih.gov/pubmed/37298681
http://dx.doi.org/10.3390/ijms24119729
_version_ 1785056525717340160
author Boskind, Madison
Nelapudi, Nikitha
Williamson, Grace
Mendez, Bobby
Juarez, Rucha
Zhang, Lubo
Blood, Arlin B.
Wilson, Christopher G.
Puglisi, Jose Luis
Wilson, Sean M.
author_facet Boskind, Madison
Nelapudi, Nikitha
Williamson, Grace
Mendez, Bobby
Juarez, Rucha
Zhang, Lubo
Blood, Arlin B.
Wilson, Christopher G.
Puglisi, Jose Luis
Wilson, Sean M.
author_sort Boskind, Madison
collection PubMed
description Intracellular Ca(2+) signals are key for the regulation of cellular processes ranging from myocyte contraction, hormonal secretion, neural transmission, cellular metabolism, transcriptional regulation, and cell proliferation. Measurement of cellular Ca(2+) is routinely performed using fluorescence microscopy with biological indicators. Analysis of deterministic signals is reasonably straightforward as relevant data can be discriminated based on the timing of cellular responses. However, analysis of stochastic, slower oscillatory events, as well as rapid subcellular Ca(2+) responses, takes considerable time and effort which often includes visual analysis by trained investigators, especially when studying signals arising from cells embedded in complex tissues. The purpose of the current study was to determine if full-frame time-series and line-scan image analysis workflow of Fluo-4 generated Ca(2+) fluorescence data from vascular myocytes could be automated without introducing errors. This evaluation was addressed by re-analyzing a published “gold standard” full-frame time-series dataset through visual analysis of Ca(2+) signals from recordings made in pulmonary arterial myocytes of en face arterial preparations. We applied a combination of data driven and statistical approaches with comparisons to our published data to assess the fidelity of the various approaches. Regions of interest with Ca(2+) oscillations were detected automatically post hoc using the LCPro plug-in for ImageJ. Oscillatory signals were separated based on event durations between 4 and 40 s. These data were filtered based on cutoffs obtained from multiple methods and compared to the published manually curated “gold standard” dataset. Subcellular focal and rapid Ca(2+) “spark” events from line-scan recordings were examined using SparkLab 5.8, which is a custom automated detection and analysis program. After filtering, the number of true positives, false positives, and false negatives were calculated through comparisons to visually derived “gold standard” datasets. Positive predictive value, sensitivity, and false discovery rates were calculated. There were very few significant differences between the automated and manually curated results with respect to quality of the oscillatory and Ca(2+) spark events, and there were no systematic biases in the data curation or filtering techniques. The lack of statistical difference in event quality between manual data curation and statistically derived critical cutoff techniques leads us to believe that automated analysis techniques can be reliably used to analyze spatial and temporal aspects to Ca(2+) imaging data, which will improve experiment workflow.
format Online
Article
Text
id pubmed-10253939
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-102539392023-06-10 Improved Workflow for Analysis of Vascular Myocyte Time-Series and Line-Scan Ca(2+) Imaging Datasets Boskind, Madison Nelapudi, Nikitha Williamson, Grace Mendez, Bobby Juarez, Rucha Zhang, Lubo Blood, Arlin B. Wilson, Christopher G. Puglisi, Jose Luis Wilson, Sean M. Int J Mol Sci Article Intracellular Ca(2+) signals are key for the regulation of cellular processes ranging from myocyte contraction, hormonal secretion, neural transmission, cellular metabolism, transcriptional regulation, and cell proliferation. Measurement of cellular Ca(2+) is routinely performed using fluorescence microscopy with biological indicators. Analysis of deterministic signals is reasonably straightforward as relevant data can be discriminated based on the timing of cellular responses. However, analysis of stochastic, slower oscillatory events, as well as rapid subcellular Ca(2+) responses, takes considerable time and effort which often includes visual analysis by trained investigators, especially when studying signals arising from cells embedded in complex tissues. The purpose of the current study was to determine if full-frame time-series and line-scan image analysis workflow of Fluo-4 generated Ca(2+) fluorescence data from vascular myocytes could be automated without introducing errors. This evaluation was addressed by re-analyzing a published “gold standard” full-frame time-series dataset through visual analysis of Ca(2+) signals from recordings made in pulmonary arterial myocytes of en face arterial preparations. We applied a combination of data driven and statistical approaches with comparisons to our published data to assess the fidelity of the various approaches. Regions of interest with Ca(2+) oscillations were detected automatically post hoc using the LCPro plug-in for ImageJ. Oscillatory signals were separated based on event durations between 4 and 40 s. These data were filtered based on cutoffs obtained from multiple methods and compared to the published manually curated “gold standard” dataset. Subcellular focal and rapid Ca(2+) “spark” events from line-scan recordings were examined using SparkLab 5.8, which is a custom automated detection and analysis program. After filtering, the number of true positives, false positives, and false negatives were calculated through comparisons to visually derived “gold standard” datasets. Positive predictive value, sensitivity, and false discovery rates were calculated. There were very few significant differences between the automated and manually curated results with respect to quality of the oscillatory and Ca(2+) spark events, and there were no systematic biases in the data curation or filtering techniques. The lack of statistical difference in event quality between manual data curation and statistically derived critical cutoff techniques leads us to believe that automated analysis techniques can be reliably used to analyze spatial and temporal aspects to Ca(2+) imaging data, which will improve experiment workflow. MDPI 2023-06-04 /pmc/articles/PMC10253939/ /pubmed/37298681 http://dx.doi.org/10.3390/ijms24119729 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Boskind, Madison
Nelapudi, Nikitha
Williamson, Grace
Mendez, Bobby
Juarez, Rucha
Zhang, Lubo
Blood, Arlin B.
Wilson, Christopher G.
Puglisi, Jose Luis
Wilson, Sean M.
Improved Workflow for Analysis of Vascular Myocyte Time-Series and Line-Scan Ca(2+) Imaging Datasets
title Improved Workflow for Analysis of Vascular Myocyte Time-Series and Line-Scan Ca(2+) Imaging Datasets
title_full Improved Workflow for Analysis of Vascular Myocyte Time-Series and Line-Scan Ca(2+) Imaging Datasets
title_fullStr Improved Workflow for Analysis of Vascular Myocyte Time-Series and Line-Scan Ca(2+) Imaging Datasets
title_full_unstemmed Improved Workflow for Analysis of Vascular Myocyte Time-Series and Line-Scan Ca(2+) Imaging Datasets
title_short Improved Workflow for Analysis of Vascular Myocyte Time-Series and Line-Scan Ca(2+) Imaging Datasets
title_sort improved workflow for analysis of vascular myocyte time-series and line-scan ca(2+) imaging datasets
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10253939/
https://www.ncbi.nlm.nih.gov/pubmed/37298681
http://dx.doi.org/10.3390/ijms24119729
work_keys_str_mv AT boskindmadison improvedworkflowforanalysisofvascularmyocytetimeseriesandlinescanca2imagingdatasets
AT nelapudinikitha improvedworkflowforanalysisofvascularmyocytetimeseriesandlinescanca2imagingdatasets
AT williamsongrace improvedworkflowforanalysisofvascularmyocytetimeseriesandlinescanca2imagingdatasets
AT mendezbobby improvedworkflowforanalysisofvascularmyocytetimeseriesandlinescanca2imagingdatasets
AT juarezrucha improvedworkflowforanalysisofvascularmyocytetimeseriesandlinescanca2imagingdatasets
AT zhanglubo improvedworkflowforanalysisofvascularmyocytetimeseriesandlinescanca2imagingdatasets
AT bloodarlinb improvedworkflowforanalysisofvascularmyocytetimeseriesandlinescanca2imagingdatasets
AT wilsonchristopherg improvedworkflowforanalysisofvascularmyocytetimeseriesandlinescanca2imagingdatasets
AT puglisijoseluis improvedworkflowforanalysisofvascularmyocytetimeseriesandlinescanca2imagingdatasets
AT wilsonseanm improvedworkflowforanalysisofvascularmyocytetimeseriesandlinescanca2imagingdatasets