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Research
Wentong Meng
Sichuan University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Background
The detection and dissection of epidermal subgroups could lead to an improved understanding of skin homeostasis and wound healing. Flow cytometric analysis provides an effective method to detect the surface markers of epidermal cells while producing high-dimensional data files.
Methods
A 9-color flow cytometric panel was optimized to reveal the heterogeneous subgroups in the epidermis of human skin. The subsets of epidermal cells were characterized using automated methods based on dimensional reduction approaches (viSNE) and clustering with Spanning-tree Progression Analysis of Density-normalized Events (SPADE).
Results
The manual analysis revealed differences in epidermal distribution between body sites based on a series biaxial gating starting with the expression of CD49f and CD29. The computational analysis divided the whole epidermal cell population into 25 clusters according to the surface marker phenotype with SPADE. This automatic analysis delineated the differences between body sites. The consistency of the results was confirmed with PhenoGraph.
Conclusion
A multicolor flow cytometry panel with a streamlined computational analysis pipeline is a feasible approach to delineate the heterogeneity of the epidermis in human skin.
Keywords
flow cytometry, heterogeneity, human epidermis, machine learning
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Peer Review Timeline
CURRENT STATUS: Published
View the published article at BioMedical Engineering OnLine.
No community comments so far
Editorial decision: Accept
On 03 Feb, 2021
Review #2 received
Received 02 Feb, 2021
Reviewer #2 agreed
On 31 Jan, 2021
Editor assigned
On 31 Jan, 2021
Reviewers invited
Invitations sent on 31 Jan, 2021
Reviewer #1 agreed
On 31 Jan, 2021
Review #1 received
Received 31 Jan, 2021
Submission checks complete
On 31 Jan, 2021
Editor invited
On 31 Jan, 2021
Version 1
Posted 05 Nov, 2020
No comments provided
Editorial decision: Major revision
On 11 Jan, 2021
Review #2 received
Received 06 Jan, 2021
Reviewer #2 agreed
On 15 Dec, 2020
Review #1 received
Received 20 Nov, 2020
Reviewers invited
Invitations sent on 17 Nov, 2020
Reviewer #1 agreed
On 17 Nov, 2020
Editor assigned
On 30 Oct, 2020
Submission checks complete
On 30 Oct, 2020
Editor invited
On 30 Oct, 2020
First submitted
On 28 Oct, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biomedical Engineering
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at BioMedical Engineering OnLine.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Wentong Meng
Sichuan University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BioMedical Engineering OnLine.
No community comments so far
Editorial decision: Accept
On 03 Feb, 2021
Review #2 received
Received 02 Feb, 2021
Reviewer #2 agreed
On 31 Jan, 2021
Editor assigned
On 31 Jan, 2021
Reviewers invited
Invitations sent on 31 Jan, 2021
Reviewer #1 agreed
On 31 Jan, 2021
Review #1 received
Received 31 Jan, 2021
Submission checks complete
On 31 Jan, 2021
Editor invited
On 31 Jan, 2021
Version 1
Posted 05 Nov, 2020
No comments provided
Editorial decision: Major revision
On 11 Jan, 2021
Review #2 received
Received 06 Jan, 2021
Reviewer #2 agreed
On 15 Dec, 2020
Review #1 received
Received 20 Nov, 2020
Reviewers invited
Invitations sent on 17 Nov, 2020
Reviewer #1 agreed
On 17 Nov, 2020
Editor assigned
On 30 Oct, 2020
Submission checks complete
On 30 Oct, 2020
Editor invited
On 30 Oct, 2020
First submitted
On 28 Oct, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biomedical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BioMedical Engineering OnLine.
Background
The detection and dissection of epidermal subgroups could lead to an improved understanding of skin homeostasis and wound healing. Flow cytometric analysis provides an effective method to detect the surface markers of epidermal cells while producing high-dimensional data files.
Methods
A 9-color flow cytometric panel was optimized to reveal the heterogeneous subgroups in the epidermis of human skin. The subsets of epidermal cells were characterized using automated methods based on dimensional reduction approaches (viSNE) and clustering with Spanning-tree Progression Analysis of Density-normalized Events (SPADE).
Results
The manual analysis revealed differences in epidermal distribution between body sites based on a series biaxial gating starting with the expression of CD49f and CD29. The computational analysis divided the whole epidermal cell population into 25 clusters according to the surface marker phenotype with SPADE. This automatic analysis delineated the differences between body sites. The consistency of the results was confirmed with PhenoGraph.
Conclusion
A multicolor flow cytometry panel with a streamlined computational analysis pipeline is a feasible approach to delineate the heterogeneity of the epidermis in human skin.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
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