dc.contributor.author |
Buckels, Emma Jane |
|
dc.contributor.author |
Ross, Jacqueline Mary |
|
dc.contributor.author |
Phua, Hui Hui |
|
dc.contributor.author |
Bloomfield, Frank Harry |
|
dc.contributor.author |
Jaquiery, Anne Louise |
|
dc.coverage.spatial |
Netherlands |
|
dc.date.accessioned |
2022-11-15T01:21:31Z |
|
dc.date.available |
2022-11-15T01:21:31Z |
|
dc.date.issued |
2022-01 |
|
dc.identifier.citation |
(2022). MethodsX, 9, 101856-. |
|
dc.identifier.issn |
2215-0161 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/61852 |
|
dc.description.abstract |
Quantification of cell populations in tissue sections is frequently examined in studies of human disease. However, traditional manual imaging of sections stained with immunohistochemistry is laborious, time-consuming, and often assesses fields of view rather than the whole tissue section. The analysis is usually manual or utilises expensive proprietary image analysis platforms. Whole-slide imaging allows rapid automated visualisation of entire tissue sections. This approach increases the quantum of data generated per slide, decreases user time compared to manual microscopy, and reduces selection bias. However, such large data sets mean that manual image analysis is no longer practicable, requiring an automated process. In the case of diabetes, the contribution of various pancreatic endocrine cell populations is often investigated in preclinical and clinical samples. We developed a two-part method to measure pancreatic endocrine cell mass, firstly describing imaging using an automated slide-scanner, and secondly, the analysis of the resulting large image data sets using the open-source software, Fiji, which is freely available to all researchers and has cross-platform compatibility. This protocol is highly versatile and may be applied either in full or in part to analysis of IHC images created using other imaging platforms and/or the analysis of other tissues and cell markers. |
|
dc.format.medium |
Electronic-eCollection |
|
dc.language |
eng |
|
dc.publisher |
Elsevier |
|
dc.relation.ispartofseries |
MethodsX |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
Semi-automated image analysis |
|
dc.subject |
Sheep pancreas |
|
dc.subject |
Type 2 diabetes mellitus |
|
dc.subject |
α-cell mass |
|
dc.subject |
β-cell mass |
|
dc.subject |
Diabetes |
|
dc.subject |
Pancreatic Cancer |
|
dc.subject |
Bioengineering |
|
dc.subject |
Digestive Diseases |
|
dc.subject |
Rare Diseases |
|
dc.subject |
Cancer |
|
dc.subject |
4.1 Discovery and preclinical testing of markers and technologies |
|
dc.subject |
4 Detection, screening and diagnosis |
|
dc.subject |
0912 Materials Engineering |
|
dc.title |
Whole-slide imaging and a Fiji-based image analysis workflow of immunohistochemistry staining of pancreatic islets. |
|
dc.type |
Journal Article |
|
dc.identifier.doi |
10.1016/j.mex.2022.101856 |
|
pubs.begin-page |
101856 |
|
pubs.volume |
9 |
|
dc.date.updated |
2022-10-15T06:42:18Z |
|
dc.rights.holder |
Copyright: The authors |
en |
dc.identifier.pmid |
36204475 (pubmed) |
|
pubs.author-url |
https://www.ncbi.nlm.nih.gov/pubmed/36204475 |
|
pubs.publication-status |
Published |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
research-article |
|
pubs.subtype |
Journal Article |
|
pubs.elements-id |
919634 |
|
pubs.org-id |
Liggins Institute |
|
pubs.org-id |
Medical and Health Sciences |
|
pubs.org-id |
Medical Sciences |
|
pubs.org-id |
Molecular Medicine |
|
pubs.org-id |
LiFePATH |
|
dc.identifier.eissn |
2215-0161 |
|
dc.identifier.pii |
S2215-0161(22)00235-7 |
|
pubs.number |
101856 |
|
pubs.record-created-at-source-date |
2022-10-15 |
|