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Tutorial: methods for three-dimensional visualization of archival tissue material
Radboud Univ Nijmegen, Netherlands.
Radboud Univ Nijmegen, Netherlands; Univ Texas MD Anderson Canc Ctr, TX 77030 USA; Canc Genom Nl CGC Nl, Netherlands.
Strateos, CA USA.
Linköpings universitet, Institutionen för biomedicinska och kliniska vetenskaper, Avdelningen för inflammation och infektion. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Diagnostikcentrum, Klinisk patologi. Leeds Teaching Hosp NHS Trust, England; Univ Leeds, England.
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2021 (Engelska)Ingår i: Nature Protocols, ISSN 1754-2189, E-ISSN 1750-2799, Vol. 16, nr 11, s. 4945-4962Artikel, forskningsöversikt (Refereegranskat) Published
Abstract [en]

The authors describe three-dimensional imaging pipelines available to analyze archival patient specimens. The pipelines facilitate the visualization of both large and small volumes of tissue with subcellular resolution. Analysis of three-dimensional patient specimens is gaining increasing relevance for understanding the principles of tissue structure as well as the biology and mechanisms underlying disease. New technologies are improving our ability to visualize large volume of tissues with subcellular resolution. One resource often overlooked is archival tissue maintained for decades in hospitals and research archives around the world. Accessing the wealth of information stored within these samples requires the use of appropriate methods. This tutorial introduces the range of sample preparation and microscopy approaches available for three-dimensional visualization of archival tissue. We summarize key aspects of the relevant techniques and common issues encountered when using archival tissue, including registration and antibody penetration. We also discuss analysis pipelines required to process, visualize and analyze the data and criteria to guide decision-making. The methods outlined in this tutorial provide an important and sustainable avenue for validating three-dimensional tissue organization and mechanisms of disease.

Ort, förlag, år, upplaga, sidor
NATURE PORTFOLIO , 2021. Vol. 16, nr 11, s. 4945-4962
Nationell ämneskategori
Bioinformatik och beräkningsbiologi
Identifikatorer
URN: urn:nbn:se:liu:diva-181069DOI: 10.1038/s41596-021-00611-4ISI: 000712724300002PubMedID: 34716449OAI: oai:DiVA.org:liu-181069DiVA, id: diva2:1612491
Anmärkning

Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [10602/2016-2]; European Research CouncilEuropean Research Council (ERC)European Commission [617430-DEEPINSIGHT]; Cancer Genomics Center (CGC.nl); [NIH-U54 CA210184-01]

Tillgänglig från: 2021-11-18 Skapad: 2021-11-18 Senast uppdaterad: 2025-02-07

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Treanor, Darren
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Avdelningen för inflammation och infektionMedicinska fakultetenKlinisk patologi
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