Heeva Baharlou1,2, Nicolas P Canete1,2, Kirstie M Bertram1,2, Kerrie J Sandgren1,2, Anthony L Cunningham1,2, Andrew N Harman1,2, Ellis Patrick1,3. 1. School of Medicine, The Westmead Institute for Medical Research, University of Sydney, Westmead, NSW, Australia. 2. School of Medical Sciences in the Faculty of Medicine and Health, Centre for Virus Research, The Westmead Institute for Medical Research, Westmead, NSW, Australia. 3. Department of Mathematics and Statistics in the Faculty of Science, The University of Sydney, 2006 Sydney, NSW, Australia 4Centre for Virus Research, The Westmead Institute for Medical Research, 2145 Sydney NSW Australia.
Abstract
MOTIVATION: Autofluorescence is a long-standing problem that has hindered the analysis of images of tissues acquired by fluorescence microscopy. Current approaches to mitigate autofluorescence in tissue are lab-based and involve either chemical treatment of sections or specialized instrumentation and software to 'unmix' autofluorescent signals. Importantly, these approaches are pre-emptive and there are currently no methods to deal with autofluorescence in acquired fluorescence microscopy images. RESULTS: To address this, we developed Autofluorescence Identifier (AFid). AFid identifies autofluorescent pixels as discrete objects in multi-channel images post-acquisition. These objects can then be tagged for exclusion from downstream analysis. We validated AFid using images of FFPE human colorectal tissue stained for common immune markers. Further, we demonstrate its utility for image analysis where its implementation allows the accurate measurement of HIV-Dendritic cell interactions in a colorectal explant model of HIV transmission. Therefore, AFid represents a major leap forward in the extraction of useful data from images plagued by autofluorescence by offering an approach that is easily incorporated into existing workflows and that can be used with various samples, staining panels and image acquisition methods. We have implemented AFid in ImageJ, Matlab and R to accommodate the diverse image analysis community. AVAILABILITY AND IMPLEMENTATION: AFid software is available at https://ellispatrick.github.io/AFid. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Autofluorescence is a long-standing problem that has hindered the analysis of images of tissues acquired by fluorescence microscopy. Current approaches to mitigate autofluorescence in tissue are lab-based and involve either chemical treatment of sections or specialized instrumentation and software to 'unmix' autofluorescent signals. Importantly, these approaches are pre-emptive and there are currently no methods to deal with autofluorescence in acquired fluorescence microscopy images. RESULTS: To address this, we developed Autofluorescence Identifier (AFid). AFid identifies autofluorescent pixels as discrete objects in multi-channel images post-acquisition. These objects can then be tagged for exclusion from downstream analysis. We validated AFid using images of FFPE human colorectal tissue stained for common immune markers. Further, we demonstrate its utility for image analysis where its implementation allows the accurate measurement of HIV-Dendritic cell interactions in a colorectal explant model of HIV transmission. Therefore, AFid represents a major leap forward in the extraction of useful data from images plagued by autofluorescence by offering an approach that is easily incorporated into existing workflows and that can be used with various samples, staining panels and image acquisition methods. We have implemented AFid in ImageJ, Matlab and R to accommodate the diverse image analysis community. AVAILABILITY AND IMPLEMENTATION: AFid software is available at https://ellispatrick.github.io/AFid. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Marianna Zazhytska; Albana Kodra; Daisy A Hoagland; John F Fullard; Hani Shayya; Arina Omer; Stuart Firestein; Qizhi Gong; Peter D Canoll; James E Goldman; Panos Roussos; Benjamin R tenOever; Jonathan B Overdevest; Stavros Lomvardas Journal: bioRxiv Date: 2021-02-09
Authors: Marianna Zazhytska; Albana Kodra; Daisy A Hoagland; Justin Frere; John F Fullard; Hani Shayya; Natalie G McArthur; Rasmus Moeller; Skyler Uhl; Arina D Omer; Max E Gottesman; Stuart Firestein; Qizhi Gong; Peter D Canoll; James E Goldman; Panos Roussos; Benjamin R tenOever; Stavros Lomvardas Journal: Cell Date: 2022-02-02 Impact factor: 66.850
Authors: Philip A Adeniyi; Katie-Anne Fopiano; Fatima Banine; Mariel Garcia; Xi Gong; C Dirk Keene; Larry S Sherman; Zsolt Bagi; Stephen A Back Journal: ASN Neuro Date: 2022 Jan-Dec Impact factor: 5.200