PT  - JOURNAL ARTICLE
AU  - Heeva Baharlou
AU  - Nicolas P Canete
AU  - Kirstie M Bertram
AU  - Kerrie J Sandgren
AU  - Anthony L Cunningham
AU  - Andrew N Harman
AU  - Ellis Patrick
TI  - AFid: A tool for automated identification and exclusion of autofluorescent objects from microscopy images
AID  - 10.1101/566315
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 566315
4099  - http://biorxiv.org/content/early/2019/11/26/566315.short
4100  - http://biorxiv.org/content/early/2019/11/26/566315.full
AB  - 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. 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.Availability and implementation https://ellispatrick.github.io/AFidContact ellis.patrick{at}sydney.edu.au