GERDA datasets

Published: 31 January 2023| Version 1 | DOI: 10.17632/8c4zbxfvwk.1
Fabian Otte


pre-ART (antiretroviral therapy), P01_46_T1 and P01_46_VOA_d14 dataset for HIV-1 reservoir screen from Swiss HIV Study Cohort using GERDA (Gag and Envelope reactivation co-detection assay). Resubmitted to Cell Reports Methods (Jan-2023) GERDA is a new detection method to decipher the HIV-1 cellular reservoir in blood (tissue or any other specimen). It integrates HIV-1 Gag and Env co-detection along with cellular surface markers to reveal 1) what cells still contain HIV-1 translation competent virus and 2) which marker the respective infected cells express. The phenotypic marker repertoire of the cells allow to make predictions on potential homing and to assess the HIV-1 (tissue) reservoir. All FACS data were acquired on a LSRFortessa BD FACS machine (markers: CCR7, CD45RA, CD28, CD4, CD25, PD1, IntegrinB7, CLA, HIV-1 Env, HIV-1 Gag) Raw FACS data (pre-gated CD4CD3+ T-cells) were arcsin transformed and dimensionally reduced using optsne. Data was further clustered using DBSCAN and either individual clusters were further analyzed for individual marker expression or expression profiles of all relevant clusters were analyzed by heatmaps. data normalization code (by Julian Spagnuolo) FACS normalized data as CSV (XXX_arcsin.csv) OMIQ conText file (_OMIQ-context_XXX) arcsin normalized FACS data after optsne dimension reduction with as CSV file (XXXarcsin.csv.csv) R pipeline with codes (XXX_commented.R)


Steps to reproduce

For all three datasets a step-by-step R coding pipeline is attached as well as R working environments for comparison.


Universitat Basel Departement Biomedizin


Clustering, Fluorescence Activated Cell Sorting