tetano
Editor, Senior Moderator
JCI Insight
. 2024 May 30:e172140.
doi: 10.1172/jci.insight.172140. Online ahead of print. iGATE analysis improves the interpretability of single-cell immune landscape of influenza infection
Brett D Hill[SUP] 1 [/SUP], Andrew J Zak[SUP] 1 [/SUP], Sanjeev Raja[SUP] 1 [/SUP], Luke F Bugada[SUP] 1 [/SUP], Syed M Rizvi[SUP] 1 [/SUP], Saiful B Roslan[SUP] 1 [/SUP], Hong Nhi Nguyen[SUP] 1 [/SUP], Judy Chen[SUP] 2 [/SUP], Hui Jiang[SUP] 3 [/SUP], Akira Ono[SUP] 4 [/SUP], Daniel R Goldstein[SUP] 2 [/SUP], Fei Wen[SUP] 1 [/SUP]
Affiliations
Influenza poses a persistent health burden worldwide. To design equitable vaccines effective across all demographics, it is essential to better understand how host factors such as genetic background and aging affect the single-cell immune landscape of influenza infection. Cytometry by time-of-flight (CyTOF) represents a promising technique in this pursuit, but interpreting its large, high-dimensional data remains difficult. We've developed a new analytical approach iGATE (in-silico gating annotating training elucidating) based on probabilistic support vector machine classification. By rapidly and accurately "gating" tens of millions of cells in silico into user-defined types, iGATE enabled us to track 25 canonical immune cell types in mouse lung over the course of influenza infection. Applying iGATE to study effects of host genetic background, we show that the lower survival of C57BL/6 mice compared to BALB/c is associated with a more rapid accumulation of inflammatory cell types and decreased IL-10 expression. Further, we demonstrate that the most prominent effect of aging is a defective T-cell response, reducing survival of aged mice. Finally, iGATE reveals that the 25 canonical immune cell types exhibit differential influenza infection susceptibility and replication permissiveness in vivo, but neither property varies with host genotype or aging. Software is available at https://github.com/UmichWenLab/iGATE.
Keywords: Bioinformatics; Immunology; Influenza; Proteomics; Virology.
. 2024 May 30:e172140.
doi: 10.1172/jci.insight.172140. Online ahead of print. iGATE analysis improves the interpretability of single-cell immune landscape of influenza infection
Brett D Hill[SUP] 1 [/SUP], Andrew J Zak[SUP] 1 [/SUP], Sanjeev Raja[SUP] 1 [/SUP], Luke F Bugada[SUP] 1 [/SUP], Syed M Rizvi[SUP] 1 [/SUP], Saiful B Roslan[SUP] 1 [/SUP], Hong Nhi Nguyen[SUP] 1 [/SUP], Judy Chen[SUP] 2 [/SUP], Hui Jiang[SUP] 3 [/SUP], Akira Ono[SUP] 4 [/SUP], Daniel R Goldstein[SUP] 2 [/SUP], Fei Wen[SUP] 1 [/SUP]
Affiliations
- PMID: 38814732
- DOI: 10.1172/jci.insight.172140
Influenza poses a persistent health burden worldwide. To design equitable vaccines effective across all demographics, it is essential to better understand how host factors such as genetic background and aging affect the single-cell immune landscape of influenza infection. Cytometry by time-of-flight (CyTOF) represents a promising technique in this pursuit, but interpreting its large, high-dimensional data remains difficult. We've developed a new analytical approach iGATE (in-silico gating annotating training elucidating) based on probabilistic support vector machine classification. By rapidly and accurately "gating" tens of millions of cells in silico into user-defined types, iGATE enabled us to track 25 canonical immune cell types in mouse lung over the course of influenza infection. Applying iGATE to study effects of host genetic background, we show that the lower survival of C57BL/6 mice compared to BALB/c is associated with a more rapid accumulation of inflammatory cell types and decreased IL-10 expression. Further, we demonstrate that the most prominent effect of aging is a defective T-cell response, reducing survival of aged mice. Finally, iGATE reveals that the 25 canonical immune cell types exhibit differential influenza infection susceptibility and replication permissiveness in vivo, but neither property varies with host genotype or aging. Software is available at https://github.com/UmichWenLab/iGATE.
Keywords: Bioinformatics; Immunology; Influenza; Proteomics; Virology.