Naotoshi Nakamura - LAVENDER extracts individual variability...
Channel: Institut des Hautes Études Scientifiques (IHÉS)
Date: 2018-03-26

Naotoshi Nakamura - LAVENDER extracts individual variability in the response to seasonal influenza vaccination

The human immune system is known to be highly variable 
among individuals, but it is not well understood how the variability changes over time, especially when faced with external perturbations. Here we analyzed individual variability in the immune system in a  cohort  of  301  Japanese  volunteers  who  received  the  same  trivalent  inactivated influenza vaccine in winter 2011. To extract important variability axes from
single-cell measurements in  a  data-driven  and unsupervised  manner,  we devised a computational method termed LAVENDER 
(latent axes visualization and evaluation by nonparametric density  estimation  and  multidimensional  scaling  reconstruction). 
It  measures  distances between  samples  using k-nearest  neighbor  density  estimation  and  Jensen-Shannon divergence,  then  reconstructs  samples  in  a  new  coordinate  space,  whose  axes  can  be compared with other omics measurements to find biological information. Application of LAVENDER to  multidimensional  flow  cytometry  datasets  of  B  and  T lymphocytes (taken before and 1, 7, 90 days after vaccination) 
uncovered an axis related to time and another  axis  related  to  individuality. We  found  that the  values  of the individuality axis
were  positively  correlated between  different  days, suggesting  that  the  axis  reflects  the baseline immunological characteristics of each individual. In fact, the value of the axis before  vaccination  was highly correlated  with the  neutrophil-to-lymphocyte  ratio, 
a clinical  marker  of  the  systemic  inflammatory response;  this  was  verified  by  the transcriptome analysis of peripheral blood. 
These results demonstrate that LAVENDER is  a  useful tool for 
identifying critical heterogeneity among similar  but  different 
single-cell datasets.