LAVENDER extracts individual variability in the response to seasonal influenza vaccination
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Nakamura, Naotoshi
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.
 And it is known that different people show different immune responses to the same immunological perturbations, such as pollen or vaccines. For example, only a fraction of people is allergic to pollen, or some people respond well to vaccines, but others don't respond so well. So the question is, how different they are? How different are they? And why are they different? And the approach of population immunology that measures a lot of people and immune responses over the course of time can potentially answer this question. So it's immediately different, because people say it's immunological responses that are allergic, they're instantaneous. But immunity response takes about three weeks, while it's immunological. This alligates instantaneous and people have, you know, the first exposure. So you don't even have time to develop any immune reactions. So why is it called immunology? Okay, so I agree that these two types of responses are different, immunologically different, but they share some characteristics, because they are related to some genetic background related to... Genetic here, but why they're both... Not even logical, they sound like a cancer. Okay, so maybe I can answer the general questions later. No, no, it's still, I mean, just answer, because it's confusing. It's not even logical. It is, it is. It is immunological. It's a different immunopath, it's still immunological. So it's an immune system which always reacts to anything, yeah? It's not learned. So for it is ready to go, or it has to be built, and then it goes. Okay. Thank you. And so I'm interested in the approach of population immunology. And we are... This time we examined the cohort of 300 volunteers who cooperated with us and received seasonal influenza vaccine in 2011. The cohort consisted of 100 males and 200 females, age 32 to 66, so they are middle-aged people. And we used trivalent inactivated influenza vaccine containing hemagglutinin proteins of these virus stocks. So they are very usual ones. And we collected peripheral blood on four time occasions, namely before vaccination and one day after and one week after, or three months after, vaccinations. And we analyzed these blood samples. And individual blood samples were split into two tubes. One tube was used for measuring six V cell markers like this. The other tube was used for measuring T cell markers like this. So as you see, they are very general markers classifying the basic subsets of V cells and T cells. And in flow cytometry, each of these markers is measured for each single cell in the blood. So what we get is a point cloud of cells in a multi-dimensional space. So seven axes, seven dimensions for T cells and six dimensions in V cells. And usually in immunology studies, we select only two axes and show two dimensional plots like this. But please note that this is only a fraction of a much bigger picture. So to analyze these multi-dimensional space, we devised a new method called lavender. Lavender is intended to uncover the dilatant axes that can explain the variability of the dataset. Suppose we have four samples showing these kinds of immunological distributions. And looking at these pictures, you might think that top two pictures might be somewhat similar because they belong to the same participant number three. But they might be a little bit different from the bottom two pictures which belong to the participant number six. So we want to quantify these differences and for that we follow the four steps. In step one, we perform density estimation of these point clouds. So these raw data are very complicated. So we determine a grid and calculate the density of cells using K-nearest-neverex method. That amounts to basically smoothing the distribution. And this method is non-parametric. That means it doesn't presuppose any distributions, be it normal or Poisson or uniform. So it can deal with any complex shape of point clouds in an unbiased manner. And in step two, we measure distances between distributions using the concept of a callback library of data in information science. And we give it a little trick to make it a genuine bona fide metric. And we use the, actually we use the distance. And in that nutshell, it is, it tends to emphasize the accumulated peaks and focus on accumulated peaks and tries to measure the intensity or the positions of these accumulated peaks. But other distances, if you like, can be used. And in step three, based on the distances we just measured, we reconstruct samples in a new coordinate space that we call lavender space. That uses the algorithm of multidimensional scaling. And like this. So the distances between points reflect the distances between original samples. So this is a huge dimensionality reduction from the very complicated raw data to just two or three dimensions. And then we can analyze access in this lavender space. So this is a result of dimensionality reduction for B cell samples and T cell samples. So as you can see, each dot corresponds to each sample taken from participants. And different colors denote different days. Zero, day one, day seven, day ninety, in which samples were taken. And it might be difficult to see, but you see that the same color, the same day samples form a cluster, but they are somewhat dispersed in a certain direction. And this trend is same for T cells. Same same day, same day, suppose a formal cluster like black, but they are dispersed. And this is a two dimensional projection that is easier to see. And we see that intuitively. And for these examples of this horizontal axis corresponds to the time dependent axis that shows the difference between days and the horizontal vertical axis vertical axis. This is a corresponding to the individuality that shows the variability between each samples, same day samples. This applies to T cells as well. This time the vertical axis is more related to time. And the horizontal axis shows the variability or individuality between samples. So these are very simple intuitive arguments, but we can make it rigorous by using tensor decomposition. So T cells are basically in this context are just three dimensional versions of matrices having dimensions and participants or different days and regular coordinates. And these tensor can be decomposing to more simple rank one tensors like this. Actually the sum of rank one tensors. It's called Cp decomposition. So if you look at each component and see the days component. So this is an ideal case, but if the days component is time dependent, this component can be considered to be time dependent. And if it is not dependent on time, it can be considered to be time independent. And actually, and we were able to separate the D'Arabenda coordinates into time dependent and individuality axis using tensor decomposition. So we analyze the individuality axis and follow it over time. And first we separated the participants into two groups based on the value of the individuality axis on day zero. So group one has a lower value of the individuality axis, whereas group two people have higher values. And as we follow over time, the axis, the value of the axis is relatively stable, which means that group one people tend to stay low, whereas group two people tend to stay high, even, but with occasional switching orders for these cells. And this trend is more conspicuous in T cells, where a group at the individuality axis is more stable than the B cell samples. And we further looked at, we tried the biological characterization of the axis at day zero, and we found that when we looked at white blood cell differential counts, a group one people had more lymphocytes that is related to adaptive immunity or producing antibodies. And group two people had more neutrophils that were related to innate immunity or inflammation. So group one seems to be more ready to respond to vaccine even before vaccination. And this trend was verified when we looked at B cell subsets. And this is the lineage of B cell differentiation from immature to naive to finally to memory or antibody producing plasma cells. And we found that group one people had more plus a larger fraction of plasma cells than group two, supporting that group one group one people are more ready to respond. So to conclude, we analyzed variability in immune system in a cohort of 300 volunteers who received seasonal influenza vaccine. And our lavender analysis enables us to extract critical access of individuality in a supervised and biased manner. In fact, in our data set, it uncovered the baseline immunological characteristics under the new response to the vaccine, i.e. adaptive, that is adaptive immunity dominant or immunity dominant. So this kind of answers the how different question. But I think that to answer the why different question, we need to look at more specific genes or specifics or subsets that were discovered in our data set. So I would like to acknowledge causes, especially Daigo Kadaf is a graduate student in Yabba, the lab, the Yama lab. And Dr. Seto, who is an immunologist who performed experiments in Matsuda lab. Thank you very much. I don't think I understood because what you showed is that there are different populations that are more, basically different immune profiles. But I didn't see the connection between that and how they are responding to the vaccination. And so, it also ignores that the patients would have been exposed in different ways to the flu historically as they were in childs, etc. So that connection is now there. So how to evaluate the output is an important question. And we measure the output of this system using the antibody titers. But unfortunately, the antibody titers were not in clear linear correlations with these coordinates we measured. But as you mentioned, these small antibody titers are more correlated with vaccination history. That is reported in other studies as well. So the people who have never been vaccinated show the larger response to the vaccination. And I think in my opinion, the antibody titers are accumulation of the workings of plasma cells. So that's kind of an integration. So I don't think it's so unnatural that if the antibody titers don't show linear relationships with what we do. So our coordinates are correlated with the fraction of plasma cells, but we're not directly correlated with antibody titers. So that's maybe, as you said, there may be a difference in concepts or what we... So yeah, and we need to be more specific in discussing these results, I think. It's related to this question. So as you move this, how do you define T and B cells population? They are very heterogeneous populations. How do I define which populations? T and B cells. Yeah, so there are a large amount of studies related to defining various subsets of B cells and T cells, which should I use? And they are just very basic ones. And for example, in a recent study, you've shown that vaccination efficacy is related to the specific subset called follicle helper follicle T cells. That was not measured in this study. So I agree that this study is kind of simplistic in terms of immunology, but we think that the same method can be used for analyzing more detailed data. One more question. You have group one and two. Do they correlate with age? So that could be a bit correlated? No, there are no...at least statistically no difference between group A and group B. And I found that one...I didn't remember which was that, but in one very volatile...in one curve showed a very volatile movement. That was young female. Young females...my delusional idea is that young females are more prone to...so immunological...more active maybe, immunologically. Alright, thank you very much, Nintoshi. Thank you very much.
 And it is known that different people show different immune responses to the same immunological  perturbations, such as pollen or vaccines.  For example, only a fraction of people is allergic to pollen, or some people respond  well to vaccines, but others don't respond so well.  So the question is, how different they are?  How different are they?  And why are they different?  And the approach of population immunology that measures a lot of people and immune responses  over the course of time can potentially answer this question.  So it's immediately different, because people say it's immunological responses that are  allergic, they're instantaneous.  But immunity response takes about three weeks, while it's immunological.  This alligates instantaneous and people have, you know, the first exposure.  So you don't even have time to develop any immune reactions.  So why is it called immunology?  Okay, so I agree that these two types of responses are different, immunologically different, but  they share some characteristics, because they are related to some genetic background  related to...  Genetic here, but why they're both...  Not even logical, they sound like a cancer.  Okay, so maybe I can answer the general questions later.  No, no, it's still, I mean, just answer, because it's confusing.  It's not even logical.  It is, it is.  It is immunological.  It's a different immunopath, it's still immunological.  So it's an immune system which always reacts to anything, yeah?  It's not learned.  So for it is ready to go, or it has to be built, and then it goes.  Okay.  Thank you.  And so I'm interested in the approach of population immunology.  And we are...  This time we examined the cohort of 300 volunteers who cooperated with us and received seasonal influenza vaccine in 2011.  The cohort consisted of 100 males and 200 females, age 32 to 66, so they are middle-aged people.  And we used trivalent inactivated influenza vaccine containing hemagglutinin proteins of these virus stocks.  So they are very usual ones.  And we collected peripheral blood on four time occasions, namely before vaccination and one day after and one week after, or three months after, vaccinations.  And we analyzed these blood samples.  And individual blood samples were split into two tubes.  One tube was used for measuring six V cell markers like this.  The other tube was used for measuring T cell markers like this.  So as you see, they are very general markers classifying the basic subsets of V cells and T cells.  And in flow cytometry, each of these markers is measured for each single cell in the blood.  So what we get is a point cloud of cells in a multi-dimensional space.  So seven axes, seven dimensions for T cells and six dimensions in V cells.  And usually in immunology studies, we select only two axes and show two dimensional plots like this.  But please note that this is only a fraction of a much bigger picture.  So to analyze these multi-dimensional space, we devised a new method called lavender.  Lavender is intended to uncover the dilatant axes that can explain the variability of the dataset.  Suppose we have four samples showing these kinds of immunological distributions.  And looking at these pictures, you might think that top two pictures might be somewhat similar  because they belong to the same participant number three.  But they might be a little bit different from the bottom two pictures which belong to the participant number six.  So we want to quantify these differences and for that we follow the four steps.  In step one, we perform density estimation of these point clouds.  So these raw data are very complicated.  So we determine a grid and calculate the density of cells using K-nearest-neverex method.  That amounts to basically smoothing the distribution.  And this method is non-parametric.  That means it doesn't presuppose any distributions, be it normal or Poisson or uniform.  So it can deal with any complex shape of point clouds in an unbiased manner.  And in step two, we measure distances between distributions using the concept of a callback library of data in information science.  And we give it a little trick to make it a genuine bona fide metric.  And we use the, actually we use the distance.  And in that nutshell, it is, it tends to emphasize the accumulated peaks and focus on accumulated peaks and tries to measure the intensity or the positions of these accumulated peaks.  But other distances, if you like, can be used.  And in step three, based on the distances we just measured, we reconstruct samples in a new coordinate space that we call lavender space.  That uses the algorithm of multidimensional scaling.  And like this.  So the distances between points reflect the distances between original samples.  So this is a huge dimensionality reduction from the very complicated raw data to just two or three dimensions.  And then we can analyze access in this lavender space.  So this is a result of dimensionality reduction for B cell samples and T cell samples.  So as you can see, each dot corresponds to each sample taken from participants.  And different colors denote different days.  Zero, day one, day seven, day ninety, in which samples were taken.  And it might be difficult to see, but you see that the same color, the same day samples form a cluster, but they are somewhat dispersed in a certain direction.  And this trend is same for T cells.  Same same day, same day, suppose a formal cluster like black, but they are dispersed.  And this is a two dimensional projection that is easier to see.  And we see that intuitively.  And for these examples of this horizontal axis corresponds to the time dependent axis that shows the difference between days and the horizontal vertical axis vertical axis.  This is a corresponding to the individuality that shows the variability between each samples, same day samples.  This applies to T cells as well.  This time the vertical axis is more related to time.  And the horizontal axis shows the variability or individuality between samples.  So these are very simple intuitive arguments, but we can make it rigorous by using tensor decomposition.  So T cells are basically in this context are just three dimensional versions of matrices having dimensions and participants or different days and regular coordinates.  And these tensor can be decomposing to more simple rank one tensors like this.  Actually the sum of rank one tensors.  It's called Cp decomposition.  So if you look at each component and see the days component.  So this is an ideal case, but if the days component is time dependent, this component can be considered to be time dependent.  And if it is not dependent on time, it can be considered to be time independent.  And actually, and we were able to separate the D'Arabenda coordinates into time dependent and individuality axis using tensor decomposition.  So we analyze the individuality axis and follow it over time.  And first we separated the participants into two groups based on the value of the individuality axis on day zero.  So group one has a lower value of the individuality axis, whereas group two people have higher values.  And as we follow over time, the axis, the value of the axis is relatively stable, which means that group one people tend to stay low, whereas group two people tend to stay high, even, but with occasional switching orders for these cells.  And this trend is more conspicuous in T cells, where a group at the individuality axis is more stable than the B cell samples.  And we further looked at, we tried the biological characterization of the axis at day zero, and we found that when we looked at white blood cell differential counts, a group one people had more lymphocytes that is related to adaptive immunity or producing antibodies.  And group two people had more neutrophils that were related to innate immunity or inflammation.  So group one seems to be more ready to respond to vaccine even before vaccination.  And this trend was verified when we looked at B cell subsets.  And this is the lineage of B cell differentiation from immature to naive to finally to memory or antibody producing plasma cells.  And we found that group one people had more plus a larger fraction of plasma cells than group two, supporting that group one group one people are more ready to respond.  So to conclude, we analyzed variability in immune system in a cohort of 300 volunteers who received seasonal influenza vaccine.  And our lavender analysis enables us to extract critical access of individuality in a supervised and biased manner.  In fact, in our data set, it uncovered the baseline immunological characteristics under the new response to the vaccine, i.e. adaptive, that is adaptive immunity dominant or immunity dominant.  So this kind of answers the how different question.  But I think that to answer the why different question, we need to look at more specific genes or specifics or subsets that were discovered in our data set.  So I would like to acknowledge causes, especially Daigo Kadaf is a graduate student in Yabba, the lab, the Yama lab. And Dr. Seto, who is an immunologist who performed experiments in Matsuda lab.  Thank you very much.  I don't think I understood because what you showed is that there are different populations that are more, basically different immune profiles.  But I didn't see the connection between that and how they are responding to the vaccination.  And so, it also ignores that the patients would have been exposed in different ways to the flu historically as they were in childs, etc.  So that connection is now there.  So how to evaluate the output is an important question. And we measure the output of this system using the antibody titers.  But unfortunately, the antibody titers were not in clear linear correlations with these coordinates we measured.  But as you mentioned, these small antibody titers are more correlated with vaccination history.  That is reported in other studies as well.  So the people who have never been vaccinated show the larger response to the vaccination.  And I think in my opinion, the antibody titers are accumulation of the workings of plasma cells. So that's kind of an integration.  So I don't think it's so unnatural that if the antibody titers don't show linear relationships with what we do.  So our coordinates are correlated with the fraction of plasma cells, but we're not directly correlated with antibody titers.  So that's maybe, as you said, there may be a difference in concepts or what we...  So yeah, and we need to be more specific in discussing these results, I think.  It's related to this question. So as you move this, how do you define T and B cells population?  They are very heterogeneous populations.  How do I define which populations?  T and B cells.  Yeah, so there are a large amount of studies related to defining various subsets of B cells and T cells, which should I use?  And they are just very basic ones.  And for example, in a recent study, you've shown that vaccination efficacy is related to the specific subset called follicle helper follicle T cells.  That was not measured in this study.  So I agree that this study is kind of simplistic in terms of immunology, but we think that the same method can be used for analyzing more detailed data.  One more question.  You have group one and two. Do they correlate with age? So that could be a bit correlated?  No, there are no...at least statistically no difference between group A and group B.  And I found that one...I didn't remember which was that, but in one very volatile...in one curve showed a very volatile movement.  That was young female.  Young females...my delusional idea is that young females are more prone to...so immunological...more active maybe, immunologically.  Alright, thank you very much, Nintoshi.  Thank you very much.
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