in-silico immune cell deconvolution of the airway ...identifying prognostic biomarkers of poor...

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In-silico immune cell deconvolution of the airway proteomes of

infants with pneumonia reveals a link between reduced airway

eosinophils and an increased risk of mortality

Charles J Sande1†, Jacqueline M Waeni, James M Njunge1, Martin N Mutunga1, Elijah Gicheru1, Nelson

K Kibinge1, Agnes Gwela1

1. KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya

† Corresponding author email: [email protected]

Keywords: Respiratory syncytial virus, proteome, pneumonia, deconvolution

.CC-BY-ND 4.0 International licenseavailable under awas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

The copyright holder for this preprint (whichthis version posted January 19, 2020. ; https://doi.org/10.1101/840090doi: bioRxiv preprint

https://doi.org/10.1101/840090

http://creativecommons.org/licenses/by-nd/4.0/

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Abstract

Rationale: Pneumonia is a leading cause of mortality in infants and young children. The

mechanisms that lead to mortality in these children are poorly understood. Studies of the

cellular immunology of the infant airway have traditionally been hindered by the limited

sample volumes available from the young, frail children who are admitted to hospital

with pneumonia. This is further compounded by the relatively low frequencies of certain

immune cell phenotypes that are thought to be critical to the clinical outcome of

pneumonia. To address this, we developed a novel in-silico deconvolution method for

inferring the frequencies of immune cell phenotypes in the airway of children with

different survival outcomes using proteomic data.

Methods: Using high-resolution mass spectrometry, we identified > 1,000 proteins

expressed in the airways of children who were admitted to hospital with clinical

pneumonia. 61 of these children were discharged from hospital and survived for more

than 365 days after discharge, while 19 died during admission. We used machine learning

by random forest to derive protein features that could be used to deconvolve immune

cell phenotypes in paediatric airway samples. We applied these phenotype-specific

signatures to identify airway-resident immune cell phenotypes that were differentially

enriched by survival status and validated the findings using a large retrospective

pneumonia cohort.

Main Results: We identified immune-cell phenotype classification features for 33

immune cell types. Eosinophil-associated features were significantly elevated in airway

samples obtained from pneumonia survivors and were downregulated in children who

subsequently died. To confirm these results, we analyzed clinical parameters from

>10,000 children who had been admitted with pneumonia in the previous 10 years. The



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results of this retrospective analysis mirrored airway deconvolution data and showed that

survivors had significantly elevated eosinophils at admission compared to fatal

pneumonia.

Conclusions: Using a proteomics bioinformatics approach, we identify airway

eosinophils as a critical factor for pneumonia survival in infants and young children.



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Introduction

Pneumonia is a leading cause of paediatric mortality word-wide. A recent study on the

global burden of paediatric pneumonia conducted in seven countries found that viruses

account for about 61% of all paediatric pneumonia infections, while about 27% of

infection were attributed to bacterial pathogens, with RSV accounting for the largest

etiological fraction of paediatric pneumonia1. More than 90% of the deaths that occur

due to pneumonia in children under 5, occur in low resource settings, mainly due to the

lack of paediatric intensive care facilities2. Very young infants especially those with

comorbidities such as HIV and malnutrition have a poor survival prognosis following

pneumonia infection. HIV-infected infants who develop a pneumonia infection are up to

10 times more likely to die from the infection than non-HIV infected children3, while those

with malnutrition are more than 15 times more likely to die after admission4. The damage

to the lungs caused by severe pneumonia appears to persist even after discharge from

hospital, with recent estimates showing that post-discharge mortality in African children

previously admitted to hospital with pneumonia being eight times greater than those

discharged with other diagnoses5.

The difference in the immunological response to pneumonia between children who

succumb to infection and those who survive it is not clear. An improved understanding of

the immunobiology of this elevated post infection mortality risk will be crucial in

identifying prognostic biomarkers of poor outcome, which will be critical in guiding care

decisions in the first critical hours after admission. Most studies on the mechanisms of

severe pneumonia in infants have been done using blood samples6, and whist these

studies have provided significant insights into disease pathology, they might not fully



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recapitulate the immune response to infection in the airway. In the case of RSV, there has

only been one study in the last 60 years that examined the lung samples of children who

died from pneumonia. Archived post-mortem lung tissue from three children who died in

the pre-intensivist era in the USA in the 1930s and 1940s was evaluated and airway

obstruction with necrotic cells, fibrin, mucus, and fluid identified as a prominent feature

in RSV lung infection7. Staining for immune cell populations was not technically feasible

in these long term archival samples.

Due to the paucity of mechanistic data on the immunological response to pneumonia in

the airway there is broad interest in understanding the dynamics of airway-resident

immune cells following pneumonia infection in infants. This effort has been hindered by

the unsuitability of routine airway sampling techniques for conventional cytometric

analyses. Samples from nasopharyngeal washings and naso- and oropharyngeal swabbing

are the most common methods of sampling the airways of sick children, and whilst ideal

for molecular diagnostics, they are less suitable for phenotyping of airway resident

immune cells using conventional cytometry techniques. Sampling of the airway by these

methods typically results in limited cell yields and the cells that are recovered are

generally highly enriched for granulocytes, complicating detailed characterisation of less

abundant phenotypes using traditional flow cytometry-based tools8. Recent analysis of

the cellular composition of upper airway by flow cytometry showed that the typical

abundances of critical effector cells like T Cells, B Cells, Mast cells, Dendritic cells and NK

cells to be less than 0.5% of all airway cells, while granulocytes were present at a median

frequency of >90%9.



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A potential way to address these shortcomings is to monitor changes in marker proteins

that are uniquely or predominantly expressed by specific immune cell phenotypes and to

use this information to infer the dynamics of the underlying cell types. We have recently

described a high-resolution mass-spectrometry-based proteomics approach for

characterizing the total proteome of the infant airway to a depth of more than 1,000

proteins10,11. The airway proteome characterized by this technique represents an unbiased

snapshot of the underlying cell populations and can be leveraged to infer changes in the

frequencies of the contributing cell phenotypes. Here, we describe an in-silico immune

cell phenotype deconvolution approach where we use protein markers that are uniquely

or predominantly expressed by specific immune cell subsets to infer the dynamics of

those phenotypes in airways of children who survived or died from pneumonia.

Phenotype-classification features were derived from a previously published data set

containing the individual proteomes of purified immune cell populations12 (deconvolution

data set) and these were then applied to airway proteome data from children with

different pneumonia outcomes (Figure 1 contains a graphical description of the study

design). Using this information, we identified an eosinophil-related protein signature that

was elevated in the airways of children who survived pneumonia but that was

downregulated in fatal pneumonia and in well controls. We subsequently validated this

association using a large retrospective pneumonia cohort of >10,000 children.

Results

To determine whether the deconvolution proteome data set contained sufficient

resolution to distinguish individual immune cell phenotypes, we used protein expression

levels from the data set to visualise phenotype segregation by nonmetric



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multidimensional scaling (NMDS). The analysis showed that major immune cell

phenotypes including B-cells, T cells, natural killer (NK) cells, dendritic cells(DC),

monocytes (MO), basophils, eosinophils and neutrophils could be distinctly segregated

on the basis of differential protein expression (figure 2a). We then set out to identify

individual proteins that could be used to accurately distinguish major and sub immune

phenotypes (sub phenotypes defined as lower-level hierarchies of the major phenotypes

– e.g. plasmacytoid and myeloid DCs within the major DC phenotype) within the

deconvolution data set. Using the recursive feature elimination-paired random forest (RF)

algorithm, boruta13, we identified classification features for 33 major and sub immune

cell phenotypes (supplementary table 1). Since only a subset of these feature proteins are

likely to be expressed within the specific context of the inflamed airway, we set out to

determine which of the features identified by the model were also expressed in the

airways of children admitted with pneumonia. For some cell types such as

monocytes/macrophages and neutrophils, a substantial proportion (>30%) of the

phenotype classification features from the feature selection model were also expressed

in the airway, while for others (e.g. NK cells), a lower proportion of the classifiers were

identified in the airway (figures 2B, D, F & H). Supplementary table 1 contains a complete

list of all the identified features and their respective expression levels in the infant airway.

Next, we undertook a more detailed analysis of the phenotype classification features that

were identified by machine learning to determine whether they could be used to

recapitulate the phenotypes in an unsupervised prediction analysis. We reasoned that the

classification features of a particular phenotype would be broadly related to its functional

properties and that when they are subjected to an independent unsupervised enrichment



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analysis, they would successfully predict the phenotypes from which they were initially

derived. Using the unsupervised enrichment platform, enrichR14,15, we found that 20 of

the 33 immune cell phenotypes were successfully predicted by their respective feature

proteins (examples in figures 2 C, E, G & I). Subsequent analysis was restricted to these

phenotypes. We visualised the performance of individual classification features by

comparing their expression levels between different immune cell phenotypes. An

example of this analysis is shown in figure 3A, where the monocyte classification feature,

SERPINB2, is shown as being expressed at substantially higher levels in activated

monocytes relative to all other cell phenotypes; the median expression level of SERPINB2

in classical activated monocytes was >104 fold higher in monocytes, relative to all other

phenotypes (figure 3B). Of the 64 classification features identified by machine learning

for activated monocytes, 17 were detected in the airway samples from children with

pneumonia (figure 3C).

To enhance the power of the identified features to resolve the constituent cell

phenotypes of a complex sample, we generated a phenotype classification profile, in

which all the classification features of a particular phenotype were aggregated and their

combined expression was plotted relative to all other phenotypes. An example of this

analysis is shown in figure 3D, where the combined expression of all monocyte-specific

features (plotted in red) was compared to the expression level of the same proteins in all

other phenotypes (plotted in gray). The results of this analysis showed that these proteins

were expressed at a significantly higher level in monocytes compared to all other

phenotypes (p<0.0001). Similar analysis for all other phenotypes is presented in

supplementary figure 1. We then used t-SNE dimensional reduction analysis to visualise



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the segregation of major and sub phenotypes on the basis of the phenotype classification

features (figure 4A). The results of this analysis showed that the feature classifier proteins

could clearly distinguish most immune cell types; phenotypes like plasma cells

(B.plasma), pDC, mDC, eosinophils, basophils, neutrophils and different monocyte sub

phenotypes were clearly distinguishable from the rest of the phenotypes. Some sub-

phenotypes such as central memory CD4 T cells (T4.CM) and effector memory CD4 T cells

(T4.EM), could not be clearly disaggregated on the basis of feature expression alone.

We then applied the phenotype classification features identified by machine learning to

airway proteome data obtained from naso/oropharyngeal samples from children who

were admitted to hospital with clinical pneumonia and who either survived and were

discharged from hospital(N=61) or ultimately died (N=19) in the course of admission. We

also obtained similar data from age-matched well controls, who were sampled from the

community (N=10). Using a false discovery rate (FDR) of 5%, we identified >1,000

proteins in the airways of these children. We then compared the expression levels of

phenotype-specific feature proteins between these groups. We found that the expression

levels of eosinophil-specific classification features were significantly overexpressed in

children who survived infection, relative to non-survivors (P<0.0001) and controls (figure

4B). This was in contrast to neutrophils, where the expression level of phenotype-specific

proteins did not vary by survival status (figure 4B). The expression level of proteins

predominantly expressed by naïve CD8 T cells (T8.naive) were also significantly elevated

in pneumonia survivors compared to non-survivors (P<0.05). We observed no significant

differences for the remaining phenotypes (figure 4C). To validate these findings, we

reviewed the clinical records of >10,000 children who had been admitted to Kilifi County



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Hospital Kenya over an approximate 10-year period with clinical pneumonia and for

whom haematological data (including eosinophil and neutrophil counts) had been

collected at admission. These data were stratified by survival status and the difference in

eosinophils and neutrophil counts between these groups was tested. We found that

eosinophil counts in children who survived pneumonia in the retrospective validation

cohort (N=10,859) were significantly greater than those of non-survivors (N=1,604,

p=0.0004 ) – Figure 5A, first panel. We then evaluated the risk of mortality in the first 10

days after admission in a subset of children who are at an elevated risk of pneumonia

mortality (undernourished children), in order to determine the association between

eosinophil counts and pneumonia survival in this group. Eosinophils counts in this group

were stratified into two groups on the basis of median counts. Undernourished children

who were admitted with pneumonia and with low (below median) eosinophil counts,

were significantly more likely to die in the first 10 days of admission, relative to those with

high eosinophil counts (Figure 5A, second panel). In contrast, no association with

mortality was observed when neutrophils were analysed using a similar strategy (Figure

5B).

Discussion

We report on a new method of deconvolving immune cell populations that are resident

in the airways of infants and children with different survival outcomes of severe

pneumonia. The study mucosal cellular immunity during very severe pneumonia has been

hindered by a number of important hurdles including low sample volumes as well as the

relatively low abundance of immune cell phenotypes that may be critical in directing the

clinical course of pneumonia. In this study, we addressed these problems by using a



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machine learning approach to identify protein markers that could be used to deconvolve

mixed immune cells. We then applied these markers to airway proteome data obtained

from children with different survival outcomes of clinical pneumonia. Our results show

that protein markers associated with eosinophils are elevated in the airways of survivors

and are diminished in children who later died from infection. The airway levels of these

eosinophil markers were no different between children who died and well controls,

suggesting that the failure to mount an appropriate eosinophil response is a potential

mechanism of pneumonia-related mortality, especially in high risk populations such as

undernourished children. To validate the findings of the airway proteome analysis, we

reviewed the hospitalisation records of >10,000 children who had been admitted to

hospital in the previous 10 years with clinical pneumonia. The results of this retrospective

analysis confirmed the observations made from analysis of the airway proteome and

showed that children who died from pneumonia, had significantly lower blood eosinophil

counts relative to those who survived. In contrast, neutrophil levels were not different

between survivors and non survivors in both the airway proteome and in the retrospective

validation cohort.

Previous studies have shown that eosinophils are activated in the airway shortly after

pneumonia infection appear to contribute significantly to airway recovery. An increase in

the expression of eosinophil-related markers in children with severe pneumonia has been

associated with a reduced requirement for supplemental oxygen16, suggesting that these

cells are a critical component in the host’s response to infecting pathogens in the airway.

Airway-resident eosinophils have not been extensively characterised in previous

pneumonia studies, although proteins expressed predominantly by eosinophils including



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leukotriene C4, eosinophil-derived neurotoxin (RNASE2) and eosinophil cationic protein

(ECP) are expressed at high levels during viral pneumonia17-19. Taken together with the

results of the current study, there appears to be a clear role for eosinophils in the response

to pneumonia infection and that the failure to sufficiently recruit them to the airway may

be a significant factor in mortality. A potential limitation of our study is that it was carried

out using samples collected from the upper airway and not the lung. While lung samples

would have undoubtedly been more informative of responses at the site of disease, the

collection of these samples is a highly invasive process and exposes children to

substantial additional risk without providing additional diagnostic value above

nasopharyngeal or peripheral blood sampling. As a result, these samples are generally

only available in settings with paediatric intensive care facilities, and even here, they are

generally collected in children with atypically severe disease.

In addition to eosinophils, previous studies have shown infections that cause severe

pneumonia such as RSV, trigger a strong cellular response, characterised by the influx of

innate immune cells20,21. The initial response to infection is characterised by the airway

recruitment of neutrophils, which express markers such as CD11b (ITGAM) and neutrophil

granule proteins such as neutrophil elastase (ELANE)22,23. Other innate immune cells such

as NK cells which expressed granzyme B are recruited in the lower airway and can be

detected in in the lungs of mechanically-ventilated children with very severe

pneumonia24,25. In addition to these cells, both myeloid and plasmacytoid dendritic cells

are typically recruited into the airways of children with pneumonia in the early stages of

infection24,26 and exhibit an activated proinflammatory phenotype24. Adaptive immune

cells including CD4+ and CD8+ T cells are present in airway samples for children with viral



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pneumonia20,27. During infection, airway frequencies of granzyme B-secreting, activated

CD8 T cells is greater in the airways of children with severe viral pneumonia25. In the

present study, we found that protein markers of different phenotypes of CD8 T cells were

significantly elevated in the airways of children who survived pneumonia relative to those

who died or well controls. Although data was not available for validation of these

phenotypes in the retrospective cohort, the data suggests a role for these cells in

pneumonia survival. Previous studies in mechanically ventilated children showed that the

frequency of lung-associated T cells increased as children recovered from infection,

suggesting that these cells are an important component of effective local immunity

against pneumonia and that deficits in the airways of children with pneumonia is a

significant risk factor for mortality. In summary, the present data identifies a new

approach for characterising the cellular immune response to pneumonia in the airway and

identified critical immune populations that appear to be critical for survival. Future

studies should aim to replicate these findings in samples collected from the lower

respiratory tract.



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Methods

Study site and population

This study recruited 80 infants and children who were admitted to Kilifi County Hospital

with clinical pneumonia, defined using World Health Organisation syndromic criteria.

Nasal samples were collected from each child for proteomic analysis. The microbial

etiology of pneumonia was determined using both blood cultures and using a 15-target

multiplex PCR panel for the detection of respiratory syncytial virus (RSV - A & B),

rhinovirus, parainfluenza virus (1, 2, 3 & 4) adenovirus, influenza (A, B & C), coronavirus

(OC43 & e229), human metapneumovirus and Mycoplasma pneumoniae. Children with

clinical pneumonia signs and a positive diagnostic result from any of these tests were

included in the analysis. Children were stratified into the survival group if they were alive

for at least 365 days after discharge(n=61) while the mortality group comprised of

children who died within 72 hours of admission (n=19). In addition to these groups, we

recruited 10 age-matched well controls as a comparator group. Written informed consent

was sought from the parents and legal guardians of all children who were sampled in this

study. Ethical approval for the conduct of this study was granted by the Kenya Medical

Research Institute’s Scientific and ethical research unit (SERU). All study procedures were

conducted in accordance with Good Clinical Laboratory Practise (GCLP) standards.

Analysis of airway proteomes using mass spectrometry

Naso- and oropharyngeal swab samples were centrifuged at 17,000xg for 10 mins at 4°C

to obtain cell pellets which were washed once using PBS and lysed by bead-vortexing for

10 minutes in cell lysis buffer (RLT, Qiagen, Germany). Proteins (as well as DNA and RNA)



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were then extracted from the lysate using the AllPrep DNA/RNA/Protein Mini Kit

(Qiagen, Germany) following manufacturers instructions. The concentration of total

protein obtained was determined using the Bradford assay (Bio-Rad, USA). Thirty

micrograms (30µg) of total protein from each sample was then reduced with 10mM

tris(2-carboxyethyl)phosphine (TCEP, Sigma-Aldrich, USA) at 55°C for 1h and

subsequently alkylated with 18mM IAA (Sigma-Aldrich, USA) for 30 minutes at room

temperature, while keeping the reaction protected from light. Proteins were precipitated

overnight at -20°C with six volumes of pre-chilled (-20°C) acetone (Sigma-Aldrich, USA).

The samples were centrifuged at 8,000xg for 10 minutes at 4°C to obtain the protein

pellets and supernatants were discarded. The protein pellet was resuspended in 100µl of

50mM Triethylammonium bicarbonate (TEAB, Sigma-Aldrich, USA). Trypsin (Sigma-

Aldrich, USA) was added to the protein samples at a trypsin-protein sample ratio of 1:10

and protein digestion was allowed to proceed overnight at 37°C with shaking. The peptide

samples were randomly assigned to 10 individual batches: each containing nine patient

samples and one pooled control sample. The pooled control sample consisted of a pool

of peptides from all patient samples. The peptide samples derived from individual

patients were then individually labelled using the TMT10plex mass tag kit (Thermo

scientific, USA) according to manufacturer’s instructions, with one isobaric tag being

exclusively used to label the pooled control sample. The labelled peptides for each 10plex

were subsequently combined to generate 10 individual pools. The labelled peptide pools

were desalted using P10 C18 pipette ZipTips (Millipore, USA) according to the

manufacturer’s instructions. Eluted peptides were dried in a Speedvac concentrator

(Thermo Scientific, USA). Peptides (8 μl) were loaded using a Dionex Ultimate 3000 nano-

flow ultra-high-pressure liquid chromatography system (Thermo Scientific, USA) on to a



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75µm x 2 cm C18 trap column (Thermo Scientific, USA) and separated on a 75µm x 50 cm

C18 reverse-phase analytical column (Thermo Scientific) at heated at 40°C. For LFQ

protein quantification; elution was carried out with mobile phase B (80% acetonitrile

with 0.1% formic acid) gradient (4 to 30%) over 310 min at a flow rate of 0.25 μl/min.

Each LC run was finished by washout with 98% B for 10 min and re-equilibration in 2% B

for 30 min. Five blanks of 40 min each were run on the column between each injection

comprising of two wash cycles with 90% B and an equilibration phase of 15 min to avoid

sample carryover. Peptides were measured using a Q Exactive Orbitrap mass

spectrometer (Thermo Scientific, USA) coupled to the chromatography system via a

nano-electrospray ion source (Thermo Scientific). On the Q Exactive , the ms^1 settings

for peptides were: Resolution, 70000; AGC target, 3e6; maximum IT, 120 ms; scan range,

400-1800 m/z; while the ms^2 settings for fragmentation spectra of peptides were:

Resolution, 17000 (35000 for labelled peptides); AGC target, 5e4; maximum IT, 120 ms ;

isolation window, 1.6 m/z. MS data were acquired by data dependent acquisition where

the top 12 (15 for labelled peptides) most intense precursor ions in positive mode were

selected for ms^2 Higher-energy C-trap dissociation fragmentation which were

subsequently excluded for the next 45 s following fragmentation event. Charge exclusion

was set to ignore peptide spectrum matches that were unassigned, singly charged, and

those with ≥+8 charges. Raw mass spectrometer files were analysed by MaxQuant

software version 1.6.0.1. by searching against the human Uniprot FASTA database

(downloaded February 2014) using the Andromeda search engine..

Airway samples were centrifuged at 17,000xg for 10 mins at 4°C to obtain cell pellets

which were washed once using PBS and lysed by bead-vortexing for 10 minutes in cell lysis



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17

buffer (RLT, Qiagen, Germany). Proteins (as well as DNA and RNA) were then extracted

from the lysate using the AllPrep DNA/RNA/Protein Mini Kit (Qiagen, Germany)

following manufacturers instructions. The concentration of total protein obtained was

determined using the Bradford assay (Bio-Rad, USA). Thirty micrograms (30µg) of total

protein from each sample was then reduced with 10mM tris(2-carboxyethyl)phosphine

(TCEP, Sigma-Aldrich, USA) at 55°C for 1h and subsequently alkylated with 18mM IAA

(Sigma-Aldrich, USA) for 30 minutes at room temperature, while keeping the reaction

protected from light. Proteins were precipitated overnight at -20°C with six volumes of

pre-chilled (-20°C) acetone (Sigma-Aldrich, USA). The samples were centrifuged at

8,000xg for 10 minutes at 4°C to obtain the protein pellets and supernatants were

discarded. The protein pellet was resuspended in 100µl of 50mM Triethylammonium

bicarbonate (TEAB, Sigma-Aldrich, USA). Trypsin (Sigma-Aldrich, USA) was added to the

protein samples at a trypsin-protein sample ratio of 1:10 and protein digestion was

allowed to proceed overnight at 37°C with shaking. The peptide samples were randomly

assigned to 10 individual batches: each containing nine patient samples and one pooled

control sample. The pooled control sample consisted of a pool of peptides from all

patient samples. The peptide samples derived from individual patients were then

individually labelled using the TMT10plex mass tag kit (Thermo scientific, USA) according

to manufacturer’s instructions, with one isobaric tag being exclusively used to label the

pooled control sample. The labelled peptides for each 10plex were subsequently

combined to generate 10 individual pools. The labelled peptide pools were desalted using

P10 C18 pipette ZipTips (Millipore, USA) according to the manufacturer’s instructions.

Eluted peptides were dried in a Speedvac concentrator (Thermo Scientific, USA). Peptides

(8 μl) were loaded using a Dionex Ultimate 3000 nano-flow ultra-high-pressure liquid



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18

chromatography system (Thermo Scientific, USA) on to a 75µm x 2 cm C18 trap column

(Thermo Scientific, USA) and separated on a 75µm x 50 cm C18 reverse-phase analytical

column (Thermo Scientific) at heated at 40°C. For LFQ protein quantification; elution was

carried out with mobile phase B (80% acetonitrile with 0.1% formic acid) gradient (4 to

30%) over 310 min at a flow rate of 0.25 μl/min. Each LC run was finished by washout

with 98% B for 10 min and re-equilibration in 2% B for 30 min. Five blanks of 40 min each

were run on the column between each injection comprising of two wash cycles with 90%

B and an equilibration phase of 15 min to avoid sample carryover. Peptides were measured

using a Q Exactive Orbitrap mass spectrometer (Thermo Scientific, USA) coupled to the

chromatography system via a nano-electrospray ion source (Thermo Scientific). On the Q

Exactive , the ms^1 settings for peptides were: Resolution, 70000; AGC target, 3e6;

maximum IT, 120 ms; scan range, 400-1800 m/z; while the ms^2 settings for

fragmentation spectra of peptides were: Resolution, 17000 (35000 for labelled peptides);

AGC target, 5e4; maximum IT, 120 ms ; isolation window, 1.6 m/z. MS data were acquired

by data dependent acquisition where the top 12 (15 for labelled peptides) most intense

precursor ions in positive mode were selected for ms^2 Higher-energy C-trap dissociation

fragmentation which were subsequently excluded for the next 45 s following

fragmentation event. Charge exclusion was set to ignore peptide spectrum matches that

were unassigned, singly charged, and those with ≥+8 charges. Raw mass spectrometer

files were analysed by MaxQuant software version 1.6.0.1. by searching against the human

Uniprot FASTA database (downloaded February 2014) using the Andromeda search

engine.

Analysis of airway-resident immune cells using flow cytometry



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1ml of nasopharyngeal and oropharyngeal swab samples obtained from children was

centrifuged at 17,000xg for 7 minutes, after which 800�l of the supernatant was removed

and discarded. The remaining 200µl were split into two aliquots of 100µl each. The first

aliquot was used for neutrophil phenotyping assays and the other was used for neutrophil

phagocytosis assays. 20µl of a pre-constituted cocktail of the following antibodies (from

ThermoFisher) was used to label both aliquots – CD45, CD16, CD14, CD3, CD19, HLA-DR,

CD66b, CD11b and a Live-dead marker. With the exception of the live/dead marker, all

other antibodies were diluted 1:100 in FACS buffer. The live/dead marker was prepared at

a 1:1000 dilution in FACS buffer. For the phagocytosis assay tube, 20�l of opsonised

Escherichia coli (E.coli) was added to the tube (pHrodo Red E. coli BioParticles;

ThermoFisher). The bacteria was initially prepared by mixing the E.coli strain with new-

born calf sera followed by a 30-minute incubation at 37oC. After this step, both tubes

were incubated at 37oC for 35 minutes. After the incubation, 20�l of a live-dead marker

was added to each tube and incubated for 10 minutes at 37oC. The reaction was stopped

by adding 500�l of 1X RBC lysis buffer followed by a 5-minute incubation. Cells in each

tube were then spun down at 2,700xg for 1 minute and the supernatant discarded. Cells

were then washed twice with FACS buffer, after which 350�l of FACS flow was added.

Cells were then analysed immediately on a BD LSR Fortessa instrument. The following

gating strategy was used to detect airway-resident neutrophils: Debris were excluded on

the basis of their forward (FSC-A) and side (SSC-A) scatter characteristics, doublets were

excluded using FSC-A versus FSC-H and dead cells were excluded using the live-dead

marker. Data was analysed using FlowJo software.

Statistical data analysis



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Data were analyzed in R. deconvolution analysis by random forest classification was done

using the Boruta package, with a maximum of 300 iterations. Protein classifiers that

whose mean expression in the test phenotype was significantly greater than alternative

phenotypes were taken forward for further analysis. T-SNE analysis was carried out using

the Rtsne, with the iterations parameter set to a maximum of 300 and a perplexity value

of 30. Analysis was carried out in two dimensional space. Cell type enrichment analysis

was done one the enrichR platform. The input search term used for enrichment analysis

was the RF protein classifier lists derived from random forest classification. All pairwise

comparisons between the expression level of RF classifiers between phenotypes and

frequency of immune cell types between survival states was done using t-tests on log10

normalised data. The deconvolution data set that was used to identify phenotype-specific

protein classifiers was obtained from a previously published paper by Rieckmann et al.12

Data availability

The proteomics data reported in this paper are available at the ProteomeXchange

Consortium database (Accession numbers: PXD009403) .

Acknowledgements

This study was supported by fellowship funding to CJS from the Wellcome Trust

(WT105882MA). The funder played no role in the conceptualization, design, data

collection, analysis, decision to publish, or preparation of the manuscript

Conflicts of interest

The authors declare that they have no conflicting interests



https://doi.org/10.1101/840090


21

Author contributions

CJS designed the study

CJS, JMN, MNM, ETG, AG conducted the experiments

CJS,NKK analysed the data

CJS wrote the manuscript

All authors reviewed and approved the manuscript



https://doi.org/10.1101/840090


22

Figures



https://doi.org/10.1101/840090


23

Figure 1: Analysis of the profile of airway-resident immune cells using flow cytometry and in-silico deconvolution. (A) To characterize the

phenotype diversity of airway-resident immune cells, fresh naso- and oropharyngeal flocked swabs were obtained from children admitted

to hospital with pneumonia and processed immediately by flow cytometry. Here the gating strategy is shown, where live, singlet cells were

initially gated on CD45 expression, followed by fluorescent antibody staining for different cell surface markers. CD3+ and CD19- cells were

gated as T-cells, CD66-, CD14+ and HLA-DR- cells were gated as macrophages/monocytes while neutrophils were gated on the basis of

CD66 and CD16 double expression. The functional activity of neutrophils was characterized by co-culturing the cells with E.coli particles

that were labelled with pHRhodo. Neutrophils that contained phagocytosed particles are shown in green in the overlay dot plot. The

bottom represented FSC and SSC plots of samples obtained from 6 different infants and demonstrates a high level of diversity in the

immune cell populations present in the airways of children with pneumonia. (B) In silico deconvolution analysis was based on high

resolution mass spectrometry data obtained from the airway. Cells were obtained from the naso and oropharyngeal sites using separate

swabs, which were both eluted in a common transport media. As shown in A above, these samples contained a broad diversity of immune

cells including T-cells, monocytes/macrophages and neutrophils. These cells were isolated from the sample by centrifugation and

processed for mass spectrometry analysis using a standard protocol (see methods). (C) In silico phenotype deconvolution was conducted

initially by identifying phenotype specific protein features in a previously-published deconvolution data set using random forest feature



https://doi.org/10.1101/840090


24

selection. These features were then applied on airway proteome data set obtained from children admitted to hospital with severe

pneumonia, and used to construct a detailed map of airway immunophenotypes and their associations with clinical outcomes.



https://doi.org/10.1101/840090


25

NEUTROPHIL

PERIPHERAL BLOOD

GRANULOCYTE

CORD BLOOD

SPLEEN (BULK TISSUE)

0 10 20

B

Basophil

DC

Eosinophil

MO

Neutrophil

NKT

−0.2

−0.1

0.0

0.1

−0.6 −0.4 −0.2 0.0 0.2NMDS1

NMDS

2

12.5%

37.5%67.5%

87.5%

0%

25%

50%

75%

NK cells

CAPN

5

CD48

RRAS

2GNLY

ORAI2

B4GA

LT4

PNOC

KLRD1

GOLIM4

FCGR3A

GOLGA8R

GZMB

MATK

TYROBP

NCR1

SH2D1B

NCR3

TPST2

CST7

EOMES

CD8A

ABCB1GZMA

NCAM1PRF1

IL2RBGZMHCD247ENPP1AKAP5

KLRC1

AOAH

TXKKIR3DL1

KIR3DL2

KIR2DS2

AFM

RGS3

ENTP

D1

ITGA1

CTSW

NCALD

GNAI1

SATB1

NFATC2IL18R1RUNX3DH

CR24

AGKMC

TP2KDE

LC1ERM

P1GPR1

14SAMD3

HAVCR2

DPF3

NEIL1

ABHD17A

CD244

LAT2

OSBPL5

IRF2BPL

APOBEC3G

GOPC

TOR4A

TBX21

MAFFSIGLEC7

HDGFRP3TNFRSF18FCGR3B

AKR1C3ANXA1

A1BG

SYK

12.5%

37.5%67.5%

87.5%

0%

25%

50%

75%

Neutrophils

POM121C

ZFP36L2

THOC1

RMND

1ATXN

3AS

AP1

ANO9

ZMYM

3RPA3OS

CLDN

D1MAR

K2TP53I11

GPSM

3CTAG

E5CALM1

PCBD2

LUC7L3

MSL1

SNAP23

TOR1A

ADAM10

NDC80

SCAMP1

SIGLEC5

SLC16A3

AQP9

RANBP6

B3GAT3

RTN3

LRATSVILGGPS1

ABL1

GLAMME

MMP7

SLC2A3

TIMP2

CBLRPS6KB

1

CXCR2

CFPPKLRWASKIR2D

L3RGS19HDGFGNG2SPCS3NTHL1CDK17SPTBN1PLAURCD47SIGLEC14APOBRSTX4MAP3K1ROCK1FAM53BNBR1MESDC2PPP2R5AVPS72TMEM132A

MTHFSDLRRFIP1C5orf22FGD3SMEK2TMEM63BHGSNATADNP2PPP1R18RICTORTAOK1GOLGA7ARHGAP30

YTHDF3RBM23

NT5DC3

HOOK3

SFR1

GPR97

RAB43

TMC8

SLC44A2

LMTK2

ORMDL3

SLC15A4

NFATC2IP

NEK7SH3TC1

BCL7CMITD1BRI3BPRAD54LDDX12PMCUR1

ARL8ACHM

P6JAKM

IP1TEFMPHF12CSF3RCIB1CRACR2A

DCTN

5RB

CK1

EDEM

3TM

6SF1

CYSTM1

DNAJC5

DEF6

CCDC

134

ZFYV

E28

POLM

HPS4

DROS

HA

AGPAT4

ARL8B

NDE1

SACS

TMOD

2

ZNF282

ADD3

LAMTOR3

NAGPA

AMFRINTS6

RAB26

PACSIN2

SMC3PL

AAATP8A1

ZNF281AK6

TSC22D4

UBIAD

1LRR

FIP2CD

177FCGR3BCEA

CAM8LSP1GNB

2L1UQCRBANXA6CASTMGAMCR1CAPN1RAP1B

OLR1PDIA3PFN1CAMP

CRISP3TNC

U2AF2SDCBPARPC2CPNE3

PGLYRP1RP2DYSFFLOT1VNN1RAB3D

HPORM1GNAI2ARG1

S100A9EPHX1P4HBMGPGNAI3

HMGB1RALB

MMP9

ITGB5

AZU1

FCAR

PRTN3

STOMGRN

ICAM3MYH9

TALDO1GYG1PGDIST1

RAB2ATMSB4XLCN2GBE1

CYP4F3LMAN2

STIM1

SPTAN1MAPRE1

MAPK14SYTL1MICU2MCU

NCSTNELM

O1

ERO1L

VAT1CHP1SLK

TMX1

APMAP

12.5%

37.5%67.5%

87.5%

0%

25%

50%

75%

DC

C12orf45

DOCK

1CY

P51A1

SRGAP

2DC

KCLAS

P2SN

CAMAP

1APLD4

CDK2

VEZF1

IRF7

HIP1

TRAFD1

IFIT3

CLIC2

TBC1D4

LY75

PFKFB2

RPS6KA4

FCHSD2

WDR47

PTBP3

ADAFBP

1

MRC1

PTPRE

NFKBIA

ITGB8

CD1CEPHA

2

PRKAR2B

GPR183

S100A3CDKN1AETV3ETV6ECE1GCLCDUSP3AKR1D1MAP2K6GABARAPL2DAB2PLCB3IRF8ZFP36L1SCRN1PAK1TARBP1IRF4CCDC6CSRP2CALCRL

DUSP5CCDC88A

ZNF672

CMPK2

SPECC1

ZNF618

TNS3

TIFAB

WDFY4

SMIM5

RUFY3

SGK223

ZDHHC17

CCDC50

PM20D2

LILRB2

KRTCAP2

OTUD6BZNF579ZNRF2LILRB4PIK3R5NR4A3

NSMAF

ISG20

UBE2W

DHX58

PPP1R14B

ACY3

DHTKD1

NGLY1

EPSTI1

UBE2E2

LGMN

REPIN1

PACSIN1

IFIH1

MTMR

12

PARP12

FAM49A

BCL11AALG2

TMEM206

CXorf21

TMEM9BPD

LIM7HE

BP1

TCP11L

1TBC

1D13SPH

K1TLR7ZNFX1EIF2A

K4DNAJB4

FEZ2HERC5PALD1USP18SH2B3SCML2CD2APST14

KIAA1598PON2

PLXNB2CTNND1ATP1B1ISG15PYGLPLEK

GSTM1IFIT2IFIT1CKB

LAMP2

PLS3

NFKB1

MX2

COMT

LAP3

CTNNA1TAGLN2RPL3

ALDH7A1IDH3A

RPL27MYO1E

TCIRG1ALCAM

DPYSL2TRIM

22CYP2S1

CORO

1BFAM

213ATUBB6

H2AFY2NDRG

2SAM

HD1HYO

U1

12.5%

37.5%67.5%

87.5%

0%

25%

50%

75%

Monocytes

SULT1A4

TSPO

CD36

PICA

LMBACH

1DS

CR3

SPTLC1

SLC31A1

TLR2

STX11

MFN2

SGPL1

F13A1

CTSL

GAA

FCGR1A

MGAT1

LRPAP1

SULT1A1

CYP27A1

LRP1

DPYD

NAIP

ATP6AP1CD300EhCG_14925SLC27A3ARHGEF10LLAMTOR1NCEH1AGTRAPSYNE3C6orf120

LDHDRHOT1

GHDC

LRRC25

PNKD

ATP6V0D2

FAM45A

CDC6

NPL

SLC12A9

TBC1D2CLPB

PLEK

HA4

TLR8

HEBP

1

STAB1

PYCA

RDIRAK3

COMM

D10

SFXN3

TM9SF4

ATP6V1G1

IDH1ND

UFB8ALD

H1A1S10

0A6ANX

A2CD1

4CYC1

ANXA5LGALS1HMOX1

IFI30

POR

TYMP

RAB5A

ATP6V1B2

UQCRC2

CES1

S100A4

BLVRB

UQCRC1

APOBEC3A

HADHA

CRATGPD2

UQCRFS1HNMT

SERPINB8ALDH3A2PLO

D1CKAP4

AHNAKNAPRT

COLG

ALT1

CD14+_Monocytes

CD33+_Myeloid

WholeBlood

CD71+_EarlyErythroid

Liver

0

100

200

300

400

500

CD56+_NKCells

Adrenalgland

AdrenalCortex

ParietalLobe

Amygdala

0

200

400

600

BDCA4+_DendriticCells

CD33+_Myeloid

WholeBlood

CD14+_Monocytes

Thymus0

200

400

600

Enrichment score Enrichment score Enrichment scoreEnrichment score

(A)

(B)

(C)

(D)

(E)

(F)

(G)

(H)

(I)



https://doi.org/10.1101/840090


26

Figure 2: Use of proteomics data to segregate immune cell phenotypes. (A) A nonmetric multidimensional scaling (NMDS) plot was used

to visualize the separation of immune cell phenotypes at the major phenotype level based on protein expression data from the

deconvolution data set. We observed that expression profile of certain cell types including neutrophils, monocytes and basophils, resulted

in a clear separation from other immune cell types. To identify specific proteins that could be used to distinguish phenotypes, we used

random forest feature selection. Using this approach, we identified protein features for 33 phenotypes that could be used to disaggregate

individual phenotypes from a complex mixture (Supplementary table 1). We then examined airway proteome data from children with

pneumonia to determine whether any of the proteins identified were detectable in the airway. An example of the results is shown in (B,D,F

& H), where the phenotype classification features for NK cells, DCs, Neutrophils and Monocytes are shown labelled on the circular tile

plot. The green tiles are the phenotype-specific feature proteins that were identified in the random forest model and that were also

detected in the airway. We used enrichment analysis to determine whether the phenotype-classifier proteins identified by the random

forest model for specific phenotypes could be used to recapitulate those the phenotypes in an unsupervised phenotype prediction analysis.

The clustergrams shown in (C,E,G & I) depict a co-expression matrix of feature proteins and their known expression in different cell types.

For 20 of the 33 phenotypes, (including the four examples shown here) the random forest phenotype classification, correctly predicted the

original phenotype in the unsupervised prediction analysis. Each row on the clustergram represents a feature protein, while each colored



https://doi.org/10.1101/840090


27

box represents the known expression profile in a particular cell types. Horizonal panels at the bottom indicate enrichment scores from the

unsupervised analysis.



https://doi.org/10.1101/840090


28

SERP

INB2

●●●● ●●●● ●● ●● ●●●● ●●●● ●●●●●

●●●

● ●●● ●● ●●

●

●

●

●●●● ●●●● ●●●● ●● ● ●●●● ●●●● ●●● ●●●● ●●● ●●●● ●●● ●●●● ●●●● ●●●● ●●●● ●●●● ●● ●● ●●●● ●●●● ●●●● ●●●● ●●●● ●●● ● ●●●● ●●●● ●●●● ●● ●● ●●●● ●●●● ●●●● ●● ●● ●●●● ●● ●●0

5,000,000

10,000,000

15,000,000

B.m

emor

y_ac

tivat

edB.

mem

ory_

stea

dy.s

tate

B.na

ive_a

ctiva

ted

B.na

ive_s

tead

y.st

ate

B.pl

asm

a_st

eady

.sta

teBa

soph

il_st

eady

.sta

teEo

sinop

hil_

stea

dy.s

tate

mDC

_act

ivate

dm

DC_s

tead

y.st

ate

MO

.cla

ssica

l_ac

tivat

edM

O.c

lass

ical_

stea

dy.s

tate

MO

.inte

rmed

iate

_ste

ady.

stat

eM

O.n

oncla

ssica

l_st

eady

.sta

tem

Treg

s_ac

tivat

edm

Treg

s_st

eady

.sta

teNe

utro

phil_

stea

dy.s

tate

NK.b

right

_act

ivate

dNK

.brig

ht_s

tead

y.st

ate

NK.d

im_a

ctiva

ted

NK.d

im_s

tead

y.st

ate

nTre

gs_a

ctiva

ted

nTre

gs_s

tead

y.st

ate

pDC_

activ

ated

pDC_

stea

dy.s

tate

T4.C

M_a

ctiva

ted

T4.C

M_s

tead

y.st

ate

T4.E

M_a

ctiva

ted

T4.E

M_s

tead

y.st

ate

T4.E

MRA

_act

ivate

dT4

.EM

RA_s

tead

y.st

ate

T4.n

aive

_act

ivate

dT4

.nai

ve_s

tead

y.st

ate

T8.C

M_a

ctiva

ted

T8.C

M_s

tead

y.st

ate

T8.E

M_a

ctiva

ted

T8.E

M_s

tead

y.st

ate

T8.E

MRA

_act

ivate

dT8

.EM

RA_s

tead

y.st

ate

T8.n

aive

_act

ivate

dT8

.nai

ve_s

tead

y.st

ate

Th1_

stea

dy.s

tate

Th17

_ste

ady.

stat

eTh

2_st

eady

.sta

te

SERPINB2

Prot

ein

copy

num

ber

ACTG1AGA

AGTRAPATG13ATG2ACCR1CD14

CD163CD36CD37

CDCA7LCEBPD

CEP152CES1

CKAP4CLEC4E

CPMCXCL5CXCL8

CYP27A1DYNLT1ELOVL7

FABP4FAM129B

FAM160A2FCAR

FCGR1AFPR1FPR2FTH1

GABARAPGNAQGUSBHLTFIL1B

IQGAP3ITGA5

LACC1LAMTOR4

LTB4RLYZ

METTL7BMPP1

MTFMTNCSTN

NINJ1OBFC1

PAMPLAC8RAD9A

RALGAPA1RBM47

RIT1RPL6

SERPINB1SERPINB13SERPINB2

SLC44A1SLC7A6STEAP3

SUN1TMEM50A

VCANZNF185

Airway expression level (log2 reporter intensity)

4.54

3.53

210.750.25

NA

SERPINB2

ABHD

10AB

HD11

ACAD

SBAC

OX1

ACSL

1AH

CYL2

AKR1

C1AL

OX5

ANPE

PAN

XA6

APAF

1AP

MAP

ARCN

1AR

PC5

AZU1

BAG3

BAZ1

BBPHL

BPI

CA4CAMK2D

CCAR2

CCDC47

CD14

CKBCMAS

CNN3CORO1B

CPNE1

CRIP1CTNNB1

CTSBCYB5BCYBBDCDDCXRDDX1DHX15DNAJA1DNAJB1DYSFEIF3AEIF3BEIF4G1EMC7EPXFTH1GCAGFM2GFPT1GNSGPD1LGRHPR

GSTO1HADHHIBADH

HIST1H1C

HIST1H3A

HNRNPAB

HNRNPR

HP1BP3

HPRT1

HSD17B11

HSP90AB1

IFI30IGHG4

ILF3IPO9ITG

AMKHDRBS1

KPNB1KRT19LCP1LM

AN2M

APRE1M

E1M

GAMM

LLT4

MM

P9M

NDA

MO

GS

MRP

L24

MRP

S22

MTP

NM

X1M

YH9

MYO

1FNA

CANCF4

NDUF

A11

NDUF

A12

NDUF

A2

NDUFA5

NMES1

NRAS

NUDT21

OLFM4

OPTNPADI4

PAFAH1B1PCPCMT1PDLIM5PEPDPGRMC2PLBD1PMPCBPOLDIP2PON2PRPF19PRRC1PRTN3PSMA3PSMB3

PSMD11PSMD8PSME1PSME2RAB10RAB7ARAP1BRBM39

RETNRPL10RPL17RPL29

RPS14

RPS16

RPS25

RPS29

RRBP1

RUVBL2

S100A12

SAP18

SBDS

SERPINA3

SERPINB10

SERPINB2SH3GLB1

SNX6SRP54

SRSF3SUGP2

SUMF2

TCEB2TNFSF13BTO

MM

40TPPP3

TRAP1TUBB8UCHL3VNN1VRK1

YWHAQ

Fold change

4.543.532.5210.750.50.250NA

SERPINB2

MO classical activated vs Th2 steady stateMO classical activated vs Th17 steady stateMO classical activated vs Th1 steady stateMO classical activated vs T8 naive steady stateMO classical activated vs T8 EMRA steady stateMO classical activated vs T8 EM steady stateMO classical activated vs T8 CM steady stateMO classical activated vs T4 naive steady stateMO classical activated vs T4 EMRA steady stateMO classical activated vs T4 EM steady stateMO classical activated vs T4 CM steady stateMO classical activated vs pDC steady stateMO classical activated vs nTregs steady stateMO classical activated vs NK dim steady stateMO classical activated vs NK bright steady stateMO classical activated vs Neutrophil steady stateMO classical activated vs mTregs steady stateMO classical activated vs MO nonclassical steady stateMO classical activated vs MO intermediate steady stateMO classical activated vs MO classical steady stateMO classical activated vs mDC steady stateMO classical activated vs Eosinophil steady stateMO classical activated vs Basophil steady stateMO classical activated vs B plasma steady stateMO classical activated vs B naive steady stateMO classical activated vs B memory steady state

●

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0

10

20

B.m

emor

y_ac

tivat

edB.

mem

ory_

stea

dy.s

tate

B.na

ive_a

ctiva

ted

B.na

ive_s

tead

y.st

ate

B.pl

asm

a_st

eady

.sta

teBa

soph

il_st

eady

.sta

teEo

sinop

hil_

stea

dy.s

tate

mDC

_act

ivate

dm

DC_s

tead

y.st

ate

MO

.cla

ssica

l_ac

tivat

edM

O.c

lass

ical_

stea

dy.s

tate

MO

.inte

rmed

iate

_ste

ady.

stat

eM

O.n

oncla

ssica

l_st

eady

.sta

tem

Treg

s_ac

tivat

edm

Treg

s_st

eady

.sta

teNe

utro

phil_

stea

dy.s

tate

NK.b

right

_act

ivate

dNK

.brig

ht_s

tead

y.st

ate

NK.d

im_a

ctiva

ted

NK.d

im_s

tead

y.st

ate

nTre

gs_a

ctiva

ted

nTre

gs_s

tead

y.st

ate

pDC_

activ

ated

pDC_

stea

dy.s

tate

T4.C

M_a

ctiva

ted

T4.C

M_s

tead

y.st

ate

T4.E

M_a

ctiva

ted

T4.E

M_s

tead

y.st

ate

T4.E

MRA

_act

ivate

dT4

.EM

RA_s

tead

y.st

ate

T4.n

aive

_act

ivate

dT4

.nai

ve_s

tead

y.st

ate

T8.C

M_a

ctiva

ted

T8.C

M_s

tead

y.st

ate

T8.E

M_a

ctiva

ted

T8.E

M_s

tead

y.st

ate

T8.E

MRA

_act

ivate

dT8

.EM

RA_s

tead

y.st

ate

T8.n

aive

_act

ivate

dT8

.nai

ve_s

tead

y.st

ate

Th1_

stea

dy.s

tate

Th17

_ste

ady.

stat

eTh

2_st

eady

.sta

te

Prot

ein

copy

num

ber (

log2

)

MO

(A)

(B)

(C)

(D)



https://doi.org/10.1101/840090


29

Figure 3: Detailed analysis of the phenotype-specific feature proteins. (A) We used a circular heat map to visualize differential expression

of phenotype-specific proteins in a selection of immune cell phenotypes. In this example, the fold difference in protein expression between

classical activated monocytes and other immune cell subsets is shown. Each spoke of the wheel represents a single protein, and each

segment on a spoke represents the log10 fold difference between protein’s expression level in classical activated monocytes and each of

the listed cell types. An increase in the intensity of the red hue indicates greater expression in classical activated monocytes, relative to

the comparator phenotype. (B) Protein expression across different phenotypes: in this example, the expression level of a feature protein

for activated monocytes (SERPINB2 – identified from the random forest model) was compared between different phenotypes and was

seen to be expressed almost exclusively by monocytes. (C) The airway proteome data from paediatric pneumonia admissions was

examined to determine whether any of the phenotype classification features for classical activated monocytes were also detected in the

airways of children. Each segment represents a single feature, with proteins that were also detected in the airway depicted in a green hue.

An increase in hue intensity, indicates increased airway expression for a particular protein. SERPINB2 (red arrow), was expressed at

relatively high levels in the infant airway. (D) The expression level of all monocyte-specific features was plotted for monocytes and other

cell types. Each black open circle denotes the expression level of a particular feature in other phenotypes, while red markers indicate their

expression in monocytes. The dashed black and red lines indicated median expression of all features in monocytes and alternate cell types

respectively.



https://doi.org/10.1101/840090


30

Figure 4: (A) Performance of all phenotype specific protein features in segregating different immune phenotypes and sub phenotypes was

visualized using a t-SNE plot. Most phenotype classification features were able to disaggregate different phenotype hierarchies in the 2D

t-SNE space. Neutrophils, eosinophils, monocytes, NK cells, dendritic cells, basophils among others were clearly separated from all other

B.memory

B.memoryB.memory

B.naiveB.naiveB.naive

B.naive

B.plasmaB.memory

B.memory

B.memory

B.memory B.naive

B.naive

B.naive

B.naive

B.plasmaB.plasma

B.plasmaBasophil

Basophil

BasophilBasophil

EosinophilEosinophil

EosinophilEosinophil

mDCmDC

mDC

mDC

mDC

mDC

mDC

mDC

MO.classical

MO.classical

MO.classical

MO.intermediateMO.intermediate

MO.nonclassical

MO.intermediate

MO.classicalMO.classical

MO.intermediate

MO.nonclassicalMO.nonclassical

MO.nonclassical

MO.classical

MO.classical

mTregs

mTregs

mTregs

mTregs

mTregs

mTregs

mTregs

Neutrophil

Neutrophil

NeutrophilNeutrophil

NK.bright

NK.brightNK.dim

NK.bright

NK.dimNK.dim

NK.bright

NK.bright

NK.dim

NK.dim

NK.dim

NK.dim

NK.bright

NK.bright

nTregsnTregs

nTregs

nTregs

nTregs

nTregsnTregs

pDC

pDC

pDC

pDC pDC

pDCpDC

pDC

T4.naive

T4.naive

T4.EMRAT4.EMRA

T4.CM

T4.EMRA

T4.CM

T4.CM

T4.EM

T4.CM

T4.naive

T4.naive

T4.naive

T4.naive

T4.naive

T4.naive

T4.EMRA

T4.CM

T4.EMRA

T4.CM T4.CM

T4.EM

T4.CM

T4.EM

T4.EM

T4.EM

T4.EM

T4.EMRA

T4.EMRA

T4.EMT4.EM

T4.EMRA

T8.CM

T8.naiveT8.naive

T8.CM

T8.naive

T8.naive

T8.EMRA

T8.EMRA

T8.naive

T8.naive

T8.CMT8.CM

T8.EM

T8.EM

T8.EM

T8.EM

T8.EMRA

T8.EMRA

T8.EMRA

T8.naive

T8.naiveT8.CM

T8.CM

T8.CMT8.CM

T8.EM

T8.EMT8.EM

T8.EM

T8.EMRA

T8.EMRAT8.EMRA

Th1

Th1 Th1

Th1

Th17

Th17

Th17

Th17

Th2

Th2

Th2

Th2

−20

−10

0

10

20

−20 −10 0 10 20

labelsB

Basophil

Eosinophil

mDC

MO

mTregs

Neutrophil

NK

nTregs

pDC

T4

T8

Th1

Th17

Th2

(A)

(B)

(C)

P<0.01 n.s.

P<0.05



https://doi.org/10.1101/840090


31

phenotypes. There was less clear segregation for T-cell sub phenotypes, including CD8, CD4 and regulatory T-cell phenotypes. (B) the

expression of eosinophil- and neutrophil-specific features was compared between children with different survival outcomes of pneumonia

as well as in well controls. Eosinophil features were expressed at significantly higher levels in survivors compared with non-survivors and

well controls. In contrast, there was no difference in the expression of neutrophil features between these groups. (C) Feature classifiers

for other immune cell phenotypes were also compared by survival status. No significant difference in expression by survival status was

observed, with the exception of the CD8 (T8) phenotype, whose features were significantly upregulated in the airways of survivors relative

to non-survivors and well controls.



https://doi.org/10.1101/840090


32

Figure 5: Validation of the phenotype associations with pneumonia mortality using a retrospective pneumonia cohort. (A, first panel)

Eosinophils counts were analysed in a retrospective cohort of >10,000 children who had been admitted to hospital with clinical pneumonia

0.0

0.5

1.0

1.5

2.0

2.5

Survived Died

Eosin

ophil

s x10

3 cells

/uL

N=10,859 N=1,604

P = 0.0004

Eosinophils(A)

0

10

20

30

40

50

Survived Died

Neut

roph

ils x1

03 cells

/uL

N=10,871 N=1,606

P = 0.26

Neutrophils(B)

++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++++++++++

++++++++++++++

+

+++

0.00

0.25

0.50

0.75

1.00

0 1 2 3 4 5 6 7 8 9 10

Surv

ival

pro

babi

lity

++

High

Low

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

++++++++++++++++++++ +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

++

0.00

0.25

0.50

0.75

1.00

0 1 2 3 4 5 6 7 8 9 10

Surv

ival

pro

babi

lity

++

High

Low

Surv

ival p

roba

bilit

ySu

rviva

l pro

babi

lity

Days of admission

Days of admission

MUAC < 11.5cm

MUAC < 11.5cm

P=0.2

P<0.0001

0.0

0.1

0.2

0.3

0.4

Eosi

noph

ils x

103 c

ells

/uL

Eosin

ophi

ls x1

03ce

lls/µ

l

High

Low

High

Low

0.0

2.5

5.0

7.5

10.0

Eosi

noph

ils x

103 c

ells

/uL

Neut

roph

ils x

103

cells

/µl



https://doi.org/10.1101/840090


33

and who either survived and were discharged or died during admission. Pneumonia survivors had significantly greater eosinophil counts

at admission compared to non-survivors. (A, second panel) In a subset of children who were acutely undernourished at admission (defined

as by a mid-upper arm (MUAC) circumference <11.5cm), children were stratified into two groups on the basis of the median eosinophil

count. In the first 10 days after admission, children whose eosinophil counts were below the median were more likely to die relative to

those with above-median counts. (B) In contrast, there was no association between neutrophil counts and survival status.



https://doi.org/10.1101/840090


34

References

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2 Shi, T. et al. Global, regional, and national disease burden estimates of acute lower respiratory infections due to respiratory syncytial virus in young children in 2015: a systematic review and modelling study. Lancet (London, England), doi:10.1016/s0140-6736(17)30938-8 (2017).

3 McNally, L. M. et al. Effect of age, polymicrobial disease, and maternal HIV status on treatment response and cause of severe pneumonia in South African children: a prospective descriptive study. Lancet (London, England) 369, 1440-1451, doi:10.1016/s0140-6736(07)60670-9 (2007).

4 Chisti, M. J., Tebruegge, M., La Vincente, S., Graham, S. M. & Duke, T. Pneumonia in severely malnourished children in developing countries - mortality risk, aetiology and validity of WHO clinical signs: a systematic review. Tropical medicine & international health : TM & IH 14, 1173-1189, doi:10.1111/j.1365-3156.2009.02364.x (2009).

5 Moisi, J. C. et al. Sensitivity of hospital-based surveillance for severe disease: a geographic information system analysis of access to care in Kilifi district, Kenya. Bulletin of the World Health Organization 89, 102-111, doi:10.2471/blt.10.080796 (2011).

6 Mejias, A. et al. Whole blood gene expression profiles to assess pathogenesis and disease severity in infants with respiratory syncytial virus infection. PLoS medicine 10, e1001549, doi:10.1371/journal.pmed.1001549 (2013).

7 Johnson, J. E., Gonzales, R. A., Olson, S. J., Wright, P. F. & Graham, B. S. The histopathology of fatal untreated human respiratory syncytial virus infection. Mod Pathol 20, 108-119, doi:10.1038/modpathol.3800725 (2007).

8 Jochems, S. P. et al. Novel Analysis of Immune Cells from Nasal Microbiopsy Demonstrates Reliable, Reproducible Data for Immune Populations, and Superior Cytokine Detection Compared to Nasal Wash. PloS one 12, e0169805, doi:10.1371/journal.pone.0169805 (2017).

9 Connors, T. J. et al. Developmental Regulation of Effector and Resident Memory T Cell Generation during Pediatric Viral Respiratory Tract Infection. Journal of immunology (Baltimore, Md. : 1950) 201, 432-439, doi:10.4049/jimmunol.1800396 (2018).

10 Sande, C. J. et al. Untargeted analysis of the airway proteomes of children with respiratory infections using mass spectrometry based proteomics. Scientific reports 8, 13814, doi:10.1038/s41598-018-32072-3 (2018).

11 Sande, C. J. et al. Airway response to respiratory syncytial virus has incidental antibacterial effects. Nature communications 10, 2218, doi:10.1038/s41467-019-10222-z (2019).

12 Rieckmann, J. C. et al. Social network architecture of human immune cells unveiled by quantitative proteomics. Nature immunology 18, 583-593, doi:10.1038/ni.3693 (2017).



https://doi.org/10.1101/840090


35

13 Kursa, M. B. & Rudnicki, W. R. Feature Selection with the Boruta Package. 2010 36, 13, doi:10.18637/jss.v036.i11 (2010).

14 Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC bioinformatics 14, 128, doi:10.1186/1471-2105-14-128 (2013).

15 Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic acids research 44, W90-97, doi:10.1093/nar/gkw377 (2016).

16 Lindemans, C. A. et al. Systemic eosinophil response induced by respiratory syncytial virus. Clinical and experimental immunology 144, 409-417, doi:10.1111/j.1365-2249.2006.03084.x (2006).

17 Kim, H. H., Lee, M. H. & Lee, J. S. Eosinophil cationic protein and chemokines in nasopharyngeal secretions of infants with respiratory syncytial virus (RSV) bronchiolitis and non-RSV bronchiolitis. Journal of Korean medical science 22, 37-42, doi:10.3346/jkms.2007.22.1.37 (2007).

18 Harrison, A. M., Bonville, C. A., Rosenberg, H. F. & Domachowske, J. B. Respiratory syncytical virus-induced chemokine expression in the lower airways: eosinophil recruitment and degranulation. American journal of respiratory and critical care medicine 159, 1918-1924, doi:10.1164/ajrccm.159.6.9805083 (1999).

19 Dimova-Yaneva, D., Russell, D., Main, M., Brooker, R. J. & Helms, P. J. Eosinophil activation and cysteinyl leukotriene production in infants with respiratory syncytial virus bronchiolitis. Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology 34, 555-558, doi:10.1111/j.1365-2222.2004.1918.x (2004).

20 Everard, M. L. et al. Analysis of cells obtained by bronchial lavage of infants with respiratory syncytial virus infection. Archives of disease in childhood 71, 428-432 (1994).

21 McNamara, P. S., Ritson, P., Selby, A., Hart, C. A. & Smyth, R. L. Bronchoalveolar lavage cellularity in infants with severe respiratory syncytial virus bronchiolitis. Archives of disease in childhood 88, 922-926, doi:10.1136/adc.88.10.922 (2003).

22 Wang, S. Z. et al. Shedding of L-selectin and PECAM-1 and upregulation of Mac-1 and ICAM-1 on neutrophils in RSV bronchiolitis. The American journal of physiology 275, L983-989, doi:10.1152/ajplung.1998.275.5.L983 (1998).

23 Wang, S. Z. et al. The apoptosis of neutrophils is accelerated in respiratory syncytial virus (RSV)-induced bronchiolitis. Clinical and experimental immunology 114, 49-54, doi:10.1046/j.1365-2249.1998.00681.x (1998).

24 Kerrin, A. et al. Differential lower airway dendritic cell patterns may reveal distinct endotypes of RSV bronchiolitis. Thorax 72, 620-627, doi:10.1136/thoraxjnl-2015-207358 (2017).

25 Bem, R. A. et al. Activation of the granzyme pathway in children with severe respiratory syncytial virus infection. Pediatric research 63, 650-655, doi:10.1203/PDR.0b013e31816fdc32 (2008).

26 Gill, M. A. et al. Mobilization of plasmacytoid and myeloid dendritic cells to mucosal sites in children with respiratory syncytial virus and other viral respiratory infections. The Journal of infectious diseases 191, 1105-1115, doi:10.1086/428589 (2005).

27 Heidema, J. et al. CD8+ T cell responses in bronchoalveolar lavage fluid and peripheral blood mononuclear cells of infants with severe primary respiratory syncytial virus infections. Journal of immunology (Baltimore, Md. : 1950) 179, 8410-8417 (2007).



https://doi.org/10.1101/840090


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https://doi.org/10.1101/840090


in-silico immune cell deconvolution of the airway ...identifying prognostic biomarkers of poor...

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