Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions

Culos, Anthony; Tsai, Amy S.; Stanley, Natalie; Becker, Martin; Ghaemi, Mohammad Sajjad; McIlwain, David R.; Fallahzadeh, Ramin; Tanada, Athena; Nassar, Huda; Espinosa, Camilo; Xenochristou, Maria; Ganio, Edward A.; Peterson, Laura S.; Han, Xiaoyuan; Stelzer, Ina A.; Ando, Kazuo; Gaudilliere, Dyani; Phongpreecha, Thanaphong; Marić, Ivana; Chang, Alan L.; Shaw, Gary M.; Stevenson, David K.; Bendall, Sean C.; Davis, Kara L.; Fantl, Wendy J.; Nolan, Garry P.; Hastie, Trevor; Tibshirani, Robert; Angst, Martin S.; Gaudillière, Brice; Aghaeepour, Nima

doi:10.1038/s42256-020-00232-8

Cited by 62 publications

(62 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given these highly correlated immune features, we constructed a multivariate model using immunological Elastic Net (iEN), a recently developed regression algorithm designed for immune signals ( 20 ), and examined its predictive power on unseen data as an alternative to multiple univariate testing ( Fig. 3 ).…”

Section: Resultsmentioning

confidence: 99%

Single-cell peripheral immunoprofiling of Alzheimer’s and Parkinson’s diseases

et al. 2020

Self Cite

View full text Add to dashboard Cite

Peripheral blood mononuclear cells (PBMCs) may provide insight into the pathogenesis of Alzheimer’s disease (AD) or Parkinson’s disease (PD). We investigated PBMC samples from 132 well-characterized research participants using seven canonical immune stimulants, mass cytometric identification of 35 PBMC subsets, and single-cell quantification of 15 intracellular signaling markers, followed by machine learning model development to increase predictive power. From these, three main intracellular signaling pathways were identified specifically in PBMC subsets from people with AD versus controls: reduced activation of PLCγ2 across many cell types and stimulations and selectively variable activation of STAT1 and STAT5, depending on stimulant and cell type. Our findings functionally buttress the now multiply-validated observation that a rare coding variant in PLCG2 is associated with a decreased risk of AD. Together, these data suggest enhanced PLCγ2 activity as a potential new therapeutic target for AD with a readily accessible pharmacodynamic biomarker.

show abstract

Section: Resultsmentioning

confidence: 99%

Single-cell peripheral immunoprofiling of Alzheimer’s and Parkinson’s diseases

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Intracellular signaling responses to stimulation were reported as the difference in arcsinh transformed value of each signaling protein between the stimulated and unstimulated conditions (arcsinh ratio over basal signal). A knowledge-based penalization matrix was applied to intracellular signaling response features in the mass cytometry data based on mechanistic immunological knowledge, as previously described ( 32 , 33 ). Importantly, mechanistic priors used in the penalization matrix are independent of immunological knowledge related to neonatal immunology and GA.…”

Section: Methodsmentioning

confidence: 99%

Single-Cell Analysis of the Neonatal Immune System Across the Gestational Age Continuum

et al. 2021

Self Cite

View full text Add to dashboard Cite

Although most causes of death and morbidity in premature infants are related to immune maladaptation, the premature immune system remains poorly understood. We provide a comprehensive single-cell depiction of the neonatal immune system at birth across the spectrum of viable gestational age (GA), ranging from 25 weeks to term. A mass cytometry immunoassay interrogated all major immune cell subsets, including signaling activity and responsiveness to stimulation. An elastic net model described the relationship between GA and immunome (R=0.85, p=8.75e-14), and unsupervised clustering highlighted previously unrecognized GA-dependent immune dynamics, including decreasing basal MAP-kinase/NFκB signaling in antigen presenting cells; increasing responsiveness of cytotoxic lymphocytes to interferon-α; and decreasing frequency of regulatory and invariant T cells, including NKT-like cells and CD8+CD161+ T cells. Knowledge gained from the analysis of the neonatal immune landscape across GA provides a mechanistic framework to understand the unique susceptibility of preterm infants to both hyper-inflammatory diseases and infections.

show abstract

“…For cell subsets in a given sample that had an event count below 20 events, that cell subset and related phospho-signal were excluded from downstream analysis. A penalization matrix, based on mechanistic immunological knowledge, was applied to the immune cell response data (Culos et al, 2020; Ghaemi et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

Feyaerts

Hédou

Gillard

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The biological determinants of the wide spectrum of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, basal signaling activity, and signaling responses to inflammatory ligands were assessed in peripheral blood from patients with mild, moderate, and severe COVID-19, at the time of diagnosis. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identified and independently validated a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Features of this high-dimensional model recapitulated recent COVID-19 related observations of immune perturbations, and revealed novel biological signatures of severity, including the mobilization of elements of the renin-angiotensin system and primary hemostasis, as well as dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for the prevention of COVID-19 progression.

show abstract

Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions

Cited by 62 publications

References 81 publications

Single-cell peripheral immunoprofiling of Alzheimer’s and Parkinson’s diseases

Single-cell peripheral immunoprofiling of Alzheimer’s and Parkinson’s diseases

Single-Cell Analysis of the Neonatal Immune System Across the Gestational Age Continuum

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

Contact Info

Product

Resources

About