Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases

Vallania, Francesco; Tam, Andrew; Lofgren, Shane; Schaffert, Steven; Azad, Tej D.; Bongen, Erika; Haynes, Winston; Alsup, Meia; Alonso, Michael N.; Davis, Mark M.; Engleman, Edgar G.; Khatri, Purvesh

doi:10.1038/s41467-018-07242-6

Cited by 141 publications

(192 citation statements)

References 27 publications

(50 reference statements)

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“…Cell-type deconvolution algorithms have recently been used with genome-wide RNA expression data to help identify changes in immune cell proportions in the blood that are associated with TB disease, prospective TB disease risk and treatment success (25,40). To identify immune cell populations that are associated with time since TB exposure in the GC6-74 study, we used the leukocyte expression signature matrix 'immunoStates' and linear regression to infer leukocyte proportions for each subject's sample (40). We found that the proportion of CD4+ α/β T cells was increased at 6 months vs. baseline time point in the Gambian and Ethiopian cohorts (P = 0.0079; linear mixed model; Figure 4A), but was not significantly changed at 18 months (P = 0.20 vs. baseline; linear mixed model, included South African cohort; Figure 4B).…”

Section: Time Since Tb Exposure In Humans Is Associated With Alteratimentioning

confidence: 99%

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Blood RNA Signatures Predict Recent Tuberculosis Exposure in Mice, Macaques and Humans

Ault

Headley

Hare

et al. 2019

Preprint

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Tuberculosis (TB) is the leading cause of death due to a single infectious disease.Knowing when a person was infected with Mycobacterium tuberculosis (M.tb) is critical as recent infection is the strongest clinical risk factor for progression to TB disease in immunocompetent individuals. However, time since M.tb infection is challenging to determine in routine clinical practice. To define a biomarker for recent TB exposure, we determined whether gene expression patterns in blood RNA correlated with time since M.tb infection or exposure.First, we found RNA signatures that accurately discriminated early and late time periods after experimental infection in mice and cynomolgus macaques. Next, we found a 6-gene blood RNA signature that identified recently exposed individuals in two independent human cohorts, including adult household contacts of TB cases and adolescents who recently acquired M.tb infection. Our work supports the need for future longitudinal studies of recent TB contacts to determine whether biomarkers of recent infection can provide prognostic information of TB disease risk in individuals and help map recent transmission in communities.

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Section: Time Since Tb Exposure In Humans Is Associated With Alteratimentioning

confidence: 99%

“…Cell type proportions in blood were estimated from RNA-seq data as previously described using the R MetaIntegrator package (25,40,43). Gene-level expression for this deconvolution was obtained from the ARCHS 4 resource (61).…”

Section: Cell Type Deconvolution Pathway and Transcriptional Module mentioning

confidence: 99%

Blood RNA Signatures Predict Recent Tuberculosis Exposure in Mice, Macaques and Humans

Ault

Headley

Hare

et al. 2019

Preprint

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“…Cell subsets were identified using a "basis set" of published marker genes called immunoStates (71). The mean expression of each gene was computed for each cluster from the scaled log normalized expression values; each cluster was then assigned to the immunoState subset with the highest Pearson correlation value.…”

Section: Single-cell Library Preparation and Gene Expression Analysismentioning

confidence: 99%

Single-cell repertoire tracing identifies rituximab refractory B cells during myasthenia gravis relapses

Jiang

Fichtner

Hoehn

et al. 2019

Preprint

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One Sentence Summary: Disease relevant B cells during post-rituximab (RTX) relapse emerge from the unsuccessful depletion of pre-existing B cell clones; single-cell transcriptomic phenotyping combined with lineage tracing using paired B cell receptor repertoires identified both persistent antibody-secreting cell as well as memory B cell subsets.Abstract: Rituximab, an anti-CD20 B cell-depleting therapy, is indicated to treat a growing number of autoimmune disorders. However, disease relapses can occur when the B cell compartment is re-established after treatment. To explore the mechanism of relapse, we studied patients with muscle-specific kinase (MuSK) myasthenia gravis (MG), a paradigm for B cellmediated autoimmune diseases in which pathology is directly associated with autoantibody production. Whether the failed depletion of specific clones leads to the persistence of autoantibody producing B cell subsets has yet to be elucidated. Here we show that rituximab (RTX) therapy is not fully effective at eliminating disease-associated B cells and provide a mechanistic understanding of relapse. We carried out single-cell transcriptional and B cell receptor (BCR) profiling on longitudinal B cell samples from the peripheral blood of MuSK MG patients who relapsed after rituximab therapy. Computational analysis of these data identified individual B cells that were refractory to rituximab depletion by linking them to clones that were present prior to therapy and continued to persist after therapy. These persistent B cell clones were present at high frequency, and included both antibody-secreting cells and memory B cells. They were disproportionately isotype switched with elevated somatic hypermutation frequencies and a gene expression signature associated with antigen-experience and B cell clonal expansion. We further identified both antibody-secreting cells and memory B cells with specificity for MuSK autoantigen that were clonally expanded during relapse and persistent. Our results provide evidence that post-rituximab relapse in MuSK MG is associated with the failed depletion of autoantigen-specific B cell subsets. This work provides insight into the durability of human B cells during treatment with therapeutic CD20-specific monoclonal antibodies. The transcriptional signatures associated with B cell resistance may represent new therapeutic avenues for treatment and biomarkers of depletion efficacy.

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“…Although examples exist of both general purpose immune signature matrices, e.g. LM22 (Newman et al, 2015) and Immunostates (Vallania et al, 2018), and more tissue specific ones e.g. M17 (Ciavarella et al, 2018), these matrices are most likely not appropriate for all diseases and tissue types.…”

Section: Introductionmentioning

confidence: 99%

ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells

Danziger¹,

Gibbs

Shmulevich

et al. 2019

Preprint

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Immune cell infiltration of tumors can be an important component for determining patient outcomes, e.g. by inferring immune cell presence by deconvolving gene expression data drawn from a heterogenous mix of cell types. ADAPTS aids deconvolution by adding custom cell types to existing cell-type signature matrices or building new matrices de novo. This R package builds a custom signature matrix from purified cell type gene expression data by automatically determining genes that uniquely identify each cell type. The package includes functions that call deconvolution algorithms which use the custom signature matrix to estimate the proportion of cell types present in heterogenous samples. Availability:The R packages ADAPTS, ADAPTSdata, ADAPTSdata2 are at GitHub (https://github.com/sdanzige/ADAPTS , etc.) and eventually will be available on CRAN

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Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases

Cited by 141 publications

References 27 publications

Blood RNA Signatures Predict Recent Tuberculosis Exposure in Mice, Macaques and Humans

Blood RNA Signatures Predict Recent Tuberculosis Exposure in Mice, Macaques and Humans

Single-cell repertoire tracing identifies rituximab refractory B cells during myasthenia gravis relapses

ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells

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