scholarly journals Infino: a Bayesian hierarchical model improves estimates of immune infiltration into tumor microenvironment

2017 ◽  
Author(s):  
Maxim Zaslavsky ◽  
Jacqueline Buros Novik ◽  
Eliza Chang ◽  
Jeffrey Hammerbacher
Keyword(s):  
T Cell ◽  
Tumor Microenvironment ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Types ◽  
Training Data ◽  
Cell Type ◽  
Sequencing Data ◽  
Effective Response ◽  
Immune Infiltration

AbstractRobust quantification of immune cell infiltration into the tumor microenvironment may shed light on why only a small proportion of patients benefit from checkpoint therapy. The immune cells surrounding a tumor have been suggested to mediate an effective response to immunotherapy. However, traditional measurement of immune cell content around a tumor by immunohistochemistry, flow cytometry, or mass cytometry allows measurement of only up to a few dozen markers at a time, limiting the number of immune cell types identified. Immune cell type abundances may instead be estimated in silico by deconvolving gene expression mixtures from bulk RNA sequencing of tumor tissue. By measuring tens of thousands of transcripts at once, bulk RNA-seq provides a rich input to algorithms that quantify cell type abundances in the tumor microenvironment, affording the potential to quantify the states of a greater number of immune cell types (given adequate training data). Here, we first review existing methods for deconvolution and evaluate their performance on synthetic mixtures. Then we develop a Bayesian inference approach, named infino, that learns to distinguish immune cell expression phenotypes and deconvolve mixtures. In contrast to earlier approaches, infino accepts RNA sequencing data, models transcript expression variability, and exploits the relationships between cell types to improve deconvolution accuracy and allow interrogation from the level of broad categories to the level of finest granularity. The resulting probability distributions of immune infiltration could be applied to numerous questions concerning the diverse ecology of immune cell types, including assessment of the association of immune infiltration with response to immunotherapy, and study of the expression profile and presence of elusive T cell subcompartments, such as T cell exhaustion.

scholarly journals MIXTURE: an improved algorithm for immune tumor microenvironment estimation based on gene expression data

2019 ◽  
Author(s):  
Elmer A. Fernández ◽  
Yamil D. Mahmoud ◽  
Florencia Veigas ◽  
Darío Rocha ◽  
Mónica Balzarini ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Immune Cell ◽  
Therapy Response ◽  
Cell Types ◽  
Gene Signature ◽  
Response To Therapy ◽  
Support Vector ◽  
Data Sets ◽  
Cell Type ◽  
Before And After

AbstractRNA sequencing has proved to be an efficient high-throughput technique to robustly characterize the presence and quantity of RNA in tumor biopsies at a given time. Importantly, it can be used to computationally estimate the composition of the tumor immune infiltrate and to infer the immunological phenotypes of those cells. Given the significant impact of anti-cancer immunotherapies and the role of the associated immune tumor microenvironment (ITME) on its prognosis and therapy response, the estimation of the immune cell-type content in the tumor is crucial for designing effective strategies to understand and treat cancer. Current digital estimation of the ITME cell mixture content can be performed using different analytical tools. However, current methods tend to over-estimate the number of cell-types present in the sample, thus under-estimating true proportions, biasing the results. We developed MIXTURE, a noise-constrained recursive feature selection for support vector regression that overcomes such limitations. MIXTURE deconvolutes cell-type proportions of bulk tumor samples for both RNA microarray or RNA-Seq platforms from a leukocyte validated gene signature. We evaluated MIXTURE over simulated and benchmark data sets. It overcomes competitive methods in terms of accuracy on the true number of present cell-types and proportions estimates with increased robustness to estimation bias. It also shows superior robustness to collinearity problems. Finally, we investigated the human immune microenvironment of breast cancer, head and neck squamous cell carcinoma, and melanoma biopsies before and after anti-PD-1 immunotherapy treatment revealing associations to response to therapy which have not seen by previous methods.

scholarly journals IMMU-08. MICROENVIRONMENT MODULATION BY TIM-3 BLOCKADE IMPROVES THE OUTCOME OF PRECLINICAL DIPG MODELS

2021 ◽  
Vol 23 (Supplement_1) ◽  
pp. i28-i28
Author(s):  
Iker Ausejo-Mauleon ◽  
Sara Labiano ◽  
Virginia Laspidea ◽  
Marc Garcia-Moure ◽  
Daniel de la Nava ◽  
...  
Keyword(s):  
T Cell ◽  
Tumor Microenvironment ◽  
Nk Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Pediatric Brain Tumors ◽  
Immune Memory ◽  
Cd8 Cells ◽  
Functional Studies

Abstract Diffuse Midline Gliomas (DMGs), encompassing Diffuse Intrinsic Pontine Gliomas (DIPGs), are the most aggressive pediatric brain tumors. Their meagre survival has not changed despite the combination of radiotherapy with targeted therapies emphasizing the urgent need for effective treatments. Recent research suggested that the DIPG tumor microenvironment is neither highly immunosuppressive nor inflammatory. These analyses showed the lack of infiltrating lymphocytes and the abundance of CD11b+ cells. TIM-3 (HAVCR2) is a member of the T-cell immunoglobulin and mucin domain protein family which is expressed on multiple immune cell types including T cells, Tregs, NK cells, monocytes, dendritic cells and microglia, where it potently regulates not only adaptive immunity but also innate immunity. Therefore, the central hypothesis of this study is that TIM-3 inhibitors could stimulate a cytotoxic immune effect and challenge several components in the tumor microenvironment including microglia, thereby providing a potential effective treatment for DMGs. In silico assessment of TIM-3 expression in a DIPG datasets showed a robust expression of this gene. Moreover, single-cell sequencing analyses of DIPG biopsies uncover its expression on tumor cells, especially in the OPCs compartment. In vivo efficacy studies showed that treatment with anti-TIM-3 antibody significantly increase the overall survival in two DIPG immunocompetent orthotopic animal models (doubling the median), lead to long-term survivors (50%) and showed immune memory. Analyses of CD45+ populations in the tumor microenvironment showed a significant increase in B, NK and CD8+ cells corresponding with a T-cell activate phenotype in treated-mice. The potential therapeutic involvement of NK cells was certified using nude mice and functional studies. Involvement of microglia in currently being analysed. In summary, these data underscore TIM-3 as a potential target DIPGs and uncover the potential involvement of NKs and other immune mechanisms in the efficacy of anti-TIM-3 therapy.

scholarly journals scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Bobby Ranjan ◽  
Florian Schmidt ◽  
Wenjie Sun ◽  
Jinyu Park ◽  
Mohammad Amin Honardoost ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Cell Types ◽  
Unsupervised Clustering ◽  
Differentially Expressed ◽  
Consensus Clustering ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Background Clustering is a crucial step in the analysis of single-cell data. Clusters identified in an unsupervised manner are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering approaches have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation. Results We present scConsensus, an $${\mathbf {R}}$$ R framework for generating a consensus clustering by (1) integrating results from both unsupervised and supervised approaches and (2) refining the consensus clusters using differentially expressed genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. Conclusions scConsensus combines the merits of unsupervised and supervised approaches to partition cells with better cluster separation and homogeneity, thereby increasing our confidence in detecting distinct cell types. scConsensus is implemented in $${\mathbf {R}}$$ R and is freely available on GitHub at https://github.com/prabhakarlab/scConsensus.

scholarly journals Higher cytolytic score correlates with an immunosuppressive tumor microenvironment and reduced survival in glioblastoma

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexander F. Haddad ◽  
Jia-Shu Chen ◽  
Taemin Oh ◽  
Matheus P. Pereira ◽  
Rushikesh S. Joshi ◽  
...  
Keyword(s):  
T Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Cd8 T Cell ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Sequencing Data ◽  
Gene Score ◽  
T Cell Infiltration ◽  
The Relationship

Abstract Cytolytic score (CYT), calculated from mRNA expression levels of granzyme and perforin, positively correlates with CD8+ T cell infiltration/activity in a variety of cancers. Unlike other cancers, higher CYT has been associated with worse prognosis in glioblastoma (GBM). To address this discrepancy, we sought to investigate the relationship between CYT and immune checkpoint gene score (ICGscore), as well as their correlation with patient survival and tumor immune cell infiltration. Clinical and RNA-sequencing data for patients with newly diagnosed GBM were obtained from The Cancer Genome Atlas. Maximally-selected rank statistics was used to dichotomize subgroups. CIBERSORT was used to estimate abudence of immune cell-types. Spearman correlation was used to characterize the relationship between CYT and ICGscore. Kaplan–Meier curves were generated for survival analysis. Overall, 28/151 patients had high CYT. High CYT was associated with a mesenchymal subtype (p < 0.001) and worse survival (7.45 vs. 12.2 months, p < 0.001). There were no differences in patient demographics, IDH/MGMT mutation status, or treatment. On subgroup analysis, patients with high CYT/ICGscore had significantly increased CD8+ infiltration (p < 0.001), as expected, and worse survival (HR 0.445, p < 0.01). Furthermore, CYT strongly correlated with ICGscore (RS = 0.675, p < 0.001). The high CYT/ICGscore subgroup was associated with greater infiltration of M2 macrophages (p = 0.011) and neutrophils (p = 0.055). Our study highlights a multidimensional immunosuppressive GBM microenvironment in patients with higher CYT and potentially identifies patients with high CYT/ICGscore as a subgroup that may particularly benefit from multi-faceted immunotherapies, given their already elevated tumor CD8+ T cell levels.

scholarly journals ACTINN: automated identification of cell types in single cell RNA sequencing

2019 ◽  
Author(s):  
Feiyang Ma ◽  
Matteo Pellegrini
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Types ◽  
Mouse Cell ◽  
Supplementary Information ◽  
Cell Type ◽  
Human T Cell ◽  
Single Cell Rna Sequencing

Abstract Motivation Cell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with three hidden layers, trains on datasets with predefined cell types and predicts cell types for other datasets based on the trained parameters. Results We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines. Availability and implementation The codes and datasets are available at https://figshare.com/articles/ACTINN/8967116. Tutorial is available at https://github.com/mafeiyang/ACTINN. All codes are implemented in python. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals FR-Match: Robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test

2020 ◽  
Author(s):  
Yun Zhang ◽  
Brian D. Aevermann ◽  
Trygve E. Bakken ◽  
Jeremy A. Miller ◽  
Rebecca D. Hodge ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
R Package ◽  
Brain Regions ◽  
Cortical Layer ◽  
Middle Temporal Gyrus ◽  
Cell Type ◽  
Sequencing Data

AbstractSingle cell/nucleus RNA sequencing (scRNAseq) is emerging as an essential tool to unravel the phenotypic heterogeneity of cells in complex biological systems. While computational methods for scRNAseq cell type clustering have advanced, the ability to integrate datasets to identify common and novel cell types across experiments remains a challenge. Here, we introduce a cluster-to-cluster cell type matching method – FR-Match – that utilizes supervised feature selection for dimensionality reduction and incorporates shared information among cells to determine whether two cell type clusters share the same underlying multivariate gene expression distribution. FR-Match is benchmarked with existing cell-to-cell and cell-to-cluster cell type matching methods using both simulated and real scRNAseq data. FR-Match proved to be a stringent method that produced fewer erroneous matches of distinct cell subtypes and had the unique ability to identify novel cell phenotypes in new datasets. In silico validation demonstrated that the proposed workflow is the only self-contained algorithm that was robust to increasing numbers of true negatives (i.e. non-represented cell types). FR-Match was applied to two human brain scRNAseq datasets sampled from cortical layer 1 and full thickness middle temporal gyrus. When mapping cell types identified in specimens isolated from these overlapping human brain regions, FR-Match precisely recapitulated the laminar characteristics of matched cell type clusters, reflecting their distinct neuroanatomical distributions. An R package and Shiny application are provided at https://github.com/JCVenterInstitute/FRmatch for users to interactively explore and match scRNAseq cell type clusters with complementary visualization tools.

scholarly journals Cellular and genetic drivers of RNA editing variation in the human brain

2021 ◽  
Author(s):  
Ryn Cuddleston ◽  
Junhao Li ◽  
Xuanjia Fan ◽  
Alexey Kozenkov ◽  
Matthew Lalli ◽  
...  
Keyword(s):  
Human Brain ◽  
Rna Sequencing ◽  
Rna Editing ◽  
Genetic Regulation ◽  
Cell Types ◽  
Brain Regions ◽  
Brain Cell ◽  
Cell Type ◽  
Sequencing Data ◽  
Regulatory Effects

Posttranscriptional adenosine-to-inosine modifications amplify the functionality of RNA molecules in the brain, yet the cellular and genetic regulation of RNA editing is poorly described. We quantified base-specific RNA editing across three major cell populations from the human prefrontal cortex: glutamatergic neurons, medial ganglionic eminence GABAergic neurons, and oligodendrocytes. We found more selective editing and RNA hyper-editing in neurons relative to oligodendrocytes. The pattern of RNA editing was highly cell type-specific, with 189,229 cell type-associated sites. The cellular specificity for thousands of sites was confirmed by single nucleus RNA-sequencing. Importantly, cell type-associated sites were enriched in GTEx RNA-sequencing data, edited ~twentyfold higher than all other sites, and variation in RNA editing was predominantly explained by neuronal proportions in bulk brain tissue. Finally, we discovered 661,791 cis-editing quantitative trait loci across thirteen brain regions, including hundreds with cell type-associated features. These data reveal an expansive repertoire of highly regulated RNA editing sites across human brain cell types and provide a resolved atlas linking cell types to editing variation and genetic regulatory effects.

scholarly journals scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data

2020 ◽  
Author(s):  
Bobby Ranjan ◽  
Florian Schmidt ◽  
Wenjie Sun ◽  
Jinyu Park ◽  
Mohammad Amin Honardoost ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Unsupervised Clustering ◽  
Differentially Expressed ◽  
Consensus Clustering ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Data Clusters

Clustering is a crucial step in the analysis of single-cell data. Clusters identified using unsupervised clustering are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering strategies have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation. We present scConsensus, an R framework for generating a consensus clustering by (i) integrating the results from both unsupervised and supervised approaches and (ii) refining the consensus clusters using differentially expressed (DE) genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. scConsensus is freely available on GitHub at https://github.com/prabhakarlab/scConsensus.

scholarly journals Automated identification of Cell Types in Single Cell RNA Sequencing

2019 ◽  
Author(s):  
Feiyang Ma ◽  
Matteo Pellegrini
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Complex Data ◽  
Cell Type ◽  
Human T Cell ◽  
Single Cell Rna Sequencing

AbstractCell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with 3 hidden layers, trains on datasets with predefined cell types, and predicts cell types for other datasets based on the trained parameters. We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines.Author SummarySingle cell RNA sequencing (scRNA-seq) provides high resolution profiling of the transcriptomes of individual cells, which inevitably results in high volumes of data that require complex data processing pipelines. Usually, one of the first steps in the analysis of scRNA-seq is to assign individual cells to known cell types. To accomplish this, traditional methods first group the cells into different clusters, then find marker genes, and finally use these to manually assign cell types for each cluster. Thus these methods require prior knowledge of cell type canonical markers, and some level of subjectivity to make the cell type assignments. As a result, the process is often laborious and requires domain specific expertise, which is a barrier for inexperienced users. By contrast, our neural network ACTINN automatically learns the features for each predefined cell type and uses these features to predict cell types for individual cells. This approach is computationally efficient and requires no domain expertise of the tissues being studied. We believe ACTINN allows users to rapidly identify cell types in their datasets, thus rendering the analysis of their scRNA-seq datasets more efficient.

scholarly journals Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion

2019 ◽  
Author(s):  
Ansuman T. Satpathy ◽  
Jeffrey M. Granja ◽  
Kathryn E. Yost ◽  
Yanyan Qi ◽  
Francesca Meschi ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Immune Cell ◽  
Single Cells ◽  
Cell Types ◽  
Cell Development ◽  
Massively Parallel ◽  
T Cell Exhaustion ◽  
Cell Type ◽  
Cell Exhaustion

AbstractUnderstanding complex tissues requires single-cell deconstruction of gene regulation with precision and scale. Here we present a massively parallel droplet-based platform for mapping transposase-accessible chromatin in tens of thousands of single cells per sample (scATAC-seq). We obtain and analyze chromatin profiles of over 200,000 single cells in two primary human systems. In blood, scATAC-seq allows marker-free identification of cell type-specificcis- andtrans-regulatory elements, mapping of disease-associated enhancer activity, and reconstruction of trajectories of differentiation from progenitors to diverse and rare immune cell types. In basal cell carcinoma, scATAC-seq reveals regulatory landscapes of malignant, stromal, and immune cell types in the tumor microenvironment. Moreover, scATAC-seq of serial tumor biopsies before and after PD-1 blockade allows identification of chromatin regulators and differentiation trajectories of therapy-responsive intratumoral T cell subsets, revealing a shared regulatory program driving CD8+T cell exhaustion and CD4+T follicular helper cell development. We anticipate that droplet-based single-cell chromatin accessibility will provide a broadly applicable means of identifying regulatory factors and elements that underlie cell type and function.

Sign in / Sign up

Export Citation Format

Share Document