Combinatorial prediction of marker panels from single‐cell transcriptomic data

Delaney, Conor P.; Schnell, Alexandra; Cammarata, Louis; Yao-Smith, Aaron; Regev, Aviv; Kuchroo, Vijay K.; Singer, Meromit

doi:10.15252/msb.20199005

Cited by 80 publications

(70 citation statements)

References 43 publications

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“…Additional studies using novel unbiased computational methods are going to be required in the future to identify and test mediators of T-cell exhaustion in the treatment of autoimmune diseases. 124 Furthermore, a better analysis of the tissue-specific molecular and cellular pathways inducing T-cell exhaustion remains mandatory in order to improve its targeting.…”

Section: Therapeutic Induction Of Exhaustion In Autoimmune Diseasesmentioning

confidence: 99%

The yin and yang of co-inhibitory receptors: toward anti-tumor immunity without autoimmunity

Schnell¹,

Bod²,

Madi³

et al. 2020

Cell Res

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141

103

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Co-inhibitory receptors are important regulators of T-cell function that define the balance between tolerance and autoimmunity. The immune regulatory function of co-inhibitory receptors, including CTLA-4, PD-1, TIM-3, TIGIT, and LAG-3, was first discovered in the setting of autoimmune disease models, in which their blockade or deficiency resulted in induction or exacerbation of the disease. Later on, co-inhibitory receptors on lymphocytes have also been found to influence outcomes in tumor and chronic viral infection settings. These receptors suppress T-cell function in the tumor microenvironment (TME), thereby making the T cells dysfunctional. Based on this observation, blockade of co-inhibitory receptors (also known as checkpoint molecules) has emerged as a successful treatment option for a number of human cancers. However, severe autoimmune-like side effects limit the use of therapeutics that block individual or combinations of co-inhibitory receptors for cancer treatment. In this review we provide an overview of the role of co-inhibitory receptors in autoimmunity and anti-tumor immunity. We then discuss current approaches and future directions to leverage our knowledge of co-inhibitory receptors to target them in tumor immunity without inducing autoimmunity.

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Section: Therapeutic Induction Of Exhaustion In Autoimmune Diseasesmentioning

confidence: 99%

The yin and yang of co-inhibitory receptors: toward anti-tumor immunity without autoimmunity

Schnell¹,

Bod²,

Madi³

et al. 2020

Cell Res

Self Cite

141

103

View full text Add to dashboard Cite

show abstract

“…To determine if better markers for isolating these cells could be identified in humans as we had done in mice, we again used COMET (Delaney et al, 2019) to evaluate surface-expressed genes (Chihara et al, 2018). For each of the six patient blood samples, COMET identified transcripts that significantly enriched for the TM component ( Fig.…”

Section: Transcriptional Analysis Suggests That Cell Surface Marker Cmentioning

confidence: 99%

“…Cells were classified as positive for the PD-1 transcript (Pdcd1 in mice, PDCD1 in humans) if they had any number of reads above zero. To classify mouse cells as positive for the Klrk1 (encoding NKG2D), Entpd1 (encoding CD39), and/or Cx3cr1 (encoding CX3CR1), a more stringent cut off was used for a cell to qualify as positive, determined by COMET (Delaney et al, 2019). The COMET-determined thresholds for all markers in the COMET database are provided for the mouse dataset in Table S4, and the human dataset in Table S10, and can be found in the column titled "cutoff_val".…”

Section: Computational Processing Of Gene Expression Datamentioning

confidence: 99%

“…COMET (Delaney et al, 2019) runs were conducted with version 0.1.12 the default X parameter (0.15) and with the L parameter set to the minimum of (1) 10*K and (2) 0.35*N, where K is the number of tumor matching cells and N is the total number of cells with at least one alpha and one beta chain annotated, to account for our willingness to allow for greater levels of contamination in the identified tumor-matching samples than allowed by default.…”

Section: Cometmentioning

confidence: 99%

“…(a-b) Logistic regression showing classification of cells as TM or non-TM based on (a) all genes and (b) a pre-selected list enriched for surface-marker genes(Chihara et al, 2018).Shown are the first two principal component projections (left), ROC curves (middle), and the Recall-Precision plots (right) with 5-fold cross validation. (c) Top surface markers for identifying TM cells from non-TM cells in the blood based on COMET analysis(Delaney et al, 2019). The20 top scoring genes by q-value are shown.…”

mentioning

confidence: 99%

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Single-cell analyses identify circulating anti-tumor CD8 T cells and markers for their enrichment

Pauken

Shahid

Lagattuta

et al. 2020

Preprint

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The ability to monitor anti-tumor CD8+ T cell responses in the blood has tremendous therapeutic potential. Here, we used paired single-cell RNA sequencing and T cell receptor (TCR) sequencing to detect and characterize tumor matching (TM) CD8+ T cells in the blood of mice with MC38 tumors and melanoma patients using the TCR as a molecular barcode. TM cells showed increased activation compared to non-matching T cells in blood, and appeared less exhausted than matching counterparts in tumor. Importantly, PD-1, which has been used to identify putative circulating anti-tumor CD8+ T cells, showed poor sensitivity for identifying TM cells. By leveraging the transcriptome we identified candidate cell surface marker panels for TM cells in mice and melanoma patients, and validated NKG2D, CD39, and CX3CR1 in mice. These data demonstrate that the TCR can be used to identify tumor-relevant populations for comprehensive characterization, reveal unique transcriptional properties of TM cells, and develop marker panels for tracking and analysis of these cells.

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Single‐Cell Transcriptomic Census of Endothelial Changes Induced by Matrix Stiffness and the Association with Atherosclerosis

Zamani

Cheng

Charbonier

et al. 2022

Adv Funct Materials

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Vascular endothelial cell (EC) plasticity plays a critical role in the progression of atherosclerosis by giving rise to mesenchymal phenotypes in the plaque lesion. Despite the evidence for arterial stiffening as a major contributor to atherosclerosis, the complex interplay among atherogenic stimuli in vivo has hindered attempts to determine the effects of extracellular matrix (ECM) stiffness on endothelial‐mesenchymal transition (EndMT). To study the regulatory effects of ECM stiffness on EndMT, an in vitro model is developed in which human coronary artery ECs are cultured on physiological or pathological stiffness substrates. Leveraging single‐cell RNA sequencing, cell clusters with mesenchymal transcriptional features are identified to be more prevalent on pathological substrates than physiological substrates. Trajectory inference analyses reveal a novel mesenchymal‐to‐endothelial reverse transition, which is blocked by pathological stiffness substrates, in addition to the expected EndMT trajectory. ECs pushed to a mesenchymal character by pathological stiffness substrates are enriched in transcriptional signatures of atherosclerotic ECs from human and murine plaques. This study characterizes at single‐cell resolution the transcriptional programs that underpin EC plasticity in both physiological or pathological milieus, and thus serves as a valuable resource for more precisely defining EndMT and the transcriptional programs contributing to atherosclerosis.

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Combinatorial prediction of marker panels from single‐cell transcriptomic data

Cited by 80 publications

References 43 publications

The yin and yang of co-inhibitory receptors: toward anti-tumor immunity without autoimmunity

The yin and yang of co-inhibitory receptors: toward anti-tumor immunity without autoimmunity

Single-cell analyses identify circulating anti-tumor CD8 T cells and markers for their enrichment

Single‐Cell Transcriptomic Census of Endothelial Changes Induced by Matrix Stiffness and the Association with Atherosclerosis

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