T cell state transition produces an emergent change detector

AbstractCell fate decisions play a pivotal role in development but technologies for dissecting them are limited. We developed a multifunction new method, Topographer to construct a ‘quantitative’ Waddington’s landscape of single-cell transcriptomic data. This method is able to identify complex cell-state transition trajectories and to estimate complex cell-type dynamics characterized by fate and transition probabilities. It also infers both marker gene networks and their dynamic changes as well as dynamic characteristics of transcriptional bursting along the cell-state transition trajectories. Applying this method to single-cell RNA-seq data on the differentiation of primary human myoblasts, we not only identified three known cell types but also estimated both their fate probabilities and transition probabilities among them. We found that the percent of genes expressed in a bursty manner is significantly higher at (or near) the branch point (∼97%) than before or after branch (below 80%), and that both gene-gene and cell-cell correlation degrees are apparently lower near the branch point than away from the branching. Topographer allows revealing of cell fate mechanisms in a coherent way at three scales: cell lineage (macroscopic), gene network (mesoscopic) and gene expression (microscopic).

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Targeted reconstruction of T cell receptor sequence from single cell RNA-sequencing links CDR3 length to T cell differentiation state

10.1101/072744 ◽

2016 ◽

Cited By ~ 4

Author(s):

Shaked Afik ◽

Kathleen B. Yates ◽

Kevin Bi ◽

Samuel Darko ◽

Jernej Godec ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Single Cell ◽

Rna Sequencing ◽

Cell Response ◽

T Cell Response ◽

Effector Memory ◽

Cell State ◽

Tcr Repertoire ◽

Single Cell Rna Sequencing

ABSTRACTThe T cell compartment must contain diversity in both TCR repertoire and cell state to provide effective immunity against pathogens1,2. However, it remains unclear how differences in the TCR contribute to heterogeneity in T cell state at the single cell level because most analysis of the TCR repertoire has, to date, aggregated information from populations of cells. Single cell RNA-sequencing (scRNA-seq) can allow simultaneous measurement of TCR sequence and global transcriptional profile from single cells. However, current protocols to directly sequence the TCR require the use of long sequencing reads, increasing the cost and decreasing the number of cells that can be feasibly analyzed. Here we present a tool that can efficiently extract TCR sequence information from standard, short-read scRNA-seq libraries of T cells: TCR Reconstruction Algorithm for Paired-End Single cell (TRAPeS). We apply it to investigate heterogeneity in the CD8+T cell response in humans and mice, and show that it is accurate and more sensitive than previous approaches3,4. We applied TRAPeS to single cell RNA-seq of CD8+T cells specific for a single epitope from Yellow Fever Virus5. We show that the recently-described "naive-like" memory population of YFV-specific CD8+T cells have significantly longer CDR3 regions and greater divergence from germline sequence than do effector-memory phenotype CD8+T cells specific for YFV. This suggests that TCR usage contributes to heterogeneity in the differentiation state of the CD8+T cell response to YFV. TRAPeS is publicly available, and can be readily used to investigate the relationship between the TCR repertoire and cellular phenotype.

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Decision letter: A lncRNA fine tunes the dynamics of a cell state transition involving Lin28, let-7 and de novo DNA methylation

10.7554/elife.23468.031 ◽

2017 ◽

Keyword(s):

Dna Methylation ◽

State Transition ◽

De Novo ◽

Cell State ◽

A Cell

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Modeling memory T cell states at single-cell resolution identifies in vivo state-dependence of eQTLs influencing disease

10.1101/2021.07.29.454316 ◽

2021 ◽

Author(s):

Aparna Nathan ◽

Samira Asgari ◽

Kazuyoshi Ishigaki ◽

Tiffany Amariuta ◽

Yang Luo ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Single Cell ◽

Cd8 T Cells ◽

Surface Protein ◽

State Dependence ◽

Cell State ◽

Fine Grained ◽

State Dependent

Many non-coding genetic variants cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by gene-regulation differences between cell states. T cells, for example, have fluid, multifaceted functional states in vivo that cannot be modeled in eQTL studies that aggregate cells. Here, we modeled T cell states and eQTLs at single-cell resolution. Using >500,000 resting memory T cells from 259 Peruvians, we found over one-third of the 6,511 cis-eQTLs had state-dependent effects. By integrating single-cell RNA and surface protein measurements, we defined continuous cell states that explained more eQTL variation than discrete states like CD4+ or CD8+ T cells and could have opposing effects on independent eQTL variants in a locus. Autoimmune variants were enriched in cell-state-dependent eQTLs, such as a rheumatoid-arthritis variant near ORMDL3 strongest in cytotoxic CD8+ T cells. These results argue that fine-grained cell state context is crucial to understanding disease-associated eQTLs.

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Drivers of dynamic intratumor heterogeneity and phenotypic plasticity

AJP Cell Physiology ◽

10.1152/ajpcell.00575.2020 ◽

2021 ◽

Vol 320 (5) ◽

pp. C750-C760

Author(s):

Antara Biswas ◽

Subhajyoti De

Keyword(s):

Phenotypic Plasticity ◽

Tumor Cells ◽

State Transition ◽

Malignant Lesion ◽

Genetic Alterations ◽

Intratumor Heterogeneity ◽

Genetic Mutations ◽

Dynamic Heterogeneity ◽

Cell State ◽

Somatic Evolution

Cancer is a clonal disease, i.e., all tumor cells within a malignant lesion trace their lineage back to a precursor somatic cell that acquired oncogenic mutations during development and aging. And yet, those tumor cells tend to have genetic and nongenetic variations among themselves—which is denoted as intratumor heterogeneity. Although some of these variations are inconsequential, others tend to contribute to cell state transition and phenotypic heterogeneity, providing a substrate for somatic evolution. Tumor cell phenotypes can dynamically change under the influence of genetic mutations, epigenetic modifications, and microenvironmental contexts. Although epigenetic and microenvironmental changes are adaptive, genetic mutations are usually considered permanent. Emerging reports suggest that certain classes of genetic alterations show extensive reversibility in tumors in clinically relevant timescales, contributing as major drivers of dynamic intratumor heterogeneity and phenotypic plasticity. Dynamic heterogeneity and phenotypic plasticity can confer resistance to treatment, promote metastasis, and enhance evolvability in cancer. Here, we first highlight recent efforts to characterize intratumor heterogeneity at genetic, epigenetic, and microenvironmental levels. We then discuss phenotypic plasticity and cell state transition by tumor cells, under the influence of genetic and nongenetic determinants and their clinical significance in classification of tumors and therapeutic decision-making.

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