Tapio Lönnberg

T cell fate and clonality inference from single-cell transcriptomes

We developed TraCeR, a computational method to reconstruct full-length, paired T cell receptor (TCR) sequences from T lymphocyte single-cell RNA sequence data. TraCeR links T cell specificity with functional response by revealing clonal relationships between cells alongside their transcriptional profiles. We found that T cell clonotypes in a mouse Salmonella infection model span early activated CD4+ T cells as well as mature effector and memory cells.

Global Chromatin State Analysis Reveals Lineage-Specific Enhancers during the Initiation of Human T helper 1 and T helper 2 Cell Polarization

Naive CD4⁺ T cells can differentiate into specific helper and regulatory T cell lineages in order to combat infection and disease. The correct response to cytokines and a controlled balance of these populations is critical for the immune system and the avoidance of autoimmune disorders. To investigate how early cell-fate commitment is regulated, we generated the first human genome-wide maps of histone modifications that reveal enhancer elements after 72 hr of in vitro polarization toward T helper 1 (Th1) and T helper 2 (Th2) cell lineages. Our analysis indicated that even at this very early time point, cell-specific gene regulation and enhancers were at work directing lineage commitment. Further examination of lineage-specific enhancers identified transcription factors (TFs) with known and unknown T cell roles as putative drivers of lineage-specific gene expression. Lastly, an integrative analysis of immunopathogenic-associated SNPs suggests a role for distal regulatory elements in disease etiology.

A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications.

RNA sequencing (RNA-seq) is a genomic approach for the detection and quantitative analysis of messenger RNA molecules in a biological sample and is useful for studying cellular responses. RNA-seq has fueled much discovery and innovation in medicine over recent years. For practical reasons, the technique is usually conducted on samples comprising thousands to millions of cells. However, this has hindered direct assessment of the fundamental unit of biology—the cell. Since the first single-cell RNA-sequencing (scRNA-seq) study was published in 2009, many more have been conducted, mostly by specialist laboratories with unique skills in wet-lab single-cell genomics, bioinformatics, and computation. However, with the increasing commercial availability of scRNA-seq platforms, and the rapid ongoing maturation of bioinformatics approaches, a point has been reached where any biomedical researcher or clinician can use scRNA-seq to make exciting discoveries. In this review, we present a practical guide to help researchers design their first scRNA-seq studies, including introductory information on experimental hardware, protocol choice, quality control, data analysis and biological interpretation.

Quantitative Proteomics Analysis of the Nuclear Fraction of Human CD4+ Cells in the Early Phases of IL-4-induced Th2 Differentiation

We used stable isotope labeling with 4-plex iTRAQ (isobaric tags for relative and absolute quantification) reagents and LC-MS/MS to investigate proteomic changes in the nucleus of activated human CD4+ cells during the early stages of Th2 cell differentiation. The effects of IL-4 stimulation upon activated naïve CD4+ cells were measured in the nuclear fractions from 6 and 24 h in three biological replicates, each using pooled cord blood samples derived from seven or more individuals. In these analyses, in the order of 800 proteins were detected with two or more peptides and quantified in three biological replicates. In addition to consistent differences observed with the nuclear localization/expression of established human Th2 and Th1 markers, there were changes that suggested the involvement of several proteins either only recently reported or otherwise not known in this context. These included SATB1 and among the novel changes detected and validated an IL-4-induced increase in the level of YB1. This unique data set from human cord blood CD4+ T cells details an extensive list of protein determinations that compares with and complements previous data determined from the Jurkat cell nucleus.

Single-cell technologies are revolutionizing the approach to rare cells

In the last lustrum single‐cell techniques such as single‐cell quantitative PCR, RNA and DNA sequencing, and the state‐of‐the‐art cytometry by time of flight (CyTOF) mass cytometer have allowed a detailed analysis of the sub‐composition of different organs from the bone marrow hematopoietic compartment to the brain. These fine‐grained analyses have highlighted the great heterogeneity within each cell compartment revealing previously unknown subpopulations of cells. In this review, we analyze how this fast technological evolution has improved our understanding of the biological processes with a particular focus on rare cells of the immune system.

Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation

Differentiation of lymphocytes is frequently accompanied by cell cycle changes, interplay that is of central importance for immunity but is still incompletely understood. Here, we interrogate and quantitatively model how proliferation is linked to differentiation in CD4+ T cells. We perform ex vivo single-cell RNA-sequencing of CD4+ T cells during a mouse model of infection that elicits a type 2 immune response and infer that the differentiated, cytokine-producing cells cycle faster than early activated precursor cells. To dissect this phenomenon quantitatively, we determine expression profiles across consecutive generations of differentiated and undifferentiated cells during Th2 polarization in vitro. We predict three discrete cell states, which we verify by single-cell quantitative PCR. Based on these three states, we extract rates of death, division and differentiation with a branching state Markov model to describe the cell population dynamics. From this multi-scale modelling, we infer a significant acceleration in proliferation from the intermediate activated cell state to the mature cytokine-secreting effector state. We confirm this acceleration both by live imaging of single Th2 cells and in an ex vivo Th1 malaria model by single-cell RNA-sequencing. The link between cytokine secretion and proliferation rate holds both in Th1 and Th2 cells in vivo and in vitro, indicating that this is likely a general phenomenon in adaptive immunity.BackgroundDifferentiation of lymphocytes is frequently accompanied by cell cycle changes, interplay that is of central importance for immunity but is still incompletely understood. Here, we interrogate and quantitatively model how proliferation is linked to differentiation in CD4+ T cells.ResultsWe perform ex vivo single-cell RNA-sequencing of CD4+ T cells during a mouse model of infection that elicits a type 2 immune response and infer that the differentiated, cytokine-producing cells cycle faster than early activated precursor cells. To dissect this phenomenon quantitatively, we determine expression profiles across consecutive generations of differentiated and undifferentiated cells during Th2 polarization in vitro. We predict three discrete cell states, which we verify by single-cell quantitative PCR. Based on these three states, we extract rates of death, division and differentiation with a branching state Markov model to describe the cell population dynamics. From this multi-scale modelling, we infer a significant acceleration in proliferation from the intermediate activated cell state to the mature cytokine-secreting effector state. We confirm this acceleration both by live imaging of single Th2 cells and in an ex vivo Th1 malaria model by single-cell RNA-sequencing.ConclusionThe link between cytokine secretion and proliferation rate holds both in Th1 and Th2 cells in vivo and in vitro, indicating that this is likely a general phenomenon in adaptive immunity.

An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation

BackgroundA proper balance between different T helper (Th) cell subsets is necessary for normal functioning of the adaptive immune system. Revealing key genes and pathways driving the differentiation to distinct Th cell lineages provides important insight into underlying molecular mechanisms and new opportunities for modulating the immune response. Previous computational methods to quantify and visualize kinetic differential expression data of three or more lineages to identify reciprocally regulated genes have relied on clustering approaches and regression methods which have time as a factor, but have lacked methods which explicitly model temporal behavior.ResultsWe studied transcriptional dynamics of human umbilical cord blood T helper cells cultured in absence and presence of cytokines promoting Th1 or Th2 differentiation. To identify genes that exhibit distinct lineage commitment dynamics and are specific for initiating differentiation to different Th cell subsets, we developed a novel computational methodology (LIGAP) allowing integrative analysis and visualization of multiple lineages over whole time-course profiles. Applying LIGAP to time-course data from multiple Th cell lineages, we identified and experimentally validated several differentially regulated Th cell subset specific genes as well as reciprocally regulated genes. Combining differentially regulated transcriptional profiles with transcription factor binding site and pathway information, we identified previously known and new putative transcriptional mechanisms involved in Th cell subset differentiation. All differentially regulated genes among the lineages together with an implementation of LIGAP are provided as an open-source resource.ConclusionsThe LIGAP method is widely applicable to quantify differential time-course dynamics of many types of datasets and generalizes to any number of conditions. It summarizes all the time-course measurements together with the associated uncertainty for visualization and manual assessment purposes. Here we identified novel human Th subset specific transcripts as well as regulatory mechanisms important for the initiation of the Th cell subset differentiation.

Simultaneously inferring T cell fate and clonality from single cell transcriptomes

The heterodimeric T cell receptor (TCR) comprises two protein chains that pair to determine the antigen specificity of each T lymphocyte. The enormous sequence diversity within TCR repertoires allows specific TCR sequences to be used as lineage markers for T cells that derive from a common progenitor. We have developed a computational method, called TraCeR, to reconstruct full-length, paired TCR sequences from T lymphocyte single-cell RNA-seq by combining existing assembly and alignment programs with a “synthetic genome” library comprising all possible TCR sequences. We validate this method with PCR to quantify its accuracy and sensitivity, and compare to other TCR sequencing methods. Our inferred TCR sequences reveal clonal relationships between T cells, which we put into the context of each cell’s functional state from the complete transcriptional landscape quantified from the remaining RNA-seq data. This provides a powerful tool to link T cell specificity with functional response in a variety of normal and pathological conditions. We demonstrate this by determining the distribution of members of expanded T cell clonotypes in response to Salmonella infection in the mouse. We show that members of the same clonotype span early activated CD4+ T cells, as well as mature effector and memory cells.

Cutting-edge single-cell genomics and modelling in immunology

More than a decade after the beginning of the genomic era that has completely reshaped medical and life sciences, we are now entering a new equally revolutionary phase, the single-cell era. To date, most of our biological knowledge has been obtained by populationlevel studies, based on the fundamental assumption that every cell within a defined population contributes equally to its characteristics and behaviour. As our understanding of the dynamics of cell populations improves, it is becoming increasingly evident that this assumption is not true in many cases. An important example is presented by the immune system, the function of which can be profoundly influenced by the activity of relatively rare cells such as antigen-specific T and B cells. The development of a plethora of single-cell technologies during the last few years has provided us with powerful tools to finally tackle this issue in a systematic way. These technologies span from transcriptomics and proteomics to imaging approaches enabling the study of living cells over time. In this Special Feature, we present a series of reviews that highlight how advances in both instrumentation and computational methodologies are transforming the study of cell biology and immunology. The inherent strength of these approaches is in the ability to resolve cellular heterogeneity at great detail in an unbiased way. This obviously opens up many new possibilities in the analyses of rare cell populations, as we discuss in our contribution.1 Importantly, single-cell resolution has also proved highly informative in studies on developmental and differentiation processes, during which cells can typically be found in a continuous gradient of transitional states. In the review by Cvejic,2 these principles are illustrated in the study of haematopoiesis. A conceptually related process with tremendous immunological significance is the diversification of T and B lymphocytes into numerous functionally specialized effector and memory cell subpopulations. The first wave of research reports has already indicated the great promise of single-cell approaches in dissecting this heterogeneity. The dense data sets generated in such experiments also provide useful opportunities for generation and validation of quantitative models of cell behaviour. In the review by Gerritsen and Pandit,3 the modelling approaches are discussed in the context of CD8 T-cell state transitions from naive to effector and memory states. When attempting to investigate the process of clonal expansion of lymphocytes, where a small number of precursor cells proliferate into expanded clones, single-cell live imaging can provide good insights into cell numbers, proliferation, death and differentiation rates. In their review, Polonsky et al.4 explore the possible applications of live imaging of single cells within microwell arrays and discuss their significant advances with respect to population analysis. One limitation of most of the single-cell technologies mentioned above lies in the lack of information about cell morphology and the original cell microenvironment. This drawback arises from the first step of sample preparation, which generally involves tissue dissociation with associated loss of any spatial information. Donati5, in his review, summarizes the development of various techniques that enable transcriptomic analysis of single cells, while retaining precise spatial information. These developments are important in the fields of stem cell biology and cancer. Finally, a unifying feature of all the single-cell -omics technologies is the large amount of high-dimensionality data that they produce. Therefore, parallel development of computational methods has been necessary. Woodhouse et al.6 discuss different methods for data mining. In particular they investigate methods to interpret and visualize transcriptomic single-cell data by focusing on the transcriptional regulatory networks. The need for new computational methods continues far into the future. At the same time, ongoing rapid technological development offers unforeseen possibilities and challenges to our thinking on how immunological questions can and should be addressed.

Holistic systems biology approaches to molecular mechanisms of human helper T cell differentiation to functionally distinct subsets.

Current knowledge of helper T cell differentiation largely relies on data generated from mouse studies. To develop therapeutical strategies combating human diseases, understanding the molecular mechanisms how human naïve T cells differentiate to functionally distinct T helper (Th) subsets as well as studies on human differentiated Th cell subsets is particularly valuable. Systems biology approaches provide a holistic view of the processes of T helper differentiation, enable discovery of new factors and pathways involved and generation of new hypotheses to be tested to improve our understanding of human Th cell differentiation and immune‐mediated diseases. Here, we summarize studies where high‐throughput systems biology approaches have been exploited to human primary T cells. These studies reveal new factors and signalling pathways influencing T cell differentiation towards distinct subsets, important for immune regulation. Such information provides new insights into T cell biology and into targeting immune system for therapeutic interventions.