Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia

Single-cell genomics-based immune and disease monitoring in blood malignancies

Achieving long-term disease control using therapeutic immunomodulation is a long-standing concept with a strong tradition in blood malignancies. Besides allogeneic hematopoietic stem cell transplantation that continues to provide potentially curative treatment for otherwise challenging diagnoses, recent years have seen impressive progress in immunotherapies for leukemias and lymphomas with immune checkpoint blockade, bispecific monoclonal antibodies, and CAR T cell therapies. Despite their success, non-response, relapse, and immune toxicities remain frequent, thus prioritizing the elucidation of the underlying mechanisms and identifying predictive biomarkers. The increasing availability of single-cell genomic tools now provides a system's immunology view to resolve the molecular and cellular mechanisms of immunotherapies at unprecedented resolution. Here, we review recent studies that leverage these technological advancements for tracking immune responses, the emergence of immune resistance, and toxicities. As single-cell immune monitoring tools evolve and become more accessible, we expect their wide adoption for routine clinical applications to catalyze more precise therapeutic steering of personal immune responses.

Mouse models of chronic lymphocytic leukemia and Richter transformation: what we have learnt and what we are missing

Although the chronic lymphocytic leukemia (CLL) treatment landscape has changed dramatically, unmet clinical needs are emerging, as CLL in many patients does not respond, becomes resistant to treatment, relapses during treatment, or transforms into Richter. In the majority of cases, transformation evolves the original leukemia clone into a diffuse large B-cell lymphoma (DLBCL). Richter transformation (RT) represents a dreadful clinical challenge with limited therapeutic opportunities and scarce preclinical tools. CLL cells are well known to highly depend on survival signals provided by the tumor microenvironment (TME). These signals enhance the frequency of immunosuppressive cells with protumor function, including regulatory CD4+ T cells and tumor-associated macrophages. T cells, on the other hand, exhibit features of exhaustion and profound functional defects. Overall immune dysfunction and immunosuppression are common features of patients with CLL. The interaction between malignant cells and TME cells can occur during different phases of CLL development and transformation. A better understanding of in vivo CLL and RT biology and the availability of adequate mouse models that faithfully recapitulate the progression of CLL and RT within their microenvironments are "conditio sine qua non" to develop successful therapeutic strategies. In this review, we describe the xenograft and genetic-engineered mouse models of CLL and RT, how they helped to elucidate the pathophysiology of the disease progression and transformation, and how they have been and might be instrumental in developing innovative therapeutic approaches to finally eradicate these malignancies.

Mouse models of CLL: In vivo modeling of disease initiation, progression, and transformation

Chronic lymphocytic leukemia (CLL) is a highly complex disease characterized by the proliferation of CD5+ B cells in lymphoid tissues. Current modern treatments have brought significant clinical benefits to CLL patients. However, there are still unmet needs. Patients relapse on Bruton's tyrosine kinase inhibitors and BCL2 inhibitors and often develop more aggressive diseases including Richter transformation (RT), an incurable complication of up to ∼10% patients. This evidence underscores the need for improved immunotherapies, combination treatment strategies, and predictive biomarkers. A mouse model that can recapitulate human CLL disease and certain components of the tumor immune microenvironment represents a promising preclinical tool for such purposes. In this review, we provide an overview of CRISPR-engineered and xenograft mouse models utilizing either cell lines, or primary CLL cells suitable for studies of key events driving the disease onset, progression and transformation of CLL. We also review how CRISPR/Cas9 established mouse models carrying loss-of-function lesions allow one to study key mutations driving disease progression. Finally, we discuss how next generation humanized mice might improve to generation of faithful xenograft mouse models of human CLL.

Unveiling aging dynamics in the hematopoietic system insights from single-cell technologies

As the demographic structure shifts towards an aging society, strategies aimed at slowing down or reversing the aging process become increasingly essential. Aging is a major predisposing factor for many chronic diseases in humans. The hematopoietic system, comprising blood cells and their associated bone marrow microenvironment, intricately participates in hematopoiesis, coagulation, immune regulation and other physiological phenomena. The aging process triggers various alterations within the hematopoietic system, serving as a spectrum of risk factors for hematopoietic disorders, including clonal hematopoiesis, immune senescence, myeloproliferative neoplasms and leukemia. The emerging single-cell technologies provide novel insights into age-related changes in the hematopoietic system. In this review, we summarize recent studies dissecting hematopoietic system aging using single-cell technologies. We discuss cellular changes occurring during aging in the hematopoietic system at the levels of the genomics, transcriptomics, epigenomics, proteomics, metabolomics and spatial multi-omics. Finally, we contemplate the future prospects of single-cell technologies, emphasizing the impact they may bring to the field of hematopoietic system aging research.

Stochastic modelling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones

Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present the first formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein-Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Interestingly, sublines previously observed to be resistant to anti-CTLA-4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression.

MNDA, a PYHIN factor involved in transcriptional regulation and apoptosis control in leukocytes

Inflammation control is critical during the innate immune response. Such response is triggered by the detection of molecules originating from pathogens or damaged host cells by pattern-recognition receptors (PRRs). PRRs subsequently initiate intra-cellular signalling through different pathways, resulting in i) the production of inflammatory cytokines, including type I interferon (IFN), and ii) the initiation of a cascade of events that promote both immediate host responses as well as adaptive immune responses. All human PYRIN and HIN-200 domains (PYHIN) protein family members were initially proposed to be PRRs, although this view has been challenged by reports that revealed their impact on other cellular mechanisms. Of relevance here, the human PYHIN factor myeloid nuclear differentiation antigen (MNDA) has recently been shown to directly control the transcription of genes encoding factors that regulate programmed cell death and inflammation. While MNDA is mainly found in the nucleus of leukocytes of both myeloid (neutrophils and monocytes) and lymphoid (B-cell) origin, its subcellular localization has been shown to be modulated in response to genotoxic agents that induce apoptosis and by bacterial constituents, mediators of inflammation. Prior studies have noted the importance of MNDA as a marker for certain forms of lymphoma, and as a clinical prognostic factor for hematopoietic diseases characterized by defective regulation of apoptosis. Abnormal expression of MNDA has also been associated with altered levels of cytokines and other inflammatory mediators. Refining our comprehension of the regulatory mechanisms governing the expression of MNDA and other PYHIN proteins, as well as enhancing our definition of their molecular functions, could significantly influence the management and treatment strategies of numerous human diseases. Here, we review the current state of knowledge regarding PYHIN proteins and their role in innate and adaptive immune responses. Emphasis will be placed on the regulation, function, and relevance of MNDA expression in the control of gene transcription and RNA stability during cell death and inflammation.

The molecular map of CLL and Richter's syndrome

Clonal expansion of B-cells, from the early stages of monoclonal B-cell lymphocytosis through to chronic lymphocytic leukemia (CLL), and then in some cases to Richter's syndrome (RS) provides a comprehensive model of cancer evolution, notable for the marked morphological transformation and distinct clinical phenotypes. High-throughput sequencing of large cohorts of patients and single-cell studies have generated a molecular map of CLL and more recently, of RS, yielding fundamental insights into these diseases and of clonal evolution. A selection of CLL driver genes have been functionally interrogated to yield novel insights into the biology of CLL. Such findings have the potential to impact patient care through risk stratification, treatment selection and drug discovery. However, this molecular map remains incomplete, with extant questions concerning the origin of the B-cell clone, the role of the TME, inter- and intra-compartmental heterogeneity and of therapeutic resistance mechanisms. Through the application of multi-modal single-cell technologies across tissues, disease states and clinical contexts, these questions can now be addressed with the answers holding great promise of generating translatable knowledge to improve patient care.

ZAP-70 augments tonic B-cell receptor and CCR7 signaling in IGHV-unmutated chronic lymphocytic leukemia

ABSTRACT

Expression of ZAP-70 in a subset of patients with chronic lymphocytic leukemia (CLL) positively correlates with the absence of immunoglobulin heavy-chain gene (IGHV) mutations and is indicative of a more active disease and shorter treatment-free survival. We recently demonstrated that ZAP-70 regulates the constitutive expression of CCL3 and CCL4, activation of AKT, and expression of MYC in the absence of an overt B-cell receptor (BCR) signal, bona fide functions of BCR activation. We, here, provide evidence that these features relate to the presence of a constitutive tonic BCR signal, exclusively found in IGHV-unmutated CLL and dependent on the ZAP-70-mediated activation of AKT and its downstream target GSK-3β. These findings are associated with increased steady-state activation of CD19 and SRC. Notably this tonic BCR signal is not present in IGHV-mutated CLL cells, discordantly expressing ZAP-70. Results of quantitative mass spectrometry and phosphoprotein analyses indicate that this ZAP-70-dependent, tonic BCR signal regulates CLL cell migration through phosphorylation of LCP1 on serine-5. Indeed, we show that CCL19- and CCL21-induced chemotaxis is regulated by and dependent on the expression of ZAP-70 through its function to enhance CCR7 signaling to LCP1. Thus, our data demonstrate that ZAP-70 converges a tonic BCR signal, exclusively present in IGHV-unmutated CLL and CCR7-mediated chemotaxis.

Decoding leukemia at the single-cell level: clonal architecture, classification, microenvironment, and drug resistance

Leukemias are refractory hematological malignancies, characterized by marked intrinsic heterogeneity which poses significant obstacles to effective treatment. However, traditional bulk sequencing techniques have not been able to effectively unravel the heterogeneity among individual tumor cells. With the emergence of single-cell sequencing technology, it has bestowed upon us an unprecedented resolution to comprehend the mechanisms underlying leukemogenesis and drug resistance across various levels, including the genome, epigenome, transcriptome and proteome. Here, we provide an overview of the currently prevalent single-cell sequencing technologies and a detailed summary of single-cell studies conducted on leukemia, with a specific focus on four key aspects: (1) leukemia's clonal architecture, (2) frameworks to determine leukemia subtypes, (3) tumor microenvironment (TME) and (4) the drug-resistant mechanisms of leukemia. This review provides a comprehensive summary of current single-cell studies on leukemia and highlights the markers and mechanisms that show promising clinical implications for the diagnosis and treatment of leukemia.

Epigenomic Characterization of Lymphoid Neoplasms

Lymphoid neoplasms represent a heterogeneous group of disease entities and subtypes with markedly different molecular and clinical features. Beyond genetic alterations, lymphoid tumors also show widespread epigenomic changes. These severely affect the levels and distribution of DNA methylation, histone modifications, chromatin accessibility, and three-dimensional genome interactions. DNA methylation stands out as a tracer of cell identity and memory, as B cell neoplasms show epigenetic imprints of their cellular origin and proliferative history, which can be quantified by an epigenetic mitotic clock. Chromatin-associated marks are informative to uncover altered regulatory regions and transcription factor networks contributing to the development of distinct lymphoid tumors. Tumor-intrinsic epigenetic and genetic aberrations cooperate and interact with microenvironmental cells to shape the transcriptome at different phases of lymphoma evolution, and intraclonal heterogeneity can now be characterized by single-cell profiling. Finally, epigenetics offers multiple clinical applications, including powerful diagnostic and prognostic biomarkers as well as therapeutic targets.

De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data

Identifying expressed somatic mutations from single-cell RNA sequencing data de novo is challenging but highly valuable. We propose RESA - Recurrently Expressed SNV Analysis, a computational framework to identify expressed somatic mutations from scRNA-seq data. RESA achieves an average precision of 0.77 on three in silico spike-in datasets. In extensive benchmarking against existing methods using 19 datasets, RESA consistently outperforms them. Furthermore, we applied RESA to analyze intratumor mutational heterogeneity in a melanoma drug resistance dataset. By enabling high precision detection of expressed somatic mutations, RESA substantially enhances the reliability of mutational analysis in scRNA-seq. RESA is available at https://github.com/ShenLab-Genomics/RESA .

Barcoded multiple displacement amplification for high coverage sequencing in spatial genomics

Determining mutational landscapes in a spatial context is essential for understanding genetically heterogeneous cell microniches. Current approaches, such as Multiple Displacement Amplification (MDA), offer high genome coverage but limited multiplexing, which hinders large-scale spatial genomic studies. Here, we introduce barcoded MDA (bMDA), a technique that achieves high-coverage genomic analysis of low-input DNA while enhancing the multiplexing capabilities. By incorporating cell barcodes during MDA, bMDA streamlines library preparation in one pot, thereby overcoming a key bottleneck in spatial genomics. We apply bMDA to the integrative spatial analysis of triple-negative breast cancer tissues by examining copy number alterations, single nucleotide variations, structural variations, and kataegis signatures for each spatial microniche. This enables the assessment of subclonal evolutionary relationships within a spatial context. Therefore, bMDA has emerged as a scalable technology with the potential to advance the field of spatial genomics significantly.

The end of the beginning: application of single-cell sequencing to chronic lymphocytic leukemia

Single-cell analysis has emerged over the past decade as a transformative technology informative for the systematic analysis of complex cell populations such as in cancers and the tumor immune microenvironment. The methodologic and analytical advancements in this realm have evolved rapidly, scaling from but a few cells at its outset to the current capabilities of processing and analyzing hundreds of thousands of individual cells at a time. The types of profiling attainable at individual cell resolution now range from genetic and transcriptomic characterization and extend to epigenomic and spatial analysis. Additionally, the increasing ability to achieve multiomic integration of these data layers now yields ever richer insights into diverse molecular disease subtypes and the patterns of cellular circuitry on a per-cancer basis. Over the years, chronic lymphocytic leukemia (CLL) consistently has been at the forefront of genomic investigation, given the ready accessibility of pure leukemia cells and immune cells from circulating blood of patients with this disease. Herein, we review the recent forays into the application of single-cell analysis to CLL, which are already revealing a new understanding of the natural progression of CLL, the impact of novel therapies, and the interactions with coevolving nonmalignant immune cell populations. As we emerge from the end of the beginning of this technologic revolution, CLL stands poised to reap the benefits of single-cell analysis from the standpoints of uncovering fresh fundamental biological knowledge and of providing a path to devising regimens of personalized diagnosis, treatment, and monitoring.

Single cell RNA-sequencing: A powerful yet still challenging technology to study cellular heterogeneity

Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic 'average' cannot outright be used as representative of the 'average cell'. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single-cell RNA sequencing (scRNA-Seq) enables the comparison of the transcriptomes of individual cells. This provides high-resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context-specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA-Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA-Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research.

Towards precision medicine in lymphoid malignancies

Careful histopathologic examination remains the cornerstone in the diagnosis of the clinically and biologically heterogeneous group of lymphoid malignancies. However, recent advances in genomic and epigenomic characterization using high-throughput technologies have significantly improved our understanding of these tumors. Although no single genomic alteration is completely specific for a lymphoma entity, some alterations are highly recurrent in certain entities and thus can provide complementary diagnostic information when integrated in the hematopathological diagnostic workup. Moreover, other alterations may provide important information regarding the clinical course, that is, prognostic or risk-stratifying markers, or response to treatment, that is, predictive markers, which may allow tailoring of the patient's treatment based on (epi)genetic characteristics. In this review, we will focus on clinically relevant diagnostic, prognostic, and predictive biomarkers identified in more common types of B-cell malignancies, and discuss how diagnostic assays designed for comprehensive molecular profiling may pave the way for the implementation of precision diagnostics/medicine approaches. We will also discuss future directions in this rapidly evolving field, including the application of single-cell sequencing and other omics technologies, to decipher clonal dynamics and evolution in lymphoid malignancies.

WNK1 in Malignant Behaviors: A Potential Target for Cancer?

Metastasis is the major cause of mortality in cancer patients. Analyses of mouse models and patient data have implicated the protein kinase WNK1 as one of a handful of genes uniquely linked to a subset of invasive cancers. WNK1 signaling pathways are widely implicated in the regulation of ion co-transporters and in controlling cell responses to osmotic stress. In this review we will discuss its actions in tumor malignancy in human cancers and present evidence for its function in invasion, migration, angiogenesis and mesenchymal transition.

New insights into Human Hematopoietic Stem and Progenitor Cells via Single-Cell Omics

Residing at the apex of the hematopoietic hierarchy, hematopoietic stem and progenitor cells (HSPCs) give rise to all mature blood cells. In the last decade, significant progress has been made in single-cell RNA sequencing as well as multi-omics technologies that have facilitated elucidation of the heterogeneity of previously defined human HSPCs. From the embryonic stage through the adult stage to aging, single-cell studies have enabled us to trace the origins of hematopoietic stem cells (HSCs), demonstrating different hematopoietic differentiation during development, as well as identifying novel cell populations. In both hematological benign diseases and malignancies, single-cell omics technologies have begun to reveal tissue heterogeneity and have permitted mapping of microenvironmental ecosystems and tracking of cell subclones, thereby greatly broadening our understanding of disease development. Furthermore, advances have also been made in elucidating the molecular mechanisms for relapse and identifying therapeutic targets of hematological disorders and other non-hematological diseases. Extensive exploration of hematopoiesis at the single-cell level may thus have great potential for broad clinical applications of HSPCs, as well as disease prognosis.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Single-Cell Technologies to Decipher the Immune Microenvironment in Myeloid Neoplasms: Perspectives and Opportunities

Myeloid neoplasms (MN) are heterogeneous clonal disorders arising from the expansion of hematopoietic stem and progenitor cells. In parallel with genetic and epigenetic dynamics, the immune system plays a critical role in modulating tumorigenesis, evolution and therapeutic resistance at the various stages of disease progression. Single-cell technologies represent powerful tools to assess the cellular composition of the complex tumor ecosystem and its immune environment, to dissect interactions between neoplastic and non-neoplastic components, and to decipher their functional heterogeneity and plasticity. In addition, recent progress in multi-omics approaches provide an unprecedented opportunity to study multiple molecular layers (DNA, RNA, proteins) at the level of single-cell or single cellular clones during disease evolution or in response to therapy. Applying single-cell technologies to MN holds the promise to uncover novel cell subsets or phenotypic states and highlight the connections between clonal evolution and immune escape, which is crucial to fully understand disease progression and therapeutic resistance. This review provides a perspective on the various opportunities and challenges in the field, focusing on key questions in MN research and discussing their translational value, particularly for the development of more efficient immunotherapies.

Single-cell RNA sequencing to track novel perspectives in HSC heterogeneity

As the importance of cell heterogeneity has begun to be emphasized, single-cell sequencing approaches are rapidly adopted to study cell heterogeneity and cellular evolutionary relationships of various cells, including stem cell populations. The hematopoietic stem and progenitor cell (HSPC) compartment contains HSC hematopoietic stem cells (HSCs) and distinct hematopoietic cells with different abilities to self-renew. These cells perform their own functions to maintain different hematopoietic lineages. Undeniably, single-cell sequencing approaches, including single-cell RNA sequencing (scRNA-seq) technologies, empower more opportunities to study the heterogeneity of normal and pathological HSCs. In this review, we discuss how these scRNA-seq technologies contribute to tracing origin and lineage commitment of HSCs, profiling the bone marrow microenvironment and providing high-resolution dissection of malignant hematopoiesis, leading to exciting new findings in HSC biology.

Natural Barcodes for Longitudinal Single Cell Tracking of Leukemic and Immune Cell Dynamics

Blood malignancies provide unique opportunities for longitudinal tracking of disease evolution following therapeutic bottlenecks and for the monitoring of changes in anti-tumor immunity. The expanding development of multi-modal single-cell sequencing technologies affords newer platforms to elucidate the mechanisms underlying these processes at unprecedented resolution. Furthermore, the identification of molecular events that can serve as in-vivo barcodes now facilitate the tracking of the trajectories of malignant and of immune cell populations over time within primary human samples, as these permit unambiguous identification of the clonal lineage of cell populations within heterogeneous phenotypes. Here, we provide an overview of the potential for chromosomal copy number changes, somatic nuclear and mitochondrial DNA mutations, single nucleotide polymorphisms, and T and B cell receptor sequences to serve as personal natural barcodes and review technical implementations in single-cell analysis workflows. Applications of these methodologies include the study of acquired therapeutic resistance and the dissection of donor- and host cellular interactions in the context of allogeneic hematopoietic stem cell transplantation.

Clonal Evolution of High-Risk Chronic Lymphocytic Leukemia: A Contemporary Perspective

Clonal evolution represents the natural process through which cancer cells continuously search for phenotypic advantages that enable them to develop and expand within microenvironmental constraints. In chronic lymphocytic leukemia (CLL), clonal evolution underpins leukemic progression and therapeutic resistance, with differences in clonal evolutionary dynamics accounting for its characteristically diverse clinical course. The past few years have witnessed profound changes in our understanding of CLL clonal evolution, facilitated by a maturing definition of high-risk CLL and an increasing sophistication of next-generation sequencing technology. In this review, we offer a modern perspective on clonal evolution of high-risk CLL, highlighting recent discoveries, paradigm shifts and unresolved questions. We appraise recent advances in our understanding of the molecular basis of CLL clonal evolution, focusing on the genetic and non-genetic sources of intratumoral heterogeneity, as well as tumor-immune dynamics. We review the technological innovations, particularly in single-cell technology, which have fostered these advances and represent essential tools for future discoveries. In addition, we discuss clonal evolution within several contexts of particular relevance to contemporary clinical practice, including the settings of therapeutic resistance to CLL targeted therapy and immunotherapy, as well as Richter transformation of CLL to high-grade lymphoma.

Distinction of lymphoid and myeloid clonal hematopoiesis

Clonal hematopoiesis (CH) results from somatic genomic alterations that drive clonal expansion of blood cells. Somatic gene mutations associated with hematologic malignancies detected in hematopoietic cells of healthy individuals, referred to as CH of indeterminate potential (CHIP), have been associated with myeloid malignancies, while mosaic chromosomal alterations (mCAs) have been associated with lymphoid malignancies. Here, we analyzed CHIP in 55,383 individuals and autosomal mCAs in 420,969 individuals with no history of hematologic malignancies in the UK Biobank and Mass General Brigham Biobank. We distinguished myeloid and lymphoid somatic gene mutations, as well as myeloid and lymphoid mCAs, and found both to be associated with risk of lineage-specific hematologic malignancies. Further, we performed an integrated analysis of somatic alterations with peripheral blood count parameters to stratify the risk of incident myeloid and lymphoid malignancies. These genetic alterations can be readily detected in clinical sequencing panels and used with blood count parameters to identify individuals at high risk of developing hematologic malignancies.

Somatic variant analysis suite: copy number variation clonal visualization online platform for large-scale single-cell genomics

The recent advance of single-cell copy number variation (CNV) analysis plays an essential role in addressing intratumor heterogeneity, identifying tumor subgroups and restoring tumor-evolving trajectories at single-cell scale. Informative visualization of copy number analysis results boosts productive scientific exploration, validation and sharing. Several single-cell analysis figures have the effectiveness of visualizations for understanding single-cell genomics in published articles and software packages. However, they almost lack real-time interaction, and it is hard to reproduce them. Moreover, existing tools are time-consuming and memory-intensive when they reach large-scale single-cell throughputs. We present an online visualization platform, single-cell Somatic Variant Analysis Suite (scSVAS), for real-time interactive single-cell genomics data visualization. scSVAS is specifically designed for large-scale single-cell genomic analysis that provides an arsenal of unique functionalities. After uploading the specified input files, scSVAS deploys the online interactive visualization automatically. Users may conduct scientific discoveries, share interactive visualizations and download high-quality publication-ready figures. scSVAS provides versatile utilities for managing, investigating, sharing and publishing single-cell CNV profiles. We envision this online platform will expedite the biological understanding of cancer clonal evolution in single-cell resolution. All visualizations are publicly hosted at https://sc.deepomics.org.

Distinct evolutionary paths in chronic lymphocytic leukemia during resistance to the graft-versus-leukemia effect

Leukemic relapse remains a major barrier to successful allogeneic hematopoietic stem cell transplantation (allo-HSCT) for aggressive hematologic malignancies. The basis for relapse of advanced lymphoid malignancies remains incompletely understood and may involve escape from the graft-versus-leukemia (GvL) effect. We hypothesized that for patients with chronic lymphocytic leukemia (CLL) treated with allo-HSCT, leukemic cell-intrinsic features influence transplant outcomes by directing the evolutionary trajectories of CLL cells. Integrated genetic, transcriptomic, and epigenetic analyses of CLL cells from 10 patients revealed that the clinical kinetics of post-HSCT relapse are shaped by distinct molecular dynamics. Early relapses after allo-HSCT exhibited notable genetic stability; single CLL cell transcriptional analysis demonstrated a cellular heterogeneity that was static over time. In contrast, CLL cells relapsing late after allo-HSCT displayed notable genetic evolution and evidence of neoantigen depletion, consistent with marked single-cell transcriptional shifts that were unique to each patient. We observed a greater rate of epigenetic change for late relapses not seen in early relapses or relapses after chemotherapy alone, suggesting that the selection pressures of the GvL bottleneck are unlike those imposed by chemotherapy. No selective advantage for human leukocyte antigen (HLA) loss was observed, even when present in pretransplant subpopulations. Gain of stem cell modules was a common signature associated with leukemia relapse regardless of posttransplant relapse kinetics. These data elucidate the biological pathways that underlie GvL resistance and posttransplant relapse.

Prognostic and Clinical Implications of WNK Lysine Deficient Protein Kinase 1 Expression in Patients With Hepatocellular Carcinoma

BACKGROUND/AIM

Hepatocellular carcinoma (HCC) is a particularly malignant form of cancer prevalent throughout the world; however, there is a pressing need for HCC biomarkers to facilitate prognosis and risk assessment.

PATIENTS AND METHODS

This paper reports on the potential prognostic value of WNK lysine deficient protein kinase 1 (WNK1) in cases of HCC. We analyzed the expression of WNK1 at the mRNA level using omics data from the UALCAN database. We then verified our findings through the immunohistochemical (IHC) staining of various human cancer tissue as well as 59 HCC samples paired with corresponding normal tissues. The prognostic value of mRNA or protein expression by WNK1 was evaluated using the Kaplan-Meier method.

RESULTS

Initial screening results revealed significantly higher WNK1 expression levels in HCC tissue compared to normal tissue. Verification using the paired HCC samples confirmed that the expression of WNK1 was indeed significantly higher in HCC tissue samples than in adjacent normal tissues. High WNK1 expression levels were significantly correlated with clinicopathological variables, including gender and histologic grade. Kaplan-Meier survival analysis revealed that high WNK1 expression levels were associated with poor HCC prognosis. Finally, univariate and multivariate analysis identified WNK1 as a prognostic factor for TNM stage in cases of HCC.

CONCLUSION

In summary, WNK1 is overexpressed at the mRNA and protein levels, and correlated with poor prognosis. Thus, WNK1 expression could potentially be used as a biomarker in HCC prognosis.

Hematopoietic mosaic chromosomal alterations increase the risk for diverse types of infection

Age is the dominant risk factor for infectious diseases, but the mechanisms linking age to infectious disease risk are incompletely understood. Age-related mosaic chromosomal alterations (mCAs) detected from genotyping of blood-derived DNA, are structural somatic variants indicative of clonal hematopoiesis, and are associated with aberrant leukocyte cell counts, hematological malignancy, and mortality. Here, we show that mCAs predispose to diverse types of infections. We analyzed mCAs from 768,762 individuals without hematological cancer at the time of DNA acquisition across five biobanks. Expanded autosomal mCAs were associated with diverse incident infections (hazard ratio (HR) 1.25; 95% confidence interval (CI) = 1.15-1.36; P = 1.8 × 10-7), including sepsis (HR 2.68; 95% CI = 2.25-3.19; P = 3.1 × 10-28), pneumonia (HR 1.76; 95% CI = 1.53-2.03; P = 2.3 × 10-15), digestive system infections (HR 1.51; 95% CI = 1.32-1.73; P = 2.2 × 10-9) and genitourinary infections (HR 1.25; 95% CI = 1.11-1.41; P = 3.7 × 10-4). A genome-wide association study of expanded mCAs identified 63 loci, which were enriched at transcriptional regulatory sites for immune cells. These results suggest that mCAs are a marker of impaired immunity and confer increased predisposition to infections.

CLL dedifferentiation to clonally related myeloid cells

Common progenitor cells exist in clonally related concomitant chronic lymphocytic leukemia and acute myeloid leukemias. CLL cells dedifferentiated to clonally related myeloid cells posttransplantation.

Single-cell technologies and analyses in hematopoiesis and hematological malignancies

In recent years, single-cell technologies have emerged as breakthrough techniques that enable the characterization of hematopoietic cell populations of normal and malignant tissue samples and will be combined in the near future with bulk technologies, currently used in clinical practice, to improve diagnosis, prognosis, and the search for novel molecular targets. These single-cell methods have the advantage of not masking cell-to-cell variation features and involve the study of genetic, epigenetic, transcriptional, and proteomic landscapes from a single-cell perspective. Latest advances in this field have enabled the development of novel strategies that significantly increase both sensitivity and high throughput. In this review, we emphasize emerging techniques aimed at assessing individual or multiomic parameters at single-cell resolution and analyze how these technologies have helped us understand hematopoietic variability and identify unknown and/or rare subpopulations. We also summarize the impact of these single-cell profiling strategies on the characterization of cell diversity within the tumor and the clonal evolution of multiple hematological malignancies in samples from untreated and treated patients, which provide valuable information for diagnosis, prognosis, and future treatments and explain why current therapies may fail. However, despite these improvements, new challenges lie ahead.

Single-cell sequencing technology in tumor research

Tumor heterogeneity is a key characteristic of malignant tumors and a significant obstacle in cancer treatment and research. Although bulk tissue sequencing has wide coverage and high accuracy, it can only represent the dominant cell signal information of each sample, while masking the unique gene expression of rare cells; therefore it cannot represent genes that are unstable within a subgroup, but unchanged in a majority of cells. With the progress of genomic technology, the emergence of single-cell sequencing (SCS) has effectively solved the above problem. Genetic, transcriptomic and epigenetic sequencing at the single-cell level provides an important basis for us to correctly classify the cell subsets of heterogeneous tumor populations and to reveal the process of complex changes in tumor cells at the molecular level. Single-cell sequencing technology has been applied to the field of cancer, revealing exciting discoveries in the potential mechanisms of tumor driver gene mutation, clonal evolution, invasion and metastasis. It also provides favorable conditions for developing new tumor biomarkers and providing more accurate and individualized targeted tumor therapy. Herein, we review the steps and methods of single-cell sequencing and highlight the application of SCS in tumor diagnosis and clinical treatment.

Multiple Myeloma: Heterogeneous in Every Way

Simple Summary

With the development of modern therapies in multiple myeloma, prognosis stratification is becoming an indispensable tool for the choice of treatment between patients. Many factors influence the prognosis in multiple myeloma; scores, mainly based on biochemical parameters and cytogenetics, have been proposed to discriminate patients. However, these scores are not perfect and fail to predict some patients’ outcomes. In this review, we describe current evaluated factors and their limitations. In the second part, we address factors with an impact on treatment escape and prognosis, but which are not available routinely yet.

Abstract

Multiple myeloma (MM) is a hematological malignancy characterized by the accumulation of tumor plasma cells (PCs) in the bone marrow (BM). Despite considerable advances in terms of treatment, patients’ prognosis is still very heterogeneous. Cytogenetics and minimal residual disease both have a major impact on prognosis. However, they do not explain all the heterogeneity seen in the outcomes. Their limitations are the result of the emergence of minor subclones missed at diagnosis, detected by sensible methods such as single-cell analysis, but also the non-exploration in the routine practice of the spatial heterogeneity between different clones according to the focal lesions. Moreover, biochemical parameters and cytogenetics do not reflect the whole complexity of MM. Gene expression is influenced by a tight collaboration between cytogenetic events and epigenetic regulation. The microenvironment also has an important impact on the development and the progression of the disease. Some of these determinants have been described as independent prognostic factors and could be used to more accurately predict patient prognosis and response to treatment.

Joint profiling of DNA and proteins in single cells to dissect genotype-phenotype associations in leukemia

Studies of acute myeloid leukemia rely on DNA sequencing and immunophenotyping by flow cytometry as primary tools for disease characterization. However, leukemia tumor heterogeneity complicates integration of DNA variants and immunophenotypes from separate measurements. Here we introduce DAb-seq, a technology for simultaneous capture of DNA genotype and cell surface phenotype from single cells at high throughput, enabling direct profiling of proteogenomic states in tens of thousands of cells. To demonstrate the approach, we analyze the disease of three patients with leukemia over multiple treatment timepoints and disease recurrences. We observe complex genotype-phenotype dynamics that illustrate the subtlety of the disease process and the degree of incongruity between blast cell genotype and phenotype in different clinical scenarios. Our results highlight the importance of combined single-cell DNA and protein measurements to fully characterize the heterogeneity of leukemia.

Allele-specific expression: applications in cancer and technical considerations

Allele-specific gene expression can influence disease traits. Non-coding germline genetic variants that alter regulatory elements can cause allele-specific gene expression and contribute to cancer susceptibility. In tumors, both somatic copy number alterations and somatic single nucleotide variants have been shown to lead to allele-specific expression of genes, many of which are considered drivers of tumor growth. Here, we review recent studies revealing the pervasive presence of this phenomenon in cancer susceptibility and progression. Furthermore, we underscore the importance of careful experimental design and computational analysis for accurate allelic expression quantification and avoidance of false positives. Finally, we discuss additional methodological challenges encountered in cancer studies and in the burgeoning field of single-cell transcriptomics.

Single-cell RNA Sequencing in Immunology

The complex immune system is involved in multiple pathological processes. Single-cell RNA sequencing (scRNA-seq) is able to analyze complex cell mixtures correct to a single cell and single molecule, thus is qualified to analyze immune reactions in several diseases. In recent years, scRNA-seq has been applied in many researching fields and has presented many innovative results. In this review, we intend to provide an overview of single-cell RNA sequencing applications in immunology and a prospect of future directions.

Hematopoietic mosaic chromosomal alterations and risk for infection among 767,891 individuals without blood cancer

Age is the dominant risk factor for infectious diseases, but the mechanisms linking the two are incompletely understood1,2. Age-related mosaic chromosomal alterations (mCAs) detected from blood-derived DNA genotyping, are structural somatic variants associated with aberrant leukocyte cell counts, hematological malignancy, and mortality3-11. Whether mCAs represent independent risk factors for infection is unknown. Here we use genome-wide genotyping of blood DNA to show that mCAs predispose to diverse infectious diseases. We analyzed mCAs from 767,891 individuals without hematological cancer at DNA acquisition across four countries. Expanded mCA (cell fraction >10%) prevalence approached 4% by 60 years of age and was associated with diverse incident infections, including sepsis, pneumonia, and coronavirus disease 2019 (COVID-19) hospitalization. A genome-wide association study of expanded mCAs identified 63 significant loci. Germline genetic alleles associated with expanded mCAs were enriched at transcriptional regulatory sites for immune cells. Our results link mCAs with impaired immunity and predisposition to infections. Furthermore, these findings may also have important implications for the ongoing COVID-19 pandemic, particularly in prioritizing individual preventive strategies and evaluating immunization responses.

Hematopoietic mosaic chromosomal alterations and risk for infection among 767,891 individuals without blood cancer

Age is the dominant risk factor for infectious diseases, but the mechanisms linking the two are incompletely understood1,2. Age-related mosaic chromosomal alterations (mCAs) detected from blood-derived DNA genotyping, are structural somatic variants associated with aberrant leukocyte cell counts, hematological malignancy, and mortality3-11. Whether mCAs represent independent risk factors for infection is unknown. Here we use genome-wide genotyping of blood DNA to show that mCAs predispose to diverse infectious diseases. We analyzed mCAs from 767,891 individuals without hematological cancer at DNA acquisition across four countries. Expanded mCA (cell fraction >10%) prevalence approached 4% by 60 years of age and was associated with diverse incident infections, including sepsis, pneumonia, and coronavirus disease 2019 (COVID-19) hospitalization. A genome-wide association study of expanded mCAs identified 63 significant loci. Germline genetic alleles associated with expanded mCAs were enriched at transcriptional regulatory sites for immune cells. Our results link mCAs with impaired immunity and predisposition to infections. Furthermore, these findings may also have important implications for the ongoing COVID-19 pandemic, particularly in prioritizing individual preventive strategies and evaluating immunization responses.

Modeling cancer progression using human pluripotent stem cell-derived cells and organoids

Conventional cancer cell lines and animal models have been mainstays of cancer research. More recently, human pluripotent stem cells (hPSCs) and hPSC-derived organoid technologies, together with genome engineering approaches, have provided a complementary platform to model cancer progression. Here, we review the application of these technologies in cancer modeling with respect to the cell-of-origin, cancer propagation, and metastasis. We further discuss the benefits and challenges accompanying the use of hPSC models for cancer research and discuss their broad applicability in drug discovery, biomarker identification, decoding molecular mechanisms, and the deconstruction of clonal and intra-tumoral heterogeneity. In summary, hPSC-derived organoids provide powerful models to recapitulate the pathogenic states in cancer and to perform drug discovery.

Single-cell transcriptomics in cancer: computational challenges and opportunities

Intratumor heterogeneity is a common characteristic across diverse cancer types and presents challenges to current standards of treatment. Advancements in high-throughput sequencing and imaging technologies provide opportunities to identify and characterize these aspects of heterogeneity. Notably, transcriptomic profiling at a single-cell resolution enables quantitative measurements of the molecular activity that underlies the phenotypic diversity of cells within a tumor. Such high-dimensional data require computational analysis to extract relevant biological insights about the cell types and states that drive cancer development, pathogenesis, and clinical outcomes. In this review, we highlight emerging themes in the computational analysis of single-cell transcriptomics data and their applications to cancer research. We focus on downstream analytical challenges relevant to cancer research, including how to computationally perform unified analysis across many patients and disease states, distinguish neoplastic from nonneoplastic cells, infer communication with the tumor microenvironment, and delineate tumoral and microenvironmental evolution with trajectory and RNA velocity analysis. We include discussions of challenges and opportunities for future computational methodological advancements necessary to realize the translational potential of single-cell transcriptomic profiling in cancer.

The actin-bundling protein L-plastin-A double-edged sword: Beneficial for the immune response, maleficent in cancer

The dynamic organization of the actin cytoskeleton into bundles and networks is orchestrated by a large variety of actin-binding proteins. Among them, the actin-bundling protein L-plastin is normally expressed in hematopoietic cells, where it is involved in the immune response. However, L-plastin is also often ectopically expressed in malignant cancer cells of non-hematopoietic origin and is even considered as a marker for cancer progression. Post-translational modification modulates L-plastin activity. In particular, L-plastin Ser5 phosphorylation has been shown to be important for the immune response in leukocytes as well as for invasion and metastasis formation of carcinoma cells. This chapter discusses the physiological and pathological role of L-plastin with a special focus on the importance of L-plastin Ser5 phosphorylation for the protein functions. The potential use of Ser5 phosphorylated L-plastin as a biomarker and/or therapeutic target will be evoked.

Surveying brain tumor heterogeneity by single-cell RNA-sequencing of multi-sector biopsies

Brain tumors are among the most challenging human tumors for which the mechanisms driving progression and heterogeneity remain poorly understood. We combined single-cell RNA-seq with multi-sector biopsies to sample and analyze single-cell expression profiles of gliomas from 13 Chinese patients. After classifying individual cells, we generated a spatial and temporal landscape of glioma that revealed the patterns of invasion between the different sub-regions of gliomas. We also used single-cell inferred copy number variations and pseudotime trajectories to inform on the crucial branches that dominate tumor progression. The dynamic cell components of the multi-region biopsy analysis allowed us to spatially deconvolute with unprecedented accuracy the transcriptomic features of the core and those of the periphery of glioma at single-cell level. Through this rich and geographically detailed dataset, we were also able to characterize and construct the chemokine and chemokine receptor interactions that exist among different tumor and non-tumor cells. This study provides the first spatial-level analysis of the cellular states that characterize human gliomas. It also presents an initial molecular map of the cross-talks between glioma cells and the surrounding microenvironment with single-cell resolution.

Single-Cell RNA Sequencing in Hematological Diseases

Hematological diseases, including leukemia, lymphoma, and multiple myeloma, are characterized by high heterogeneity with diverse cellular subpopulations. Single-cell RNA sequencing (scRNA-seq), a transformational technology, provides deep insights into cell-to-cell variation in tumor and microenvironment, allows high-resolution dissection of the pathogenic mechanisms of diseases, and affords potential clinical utilities. Recent developments in single-cell transcriptomics and associated technologies and their applications in hematological disorders for unraveling cellular subpopulations, disease pathogenesis, patient stratification, and therapeutic responses are summarized.

Advancing Cancer Research and Medicine with Single-Cell Genomics

Single-cell sequencing (SCS) has impacted many areas of cancer research and improved our understanding of intratumor heterogeneity, the tumor microenvironment, metastasis, and therapeutic resistance. The development and refinement of SCS technologies has led to massive reductions in costs, increased cell throughput, and improved reproducibility, paving the way for clinical applications. However, before translational applications can be realized, there are a number of logistical and technical challenges that must be overcome. This review discusses past cancer research studies, emerging technologies, and future clinical applications that are bound to transform cancer medicine.

Chronic lymphocytic leukemia with TP53 gene alterations: a detailed clinicopathologic analysis

TP53 alteration in chronic lymphocytic leukemia indicates a high-risk disease that is usually refractory to chemotherapy. It may be caused by deletion of 17p involving the loss of TP53 gene, which occurs in low percentage of patients at diagnosis but can be acquired as the disease progresses. Since patients may harbor TP53 mutation without chromosome 17p deletion, consensus recommendations call for both cytogenetic and PCR mutation analysis of TP53 in chronic lymphocytic leukemia. We conducted a single-institution retrospective study to investigate the clinicopathologic features of chronic lymphocytic leukemia with TP53 alterations as well as the utility of different diagnostic modalities to identify p53 alterations. Forty percent of chronic lymphocytic leukemia patients with TP53 alterations demonstrated atypical lymphocytes with cleaved/irregularly shaped nuclei and/or large atypical lymphoid cells with abundant cytoplasm in the peripheral blood. Progression was also observed in lymph node and bone marrow samples (21% with Richter transformation; 33% with findings suggestive of "accelerated phase" of chronic lymphocytic leukemia including prominent proliferation centers and/or increased numbers of prolymphocytes). However, the presence of the morphologic features suggestive of "accelerated phase" had no effect on overall survival within the chronic lymphocytic leukemia group with TP53 abnormalities (p > 0.05). As previously reported by others, a subset of patients with TP53 alterations were only identified by either PCR mutation analysis (12%) or cytogenetic studies (14%). p53 immunostain positivity was only identified in approximately half of the patients with TP53 alterations identified by either method, and it failed to identify any additional patients with p53 abnormalities. In summary, chronic lymphocytic leukemia patients with TP53 alterations frequently show atypical morphologic features. Use of multiple modalities to identify p53 abnormalities is recommended to ensure optimal sensitivity and specificity.

CRISPR/Cas9-generated models uncover therapeutic vulnerabilities of del(11q) CLL cells to dual BCR and PARP inhibition

The deletion of 11q (del(11q)) invariably comprises ATM gene in chronic lymphocytic leukemia (CLL). Concomitant mutations in this gene in the remaining allele have been identified in 1/3 of CLL cases harboring del(11q), being the biallelic loss of ATM associated with adverse prognosis. Although the introduction of targeted BCR inhibition has significantly favored the outcomes of del(11q) patients, responses of patients harboring ATM functional loss through biallelic inactivation are unexplored, and the development of resistances to targeted therapies have been increasingly reported, urging the need to explore novel therapeutic approaches. Here, we generated isogenic CLL cell lines harboring del(11q) and ATM mutations through CRISPR/Cas9-based gene-editing. With these models, we uncovered a novel therapeutic vulnerability of del(11q)/ATM-mutated cells to dual BCR and PARP inhibition. Ex vivo studies in the presence of stromal stimulation on 38 CLL primary samples confirmed a synergistic action of the combination of olaparib and ibrutinib in del(11q)/ATM-mutated CLL patients. In addition, we showed that ibrutinib produced a homologous recombination repair impairment through RAD51 dysregulation, finding a synergistic link of both drugs in the DNA damage repair pathway. Our data provide a preclinical rationale for the use of this combination in CLL patients with this high-risk cytogenetic abnormality.

Tracking the important role of JUNB in hepatocellular carcinoma by single-cell sequencing analysis

Hepatocellular carcinoma (HCC) is the most commonly diagnosed liver cancer, accounting for ~90% of all primary malignancy of the liver. Although various medical treatments have been used as systemic therapies, patient survival time may be extended by only a few months. Moreover, the underlying mechanisms of HCC development and progression remain poorly understood. In the present study, the single-cell transcriptome of one in vivo HCC tumor sample, two in vitro HCC cell lines and normal peripheral blood mononuclear cells were analysed in order to identify the potential mechanism underlying the development and progression of HCC. Interestingly, JunB proto-oncogene was identified to serve a role in the immune response and in development and progression of HCC, potentially contributing to the development of novel therapeutics for HCC patients.

Systems Immunology: Learning the Rules of the Immune System

Given the many cell types and molecular components of the human immune system, along with vast variations across individuals, how should we go about developing causal and predictive explanations of immunity? A central strategy in human studies is to leverage natural variation to find relationships among variables, including DNA variants, epigenetic states, immune phenotypes, clinical descriptors, and others. Here, we focus on how natural variation is used to find patterns, infer principles, and develop predictive models for two areas: (a) immune cell activation-how single-cell profiling boosts our ability to discover immune cell types and states-and (b) antigen presentation and recognition-how models can be generated to predict presentation of antigens on MHC molecules and their detection by T cell receptors. These are two examples of a shift in how we find the drivers and targets of immunity, especially in the human system in the context of health and disease.

Transcriptional Regulation in the Immune System: One Cell at a Time

Transcriptional regulation of cells in the immune system must be strictly controlled at multiple levels to ensure that a proper immune response is elicited only when required. Analysis in bulk, or ensemble of cells, provides a wealth of important information leading to a better understanding of the various molecular steps and mechanisms involved in regulating gene expression in immune cells. However, given the substantial heterogeneity of these cells, it is imperative now to decipher these mechanisms at a single cell level. Here I bring together several recent examples to review our understanding of transcriptional regulation of the immune system via single cell analysis and to further illustrate the immense power of such analyses to interrogate immune cell heterogeneity.

Presence of recombination hotspots throughout SLC6A3

The human dopamine transporter gene SLC6A3 is involved in substance use disorders (SUDs) among many other common neuropsychiatric illnesses but allelic association results including those with its classic genetic markers 3'VNTR or Int8VNTR remain mixed and unexplainable. To better understand the genetics for reproducible association signals, we report the presence of recombination hotspots based on sequencing of the entire 5' promoter regions in two small SUDs cohorts, 30 African Americans (AAs) and 30 European Americans (EAs). Recombination rate was the highest near the transcription start site (TSS) in both cohorts. In addition, each cohort carried 57 different promoter haplotypes out of 60 and no haplotypes were shared between the two ethnicities. A quarter of the haplotypes evolved in an ethnicity-specific manner. Finally, analysis of five hundred subjects of European ancestry, from the 1000 Genome Project, confirmed the promoter recombination hotspots and also revealed several additional ones in non-coding regions only. These findings provide an explanation for the mixed results as well as guidance for selection of effective markers to be used in next generation association validation (NGAV), facilitating the delineation of pathogenic variation in this critical neuropsychiatric gene.

Innovative strategies for annotating the "relationSNP" between variants and molecular phenotypes

Characterizing how variation at the level of individual nucleotides contributes to traits and diseases has been an area of growing interest since the completion of sequencing the first human genome. Our understanding of how a single nucleotide polymorphism (SNP) leads to a pathogenic phenotype on a genome-wide scale is a fruitful endeavor for anyone interested in developing diagnostic tests, therapeutics, or simply wanting to understand the etiology of a disease or trait. To this end, many datasets and algorithms have been developed as resources/tools to annotate SNPs. One of the most common practices is to annotate coding SNPs that affect the protein sequence. Synonymous variants are often grouped as one type of variant, however there are in fact many tools available to dissect their effects on gene expression. More recently, large consortiums like ENCODE and GTEx have made it possible to annotate non-coding regions. Although annotating variants is a common technique among human geneticists, the constant advances in tools and biology surrounding SNPs requires an updated summary of what is known and the trajectory of the field. This review will discuss the history behind SNP annotation, commonly used tools, and newer strategies for SNP annotation. Additionally, we will comment on the caveats that distinguish approaches from one another, along with gaps in the current state of knowledge, and potential future directions. We do not intend for this to be a comprehensive review for any specific area of SNP annotation, but rather it will be an excellent resource for those unfamiliar with computational tools used to functionally characterize SNPs. In summary, this review will help illustrate how each SNP annotation method impacts the way in which the genetic and molecular etiology of a disease is explored in-silico.