A track of the clones: new developments in cellular barcoding

Advancements in prospective single-cell lineage barcoding and their applications in research

Single-cell lineage tracing (scLT) has emerged as a powerful tool, providing unparalleled resolution to investigate cellular dynamics, fate determination, and the underlying molecular mechanisms. This review thoroughly examines the latest prospective lineage DNA barcode tracing technologies. It further highlights pivotal studies that leverage single-cell lentiviral integration barcoding technology to unravel the dynamic nature of cell lineages in both developmental biology and cancer research. Additionally, the review navigates through critical considerations for successful experimental design in lineage tracing and addresses challenges inherent in this field, including technical limitations, complexities in data analysis, and the imperative for standardization. It also outlines current gaps in knowledge and suggests future research directions, contributing to the ongoing advancement of scLT studies.

A computational tool suite to facilitate single-cell lineage tracing analyses

Tracking the lineage relationships of cell populations is of increasing interest in diverse biological contexts. In this issue of Cell Reports Methods, Holze et al. present a suite of computational tools to facilitate such analyses and encourage their broader application.

Analysis of synthetic cellular barcodes in the genome and transcriptome with BARtab and bartools

Cellular barcoding is a lineage-tracing methodology that couples heritable synthetic barcodes to high-throughput sequencing, enabling the accurate tracing of cell lineages across a range of biological contexts. Recent studies have extended these methods by incorporating lineage information into single-cell or spatial transcriptomics readouts. Leveraging the rich biological information within these datasets requires dedicated computational tools for dataset pre-processing and analysis. Here, we present BARtab, a portable and scalable Nextflow pipeline, and bartools, an open-source R package, designed to provide an integrated end-to-end cellular barcoding analysis toolkit. BARtab and bartools contain methods to simplify the extraction, quality control, analysis, and visualization of lineage barcodes from population-level, single-cell, and spatial transcriptomics experiments. We showcase the utility of our integrated BARtab and bartools workflow via the analysis of exemplar bulk, single-cell, and spatial transcriptomics experiments containing cellular barcoding information.

Extracting, filtering and simulating cellular barcodes using CellBarcode tools

Identifying true DNA cellular barcodes among polymerase chain reaction and sequencing errors is challenging. Current tools are restricted in the diversity of barcode types supported or the analysis strategies implemented. As such, there is a need for more versatile and efficient tools for barcode extraction, as well as for tools to investigate which factors impact barcode detection and which filtering strategies to best apply. Here we introduce the package CellBarcode and its barcode simulation kit, CellBarcodeSim, that allows efficient and versatile barcode extraction and filtering for a range of barcode types from bulk or single-cell sequencing data using a variety of filtering strategies. Using the barcode simulation kit and biological data, we explore the technical and biological factors influencing barcode identification and provide a decision tree on how to optimize barcode identification for different barcode settings. We believe that CellBarcode and CellBarcodeSim have the capability to enhance the reproducibility and interpretation of barcode results across studies.

Quantitative association between gene expression and blood cell production of individual hematopoietic stem cells in mice

Individual hematopoietic stem cells (HSCs) produce different amounts of blood cells upon transplantation. Taking advantage of the intercellular variation, we developed an experimental and bioinformatic approach to evaluating the quantitative association between gene expression and blood cell production across individual HSCs. We found that most genes associated with blood production exhibit the association only at some levels of blood production. By mapping gene expression with blood production, we identified four distinct patterns of their quantitative association. Some genes consistently correlate with blood production over a range of levels or across all levels, and these genes are found to regulate lymphoid but not myeloid production. Other genes exhibit one or more clear peaks of association. Genes with overlapping peaks are found to be coexpressed in other tissues and share similar molecular functions and regulatory motifs. By dissecting intercellular variations, our findings revealed four quantitative association patterns that reflect distinct dose-response molecular mechanisms modulating the blood cell production of HSCs.

Hematopoietic System under Physiological Conditions and Following Hematopoietic Reconstitution or Stress

The hematopoietic system performs the most vital functions in the human body, integrating the work of various organs while producing enormous numbers of mature cells daily [...].

Exploiting somatic mutations to decipher human blood production: a natural lineage-tracing strategy

Lineage tracing using fluorescent proteins, genetic barcodes, and various other strategies has provided critical insights into the dynamics of both fetal and adult hematopoiesis in model organisms. However, these technologies cannot be readily used to study hematopoiesis in human beings. Therefore, there is a critical need to develop strategies to assess cellular dynamics within human hematopoietic tissues in vivo. Recently, researchers have used naturally acquired somatic mutations, coupled with other single-cell technologies, to retrospectively analyze clonal cellular dynamics. In summer 2022, the International Society for Experimental Hematology's New Investigator Committee hosted a webinar focused on novel approaches to dissect fetal and adult hematopoiesis, with presentations from Drs. Ana Cvejic and Vijay Sankaran. Here, we provide an overview of these exciting technological advances and some of the novel insights they have already provided in studying human hematopoiesis.

CellDestiny: A RShiny application for the visualization and analysis of single-cell lineage tracing data

Single-cell lineage tracing permits the labeling of individual cells with a heritable marker to follow the fate of each cell's progeny. Over the last twenty years, several single-cell lineage tracing methods have emerged, enabling major discoveries in developmental biology, oncology and gene therapies. Analytical tools are needed to draw meaningful conclusions from lineage tracing measurements, which are characterized by high variability, sparsity and technical noise. However, the single cell lineage tracing field lacks versatile and easy-to-use tools for standardized and reproducible analyses, in particular tools accessible to biologists. Here we present CellDestiny, a RShiny app and associated web application developed for experimentalists without coding skills to perform visualization and analysis of single cell lineage-tracing datasets through a graphical user interface. We demonstrate the functionality of CellDestiny through the analysis of (i) lentiviral barcoding datasets of murine hematopoietic progenitors; (ii) published integration site data from Wiskott-Aldrich Symdrome patients undergoing gene-therapy treatment; and (iii) simultaneous barcoding and transcriptomic analysis of murine hematopoietic progenitor differentiation in vitro. In summary, CellDestiny is an easy-to-use and versatile toolkit that enables biologists to visualize and analyze single-cell lineage tracing data.

The Reconstitution Dynamics of Cultivated Hematopoietic Stem Cells and Progenitors Is Independent of Age

Hematopoietic stem cell transplantation (HSCT) represents the only curative treatment option for numerous hematologic malignancies. While the influence of donor age and the composition of the graft have already been examined in clinical and preclinical studies, little information is available on the extent to which different hematological subpopulations contribute to the dynamics of the reconstitution process and on whether and how these contributions are altered with age. In a murine model of HSCT, we therefore simultaneously tracked different cultivated and transduced hematopoietic stem and progenitor cell (HSPC) populations using a multicolor-coded barcode system (BC32). We studied a series of age-matched and age-mismatched transplantations and compared the influence of age on the reconstitution dynamics. We show that reconstitution from these cultured and assembled grafts was substantially driven by hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) independent of age. The reconstitution patterns were polyclonal and stable in all age groups independently of the variability between individual animals, with higher output rates from MPPs than from HSCs. Our experiments suggest that the dynamics of reconstitution and the contribution of cultured and individually transduced HSPC subpopulations are largely independent of age. Our findings support ongoing efforts to expand the application of HSCT in older individuals as a promising strategy to combat hematological diseases, including gene therapy applications.

Interrogation of clonal tracking data using barcodetrackR

Clonal tracking methods provide quantitative insights into the cellular output of genetically labelled progenitor cells across time and cellular compartments. In the context of gene and cell therapies, clonal tracking methods have enabled the tracking of progenitor cell output both in humans receiving therapies and in corresponding animal models, providing valuable insight into lineage reconstitution, clonal dynamics, and vector genotoxicity. However, the absence of a toolbox for analysis of clonal tracking data has precluded the development of standardized analytical frameworks within the field. Thus, we developed barcodetrackR, an R package and accompanying Shiny app containing diverse tools for the analysis and visualization of clonal tracking data. We demonstrate the utility of barcodetrackR in exploring longitudinal clonal patterns and lineage relationships in a number of clonal tracking studies of hematopoietic stem and progenitor cells (HSPCs) in humans receiving HSPC gene therapy and in animals receiving lentivirally transduced HSPC transplants or tumor cells.

Clonal tracking of haematopoietic cells: insights and clinical implications

Recent advances in high-throughput genomics have enabled the direct tracking of outputs from many cell types, greatly accelerating the study of developmental processes and tissue regeneration. The capacity for long-term self-renewal with multilineage differentiation potential characterises the cellular dynamics of a special set of developmental states that are critical for maintaining homeostasis. In haematopoiesis, the archetypal model for development, lineage-tracing experiments have elucidated the roles of haematopoietic stem cells to ongoing blood production and the importance of long-lived immune cells to immunological memory. An understanding of the biology and clonal dynamics of these cellular fates and states can provide clues to the response of haematopoiesis to ageing, the process of malignant transformation, and are key to designing more efficacious and durable clinical gene and cellular therapies.

genBaRcode: a comprehensive R-package for genetic barcode analysis

MOTIVATION

Genetic barcodes have been established as an efficient method to trace clonal progeny of uniquely labeled cells by introducing artificial genetic sequences into the corresponding genomes. The assessment of those sequences relies on next generation sequencing and the subsequent analysis aiming to identify sequences of interest and correctly quantifying their abundance.

RESULTS

We developed the genBaRcode package as a toolbox combining the flexibility of digesting next generation sequencing reads with or without a sophisticated barcode structure, with a variety of error-correction approaches and the availability of several types of visualization routines. Furthermore, a graphical user interface was incorporated to allow also less experienced R users package-based analyses. Finally, the provided tool is intended to bridge the gap between generating and analyzing barcode data and thereby supporting the establishment of standardized and reproducible analysis strategies.

AVAILABILITY AND IMPLEMENTATION

The genBaRcode package is available at CRAN (https://cran.r-project.org/package=genBaRcode).

Emerging single-cell tools are primed to reveal functional and molecular heterogeneity in malignant hematopoietic stem cells

PURPOSE OF REVIEW

The recent emergence of single-cell technologies has permitted unprecedented insight into the molecular drivers of fate choice in blood stem and progenitor cells. This review gives a broad overview of current efforts to understand the molecular regulators of malignant hematopoietic stem cells (HSCs) at the single-cell level.

RECENT FINDINGS

The large-scale adoption of single-cell approaches has allowed extensive description of the transcriptional profiles and functional properties of single HSCs. These techniques are now beginning to be applied to malignant HSCs isolated directly from patients or from mouse models of malignancy. However, these studies have generally struggled to pinpoint the functional regulators of malignant characteristics, since malignant HSCs often differ in more than one property when compared with normal HSCs. Moreover, both normal and malignant populations are complicated by HSC heterogeneity.

SUMMARY

Despite the existence of single-cell gene expression profiling tools, relatively few publications have emerged. Here, we review these studies from recent years with a specific focus on those undertaking single-cell measurements in malignant stem and progenitor cells. We anticipate this to be the tip of the iceberg, expecting the next 2-3 years to produce datasets that will facilitate a much broader understanding of malignant HSCs.

Advancing Stem Cell Research through Multimodal Single-Cell Analysis

Technological advances play a key role in furthering our understanding of stem cell biology, and advancing the prospects of regenerative therapies. Highly parallelized methods, developed in the last decade, can profile DNA, RNA, or proteins in thousands of cells and even capture data across two or more modalities (multiomics). This allows unbiased and precise definition of molecular cell states, thus allowing classification of cell types, tracking of differentiation trajectories, and discovery of underlying mechanisms. Despite being based on destructive techniques, novel experimental and bioinformatic approaches enable embedding and extraction of temporal information, which is essential for deconvolution of complex data and establishing cause and effect relationships. Here, we provide an overview of recent studies pertinent to stem cell biology, followed by an outlook on how further advances in single-cell molecular profiling and computational analysis have the potential to shape the future of both basic and translational research.

Clonal tracking using embedded viral barcoding and high-throughput sequencing

Embedded viral barcoding in combination with high-throughput sequencing is a powerful technology with which to track single-cell clones. It can provide clonal-level insights into cellular proliferation, development, differentiation, migration, and treatment efficacy. Here, we present a detailed protocol for a viral barcoding procedure that includes the creation of barcode libraries, the viral delivery of barcodes, the recovery of barcodes, and the computational analysis of barcode sequencing data. The entire procedure can be completed within a few weeks. This barcoding method requires cells to be susceptible to viral transduction. It provides high sensitivity and throughput, and enables precise quantification of cellular progeny. It is cost efficient and does not require any advanced skills. It can also be easily adapted to many types of applications, including both in vitro and in vivo experiments.

Tracking hematopoietic stem cells and their progeny using whole-genome sequencing

Despite decades of progress in our understanding of hematopoiesis through the study of animal models and transplantation in humans, investigating physiological human hematopoiesis directly has remained challenging. Questions on the clonal structure of the human hematopoietic stem cell (HSC) pool, such as "how many HSCs are there?" and "do all HSC clones actively produce all blood cell types in equal proportions?" remain open. These questions have inherent value for understanding normal human physiology, but also directly inform our comprehension of the process by which the system is subverted to drive diseases of the blood, in particular blood cancers and bone marrow failure syndromes. The critical link between normal and abnormal hematopoiesis is perhaps best illustrated by the recent discovery of clonal hematopoiesis in healthy people with no abnormal blood parameters. In such individuals, large clones derived from single cells are present and are dominant relative to their normal counterparts, but their presence does not necessitate abnormal blood cell production. Intriguingly, however, these individuals are also at a significantly greater risk of developing leukemias and of cardiovascular events, underscoring the importance of understanding how blood stem cell clones compete against each other.

Donor-to-Donor Heterogeneity in the Clonal Dynamics of Transplanted Human Cord Blood Stem Cells in Murine Xenografts

Umbilical cord blood (UCB) provides an alternative source of hematopoietic stem cells (HSCs) for allogeneic transplantation. Administration of sufficient donor HSCs is critical to restore recipient hematopoiesis and to maintain long-term polyclonal blood formation. However, due to lack of unique markers, the frequency of HSCs among UCB CD34⁺ cells is the subject of ongoing debate, urging for reproducible strategies for their counting. Here, we used cellular barcoding to determine the frequency and clonal dynamics of human UCB HSCs and to determine how data analysis methods affect these parameters. We transplanted lentivirally barcoded CD34⁺ cells from 20 UCB donors into Nod/Scid/IL2Ry^-/- (NSG) mice (n = 30). Twelve recipients (of 8 UCB donors) engrafted with >1% GFP⁺ cells, allowing for clonal analysis by multiplexed barcode deep sequencing. Using multiple definitions of clonal diversity and strategies for data filtering, we demonstrate that differences in data analysis can change clonal counts by several orders of magnitude and propose methods to improve their consistency. Using these methods, we show that the frequency of NSG-repopulating cells was low (median ∼1 HSC/10⁴ CD34⁺ UCB cells) and could vary up to 10-fold between donors. Clonal patterns in blood became increasingly consistent over time, likely reflecting initial output of transient progenitors, followed by long-term HSCs with stable hierarchies. The majority of long-term clones displayed multilineage output, yet clones with lymphoid- or myeloid-biased output were also observed. Altogether, this study uncovers substantial interdonor and analysis-induced variability in the frequency of UCB CD34⁺ clones that contribute to post-transplant hematopoiesis. As clone tracing is increasingly relevant, we urge for universal and transparent methods to count HSC clones during normal aging and upon transplantation.