Barcodes based on nucleic acid sequences: Applications and challenges (Review)

Cells are the fundamental structural and functional units of living organisms and the study of these entities has remained a central focus throughout the history of biological sciences. Traditional cell research techniques, including fluorescent protein tagging and microscopy, have provided preliminary insights into the lineage history and clonal relationships between progenitor and descendant cells. However, these techniques exhibit inherent limitations in tracking the full developmental trajectory of cells and elucidating their heterogeneity, including sensitivity, stability and barcode drift. In developmental biology, nucleic acid barcode technology has introduced an innovative approach to cell lineage tracing. By assigning unique barcodes to individual cells, researchers can accurately identify and trace the origin and differentiation pathways of cells at various developmental stages, thereby illuminating the dynamic processes underlying tissue development and organogenesis. In cancer research, nucleic acid barcoding has played a pivotal role in analyzing the clonal architecture of tumor cells, exploring their heterogeneity and resistance mechanisms and enhancing our understanding of cancer evolution and inter‑clonal interactions. Furthermore, nucleic acid barcodes play a crucial role in stem cell research, enabling the tracking of stem cells from diverse origins and their derived progeny. This has offered novel perspectives on the mechanisms of stem cell self‑renewal and differentiation. The present review presented a comprehensive examination of the principles, applications and challenges associated with nucleic acid barcode technology.

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.

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.

HSPCs display within-family homogeneity in differentiation and proliferation despite population heterogeneity

High-throughput single-cell methods have uncovered substantial heterogeneity in the pool of hematopoietic stem and progenitor cells (HSPCs), but how much instruction is inherited by offspring from their heterogeneous ancestors remains unanswered. Using a method that enables simultaneous determination of common ancestor, division number, and differentiation status of a large collection of single cells, our data revealed that murine cells that derived from a common ancestor had significant similarities in their division progression and differentiation outcomes. Although each family diversifies, the overall collection of cell types observed is composed of homogeneous families. Heterogeneity between families could be explained, in part, by differences in ancestral expression of cell surface markers. Our analyses demonstrate that fate decisions of cells are largely inherited from ancestor cells, indicating the importance of common ancestor effects. These results may have ramifications for bone marrow transplantation and leukemia, where substantial heterogeneity in HSPC behavior is observed.

In-vivo differentiation of adult hematopoietic stem cells from a single-cell point of view

PURPOSE OF REVIEW

Although hematopoietic stem cell (HSC) function has long been studied by transplantation assays, this does not reflect what HSCs actually do in their native context. Here, we review recent technologic advances that facilitate the study of HSCs in their native context focusing on inducible HSC-specific lineage tracing and inference of hematopoietic trajectories through single-cell RNA sequencing (scRNA-Seq).

RECENT FINDINGS

Lineage tracing of HSCs at the population level using multiple systems has suggested that HSCs make a major contribution to steady-state hematopoiesis. Although several genetic systems and novel methods for lineage tracing individual hematopoietic clones have been described, the technology for tracking these cellular barcodes (in particular mutations or insertion sites) is still in its infancy. Thus, lineage tracing of HSC clones in the adult bone marrow remains elusive. Static snapshots of scRNA-Seq of hematopoietic populations have captured the heterogeneity of transcriptional profiles of HSCs and progenitors, with some cells displaying a unilineage signature as well as others with bi or multipotent lineage profiles. Kinetic analysis using HSC-specific lineage tracing combined with scRNA-Seq confirmed this heterogeneity of progenitor populations and revealed a rapid and early emergence of megakaryocytic progeny, followed by erythroid and myeloid lineages, whereas lymphoid differentiation emerged last.

SUMMARY

New approaches to study HSCs both in vivo through lineage tracing and at a high-resolution molecular level through scRNA-Seq are providing key insight into HSC differentiation in the absence of transplantation. Recent studies using these approaches are discussed here. These studies pave the way for integration of in-vivo clonal analysis of HSC behavior over time with single-cell sequencing data, including but not limited to transcriptomic, proteomic, and epigenomic, to establish a comprehensive molecular and cellular map of hematopoiesis.

Diversity in biology: definitions, quantification and models

Diversity indices are useful single-number metrics for characterizing a complex distribution of a set of attributes across a population of interest. The utility of these different metrics or sets of metrics depends on the context and application, and whether a predictive mechanistic model exists. In this topical review, we first summarize the relevant mathematical principles underlying heterogeneity in a large population, before outlining the various definitions of 'diversity' and providing examples of scientific topics in which its quantification plays an important role. We then review how diversity has been a ubiquitous concept across multiple fields, including ecology, immunology, cellular barcoding experiments, and socioeconomic studies. Since many of these applications involve sampling of populations, we also review how diversity in small samples is related to the diversity in the entire population. Features that arise in each of these applications are highlighted.

Tree-ensemble analysis assesses presence of multifurcations in single cell data

We introduce TreeTop, an algorithm for single cell data analysis to identify and assign a branching score to branch points in biological processes which may have multi-level branching hierarchies. We demonstrate branch point identification for processes with varying topologies, including T-cell maturation, B-cell differentiation and hematopoiesis. Our analyses are consistent with recent experimental studies suggesting a shallower hierarchy of differentiation events in hematopoiesis, rather than the classical multi-level hierarchy.

Hematopoietic Stem and Progenitor Cells (HSPCs)

Hematopoietic stem/progenitor cells (HSPCs) isolated from bone marrow have been successfully employed for 50 years in hematological transplantations. Currently, these cells are more frequently isolated from mobilized peripheral blood or umbilical cord blood. In this chapter, we overview several topics related to these cells including their phenotype, methods for isolation, and in vitro and in vivo assays to evaluate their proliferative potential. The successful clinical application of HSPCs is widely understood to have helped establish the rationale for the development of stem cell therapies and regenerative medicine.

The impact of single-cell RNA sequencing on understanding the functional organization of the immune system

Application of single-cell genomics technologies has revolutionized our approach to study the immune system. Unravelling the functional diversity of immune cells and their coordinated response is key to understanding immunity. Single-cell transcriptomics technologies provide high-dimensional assessment of the transcriptional states of immune cells and have been successfully applied to discover new immune cell types, reveal haematopoietic lineages, identify gene modules dictating immune responses and investigate lymphocyte antigen receptor diversity. In this review, we discuss the impact and applications of single-cell RNA sequencing technologies in immunology.

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.