Colon stem cell and crypt dynamics exposed by cell lineage reconstruction

The landscape of cell lineage tracing

Cell fate changes play a crucial role in the processes of natural development, disease progression, and the efficacy of therapeutic interventions. The definition of the various types of cell fate changes, including cell expansion, differentiation, transdifferentiation, dedifferentiation, reprogramming, and state transitions, represents a complex and evolving field of research known as cell lineage tracing. This review will systematically introduce the research history and progress in this field, which can be broadly divided into two parts: prospective tracing and retrospective tracing. The initial section encompasses an array of methodologies pertaining to isotope labeling, transient fluorescent tracers, non-fluorescent transient tracers, non-fluorescent genetic markers, fluorescent protein, genetic marker delivery, genetic recombination, exogenous DNA barcodes, CRISPR-Cas9 mediated DNA barcodes, and base editor-mediated DNA barcodes. The second part of the review covers genetic mosaicism, genomic DNA alteration, TCR/BCR, DNA methylation, and mitochondrial DNA mutation. In the final section, we will address the principal challenges and prospective avenues of enquiry in the field of cell lineage tracing, with a particular focus on the sequencing techniques and mathematical models pertinent to single-cell genetic lineage tracing, and the value of pursuing a more comprehensive investigation at both the spatial and temporal levels in the study of cell lineage tracing.

From Hematopoietic Stem Cells to Platelets: Unifying Differentiation Pathways Identified by Lineage Tracing Mouse Models

Platelets are the terminal progeny of megakaryocytes, primarily produced in the bone marrow, and play critical roles in blood homeostasis, clotting, and wound healing. Traditionally, megakaryocytes and platelets are thought to arise from multipotent hematopoietic stem cells (HSCs) via multiple discrete progenitor populations with successive, lineage-restricting differentiation steps. However, this view has recently been challenged by studies suggesting that (1) some HSC clones are biased and/or restricted to the platelet lineage, (2) not all platelet generation follows the "canonical" megakaryocytic differentiation path of hematopoiesis, and (3) platelet output is the default program of steady-state hematopoiesis. Here, we specifically investigate the evidence that in vivo lineage tracing studies provide for the route(s) of platelet generation and investigate the involvement of various intermediate progenitor cell populations. We further identify the challenges that need to be overcome that are required to determine the presence, role, and kinetics of these possible alternate pathways.

How mutation accumulation depends on the structure of the cell lineage tree

All the cells of a multicellular organism are the product of cell divisions that trace out a single binary tree, the so-called cell lineage tree. Because cell divisions are accompanied by replication errors, the shape of the cell lineage tree is a key determinant of how somatic evolution, which can potentially lead to cancer, proceeds. Carcinogenesis requires the accumulation of a certain number of driver mutations. By mapping the accumulation of mutations into a graph theoretical problem, we present an exact numerical method to calculate the probability of collecting a given number of mutations and show that for low mutation rates it can be approximated with a simple analytical formula, which depends only on the distribution of the lineage lengths, and is dominated by the longest lineages. Our results are crucial in understanding how natural selection can shape the cell lineage trees of multicellular organisms and curtail somatic evolution.

Reconstructing cell lineage trees with genomic barcoding: approaches and applications

In multicellular organisms, developmental history of cell divisions and functional annotation of terminal cells can be organized into a cell lineage tree (CLT). The reconstruction of the CLT has long been a major goal in developmental biology and other related fields. Recent technological advancements, especially those in editable genomic barcodes and single-cell high-throughput sequencing, have sparked a new wave of experimental methods for reconstructing CLTs. Here we review the existing experimental approaches to the reconstruction of CLT, which are broadly categorized as either image-based or DNA barcode-based methods. In addition, we present a summary of the related literature based on the biological insight provided by the obtained CLTs. Moreover, we discuss the challenges that will arise as more and better CLT data become available in the near future. Genomic barcoding-based CLT reconstructions and analyses, due to their wide applicability and high scalability, offer the potential for novel biological discoveries, especially those related to general and systemic properties of the developmental process.

Lineage Tracing by Single-Cell Transcriptomics Decoding Dynamics of Lineage Commitment

Tracing the fate of individual cells and their progeny is necessary and significant for stem cell research and cancer research. Changes in lineage-specific transcription factor levels during lineage commitment are gradual and continuous. Development of single-cell sequencing technology allows many different states of cells to be sequenced at an unprecedented resolution, and it has been proved that single-cell transcriptomics meets lineage tracing. Here, we introduce a detailed protocol for the lineage tracing by single-cell transcriptomics to clarify the dynamics of lineage commitment.

Cellular barcoding: From developmental tracing to anti-tumor drug discovery

Clonal evolution has gained immense attention in explaining cancer cell status, history, and fate during cancer progression. Current single-cell or spatial transcriptome technologies have broadened our understanding of various mechanisms underlying cancer initiation, relapse, and drug resistance. However, technical challenges still hinder a better understanding of the dynamics of distinctive phenotypic states and abnormal trajectories from normal physiological transition to malignant stages. Cellular barcoding enabled lineage tracing on parallelly massive cells at single-cell resolution through different mechanisms lately, enabling new insights into exploring developmental trajectories, cancer progression, and targeted therapies. This review summarizes the latest noteworthy and robust strategies for different types of cellular barcodes. To introduce the major characteristics, advantages and limitations of these different strategies, this review will further guide in choosing or improving cellular barcoding technologies and their applications in cancer research.

Best Practices in Designing, Sequencing, and Identifying Random DNA Barcodes

Random DNA barcodes are a versatile tool for tracking cell lineages, with applications ranging from development to cancer to evolution. Here, we review and critically evaluate barcode designs as well as methods of barcode sequencing and initial processing of barcode data. We first demonstrate how various barcode design decisions affect data quality and propose a new design that balances all considerations that we are currently aware of. We then discuss various options for the preparation of barcode sequencing libraries, including inline indices and Unique Molecular Identifiers (UMIs). Finally, we test the performance of several established and new bioinformatic pipelines for the extraction of barcodes from raw sequencing reads and for error correction. We find that both alignment and regular expression-based approaches work well for barcode extraction, and that error-correction pipelines designed specifically for barcode data are superior to generic ones. Overall, this review will help researchers to approach their barcoding experiments in a deliberate and systematic way.

Replicative history marks transcriptional and functional disparity in the CD8+ T cell memory pool

Clonal expansion is a core aspect of T cell immunity. However, little is known with respect to the relationship between replicative history and the formation of distinct CD8⁺ memory T cell subgroups. To address this issue, we developed a genetic-tracing approach, termed the DivisionRecorder, that reports the extent of past proliferation of cell pools in vivo. Using this system to genetically ‘record’ the replicative history of different CD8⁺ T cell populations throughout a pathogen-specific immune response, we demonstrate that the central memory T cell (T_CM) pool is marked by a higher number of prior divisions than the effector memory T cell pool, due to the combination of strong proliferative activity during the acute immune response and selective proliferative activity after pathogen clearance. Furthermore, by combining DivisionRecorder analysis with single cell transcriptomics and functional experiments, we show that replicative history identifies distinct cell pools within the T_CM compartment. Specifically, we demonstrate that lowly divided T_CM display enriched expression of stem-cell-associated genes, exist in a relatively quiescent state, and are superior in eliciting a proliferative recall response upon activation. These data provide the first evidence that a stem cell like memory T cell pool that reconstitutes the CD8⁺ T cell effector pool upon reinfection is marked by prior quiescence.

Temporal modelling using single-cell transcriptomics

Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.

Comparing Phylogenetic Approaches to Reconstructing Cell Lineage From Microsatellites With Missing Data

Due to the imperfect fidelity of DNA replication, somatic cells acquire DNA mutations at each division which record their lineage history. Microsatellites, tandem repeats of DNA nucleotide motifs, mutate more frequently than other genomic regions and by observing microsatellite lengths in single cells and implementing suitable inference procedures, the cell lineage tree of an organism can be reconstructed. Due to recent advances in single cell Next Generation Sequencing (NGS) and the phylogenetic methods used to infer lineage trees, this work investigates which computational approaches best exploit the lineage information found in single cell NGS data. We simulated trees representing cell division with mutating microsatellites, and tested a range of available phylogenetic algorithms to reconstruct cell lineage. We found that distance-based approaches are fast and accurate with fully observed data. However, Maximum Parsimony and the computationally intensive probabilistic methods are more robust to missing data and therefore better suited to reconstructing cell lineage from NGS datasets. We also investigated how robust reconstruction algorithms are to different tree topologies and mutation generation models. Our results show that the flexibility of Maximum Parsimony and the probabilistic approaches mean they can be adapted to allow good reconstruction across a range of biologically relevant scenarios.

Applications of Single-Cell DNA Sequencing

Over the past decade, genomic analyses of single cells-the fundamental units of life-have become possible. Single-cell DNA sequencing has shed light on biological questions that were previously inaccessible across diverse fields of research, including somatic mutagenesis, organismal development, genome function, and microbiology. Single-cell DNA sequencing also promises significant future biomedical and clinical impact, spanning oncology, fertility, and beyond. While single-cell approaches that profile RNA and protein have greatly expanded our understanding of cellular diversity, many fundamental questions in biology and important biomedical applications require analysis of the DNA of single cells. Here, we review the applications and biological questions for which single-cell DNA sequencing is uniquely suited or required. We include a discussion of the fields that will be impacted by single-cell DNA sequencing as the technology continues to advance.

The Intestinal Epithelium - Fluid Fate and Rigid Structure From Crypt Bottom to Villus Tip

The single-layered, simple epithelium of the gastro-intestinal tract controls nutrient uptake, coordinates our metabolism and shields us from pathogens. Despite its seemingly simple architecture, the intestinal lining consists of highly distinct cell populations that are continuously renewed by the same stem cell population. The need to maintain balanced diversity of cell types in an unceasingly regenerating tissue demands intricate mechanisms of spatial or temporal cell fate control. Recent advances in single-cell sequencing, spatio-temporal profiling and organoid technology have shed new light on the intricate micro-structure of the intestinal epithelium and on the mechanisms that maintain it. This led to the discovery of unexpected plasticity, zonation along the crypt-villus axis and new mechanism of self-organization. However, not only the epithelium, but also the underlying mesenchyme is distinctly structured. Several new studies have explored the intestinal stroma with single cell resolution and unveiled important interactions with the epithelium that are crucial for intestinal function and regeneration. In this review, we will discuss these recent findings and highlight the technologies that lead to their discovery. We will examine strengths and limitations of each approach and consider the wider impact of these results on our understanding of the intestine in health and disease.

Identification of a subpopulation of long-term tumor-initiating cells in colon cancer

Long-term tumor-initiating cells (LT-TICs) are viewed as a quantifiable target for colon cancer therapy owing to their extensive self-renewal and tumorigenic and metastatic capacities. However, it is unknown which subpopulation of colon cancer cells contains LT-TICs. Here, based on the methods for isolating and identifying cancer stem cells (CSCs) and the functional features of LT-TICs, we aimed to identify a subpopulation of LT-TICs. Among the six cell lines assessed, our results showed that CD133 and CD44 coexpression was only detected in HCT116 and HT29 cell lines. In HCT116 and HT29 cells, CD133⁺CD44⁺ cells not only shared the extensive tumorigenic potential of LT-TICs but also functionally reproduced the behaviors of LT-TICs that drive tumor metastasis (TM) formation, suggesting that CD133⁺CD44⁺ cells are a typical representation of LT-TICs in colon cancer. Mechanistically, the enhanced capacity of CD133⁺CD44⁺ cells to drive metastasis involves the up-regulated expression of Wnt-, epithelial–mesenchymal transition (EMT)-, and metastasis-related genes in these cells. Additionally, CD133⁺CD44⁺ cells presented significant chemoresistance compared with corresponding nontumorigenic CD133⁻CD44⁻ cells following exposure to oxaliplatin (OXLP) or 5-fluorouracil (5-FU). Accordingly, CD133⁺CD44⁺ cells contained lower reactive oxygen species (ROS) levels than CD1133⁻CD44⁻ cells, and the low ROS levels in CD133⁺CD44⁺ cells were related to the enhancement of antioxidant defense systems. More importantly, CD133⁺CD44⁺ cells developed less DNA damage after exposure to chemotherapeutics than CD133⁻CD44⁻ cells. In conclusion, we identified a subpopulation of LT-TICs in colon cancer.

Single-Cell Sequencing and Organoids: A Powerful Combination for Modelling Organ Development and Diseases

The development and function of a particular organ and the pathogenesis of various diseases remain intimately linked to the features of each cell type in the organ. Conventional messenger RNA- or protein-based methodologies often fail to elucidate the contribution of rare cell types, including some subpopulations of stem cells, short-lived progenitors and circulating tumour cells, thus hampering their applications in studies regarding organ development and diseases. The scRNA-seq technique represents a new approach for determining gene expression variability at the single-cell level. Organoids are new preclinical models that recapitulate complete or partial features of their original organ and are thought to be superior to cell models in mimicking the sophisticated spatiotemporal processes of the development and regeneration and diseases. In this review, we highlight recent advances in the field of scRNA-seq, organoids and their current applications and summarize the advantages of using a combination of scRNA-seq and organoid technology to model diseases and organ development.

Progress and applications of single-cell sequencing techniques

Single-cell sequencing (SCS) is a next-generation sequencing method that is mainly used to analyze differences in genetic and protein information between cells, to obtain genetic information on microorganisms that are difficult to cultivate at a single-cell level and to better understand their specific roles in the microenvironment. By sequencing the whole genome, transcriptome and epigenome of a single cell, the complex heterogeneous mechanisms involved in disease occurrence and progression can be revealed, further improving disease diagnosis, prognosis prediction and monitoring of the therapeutic effects of drugs. In this study, we mainly summarized the methods and application fields of SCS, which may provide potential references for its future clinical applications, including the analysis of embryonic and organ development, the immune system, cancer progression, and parasitic and infectious diseases as well as stem cell research, antibody screening, and therapeutic research and development.

Unravelling cellular relationships during development and regeneration using genetic lineage tracing

Tracking the progeny of single cells is necessary for building lineage trees that recapitulate processes such as embryonic development and stem cell differentiation. In classical lineage tracing experiments, cells are fluorescently labelled to allow identification by microscopy of a limited number of cell clones. To track a larger number of clones in complex tissues, fluorescent proteins are now replaced by heritable DNA barcodes that are read using next-generation sequencing. In prospective lineage tracing, unique DNA barcodes are introduced into single cells through genetic manipulation (using, for example, Cre-mediated recombination or CRISPR-Cas9-mediated editing) and tracked over time. Alternatively, in retrospective lineage tracing, naturally occurring somatic mutations can be used as endogenous DNA barcodes. Finally, single-cell mRNA-sequencing datasets that capture different cell states within a developmental or differentiation trajectory can be used to recapitulate lineages. In this Review, we discuss methods for prospective or retrospective lineage tracing and demonstrate how trajectory reconstruction algorithms can be applied to single-cell mRNA-sequencing datasets to infer developmental or differentiation tracks. We discuss how these approaches are used to understand cell fate during embryogenesis, cell differentiation and tissue regeneration.

Sample path properties of the average generation of a Bellman-Harris process

Motivated by a recently proposed design for a DNA coded randomised algorithm that enables inference of the average generation of a collection of cells descendent from a common progenitor, here we establish strong convergence properties for the average generation of a super-critical Bellman-Harris process. We further extend those results to a two-type Bellman-Harris process where one type can give rise to the other, but not vice versa. These results further affirm the estimation method's potential utility by establishing its long run accuracy on individual sample-paths, and significantly expanding its remit to encompass cellular development that gives rise to differentiated offspring with distinct population dynamics.

Lineage tracing using a Cas9-deaminase barcoding system targeting endogenous L1 elements

Determining cell lineage and function is critical to understanding human physiology and pathology. Although advances in lineage tracing methods provide new insight into cell fate, defining cellular diversity at the mammalian level remains a challenge. Here, we develop a genome editing strategy using a cytidine deaminase fused with nickase Cas9 (nCas9) to specifically target endogenous interspersed repeat regions in mammalian cells. The resulting mutation patterns serve as a genetic barcode, which is induced by targeted mutagenesis with single-guide RNA (sgRNA), leveraging substitution events, and subsequent read out by a single primer pair. By analyzing interspersed mutation signatures, we show the accurate reconstruction of cell lineage using both bulk cell and single-cell data. We envision that our genetic barcode system will enable fine-resolution mapping of organismal development in healthy and diseased mammalian states.

Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging

Tracking the fate of individual cells and their progeny through lineage tracing has been widely used to investigate various biological processes including embryonic development, homeostatic tissue turnover, and stem cell function in regeneration and disease. Conventional lineage tracing involves the marking of cells either with dyes or nucleoside analogues or genetic marking with fluorescent and/or colorimetric protein reporters. Both are imaging-based approaches that have played a crucial role in the field of developmental biology as well as adult stem cell biology. However, imaging-based lineage tracing approaches are limited by their scalability and the lack of molecular information underlying fate transitions. Recently, computational biology approaches have been combined with diverse tracing methods to overcome these limitations and so provide high-order scalability and a wealth of molecular information. In this review, we will introduce such novel computational methods, starting from single-cell RNA sequencing-based lineage analysis to DNA barcoding or genetic scar analysis. These novel approaches are complementary to conventional imaging-based approaches and enable us to study the lineage relationships of numerous cell types during vertebrate, and in particular human, development and disease.

Single-Cell Transcriptomics Meets Lineage Tracing

Reconstructing lineage relationships between cells within a tissue or organism is a long-standing aim in biology. Traditionally, lineage tracing has been achieved through the (genetic) labeling of a cell followed by the tracking of its offspring. Currently, lineage trajectories can also be predicted using single-cell transcriptomics. Although single-cell transcriptomics provides detailed phenotypic information, the predicted lineage trajectories do not necessarily reflect genetic relationships. Recently, techniques have been developed that unite these strategies. In this Review, we discuss transcriptome-based lineage trajectory prediction algorithms, single-cell genetic lineage tracing, and the promising combination of these techniques for stem cell and cancer research.

Whole-organism clone tracing using single-cell sequencing

Embryonic development is a crucial period in the life of a multicellular organism, during which limited sets of embryonic progenitors produce all cells in the adult body. Determining which fate these progenitors acquire in adult tissues requires the simultaneous measurement of clonal history and cell identity at single-cell resolution, which has been a major challenge. Clonal history has traditionally been investigated by microscopically tracking cells during development, monitoring the heritable expression of genetically encoded fluorescent proteins and, more recently, using next-generation sequencing technologies that exploit somatic mutations, microsatellite instability, transposon tagging, viral barcoding, CRISPR-Cas9 genome editing and Cre-loxP recombination. Single-cell transcriptomics provides a powerful platform for unbiased cell-type classification. Here we present ScarTrace, a single-cell sequencing strategy that enables the simultaneous quantification of clonal history and cell type for thousands of cells obtained from different organs of the adult zebrafish. Using ScarTrace, we show that a small set of multipotent embryonic progenitors generate all haematopoietic cells in the kidney marrow, and that many progenitors produce specific cell types in the eyes and brain. In addition, we study when embryonic progenitors commit to the left or right eye. ScarTrace reveals that epidermal and mesenchymal cells in the caudal fin arise from the same progenitors, and that osteoblast-restricted precursors can produce mesenchymal cells during regeneration. Furthermore, we identify resident immune cells in the fin with a distinct clonal origin from other blood cell types. We envision that similar approaches will have major applications in other experimental systems, in which the matching of embryonic clonal origin to adult cell type will ultimately allow reconstruction of how the adult body is built from a single cell.

W. A. H, Lewis RJ, Vetter I. Transcriptomics in pain research: insights from new and old technologies

Physiological and pathological pain involves a complex interplay of multiple cell types and signaling pathways.

Quantitative Analysis of Synthetic Cell Lineage Tracing Using Nuclease Barcoding

Lineage tracing by the determination and mapping of progeny arising from single cells is an important approach enabling the elucidation of mechanisms underlying diverse biological processes ranging from development to disease. We developed a dynamic sequence-based barcode system for synthetic lineage tracing and have demonstrated its performance in C. elegans, a model organism whose lineage tree is well established. The strategy we use creates lineage trees based upon the introduction of synthetically controlled mutations into cells and the propagation of these mutations to daughter cells at each cell division. We analyzed this experimental proof of concept along with a corresponding simulation and analytical model to gain a deeper understanding of the coding capacity of the system. Our results provide specific bounds on the fidelity of lineage tracing using such approaches.

Building a lineage from single cells: genetic techniques for cell lineage tracking

Resolving lineage relationships between cells in an organism is a fundamental interest of developmental biology. Furthermore, investigating lineage can drive understanding of pathological states, including cancer, as well as understanding of developmental pathways that are amenable to manipulation by directed differentiation. Although lineage tracking through the injection of retroviral libraries has long been the state of the art, a recent explosion of methodological advances in exogenous labelling and single-cell sequencing have enabled lineage tracking at larger scales, in more detail, and in a wider range of species than was previously considered possible. In this Review, we discuss these techniques for cell lineage tracking, with attention both to those that trace lineage forwards from experimental labelling, and those that trace backwards across the life history of an organism.

The role of the Hes1 crosstalk hub in Notch-Wnt interactions of the intestinal crypt

The Notch pathway plays a vital role in determining whether cells in the intestinal epithelium adopt a secretory or an absorptive phenotype. Cell fate specification is coordinated via Notch's interaction with the canonical Wnt pathway. Here, we propose a new mathematical model of the Notch and Wnt pathways, in which the Hes1 promoter acts as a hub for pathway crosstalk. Computational simulations of the model can assist in understanding how healthy intestinal tissue is maintained, and predict the likely consequences of biochemical knockouts upon cell fate selection processes. Chemical reaction network theory (CRNT) is a powerful, generalised framework which assesses the capacity of our model for monostability or multistability, by analysing properties of the underlying network structure without recourse to specific parameter values or functional forms for reaction rates. CRNT highlights the role of β-catenin in stabilising the Notch pathway and damping oscillations, demonstrating that Wnt-mediated actions on the Hes1 promoter can induce dynamic transitions in the Notch system, from multistability to monostability. Time-dependent model simulations of cell pairs reveal the stabilising influence of Wnt upon the Notch pathway, in which β-catenin- and Dsh-mediated action on the Hes1 promoter are key in shaping the subcellular dynamics. Where Notch-mediated transcription of Hes1 dominates, there is Notch oscillation and maintenance of fate flexibility; Wnt-mediated transcription of Hes1 favours bistability akin to cell fate selection. Cells could therefore regulate the proportion of Wnt- and Notch-mediated control of the Hes1 promoter to coordinate the timing of cell fate selection as they migrate through the intestinal epithelium and are subject to reduced Wnt stimuli. Furthermore, mutant cells characterised by hyperstimulation of the Wnt pathway may, through coupling with Notch, invert cell fate in neighbouring healthy cells, enabling an aberrant cell to maintain its neighbours in mitotically active states.

A generic, cost-effective, and scalable cell lineage analysis platform

Advances in single-cell genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells. Current sequencing-based methods for cell lineage analysis depend on low-resolution bulk analysis or rely on extensive single-cell sequencing, which is not scalable and could be biased by functional dependencies. Here we show an integrated biochemical-computational platform for generic single-cell lineage analysis that is retrospective, cost-effective, and scalable. It consists of a biochemical-computational pipeline that inputs individual cells, produces targeted single-cell sequencing data, and uses it to generate a lineage tree of the input cells. We validated the platform by applying it to cells sampled from an ex vivo grown tree and analyzed its feasibility landscape by computer simulations. We conclude that the platform may serve as a generic tool for lineage analysis and thus pave the way toward large-scale human cell lineage discovery.

The role of cell location and spatial gradients in the evolutionary dynamics of colon and intestinal crypts

BACKGROUND

Colon and intestinal crypts serve as an important model system for adult stem cell proliferation and differentiation. We develop a spatial stochastic model to study the rate of somatic evolution in a normal crypt, focusing on the production of two-hit mutants that inactivate a tumor suppressor gene. We investigate the effect of cell division pattern along the crypt on mutant production, assuming that the division rate of each cell depends on its location.

RESULTS

We find that higher probability of division at the bottom of the crypt, where the stem cells are located, leads to a higher rate of double-hit mutant production. The optimal case for delaying mutations occurs when most of the cell divisions happen at the top of the crypt. We further consider an optimization problem where the "evolutionary" penalty for double-hit mutant generation is complemented with a "functional" penalty that assures that fully differentiated cells at the top of the crypt cannot divide.

CONCLUSION

The trade-off between the two types of objectives leads to the selection of an intermediate division pattern, where the cells in the middle of the crypt divide with the highest rate. This matches the pattern of cell divisions obtained experimentally in murine crypts.

REVIEWERS

This article was reviewed by David Axelrod (nominated by an Editorial Board member, Marek Kimmel), Yang Kuang and Anna Marciniak-Czochra. For the full reviews, please go to the Reviewers' comments section.

Accuracy of Answers to Cell Lineage Questions Depends on Single-Cell Genomics Data Quality and Quantity

Advances in single-cell (SC) genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells, as determined by phylogenetic analysis of the somatic mutations harbored by each cell. Theoretically, complete and accurate knowledge of the genome of each cell of an individual can produce an extremely accurate cell lineage tree of that individual. However, the reality of SC genomics is that such complete and accurate knowledge would be wanting, in quality and in quantity, for the foreseeable future. In this paper we offer a framework for systematically exploring the feasibility of answering cell lineage questions based on SC somatic mutational analysis, as a function of SC genomics data quality and quantity. We take into consideration the current limitations of SC genomics in terms of mutation data quality, most notably amplification bias and allele dropouts (ADO), as well as cost, which puts practical limits on mutation data quantity obtained from each cell as well as on cell sample density. We do so by generating in silico cell lineage trees using a dedicated formal language, eSTG, and show how the ability to answer correctly a cell lineage question depends on the quality and quantity of the SC mutation data. The presented framework can serve as a baseline for the potential of current SC genomics to unravel cell lineage dynamics, as well as the potential contributions of future advancement, both biochemical and computational, for the task.

A human cell lineage tree describes the entire developmental dynamics of a person starting from the zygote and ending with each and every extant cell. Fundamental open problems in biology and medicine are in fact questions about the human cell lineage tree: its structure and its dynamics in development, growth, renewal, aging, and disease. Consequently, a method to know the human cell lineage tree would allow resolving these problems and enable a leapfrog advance in human knowledge and health. Recent advancements in single-cell genomics have the potential to uncover various properties of the human cell lineage tree and thus promote our understanding of various biological phenomena. In this paper we present a computational framework along with specific results, which enable to understand what can be achieved using the limitations of current technologies and predict future capabilities based on future improvements. This approach can serve as a valuable tool for researchers who plan to perform lineage experiments both in designing and optimizing the actual experimental needs and predicting the costs and limitations of the plan. This work can also help researchers focus on developing what is needed for future advancements.

Inferring average generation via division-linked labeling

For proliferating cells subject to both division and death, how can one estimate the average generation number of the living population without continuous observation or a division-diluting dye? In this paper we provide a method for cell systems such that at each division there is an unlikely, heritable one-way label change that has no impact other than to serve as a distinguishing marker. If the probability of label change per cell generation can be determined and the proportion of labeled cells at a given time point can be measured, we establish that the average generation number of living cells can be estimated. Crucially, the estimator does not depend on knowledge of the statistics of cell cycle, death rates or total cell numbers. We explore the estimator's features through comparison with physiologically parameterized stochastic simulations and extrapolations from published data, using it to suggest new experimental designs.

Somatic mosaicism: on the road to cancer

Diversity is the basis of fitness selection. Although the genome of an individual is considered to be largely stable, there is theoretical and experimental evidence--both in model organisms and in humans--that genetic mosaicism is the rule rather than the exception. The continuous generation of cell variants, their interactions and selective pressures lead to life-long tissue dynamics. Individuals may thus enjoy 'clonal health', defined as a clonal composition that supports healthy morphology and physiology, or suffer from clonal configurations that promote disease, such as cancer. The contribution of mosaicism to these processes starts during embryonic development. In this Opinion article, we argue that the road to cancer might begin during these early stages.

Aging, clonal hematopoiesis and preleukemia: not just bad luck?

Chronological human aging is associated with a number of changes in the hematopoietic system, occurring at many levels from stem to mature cells, and the marrow microenvironment as well. This review will focus mainly on the aging of hematopoietic stem and progenitor cells (HSPCs), and on the associated increases in the incidence of hematological malignancies. HSPCs manifest reduced function and acquire molecular changes with chronological aging. Furthermore, while for many years it has been known that the human hematopoietic system becomes increasingly clonal with chronological aging (clonal hematopoiesis), only in the last few years has it become clear that clonal hematopoiesis may result from the accumulation of preleukemic mutations in HSPCs. Such mutations confer a selective advantage that leads to clonal hematopoiesis, and that may occasionally result in the development of leukemia, and define the existence of both preleukemic stem cells, and of 'preleukemia' as a clinical entity. While it is well appreciated that clonal hematopoiesis is very common in the elderly, several questions remain unanswered: why and how does clonal hematopoiesis develop? How is clonal hematopoiesis related to the age-related changes observed in the hematopoietic system? And why do only some individuals with clonal hematopoiesis develop leukemia?

Cryosectioning Method for Microdissection of Murine Colonic Mucosa

The colonic mucosal tissue provides a vital barrier to luminal antigens. This barrier is composed of a monolayer of simple columnar epithelial cells. The colonic epithelium is dynamically turned over and epithelial cells are generated in the stem cell containing crypts of Lieberkühn. Progenitor cells produced in the crypt-bases migrate toward the luminal surface, undergoing a process of cellular differentiation before being shed into the gut lumen. In order to study these processes at the molecular level, we have developed a simple method for the microdissection of two spatially distinct regions of the colonic mucosa; the proliferative crypt zone, and the differentiated surface epithelial cells. Our objective is to isolate specific crypt and surface epithelial cell populations from mouse colonic mucosa for the isolation of RNA and protein.

The protective role of symmetric stem cell division on the accumulation of heritable damage

Stem cell divisions are either asymmetric—in which one daughter cell remains a stem cell and one does not—or symmetric, in which both daughter cells adopt the same fate, either stem or non-stem. Recent studies show that in many tissues operating under homeostatic conditions stem cell division patterns are strongly biased toward the symmetric outcome, raising the question of whether symmetry confers some benefit. Here, we show that symmetry, via extinction of damaged stem-cell clones, reduces the lifetime risk of accumulating phenotypically silent heritable damage (mutations or aberrant epigenetic changes) in individual stem cells. This effect is greatest in rapidly cycling tissues subject to accelerating rates of damage accumulation over time, a scenario that describes the progression of many cancers. A decrease in the rate of cellular damage accumulation may be an important factor favoring symmetric patterns of stem cell division.

Recently, highly symmetric patterns of stem cell division have been observed in a variety of adult mammalian somatic tissues. Here we identify conditions under which this behavior serves as a strategy to protect the organism against mutation accumulation. First, we find that a sufficient number of lifetime stem cell divisions must occur, potentially explaining why stem cell pools with the most symmetric divisions are rapidly cycling. Second, we find that late-occurring mutations must occur rapidly, a scenario known in cancer biology as genetic instability. These findings provide a potential explanation for the observation that cancer risks among large, long-lived organisms fail to rise as expected with lifespan and body size.

Ten years of next-generation sequencing technology

Ten years ago next-generation sequencing (NGS) technologies appeared on the market. During the past decade, tremendous progress has been made in terms of speed, read length, and throughput, along with a sharp reduction in per-base cost. Together, these advances democratized NGS and paved the way for the development of a large number of novel NGS applications in basic science as well as in translational research areas such as clinical diagnostics, agrigenomics, and forensic science. Here we provide an overview of the evolution of NGS and discuss the most significant improvements in sequencing technologies and library preparation protocols. We also explore the current landscape of NGS applications and provide a perspective for future developments.

Lineage grammars: describing, simulating and analyzing population dynamics

Background

Precise description of the dynamics of biological processes would enable the mathematical analysis and computational simulation of complex biological phenomena. Languages such as Chemical Reaction Networks and Process Algebras cater for the detailed description of interactions among individuals and for the simulation and analysis of ensuing behaviors of populations. However, often knowledge of such interactions is lacking or not available. Yet complete oblivion to the environment would make the description of any biological process vacuous. Here we present a language for describing population dynamics that abstracts away detailed interaction among individuals, yet captures in broad terms the effect of the changing environment, based on environment-dependent Stochastic Tree Grammars (eSTG). It is comprised of a set of stochastic tree grammar transition rules, which are context-free and as such abstract away specific interactions among individuals. Transition rule probabilities and rates, however, can depend on global parameters such as population size, generation count, and elapsed time.

Results

We show that eSTGs conveniently describe population dynamics at multiple levels including cellular dynamics, tissue development and niches of organisms. Notably, we show the utilization of eSTG for cases in which the dynamics is regulated by environmental factors, which affect the fate and rate of decisions of the different species. eSTGs are lineage grammars, in the sense that execution of an eSTG program generates the corresponding lineage trees, which can be used to analyze the evolutionary and developmental history of the biological system under investigation. These lineage trees contain a representation of the entire events history of the system, including the dynamics that led to the existing as well as to the extinct individuals.

Conclusions

We conclude that our suggested formalism can be used to easily specify, simulate and analyze complex biological systems, and supports modular description of local biological dynamics that can be later used as “black boxes” in a larger scope, thus enabling a gradual and hierarchical definition and simulation of complex biological systems. The simple, yet robust formalism enables to target a broad class of stochastic dynamic behaviors, especially those that can be modeled using global environmental feedback regulation rather than direct interaction between individuals.

Comparing algorithms that reconstruct cell lineage trees utilizing information on microsatellite mutations

Organism cells proliferate and die to build, maintain, renew and repair it. The cellular history of an organism up to any point in time can be captured by a cell lineage tree in which vertices represent all organism cells, past and present, and directed edges represent progeny relations among them. The root represents the fertilized egg, and the leaves represent extant and dead cells. Somatic mutations accumulated during cell division endow each organism cell with a genomic signature that is unique with a very high probability. Distances between such genomic signatures can be used to reconstruct an organism's cell lineage tree. Cell populations possess unique features that are absent or rare in organism populations (e.g., the presence of stem cells and a small number of generations since the zygote) and do not undergo sexual reproduction, hence the reconstruction of cell lineage trees calls for careful examination and adaptation of the standard tools of population genetics. Our lab developed a method for reconstructing cell lineage trees by examining only mutations in highly variable microsatellite loci (MS, also called short tandem repeats, STR). In this study we use experimental data on somatic mutations in MS of individual cells in human and mice in order to validate and quantify the utility of known lineage tree reconstruction algorithms in this context. We employed extensive measurements of somatic mutations in individual cells which were isolated from healthy and diseased tissues of mice and humans. The validation was done by analyzing the ability to infer known and clear biological scenarios. In general, we found that if the biological scenario is simple, almost all algorithms tested can infer it. Another somewhat surprising conclusion is that the best algorithm among those tested is Neighbor Joining where the distance measure used is normalized absolute distance. We include our full dataset in Tables S1, S2, S3, S4, S5 to enable further analysis of this data by others.

Continuous clonal labeling reveals small numbers of functional stem cells in intestinal crypts and adenomas

Lineage-tracing approaches, widely used to characterize stem cell populations, rely on the specificity and stability of individual markers for accurate results. We present a method in which genetic labeling in the intestinal epithelium is acquired as a mutation-induced clonal mark during DNA replication. By determining the rate of mutation in vivo and combining this data with the known neutral-drift dynamics that describe intestinal stem cell replacement, we quantify the number of functional stem cells in crypts and adenomas. Contrary to previous reports, we find that significantly lower numbers of "working" stem cells are present in the intestinal epithelium (five to seven per crypt) and in adenomas (nine per gland), and that those stem cells are also replaced at a significantly lower rate. These findings suggest that the bulk of tumor stem cell divisions serve only to replace stem cell loss, with rare clonal victors driving gland repopulation and tumor growth.

Single-cell sequencing-based technologies will revolutionize whole-organism science

The unabated progress in next-generation sequencing technologies is fostering a wave of new genomics, epigenomics, transcriptomics and proteomics technologies. These sequencing-based technologies are increasingly being targeted to individual cells, which will allow many new and longstanding questions to be addressed. For example, single-cell genomics will help to uncover cell lineage relationships; single-cell transcriptomics will supplant the coarse notion of marker-based cell types; and single-cell epigenomics and proteomics will allow the functional states of individual cells to be analysed. These technologies will become integrated within a decade or so, enabling high-throughput, multi-dimensional analyses of individual cells that will produce detailed knowledge of the cell lineage trees of higher organisms, including humans. Such studies will have important implications for both basic biological research and medicine.

Age-dependent transition from cell-level to population-level control in murine intestinal homeostasis revealed by coalescence analysis

In multi-cellular organisms, tissue homeostasis is maintained by an exquisite balance between stem cell proliferation and differentiation. This equilibrium can be achieved either at the single cell level (a.k.a. cell asymmetry), where stem cells follow strict asymmetric divisions, or the population level (a.k.a. population asymmetry), where gains and losses in individual stem cell lineages are randomly distributed, but the net effect is homeostasis. In the mature mouse intestinal crypt, previous evidence has revealed a pattern of population asymmetry through predominantly symmetric divisions of stem cells. In this work, using population genetic theory together with previously published crypt single-cell data obtained at different mouse life stages, we reveal a strikingly dynamic pattern of stem cell homeostatic control. We find that single-cell asymmetric divisions are gradually replaced by stochastic population-level asymmetry as the mouse matures to adulthood. This lifelong process has important developmental and evolutionary implications in understanding how adult tissues maintain their homeostasis integrating the trade-off between intrinsic and extrinsic regulations.

In multi-cellular organisms, there is a static equilibrium maintaining cells of various forms. This homeostasis is achieved by an exquisite balance between stem cell proliferation and differentiation. Understanding how different species and organ types maintain this dynamic equilibrium has been an interesting question for both evolutionary and developmental biologists. Using population genetic theory together with previously published single-cell sequencing data collected from mouse intestinal crypts at two points in development, we have revealed a dynamic picture of stem cell renewal in intestinal crypts. We found that intestinal equilibrium is maintained at the single-cell level through predominantly asymmetric stem cell divisions at early life stages, but progressively switches to a population level homeostasis with only symmetric divisions as the mouse matures to adulthood. This dynamic process, likely to be conserved across species, has important developmental and evolutionary implications in understanding how adult tissues maintain their homeostasis integrating lifelong trade-offs between intrinsic and extrinsic factors.

Stem cell clonality -- theoretical concepts, experimental techniques, and clinical challenges

Here we report highlights of discussions and results presented at an International Workshop on Concepts and Models of Stem Cell Organization held on July 16th and 17th, 2012 in Dresden, Germany. The goal of the workshop was to undertake a systematic survey of state-of-the-art methods and results of clonality studies of tissue regeneration and maintenance with a particular emphasis on the hematopoietic system. The meeting was the 6th in a series of similar conceptual workshops, termed StemCellMathLab,(2) all of which have had the general objective of using an interdisciplinary approach to discuss specific aspects of stem cell biology. The StemCellMathLab 2012, which was jointly organized by the Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Dresden University of Technology and the Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, brought together 32 scientists from 8 countries, with scientific backgrounds in medicine, cell biology, virology, physics, computer sciences, bioinformatics and mathematics. The workshop focused on the following questions: (1) How heterogeneous are stem cells and their progeny? and (2) What are the characteristic differences in the clonal dynamics between physiological and pathophysiological situations? In discussing these questions, particular emphasis was placed on (a) the methods for quantifying clones and their dynamics in experimental and clinical settings and (b) general concepts and models for their description. In this workshop summary we start with an introduction to the current state of clonality research and a proposal for clearly defined terminology. Major topics of discussion include clonal heterogeneity in unperturbed tissues, clonal dynamics due to physiological and pathophysiological pressures and conceptual and technical issues of clone quantification. We conclude that an interactive cross-disciplinary approach to research in this field will continue to promote a conceptual understanding of tissue organization.

Cell lineage analysis of acute leukemia relapse uncovers the role of replication-rate heterogeneity and microsatellite instability

Human cancers display substantial intratumoral genetic heterogeneity, which facilitates tumor survival under changing microenvironmental conditions. Tumor substructure and its effect on disease progression and relapse are incompletely understood. In the present study, a high-throughput method that uses neutral somatic mutations accumulated in individual cells to reconstruct cell lineage trees was applied to hundreds of cells of human acute leukemia harvested from multiple patients at diagnosis and at relapse. The reconstructed cell lineage trees of patients with acute myeloid leukemia showed that leukemia cells at relapse were shallow (divide rarely) compared with cells at diagnosis and were closely related to their stem cell subpopulation, implying that in these instances relapse might have originated from rarely dividing stem cells. In contrast, among patients with acute lymphoid leukemia, no differences in cell depth were observed between diagnosis and relapse. In one case of chronic myeloid leukemia, at blast crisis, most of the cells at relapse were mismatch-repair deficient. In almost all leukemia cases, > 1 lineage was observed at relapse, indicating that diverse mechanisms can promote relapse in the same patient. In conclusion, diverse relapse mechanisms can be observed by systematic reconstruction of cell lineage trees of patients with leukemia.

Cell lineage analysis of the mammalian female germline

Fundamental aspects of embryonic and post-natal development, including maintenance of the mammalian female germline, are largely unknown. Here we employ a retrospective, phylogenetic-based method for reconstructing cell lineage trees utilizing somatic mutations accumulated in microsatellites, to study female germline dynamics in mice. Reconstructed cell lineage trees can be used to estimate lineage relationships between different cell types, as well as cell depth (number of cell divisions since the zygote). We show that, in the reconstructed mouse cell lineage trees, oocytes form clusters that are separate from hematopoietic and mesenchymal stem cells, both in young and old mice, indicating that these populations belong to distinct lineages. Furthermore, while cumulus cells sampled from different ovarian follicles are distinctly clustered on the reconstructed trees, oocytes from the left and right ovaries are not, suggesting a mixing of their progenitor pools. We also observed an increase in oocyte depth with mouse age, which can be explained either by depth-guided selection of oocytes for ovulation or by post-natal renewal. Overall, our study sheds light on substantial novel aspects of female germline preservation and development.

Many aspects of mammalian female germline development during embryogenesis and throughout adulthood are either unknown or under debate. In this study we applied a novel method for the reconstruction of cell lineage trees utilizing microsatellite mutations, accumulated during mouse life, in oocytes and other cells, sampled from young and old mice. Analysis of the reconstructed cell lineage trees shows that oocytes are clustered separately from bone-marrow derived cells, that oocytes from different ovaries share common progenitors, and that oocyte depth (number of cell divisions since the zygote) increases significantly with mouse age.

Muscle-bound primordial stem cells give rise to myofiber-associated myogenic and non-myogenic progenitors

Myofiber cultures give rise to myogenic as well as to non-myogenic cells. Whether these myofiber-associated non-myogenic cells develop from resident stem cells that possess mesenchymal plasticity or from other stem cells such as mesenchymal stem cells (MSCs) remain unsolved. To address this question, we applied a method for reconstructing cell lineage trees from somatic mutations to MSCs and myogenic and non-myogenic cells from individual myofibers that were cultured at clonal density.

Our analyses show that (i) in addition to myogenic progenitors, myofibers also harbor non-myogenic progenitors of a distinct, yet close, lineage; (ii) myofiber-associated non-myogenic and myogenic cells share the same muscle-bound primordial stem cells of a lineage distinct from bone marrow MSCs; (iii) these muscle-bound primordial stem-cells first part to individual muscles and then differentiate into myogenic and non-myogenic stem cells.