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

High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutations

In vivo lineage tracing holds great potential to reveal fundamental principles of tissue development and homeostasis. However, current lineage tracing in humans relies on extremely rare somatic mutations, which has limited temporal resolution and lineage accuracy. Here, we developed a generic lineage-tracing tool based on frequent epimutations on DNA methylation, enabled by our computational method MethylTree. Using single-cell genome-wide DNA methylation datasets with known lineage and phenotypic labels, MethylTree reconstructed lineage histories at nearly 100% accuracy across different cell types, developmental stages, and species. We demonstrated the epimutation-based single-cell multi-omic lineage tracing in mouse and human blood, where MethylTree recapitulated the differentiation hierarchy in hematopoiesis. Applying MethylTree to human embryos, we revealed early fate commitment at the four-cell stage. In native mouse blood, we identified ~250 clones of hematopoietic stem cells. MethylTree opens the door for high-resolution, noninvasive and multi-omic lineage tracing in humans and beyond.

Protocol for mitochondrial variant enrichment from single-cell RNA sequencing using MAESTER

Single-cell RNA sequencing (scRNA-seq) enables detailed characterization of cell states but often lacks insights into tissue clonal structures. Here, we present a protocol to probe cell states and clonal information simultaneously by enriching mitochondrial DNA (mtDNA) variants from 3'-barcoded full-length cDNA. We describe steps for input library preparation, mtDNA enrichment, PCR product cleanup, and paired-end sequencing. We then detail computational steps for running maegatk, variant calling, and data integration to illuminate cell states and clonal dynamics in primary human tissues. For complete details on the use and execution of this protocol, please refer to Miller et al.1.

scLTdb: a comprehensive single-cell lineage tracing database

Single-cell lineage tracing (scLT) is a powerful technique that integrates cellular barcoding with single-cell sequencing technologies. This new approach enables the simultaneous measurement of cell fate and molecular profiles at single-cell resolution, uncovering the gene regulatory program of cell fate determination. However, a comprehensive scLT database is not yet available. Here, we present the single-cell lineage tracing database (scLTdb, https://scltdb.com) containing 109 datasets that are manually curated and analyzed through a standard pipeline. The scLTdb provides interactive analysis modules for visualizing and re-analyzing scLT datasets, especially the comprehensive cell fate analysis and lineage relationship analysis. Importantly, scLTdb also allows users to identify fate-related gene signatures. In conclusion, scLTdb provides an interactive interface of scLT data exploration and analysis, and will facilitate the understanding of cell fate decision and lineage commitment in development and diseases.

Dual-nuclease single-cell lineage tracing by Cas9 and Cas12a

Single-cell lineage tracing based on CRISPR-Cas9 gene editing enables the simultaneous linkage of cell states and lineage history at a high resolution. Despite its immense potential in resolving the cell fate determination and genealogy within an organism, existing implementations of this technology suffer from limitations in recording capabilities and considerable barcode dropout. Here, we introduce DuTracer, a versatile tool that utilizes two orthogonal gene editing systems to record cell lineage history at single-cell resolution in an inducible manner. DuTracer shows the ability to enhance lineage recording with minimized target dropouts and potentially deeper tree depths. Applying DuTracer in mouse embryoid bodies and neuromesodermal organoids illustrates the lineage relationship of different cell types and proposes potential lineage-biased molecular drivers, showcased by identifying transcription factor Foxb1 as a modulator in the cell fate determination of neuromesodermal progenitors. Collectively, DuTracer facilitates the precise and regulatory interrogation of cellular lineages of complex biological processes.

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.

The lives of cells, recorded

A paradigm for biology is emerging in which cells can be genetically programmed to write their histories into their own genomes. These records can subsequently be read, and the cellular histories reconstructed, which for each cell could include a record of its lineage relationships, extrinsic influences, internal states and physical locations, over time. DNA recording has the potential to transform the way that we study developmental and disease processes. Recent advances in genome engineering are driving the development of systems for DNA recording, and meanwhile single-cell and spatial omics technologies increasingly enable the recovery of the recorded information. Combined with advances in computational and phylogenetic inference algorithms, the DNA recording paradigm is beginning to bear fruit. In this Perspective, we explore the rationale and technical basis of DNA recording, what aspects of cellular biology might be recorded and how, and the types of discovery that we anticipate this paradigm will enable.

Isotype-aware inference of B cell clonal lineage trees from single-cell sequencing data

Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the micro-evolutionary processes of B cells during an adaptive immune response, capturing features of somatic hypermutation (SHM) and class switch recombination (CSR). Existing phylogenetic approaches for reconstructing B cell evolution have primarily focused on the SHM process alone. Here, we present tree inference of B cell clonal lineages (TRIBAL), an algorithm designed to optimally reconstruct the evolutionary history of B cell clonal lineages undergoing both SHM and CSR from scRNA-seq data. Through simulations, we demonstrate that TRIBAL produces more comprehensive and accurate B cell lineage trees compared to existing methods. Using real-world datasets, TRIBAL successfully recapitulates expected biological trends in a model affinity maturation system while reconstructing evolutionary histories with more parsimonious class switching than state-of-the-art methods. Thus, TRIBAL significantly improves B cell lineage tracing, useful for modeling vaccine responses, disease progression, and the identification of therapeutic antibodies.

Tracking of lineage mass via quantitative phase imaging and confinement in low refractive index microwells

Measurements of cell lineages are central to a variety of fundamental biological questions, ranging from developmental to cancer biology. However, accurate lineage tracing requires nearly perfect cell tracking, which can be challenging due to cell motion during imaging. Here we demonstrate the integration of microfabrication, imaging, and image processing approaches to demonstrate a platform for cell lineage tracing. We use quantitative phase imaging (QPI), a label-free imaging approach that quantifies cell mass. This gives an additional parameter, cell mass, that can be used to improve tracking accuracy. We confine lineages within microwells fabricated to reduce cell adhesion to sidewalls made of a low refractive index polymer. This also allows the microwells themselves to serve as references for QPI, enabling measurement of cell mass even in confluent microwells. We demonstrate application of this approach to immortalized adherent and nonadherent cell lines as well as stimulated primary B cells cultured ex vivo. Overall, our approach enables lineage tracking, or measurement of lineage mass, in a platform that can be customized to varied cell types.

Effect of photoconversion conditions on the spectral and cytotoxic properties of photoconvertible fluorescent polymer markers

Fluorescence labeling of cells is a versatile tool used to study cell behavior, which is of significant importance in biomedical sciences. Fluorescent photoconvertible markers based on polymer microcapsules have been recently considered as efficient and perspective ones for long-term tracking of individual cells. However, the dependence of photoconversion conditions on the polymeric capsule structure is still not sufficiently clear. Here, we have studied the structural and spectral properties of fluorescent photoconvertible polymeric microcapsules doped with Rhodamine B and irradiated using a pulsed laser in various regimes, and shown the dependence between the photoconversion degree and laser irradiation intensity. The effect of microcapsule composition on the photoconversion process was studied by monitoring structural changes in the initial and photoconverted microcapsules using X-ray diffraction analysis with synchrotron radiation source, and Fourier transform infrared, Raman and fluorescence spectroscopy. We demonstrated good biocompatibility of free-administered initial and photoconverted microcapsules through long-term monitoring of the RAW 264.7 monocyte/macrophage cells with unchanged viability. These data open new perspectives for using the developed markers as safe and precise cell labels with switchable fluorescent properties.

Cerebral organoids display dynamic clonal growth and tunable tissue replenishment

During brain development, neural progenitors expand through symmetric divisions before giving rise to differentiating cell types via asymmetric divisions. Transition between those modes varies among individual neural stem cells, resulting in clones of different sizes. Imaging-based lineage tracing allows for lineage analysis at high cellular resolution but systematic approaches to analyse clonal behaviour of entire tissues are currently lacking. Here we implement whole-tissue lineage tracing by genomic DNA barcoding in 3D human cerebral organoids, to show that individual stem cell clones produce progeny on a vastly variable scale. By using stochastic modelling we find that variable lineage sizes arise because a subpopulation of lineages retains symmetrically dividing cells. We show that lineage sizes can adjust to tissue demands after growth perturbation via chemical ablation or genetic restriction of a subset of cells in chimeric organoids. Our data suggest that adaptive plasticity of stem cell populations ensures robustness of development in human brain organoids.

Single-cell lineage tracing with endogenous markers

Resolving lineage relationships between cells in an organism provides key insights into the fate of individual cells and drives a fundamental understanding of the process of development and disease. A recent rapid increase in experimental and computational advances for detecting naturally occurring somatic nuclear and mitochondrial mutation at single-cell resolution has expanded lineage tracing from model organisms to humans. This review discusses the advantages and challenges of experimental and computational techniques for cell lineage tracing using somatic mutation as endogenous DNA barcodes to decipher the relationships between cells during development and tumour evolution. We outlook the advantages of spatial clonal evolution analysis and single-cell lineage tracing using endogenous genetic markers.

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.

DNA Barcoding Technology for Lineage Recording and Tracing to Resolve Cell Fate Determination

In various biological contexts, cells receive signals and stimuli that prompt them to change their current state, leading to transitions into a future state. This change underlies the processes of development, tissue maintenance, immune response, and the pathogenesis of various diseases. Following the path of cells from their initial identity to their current state reveals how cells adapt to their surroundings and undergo transformations to attain adjusted cellular states. DNA-based molecular barcoding technology enables the documentation of a phylogenetic tree and the deterministic events of cell lineages, providing the mechanisms and timing of cell lineage commitment that can either promote homeostasis or lead to cellular dysregulation. This review comprehensively presents recently emerging molecular recording technologies that utilize CRISPR/Cas systems, base editing, recombination, and innate variable sequences in the genome. Detailing their underlying principles, applications, and constraints paves the way for the lineage tracing of every cell within complex biological systems, encompassing the hidden steps and intermediate states of organism development and disease progression.

MIL-CELL: a tool for multi-scale simulation of yeast replication and prion transmission

The single-celled baker's yeast, Saccharomyces cerevisiae, can sustain a number of amyloid-based prions, the three most prominent examples being [URE3], [PSI+], and [PIN+]. In the laboratory, haploid S. cerevisiae cells of a single mating type can acquire an amyloid prion in one of two ways (i) spontaneous nucleation of the prion within the yeast cell, and (ii) receipt via mother-to-daughter transmission during the cell division cycle. Similarly, prions can be lost due to (i) dissolution of the prion amyloid by its breakage into non-amyloid monomeric units, or (ii) preferential donation/retention of prions between the mother and daughter during cell division. Here we present a computational tool (Monitoring Induction and Loss of prions in Cells; MIL-CELL) for modelling these four general processes using a multiscale approach describing both spatial and kinetic aspects of the yeast life cycle and the amyloid-prion behavior. We describe the workings of the model, assumptions upon which it is based and some interesting simulation results pertaining to the wave-like spread of the epigenetic prion elements through the yeast population. MIL-CELL is provided as a stand-alone GUI executable program for free download with the paper. MIL-CELL is equipped with a relational database allowing all simulated properties to be searched, collated and graphed. Its ability to incorporate variation in heritable properties means MIL-CELL is also capable of simulating loss of the isogenic nature of a cell population over time. The capability to monitor both chronological and reproductive age also makes MIL-CELL potentially useful in studies of cell aging.

Initial refinement of data from video-based single-cell tracking

Background

Video recording of cells offers a straightforward way to gain valuable information from their response to treatments. An indispensable step in obtaining such information involves tracking individual cells from the recorded data. A subsequent step is reducing such data to represent essential biological information. This can help to compare various single-cell tracking data yielding a novel source of information. The vast array of potential data sources highlights the significance of methodologies prioritizing simplicity, robustness, transparency, affordability, sensor independence, and freedom from reliance on specific software or online services.

Methods

The provided data presents single-cell tracking of clonal (A549) cells as they grow in two-dimensional (2D) monolayers over 94 hours, spanning several cell cycles. The cells are exposed to three different concentrations of yessotoxin (YTX). The data treatments showcase the parametrization of population growth curves, as well as other statistical descriptions. These include the temporal development of cell speed in family trees with and without cell death, correlations between sister cells, single-cell average displacements, and the study of clustering tendencies.

Results

Various statistics obtained from single-cell tracking reveal patterns suitable for data compression and parametrization. These statistics encompass essential aspects such as cell division, movements, and mutual information between sister cells.

Conclusion

This work presents practical examples that highlight the abundant potential information within large sets of single-cell tracking data. Data reduction is crucial in the process of acquiring such information which can be relevant for phenotypic drug discovery and therapeutics, extending beyond standardized procedures. Conducting meaningful big data analysis typically necessitates a substantial amount of data, which can stem from standalone case studies as an initial foundation.

The technological landscape and applications of single-cell multi-omics

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

Multimodal deep learning approaches for single-cell multi-omics data integration

Integrating single-cell multi-omics data is a challenging task that has led to new insights into complex cellular systems. Various computational methods have been proposed to effectively integrate these rapidly accumulating datasets, including deep learning. However, despite the proven success of deep learning in integrating multi-omics data and its better performance over classical computational methods, there has been no systematic study of its application to single-cell multi-omics data integration. To fill this gap, we conducted a literature review to explore the use of multimodal deep learning techniques in single-cell multi-omics data integration, taking into account recent studies from multiple perspectives. Specifically, we first summarized different modalities found in single-cell multi-omics data. We then reviewed current deep learning techniques for processing multimodal data and categorized deep learning-based integration methods for single-cell multi-omics data according to data modality, deep learning architecture, fusion strategy, key tasks and downstream analysis. Finally, we provided insights into using these deep learning models to integrate multi-omics data and better understand single-cell biological mechanisms.

Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated "Omics" Approaches to Explore Measurable Metrics

Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.

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.

Mitochondrial single-cell ATAC-seq for high-throughput multi-omic detection of mitochondrial genotypes and chromatin accessibility

Natural sequence variation within mitochondrial DNA (mtDNA) contributes to human phenotypes and may serve as natural genetic markers in human cells for clonal and lineage tracing. We recently developed a single-cell multi-omic approach, called 'mitochondrial single-cell assay for transposase-accessible chromatin with sequencing' (mtscATAC-seq), enabling concomitant high-throughput mtDNA genotyping and accessible chromatin profiling. Specifically, our technique allows the mitochondrial genome-wide inference of mtDNA variant heteroplasmy along with information on cell state and accessible chromatin variation in individual cells. Leveraging somatic mtDNA mutations, our method further enables inference of clonal relationships among native ex vivo-derived human cells not amenable to genetic engineering-based clonal tracing approaches. Here, we provide a step-by-step protocol for the use of mtscATAC-seq, including various cell-processing and flow cytometry workflows, by using primary hematopoietic cells, subsequent single-cell genomic library preparation and sequencing that collectively take ~3-4 days to complete. We discuss experimental and computational data quality control metrics and considerations for the extension to other mammalian tissues. Overall, mtscATAC-seq provides a broadly applicable platform to map clonal relationships between cells in human tissues, investigate fundamental aspects of mitochondrial genetics and enable additional modes of multi-omic discovery.

Principles of neural stem cell lineage progression: Insights from developing cerebral cortex

How to generate a brain of correct size and with appropriate cell-type diversity during development is a major question in Neuroscience. In the developing neocortex, radial glial progenitor (RGP) cells are the main neural stem cells that produce cortical excitatory projection neurons, glial cells, and establish the prospective postnatal stem cell niche in the lateral ventricles. RGPs follow a tightly orchestrated developmental program that when disrupted can result in severe cortical malformations such as microcephaly and megalencephaly. The precise cellular and molecular mechanisms instructing faithful RGP lineage progression are however not well understood. This review will summarize recent conceptual advances that contribute to our understanding of the general principles of RGP lineage progression.

Mapping of clonal lineages across developmental stages in human neural differentiation

The cell lineages across developmental stages remain to be elucidated. Here, we developed single-cell split barcoding (SISBAR) that allows clonal tracking of single-cell transcriptomes across stages in an in vitro model of human ventral midbrain-hindbrain differentiation. We developed "potential-spective" and "origin-spective" analyses to investigate the cross-stage lineage relationships and mapped a multi-level clonal lineage landscape depicting the whole differentiation process. We uncovered many previously uncharacterized converging and diverging trajectories. Furthermore, we demonstrate that a transcriptome-defined cell type can arise from distinct lineages that leave molecular imprints on their progenies, and the multilineage fates of a progenitor cell-type represent the collective results of distinct rather than similar clonal fates of individual progenitors, each with distinct molecular signatures. Specifically, we uncovered a ventral midbrain progenitor cluster as the common clonal origin of midbrain dopaminergic (mDA) neurons, midbrain glutamatergic neurons, and vascular and leptomeningeal cells and identified a surface marker that can improve graft outcomes.

Single-cell RNA sequencing to identify cellular heterogeneity and targets in cardiovascular diseases: from bench to bedside

The mechanisms of cardiovascular diseases (CVDs) remain incompletely elucidated. Single-cell RNA sequencing (scRNA-seq) has enabled the profiling of single-cell transcriptomes at unprecedented resolution and throughput, which is critical for deciphering cardiovascular cellular heterogeneity and underlying disease mechanisms, thereby facilitating the development of therapeutic strategies. In this review, we summarize cellular heterogeneity in cardiovascular homeostasis and diseases as well as the discovery of potential disease targets based on scRNA-seq, and yield new insights into the promise of scRNA-seq technology in precision medicine and clinical application.

Molecular recording: transcriptional data collection into the genome

Advances in regenerative medicine depend upon understanding the complex transcriptional choreography that guides cellular development. Transcriptional molecular recorders, tools that record different transcriptional events into the genome of cells, hold promise to elucidate both the intensity and timing of transcriptional activity at single-cell resolution without requiring destructive multitime point assays. These technologies are dependent on DNA writers, which translate transcriptional signals into stable genomic mutations that encode the duration, intensity, and order of transcriptional events. In this review, we highlight recent progress toward more informative and multiplexable transcriptional recording through the use of three different types of DNA writing - recombineering, Cas1-Cas2 acquisition, and prime editing - and the architecture of the genomic data generated.

Grave-to-cradle: human embryonic lineage tracing from the postmortem body

Curiosity concerning the process of human creation has been around for a long time. Relevant questions seemed to be resolved with the knowledge of how cells divide after fertilization obtained through in vitro fertilization experiments. However, we still do not know how human life is created at the cellular level. Recently, the value of cadavers as a resource from which to obtain "normal" cells and tissues has been established, and human research using postmortem bodies has attracted growing scientific attention. As the human genome can be analyzed at the level of nucleotides through whole-genome sequencing, individual cells in a postmortem body can be traced back to determine what developmental processes have transpired from fertilization. These retrospective lineage tracing studies have answered several unsolved questions on how humans are created. This review covers the methodologies utilized in lineage tracing research in a historical context and the conceptual basis for reconstructing the division history of cells in a retrospective manner using postzygotic somatic variants in postmortem tissue. We further highlight answers that postmortem research could potentially address and discuss issues that wait to be solved in the future.

Serial enrichment of heteroduplex DNA using a MutS-magnetic bead system

Numerous applications in molecular biology and genomics require characterization of mutant DNA molecules present at low levels within a larger sample of non-mutant DNA. This is often achieved either by selectively amplifying mutant DNA, or by sequencing all the DNA followed by computational identification of the mutant DNA. However, selective amplification is challenging for insertions and deletions (indels). Additionally, sequencing all the DNA in a sample may not be cost effective when only the presence of a mutation needs to be ascertained rather than its allelic fraction. The MutS protein evolved to detect DNA heteroduplexes in which the two DNA strands are mismatched. Prior methods have utilized MutS to enrich mutant DNA by hybridizing mutant to non-mutant DNA to create heteroduplexes. However, the purity of heteroduplex DNA these methods achieve is limited because they can only feasibly perform one or two enrichment cycles. We developed a MutS-magnetic bead system that enables rapid serial enrichment cycles. With six cycles, we achieve complete purification of heteroduplex indel DNA originally present at a 5% fraction and over 40-fold enrichment of heteroduplex DNA originally present at a 1% fraction. This system may enable novel approaches for enriching mutant DNA for targeted sequencing.

Connecting past and present: single-cell lineage tracing

Central to the core principle of cell theory, depicting cells' history, state and fate is a fundamental goal in modern biology. By leveraging clonal analysis and single-cell RNA-seq technologies, single-cell lineage tracing provides new opportunities to interrogate both cell states and lineage histories. During the past few years, many strategies to achieve lineage tracing at single-cell resolution have been developed, and three of them (integration barcodes, polylox barcodes, and CRISPR barcodes) are noteworthy as they are amenable in experimentally tractable systems. Although the above strategies have been demonstrated in animal development and stem cell research, much care and effort are still required to implement these methods. Here we review the development of single-cell lineage tracing, major characteristics of the cell barcoding strategies, applications, as well as technical considerations and limitations, providing a guide to choose or improve the single-cell barcoding lineage tracing.

Single-cell genome sequencing of human neurons identifies somatic point mutation and indel enrichment in regulatory elements

Accurate somatic mutation detection from single-cell DNA sequencing is challenging due to amplification-related artifacts. To reduce this artifact burden, an improved amplification technique, primary template-directed amplification (PTA), was recently introduced. We analyzed whole-genome sequencing data from 52 PTA-amplified single neurons using SCAN2, a new genotyper we developed to leverage mutation signatures and allele balance in identifying somatic single-nucleotide variants (SNVs) and small insertions and deletions (indels) in PTA data. Our analysis confirms an increase in nonclonal somatic mutation in single neurons with age, but revises the estimated rate of this accumulation to 16 SNVs per year. We also identify artifacts in other amplification methods. Most importantly, we show that somatic indels increase by at least three per year per neuron and are enriched in functional regions of the genome such as enhancers and promoters. Our data suggest that indels in gene-regulatory elements have a considerable effect on genome integrity in human neurons.

De novo mutations, genetic mosaicism and human disease

Mosaicism—the existence of genetically distinct populations of cells in a particular organism—is an important cause of genetic disease. Mosaicism can appear as de novo DNA mutations, epigenetic alterations of DNA, and chromosomal abnormalities. Neurodevelopmental or neuropsychiatric diseases, including autism—often arise by de novo mutations that usually not present in either of the parents. De novo mutations might occur as early as in the parental germline, during embryonic, fetal development, and/or post-natally, through ageing and life. Mutation timing could lead to mutation burden of less than heterozygosity to approaching homozygosity. Developmental timing of somatic mutation attainment will affect the mutation load and distribution throughout the body. In this review, we discuss the timing of de novo mutations, spanning from mutations in the germ lineage (all ages), to post-zygotic, embryonic, fetal, and post-natal events, through aging to death. These factors can determine the tissue specific distribution and load of de novo mutations, which can affect disease. The disease threshold burden of somatic de novo mutations of a particular gene in any tissue will be important to define.

Identification of Somatic Mutations From Bulk and Single-Cell Sequencing Data

Somatic mutations are DNA variants that occur after the fertilization of zygotes and accumulate during the developmental and aging processes in the human lifespan. Somatic mutations have long been known to cause cancer, and more recently have been implicated in a variety of non-cancer diseases. The patterns of somatic mutations, or mutational signatures, also shed light on the underlying mechanisms of the mutational process. Advances in next-generation sequencing over the decades have enabled genome-wide profiling of DNA variants in a high-throughput manner; however, unlike germline mutations, somatic mutations are carried only by a subset of the cell population. Thus, sensitive bioinformatic methods are required to distinguish mutant alleles from sequencing and base calling errors in bulk tissue samples. An alternative way to study somatic mutations, especially those present in an extremely small number of cells or even in a single cell, is to sequence single-cell genomes after whole-genome amplification (WGA); however, it is critical and technically challenging to exclude numerous technical artifacts arising during error-prone and uneven genome amplification in current WGA methods. To address these challenges, multiple bioinformatic tools have been developed. In this review, we summarize the latest progress in methods for identification of somatic mutations and the challenges that remain to be addressed in the future.

TiC2D: Trajectory Inference From Single-Cell RNA-Seq Data Using Consensus Clustering

Cellular programs often exhibit strong heterogeneity and asynchrony in the timing of program execution. Single-cell RNA-seq technology has provided an unprecedented opportunity for characterizing these cellular processes by simultaneously quantifying many parameters at single-cell resolution. Robust trajectory inference is a critical step in the analysis of dynamic temporal gene expression, which can shed light on the mechanisms of normal development and diseases. Here, we present TiC2D, a novel algorithm for cell trajectory inference from single-cell RNA-seq data, which adopts a consensus clustering strategy to precisely cluster cells. To evaluate the power of TiC2D, we compare it with three state-of-the-art methods on four independent single-cell RNA-seq datasets. The results show that TiC2D can accurately infer developmental trajectories from single-cell transcriptome. Furthermore, the reconstructed trajectories enable us to identify key genes involved in cell fate determination and to obtain new insights about their roles at different developmental stages.

CoSpar identifies early cell fate biases from single-cell transcriptomic and lineage information

A goal of single-cell genome-wide profiling is to reconstruct dynamic transitions during cell differentiation, disease onset and drug response. Single-cell assays have recently been integrated with lineage tracing, a set of methods that identify cells of common ancestry to establish bona fide dynamic relationships between cell states. These integrated methods have revealed unappreciated cell dynamics, but their analysis faces recurrent challenges arising from noisy, dispersed lineage data. In this study, we developed coherent, sparse optimization (CoSpar) as a robust computational approach to infer cell dynamics from single-cell transcriptomics integrated with lineage tracing. Built on assumptions of coherence and sparsity of transition maps, CoSpar is robust to severe downsampling and dispersion of lineage data, which enables simpler experimental designs and requires less calibration. In datasets representing hematopoiesis, reprogramming and directed differentiation, CoSpar identifies early fate biases not previously detected, predicting transcription factors and receptors implicated in fate choice. Documentation and detailed examples for common experimental designs are available at https://cospar.readthedocs.io/ .

Accounting for errors in data improves divergence time estimates in single-cell cancer evolution

Single-cell sequencing provides a new way to explore the evolutionary history of cells. Compared to traditional bulk sequencing, where a population of heterogeneous cells is pooled to form a single observation, single-cell sequencing isolates and amplifies genetic material from individual cells, thereby preserving the information about the origin of the sequences. However, single-cell data is more error-prone than bulk sequencing data due to the limited genomic material available per cell. Here, we present error and mutation models for evolutionary inference of single-cell data within a mature and extensible Bayesian framework, BEAST2. Our framework enables integration with biologically informative models such as relaxed molecular clocks and population dynamic models. Our simulations show that modeling errors increase the accuracy of relative divergence times and substitution parameters. We reconstruct the phylogenetic history of a colorectal cancer patient and a healthy patient from single-cell DNA sequencing data. We find that the estimated times of terminal splitting events are shifted forward in time compared to models which ignore errors. We observed that not accounting for errors can overestimate the phylogenetic diversity in single-cell DNA sequencing data. We estimate that 30-50% of the apparent diversity can be attributed to error. Our work enables a full Bayesian approach capable of accounting for errors in the data within the integrative Bayesian software framework BEAST2.

Asymmetric Contribution of Blastomere Lineages of First Division of the Zygote to Entire Human Body Using Post-Zygotic Variants

BACKGROUND

In humans, after fertilization, the zygote divides into two 2n diploid daughter blastomeres. During this division, DNA is replicated, and the remaining mutually exclusive genetic mutations in the genome of each cell are called post-zygotic variants. Using these somatic mutations, developmental lineages can be reconstructed. How these two blastomeres are contributing to the entire body is not yet identified. This study aims to evaluate the cellular contribution of two blastomeres of 2-cell embryos to the entire body in humans using post-zygotic variants based on whole genome sequencing.

METHODS

Tissues from different anatomical areas were obtained from five donated cadavers for use in single-cell clonal expansion and bulk target sequencing. After conducting whole genome sequencing, computational analysis was applied to find the early embryonic mutations of each clone. We developed our in-house bioinformatics pipeline, and filtered variants using strict criteria, composed of mapping quality, base quality scores, depth, soft-clipped reads, and manual inspection, resulting in the construction of embryological phylogenetic cellular trees.

RESULTS

Using our in-house pipeline for variant filtering, we could extract accurate true positive variants, and construct the embryological phylogenetic trees for each cadaver. We found that two daughter blastomeres, L1 and L2 (lineage 1 and 2, respectively), derived from the zygote, distribute unequally to the whole body at the clonal level. From bulk target sequencing data, we validated asymmetric contribution by means of the variant allele frequency of L1 and L2. The asymmetric contribution of L1 and L2 varied from person to person.

CONCLUSION

We confirmed that there is asymmetric contribution of two daughter blastomeres from the first division of the zygote across the whole human body.

Leveraging single cell sequencing to unravel intra-tumour heterogeneity and tumour evolution in human cancers

Intra-tumour heterogeneity and tumour evolution are well-documented phenomena in human cancers. While the advent of next-generation sequencing technologies has facilitated the large-scale capture of genomic data, the field of single cell genomics is nascent but rapidly advancing and generating many new insights into the complex molecular mechanisms of tumour biology. In this review, we provide an overview of current single cell DNA sequencing technologies, exploring how recent methodological advancements have enumerated new insights into intra-tumour heterogeneity and tumour evolution. Areas highlighted include the potential power of single cell genome sequencing studies to explore evolutionary dynamics contributing to tumourigenesis through to progression, metastasis and therapy resistance. We also explore the use of in-situ sequencing technologies to study intra-tumour heterogeneity in a spatial context, as well as examining the use of single cell genomics to perform lineage tracing in both normal and malignant tissues. Finally, we consider the use of multi-modal single cell sequencing technologies. Taken together, it is hoped that these many facets of single cell genome sequencing will improve our understanding of tumourigenesis, progression and lethality in cancer leading to the development of novel therapies. This article is protected by copyright. All rights reserved.

Single-Cell Epigenomics Reveals Mechanisms of Cancer Progression

Cancer initiation is driven by the cooperation between genetic and epigenetic aberrations that disrupt gene regulatory programs critical to maintaining specialized cellular functions. After initiation, cells acquire additional genetic and epigenetic alterations influenced by tumor-intrinsic and -extrinsic mechanisms, which increase intratumoral heterogeneity, reshape the cell's underlying gene regulatory networks and promote cancer evolution. Furthermore, environmental or therapeutic insults drive the selection of heterogeneous cell states, with implications for cancer initiation, maintenance, and drug resistance. The advancement of single-cell genomics has begun to uncover the full repertoire of chromatin and gene expression states (cell states) that exist within individual tumors. These single-cell analyses suggest that cells diversify in their regulatory states upon transformation by co-opting damage-induced and nonlineage regulatory programs that can lead to epigenomic plasticity. Here, we review these recent studies related to regulatory state changes in cancer progression and highlight the growing single-cell epigenomics toolkit poised to address unresolved questions in the field.

Somatic mosaicism reveals clonal distributions of neocortical development

The structure of the human neocortex underlies species-specific traits and reflects intricate developmental programs. Here we sought to reconstruct processes that occur during early development by sampling adult human tissues. We analysed neocortical clones in a post-mortem human brain through a comprehensive assessment of brain somatic mosaicism, acting as neutral lineage recorders1,2. We combined the sampling of 25 distinct anatomic locations with deep whole-genome sequencing in a neurotypical deceased individual and confirmed results with 5 samples collected from each of three additional donors. We identified 259 bona fide mosaic variants from the index case, then deconvolved distinct geographical, cell-type and clade organizations across the brain and other organs. We found that clones derived after the accumulation of 90-200 progenitors in the cerebral cortex tended to respect the midline axis, well before the anterior-posterior or ventral-dorsal axes, representing a secondary hierarchy following the overall patterning of forebrain and hindbrain domains. Clones across neocortically derived cells were consistent with a dual origin from both dorsal and ventral cellular populations, similar to rodents, whereas the microglia lineage appeared distinct from other resident brain cells. Our data provide a comprehensive analysis of brain somatic mosaicism across the neocortex and demonstrate cellular origins and progenitor distribution patterns within the human brain.

Blocker displacement amplification mediated PCR based screen-printed carbon electrode biosensor and lateral flow strip strategy for CYP2C19*2 genotyping

Single nucleotide variants in CYP2C19*2 are associated with clopidogrel resistance in coronary heart disease. In order the guidance the dosage of drug and personalized medicine, blocker displacement amplification was first used to specific amplify G site and A site alleles. For electrochemical strategy, forward primers were labeled electrochemical active methyl blue and ferrocene, generates signals on -0.26 for G site and 0.22 V for A site. For lateral flow strip assay, primers with specific modification were used to generates unique color in test line 1 for G site and test line 2 for A site. In conclusion, we developed a sensitive screen-printed carbon electrodes based electrochemical sensor and gold nano particle based lateral flow strip assay strategy to successfully genotyping CYP2C19*2 GG, GA and AA genotype. The proposed method can realize CYP2C19*2 analysis from multiple biological samples including whole blood, buccal swab, saliva and hair root, and showed good consistency with Sequencing. Due to the fact our proposed strategy merely relies on thermal cycler instrument and visual strip detection, this platform shows great potential in source-limited regions genotyping.

Towards Tabula Gallus

The Tabula Gallus is a proposed project that aims to create a map of every cell type in the chicken body and chick embryos. Chickens (Gallus gallus) are one of the most recognized model animals that recapitulate the development and physiology of mammals. The Tabula Gallus will generate a compendium of single-cell transcriptome data from Gallus gallus, characterize each cell type, and provide tools for the study of the biology of this species, similar to other ongoing cell atlas projects (Tabula Muris and Tabula Sapiens/Human Cell Atlas for mice and humans, respectively). The Tabula Gallus will potentially become an international collaboration between many researchers. This project will be useful for the basic scientific study of Gallus gallus and other birds (e.g., cell biology, molecular biology, developmental biology, neuroscience, physiology, oncology, virology, behavior, ecology, and evolution). It will eventually be beneficial for a better understanding of human health and diseases.

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.

Somatic Mutation Analysis in Salix suchowensis Reveals Early-Segregated Cell Lineages

Long-lived plants face the challenge of ever-increasing mutational burden across their long lifespan. Early sequestration of meristematic stem cells is supposed to efficiently slow down this process, but direct measurement of somatic mutations that accompanies segregated cell lineages in plants is still rare. Here, we tracked somatic mutations in 33 leaves and 22 adventitious roots from 22 stem-cuttings across eight major branches of a shrub willow (Salix suchowensis). We found that most mutations propagated separately in leaves and roots, providing clear evidence for early segregation of underlying cell lineages. By combining lineage tracking with allele frequency analysis, our results revealed a set of mutations shared by distinct branches, but were exclusively present in leaves and not in roots. These mutations were likely propagated by rapidly dividing somatic cell lineages which survive several iterations of branching, distinct from the slowly dividing axillary stem cell lineages. Leaf is thus contributed by both slowly and rapidly dividing cell lineages, leading to varied fixation chances of propagated mutations. By contrast, each root likely arises from a single founder cell within the adventitious stem cell lineages. Our findings give straightforward evidence that early segregation of meristems slows down mutation accumulation in axillary meristems, implying a plant "germline" paralog to the germline of animals through convergent evolution.

Intervertebral disc repair and regeneration: Insights from the notochord

The vertebrate notochord plays an essential role in patterning multiple structures during embryonic development. In the early 2000s, descendants of notochord cells were demonstrated to form the entire nucleus pulposus of the intervertebral disc in addition to their key role in embryonic patterning. The nucleus pulposus undergoes degeneration during postnatal life, which can lead to back pain. Recently, gene and protein profiles of notochord and nucleus pulposus cells have been identified. These datasets, coupled with the ability to differentiate human induced pluripotent stem cells (iPSCs) into cells that resemble nucleus pulposus cells, provide the possibility of generating a cell-based therapy to halt and/or reverse disc degeneration.

Mapping single-cell-resolution cell phylogeny reveals cell population dynamics during organ development

Mapping the cell phylogeny of a complex multicellular organism relies on somatic mutations accumulated from zygote to adult. Available cell barcoding methods can record about three mutations per barcode, enabling only low-resolution mapping of the cell phylogeny of complex organisms. Here we developed SMALT, a substitution mutation-aided lineage-tracing system that outperforms the available cell barcoding methods in mapping cell phylogeny. We applied SMALT to Drosophila melanogaster and obtained on average more than 20 mutations on a three-kilobase-pair barcoding sequence in early-adult cells. Using the barcoding mutations, we obtained high-quality cell phylogenetic trees, each comprising several thousand internal nodes with 84-93% median bootstrap support. The obtained cell phylogenies enabled a population genetic analysis that estimates the longitudinal dynamics of the number of actively dividing parental cells (Np) in each organ through development. The Np dynamics revealed the trajectory of cell births and provided insight into the balance of symmetric and asymmetric cell division.

Single-cell RNA sequencing analysis of mouse follicular somatic cells†

Within the development of ovarian follicle, in addition to cell proliferation and differentiation, sophisticated cell-cell cross talks are established among follicular somatic cells such as granulosa cells (GCs) and theca cells. To systematically reveal the cell differentiation and signal transductions in follicular somatic cells, we collected the mouse follicular somatic cells from secondary to ovulatory stage, and analyzed the single cell transcriptomes. Having data filtered and screened, we found 6883 high variable genes in 4888 single cells. Then follicular somatic cells were clustered into 26 cell clusters, including 18 GC clusters, 4 theca endocrine cell (TEC) clusters, and 4 other somatic cell clusters, which include immune cells and Acta2 positive theca externa cells. From our data, we found there was metabolic reprogramming happened during GC differentiation. We also found both Cyp19a1 and Cyp11a1 could be expressed in TECs. We analyzed the expression patterns of genes associated with cell-cell interactions such as steroid hormone receptor genes, insulin signaling genes, and cytokine/transformation growth factor beta associated genes in all cell clusters. Lastly, we clustered the highly variable genes into 300 gene clusters, which could be used to search new genes involved in follicle development. These transcriptomes of follicular somatic cells provide us potential clues to reveal how mammals regulating follicle development and could help us find targets to improve oocyte quality for women with low fertility.

Advanced Technologies for Local Neural Circuits in the Cerebral Cortex

The neural network in the brain can be viewed as an integrated system assembled from a large number of local neural circuits specialized for particular brain functions. Activities of neurons in local neural circuits are thought to be organized both spatially and temporally under the rules optimized for their roles in information processing. It is well perceived that different areas of the mammalian neocortex have specific cognitive functions and distinct computational properties. However, the organizational principles of the local neural circuits in different cortical regions have not yet been clarified. Therefore, new research principles and related neuro-technologies that enable efficient and precise recording of large-scale neuronal activities and synaptic connections are necessary. Innovative technologies for structural analysis, including tissue clearing and expansion microscopy, have enabled super resolution imaging of the neural circuits containing thousands of neurons at a single synapse resolution. The imaging resolution and volume achieved by new technologies are beyond the limits of conventional light or electron microscopic methods. Progress in genome editing and related technologies has made it possible to label and manipulate specific cell types and discriminate activities of multiple cell types. These technologies will provide a breakthrough for multiscale analysis of the structure and function of local neural circuits. This review summarizes the basic concepts and practical applications of the emerging technologies and new insight into local neural circuits obtained by these technologies.

DNA barcode to trace the development and differentiation of cord blood stem cells (Review)

Umbilical cord blood transplantation was first reported in 1980. Since then, additional research has indicated that umbilical cord blood stem cells (UCBSCs) have various advantages, such as multi-lineage differentiation potential and potent renewal activity, which may be induced to promote their differentiation into a variety of seed cells for tissue engineering and the treatment of clinical and metabolic diseases. Recent studies suggested that UCBSCs are able to differentiate into nerve cells, chondrocytes, hepatocyte-like cells, fat cells and osteoblasts. The culture of UCBSCs has developed from feeder-layer to feeder-free culture systems. The classical techniques of cell labeling and tracing by gene transfection and fluorescent dye and nucleic acid analogs have evolved to DNA barcode technology mediated by transposon/retrovirus, cyclization recombination-recombinase and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 strategies. DNA barcoding for cell development tracing has advanced to include single cells and single nucleic acid mutations. In the present study, the latest research findings on the development and differentiation, culture techniques and labeling and tracing of UCBSCs are reviewed. The present study may increase the current understanding of UCBSC biology and its clinical applications.

Spatial Organization of Chromatin: Emergence of Chromatin Structure During Development

Nuclei are central hubs for information processing in eukaryotic cells. The need to fit large genomes into small nuclei imposes severe restrictions on genome organization and the mechanisms that drive genome-wide regulatory processes. How a disordered polymer such as chromatin, which has vast heterogeneity in its DNA and histone modification profiles, folds into discernibly consistent patterns is a fundamental question in biology. Outstanding questions include how genomes are spatially and temporally organized to regulate cellular processes with high precision and whether genome organization is causally linked to transcription regulation. The advent of next-generation sequencing, super-resolution imaging, multiplexed fluorescent in situ hybridization, and single-molecule imaging in individual living cells has caused a resurgence in efforts to understand the spatiotemporal organization of the genome. In this review, we discuss structural and mechanistic properties of genome organization at different length scales and examine changes in higher-order chromatin organization during important developmental transitions.

Decoding and recoding plant development

The development of multicellular organisms has been studied for centuries, yet many critical events and mechanisms of regulation remain challenging to observe directly. Early research focused on detailed observational and comparative studies. Molecular biology has generated insights into regulatory mechanisms, but only for a limited number of species. Now, synthetic biology is bringing these two approaches together, and by adding the possibility of sculpting novel morphologies, opening another path to understanding biology. Here, we review a variety of recently invented techniques that use CRISPR/Cas9 and phage integrases to trace the differentiation of cells over various timescales, as well as to decode the molecular states of cells in high spatiotemporal resolution. Most of these tools have been implemented in animals. The time is ripe for plant biologists to adopt and expand these approaches. Here, we describe how these tools could be used to monitor development in diverse plant species, as well as how they could guide efforts to recode programs of interest.